Strategic Investment Plan for the Water and Environment Sector, Uganda (2018-2030)

STRATEGIC INVESTMENT PLAN FOR THE WATER AND ENVIRONMENT SECTOR, UGANDA (2018-2030) FINAL REPORT VOLUME 1. FUNDING REQUIREMENTS TO MEET NATIONAL 2030 TARGETS FOR THE WATER AND ENVIRONMENT SECTOR VOLUME 2. CONSOLIDATED STRATEGIC SECTOR INVESTMENT PLAN VOLUME 3. SUBSECTOR STRATEGIC INVESTMENT PLANS VOLUME 4. SECTOR INVESTMENT MODEL prepared for: Ministry of Water and Environment Kampala, Uganda prepared by: Kenneth Strzepek, Brent Boehlert, and Jacqueline Willwerth Industrial Economics, Incorporated 2067 Massachusetts Avenue Cambridge, MA 02140 617/354-0074 Final – March 31, 2018 i TABLE OF CONTENTS ACKNOWLEDGEMENTS ABSTRACTS SSIP IMPLEMENTATION GUIDE VOLUME 1. FUNDING REQUIREMENTS TO MEET NATIONAL 2030 TARGETS FOR THE WATER AND ENVIRONMENT SECTOR 1.1. Introduction 1.2. Water and Environment Sector National Indicators and 2030 Targets 1.3. Target Costing Methods 1.4. Annual Funding Requirements 1.5. Projected Target Shortfall Under Current Funding Levels VOLUME 2. CONSOLIDATED STRATEGIC SECTOR INVESTMENT PLAN 2.1. Background and Motivation 2.2. Components of the Strategic Sector Investment Plan 2.3. Investment Planning Methods 2.4. Findings of the Consolidated Investment Plans VOLUME 3. SUBSECTOR STRATEGIC INVESTMENT PLANS 3.1. Background and Motivation 3.2. Investment Planning Methods 3.3. Strategic Subsector Investment Plans VOLUME 4. SECTOR INVESTMENT MODEL 4.1. Introduction and Purpose 4.2. Sector Investment Modeling Methodology 4.3. Sector Investment Model Components 4.4. SIM Caveats and Recommendations Annex 4A. August 2017 Prioritization Workshop Annex 4B. Biophysical Models Annex 4C. Investment Multipliers Annex 4D. Detailed Indicator Performance Tables Annex 4E. Detailed SIM Input Tables Final – March 31, 2018 ii ACKNOWLEDGEMENTS The study is a partnership between the Industrial Economics (IEc) expert team and staff, the Ministry of Water and Environment (MWE) team, and other local counterparts in Uganda. From the government of Uganda, we are grateful for policy guidance and technical support provided by the Ministry of Water and Environment, the Ministry of Finance, Planning and Economic Development, the Nile Basin Initiative, and to the study focal points within MWE whose participation made this study possible. The study greatly benefitted from the important contributions made through valuable inputs, comments, advice, and support provided by academia, civil society and NGOs, the donor community, and development partners in Uganda throughout this work. The consulting team includes experts from IEc, the Massachusetts Institute of Technology (MIT), and organizations within Uganda. Study authors include team leader Prof. Kenneth Strzepek, project manager Dr. Brent Boehlert and assistant project manager Jacqueline Willwerth, as well as five other experts. Prof. Kenneth Strzepek is the project team leader and an expert in water resources economics. Prof. Strzepek is an Emeritus Professor in the Water Resources Engineering program at the University of Colorado at Boulder, currently a Research Scientist at the Joint Program on Climate Change at the Massachusetts Institute of Technology and a Non-Resident Fellow at the UNU-World Institute for Development Economics Research. Dr. Brent Boehlert is the project manager on this study and a water resources engineer. Dr. Boehlert is a Principal at IEc with over 10 years of experience managing complex multi-disciplinary teams of consultants, subcontractors, and in-country collaborators both in the U.S. and internationally. Jacqueline Willwerth is the assistant project manager for this effort. She is a Senior Associate at IEc with over six years of consulting and academic experience in natural resource and environmental economics. Other IEc team experts who contributed to the analysis, reporting, and capacity building include: • Benjamin Ssekamuli is a water resources specialist with expertise in a range of water systems modeling software platforms. He is leading the capacity building activities for this effort, and has led dozens of training workshops focused on analytical tools and techniques. • Eng. Emmanuel Olet, an expert on energy economics, and a consulting civil engineer with 15 years of experience. His extensive project experience is heavily focused on work within Sub Saharan Africa and, specifically, Uganda. • James Neumann, a Principal at IEc with over 25 years of consulting experience in environmental economics. Mr. Neumann specializes in natural resource economics, and is one of the lead authors for a USEPA analysis that links benefit-cost analysis with economy-wide modeling for a study of U.S. air pollution policy. • Dr. Susan Murcott is a water supply and sanitation expert with 15 years of experience in water and wastewater consulting. • Dr. Krishna Prasad is a civil and water resource engineer with over 20 years of experience on irrigation and institutions. ABOUT THE AUTHORS Final – March 31, 2018 iii SSIP IMPLEMENTATION GUIDE This Strategic Sector Investment Plan (SSIP) is intended to be a living plan that is updated regularly as new information becomes available and priorities change. The SSIP can be updated using the Sector Investment Model (SIM), which is described in Volume 4. This brief note provides (a) recommended best practices for utilization of the SIM and (b) priority improvements to the tool. BEST PRACTICES FOR UTIL IZATION OF THE S IM AND INFORMATION MANAGEMENT Two best practices are recommended for ensuring the SIM is used effectively in the planning process: • Establish a SIM expert team within the Sector. Establish a small group of expert staff within the Sector who are responsible for operating and modifying the SIM. This group must be deeply familiar with how the SIM operates, and would ideally be representative of a broad cross section of the sector (e.g., representatives from each of the nine subsectors). A good starting point for this group would be the team of Sector partners who participated in the SIM capacity building program over the last year. • Maintain a master version of the SIM. Although it will be important to disseminate the SIM to the various subsectors in order to build transparency and to facilitate the planning process, it will be critical to maintain a single master version of the SIM that houses the official information, priorities, and updates from the sector planning process. It is recommended that the expert team gather updated data and build consensus on priorities within their respective sectors, and then meet at least every six months to update the SIM with the latest information. USING THE S IM IN THE BUDGET PLANNING PROCESS The SIM provides a logical, transparent rationale behind cross-subsector spending, and thus can be used to inform sector-wide budgeting beginning in 2019/2020. However, given the limited scope of this current study, there are several uncertainties in the SIM results that must be addressed before the tool can be used to inform budget allocations. The following are the priority SIM improvements: • Improve spending preference weights. Sector spending preferences in the SIM have a huge effect on investment planning outcomes. A more in-depth workshop to elicit the spending preferences of the Sector is needed, involving a broad representation of decision-makers at the highest levels. • Enhance cost estimates across space and time. Investment cost estimates and their indicators also have a pronounced effect on spending. Further refinement of these estimates by additional analysis and modeling, and by reaching out to regional experts for catchment level results would greatly improve the reliability of SIM results. • Improve indicator information. Measurement of baseline indicator levels and target definitions could be refined, and the indicators can be enhanced through a process to identify the “best” list of investment indicators from the numerous options available. • Better characterize vulnerability to uncertainty. Running the SIM across a wide range of future combinations of inputs would allow for a better characterization of which parameters are most sensitive to uncertainty. Final – March 31, 2018 iv ABSTRACTS VOLUME 1: FUNDING REQUIREMENTS TO MEET NATIONAL 2030 TARGETS FOR THE WATER AND ENVIRONMENT SECTOR The Water and Environment Sector currently receives about 800 billion UGX per year for investment in activities modeled in the SSIP. With this funding, the Sector has managed to make progress across a number of indicators that track the Sector’s performance across its major areas of intervention. The Sector has stated a set of targets for these indicators to be met by 2030, in line with the United Nations Sustainable Development Goals (SDG). Based on the Sector investment modeling and planning undertaken in this report, the current level of funding with an assumed moderate annual growth rate of 3 percent (business as usual, or BAU scenario) will not be sufficient for the Sector to meet its targets. In this funding requirements assessment, accounting for external trends such as population growth, it is estimated that the Sector will need an average annual budget of almost 7.6 trillion UGX over the next 13 years to 2030. This is about nine times the current funding allocated to SSIP investments. This volume describes the methods used in costing analysis and presents the results in terms of both the full sector funding requirement from 2018 to 2030 and an analysis of how the sector indicators will perform by 2030 in the business as usual budget allocation. VOLUME 2: CONSOLIDATED STRATEGIC SECTOR INVESTMENT PLAN (SS IP) Through various development plans and international agreements, MWE and the Government of Uganda have agreed to achieve certain targets in the areas of water resource development and environmental management. Given the current level of funding, limited growth in future funding and a rapidly growing population, reaching these targets will be a challenge. The SSIP seeks to answer this question: in which types of investment should the Water and Environment Sector (WES) invest in order to maximize performance, as measured by 24 indicators, and best reflect the priorities of the sector? While Volume 1 of this Strategic Sector Investment Plan (SSIP) focuses on the spending requirements needed to reach 2030 targets, Volume 2 analyzes how to allocate resources within the WES given limited budgets. The volume considers three budget scenarios—all of which are considerably below what is needed to achieve 2030 indicator targets—and given these constraints, describes spending pathways for water and environmental investments that are aligned with WES priorities. The emphasis of this volume is on reporting insights from developing a strategic investment plan at the WES level; details on subsector level planning are documented in Volume 3. This volume first reviews the measurement metrics and spatial units that drive the SSIP, then provides an overview of the Sector Investment Model (SIM), and lastly reviews findings and lessons learned under the three budget scenarios. These include a BAU scenario (current funding), a moderate investment scenario (1.5x current funding), and a high investment scenario (3x current funding). The three spending scenarios result in considerably different levels of indicator achievement, driven by differences in unit costs and indicator gaps that drive differences in funding allocation within the SIM. Several of the high performing indicators—including Water for Production Functionality, Permit Compliance, and Operational weather stations—have low unit costs relative to the spending allocation provided by Sector preferences. Others, such as Village water supply, benefit from receiving “ancillary benefits” that spill over from investing in achieving other indicators. On the Final – March 31, 2018 v other hand, the poorer performing indicators tend to be comparably expensive, in terms of the cost to reduce one percent of the gap. Examples include safely managed drinking water, which requires investing in expensive piped water supply, and forest coverage, which requires rehabilitation of forests. Sector outcomes tend to rise under all budget scenarios, although much more modestly under the BAU scenario. From a spatial perspective, a number of factors vary by catchment (the spatial scale used for this analysis—23 hydro-geographic regions of Uganda), including population, unit costs of investment (e.g., storage construction costs vary based on topographic characteristics), and the 2017 starting level for several of the indicators. These differences drive a non-uniform pattern of spending across space, where catchments with higher unit costs, higher populations (or units of investment such as irrigable hectares), and lower 2017 indicator levels receive a greater share of the funding. Lastly, O&M expenditures on both existing and new investments are assumed to be part of the allocated budget. Under the high budget scenario, approximately 20% of the total budget is spent on O&M expenses. VOLUME 3: SUBSECTOR STRATEGIC INVESTMENT PLANS The current Sector planning effort marks the first time the Water and Environment Sector has undertaken strategic planning as a sector rather than subsectors. In doing so, the Sector is able to develop a plan that best meets the priorities of the sector as a whole, acknowledging that many costs and benefits are shared across subsectors. The previous volume (Volume 2) in this report describes the sector-wide results of this planning process. This volume presents the subsector investment plans under three potential funding scenarios in terms of the implications for each subsector within the Water and Environment Sector of Uganda. The results show that at business as usual funding levels, very few subsectors will achieve any of the targets set for 2030. As the budget is increased, and indicator achievement becomes more prevalent across various indicators, the sector can shift funding allocation across subsectors to support indicators that have lower achievement levels, either due to high costs or lower initial prioritization within the sector. This type of funding reallocation is most common in the Moderate-High funding scenario, when a budget of 2400 billion UGX per year is assumed. VOLUME 4: SECTOR INVESTMENT MODEL The Sector Investment Model (SIM) is the engine of the Strategic Investment Plan for the Water and Environment Sector (SSIP), and is the collaborative product of both Sector and outside experts. Volumes 1 through 3 of this report describe the SSIP findings, and this volume focuses on the SIM itself. The SIM is a decision support tool that aids in annual investment planning across the 23 catchments in Uganda. The model is designed to work in two modes to accomplish its two main objectives. The first is the funding requirements mode which uses information on indicator costs and achievement gaps to estimate the total funding requirements to meet Sector goals (documented in Volume 1 and 3). The second is the strategic allocation mode (Volume 2 and 3). In this mode, distribution of funding is based on both the cost of improvement in each indicator and a prioritization algorithm. Priorities are defined in the SIM based on several factors, including current budget allocation, the remaining gap between indicator baselines and targets, and the Sector’s preferences, as reporting during the stakeholder engagement process. The two modes of the SIM rely on the same databases of information that characterize the status, costs, preferences, and targets of the Sector indicators. SIM outputs are thus either a budget requirement, or an investment mix and subsequent indicator achievement trajectory. STRATEGIC INVESTMENT PLAN FOR THE WATER AND ENVIRONMENT SECTOR, UGANDA (2018-2030) VOLUME 1: FUNDING REQUIREMENTS TO MEET NATIONAL 2030 TARGETS FOR THE WATER AND ENVIRONMENT SECTOR FINAL REPORT prepared for: Ministry of Water and Environment Kampala, Uganda prepared by: Kenneth Strzepek, Brent Boehlert, and Jacqueline Willwerth Industrial Economics, Incorporated 2067 Massachusetts Avenue Cambridge, MA 02140 617/354-0074 Final – March 31, 2018 i TABLE OF CONTENTS ACRONYMS ABSTRACT 1.1 INTRODUCTION 1.2 WATER AND ENVIRONMENT SECTOR NATIONAL INDICATORS AND 2030 TARGETS 1.3 TARGET COSTING METHODS 1.3.1 External Trend and Indicator Trajectory Assumptions 4 1.3.2 Estimating the Remaining Gap to Achieve 2030 Targets 5 1.3.3. Unit Costs: Methods and Review Process 6 1.4 ANNUAL FUNDING REQUIREMENTS 1.5 PROJECTED TARGET SHORTFALL UNDER CURRENT FUNDING LEVELS Final – March 31, 2018 ii ACRONYM LIST BAU Business-as-Usual GHG Greenhouse Gas O&M Operations and Maintenance SDG Sustainable Development Goals SIM Sector Investment Model SSIP Strategic Sector Investment Plan UGX Ugandan Shilling WfP Water for Production Final – March 31, 2018 iii ABSTRACT The Water and Environment Sector currently receives about 800 billion UGX per year for investment in activities modeled in the SSIP. With this funding, the Sector has managed to make progress across a number of indicators that track the Sector’s performance across its major areas of intervention. The Sector has stated a set of targets for these indicators to be met by 2030, in line with the United Nations Sustainable Development Goals (SDG). Based on the Sector investment modeling and planning undertaken in this report, the current level of funding with an assumed moderate annual growth rate of 3 percent (business as usual, or BAU scenario) will not be sufficient for the Sector to meet its targets. In this funding requirements assessment, accounting for external trends such as population growth, it is estimated that the Sector will need an average annual budget of about 7.6 trillion UGX over the next 13 years to 2030. This is about nine times the current funding allocated to SSIP investments. This volume describes the methods used in costing analysis and presents the results in terms of both the full sector funding requirement from 2018 to 2030 and an analysis of how the sector indicators will perform by 2030 in the business as usual budget allocation. Final – March 31, 2018 1 1. VOLUME 1: FUNDING REQUIREMENTS TO MEET NATIONAL 2030 TARGETS FOR THE WATER AND ENVIRONMENT SECTOR 1.1 INTRODUCTION The Water and Environment Sector has historical received funding at a level of less than 1% of GDP. With this level of funding, the Sector has not been able to make adequate progress towards its targets for resource protection and service provision. These targets, beyond representing major improvements in services vital to the Ugandan people, also represent international commitments of the Ugandan government. Of the 24 indicators included in the SSIP, 18 are directly related to Sustainable Development Goals, the United Nations global program for poverty alleviation and environmental protection. While not legally binding, the country’s participation in the program is monitored closely and has helped shape national priorities. The purpose of this volume is to outline the amount of funding required annually for the Sector to reach the 2030 targets on schedule. The Sector will need this funding in order to meet its targets and remain in compliance with the international commitments of the SDGs. Investment at current levels will not results in met targets, especially in the SDG indicators which tend to be more expensive to achieve. Indicator target costing is calculated within the Sector Investment Model or SIM. In addition to its use as budget allocation model, the SIM also serves as a consolidated database of investment unit costs, targets and baselines, and indicator units (e.g. population, GHG emissions, etc.) across all years of the SSIP (2017-2030) and all water management catchments in Uganda (see Volume 4 for more information on the SIM). The results of this analysis show that at the current level of funding, very few, if any, of the 24 indicator targets will be achieved by 2030. In order for Uganda to meet not only the international standards agreed upon in the SDGs, but also to meet the important priorities defined by the Water and Environment Sector, the Sector requires funding of almost 5 trillion UGX in 2018, increasing to almost 10 trillion by 2030, accounting for population growth. This volume provides more detailed results of this analysis. First, however, the inputs and methods of this calculation are described. The remaining sections of this volume begin with a description of the indicators tracked in the SIM and provide current levels of achievement and 2030 target figures. This is followed by a discussion of the cost accounting methods used to quantify the funding requirement. Next, results of two funding scenarios are presented. First is the full funding requirement of the Sector. Second are the results of underfunding based on a projected business as usual funding allocation Final – March 31, 2018 2 1.2 WATER AND ENVIRONMENT SECTOR NATIONAL INDICATORS AND 2030 TARGETS During the initial phase of this study, Sector stakeholders selected a set of indicators currently used in the Sector to monitor performance for inclusion in the SIM (see the 2017 Sector Performance Report). These indicators cover all major functions of the Sector. The group considered only indicators for which there was a link between investment and indicator performance. For example, indicators related to good governance, funding, reporting, and gender are not included in the SSIP or SIM. Table 1-1 below presents the 24 indicators included in the SIM and this analysis along with their 2017 baseline status and 2030 goals. Note that for many of these indicators the Sector did not have an official 2030 target selected. In these instances (the non-bolder figures in the table), the study team selected a reasonable target based on expert judgement. Three indicators (5-Per capita investment cost; 19-Level of Water Stress; and 23 Climate Vulnerability Index) are reporting indicators, meaning they do not link directly to investments within the SIM. Rather they are the byproduct of progress in other indicators. Their status is reported however no costs for achieving these indicators are included in this analysis. TABLE 1-1. INDICATORS MONITORED IN THE FUNDING REQUIREMENT ANALYSIS INDICATOR DEFINITION B A SE LI N E 1 T A R G E T RELATED SDG OTHER TARGET 1 Village water supply Percentage of villages with a source of safe water supply 66% 100% Presidential Directive 2 Functional rural water sources Functionality: rural: % of water sources functional at time of spot-check 85% 100% 6.1.1 3 Improved drinking water Basic water: Percentage of population using an improved drinking water source 70% 100% 6.1.1 NDPII 4 Safely managed drinking water Safely managed water: Percentage of population using safely managed drinking water services located on premises 7%2 100% 6.1.1 5 Per capita investment cost Per Capita Investment Cost: Average cost per beneficiary of new water and sanitation schemes (USD) $32 $75 6 Urban water service functionality Functionality: urban: % piped water service availability 92% 100% 6.1.1 7 Solid waste disposal % urban solid waste safely disposed of or recycled in municipalities 68% 90% 11.6.1 8 Improved sanitation Basic sanitation: Percentage of population using an improved sanitation facility not shared with other households 19%2 50% 6.2.1 NDPII 9 Safely managed sanitation Safely managed sanitation: Percentage of 9% 100% 6.2.1 Final – March 31, 2018 3 INDICATOR DEFINITION B A SE LI N E 1 T A R G E T RELATED SDG OTHER TARGET population using safely managed sanitation services 10 Handwashing at home Hand washing: Percentage of population with hand washing facilities with soap and water at home 37% 90% 6.2.1 NDPII 11 Handwashing at school Schools: Percentage of pupils enrolled in schools with basic hand washing facilities 35% 90% 6.2.1 12 Irrigation Irrigation: Proportion of irrigation potential utilized 0.49% 4% 6.4 13 Water for Production (WfP) functionality WfP Functionality: % of water for production facilities that are functional at time of spot-check 85% 100% 14 Storage Capacity Cumulative Water for Production Storage Capacity (million m³) 38.87 163.67 NDPII 15 Compliance with water standards Drinking water quality: % of water samples taken that comply with national standards: Point water sources / Piped schemes or to be combined? 64% 90% 6.1.1 16 Permit compliance Compliance with permit conditions: % of permit holders complying with permit conditions 71% 90% 17 Wastewater treatment Proportion of wastewater safely treated 20% 60% 6.3.1 18 Ambient water quality Proportion of bodies of water with good ambient water quality 0% 100% 6.3.2 NDPII 19 Level of water stress Level of water stress: Water withdrawal as a proportion of available water resources 60% 100% 6.4.2 20 Wetlands coverage % Uganda’s land surface area covered by forest 8.6%3 13% 6.6.1 NDPII 21 Forest Coverage % Uganda’s land surface area covered by wetlands 9% 24% 15.1.1 NDPII 22 GHG emissions CC Mitigation: % change in direct and indirect GHG emissions 0% 22% 13.2 NDPII Final – March 31, 2018 4 INDICATOR DEFINITION B A SE LI N E 1 T A R G E T RELATED SDG OTHER TARGET 23 Climate vulnerability index % change in Uganda’s climate change vulnerability index 50% 100% 13.1 24 Operational weather stations % weather observation stations operational and submitting data throughout the year 43% 100% NDPII 1Bold baseline and target figures are from the 2017 Water and Environment Sector Performance Report unless otherwise noted. 2Source: UNICEF Joint Monitoring Program (JMP). 3Personal communications with Wetlands division staff. The three indicators related to functionality of infrastructure/facilities (urban, rural, and water for production; indicators 2, 6, and 13) are within 15 percent of their targets—the closest out of all indicators. The sanitation subsector indicators are among the further from their targets as a group. As may be expected, the advanced coverage indicators such as safely managed drinking water (4) and safely managed sanitation (9) are currently well below the targets. These indicators will require a large amount of funding due to the combination of the large gap to close and high unit costs of achievement. 1.3 TARGET COSTING METHODS The basic approach used to estimate funding requirements is to define external growth assumptions and pathways to achieving targets, estimate the gap in unit coverage between the baseline year and the 2030 target, define unit costs and multiply the annual units covered (i.e. gap closure) by the year-specific unit costs. Note that the process below applies to the National level however the underlying analysis was conducted at the catchment level. In this analysis, the catchments refer to 23 catchments of the country, which are a fusion of regions used for catchment management and administrative boundaries (i.e. districts). For more information and a map of the catchments, see Volume 2 of this report. 1.3.1 EXTERNAL TREND AND INDICATOR TRAJECTORY ASSUMPTIONS The estimate of funding required to reach 2030 targets must take into account trends in external factors that impact either the unit costs or affected populations. Population growth is one of these important assumptions as many of the indicators have population or units anchored to population. Some indicators, such as the clean water supply indicators, are directly linked to population growth. Others indicators are based on units for which specific growth trend information is not available. In many cases, these units are assumed to scale with population growth. For example, the growth forecast for number of permits issued (Indicator 16-Permit compliance) is unknown. Therefore the number of permits issued is assumed to scale with population growth. For this Final – March 31, 2018 5 analysis, an overall population growth rate of 3.05% is used. 1 A full report of indicator populations (units) by catchment and year is available in the annexes of Volume 4. This costing exercise also assumes that all indicators move linearly from baseline achievement to target levels. In practice, some may reach their targets well before 2030 and others may require a large push of funding leading up to 2030 (i.e. a last mile investment). Without further forecasting information available, this assumption provides a reasonable trajectory. 1.3.2 ESTIMATING THE REMAINING GAP TO ACHIEVE 2030 TARGETS The first step in the funding requirement analysis is to calculate the number of additional units to be covered by 2030 above the baseline coverage to meet 2030 targets. As examples, this could be the number of people to be covered with safely managed drinking water, the number of hectares of degraded wetland to be rehabilitated, or the number of weather stations to replace. These gaps are designed to incorporate outside trends. In the previous examples, the number of people to be covered by safe drinking water in 2030 is based on the projected 2030 population and the number of wetlands to be rehabilitated includes an encroachment trend (signaling if no investment is made, the wetlands area will decrease). Unit gaps for each indicator are presented in Table 1-2. TABLE 1-2. UNIT GAPS TO ACHIEVING 2030 TARGETS INDICATOR GAP UNITS 1 Village water supply 47,349 villages 2 Functional rural water sources 68,785 rural water sources 3 Improved drinking water 28,780,503 people 4 Safely managed drinking water 52,272,805 people 5 Per capita investment cost [reporting] US dollars 6 Urban water service functionality 10,448 sources 7 Solid waste disposal 610,906 tonnes 8 Improved sanitation 47,595,980 people 9 Safely managed sanitation 51,151,465 people 10 Handwashing at home 35,532,276 people 11 Handwashing at school 12,296,652 students 12 Irrigation 104,156 hectares 13 WfP functionality 756 sources 14 Storage Capacity 125 Million cubic meters (MCM) 15 Compliance with water standards 643 samples 16 Permit compliance 700 permits 17 Wastewater treatment 25,416,614 people 18 Ambient water quality 1,823,421 tonnes of biological oxygen demand (BOD) 1 Population growth is assumed to be at a constant rate of 3.05% per year, in accordance with the 2018-2020 average UBOS 2014 Population Projection figures. Final – March 31, 2018 6 INDICATOR GAP UNITS 19 Level of water stress [reporting] 20 Wetlands coverage 1,180,830 hectares 21 Forest Coverage 4,025,558 hectares 22 GHG emissions 18,053,293 tonnes CO2 23 Climate vulnerability index [reporting] 24 Operational weather stations 1,280 weather stations 1.3.3 UNIT COSTS: METHODS AND REVIEW PROCESS The next step in the target costing analysis is to define unit costs for each indicator. In order to define unit costs, each indicator must first be linked to one or more investment in the Sector. Next, a per-unit cost is estimated for each relevant investment category. Table 1-3. shows the 24 indicators and their related investments across three investment types: Capital, Operations and Maintenance (O&M), and Software. TABLE 1-3. INDICATORS AND RELATED INVESTMENTS INDICATOR RELATED INVESTMENTS CAPITAL O&M SOFTWARE 1 Village water supply • Water Supply-Basic • Water Supply-Piped • Water Supply Basic • Water Supply Piped 2 Functional rural water sources • Water Source Repair-Rural 3 Improved drinking water • Water Supply-Basic • Water Supply-Piped • Water Supply Basic • Water Supply Piped 4 Safely managed drinking water • Water Supply Piped • Water Supply Piped 5 Per capita investment cost Reporting Indicator 6 Urban water service functionality • Water Source Repair-Urban 7 Solid waste disposal • Solid Waste Disposal 8 Improved sanitation • Faecal Sludge Management • Faecal Sludge Management • Basic Sanitation Campaign 9 Safely managed sanitation • Faecal Sludge Management • Faecal Sludge Management • Safely Managed Sanitation Campaign 10 Handwashing at home • Home Handwashing Campaign 11 Handwashing at school • School Handwashing Final – March 31, 2018 7 INDICATOR RELATED INVESTMENTS CAPITAL O&M SOFTWARE Campaign 12 Irrigation • Irrigation • Irrigation 13 WfP functionality • Water Source Repair-WfP • Water for Production Local Training 14 Storage Capacity • Storage • Storage 15 Compliance with water standards • Drinking Water Samples 16 Permit compliance • Monitoring Equipment • Permit Compliance Monitoring 17 Wastewater treatment • Wastewater Treatment • Wastewater Treatment 18 Ambient water quality • Wetlands Restoration • Forest Rehabilitation • Wastewater Treatment • Wetlands Restoration • Forest Rehabilitation • Wastewater Treatment 19 Level of water stress Reporting Indicator 20 Wetlands coverage • Wetlands Restoration • Wetlands Restoration 21 Forest Coverage • Forest Rehabilitation • Forest Rehabilitation 22 GHG emissions • Forest Rehabilitation • Forest Rehabilitation 23 Climate vulnerability index Reporting Indicator 24 Operational weather stations • Weather Stations • Weather Stations Note: Bolded investments represent the most efficient set of investments to achieve progress. For Indicators 1 (Village Water Supply), 3 (Improved Drinking Water), and 18 (Ambient Water Quality), there are alternative investments to achieving the intended improvement. If the indicator were to be achieved in isolation, the least expensive alternative would be the likely investment choice (shown in bold in Table 3). Therefore, when the unit costs are calculated, only the least expensive, or most efficient , investment costs are included. In reality, however, the Sector is trying to achieve multiple goals at the same time. Each of the less-efficient investments for the three indicators mentioned is an efficient investment for another indicator. Therefore, as the Sector allocates funding to those investments primarily for the purpose of achieving one goal, the other indicators will receive ancillary benefits. For example, the Sector will choose to invest in wetlands restoration to improve indicators 18, but as it invests in forest rehabilitation and wastewater treatment for indicators 17 and 21, respectively, there will also be an improvement seen in the ambient water quality. Final – March 31, 2018 8 The study team developed the unit costs with input and review from Sector staff. Where information was available, costs were estimated specifically for each catchment and year. A full description of the cost estimates can be found in the annexes of Volume 4. Table 1-4 gives the unit cost per investment for the first year of the model. As noted, there are co-benefits of certain investments within the sector so the total cost of achieving 2030 targets is less than the product of indicator unit gaps and unit costs. Annual unit costs by catchment are also available in the Volume 4 annexes. TABLE 1-4. 2018 NATIONAL AVERAGE UNIT COSTS BY INDICATOR INDICATOR UNIT COST (THOUS. UGX) UNITS 1 Village water supply 25,136 villages 2 Functional rural water sources 5,546 rural water sources 3 Improved drinking water 85 people 4 Safely managed drinking water 387 people 5 Per capita investment cost reporting indicator 6 Urban water service functionality 8,298 sources 7 Solid waste disposal 256 tonnes 8 Improved sanitation 20 people 9 Safely managed sanitation 190 people 10 Handwashing at home 20 people 11 Handwashing at school 20 students 12 Irrigation 43,545 hectares 13 WfP functionality 32,913 sources 14 Storage Capacity 19,578,654 MCM 15 Compliance with water standards 495,881 samples 16 Permit compliance 32,211 permits 17 Wastewater treatment 387 people 18 Ambient water quality 13,058 tonnes of BOD 19 Level of water stress reporting indicator 20 Wetlands coverage 10,369 hectares 21 Forest Coverage 6,369 hectares 22 GHG emissions 251 tonnes CO2 23 Climate vulnerability index reporting indicator 24 Operational weather stations 218,949 weather stations The magnitude of unit costs varies widely, as does the magnitude of unit gaps. This, along with the inclusion of co-benefits, makes direct comparisons of funding requirement by indicator difficult. In general, advanced coverage indicators (i.e. 4-Safely managed drinking water, and 9 Safely managed sanitation) are most costly, as are the environmental protection indicators (i.e. 20-Wetlands coverage, and 21-Forest Coverage). However each of these four indicators Final – March 31, 2018 9 mentioned offer co-benefits, so while expensive to achieve, investment in these indicators improves achievement across a wide range of indicators. 1.4 ANNUAL FUNDING REQUIREMENTS Using the data described above, the SIM calculated the annual costs required to achieve all indicator targets by 2030. The SIM is able to capture co-benefits of investment in certain categories, such as the water quality benefits of wetlands restoration and the improvement in the number of villages with a safe water source from safely managed water supply investment. Figure 1-1 shows the required funding per year and includes the SSIP investment Sector budget allocation for FY2017-2018 for comparison. 2 For example, costs related to policy and planning are not included in the SSIP and therefore not included in the red bar in 2017. FIGURE 1-1. ANNUAL SECTOR BUDGET REQUIREMENTS TO REACH 2030 TARGETS The Sector’s average annual funding requirement is 7.6 trillion UGX over the next 13 years. As seen in Figure 1, this is about nine times the current funding allocated to SSIP investments. While capital investments (shown in blue) make up the majority of these costs, a growing component of the total budget will also need to be devoted to O&M and replacement to keep up the functionality of a growing and aging capital stock. By 2030, O&M and replacement are estimated to be about 16% of the total budget requirement. Several important yet high-cost indicators comprise the majority of this requirement. Figure 1-2 presents the funding required by indicator to reach 2030 targets. Note that these costs do not account for co-benefits, and therefore, the cost of simultaneously achieving all indicators is less than the sum of the figures below. In particular, several of the high cost indicators (i.e. forest coverage, ambient water quality, safely managed drinking water, and safely managed sanitation) 2 Released budget related to SSIP activities, as found in the 2017 Sector Performance Report. Final – March 31, 2018 10 are influenced by investments that also improve other indicators. Therefore incurring these costs may negate the need for the investments directed to the indicators receiving the ancillary benefits. F IGURE 1-2. TOTAL FUNDING REQUIREMENTS BY INDICATOR (2018-2030) Note: These costs do not account for co-benefits. The cost of simultaneously achieving all indicators is less than the sum of the above requirements. 1.5 PROJECTED TARGET SHORTFALL UNDER CURRENT FUNDING LEVELS If instead of receiving the funding required to meet 2030 targets the Sector continues to receive funding at the current level, the 2030 targets will not be realized, and many will remain far from the targets. Figure 1-3 presents the Business-as-Usual (BAU) funding trajectory, assuming the Sector continues to receive the current level of funding, growing at 3 percent each year. Final – March 31, 2018 11 FIGURE 1-3. BUSINESS-AS-USUAL FUNDING TRAJECTORY Under this funding scenario, in 2030 many indicators will not reach their targets, as shown in Table 1-5. Fifteen of the 21 indicators costed for this analysis will be less than halfway to their 2030 targets compared to the 2017 baseline and only three will be within ten percent. Current high-priority indicators such as the improved and safely managed drinking water and sanitation are well below targets (greater than 70 percent of the gap from baseline to target would still need to be closed in each of the indicators). In the wastewater treatment indicator, the gap becomes larger by 2030, meaning the funding available is not sufficient to keep up with the growing population. Final – March 31, 2018 12 TABLE 1-5. SHORTFALLS BY INDICATOR If the Sector does not receive the funding necessary to meet indicator targets by 2030, it will be increasingly difficult to do so in subsequent years given the rate of population growth. Small increases in Sector funding will not be sufficient to close these gaps. A substantial increase in annual funding, on the order of six times current levels growing by more than the current three percent on average, is necessary to meet the Sector goals. Baseline Target 2030-BAU 1 Village water supply 66% 100% 90% 2 Functional rural water sources 85% 100% 88% 3 Improved drinking water 70% 100% 71% 4 Safely managed drinking water 7% 100% 10% 5 Per capita investment cost 32.00 [reporting] 55.02 6 Urban water service functionality 92% 100% 99% 7 Solid waste disposal 68% 90% 75% 8 Improved sanitation 19% 100% 50% 9 Safely managed sanitation 9% 100% 13% 10 Handwashing at home 37% 90% 38% 11 Handwashing at school 35% 90% 53% 12 Irrigation 0.49% 4% 1% 13 WfP functionality 85% 100% 99% 14 Storage Capacity 38.87 163.67 59.62 15 Compliance with water standards 64% 90% 73% 16 Permit compliance 71% 90% 88% 17 Wastewater treatment 20% 60% 16% 18 Ambient water quality 0% 100% 8% 19 Level of water stress [reporting] 4% 20 Wetlands coverage 9% 13% 11% 21 Forest Coverage 9% 24% 10% 22 GHG emissions 0% 22% 4% 23 Climate vulnerability index [reporting] 34.90 24 Operational weather stations 43% 100% 74% Legend: % of gap between baseline and target remaining Target met Within 5% of target Indicator Less than 50% of target achieved Within 50% of target STRATEGIC INVESTMENT PLAN FOR THE WATER AND ENVIRONMENT SECTOR, UGANDA (2018-2030) VOLUME 2: CONSOLIDATED STRATEGIC SECTOR INVESTMENT PLAN FOR THE WATER AND ENVIRONMENT SECTOR OF UGANDA FINAL REPORT prepared for: Ministry of Water and Environment Kampala, Uganda prepared by: Kenneth Strzepek, Brent Boehlert, and Jacqueline Willwerth Industrial Economics, Incorporated 2067 Massachusetts Avenue Cambridge, MA 02140 617/354-0074 Final – March 31, 2018 i TABLE OF CONTENTS ACRONYMS ABSTRACT 2.1 BACKGROUND AND MOTIVATION 2.1.1. Objective of the Consolidated SSIP 1 2.1.2. Structure of the SSIP Documentation 2 2.2 COMPONENTS OF THE STRATEGIC SECTOR INVESTMENT PLAN 2.2.1. Subsectors 2 2.2.2. Outcomes 3 2.2.3. Indicators 4 2.2.4. Investments 5 2.2.5. Catchments 5 2.3 INVESTMENT PLANNING METHODS 2.3.1. SIM Framework 6 2.3.2. External Trends 7 2.3.3. Spending Drivers in the SIM 8 2.3.4. Budget Scenarios 9 2.3.5. O&M Expenditures and Replacement Costs in the SIM 9 2.4 FINDINGS OF THE CONSOLIDATED INVESTMENT PLANS 2.4.1. Overall Performance Through 2030 Under Budget Scenarios 10 2.4.2. Sector Funding Over Time 11 2.4.3. Catchment-Level Spending and Indicator Achievement 12 2.4.4. Operations and Maintenance Expenditures Over Time 14 2.4.5. Investment Summary 14 REFERENCES Final – March 31, 2018 ii ACRONYM LIST BAU Business-As-Usual MAAIF Ministry of Agriculture, Animal Industry, and Fisheries MoH Ministry of Health MWE Ministry of Water and Environment O&M Operations and Maintenance OECD Organisation for Economic Co-operation and Development SIM Sector Investment Model SSIP Strategic Sector Investment Plan UGX Ugandan Shillings WES Water and Environment Sector Final – March 31, 2018 iii ABSTRACT Through various development plans and international agreements, MWE and the Government of Uganda have agreed to achieve certain targets in the areas of water resource development and environmental management. Given the current level of funding, limited growth in future funding and a rapidly growing population, reaching these targets will be a challenge. The SSIP seeks to answer this question: in which types of investment should the Water and Environment Sector (WES) invest in order to maximize performance, as measured by 24 indicators, and best reflect the priorities of the sector? While Volume 1 of this Strategic Sector Investment Plan (SSIP) focuses on the spending requirements needed to reach 2030 targets, Volume 2 analyzes how to allocate resources within WES given limited budgets. The volume considers three budget scenarios—all of which are considerably below what is needed to achieve 2030 indicator targets—and given these constraints, describes spending pathways for water and environmental investments that are aligned with WES priorities. The emphasis of this volume is on reporting insights from developing a strategic investment plan at the WES level; details on subsector level planning are documented in Volume 3. This volume first reviews the measurement metrics and spatial units that drive the SSIP, then provides an overview of the Sector Investment Model (SIM), and lastly reviews findings and lessons learned under the three budget scenarios. These include a BAU scenario (current funding), a moderate-low investment scenario (1.5x current funding), and a moderate-high investment scenario (3x current funding). The three spending scenarios result in considerably different levels of indicator achievement, driven by differences in unit costs and indicator gaps that drive differences in funding allocation within the SIM. Several of the high performing indicators—including Water for Production Functionality, Permit Compliance, and Operational weather stations—have low unit costs relative to the spending allocation provided by Sector preferences. Others, such as Village water supply, benefit from receiving “ancillary benefits” that spill over from investing in achieving other indicators. On the other hand, the poorer performing indicators tend to be comparably expensive, in terms of the cost to reduce one percent of the gap. Examples include safely managed drinking water, which requires investing in expensive piped water supply, and forest coverage, which requires rehabilitation of forests. Sector outcomes tend to rise under all budget scenarios, although much more modestly under the BAU scenario. From a spatial perspective, a number of factors vary by catchment (the spatial scale used for this analysis—23 hydro-geographic regions of Uganda), including population, unit costs of investment (e.g., storage construction costs vary based on topographic characteristics), and the 2017 starting level for several of the indicators. These differences drive a non-uniform pattern of spending across space, where catchments with higher unit costs, higher populations (or units of investment such as irrigable hectares), and lower 2017 indicator levels receive a greater share of the funding. Lastly, O&M expenditures on both existing and new investments are assumed to be part of the allocated budget. Under the moderate-high budget scenario, approximately 20% of the total budget is spent on O&M expenses. Final – March 31, 2018 1 2. VOLUME 2: CONSOLIDATED STRATEGIC SECTOR INVESTMENT PLAN FOR THE WATER AND ENVIRONMENT SECTOR OF UGANDA While Volume 1 of this Strategic Sector Investment Plan (SSIP) focuses on the spending requirements needed to reach 2030 targets, Volume 2 analyzes how to allocate resources within the Water and Environment Sector (WES) given limited budgets. The volume considers three budget scenarios—all of which are considerably below what is needed to achieve 2030 indicator targets—and given these constraints, describes spending pathways for water and environmental investments that are aligned with WES priorities. The emphasis of this volume is on reporting insights from developing a strategic investment plan at the WES level; details on subsector level planning are documented in Volume 3. 2.1 BACKGROUND AND MOTIVATION Through various policies, strategies, and strategic plans, the water and environment sector and the Government of Uganda have agreed to achieve certain targets in the areas of water resource development and environmental management. Given the current level of funding, limited growth in future funding, and a rapidly growing population, reaching these targets will be a challenge. The SSIP seeks to answer this question: in which types of investment should MWE invest in order to maximize indicator performance and best reflect the priorities of the sector? 2.1.1 OBJECTIVE OF THE CONSOLIDATED SSIP The SSIP provides a bridge from policy to budgeting by identifying investment priorities that efficiently improve indicator achievements and are feasible given projected financing. With a sound and thorough investment plan, not only can the sector make the most of its allotted funding, but also it will demonstrate to donor organizations and national budget officials that the sector has a clear and direct plan for using any funds received. This investment plan is also built to work in conjunction with other planning efforts in the sector: most notably, the Water and Environment Sector Development Plan 2020 and 2030 Sustainable Development Goals. The recent work done to align indicators across all planning documents has been carried through in the SSIP. The SSIP does not identify investments at the project level. It is designed as a screening level assessment of the funding needs and priorities by investment and region. The SIM also does not comprehensively cover all functions and goals of the sector. Indicators related to crosscutting issues and governance such as gender equity and sector funding targets are not included in the SSIP, as progress in these indicators is more a function of policy than investment. The 2030 SSIP represents the first time the water and environment sector will present one consolidated investment plan. By developing a strategic, consolidated plan, the Ministry can consider the tradeoffs required to distribute funds, and more importantly, sector planners can Final – March 31, 2018 2 model and communicate how certain investments have crosscutting impacts. In the context of the SSIP, the nine sub-sectors within the water and environment sector are: Rural Water and Sanitation, Urban Water and Sewerage, Water for Production, Water Resource Management, Wetlands Management, Forestry, Sanitation and Hygiene, Climate Change, and Meteorology. 2.1.2 STRUCTURE OF THE SSIP DOCUMENTATION The results and methodologies of the SSIP process are documented in four volumes. Volume 1 is the Funding Requirements to Meet National 2030 Targets for the Water and Environment Sector. Volume 1 outlines the budget required to reach 2030 indicator targets, and the consequences of underfunding. Volume 2 (the current document) is the Consolidated Strategic Sector Investment Plan. This document provides the SSIP results in the context of the comprehensive water and environment sector. Volume 3 contains the Subsector Strategic Investment Plans. Volume 3 provides more details about the SSIP findings that are particularly relevant to each subsector. Volume 4 and its associated annexes, describe the details of the Sector Investment Model, or SIM. Users of the SIM may also be interested in the SIM User Guide, which provide more technical direction on the use of SIM Excel tool. Finally, the key findings of the SSIP are also presented in a four page Popular SSIP, which can be used to communicate the investment plan results to a broader audience. The remainder of this volume is organized as follows; first the definitions of the SSIP components are laid out (including subsectors, indicators, and investments). Next, the modeling framework of the SIM is presented. The results of the investment planning process are then discussed, focusing on strategic allocation of limited funds, and other insights. 2.2 COMPONENTS OF THE STRATEGIC SECTOR INVESTMENT PLAN There are several terms related to the organization and components of this document and the underlying analysis that are important to define because many of these terms have multiple definitions outside of the SSIP context. This section defines those components.1 2.2.1 SUBSECTORS MWE has defined nine subsectors that align with the directorates and agencies in the water and environment sector to organize the results of the SSIP. These subsectors and their related directorates, agencies, and organizations, both inside and beyond MWE, are presented below in Table 2-1 below. TABLE 2-1. WATER AND ENVIRONMENT SUBSECTORS AND RELATED AGENCIES SUBSECTOR RELATED AGENCIES AND ORGANIZATIONS Rural Water and Sanitation • Rural Water Supply and Sanitation Department (RWSD) • Urban Water and Sewerage Department (UWSD) 1 Further description of the SIM components can be found in Volume 4. Final – March 31, 2018 3 SUBSECTOR RELATED AGENCIES AND ORGANIZATIONS Urban Water and Sewerage • Urban Water and Sewerage Department (UWSD) • National Water and Sewerage Corporation (NWSC) Sanitation and Hygiene • Kampala Capital City Authority (KCCA) • Rural Water Supply and Sanitation Department (RWSD) • Urban Water and Sewerage Department (UWSD) Water for Production • Water for Production Department (WFPD) Water Resource Management • National Environmental Management Authority (NEMA) • Water Quality Management Department (WQMD) • Water Resource Monitoring and Assessment Department (WRMAD) • Water Resource Planning and Regulation Department (WRPRD) Wetlands Management • Wetlands Management Department (WMD) Forestry • National Forest Authority (NFA) • Forest Sector Support Department (FSSD) Climate Change • Climate Change Department (CCD) • Directorate of Environmental Affairs (DEA) • Uganda National Meteorological Authority (UNMA) Meteorology • Uganda National Meteorological Authority (UNMA) Organizations Relevant for All Subsectors • Environmental Support Services Department (ESSD) • District Local Governments • Development Partners (DP) • Non-governmental Organizations (NGO) • Community Supported Organizations (CSO) It is important to note that the conclusions presented in this plan are for investments made by organizations within the water and environment sector, shown above. Many of the objectives of the sector rely on support from outside Ministries and Agencies, including most notably, Ministry of Agriculture, Animal Industry, and Fisheries (MAAIF) and Ministry of Health (MoH). This plan assumes outside agencies will continue to fund complementary projects at the current level. 2.2.2 OUTCOMES The nine subsectors align with the institutional structure of the sector. For the purposes of investment planning, a second categorization of sector functions was created, organized around the primary outcomes of the sector; in other words, the primary services for which the sector is responsible. As shown in Table 2-2, the seven outcomes align fairly well with the nine subsectors. Final – March 31, 2018 4 2.2.3 INDICATORS In the SSIP, benefits of investment are measured by indicator progress. Twenty five indicators, covering the primary functions of the sector, are included in the plan. The indicators are selected from the list of revised indicators developed in the 2016 Review of the Water and Environment Sector Performance (Measurement) Monitoring Framework (hydrophil GmbH 2016) with stakeholder consultation at the beginning of the SSIP 2030 process. Where baseline and target information in missing, as is the case with several of the indicators developed during the 2016 Review, placeholder values have been assigned. The indicators and associated baseline and target information are presented in Table 2-2. TABLE 2-2. 25 INDICATORS BY OUTCOME AND SUBSECTOR Outcomes Subsector(s) Indicators Baseline Target Clean Water supply Rural Water and Sanitation 1. Village water supply 66% 100% 2. Functional rural water sources 85% 100% Rural Water and Sanitation/Urban Water and Sewerage 3. Improved drinking water 70% 100% 4. Safely managed drinking water 7% 100% *5. Per capita investment cost $32 $75 Urban Water and Sewerage 6. Urban water service functionality 92% 100% Sanitation and Public Health Sanitation and Hygiene 7. Solid waste disposal 68% 90% 8. Improved sanitation 19% 50% 9. Safely managed sanitation 9% 100% 10. Handwashing at home 37% 90% 11. Handwashing at school 35% 90% Water for Food Production Water for Production 12. Irrigation 0.49% 4% Water for Economic Activity 13. WfP functionality 85% 100% 14. Storage Capacity 38.87 163.67 Water Quality and Quantity Water Resource Management 15. Compliance with water standards 64% 90% 16. Permit compliance 71% 90% WRM/ Sanitation and Hygiene 17. Wastewater treatment 20% 60% Water Resource Management 18. Ambient water quality 0% 100% *19. Level of water stress 60% 100% Ecosystem Protection Wetlands Management 20. Wetlands coverage 8.6% 13% Forestry 21. Forest Coverage 9% 24% Climate Change Adaptation and Mitigation Climate Change 22. GHG emissions 0% 22% *23. Climate vulnerability index 50% 100% Meteorology 24. Operational weather stations 43% 100% Notes: Bold baseline and target figures are from the 2017 Water and Environment Sector Performance Report unless otherwise noted. *Indicator is only reported and not included in the objective function Final – March 31, 2018 5 2.2.4 INVESTMENTS Funding received by the sector is distributed among a set of investments, each with a defined relationship to at least one of the 24 indicators. The investment categories are intentionally somewhat general, as this SSIP does not attempt to identify investments at the project level; however the 14 investment identify general areas where funding is needed to impact indicator achievement. These 14 investments are further broken down in the SIM to separately model the components of each investment category related to capital, O&M, replacement, and software. The investment categories are described in Table 2-3. TABLE 2-3. SS IP INVESTMENT CATEGORIES INVESTMENT EXAMPLES Investments for Service Provision Reservoir construction, irrigation systems, water supply investments, wastewater treatment Investments for Monitoring Weather monitoring, water resources monitoring and permitting. Operations, Maintenance & Repair Recurring O&M costs Replacement Infrastructure replacement Software Permit Compliance Capacity Development Staff training and education campaigns 2.2.5 CATCHMENTS Investments in the SSIP are reported nationally and regionally. The SSIP Regions were defined to: 1. Be specific enough to provide meaningful detail to the investment plan without requiring an unreasonable amount of data collection and without inferring localized results from non-localized data; 2. Align with existing MWE and Government of Uganda planning and administrative regions; and 3. Map to hydrological features, demographic trends, and land use patterns that may influence investment needs. With these objectives in mind, the country was divided into 23 regions, as shown in Figure 2-1, which are referred to as catchments because they are primarily based on the catchments of Uganda. The catchments are primarily based on hydrological features, and where possible, considerations were made to make the catchment boundaries align with political boundaries, particularly along the border of the county. Several data sources used in this analysis provided information at the district level (112 total) . To translate this information, a series of weights was developed that creates catchment level averages out of district level data. The same method can be used to reverse this process: i.e. to calculate district level data from the catchment results. Final – March 31, 2018 6 FIGURE 2-1. TWENTY THREE SS IP CATCHMENTS OF UGANDA Further details about the components discussed above can be found in Volumes 3 and 4. 2.3 INVESTMENT PLANNING METHODS The elements above are organized into a Sector Investment Model (SIM) that formulates investment plans within the SSIP. As a decision support tool, the SIM allocates budget resources to investments across time and space based on a set of Sector priorities. The model determines investments annually over the 2018 to 2030 period, and for each of the 23 catchments described above. The SIM is built in Microsoft Excel and is intended to be accessible for all decision makers within the Sector. More detail is provided on the SIM in Volume 4 and the User Guidelines, but a brief overview is provided here for context. 2.3.1 S IM FRAMEWORK The SIM has two primary functions: to determine budget requirements needed to meet indicator targets, and to allocate limited funds to optimize indicator achievement (Figure 2-2). In allocation mode, external trends such as population growth are combined with information on Sector budget allocation, preferences (described below), and unit investment costs (e.g., cost per person for water supply) to specify how investments should be made to move toward indicator targets. In budget requirements mode, the problem is reversed, where indicator targets and unit investment costs drive investment decisions in order to estimate required funding levels annually. Final – March 31, 2018 7 Each indicator may be affected by several investments, and each investment may affect multiple indicators. Unit investment costs vary by catchment, as costs and impacts may vary spatially. FIGURE 2-2. SECTOR INVESTMENT MODEL (S IM) STRUCTURE The allocation algorithm distributes funds based on a set of weights which represent the relative preference of each indicator and the gap remaining between the indicator’s status and its target. 2.3.2 EXTERNAL TRENDS As noted above a major driver of investing decisions is how external drivers change over time and across catchments. Between 2017 and 2030, population in Uganda is anticipated to increase from 37 million to 57 million people. This means that maintaining current indicator performance in areas such as clean water supply provision and wastewater treatment will already require major investments, and that making forward progress will require considerably more. Figure 2-3 shows the population distribution in each of the 23 catchments, in both 2017 and 2030. Final – March 31, 2018 8 FIGURE 2-3. POPULATION DENSITY IN 2017 AND 2030 (PEOPLE PER SQUARE KM) 2.3.3 SPENDING DRIVERS IN THE S IM Of notable importance, spending in the SIM is allocated across indicators based on two drivers: (1) “preferences” that are Sector-specified weights on indicators that externally drive investment choices; and (2) how far the indicator is from reaching its target (the “gap”), which is a weight that is determined within the SIM each year as indicator gaps are closed. All else equal, indicators receive higher levels of funding when they have higher preferences and larger gaps; the relative importance of these two drivers can be determined by the SIM user. Indicator performance gap is also the driver of budget allocations across catchments. Sector preference weights are based on past budget allocation decisions that best reflect near term priorities, and the Sector’s stated relative preferences between indicators. The stated preferences were first elicited at a Workshop aimed at distilling longer term spending priorities.2 These preferences were then further refined based on a small group discussion with Sector representatives who considered both the preferences reported at the Workshop and the costs of achieving each indicator. The 2017/2018 budget allocation drives preferences in 2018, and these linearly move toward the preferences participants provided during the Workshop (Table 2-4). Note that while the results presented in the SSIP and Subsector Investment Plans represent the current priorities of the sector, the SIM can be used to test other scenarios and to explore how changing the assumptions of the SSIP may lead to different investment priorities. 2 The Workshop was held in Kampala in August 2017, and included Sector decision-makers. See details of this process in Volume 4, Annex A. Final – March 31, 2018 9 TABLE 2-4. INDICATOR SPENDING PREFERENCES OVER TIME Note: Blue represents higher preferences and orange represents lower preferences relative to other indicators 2.3.4 BUDGET SCENARIOS The SIM develops investment plans under three budget scenarios, all of which are assumed to escalate at 3% per year between 2018 and 2030. Starting points under the three scenarios are as follows: • Business-As-Usual (BAU). Under the BAU scenario, the annual budget is assumed to remain similar to historical levels, and starts at roughly the allocation available during the 2017/2018 fiscal year of 800 billion UGX (for details, see Volume 4). • Moderate-Low. A modest increase of 50% over the BAU scenario, to 1.2 trillion UGX per year. • Moderate-High. A much more aggressive increase of 200% over the BAU scenario, to 2.4 trillion UGX per year. Each of these budget scenarios is run through the SIM to evaluate how investment decisions are made, and what indicator levels result over time and across catchments. Figure 2-3 shows budget availability over time. 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Village water supply 2% 3% 3% 3% 3% 3% 3% 4% 4% 4% 4% 4% 4% 4% Functional rural water sources 2% 2% 2% 2% 2% 2% 2% 3% 3% 3% 3% 3% 3% 3% Improved drinking water 12% 11% 11% 10% 10% 9% 9% 8% 8% 7% 7% 6% 6% 5% Safely managed drinking water 31% 30% 28% 26% 25% 23% 21% 20% 18% 16% 14% 13% 11% 9% Urban water service functionality 10% 10% 9% 9% 9% 8% 8% 7% 7% 7% 6% 6% 5% 5% Solid waste disposal 1% 1% 1% 1% 1% 2% 2% 2% 2% 2% 2% 2% 2% 2% Improved sanitation 4% 4% 4% 4% 4% 4% 4% 3% 3% 3% 3% 3% 3% 3% Safely managed sanitation 2% 2% 2% 2% 3% 3% 3% 3% 4% 4% 4% 4% 5% 5% Handwashing at home 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% Handwashing at school 1% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 3% 3% Irrigation 10% 10% 9% 9% 9% 9% 9% 8% 8% 8% 8% 7% 7% 7% WfP functionality 2% 3% 3% 3% 3% 3% 3% 3% 3% 3% 3% 3% 3% 3% Storage Capacity 2% 3% 3% 4% 4% 4% 5% 5% 6% 6% 6% 7% 7% 8% Compliance with water standards 1% 1% 1% 1% 2% 2% 2% 3% 3% 3% 3% 4% 4% 4% Permit compliance 1% 1% 1% 1% 1% 2% 2% 2% 2% 2% 2% 3% 3% 3% Wastewater treatment 1% 2% 2% 2% 2% 2% 3% 3% 3% 3% 4% 4% 4% 4% Ambient water quality 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 6% 6% Wetlands coverage 1% 2% 2% 2% 3% 3% 4% 4% 4% 5% 5% 6% 6% 7% Forest Coverage 6% 6% 7% 7% 7% 7% 7% 7% 7% 7% 7% 7% 8% 8% GHG emissions 0.4% 1% 1% 1% 1% 1% 1% 2% 2% 2% 2% 2% 3% 3% Operational weather stations 2% 2% 3% 3% 4% 4% 4% 5% 5% 5% 6% 6% 7% 7% Final – March 31, 2018 10 FIGURE 2-4. INDICATOR SPENDING PREFERENCES OVER TIME 2.3.5 O&M EXPENDITURES AND REPLACEMENT COSTS IN THE S IM Operations and maintenance (O&M) and infrastructure replacement are critical in order to maintain long-lived infrastructure investments. Although ideally, O&M and replacement for productive infrastructure that have tariffs (e.g., water supply or treatment systems) would be funded by ratepayers, history suggests this is often not the case. For instance, in the case of water supply and sanitation, the OECD finds that neglect of O&M budgets is the primary reason for non-functionality observed in many rural systems.3 As such, the SIM assumes that O&M and replacement expenditures for both existing infrastructure and all future investments come out of the available spending under each budget scenario. The existing stock of water supply, sanitation, storage, and other infrastructure is estimated to cost 137 billion UGX to maintain (i.e., roughly 17% of the 2018 BAU budget) and requires 127 billion UGX in replacement in 2018 (17% of 2018 BAU budget) to maintain existing coverage. Spending this sum each year is assumed to maintain existing infrastructure in functioning condition, and means that only 526 billion UGX out of an original budget of 800 billion UGX is available for spending on new investments in 2018. Replacement costs are estimated through a replacement schedule for existing infrastructure (i.e. pre-SSIP investment) based on the known age of a sample of existing piped water systems for water supply infrastructure and a constant rate of 1/lifespan units requiring replacement per year for other infrastructure. All projects built within the SSIP have assumed lifespans that extend past 2030 and therefore no replacement costs are incurred through 2030 for investments put in place after 2017. Costs of future O&M are in addition to this 146 billion UGX figure. Implications for future O&M expenditures are explored in the next section. Caveats and uncertainties of the SIM model are discussed in Volume 4 of this report. 3 OECD. 2009. Strategic Financial Planning for Water Supply and Sanitation. Accessed on November 15, 2017 from https://www.oecd.org/env/resources/43949580.pdf. Final – March 31, 2018 11 2.4 F INDINGS OF THE CONSOLIDATED INVESTMENT PLANS As noted above, the consolidated SSIP aims to map out a Sector-wide investment plan that is consistent with the spending priorities of the Sector. This section summarizes the key characteristics of the three plans that were developed under the BAU, moderate-low, and moderate-high budget scenarios. 2.4.1 OVERALL PERFORMANCE THROUGH 2030 UNDER BUDGET SCENARIOS The three spending scenarios result in considerably different levels of indicator achievement. Table 2-5 reports the 2030 level of each indicator under the three budget scenarios, along with the percentage of the gap between 2017 and each target that was closed by the investment plan. As described above, spending decisions each year are driven by two factors: the Sector preference and the gap that year relative to the target. Several of the high performing indicators—including Water for Production Functionality, Permit Compliance, and Operational weather stations—have low unit costs relative to the spending allocation provided by Sector preferences. Others, such as Village water supply, benefit from receiving “ancillary benefits” that spill over from investing in achieving other indicators. In that example, village water supply coverage increases as investments occur to improve drinking water and the safety of managed drinking water supplies. On the other hand, the poorer performing indicators tend to be comparably expensive, in terms of the cost to reduce one percent of the gap. Examples include safely managed drinking water, which requires investing in expensive piped water supply, and forest coverage, which requires rehabilitation of forests. The poorest performing case is wastewater treatment, which is both extremely expensive and, as seen in the indicator spending preferences table above, relatively low priority according to Sector budgets. Although investment in wastewater treatment within the SIM is considerable because of the large gap, the indicator falls because investment cannot keep up with the population growth rate , which rises over 50 percent over the study period. Final – March 31, 2018 12 TABLE 2-5. INDICATOR ACHIEVEMENT LEVELS UNDER EACH BUDGET SCENARIO BY 2030 *Excludes three reporting indicators: cost per capita, water stress, and climate change vulnerability In coloring, green means closer to target, whereas red means far from target. 2.4.2 SECTOR FUNDING OVER TIME Figure 2-5 reports share of funding over time by investment under the BAU scenario, which as can be seen, presents a very different picture in 2018 than in 2030. Again, these changes are driven by shifting Sector priorities over time, rising population, and the relative year-by-year indicator gaps that unfold as investment decisions occur. BAU Mod High BAU Mod High Village water supply 66% 100% 90% 100% 100% 70% 100% 100% Functional rural water sources 85% 100% 88% 93% 98% 20% 50% 86% Improved drinking water 70% 100% 71% 80% 97% 2% 34% 91% Safely managed drinking water 7% 100% 10% 16% 35% 4% 10% 31% Urban water service functionality 92% 100% 99% 100% 100% 84% 100% 100% Solid waste disposal 68% 90% 75% 81% 87% 31% 61% 88% Improved sanitation 19% 100% 50% 68% 96% 39% 61% 94% Safely managed sanitation 10% 100% 13% 19% 39% 3% 10% 33% Handwashing at home 37% 90% 38% 49% 70% 2% 22% 62% Handwashing at school 35% 90% 53% 66% 81% 34% 57% 84% Irrigation 0.49% 4% 1% 1% 2% 10% 20% 50% WfP functionality 85% 100% 99% 100% 100% 90% 100% 100% Storage Capacity 24% 100% 36% 48% 74% 17% 31% 66% Compliance with water standards 61% 90% 73% 80% 87% 35% 62% 88% Permit compliance 71% 90% 88% 90% 90% 88% 97% 100% Wastewater treatment 20% 60% 16% 18% 26% -11% -6% 16% Ambient water quality 0% 100% 8% 16% 43% 8% 16% 43% Wetlands coverage 8.60% 130% 8% 17% 44% 8% 17% 44% Forest Coverage 9% 24% 3% 7% 20% 3% 7% 20% GHG emissions 0% 22% 4% 9% 22% 20% 40% 100% Operational weather stations 43% 100% 74% 85% 97% 54% 74% 94% TargetIndicator Base 2030 Achievement Percent of Gap 2017 Closed Final – March 31, 2018 13 FIGURE 2-5. FUNDING DISTRIBUTION OVER TIME ACROSS INVESTMENTS UNDER THE BAU BUDGET SCENARIO Note: “CB” denotes investments in software. 2.4.3 CATCHMENT-LEVEL SPENDING AND INDICATOR ACHIEVEMENT A number of factors vary by catchment, including population, unit costs of investment (e.g., storage construction costs vary based on topographic characteristics), and the 2017 starting level for several of the indicators. These differences drive a non-uniform pattern of spending across space, where catchments with higher unit costs, higher populations (or units of investment such as irrigable hectares), and lower 2017 indicator levels receive a greater share of the funding. Figure 2-6 shows the average annual per capita allocation of funds within each of the catchments and across budget scenarios. As expected, BAU spending is far lower, with average annual spending at roughly 10,000 to 30,000 UGX per capita. The one catchment that falls well outside of this range is the Kiryandongo catchment in the western part of the country. This catchment has a very small population yet comparably high irrigation potential, so the spending per capita is disproportionately high. Under the moderate-high scenario, per capita spending rises to between 50,000 and 80,000 UGX on these water investments. Final – March 31, 2018 14 FIGURE 2-6. AVERAGE ANNUAL PER CAPITA INVESTMENTS ACROSS CATCHMENTS UNDER THE THREE BUDGET SCENARIOS (1000S OF UGX) To visualize how indicator levels that differ across catchments tend to converge over time because of gap weighting, Figure 2-7 presents the case of functional rural water sources under the moderate-low budget scenario where each of the colored lines represent the trajectory of an individual catchment and the black dotted line represents the National average. As can be seen, the range of performance levels in 2017 ranges from 67% to 93%, but by 2024 these differences have largely disappeared. The SIM allocates a greater share of funding to catchments that are performing at lower levels, with the aim of generating equal levels of performance across catchments. Clearly, closing this gap will not be feasible across all indicators, because some regional differences are too significant to close over the SSIP study period. FIGURE 2-7. FUNCTIONAL RURAL WATER SOURCES ACROSS CATCHMENTS, UNDER THE BAU BUDGET SCENARIO, 2017-2030 Note: The dashed line is the National average achievement level across catchments. Final – March 31, 2018 15 2.4.4 OPERATIONS & MAINTENANCE AND REPLACEMENT EXPENDITURES OVER TIME As noted above, O&M expenditures on both existing and new investments are assumed to be part of the allocated budget. Figure 2-8 shows the balance between capital, O&M, and replacement cost spending over the 2018 period and under each of the budget scenarios. Under the moderate high budget scenario, approximately 700 million UGX, or 20% of the total budget is being spent on O&M and replacement expenses. The share of O&M rises more rapidly under the moderate high budget scenario because several investments with high O&M expenses (e.g., wastewater treatment) comprise a larger share of spending. Replacement costs do not vary by investment scenario because they depend on existing infrastructure. By 2030, approximately 300 million UGX is needed annually to replace aging infrastructure. FIGURE 2-8. SPENDING ON CAPITAL AND O&M UNDER THE THREE BUDGET SCENARIOS 2.4.5 INVESTMENT SUMMARY Table 2-6 shows the expected level of investment by investment category in the SSIP from 2018 to 2030. TABLE 2-6. INVESTMENT LEVELS BY SCENARIO (BILLIONS OF UGX) Year Investment Scenario 2018 2019 2020 2021 2022 2023 2024 2025 20301 Water Supply-Basic BAU 183 171 161 169 150 155 156 216 MODL 277 223 187 183 156 159 158 222 MODH 559 216 153 141 106 108 106 177 Full 59 52 52 74 56 67 70 144 Final – March 31, 2018 16 Year Investment Scenario 2018 2019 2020 2021 2022 2023 2024 2025 20301 Water Supply-Piped BAU 115 123 131 141 140 145 148 173 MODL 183 211 233 248 250 256 259 282 MODH 399 535 583 605 610 615 619 657 Full 1119 1202 1293 1399 1490 1601 1713 2186 Sewerage BAU 57 63 67 66 70 70 70 61 MODL 94 112 123 124 129 128 127 116 MODH 211 287 309 311 314 310 306 294 Full 533 572 613 656 700 747 796 990 Irrigation BAU 47 51 55 58 63 66 70 76 MODL 59 68 78 84 94 101 108 121 MODH 100 134 159 178 200 215 229 242 Full 381 381 381 381 381 381 381 381 Storage BAU 38 42 47 51 58 62 68 77 MODL 45 54 64 74 85 94 102 118 MODH 67 97 125 149 173 189 200 193 Full 218 221 225 229 233 236 240 253 Wastewater Treatment BAU 130 133 137 139 144 147 150 157 MODL 145 153 161 167 176 182 190 210 MODH 194 229 252 271 293 314 336 417 Full 654 695 737 782 829 878 930 1133 Wetlands Restoration BAU 57 62 68 70 80 84 90 99 MODL 111 121 136 144 160 170 181 204 MODH 296 364 387 403 430 450 472 521 Full 721 768 816 866 918 972 1028 1243 Forest Rehab. BAU 51 57 63 65 74 77 82 87 MODL 96 111 126 133 146 154 162 181 MODH 246 320 349 367 394 416 441 534 Full 1510 1608 1709 1814 1923 2035 2152 2602 Other2 BAU 123 121 119 114 122 121 122 114 MODL 189 182 166 154 155 149 146 136 MODH 328 289 231 197 181 166 155 147 Full 128 131 138 146 153 162 170 202 Note: Includes capital, O&M, replacement, and software costs. 1 2025-2030 values are annual averages. 2Other investments include solid waste disposal, weather station, water source repair (rural, urban, and water for production), drinking water samples, permit compliance, basic sanitation, and home and school handwashing. Final – March 31, 2018 17 REFERENCES Government of Uganda. 2017. Water and Environment Sector Performance Report 2017. Ministry of Water and Environment. Government of Uganda. 2017. Water and Environment Sector Development Plan, 2015/2016-2019/2020. Ministry of Water and Environment. Accessed on February 15, 2018 from http://www.mwe.go.ug/sites/default/files/library/Sector%20Development%20Plan%20%28SDP%29 %2023%20June%202017.pdf. OECD. 2009. Strategic Financial Planning for Water Supply and Sanitation. Accessed on November 15, 2017 from https://www.oecd.org/env/resources/43949580.pdf. 1 STRATEGIC INVESTMENT PLAN FOR THE WATER AND ENVIRONMENT SECTOR, UGANDA (2018-2030) VOLUME 3: STRATEGIC SUBSECTOR INVESTMENT PLANS FOR THE WATER AND ENVIRONMENT SECTOR OF UGANDA FINAL REPORT prepared for: Ministry of Water and Environment Kampala, Uganda prepared by: Kenneth Strzepek, Brent Boehlert, and Jacqueline Willwerth Industrial Economics, Incorporated 2067 Massachusetts Avenue Cambridge, MA 02140 617/354-0074 Final – March 31, 2018 i TABLE OF CONTENTS ACRONYMS ABSTRACT 3.1 BACKGROUND AND MOTIVATION 3.2 INVESTMENT PLANNING METHODS 3.3 STRATEGIC SUBSECTOR INVESTMENT PLANS 3.3.1. Rural Water Supply and Sanitation 3 3.3.2. Urban Water Supply and Sewerage 9 3.3.3. Sanitation and Hygiene 13 3.3.4. Water for Production 18 3.3.5. Water Resource Management 22 3.3.6. Forestry 27 3.3.7. Wetlands Management 31 3.3.8. Meteorology 34 3.3.9. Climate Change 38 REFERENCES Final – March 31, 2018 ii ACRONYM LIST BAU Business-As-Usual MWE Ministry of Water and Environment O&M Operations and Maintenance OECD Organisation for Economic Co-operation and Development SIM Sector Investment Model SSIP Strategic Sector Investment Plan UGX Ugandan Shillings WES Water and Environment Sector Final – March 31, 2018 iii ABSTRACT This volume reports subsector findings from the Strategic Investment Plan for the Water and Environment Sector, Uganda (2018-2030). This Sector planning effort marks the first time the Water and Environment Sector has undertaken strategic planning as a sector rather than subsectors. In doing so, the Sector is able to develop a plan that best meets the priorities of the sector as a whole, acknowledging that many costs and benefits are shared across subsectors. The plan is developed using a Sector Investment Model (SIM) to model various pathways for the Sector to achieve 24 indicators selected to represent the Sector’s main intervention areas. The previous volume (Volume 2) in this report describes the sector-wide results of this planning process. This volume presents subsector results under three potential funding scenarios. The results show that at business as usual funding levels, very few subsectors will achieve any of the targets set for 2030. As the budget is increased, and indicator achievement becomes more prevalent across various indicators, the Sector can shift funding allocation across subsectors to support indicators that have lower achievement levels, either due to high costs or lower initial prioritization within the sector. This type of funding reallocation is most common in the Moderate-High funding scenario, when a budget of 2400 billion UGX per year is assumed. Final – March 31, 2018 1 3. VOLUME 3: STRATEGIC SUBSECTOR INVESTMENT PLANS FOR THE WATER AND ENVIRONMENT SECTOR OF UGANDA The subsector investment plans that follow are part of the Strategic Sector Investment Planning process. The results of this process are documented across the four volumes of this report. Volume 1 of the report presents the full cost to the Sector of achieving all 2030 targets for 24 indicators selected to represent the major functions of the Sector. Volume 2 is the Consolidated Subsector Investment Plan, where the strategic investment results are discussed in terms of overall progress towards the primary goals of the Sector. The current volume (Volume 3) provides subsector details that underlie the results of the Consolidated Plan. The Sector Investment Model, or SIM, which produces these results, is described in more detail in Volume 4. Volume 4 also contains the annexes of the report, including data tables of SIM inputs and outputs. 3.1. BACKGROUND AND MOTIVATION This volume presents the results of three investment planning scenarios in terms of the implications for each subsector within the Water and Environment Sector of Uganda. Prior to this current strategic planning effort, the water sector and the environment and natural resources subsector were responsible for producing their own investment plan. Now the Sector has moved to a consolidated funding request system. As a unified sector, investment planning can be coordinated in a manner that best achieves the mandates of the sector given various funding levels. This volume covers the nine subsectors listed in Table 3-1. The subsector investment plans are extracted from the consolidated sector investment plans, as modeled by the SIM and described in Volume 2. Three future funding scenarios are explored in these investment plans: • Business-As-Usual (BAU). Under the BAU scenario, the annual budget is assumed to remain similar to historical levels, and starts at roughly the allocation available during the 2017/2018 fiscal year of 800 billion UGX (for details, see Volume 4). • Moderate-Low. An increase of 50% over the BAU scenario, to 1.2 trillion UGX per year. • Moderate-High. A much more aggressive increase of 200% over the BAU scenario, to 2.4 trillion UGX per year. Each budget constraint is modeled in the SIM and the results are presented in terms of improvement in 24 indicators that represent the primary interventions of the sector. Each subsector has at least one indicator in the SIM. Final – March 31, 2018 2 The SIM allocates these limited resources across investment categories and catchments according to weights that represent 1) the relative priority of indicators impacted by each investment, and 2) the gap for each impacted indicator between baseline levels and 2030 targets. TABLE 3-1. SUBSECTORS AND RELATED AGENCIES IN THE SECTOR SUBSECTOR RELATED AGENCIES AND ORGANIZATIONS Rural Water and Sanitation • Rural Water Supply and Sanitation Department (RWSD) • Urban Water and Sewerage Department (UWSD) Urban Water and Sewerage • Urban Water and Sewerage Department (UWSD) • National Water and Sewerage Corporation (NWSC) Sanitation and Hygiene • Kampala Capital City Authority (KCCA) • National Water and Sewerage Corporation (NWSC) • Rural Water Supply and Sanitation Department (RWSD) • Urban Water and Sewerage Department (UWSD) Water for Production • Water for Production Department (WFPD) Water Resource Management • National Environmental Management Authority (NEMA) • Water Quality Management Department (WQMD) • Water Resource Monitoring and Assessment Department (WRMAD) • Water Resource Planning and Regulation Department (WRPRD) Wetlands Management • Wetlands Management Department (WMD) Forestry • National Forest Authority (NFA) • Forest Sector Support Department (FSSD) Climate Change • Climate Change Department (CCD) • Directorate of Environmental Affairs (DEA) • Uganda National Meteorological Authority (UNMA) Meteorology • Uganda National Meteorological Authority (UNMA) Cross-cutting agencies • Environmental Support Services Department (ESSD) • District Local Governments • Development Partners (DP) • Non-governmental Organizations (NGO) • Community Supported Organizations (CSO) This volume also presents the results of the full funding requirement analysis. Using information on anticipated annual unit costs for each indicator and assuming a linear trajectory from all baseline levels to 2030 targets, the SIM reports the annual funding required to meet all indicator targets by 2030. The results in this volume are presented at the subsector level; however the results still take into account the important links between subsectors. In order for the Sector to achieve 2030 goals in the most efficient manner possible, the various subsectors must first have well defined priorities within each subsector, and coordinate investments to best make use of the co-benefits available. Final – March 31, 2018 3 The remainder of this volume begins with an overview of the investment planning process and a description of funding allocation in the SIM. Next, investment plans from each of the nine subsectors are presented. Each investment plan contains information about indicator achievement and investment under three future scenarios. 3.2. INVESTMENT PLANNING METHODS The SIM has two primary functions: to determine budget requirements needed to meet indicator targets, and to allocate limited funds to optimize indicator achievement. Both of these analyses incorporate assumptions on external trends such as population growth. This is an important aspect of the investment plans: many subsectors are providing services and resource on a per capita basis, meaning even with no improvement in the indicator score, additional funding is necessary to maintain current provision rates. Unit investment costs vary by catchment, as costs and impacts may vary spatially. Another important aspect of the SIM framework is the modeling of co-benefits between subsector investments. Each indicator may be affected by several investments, and each investment may affect multiple indicators. For a full discussion of the investment planning methods used to define the subsector investment plans that follow, please see Volume 4. 3.3. STRATEGIC SUBSECTOR INVESTMENT PLANS The sections below detail the subsector investment plans for the nine subsectors of the Water and Environment Sector. Each section contains an introduction to the subsector and an explanation of the indicators included in the SSIP, a discussion of the funding requirements for the subsector to reach the 2030 targets, and the strategic investment plans for the subsector under three possible future scenarios. 3.3.1. RURAL WATER SUPPLY AND SANITATION 3.3.1.1. Introduct ion As of the 2017 Water and Environment Sector Performance Report (MWE 2017), national safe water coverage for rural areas is estimated at 70 percent, up from 67 percent in June of the previous year. Access to basic rural sanitation has reached 80 percent by June 2017, above the 2014/2015 target of 77 percent coverage. Investments in key rural water supply technologies and further sanitation education programs are crucial to maintaining and improving this coverage. While coverage may have increased, functionality decreased to 85 percent in 2017 from 86 percent in 2016 and 88 percent in 2014. Water is supplied through a variety of sources including water supply, boreholes, shallow wells, and protective springs, which make up 88 percent of the technology mix. The remaining 12 percent is supplied by piped systems and rainwater harvesting tanks. Future targets for water supply specify certain technologies. For example, a Vision 2040 goal is to have 100 percent of the Final – March 31, 2018 4 population with access to safe piped water by 2040, which will require additional funding. The SDGs do not explicitly require certain technologies, instead setting the goal as “safe and affordable drinking water” for 100 percent of the population by 2030. The strategic investment plan considers the technology mix employed when costing out increased coverage. The plan also weights the tradeoffs between efficiency of water provision technologies and equitable distribution of water provision, particularly in the context of rapid population growth of 3.05 percent in Uganda in 2016 (UBOS 2014). The functions of the Rural Water Supply and Sanitation subsector are represented in the SIM by the five indicators presented in Table 3-2. The village water supply indicator is a presidential directive mandating one improved water source per village. Improved drinking water is defined as coverage by any water source (i.e. piped supply, boreholes, shallow wells, protected streams, and rainwater harvesting tanks). Safely managed drinking water represents piped water coverage. TABLE 3-2. RURAL WATER SUPPLY AND SANITATION INDICATORS VILLAGE WATER SUPPLY FUNCTIONAL RURAL WATER SOURCES IMPROVED DRINKING WATER SAFELY MANAGED DRINKING WATER PER CAPITA INVESTMENT COST BASELINE1 66% 85% 70% 7% $32 TARGET1 100% 100% 100% 100% $75 UNITS villages sources people people USD 2018 UNIT COST (KUGX) 25,136 5,546 85 387 Reporting Indicator IN V E ST M E N T S CAPITAL Water Supply Basic Water Supply Piped Water Source Repair-Rural Water Supply Basic Water Supply Piped Water Supply Piped O&M Water Supply Basic Water Supply Piped n/a Water Supply Basic Water Supply Piped Water Supply Piped REPLACEMENT Water Supply Basic Water Supply Piped n/a Water Supply Basic Water Supply Piped Water Supply Piped SOFTWARE n/a n/a n/a n/a 1Bold baselines and targets are official figures from the Sector. Non-bold figures are estimates. 3.3.1.2. Funding Requirements to Ach ieve Ind icator Targets Table 3-3 presents the annual funding requirement to reach the targets of the rural water supply subsector. A continuously growing population is one of the main drivers of funding requirement to achieve the indicators in the rural water supply subsector. Each year, the number of people represented by a one percent coverage increase is rising. This is reflected in the growing annual cost estimates across all indicators, as seen in Table 3-3 despite the assumption of a linear path Final – March 31, 2018 5 from baseline to target in indicator terms. These figures do not include the impacts of benefits accruing across indicators for a given investment. An example of the shared benefits can be seen in the two water supply indicators (improved and safely managed drinking water), where under the benefits sharing 2030 trajectory model, there is no funding in basic water supply (i.e. shallow wells, boreholes, and protected springs) because by reaching the safely managed water supply goal of piped water to every household, the improved water indicator is also achieved and requires no additional funding. The majority of requirement is for capital investments, however O&M and replacements play an important role. TABLE 3-3. INDIVIDUAL INDICATOR FUNDING REQUIREMENTS (UGX BILLION) Note in the table above, the costs for improved drinking water and safely managed drinking water pertain to the cost required to cover only the rural population. Additional funding is necessary to meet this goal for the urban population (see Table 3-6). 3.3.1.3. Strateg ic A l locat ion of Resources under L imited Funding Indicators in the rural water supply subsector currently receive a relatively large share of the budget, and therefore the starting weights for these indicators are high and investment levels in the first several years are high. The 2030 preferences in the model put relatively less weight on these indicators, as seen in Figure 3-1, which causes a slow down in indicator achievement over time in the underfunding scenarios. However, the subsector continues to have one of the highest total weights in 2030. Note that the preferences shown in Figure 3-1 are only one input to the investment allocation algorithm and therefore do not direct correlate the share of funding received. Rural Water Supply 2018 2019 2020 2021 2022 2023 2024 2025 2030 Total Capital O&M Replace Software Village Water Supply 97 98 103 118 115 126 133 146 222 1,934 60% 20% 20% 0% Functional rural water sources 23 24 25 26 27 28 29 30 37 382 100% 0% 0% 0% Improved drinking water 170 171 179 207 199 218 231 254 390 3,386 58% 20% 22% 0% Safely managed drinking water 913 981 1,055 1,142 1,216 1,307 1,398 1,498 2,075 18,717 86% 12% 2% 0% Cost per Capita Note: These figures do not account for co-benefits between indicators Investment Type Mix Final – March 31, 2018 6 FIGURE 3-1. PREFERENCE WEIGHTS ON RURAL WATER SUPPLY AND SANITATION INDICATORS OVER TIME Unlike the full funding scenario, investment is still made in basic water supply in each underfunding scenario, particularly in the near term, as it is a less costly way to provide clean drinking water and results in progress in Indicators 1(village water supply) and 3 (improved water supply). Achievement of Indicator 4 (safely managed drinking water) is expensive relative to the other indicators and the gap between baseline and target is large; even the moderately high levels of investment across all years results in modest indicator achievement (10 percent in BAU and 35 percent in high funding, relative to 100 percent target). FIGURE 3-2. RURAL WATER SUPPLY AND SANITATION SUBSECTOR INDICATOR FUNDING BY SCENARIO Note: Funding above does not include replacement costs for existing infrastructure. Final – March 31, 2018 7 FIGURE 3-3. RURAL WATER AND SANITATION SECTOR INDICATOR ACHIEVEMENT RELATIVE TO BASELINE AND TARGET LEVELS The distribution of investments in this subsector across space is primarily driven by population distribution however other factors such as variation in baseline coverage and existing capital requiring maintenance also determine funding allocation. As seen in Figure 3-4, per capita spending across all indicators in the subsector is highest in the north. Final – March 31, 2018 8 FIGURE 3-4. RURAL WATER SUPPLY AND SANITATION AVERAGE ANNUAL SUBSECTOR INVESTMENT BY CATCHMENT 3.3.1.4. Subsector Planning Resul ts Summary Under all funding scenarios, achievement in village water supply is close to reaching 2030 targets. Rural water functionality and improved drinking water are relatively underachieveing in the BAU and Moderate-Low funding scenarios but get close to full achievement in the Moderate High scenario. Due to its high cost and large gap between baseline and target, particularly in the face of a growing population, reaching the target for safely managed drinking water would require a higher level of funding than currently allocated in the Moderate-High funding scenario, either through an increase in total budget or an adjustment in Sector preferences. TABLE 3-4. RURAL WATER SUPPLY AND SANITATION INDICATOR ACHIEVEMENT BY SCENARIO 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 Target Low Funding: 800 UGXbn Village Water Supply 66% 73% 79% 83% 86% 88% 90% 91% 92% 90% 100% Functional rural water sources 85% 89% 91% 92% 92% 92% 92% 91% 91% 88% 100% Improved drinking water 70% 72% 74% 75% 76% 76% 76% 76% 75% 71% 100% Safely managed drinking water 7% 7% 7% 8% 8% 9% 9% 9% 9% 10% 100% Cost per Capita $29 $34 $38 $42 $46 $49 $51 $54 $55 $75 Moderate Low Funding: 1200 UGXbn Village Water Supply 66% 80% 89% 95% 99% 100% 100% 100% 100% 100% 100% Functional rural water sources 85% 91% 94% 95% 95% 95% 95% 95% 95% 93% 100% Improved drinking water 70% 76% 79% 82% 83% 84% 84% 84% 84% 80% 100% Safely managed drinking water 7% 7% 8% 9% 10% 11% 12% 13% 14% 16% 100% Cost per Capita $30 $38 $47 $54 $59 $62 $65 $67 $69 $75 Moderate High Funding: 2400 UGXbn Village Water Supply 66% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% Functional rural water sources 85% 91% 95% 97% 98% 98% 99% 98% 98% 98% 100% Improved drinking water 70% 86% 91% 94% 96% 97% 98% 98% 98% 97% 100% Safely managed drinking water 7% 9% 12% 15% 18% 21% 23% 25% 28% 35% 100% Cost per Capita $30 $60 $74 $85 $91 $95 $98 $99 $99 $75 Final – March 31, 2018 9 3.3.2. URBAN WATER SUPPLY AND SEWERAGE 3.3.2.1. Introduct ion While percent safe water coverage in rural areas has increased over the past year, urban water supply has stagnated at 71 percent. Urban Water and Sewerage faces many of the same challenges as the rural water supply, with the additional pressure of population growth in urban areas due to urbanization and reclassification of rural areas to urban centers. At the same time, the goals for urban improved water supply follow a more aggressive schedule, with 100 percent coverage targeted by 2020. This would also achieve the SDG of 100 percent coverage by 2030. The functions of the Urban Water Supply and Sanitation subsector are represented in the SIM by the four indicators presented in Table 3-5. Note that two of these indicators (improved drinking water and safely managed drinking water) are shared with the rural water supply subsector. TABLE 3-5. URBAN WATER SUPPLY AND SEWERAGE INDICATORS INDICATOR IMPROVED DRINKING WATER SAFELY MANAGED DRINKING WATER PER CAPITA INVESTMENT COST URBAN WATER SERVICE FUNCTIONALITY BASELINE1 70% 7% $32 92% TARGET1 100% 100% $75 100% UNITS people people USD sources 2018 UNIT COST (KUGX) 85 387 Reporting Indicator 8,298 IN V E ST M E N T S Capital Water Supply Piped Water Supply Piped Water Source Repair-Urban O&M Water Supply Piped Water Supply Piped n/a Replacement Water Supply Piped Water Supply Piped n/a Software n/a n/a n/a 1Bold baselines and targets are official figures from the Sector. Non-bold figures are estimates. 3.3.2.2. Funding Requirements to Ach ieve Ind icator Targets As is the case in the rural water supply subsector, the safely managed drinking water indicator is the most costly to achieve. Also, as in the rural supply subsector, the full funding requirement to reach subsector goals is less than the sum of these three costs because meeting the safely managed drinking water target will also meet the improved drinking water target. Final – March 31, 2018 10 TABLE 3-6. INDIVIDUAL INDICATOR FUNDING REQUIREMENTS (UGX BILLION) As noted in the rural water supply investment plan, the responsibility for improved drinking water and safely managed drinking water is shared between the urban and rural water supply subsectors. The investment needs above are the costs required to cover the urban population. Additional funding is necessary to meet this goal for the rural population (see Table 3-3). 3.3.2.3. Strateg ic A l locat ion of Resources under L imited Funding Indicators in the urban water supply subsector currently receive a relatively large share of the budget, and therefore the starting weights for these indicators are high and investment levels in the first several years are large. The 2030 preferences in the model put relatively less weight on these indicators, as seen in Figure 3-5, which causes a slow down in indicator achievement over time in the underfunding scenarios. Note that the preferences shown in Figure 3-5 are only one input to the investment allocation algorithm and therefore do not direct correlate the share of funding received. FIGURE 3-5. PREFERENCE WEIGHTS ON URBAN WATER SUPPLY AND SANITATION INDICATORS OVER TIME Investment in urban water supply indicators remains relatively constant over time in the BAU and Moderate-Low funding scenarios. Although the relative preference to the subsector diminishes over time, the rapidly growing population causes a growing gap between current achievement and target levels in the two lowest funding scenarios, which brings investment back into the subsector. In all scenarios, capital investment levels stay relatively constant across time, however increasing shares of O&M and replacement are needed to maintain existing infrastructure. Urban Water Supply 2018 2019 2020 2021 2022 2023 2024 2025 2030 Total Capital O&M Replace Software Improved drinking water 38 39 40 47 45 49 52 57 88 763 58% 20% 22% 0% Safely managed drinking water 206 221 238 257 274 294 315 337 467 4,216 86% 12% 2% 0% Cost per Capita Urban water service functionality 5 6 6 6 6 6 7 7 8 87 100% 0% 0% 0% Note: These figures do not account for co-benefits between indicators Investment Type Mix Final – March 31, 2018 11 FIGURE 3-6. URBAN WATER SUPPLY AND SEWERAGE SUBSECTOR INDICATOR FUNDING BY SCENARIO Note: Funding above does not include replacement costs for existing infrastructure. FIGURE 3-7. URBAN WATER AND SEWERAGE SECTOR INDICATOR ACHIEVEMENT RELATIVE TO BASELINE AND TARGET LEVELS Figure 3-8 shows the distribution of investments in this subsector over 23 catchments, including both capital and O&M costs. Funding requirements per capita over space are concentrated in Final – March 31, 2018 12 areas with high urban populations where either existing infrastructure needs to be maintained or where capital investment is made to close the achievement gap under each funding scenario. FIGURE 3-8. URBAN WATER SUPPLY AND SEWERAGE AVERAGE ANNUAL SUBSECTOR INVESTMENT BY CATCHMENT 3.3.2.4. Subsector Planning Resul ts Summary Table 3-7 shows that current funding levels will likely be enough to reach the water source functionality goal however even a tripling of the current budget will not be adequate to meet the safely managed drinking water target. Without continue high levels of investment, the subsector will not be able to achieve its goals due to rapid population growth and urbanization. TABLE 3-7. URBAN WATER SUPPLY AND SEWERAGE INDICATOR ACHIEVEMENT BY SCENARIO 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 Target Low Funding: 800 UGXbn Improved drinking water 70% 72% 74% 75% 76% 76% 76% 76% 75% 71% 100% Safely managed drinking water 7% 7% 7% 8% 8% 9% 9% 9% 9% 10% 100% Cost per Capita $29.24 $33.54 $38.06 $42.24 $45.65 $48.78 $51.38 $53.64 $55.02 $0 Urban water service functionality 92% 95% 96% 97% 98% 98% 99% 99% 99% 99% 100% Moderate Low Funding: 1200 UGXbn Improved drinking water 70% 76% 79% 82% 83% 84% 84% 84% 84% 80% 100% Safely managed drinking water 7% 7% 8% 9% 10% 11% 12% 13% 14% 16% 100% Cost per Capita $29.52 $38.03 $46.71 $53.78 $58.71 $62.44 $65.12 $67.31 $69.30 $0 Urban water service functionality 92% 95% 96% 97% 98% 98% 99% 99% 99% 100% 100% Moderate High Funding: 2400 UGXbn Improved drinking water 70% 86% 91% 94% 96% 97% 98% 98% 98% 97% 100% Safely managed drinking water 7% 9% 12% 15% 18% 21% 23% 25% 28% 35% 100% Cost per Capita $0.00 $29.99 $59.68 $74.16 $84.57 $91.12 $95.33 $97.84 $99.34 $99.24 $0 Urban water service functionality 92% 95% 96% 97% 98% 98% 99% 99% 99% 100% 100% Final – March 31, 2018 13 3.3.3. SANITATION AND HYGIENE 3.3.3.1. Introduct ion The sanitation and hygiene subsector is a joint effort across a number of Ministries, as defined in the Sanitation Memorandum of Understanding (GoU 2011). Indicators such as the percent of people with access to improved sanitation and hand-washing facilities have increased moderately in recent years (from 70 to 80 percent of the rural population and 77 to 85.5 percent of the urban population, MWE 2017). An estimated 37 percent of the rural population and 40 percent of the urban population have access to a facility for handwashing as of 2017 (MWE 2017). The costs of poor sanitation in Uganda are inequitably distributed with the highest economic burden falling disproportionately on the poorest (Mutano et al. 2015). MWE requires the necessary funding to fulfill its obligations so that the group of ministries can achieve their common goals (e.g., faecal sludge management and educational campaigns for safe sanitation behaviors). Similar to the water supply subsectors, distributional equity will be an important consideration of this investment plan. Increased investments in sanitation and hygiene promotion are required not only to realize health and welfare benefits of sanitation but also to avert large economic losses. As in many other subsectors, interagency coordination is key to the success of the Sanitation and Hygiene subsector. The relationship between the MWE and Urban City Councils like Kampala Capital City Authority (KCCA), who manage urban storm water drainage and solid waste management, is also key for the sanitation sub sector. MWE will need to work with urban local government to address the lack of treatment and disposal facilities. Most urban centers also have poor or unmaintained drainage networks. This problem is further compounded by indiscriminate dumping of solid waste in the drains causing silting and flooding. The sanitation and hygiene subsector is represented in the sector strategic investment planning process by the five indicators described in Table 3-8. Note that improved sanitation refers to access to private facilities and safely managed sanitation refers to the manner in which the facility is maintained. TABLE 3-8. SANITATION AND HYGIENE INDICATORS INDICATOR SOLID WASTE DISPOSAL IMPROVED SANITATION SAFELY MANAGED SANITATION HANDWASHING AT HOME HANDWASHING AT SCHOOL BASELINE1 68% 19% 9% 37% 35% TARGET1 90% 100% 100% 90% 90% UNITS tonnes people people people students 2018 UNIT COST (KUGX) 256 20 190 20 20 IN V E ST CAPITAL Solid Waste Disposal Fecal Sludge Management Fecal Sludge Management n/a n/a O&M n/a Fecal Sludge Fecal Sludge n/a n/a Final – March 31, 2018 14 INDICATOR SOLID WASTE DISPOSAL IMPROVED SANITATION SAFELY MANAGED SANITATION HANDWASHING AT HOME HANDWASHING AT SCHOOL Management Management REPLACEMENT n/a n/a n/a n/a n/a SOFTWARE n/a Basic Sanitation Campaign Safely Managed Sanitation Campaign Home Handwashing Campaign School Handwashing Campaign 1Bold baselines and targets are official figures from the Sector. Non-bold figures are estimates. 3.3.3.2. Funding Requirements to Ach ieve Ind icator Targets As is the case in the water supply subsectors, funding requirements in the sanitation subsector are heavily influenced by population growth. This subsector also includes investments in a number of software, or capacity building costs and less investment in capital infrastructure, as much of the responsibility for infrastructure development, such as latrines or handwashing supplies falls on individuals and communities and is not funded by the subsector. TABLE 3-9. INDIVIDUAL INDICATOR FUNDING REQUIREMENTS (UGX BILLION) 3.3.3.3. Strateg ic A l locat ion of Resources under L imited Funding Currently the sanitation subsector receives about 20 percent of the overall sector budget. The 2030 preferences give relatively less weight to the indicators in this subsector. FIGURE 3-9. PREFERENCE WEIGHTS ON SANITATION INDICATORS OVER TIME Sanitation and Public Health 2018 2019 2020 2021 2022 2023 2024 2025 2030 Total Capital O&M Replace Software Solid waste disposal 15 10 10 11 11 11 12 12 13 156 100% 0% 0% 0% Improved Sanitation 158 168 177 187 198 209 220 232 301 2,912 67% 0% 0% 33% Safely managed sanitation 695 744 795 848 904 962 1,023 1,087 1,453 13,557 82% 7% 0% 11% Handwashing at home 116 122 129 135 143 150 158 166 212 2,084 67% 0% 0% 33% Note: These figures do not account for co-benefits between indicators Investment Type Mix Final – March 31, 2018 15 As seen in Figure 3-10, improved sanitation investments are also substituted with safely managed sanitation investments over time, as increases in safely managed coverage directly results in increases in improved coverage. As the improved coverage achievements near the target, funding allocation is transferred to other indicators, including safely managed sanitation, which continue to have a large gap between achievement and target levels at all modeled funding levels. FIGURE 3-10. SANITATION SUBSECTOR INDICATOR FUNDING BY SCENARIO Final – March 31, 2018 16 FIGURE 3-11. SANITATION AND HYGIENE SECTOR INDICATOR ACHIEVEMENT RELATIVE TO BASELINE AND TARGET LEVELS Under the BAU scenario, the solid waste disposal and handwashing at school indicators are able to close about half of the gap between baseline and target levels. Handwashing at home, improved sanitation, and safely managed sanitation close less than half of their gaps. Between the Moderate-High and Moderate-Low funding scenarios, which represents a doubling of budget, the gap closure more than doubles. In the Moderate-High scenario, as other indicators approach target levels, more funds become available for currently underachieving indicators. Baseline information for the sanitation indicators is not available at a spatially disaggregated level therefore the per capita spending is relatively unvaried across space. The exception being catchments with large towns, which receive funding for solid waste disposal in urban areas, while rural catchments do not. Final – March 31, 2018 17 FIGURE 3-12. SANITATION AND HYGIENE AVERAGE ANNUAL SUBSECTOR INVESTMENT BY CATCHMENT 3.3.3.4. Subsector Planning Resul ts Summary Given the large achievement gaps currently facing the sanitation subsector, a substantial increase in funding is necessary to reach 2030 targets, particularly for safely managed sanitation. Less expensive indicators such as handwashing and home and school do approach the target levels by 2030 in the Moderate-High funding scenario, but because of the low preference weights assigned to these indicators in favor of other measures, they significantly underachieve in the BAU scenario. TABLE 3-10. SANITATION INDICATOR ACHIEVEMENT BY SCENARIO 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 Target Low Funding: 800 UGXbn Solid waste disposal 68% 64% 63% 62% 63% 64% 66% 67% 69% 75% 90% Improved Sanitation 19% 25% 31% 36% 40% 43% 46% 48% 49% 50% 100% Safely managed sanitation 10% 10% 10% 11% 11% 12% 12% 12% 12% 13% 100% Handwashing at home 37% 36% 36% 35% 35% 35% 35% 35% 36% 38% 90% Handwashing at school 35% 35% 35% 35% 37% 38% 40% 43% 45% 53% 90% Moderate Low Funding: 1200 UGXbn Solid waste disposal 68% 64% 64% 65% 67% 70% 72% 74% 76% 81% 90% Improved Sanitation 19% 31% 41% 49% 55% 59% 62% 65% 66% 68% 100% Safely managed sanitation 10% 10% 12% 13% 14% 15% 15% 16% 17% 19% 100% Handwashing at home 37% 36% 36% 36% 37% 38% 39% 40% 42% 49% 90% Handwashing at school 35% 35% 36% 38% 41% 45% 49% 52% 56% 66% 90% Moderate High Funding: 2400 UGXbn Solid waste disposal 68% 65% 67% 72% 77% 80% 82% 84% 85% 87% 90% Improved Sanitation 19% 46% 65% 76% 82% 86% 89% 92% 93% 96% 100% Safely managed sanitation 10% 12% 15% 19% 22% 25% 27% 30% 32% 39% 100% Handwashing at home 37% 37% 38% 41% 43% 47% 50% 54% 57% 70% 90% Handwashing at school 35% 36% 41% 48% 55% 61% 67% 71% 74% 81% 90% Final – March 31, 2018 18 3.3.4. WATER FOR PRODUCTION 3.3.4.1. Introduct ion Water for Production (WfP) includes water supply to industry and services, as well as irrigation and livestock water provision. While some industries self-supply water, it would be beneficial to increase MWE’s capacity and ability to serve all customers in order to facilitate management of water resources. Expanded irrigation coverage as proposed in the national Irrigation Master Plan (MWE 2011) and the Agriculture Sector Investment Strategy (MAAIF 2011), has the benefit of improved crop yields, improving income for farmers and increasing available food supply. There is also a secondary benefit of protecting wetlands by moving irrigated crops from wetlands to upland areas equipped with irrigation infrastructure. Similarly, livestock water production improves livestock yields and moves livestock out of riverbed, improving downstream water quality. These water quality benefits can also be realized with improved water harvesting and sustainable land use policies in rainfed areas. At the time of the 2016 Sector Performance Report, MWE had 37.2 MCM of storage capacity for Water for Production and a goal of between 28 and 55 MCM by 2040, emphasizing the construction of large and small scale water schemes for irrigation, livestock and rural industries (GoU NDPII). However, total volume added by investments in the area in FY 2016/2017 was 1,680,000 m3 compared to 5,845,000 m3 of volume in FY 2015/2016. The area of irrigated land in Uganda is targeted to increase sevenfold in the next twenty years. This will require strategic planning to make sure new and expanded irrigation schemes are in the most advantageous areas, and that the hydrology of the region supports the plans. This may be accomplished by an agriculture value chain approach, as proposed in the NDP II. Careful collaboration between the responsible agencies (e.g. MWE, MAAIF, and the Ministry of Local Government) is a particularly important consideration in this subsector. The water of production subsector is represented by indicators covering irrigation, water for production source functionality, and storage capacity, as seen in Table 3-11. WfP subsector irrigation investments include construction headworks and primary and secondary canals. TABLE 3-11. WATER FOR PRODUCTION INDICATORS INDICATOR IRRIGATION WFP FUNCTIONALITY STORAGE CAPACITY BASELINE1 0.49% 85% 38.87 MCM TARGET1 4% 100% 163.67 MCM UNITS hectares sources MCM (million cubic meters) 2018 UNIT COST (KUGX) 43,545 32,913 19,578,654 IN V E ST M E N T S CAPITAL Irrigation Water Source Repair-WfP Storage O&M Irrigation n/a Storage REPLACEMENT Irrigation n/a Storage SOFTWARE n/a Water for Production Local Training n/a 1Bold baselines and targets are official figures from the Sector. Non-bold figures are estimates. Final – March 31, 2018 19 3.3.4.2. Funding Requirements to Ach ieve Ind icator Targets Unlike the sanitation and water supply subsector indicators, the majority of water for production indicators are not directly tied to population growth. WfP sources are assumed to grow in proportion to population to keep up with growing demand however irrigation and storage targets are fixed over time. This results in a constant spending trend assumption in the full funding scenario as shown in Table 3-12. The majority of the requirement is in capital investments; however replacement and O&M do also play a role. Note that no O&M for irrigation is included in the costs as this cost is intended to be the responsibility of the farmers benefiting from the initial investment. TABLE 3-12. INDIVIDUAL INDICATOR FUNDING REQUIREMENTS (UGX BILLION) 3.3.4.3. Strateg ic A l locat ion of Resources under L imited Funding The preference weights for water for production indicators grow over time, leading to an increasing trend in funding within the sector over time across all scenarios. There is a slight drop in funding amounts in the high scenario as these indicators reach their targets, freeing up funds for other subsectors. FIGURE 3-13. PREFERENCE WEIGHTS ON WATER FOR PRODUCTION INDICATORS OVER TIME Water for Production 2018 2019 2020 2021 2022 2023 2024 2025 2030 Total Capital O&M Replace Software Irrigation 381 381 381 381 381 381 381 381 381 4,955 92% 0% 8% 0% WfP functionality 3 3 3 3 3 3 3 4 4 44 78% 0% 0% 22% Storage Capacity 218 221 225 229 233 236 240 244 262 3,119 77% 17% 6% 0% Note: These figures do not account for co-benefits between indicators Investment Type Mix Final – March 31, 2018 20 FIGURE 3-14. WATER FOR PRODUCTION SUBSECTOR INDICATOR FUNDING BY SCENARIO Note: Funding above does not include replacement costs for existing infrastructure. Under all three underfunding scenarios, irrigation and storage capacity miss their targets. Water for production functionality, which is a relatively inexpensive indicator, reaches the target in all three scenarios. FIGURE 3-15. WATER FOR PRODUCTION SECTOR INDICATOR ACHIEVEMENT RELATIVE TO BASELINE AND TARGET LEVELS Final – March 31, 2018 21 Funding is distributed over space a seen in Figure 3-16. Areas with existing irrigation infrastructure receive the most funding both to cover O&M for existing schemes and promote expansion. FIGURE 3-16. WATER FOR PRODUCTION AVERAGE ANNUAL SUBSECTOR INVESTMENT BY CATCHMENT 3.3.4.4. Subsector Planning Resul ts Summary While source functionality is easily achieved with existing funding levels, both irrigation and storage capacity require more than tripling of funding to achieve 2030 targets. Alternatively, a shifting of sector priorities further towards these indicators would increase achievement for these indicators. TABLE 3-13. WATER FOR PRODUCTION INDICATOR ACHIEVEMENT BY SCENARIO 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 Target Low Funding: 800 UGXbn Irrigation 0.49% 0.50% 0.52% 0.53% 0.55% 0.57% 0.60% 0.63% 0.66% 0.83% 4% WfP functionality 85% 91% 95% 97% 98% 98% 98% 99% 99% 99% 100% Storage Capacity 38.9 39.3 39.9 40.8 41.8 43.2 44.8 46.6 48.6 59.6 163.67 Moderate Low Funding: 1200 UGXbn Irrigation 0.49% 0.51% 0.54% 0.57% 0.61% 0.66% 0.71% 0.77% 0.83% 1.17% 4% WfP functionality 85% 91% 95% 97% 98% 98% 99% 99% 99% 100% 100% Storage Capacity 38.9 39.6 40.8 42.6 44.7 47.4 50.5 54.0 57.8 78.1 163.67 Moderate High Funding: 2400 UGXbn Irrigation 0.49% 0.54% 0.62% 0.71% 0.82% 0.95% 1.09% 1.24% 1.40% 2.19% 4% WfP functionality 85% 91% 95% 97% 98% 98% 99% 99% 99% 100% 100% Storage Capacity 38.9 40.8 44.1 48.9 54.8 61.8 69.5 77.6 85.7 120.6 163.67 Final – March 31, 2018 22 3.3.5. WATER RESOURCE MANAGEMENT 3.3.5.1. Introduct ion Water resource management is critical to the success of all other subsectors. Reliable and sufficient water availability and quality are imperative to meeting many of the SDGs. Indicators for the Water Resource Management sector have evolved over time, but indicators focus on the percent of water abstraction and discharge permit holders complying with permit conditions (MWE 2017). From FY 2015/2016 to FY 2016/2017, there was an increase in percent compliance from 74 to 75 percent for surface water abstraction and 56 to 59 for wastewater discharge. For groundwater extraction and drilling, these numbers decreased slightly, from 74 to 71 percent and 90 to 89 percent, respectively. As a whole, several sectors have begun to have a significant impact on water quality: pollution from urban expansion and industry, particularly the oil industry; bacterial contamination from poor sanitation; run-off of agricultural nutrients; and pollution from aquaculture. As Uganda’s economy and population have grown, several significant water resources issues have become apparent. Although overall water availability remains high, there have been recurrent droughts and local water shortages. The envisaged expansion of irrigation could substantially impair the hydrological balance and the environment unless a strategic and regulatory approach is taken. Likewise, unmanaged intensification and expansion of rainfed farming will increasingly contribute to the deterioration of land and water resources in many catchments. Perhaps most importantly, the climate is expected to become warmer and less predictable in the coming years, with more frequent and pronounced extreme events such as floods and droughts bearing consequences for water availability and quality. The investment planning process includes five indicators from the water resources management subsector, shown in Table 3-14. TABLE 3-14. WATER RESOURCE MANAGEMENT INDICATORS INDICATOR COMPLIANCE WITH WATER STANDARDS PERMIT COMPLIANCE WASTEWATER TREATMENT AMBIENT WATER QUALITY LEVEL OF WATER STRESS BASELINE1 61% 71% 20% 0% Low TARGET1 90% 90% 60% 100% High UNITS water samples permits people tonnes of BOD 2018 UNIT COST (KUGX) 495,881 32,211 387 13,058 Reporting Variable IN V E ST M E N T S CAPITAL Drinking Water Samples n/a Wastewater Treatment Wetlands Restoration Forest Rehabilitation Wastewater Treatment O&M n/a n/a Wastewater Treatment Wetlands Restoration Forest Rehabilitation Final – March 31, 2018 23 INDICATOR COMPLIANCE WITH WATER STANDARDS PERMIT COMPLIANCE WASTEWATER TREATMENT AMBIENT WATER QUALITY LEVEL OF WATER STRESS Wastewater Treatment REPLACEMENT n/a n/a Wastewater Treatment n/a SOFTWARE n/a Permit Compliance Monitoring2 n/a n/a 1Bold baselines and targets are official figures from the Sector. Non-bold figures are estimates. 2Note although listed as a software cost, permit compliance monitoring does include some investment for monitoring equipment. 3.3.5.2. Funding Requirements to Ach ieve Ind icator Targets The water resources management indicators require investments across several subsectors. For example, there are important shared benefits between investments in ecosystem protection (i.e. wetlands and forest rehabilitation) and ambient water quality. The costs of achieving the water quality target in isolation are likely more expensive than would be politically feasible to fund; however the same investments produce benefits in wetlands coverage and forest coverage making the total benefits of these types of investments more in line with the costs. TABLE 3-15. INDIVIDUAL INDICATOR FUNDING REQUIREMENTS (UGX BILLION) 3.3.5.3. Strateg ic A l locat ion of Resources under L imited Funding Water resources management indicators benefit in this strategic plan from increasing relative preference weights. It is common in developing economies to shift priorities towards water resource and ecosystem protection over time as basic human services (i.e. clean water provision and sanitation) are met. Water Resources Management 2018 2019 2020 2021 2022 2023 2024 2025 2030 Total Capital O&M Replace Software Compliance with water standards 19 19 20 21 22 23 24 25 31 319 100% 0% 0% 0% Permit compliance 4 4 4 5 5 5 5 5 7 68 67% 0% 0% 33% Wastewater treatment 654 695 737 782 829 878 930 984 1,292 12,306 78% 16% 6% 0% Ambient water quality 1,832 1,867 1,903 1,939 1,975 2,011 2,047 2,083 2,263 26,612 88% 12% 0% 0% Water Stress Note: These figures do not account for co-benefits between indicators Investment Type Mix Final – March 31, 2018 24 FIGURE 3-17. PREFERENCE WEIGHTS ON WATER RESOURCES MANAGEMENT INDICATORS OVER TIME Investment in this subsector grows over time in all three scenarios, particularly in the Moderate High funding scenario, where funds from other subsectors nearing their targets before 2030 become available for these indicators. O&M in this subsector includes both wastewater treatment capital maintenance, and annual regulatory and enforcement activities on rehabilitated ecosystems. FIGURE 3-18. WATER RESOURCES MANAGEMENT SUBSECTOR INDICATOR FUNDING BY SCENARIO Note: Funding above does not include replacement costs for existing infrastructure. Despite the growing budgets for these indicators, significant gaps in achievement remain, even in the Moderate-High funding scenario for wastewater treatment and ambient water quality. Permitting and drinking water sample compliance are relatively inexpensive and meet or nearly meet the target at least in the Moderate-High funding scenario. Ambient water quality and wastewater treatment are significantly more expensive to achieve. Final – March 31, 2018 25 FIGURE 3-19. WATER RESOURCES MANAGEMENT SECTOR INDICATOR ACHIEVEMENT RELATIVE TO BASELINE AND TARGET LEVELS The demand for wastewater treatment grows over time with population. In the BAU and Moderate-Low funding scenarios, the incremental increases in funding are not enough to meet this growing demand. As a result, the indicator achievement drops below current levels. Catchments with large water bodies (e.g. Lake Victoria and Lake Kyoga) receive the majority of funding for ambient water quality improvement, although that funding is distributed across all catchments with consideration for the distribution surface water area. Population centers also receive more total funding due to the per capita nature of the wastewater treatment indicator. Final – March 31, 2018 26 FIGURE 3-20. WATER RESOURCES MANAGEMENT AVERAGE ANNUAL SUBSECTOR INVESTMENT BY CATCHMENT 3.3.5.4. Subsector Planning Resul ts Summary The indicators of the water resources management subsector are relatively costly to achieve, however they share benefits across subsectors, particularly with the forestry and wetlands subsectors. Although the preference weights for these indicators grow as the allocation shifts to the stakeholder-defined preference, it is not enough to achieve the subsector targets, particularly wastewater treatment and ambient water quality. In the high funding scenario, although the ambient water quality is still only halfway to the ultimate target, there is a significant increase in the measurement, which represents a large improvement in ambient water quality and ecosystem health across Uganda. Final – March 31, 2018 27 TABLE 3-16. WATER RESOURCES MANAGEMENT INDICATOR ACHIEVMENT BY SCENARIO 3.3.6. FORESTRY 3.3.6.1. Introduct ion Forestry plays a significant role in the national economy through its contributions to energy (firewood and charcoal), building materials, and ecological balance. Forested area in Uganda has been steadily decreasing over the past few years and currently covers about 9 percent of the land area, as of FY 2016/2017.There are 1.27 million ha of gazetted forest reserves while another 730,000 ha are located in national parks and game reserves. Under NDP goals, this coverage is targeted to increase to 18 percent by 2020 and back to natural conditions of 24 percent coverage by 2040. The National Forest Authority is actively supporting the continued management of Central Forest Reserves (CR) and formation of Collaborative Forest Management (CFM) agreements. MWE, along with the National Forest Authority (NFA) will need support for encroachment enforcement to stop deforestation in addition to funds for reforestation. A large portion of encroachment is due to firewood collection. Although not modeled in this SSIP, if MWE and the NFA are successful at enforcing firewood collection limits, the country will see the added benefits of switching fuels to cleaner sources that have fewer health risks and require less time for collection. Institutional coordination with the Ministry of Energy and Mineral Development (Rural Electrification Agency) will be key as rural electrification could reduce the dependence on biomass, allowing the restoration of ecological balance. The objectives of the forestry subsector are represented by the forest coverage indicator, as seen in Table 3-17. Forestry “O&M” refers to annual enforcement and monitoring activities. 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 Target Low Funding: 800 UGXbn Compliance with water standard 64% 64% 64% 65% 66% 67% 68% 69% 70% 73% 90% Permit compliance 71% 79% 84% 86% 86% 87% 87% 87% 87% 88% 90% Wastewater treatment 20% 20% 19% 19% 18% 18% 18% 17% 17% 16% 60% Ambient water quality 0% 0% 1% 1% 2% 2% 3% 4% 4% 8% 100% Water Stress 2% 3% 3% 3% 3% 3% 3% 3% 3% 4% $0 Moderate Low Funding: 1200 UGXbn Compliance with water standard 64% 65% 67% 69% 71% 73% 75% 76% 77% 80% 90% Permit compliance 71% 79% 84% 87% 88% 88% 89% 89% 89% 90% 90% Wastewater treatment 20% 20% 19% 19% 19% 19% 18% 18% 18% 18% 60% Ambient water quality 0% 1% 2% 3% 4% 5% 6% 7% 9% 16% 100% Water Stress 2% 3% 3% 3% 3% 3% 3% 3% 3% 4% $0 Moderate High Funding: 2400 UGXbn Compliance with water standard 64% 68% 74% 78% 81% 82% 83% 84% 85% 87% 90% Permit compliance 71% 79% 84% 87% 88% 88% 89% 89% 89% 90% 90% Wastewater treatment 20% 20% 20% 20% 21% 21% 22% 22% 23% 26% 60% Ambient water quality 0% 2% 5% 8% 10% 14% 17% 20% 24% 43% 100% Water Stress 2% 3% 3% 3% 3% 3% 3% 3% 3% 4% $0 Final – March 31, 2018 28 TABLE 3-17. FORESTRY INDICATORS INDICATOR FOREST COVERAGE BASELINE1 9% TARGET1 24% UNITS hectares 2018 UNIT COST (KUGX) 6,369 IN V E ST M E N T S CAPITAL Forest Rehabilitation O&M Forest Rehabilitation REPLACMENT n/a SOFTWARE n/a 1Bold baselines and targets are official figures from the Sector. Non-bold figures are estimates. 3.3.6.2. Funding Requirements to Ach ieve Ind icator Targets The required funding for forest coverage takes into account not only the current deficit in proportion of land area covered in forests, but also the continuous encroachment on forests. With no investment in this subsector, the forested area in Uganda would decline at a rate of 2.5 percent per year. This is reflected in the annual funding requirements: the growth over time is a result of both recovering additional lost area each year and the accumulating enforcement and monitoring costs. As discussed in the water resources management plan, the forest rehabilitation and regulation investments provide benefits beyond just the forestry subsector. TABLE 3-18. INDIVIDUAL INDICATOR FUNDING REQUIREMENTS (UGX BILLION) 3.3.6.3. Strateg ic A l locat ion of Resources under L imited Funding The relative preference weight for the forestry indicator remains almost the same between the current funding mix and the 2030 preference weights, at just under 10 percent of the Sector total. Forests 2018 2019 2020 2021 2022 2023 2024 2025 2030 Total Capital O&M Replace Software Forest Coverage 1,510 1,608 1,709 1,814 1,923 2,035 2,152 2,273 2,947 28,363 89% 11% 0% 0% Note: These figures do not account for co-benefits between indicators Investment Type Mix Final – March 31, 2018 29 FIGURE 3-21. PREFERENCE WEIGHTS ON FORESTRY INDICATORS OVER TIME The subsector receives a more than doubling of funding between the Moderate-Low and Moderate-High funding scenarios, particularly in the final years of the period. As other indicators near achievement more funds are available for forestry rehabilitation. A similar trend can be seen in the indicator performance over time across the three scenarios. The progress also benefits from increasing preferences in the water resource management indicators which also require increasing forested area in the country. FIGURE 3-22. FORESTRY SUBSECTOR INDICATOR FUNDING BY SCENARIO Final – March 31, 2018 30 FIGURE 3-23. FORESTRY SECTOR INDICATOR ACHIEVEMENT RELATIVE TO BASELINE AND TARGET LEVELS Funding allocation across space is assumed to scale with existing forested area by catchment. This analysis does not consider specific afforestation project plans but instead relies on the assumption that forests will be rehabilitated across the catchments in direct proportion to the existing distribution of forested area. FIGURE 3-24. FORESTRY AVERAGE ANNUAL SUBSECTOR INVESTMENT BY CATCHMENT 3.3.6.4. Subsector Planning Resul ts Summary The subsector is unable to fully reach its targets by 2030 even under the Moderate-High funding scenario, however the gap between baseline and target is closed about 25 percent with the high Final – March 31, 2018 31 level of funding. In the BAU scenario, the amount of funding is sufficient to maintain the status quo forested area and supports a small increase in the total area. TABLE 3-19. FORESTRY INDICATOR ACHIEVEMENT BY SCENARIO 3.3.7. WETLANDS MANAGEMENT 3.3.7.1. Introduct ion Wetlands in Uganda provide many benefits to the people and the economy through their provision of materials, food source habitats, and environmental protections. Currently wetlands cover about 8.6 percent of the land area, and are targeted to increase to 13 percent by 2020, holding steady at 13 percent through 2040 according to NDP II. Only a fraction of this area is under management plans (11.3 percent in 2016). To reach these goals, MWE needs to not only protect the remaining wetlands area but also actively restore degraded and filled wetlands areas. In FY 2016/2017, MWE has restored 476 hectares (ha) of wetlands and set aside 167.7 kilometers of critical wetlands (MWE 2017). The wetlands subsector is represented by the indicator measuring the proportion of Uganda’s land area covered by wetlands. Wetlands “O&M” refers to annual enforcement and monitoring activities. TABLE 3-20. WETLANDS MANAGEMENT INDICATORS INDICATOR WETLANDS COVERAGE BASELINE1 8.6% TARGET1 13% UNITS hectares 2018 UNIT COST (KUGX) 10,369 IN V E ST M E N T S CAPITAL Wetlands Restoration O&M Wetlands Restoration REPLACEMENT n/a SOFTWARE n/a 1Bold baselines and targets are official figures from the Sector. Non-bold figures are estimates. 3.3.7.2. Funding Requirements to Ach ieve Ind icator Targets 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 Target Low Funding: 800 UGXbn Forest Coverage 9.0% 9.0% 9.1% 9.1% 9.2% 9.2% 9.3% 9.3% 9.4% 9.7% 24% Moderate Low Funding: 1200 UGXbn Forest Coverage 9.0% 9.1% 9.2% 9.3% 9.4% 9.5% 9.6% 9.7% 9.8% 10.4% 24% Moderate High Funding: 2400 UGXbn Forest Coverage 9.0% 9.2% 9.4% 9.7% 10.0% 10.3% 10.6% 10.9% 11.2% 13.1% 24% Final – March 31, 2018 32 As is the case in the forestry subsector, the required funding for wetlands coverage takes into account not only the current deficit in proportion of land area covered in wetlands, but also an assumed trajectory of wetlands degradation, if no additional funding were allocated, of 2.5 percent per year. This is reflected in the annual funding requirements: the growth over time is a result of both recovering additional lost area each year and the accumulating enforcement and monitoring costs. Wetlands coverage also provides benefits to water resources management targets, therefore the total costs seen in the second panel below could be shared by the two subsectors. TABLE 3-21. INDIVIDUAL INDICATOR FUNDING REQUIREMENTS (UGX BILLION) 3.3.7.3. Strateg ic A l locat ion of Resources under L imited Funding The preference weight for wetlands rehabilitation is fairly constant at 5 to 7 percent over time. FIGURE 3-25. PREFERENCE WEIGHTS ON WETLANDS INDICATORS OVER TIME O&M spending increases over time as new hectares of wetlands will require additional monitoring and enforcement against degradation. The Moderate-High scenario sees more than double the funding of the Moderate-Low scenario, although the total budget has doubled, because additional funds are freed up from other indicators nearing their targets. Wetlands 2018 2019 2020 2021 2022 2023 2024 2025 2030 Total Capital O&M Replace Software Wetlands coverage 721 768 816 866 918 972 1,028 1,086 1,407 13,546 89% 11% 0% 0% Note: These figures do not account for co-benefits between indicators Investment Type Mix Final – March 31, 2018 33 FIGURE 3-26. WETLANDS SUBSECTOR INDICATOR FUNDING BY SCENARIO In the BAU scenario, about 20 percent of the gap between baseline and target wetlands coverage is closed. Because the gap is currently relatively small (11% baseline and 13% target) the final status of wetland coverage in Uganda is fairly good. In the High funding scenario, the target is nearly met but still remains about 10 percent below target levels. FIGURE 3-27. WETLANDS SECTOR INDICATOR ACHIEVEMENT RELATIVE TO BASELINE AND TARGET LEVELS Following the same assumption that is used in the forestry subsector, the growth in wetlands coverage is assumed to occur across catchments in proportion to existing wetlands coverage. Increased spending in catchments with large lakes provides the ancillary benefit of improving ambient water quality. Final – March 31, 2018 34 FIGURE 3-28. WETLANDS AVERAGE ANNUAL SUBSECTOR INVESTMENT BY CATCHMENT 3.3.7.4. Subsector Planning Resul ts Summary Due to its increasing preference weight and ancillary benefits in other subsectors, wetlands restoration and rehabilitation receives sufficient funding to make at least modest progress even in the BAU scenario. In the Moderate-High funding scenario the target is achieved, with wetlands coverage surpassing the goal (due to the linked benefits of wetlands for other indicators) TABLE 3-22. WETLANDS INDICATOR ACHIEVEMENT BY SCENARIO 3.3.8. METEOROLOGY 3.3.8.1. Introduct ion The Uganda National Meteorological Authority (UNMA) is responsible for monitoring weather and climate and providing forecasts and advisories to government and other stakeholders for use in sustainable development. Meteorology is another subsector whose investments produce benefits across a number of development indicators. The dedicated meteorology “Platinum Indicator” has not been consistently measured in recent years making it difficult to track trends in 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 Target Low Funding: 800 UGXbn Wetlands coverage 10.9% 10.9% 11.0% 11.0% 11.0% 11.1% 11.1% 11.1% 11.2% 11.4% 13% Moderate Low Funding: 1200 UGXbn Wetlands coverage 10.9% 11.0% 11.0% 11.1% 11.1% 11.2% 11.3% 11.4% 11.4% 11.9% 13% Moderate High Funding: 2400 UGXbn Wetlands coverage 10.9% 11.0% 11.2% 11.4% 11.6% 11.8% 12.0% 12.2% 12.4% 13.5% 13% Final – March 31, 2018 35 achievement, although currently the network includes 95 stations cover over 60 percent of the country (MWE 2016). As of June 2017, Automatic Weather Stations (AWS) coverage had improved against the NDPII target; however, only 34 out of 100 rain gauges regularly report data. Coordination between Meteorological authority and the DWRM is essential particularly on interventions for flood and drought forecasting and early warning systems. Furthermore, in order to reach the 2040 target for operational weather stations, further work is needed to improve the functionality of current weather stations and replace weather stations degraded beyond repair. The meteorology subsector is represented in the SIM by the operational weather stations indicator, as described in Table 3-23. TABLE 3-23. METEOROLOGY INDICATORS INDICATOR OPERATIONAL WEATHER STATIONS BASELINE1 43% TARGET1 100% UNITS stations 2018 UNIT COST (KUGX) 218,949 IN V E ST M E N T S CAPITAL Weather Stations O&M Weather Stations O&M REPLACEMENT Weather Stations SOFTWARE n/a 1Bold baselines and targets are official figures from the Sector. Non-bold figures are estimates. 3.3.8.2. Funding Requirements to Ach ieve Ind icator Targets Weather station construction and O&M costs increase over time in proportion to population growth. The total funding required to achieve this target is relatively small, however existing funding allocation to this indicator is minimal. TABLE 3-24. INDIVIDUAL INDICATOR FUNDING REQUIREMENTS (UGX BILLION) 3.3.8.3. Strateg ic A l locat ion of Resources under L imited Funding Although the subsector currently receives limited funding, the preference weights are scheduled to increase the priority of this subsector on the path to 2030, as seen in Figure 3-29. This is a large increase given the small starting weight on the subsector (1 percent in 2018 to 7 percent in 2030, of total weight). Meteorology 2018 2019 2020 2021 2022 2023 2024 2025 2030 Total Capital O&M Replace Software Operational weather stations 12 14 16 18 20 22 24 26 37 314 88% 11% 2% 0% Note: These figures do not account for co-benefits between indicators Investment Type Mix Final – March 31, 2018 36 FIGURE 3-29. PREFERENCE WEIGHTS ON METEOROLOGY INDICATORS OVER TIME Under all scenarios, the subsector receives adequate funding to make significant progress towards the target given the existing network of weather stations. A slight dip in investment around 2017 may be related to an increase in the required replacement cost in water supply infrastructure in that year. That is when a large proportion of existing water supply infrastructure is scheduled to reach the end of its useful life. FIGURE 3-30. METEOROLOGY SUBSECTOR INDICATOR FUNDING BY SCENARIO Note: Funding above does not include replacement costs for existing infrastructure. Final – March 31, 2018 37 FIGURE 3-31. METEOROLOGY INDICATOR ACHIEVEMENT RELATIVE TO BASELINE AND TARGET LEVELS The spatial distribution of funds is assumed to be proportional to the existing distribution of monitoring stations. This applies to both O&M funds and new capital investment. FIGURE 3-32. METEOROLOGY AVERAGE ANNUAL SUBSECTOR INVESTMENT BY CATCHMENT 3.3.8.4. Subsector Planning Resul ts Summary The meteorology subsector is able to make significant progress towards the 2030 targets in all funding scenarios (closing the gap by nearly 50 percent in BAU and nearly reaching the target in Moderate-High). Final – March 31, 2018 38 TABLE 3-25. METEOROLOGY INDICATOR ACHIEVEMENT BY SCENARIO 3.3.9. CLIMATE CHANGE 3.3.9.1. Introduct ion Climate change is another cross-cutting subsector whose success will be seen across a number of development indicators. According to the Intergovernmental Panel on Climate Change (IPCC), the climate in Uganda is expected to be hotter on average in the next 20 to 30 years and have more uncertain precipitation. While some of the impacts of these changes may be seen in the same time period as this SSIP, it is also possible that the larger impacts will not be seen until beyond the time period of this study. A challenge for this subsector will be to justify funding to protect against impacts that may not be borne until beyond the typical time period of interest. The climate change subsector is represented by two indicators in the model: greenhouse gas emission reduction and climate vulnerability index, the latter of which is a reporting indicator in the model, meaning no investments are specifically allocated to achieving the goal but the metric is tracked and reported. There is not currently an official climate vulnerability index designated for this indicator by the sector. This report uses the Notre Dame University Global Adaptation Initiative (ND-GAIN) index which incorporates metrics from the water sector and beyond. When reporting future results, all other sectors are assumed to hold performance constant. The 22 percent target for greenhouse gas (GHG) emissions is a reduction in relation to a no mitigation baseline (i.e. 2030 emissions, unmitigated). While there are many avenues for GHG emission reduction, this analysis considers only afforestation, as it falls under the domain of the Sector. Other efforts and policies to reduce household, vehicle, and industrial emissions are also important to reach this goal but are not modeled here. TABLE 3-26. CLIMATE CHANGE INDICATORS INDICATOR GHG EMISSIONS CLIMATE VULNERABILITY INDEX BASELINE1 0% 50% TARGET1 22% 100% UNITS tonnes CO2 ND-GAIN Index 2018 UNIT COST (KUGX) 251 Reporting Variable IN V E ST M E N T S CAPITAL Forest Rehabilitation O&M Forest Rehabilitation REPLACEMENT n/a 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 Target Low Funding: 800 UGXbn Operational weather stations 43% 44% 50% 56% 59% 63% 66% 68% 70% 74% 100% Moderate Low Funding: 1200 UGXbn Operational weather stations 43% 54% 65% 72% 75% 78% 79% 81% 82% 85% 100% Moderate High Funding: 2400 UGXbn Operational weather stations 43% 65% 77% 84% 89% 92% 94% 94% 95% 97% 100% Final – March 31, 2018 39 INDICATOR GHG EMISSIONS CLIMATE VULNERABILITY INDEX SOFTWARE n/a 1Bold baselines and targets are official figures from the Sector. Non-bold figures are estimates. 3.3.9.2. Funding Requirements to Ach ieve Ind icator Targets Greenhouse gas emission reduction, as a product of Water and Environment sector investments, is mitigated through afforestation. Therefore this subsector shares an investment channel with the forestry subsector. The full funding requirements to meet this target are presented below, and include an assumed increase in per capita emissions, coupled with population growth, over time. Note that the funding requirements for the forest rehabilitation investment in the second panel are larger than the total funding requirement for the GHG Emissions indicator. This implies that if the forestry coverage indicator target is met (24% of Uganda’s land area covered in forests) this will achieve the emissions reduction targets. This result is caveated by a note that the actual benefit of a forested hectare, in terms of emissions reduction, is dependent on the existing land use of the rehabilitated area. TABLE 3-27. INDIVIDUAL INDICATOR FUNDING REQUIREMENTS (UGX BILLION) 3.3.9.3. Strateg ic A l locat ion of Resources under L imited Funding The climate change sector, which receives minimal funding under the current budget allocation, sees a slight increase in relative preference weight by 2030 (from less than one percent to three percent), as seen in Figure 3-33. FIGURE 3-33. PREFERENCE WEIGHTS ON CLIMATE CHANGE INDICATORS OVER TIME Funding under BAU and Moderate-Low scenarios increases over time due to the increasing preference weight on the subsector, and the growing emission potential—a result of both the growing population and increasing per capita emission rates. In the Moderate-High funding Climate Change 2018 2019 2020 2021 2022 2023 2024 2025 2030 Total Capital O&M Replace Software GHG Emissions 77 112 150 192 237 287 342 401 777 4,872 91% 9% 0% 0% Climate Vulnerability Index Note: These figures do not account for co-benefits between indicators Investment Type Mix Final – March 31, 2018 40 scenario, the target is met before 2030. Once this occurs, the subsector receives funding for O&M but no additional capital investments. Note that the indicator achievement continues to grow even after additional funding is allocated to this indicator because of the shared benefits between this indicator and afforestation for the forestry coverage indicator. FIGURE 3-34. CLIMATE CHANGE SUBSECTOR INDICATOR FUNDING BY SCENARIO FIGURE 3-35. CLIMATE CHANGE SECTOR INDICATOR ACHIEVEMENT RELATIVE TO BASELINE AND TARGET LEVELS Climate change mitigation does not have a localized impact. Therefore the investment across catchments is equal on a per capita basis. The differences in total funding across catchments are a product of variation in population. Final – March 31, 2018 41 FIGURE 3-36. CLIMATE CHANGE AVERAGE ANNUAL SUBSECTOR INVESTMENT BY CATCHMENT 3.3.9.4. Subsector Planning Resul ts Summary The two climate change indicators are tied to investments across subsectors. In the case of greenhouse gas emissions, the large gap between the baseline and target in the forestry subsector draws in additional funds which provide benefits to the sector. The climate vulnerability score, while improved slightly in this each scenario, is relatively unchanged due to the small impact of Sector investments on the overall score. TABLE 3-28. CLIMATE CHANGE INDICATOR ACHIEVEMENT BY SCENARIO 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 Target Low Funding: 800 UGXbn GHG Emissions 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 22% Climate Vulnerability Index 34.39 34.55 34.70 34.81 34.88 34.94 34.98 35.00 35.01 34.90 Moderate Low Funding: 1200 UGXbn GHG Emissions 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 22% Climate Vulnerability Index 34.39 34.73 35.00 35.19 35.32 35.41 35.47 35.51 35.53 35.46 Moderate High Funding: 2400 UGXbn GHG Emissions 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 25.3% 22% Climate Vulnerability Index 34.39 35.27 35.73 35.98 36.13 36.24 36.31 36.35 36.38 36.38 Final – March 31, 2018 42 REFERENCES Government of Uganda. "National Development Plan (NDPII) 2015/16 - 2019/20." The Republic of Uganda. Government of Uganda. 2011. Sanitation Memorandum of Understanding. Government of Uganda. 2014.Population Projections 2015-2020. Uganda Bureau of Statistics. http://www.ubos.org/population-projections/ Government of Uganda. 2015. Concept Note of Uganda’s Proposal to Finance “Uganda small towns and rural growth centres faecal sludge and sanitation project”. Urban Water and Sewerage Services Department, Urban Sanitation Division. Government of Uganda. 2017. Water and Environment Sector Performance Report 2017. Ministry of Water and Environment. Ministry of Agriculture, Animal Industry & Fisheries. 2010. Agricultural Sector Development Strategy and Investment Plan: 2010/1-2015/15. March 2010. Mutano, S., Kleemeier, E., Nkengne, C.T., Tumusiime, F. 2015. Water and Sanitation for the Poor and Bottom 40% in Uganda: A Review of Strategy and Practice since 2006. April 24. MWE. 2016. Sector Performance Report. Kampala, Uganda: Ministry of Water and Environment, Government of Uganda. Accessed on March 27, 2017 from http://www.mwe.go.ug/index.php?option=com docman&task=cat view&gid=15&Ite mid=55. MWE. 2017. Sector Performance Report. Ministry of Water and Environment, Government of Uganda. Uganda Bureau of Statistics. 2014.Population Projections, 2015 to 2020. http://www.ubos.org/population-projections/ The World Bank (2017) Population Growth (% annual), Available at: https://data.worldbank.org/indicator/SP.POP.GROW?locations=UG (Accessed: 22nd November 2017) http://www.ubos.org/population-projections/ http://www.mwe.go.ug/index.php?option=com docman&task=cat view&gid=15&Itemid=55 http://www.mwe.go.ug/index.php?option=com docman&task=cat view&gid=15&Itemid=55 STRATEGIC INVESTMENT PLAN FOR THE WATER AND ENVIRONMENT SECTOR, UGANDA (2018-2030) VOLUME 4: UGANDA WATER AND ENVIRONMENT SECTOR INVESTMENT MODEL FINAL REPORT prepared for: Ministry of Water and Environment Kampala, Uganda prepared by: Kenneth Strzepek, Brent Boehlert, and Jacqueline Willwerth Industrial Economics, Incorporated 2067 Massachusetts Avenue Cambridge, MA 02140 617/354-0074 Final – March 31, 2018 i TABLE OF CONTENTS ACRONYMS ABSTRACT 4.1 INTRODUCTION AND PURPOSE 4.1.1. Purpose of the SIM 1 4.1.2. Structure of the SSIP Documentation 1 4.2 SECTOR INVESTMENT MODELING METHODOLOGY 4.2.1. SIM Framework 2 4.2.2. External Trends 3 4.2.3. The Allocation Algorithm: Spending Drivers 3 4.2.4. Budget Scenarios in the Current SIM 5 4.3 SECTOR INVESTMENT MODEL COMPONENTS 4.3.1. Indicators and Outcomes 5 4.3.2. Investment to Indicator Multipliers 7 4.3.3. Investments 8 4.3.4. Regions 9 4.4 SIM CAVEATS AND RECOMMENDATIONS 4.4.1. SIM Caveats 10 4.4.2. Recommendations and Next Steps 11 REFERENCES ANNEX 4A. AUGUST 2017 PRIORITIZATION WORKSHOP ANNEX 4B. BIOPHYSICAL MODELS ANNEX 4C. INDICATOR DETAILS ANNEX 4D. DETAILED INDICATOR PERFORMANCE TABLES ANNEX 4E. DETAILED SIM INPUT TABLES Final – March 31, 2018 ii ACRONYM LIST BAU Business-As-Usual MWE Ministry of Water and Environment O&M Operations and Maintenance SIM Sector Investment Model SSIP Strategic Sector Investment Plan UGX Ugandan Shillings Final – March 31, 2018 iii ABSTRACT The Sector Investment Model (SIM) is the engine of the Strategic Investment Plan for the Water and Environment Sector (SSIP), and is the collaborative product of both Sector and outside experts. Volumes 1 through 3 of this report describe the SSIP findings, and this volume focuses on the SIM itself. The SIM is a decision support tool that aids in annual investment planning over time and across the 23 catchments in Uganda. The model is designed to work in two modes to accomplish its two main objectives. The first is the funding requirements mode which uses information on indicator costs and achievement gaps to estimate the total funding requirements to meet Sector goals (documented in Volume 1 and 3). The second is the strategic allocation mode (Volume 2 and 3). In this mode, distribution of funding is based on both the cost of improvement in each indicator and a prioritization algorithm. Priorities are defined in the SIM based on several factors, including current budget allocation, the remaining gap between indicator baselines and targets, and the Sector’s preferences, as reporting during the stakeholder engagement process. The two modes of the SIM rely on the same databases of information that characterize the status, costs, preferences, and targets of the Sector indicators. SIM outputs are thus either a budget requirement, or an investment mix and subsequent indicator achievement trajectory. Of central importance, the model can be used flexibly, with several available options in the current version (e.g., budget or weights modification), and more that could be built into the tool in future versions (e.g., spatial resolution, period of analysis). The SIM provides a logical, transparent rationale behind cross-subsector spending, and thus can be used to inform sector-wide budgeting beginning in 2019/2020. However, given the limited scope of this current study, there are several uncertainties in the SIM results that must be addressed before the tool can be used to inform budget allocations. The following are the priority SIM improvements: • Improve spending preference weights. Sector spending preferences in the SIM have a huge effect on investment planning outcomes. A more in-depth workshop to elicit the spending preferences of the Sector is needed, involving a broad representation of decision-makers at the highest levels. • Enhance cost estimates across space and time. Investment cost estimates and their indicators also have a pronounced effect on spending. Further refinement of these estimates by additional analysis and modeling, and by reaching out to regional experts for catchment level results would greatly improve the reliability of SIM results. • Improve indicator information. Measurement of baseline indicator levels and target definitions could be refined, and the indicators can be enhanced through a process to identify the “best” list of investment indicators from the numerous options available. • Better characterize vulnerability to uncertainty. Running the SIM across a wide range of future combinations of inputs would allow for a better characterization of which parameters are most sensitive to uncertainty. Final – March 31, 2018 1 4. VOLUME 4: UGANDA WATER AND ENVIRONMENT SECTOR INVESTMENT MODEL The Sector Investment Model (SIM) is the engine of the Strategic Investment Plan for the Water and Environment Sector (SSIP), and is the collaborative product of both Sector and outside experts. Volumes 1 through 3 of this report describe the SSIP findings, and this volume focuses on the SIM itself. The SIM is a decision support tool that aids in investment planning over space and time. In the case of the current SSIP, the SIM focuses on the 2018 to 2030 period, analyzes results over 23 catchments, and considers three specific budget scenarios. The model, however, can be used more flexibly, with several available options in the current version (e.g., budget or weights modification), and more that could be built into the tool in future versions (e.g., spatial resolution, period). 4.1. INTRODUCTION AND PURPOSE While the results presented in the SSIP and Subsector Investment Plans represent the current priorities of the sector, the SIM can be used to test other scenarios and to explore how changing the assumptions of the SSIP may lead to different investment priorities. 4.1.1 PURPOSE OF THE S IM The SIM is designed to work in two modes to accomplish its two main objectives. The first is the funding requirements mode which uses information on indicator costs and achievement gaps to estimate the total funding requirements to meet Sector goals. The second is the strategic allocation mode. In this mode, distribution of funding is based on both the cost of improvement in each indicator and a prioritization algorithm. Priorities are defined in the SIM based on several factors, including current budget allocation, the remaining gap between indicator baselines and targets, and the Sector’s preferences, as reporting during the stakeholder engagement process. The SIM is built in Microsoft Excel and is intended to be accessible for all decision makers within the Sector. For more details on the technical aspects and use of the SIM, please see the SIM User Guidelines. 4.1.2 STRUCTURE OF THE SSIP DOCUMENTATION The results and methodologies of the SSIP process are documented in four volumes. Volume 1 is the Funding Requirements to Meet National 2030 Targets for the Water and Environment Sector. Volume 1 outlines the budget required to reach 2030 indicator targets, and the consequences of underfunding. Volume 2 is the Consolidated Strategic Sector Investment Plan. This document provides the SSIP results in the context of the comprehensive water and environment sector. Volume 3 contains the Subsector Strategic Investment Plans. Volume 2 provides more details about the SSIP findings that are particularly relevant to each subsector. Volume 4 (the current document) and its associated annexes, describe the details of the Sector Investment Model, or SIM. Users of the SIM may also be interested in the SIM User Guide, which provide more technical direction on the Final – March 31, 2018 2 use of SIM Excel tool. Finally, the key findings of the SSIP are also presented in a four page Popular SSIP, which can be used to communicate the investment plan results to a broader audience. The remainder of this volume is organized as follows. First, the methodology of the SIM is explained, next the key components of the SIM are described, and lastly, the key SIM caveats and a set of recommendations are provided. 4.2. SECTOR INVESTMENT MODELING METHODOLOGY As a decision support tool, the SIM allocates budget resources to investments across time and space based on a set of Sector priorities. The model determines investments annually over the 2018 to 2030 period, and for each of 23 catchments. This section describes SIM’s key methodological elements. 4.2.1 S IM FRAMEWORK The two modes of the SIM rely on the same databases of information that characterize the status, costs, preferences, and targets of the Sector indicators. Figure 4-1 depicts how the SIM brings together these various pieces of information to output either a budget requirement or investment mix and subsequent indicator achievement trajectory. In the Strategic Allocation mode, the primary user input is the annual Sector budget. Other important inputs (seen across the top of Figure 4-1), such as preference and cost information, are stored within the SIM and may be updated as new information becomes available. The model processes this information through the allocation algorithm to provide annual investments and indicator achievements by catchment. In the Budget Requirement mode, the problem is reversed, where indicator targets and unit investment costs drive investment decisions in order to estimate required funding levels annually. Each indicator may be affected by several investments, and each investment may affect multiple indicators. Unit investment costs vary by catchment, as costs and impacts may vary spatially. FIGURE 4-1. S IM STRUCTURE Final – March 31, 2018 3 4.2.2 EXTERNAL TRENDS As noted above a major driver of investing decisions is how external drivers change over time and across catchments. Between 2017 and 2030, population in Uganda is anticipated to increase from 37 million to 55 million people. This means that maintaining current indicator performance in areas such as clean water supply provision and wastewater treatment will already require major investments, and that making forward progress will require considerably more. Figure 4-2 shows the population distribution in each of the 23 catchments, in both 2017 and 2030. FIGURE 4-2. POPULATION DENSITY IN 2017 AND 2030 (PEOPLE PER SQUARE KM) 4.2.3 THE ALLOCATION ALGORITHM: SPENDING DRIVERS Of notable importance, spending in the SIM is allocated across indicators based on two drivers: (1) “preferences” that are Sector-specified weights on indicators that externally drive investment choices; and (2) how far the indicator is from reaching its target (the “gap”), which is a weight that is determined within the SIM each year as indicator gaps are closed. All else equal, indicators receive higher levels of funding when they have higher preferences and larger gaps; the relative importance of these two drivers can be determined by the SIM user. Indicator performance gap is also the driver of budget allocations across catchments. Sector preference weights are based on past budget allocation decisions that best reflect near term priorities, and the findings of a Workshop aimed at distilling longer term spending priorities. For the near term allocation, the SIM relies on the Water and Environment Sector Performance Report for fiscal years 2014/2015, 2015/2016, and 2016/2017, which provide the breakdown of the proposed budget across different investments within the Water and Environment sector (MWE 2015, 2016, and 2017). For each vote, containing different budget items, each funding category is labeled by first the relevant outcome in the SSIP and then by the most relevant indicators to that budget item. For example, in the 2017 Sector Performance Report, Vote 19, “V4:0902 Urban Water Supply and Final – March 31, 2018 4 Sanitation” is allocated evenly between the sanitation and public health and clean water supply indicators, with values of 0.2 for each indicator. Some budget items, such as hydropower and planning, are not included in the classification by outcome and indicator as they are not included in the SSIP. For longer-term weights, the SIM employs weights based on preferences of a representative cross section of the sector that participated in the SIM capacity building program. These were informed by the findings of a prioritization workshop held in August 2017 with Sector experts and decisionmakers. During that workshop, there are two types of weights that were elicited: • Outcome ratings represent the relative spending priority of each outcome to the overall goals of the sector. These weights are used in two ways. First, they are used to create outcome spending ratios which define the relative importance across outcomes. • Importance weights define how each indicator contributes to the score for the outcome, and are interpreted as the spending priorities within an outcome. The total outcome score is the weighted sum of all related indicators. These weights are combined with the outcome ratings to define final spending weights for each indicator. Values for each of these ratings and weights were elicited from sector stakeholders through both an in-person discussion and an online survey. The group of participants included MWE and GoU agency staff as well as development partner and non-governmental agency representatives. Detailed discussion and results of this process can be found in Annex 4A. The representative group used the information from this workshop, along with information about the relative costs of achieving each indicator to develop the final set of weights used in the model. The first and last columns of Table 4-1 report the starting and final indicator weights developed by the capacity building participants. The most recent years of available budget allocations drive preferences in 2018, and these linearly move toward the sector preferences (Table 4-1). Note that while the results presented in the SSIP and Subsector Investment Plans represent the current priorities of the sector, the SIM can be used to test other scenarios and to explore how changing the assumptions of the SSIP may lead to different investment priorities. Final – March 31, 2018 5 TABLE 4-1. INDICATOR SPENDING PREFERENCES OVER TIME Note: Blue represents higher preferences and orange represents lower preferences relative to other indicators 4.2.4 BUDGET SCENARIOS IN THE CURRENT S IM The SIM develops investment plans under three budget scenarios, all of which are currently assumed to escalate at 3% per year between 2018 and 2030. Starting points under the three scenarios are as follows: • Business-As-Usual (BAU). Under the BAU scenario, the annual budget is assumed to remain similar to historical levels, and starts at roughly the allocation available during the 2017/2018 fiscal year of 800 billion UGX (for details, see Volume 4). • Moderate-Low. A modest increase of 50% over the BAU scenario, to 1.2 trillion UGX per year. • Moderate-High. A much more aggressive increase of 200% over the BAU scenario, to 2.4 trillion UGX per year. Each of these budget scenarios is run through the SIM to evaluate how investment decisions are made, and what indicator levels result over time and across catchments. Both initial budget and escalation rate over time are user inputs, and can be easily adjusted in the SIM to evaluate other budget scenarios. 4.3. SECTOR INVESTMENT MODEL COMPONENTS Several elements build the SIM investment framework. Each of these elements is discussed below. 4.3.1 INDICATORS AND OUTCOMES In the SSIP, return on investment is measured by indicator progress. Twenty -four indicators, covering the primary functions of the sector, are included. The indicators are selected from the list of revised indicators developed in the 2016 Review of the Water and Environment Sector Performance 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Village water supply 2% 3% 3% 3% 3% 3% 3% 4% 4% 4% 4% 4% 4% 4% Functional rural water sources 2% 2% 2% 2% 2% 2% 2% 3% 3% 3% 3% 3% 3% 3% Improved drinking water 12% 11% 11% 10% 10% 9% 9% 8% 8% 7% 7% 6% 6% 5% Safely managed drinking water 31% 30% 28% 26% 25% 23% 21% 20% 18% 16% 14% 13% 11% 9% Urban water service functionality 10% 10% 9% 9% 9% 8% 8% 7% 7% 7% 6% 6% 5% 5% Solid waste disposal 1% 1% 1% 1% 1% 2% 2% 2% 2% 2% 2% 2% 2% 2% Improved sanitation 4% 4% 4% 4% 4% 4% 4% 3% 3% 3% 3% 3% 3% 3% Safely managed sanitation 2% 2% 2% 2% 3% 3% 3% 3% 4% 4% 4% 4% 5% 5% Handwashing at home 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% Handwashing at school 1% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 2% 3% 3% Irrigation 10% 10% 9% 9% 9% 9% 9% 8% 8% 8% 8% 7% 7% 7% WfP functionality 2% 3% 3% 3% 3% 3% 3% 3% 3% 3% 3% 3% 3% 3% Storage Capacity 2% 3% 3% 4% 4% 4% 5% 5% 6% 6% 6% 7% 7% 8% Compliance with water standards 1% 1% 1% 1% 2% 2% 2% 3% 3% 3% 3% 4% 4% 4% Permit compliance 1% 1% 1% 1% 1% 2% 2% 2% 2% 2% 2% 3% 3% 3% Wastewater treatment 1% 2% 2% 2% 2% 2% 3% 3% 3% 3% 4% 4% 4% 4% Ambient water quality 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 6% 6% Wetlands coverage 1% 2% 2% 2% 3% 3% 4% 4% 4% 5% 5% 6% 6% 7% Forest Coverage 6% 6% 7% 7% 7% 7% 7% 7% 7% 7% 7% 7% 8% 8% GHG emissions 0.4% 1% 1% 1% 1% 1% 1% 2% 2% 2% 2% 2% 3% 3% Operational weather stations 2% 2% 3% 3% 4% 4% 4% 5% 5% 5% 6% 6% 7% 7% Final – March 31, 2018 6 (Measurement) Monitoring Framework (MWE 2017) with stakeholder consultation at the beginning of the SSIP 2030 process. Some of the indicators are new this year (2017) and do not have baseline or target information available. In these instances a placeholder value has been estimated. Four of the 24 indicators are entered in the SIM only as reporting variables. Progress on these indicators is not explicitly valued in the SIM because it is either an indirect outcome of achieving other indicators, or the direction of progress is not clear.1 Indicators and associated baseline and target information are presented in Volume 2 of this report. The Sectors progress on indicators can be translated into achievement of the core functions of the Sector, namely the outcomes. Strategic investment planning requires making tradeoffs between objectives. The seven outcomes of the Sector are: Clean Water Supply Improved Sanitation and Public Health Sufficient Water for Food Production Sufficient Water for Economic Activity Improved Water Quality and Quantity Ecosystem Protection Climate Change Adaptation and Mitigation Achievement of an outcome is scored by aggregating the weighted achievement in related indicators. For example, as seen in Figure 4-3, the outcome “Clean Water Supply” is measured by six related indicators. The indicator weights are used to find a weighted sum of improvement in the outcome. The total improvement measure is used to track how closely investment in the indicator matches historic funding allocations and preferences. FIGURE 4-3. EXAMPLE OF INDICATORS TO OUTCOMES 1 These indicators are Per Capita Investment Cost, Water withdrawal as a proportion of available water resources, and Percent change in Uganda’s climate change vulnerability index. Final – March 31, 2018 7 4.3.2 INVESTMENT TO INDICATOR MULTIPLIERS Multipliers define the relationship between indicators and investments in the SIM. They define the amount of shillings in a specific investment that are needed to move the indicator forward by one unit. So for example, the cost to deliver clean water supply to one person. The model consists of a matrix of indicators by investments for each region. Each cell in the matrix contains a multiplier which relates the given investment to indicator achievement. Many cells in the matrix are zeros, as not all investments impact all indicators. However, certain investments, such as expanded water storage, are important for a variety of indicators and have multipliers that reflect this role. The multipliers were defined by first identifying all likely linkages and then identifying the few key links that are 1) quantifiable and 2) primary drivers of indicator achievement. In some cases, the relationship between investment and indicator is clear and well-studied, and therefore there is a high degree of confidence in the stated relationship. These values may come from academic literature, historical performance data, or biophysical models. In other cases, while it is understood that a relationship exists, documentation in support of any specific value is not available. In those cases, a team of experts from the consultant team utilized the information that is available to define this relationship. These values may always be refined as more information becomes available by future users of the SIM. Table 4-2 defines each cell in the matrix where the relationship was strong enough to include in the SIM. The structure of the SIM is such that as additional information becomes available, or new linkages are discovered, the user can input new multipliers by following steps laid out in the User Guidelines. Annex 4B describes biophysical models used to develop multipliers. TABLE 4-2. MULTIPLIER MATRIX: INCLUSION IN S IM Note: Dark green boxes represent primary linkages. Light green boxes represent secondary linkages. Investment In di ca to r 1. Vi llH 2O 2. Rf un ct 3. Im pH 2O 4. Sa fe H 2O 5. Co st Ca pi ta 6. U fu nc t 7. Sl dW as te 8. Bs an 9. Sa fe Sa n 10 .H om eH an d 11 .S ch lH an ds 12 .Ir r 13 .W fP Fu nc t 14 .S to ra ge Ca pa ci ty 15 .D rin kS am pl es 16 .P er m itC om pl ia nc e 17 .W W T 18 .A m bW Q 19 .H 2O St re ss 20 .W et ln ds 21 .F or es t 22 .G H G 23 .C liV ul n 24 .W ea th er St Water Supply Basic 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Water Supply Piped 2 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Solid Waste Disposal 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sewerage 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Irrigation 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Storage 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 Wastewater Treatment 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 Wetlands Restoration 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 Forest Rehabilitation 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 0 0 Weather Stations 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Water Source Repair-Rural 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Water Source Repair-Urban 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WfP Source Repair 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Drinking water samples 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 Basic Sanitation CB 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Safe Sanitation CB 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Home Handwashing CB 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 School Handwashing CB 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 WfP Source CB 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 Permit Compliance CB 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 Water Supply Basic O&M 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Water Supply Piped O&M 2 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sewerage O&M 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Irrigation O&M 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Storage O&M 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 Wastewater Treatment O&M 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 Wetlands Regulation O&M 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 Forest Regulation O&M 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 0 0 Weather Station O&M 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Final – March 31, 2018 8 Multiplier values may vary by catchment based on hydrologic, geographic, or population variation. For example, irrigation costs are higher in dryer catchments, particularly those far from commercial centers, making materials more expensive. Further details on the values and methods of determination for each multiplier in the SIM can be found in Annex 4C. 4.3.3 INVESTMENTS Achievement in each indicator is influenced by funding channeled through investments. The investments represent the types of projects that would need to be implemented to improve the indicator scores. There are three general categories of investment: capital, annual operations and maintenance (O&M), and software. Investments by category are listed in Table 4-3 below. TABLE 4-3. INVESTMENTS MODELED IN THE S IM Category Capital Investments Annual O&M Software Investment Water Supply Water Supply-Basic* Water Supply-Basic O&M Water Supply-Piped* Water Supply-Piped O&M Water Source Repair-Rural Water Source Repair Urban Sanitation Solid Waste Disposal Sewerage O&M Basic Sanitation Campaigns Wastewater Treatment* Wastewater Treatment O&M Safe Sanitation Campaigns Fecal Sludge Management Home Handwashing Campaigns School Handwashing Campaigns Water for Production Off-Farm Irrigation* Irrigation O&M Water for Production Local Training Water Storage* Water storage O&M Water for Production Source Repair Water Resource Management Drinking water samples Permit Compliance Monitoring Ecosystem Protection Wetlands Restoration Wetlands Management Forest Rehabilitation Forest Management Meteorology Weather Stations* Weather Station O&M *Star notates capital investment also requires replacement Capital and software investments are assumed to be one-time, upfront costs, whereas annual O&M costs occur each year going forward. The SIM assumes that O&M expenditures for both existing infrastructure and all future investments come out of the available spending under each budget scenario. The existing stock of water supply, sanitation, storage, and other infrastructure is estimated to cost 137 billion UGX to maintain (i.e., roughly 17% of the 2018 BAU budget); spending this sum each year is assumed to maintain existing infrastructure in functioning condition, and means that only 663 billion UGX is available. Costs of future O&M are in addition to this 137 billion UGX figure. Implications for future O&M expenditures are explored in Volume 2.Replacement costs for existing infrastructure set for replacement based on a screening level lifecycle analysis, is also included as a Final – March 31, 2018 9 mandatory cost before new spending can occur. Replacement costs are estimated to be 137 billion UGX in 2018 (i.e. 17% of the 2018 BAU budget). After O&M and replacement, the sector has 526 billion UGX out of the original 800 billion UGX budget for spending on new investments in 2018. 4.3.4 REGIONS Investments in the SSIP are reported nationally and regionally. Multipliers, or returns on investment as discussed above, may vary by catchment based on factors such as land use patterns, populations, baseline indicator status, and water availability (where data are available). National level results are generated using the weighted average (weighted by either population or land area, depending on the measure) of the catchment values. The SSIP Regions were defined to: 1. Be specific enough to provide meaningful detail to the investment plan without requiring an unreasonable amount of data collection and without inferring localized results from non localized data; 2. Align with existing MWE and Government of Uganda planning and administrative regions; and 3. Map to hydrological features, demographic trends, and land use patterns that may influence investment needs. With these objectives in mind, the SIM has been built for the 23 catchments outlined in a 2010 MWE report, “Operationalisation of Catchment-based Water Resources Management” (MWE, 2010). These 23 catchments are based on hydrologic features and are used for catchment management planning in the Sector. A map of the 23 regions is shown in Figure 4-4. FIGURE 4-4. TWENTY THREE SS IP CATCHMENTS OF UGANDA Final – March 31, 2018 10 4.4 S IM CAVEATS AND RECOMMENDATIONS A broad nation-wide investment planning effort will necessarily make broad assumptions. This section describes the key caveats of the SIM, and provides a set of priority recommendations to address some of the most critical uncertainties. 4.4.1 S IM CAVEATS The SIM has several key uncertainties that affect the reliability of the results. Some of these uncertainties, such as absence of knowledge about macroeconomic conditions in the future, are unavoidable. Others may be alleviated through enhancements to the data inputs of the SIM, or at a minimum, better characterization of the relative importance of each source of uncertainty. TABLE 4-4. CAVEATS OF THE STUDY Category Caveat or Uncertainty Cost estimates The SIM results are highly sensitive to the costs of investments. Although the cost estimates in this study are a significant step forward relative to what was previously available, given the study’s scope, many of the cost estimates are estimated based on global rather than local information. This is a priority recommendation below. Sector preferences for spending allocation One major component of how spending is allocated across indicators is the spending preferences of the Sector. Within the SIM, these start in 2018 with 2017/2018 budget allocations, and ramp to preferences elicited during the August 2017 workshop. As described in the recommendations below, these preferences would be improved considerably with a more in-depth preference elicitation process. Gap weights for spending allocation Spending choices within the SIM are also driven by a “gap weight”, that is, when the gap between the level of an indicator and its 2030 target is relatively large, more funding will be allocated to that indicator, all else equal. This same mechanism drives allocation of investments across the catchments. Although a sensible formulation, alternative approaches can be evaluated that may be more relevant for how the Sector allocates resources. Baseline and targets for indicators, and indicator definitions. As noted in the recommendations below, the baseline and target levels for the indicators could be improved at the national level, and significantly enhanced at the regional level. There may also be room to improve the indicator selection process. Infrastructure condition and O&M costs Existing and new infrastructure is assumed to remain functional if O&M is conducted, and O&M is compulsory in the current version of the SIM. Variants could be built into the SIM that allow infrastructure to degrade over time with or without O&M. This would likely show the comparative advantage of spending on O&M relative to the depreciation that would occur otherwise, and also allow for consideration of a more realistic situation. Macroeconomic and other uncertainties There are many uncertainties that influence how sector investment planning would actually unfold. These include changing infrastructure prices due to macroeconomic considerations, changes in priorities and budget levels, demographic shifts, and physical changes in the environment. These could be considered in the SIM through an uncertainty analysis. Final – March 31, 2018 11 4.4.2 RECOMMENDATIONS AND NEXT STEPS The SIM provides a logical, transparent rationale behind cross-subsector spending, and thus can be used to inform sector-wide budgeting beginning in 2019/2020. However, given the limited scope of this current study, there are several uncertainties in the SIM results that must be addressed before the tool can be used to inform budget allocations. The following are the priority SIM ‘upgrades’: • Improve spending preference weights. As described above, spending is allocated within the SIM based on (1) the spending preferences of the Ministry in the short term (2017/2018 budgets) and long term (elicited through the August 2017 workshop). The Sector spending preferences have a major impact on how resources are allocated, and although the process to generate these preferences was a large step forward from the prior SSIP, it was very preliminary. A more in-depth workshop to elicit the spending preferences of the Sector is needed, involving a broad representation of decision-makers, including ideally the Permanent Secretary and Minister of Water and Environment. • Enhance cost estimates across space and time. The investment cost estimates and their impact on indicators (i.e., the “multipliers”) within the current SIM are the product of collaboration between the consulting team and Sector experts, and represent a big step forward relative to earlier estimates. There is still considerable room for improvement in these estimates, however; the investment plans are highly sensitive to the magnitudes of both cost estimates and multipliers and many of the estimates were very rough due to the scope of this study. Further refinement of these estimates by additional analysis and modeling, and by reaching out to regional experts for catchment level results would greatly improve the reliability of SIM results. • Improve indicator information. Several items related to the indicators can be improved. First, measurement of baseline indicator levels within the catchments should be enhanced to ensure that spatial allocation of resources is occurring properly. Several indicators currently have the same level of baseline performance across all catchments. Next, target definitions for many of the indicators could be specified more clearly, and perhaps refined to accommodate greater flexibility. For example, reaching the wastewater treatment target in the SIM is exceptionally costly; reducing this goal would allow for more rapid achievement of less expensive indicators. Lastly, although the indicators used in this study were selected as part of a workshop with Sector representatives, there may be room for a more inclusive and comprehensive process to identify the “best” list of indicators from the numerous options available. • Better characterize vulnerability to uncertainty. Because the SIM is a deterministic tool, running a Monte Carlo analysis on the inputs would be a relatively straightforward exercise, given assumed statistical distributions of the uncertain input parameters (e.g., population growth rate, indicator preference level, total budget). Running the SIM across a wide range of future combinations of inputs would allow for a better characterization of which parameters are most sensitive. Final – March 31, 2018 12 REFERENCES Ministry of Water and Environment. 2010. Operationalisation of Catchment-based Water Resources Management. COWI Uganda. September 2010. Ministry of Water and Environment. 2015. Water and Environment Sector Performance Report 2015. Ministry of Water and Environment. 2016. Water and Environment Sector Performance Report 2016. Ministry of Water and Environment. 2017. Water and Environment Sector Performance Report 2017. Final – March 31, 2018 13 ANNEX 4A. AUGUST 2017 PRIORITIZATION WORKSHOP As an optimization tool, without any constraints on the distribution of investment, the SIM would allocate all funds to the indictor and related investment with the largest return on investment. While economically efficient, this does not reflect the reality of the budgeting process, nor would it result in an outcome that reflects the mission of the sector. Growth across all indicators is important for success in the sector. The balance between economic efficiency and concurrent growth across subsectors is operationalized in the SIM via prioritization weights. These weights, which were developed based on stakeholder consultation, reflect the relative importance of each indicator tracked in the SIM. This annex discusses the stakeholder consultation undertaken to develop the weights and the process by which the weights are used to incentivize distributed investment in the SIM. PRIORITIZATION WEIGHT STAKEHOLDER CONSULTATION The weights used in the SIM were set in consultation with water and environment sector stakeholders. Preferences were elicited through both an in-person group activity and discussion, and an online survey. • The online survey was disseminated by MWE to members of the sector including MWE staff, development partners, and NGO staff. A total of 53 responses were received. • The in-person group activity took place during an interim workshop for the SSIP project. Approximately 40 participants formed five groups, and within each group, they filled out one modified version of the original online survey. Respondents were encouraged to participate in both versions of the survey. The responses were ultimately combined to create a weighted average (75% group results and 25% survey responses). The heavier weight on the group results acknowledges that the group results reflect a partial consensus, and are closer to the overall preferences of the sector. In both formats respondents were first asked to rank the seven outcomes. The results of these rankings are shown in Figure 4A-1. There are more unique responses to the online survey that did not undergo the same compromising activity as was done in the group setting which explains the larger variation in results. Final – March 31, 2018 14 FIGURE 4A-1. OUTCOME RANKING RESULTS BY RESPONDENT GROUP Next, respondents were asked to either rank (online version) or distribute weight amongst (in-person version) the indicators associated with each outcome. Figure 4A-2 shows the average ranking decisions by affiliation from the online survey. Final – March 31, 2018 15 FIGURE 4A-2. ONLINE SURVEY RESPONDENT CLEAN WATER INDICATOR IMPORTANCE RATINGS BY AFFILIATION Improved drinking water was consistently the highest ranked across all groups. Respondents from outside MWE but within the Government ranked per capita investment cost and safely managed drinking water fairly high compared to other groups. Respondents from other affiliations not covered in the survey gave the lowest importance rankings overall compared to other groups, particularly in per capita investment cost and functional rural water sources. The respondents to the online survey spanned a wide variety of affiliations and years of experience, as seen in Figure 4A-3. This is important, as the purpose of the survey was to gather data from as many stakeholders, with as many opinions and preferences, as possible to ensure the final results reflect the sector’s priorities. FIGURE 4A-3 ONLINE SURVEY RESPONDENT CHARACTERISTICS The rankings and ratings collected through online survey and group activities were combined to create the final set of weights discussed in Volume 3, Sector Investment Model. Final – March 31, 2018 16 ANNEX 4B. BIOPHYSICAL MODELS This annex provides technical details on the biophysical models used in the SSIP analysis. 4.B.1 LAKE-BASIN WATER QUALITY TOOL The Lake-Basin Water Quality (LBWQ) tool generates water quality and availability implications of land use changes, population increases, and climate changes for a user-specified lake and basin. As shown in Figure 4B-1, the tool takes monthly meteorological time series as inputs – along with user inputs on land use, basin and lake characteristics, and planning scenarios – to generate time series of lake inflows, temperature, nutrient levels, and dissolved oxygen, among others. The purpose of the tool is to provide an understanding of the possible implications of expected changes in land use and climate. For the current study, the LBWQ tool was used to evaluate water quality implications of land use and management changes in the four major lakes of Uganda: Victoria, Edwards, Kyoga, and Albert. FIGURE 4B-1 SCHEMATIC OF THE LAKE BASIN WATER QUALITY TOOL The remainder of this document is divided into four parts: (1) the methodologies used in modeling water quality and rainfall runoff, (2) a case study example using meteorological data and climate projections from Tbilisi, Georgia, and (3) summary of the approach and results of the Uganda analysis. Part 2 also provides explanations of model inputs and assumptions. PART 1. MODELING METHODOLOGY The purpose of this section is to provide an overview of the physical relationships that are modeled in this tool. These include river runoff, temperature balancing, and nutrient/dissolved oxygen (DO) balancing. The organization of the modeled elements is as follows: Rainfall runoff model • Runoff (mm/day) • Basin PET (mm/day) • Soil moisture (mm) • Shallow groundwater (mm) Heat balance model • Lake water temperature (°C) • Total heat flux (cal/cm2/day) • Daily mean solar radiation (cal/cm2/day) Final – March 31, 2018 17 • Atmospheric longwave flux (cal/cm2/day) • Longwave back radiation (cal/cm2/day) • Conduction/convection flux (cal/cm2/day) • Evaporation/condensation flux (cal/cm2/day) • Daily max solar radiation (cal/cm2/day) • Photoperiod (hours) • Lake evaporation (mm/day) Nutrient and dissolved oxygen models • Phosphorus concentration (μg/L) • Nitrogen concentration (μg/L) • Dissolved oxygen concentration (mg/L) 1a. Ra infa l l runoff River runoff entering the lake can either be calculated using a rainfall runoff model that is built into LBWQ, or be estimated outside of the tool and entered directly. The built in model is a one dimensional model that apportions rainfall into one of several categories: (1) runoff to the lake, (2) soil moisture, (3) losses to PET (calculation approach discussed below), or (4) shallow groundwater. It is the “Runoff” module within the VBA code of this tool. The initial allocation of rainfall is driven by the runoff coefficient, which is driven by land use and relative magnitude of the precipitation event. 𝑓𝑓𝐺𝐺𝐺𝐺 = 1 − 0.5 (𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚 + 𝑃𝑃𝑡𝑡) 𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚 𝑐𝑐𝑟𝑟 The variable fGW is the fraction of water that is available for soil moisture, groundwater, or to be evapotranspired. 1-fGW is therefore the volume of water that enters the stream directly. Pmax is the maximum precipitation in the record, Pt is the precipitation during this period, and cr is the runoff coefficient. The purpose of the fraction in front of the runoff coefficient is to let more runoff enter the stream directly during high runoff events. Next, 𝑓𝑓𝐺𝐺𝐺𝐺𝑃𝑃𝑡𝑡 is available for entry into either soil moisture or shallow groundwater, but only if PETt does not exceed the soil moisture content. Define a temporary soil moisture variable: 𝑆𝑆𝑆𝑆𝑡𝑡𝑡𝑡𝑚𝑚𝑡𝑡 = 𝑆𝑆𝑆𝑆𝑡𝑡−1 + 𝑓𝑓𝐺𝐺𝐺𝐺𝑃𝑃𝑡𝑡 − 𝑓𝑓𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 Where SM is soil moisture, fPET is the fraction of PET that can be removed from soil moisture in any period (user-specified), and PETt is the PET during this time period. If SMtemp < 0, then SMt = 0 and GWt = GWt-1. If SMtemp is greater than the maximum allowable soil moisture (SMmax), then SMt = SMmax, and GWt = GWt-1 + SMtemp - SMmax. That is, surplus water enters the shallow groundwater system. Lastly, if neither are true, then SMt = SMtemp, and GWt = GWt-1. At elevations higher than a “preferred” groundwater level, the groundwater system will be increasingly forced to release water. Groundwater is also released in response to exceptionally low precipitation availability. Thus, the additional flow generated by groundwater is: 𝑄𝑄𝑡𝑡 𝐺𝐺𝐺𝐺 = � 𝐺𝐺𝐺𝐺𝑡𝑡 𝐺𝐺𝐺𝐺𝑠𝑠 � 3 + �1.2 − �1.2 𝑃𝑃𝑡𝑡 𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚 �� 6 Where GWs is a stable groundwater elevation (in the example presented in Part 3, this is 50 mm). The coefficient of 1.2 in the precipitation driver was chosen based on reasonableness of model results. If 𝑄𝑄𝑡𝑡 𝐺𝐺𝐺𝐺 exceeds current groundwater availability, then GWt = GWt-1 and Qt = (1-fGW)Pt. Otherwise, this value is removed from groundwater and added to streamflow. Final – March 31, 2018 18 1b. Heat balance model The differential equation for the heat balance, recognizing a constant volume and that the density and specific heat of water are unit, is as follows: 𝑑𝑑𝑃𝑃 𝑑𝑑𝑑𝑑 = 𝑄𝑄𝑃𝑃𝑡𝑡 𝑖𝑖𝑖𝑖 − (𝑄𝑄 − 𝑄𝑄𝑃𝑃)𝑃𝑃 + 𝐴𝐴𝑠𝑠𝐽𝐽 For simplicity, the inflow water temperature is assumed to be the same as the air temperature. The outflow temperature is the variable of interest, and QE is the flow removed through lake evaporation. The total heat flux, J, is defined as: 𝐽𝐽 = 𝐽𝐽𝑠𝑠𝑖𝑖 + 𝐽𝐽𝑚𝑚𝑖𝑖 − (𝐽𝐽𝑏𝑏𝑟𝑟 + 𝐽𝐽𝑐𝑐 + 𝐽𝐽𝑡𝑡) Jsn is estimated using methodologies outlined by FAO (1998). The tool generates extraterrestrial solar radiation, which is then translated to net solar shortwave radiation using percentage of cloud cover. Inputs to the extraterrestrial solar radiation calculation are simply latitude and day of year. The tool also calculates PET using the Modified Hargreaves approach (Allen et al. 2002), as well as photoperiod, in hours. The remaining terms in the flux balance are calculated following methodologies documented in Chapra (1997). These use the Stefan-Boltzmann law for atmospheric longwave radiation and back radiation, and wind speed for the conduction/convection and evaporation/condensation terms. 1c. Phosphorus model The phosphorus model is based on the budgeting approach reviewed by Chapra (1997). This approach is best suited to longer time-scale modeling efforts, and ignores the specific mechanisms that circulate phosphorus though the lake system (such as uptake and release by organisms). Because the lake is assumed to be relatively large and operate at a monthly time step, this is reasonable. 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 = 𝐺𝐺𝑡𝑡𝑝𝑝 + 𝐺𝐺𝑡𝑡𝑚𝑚 𝑉𝑉 − (𝑄𝑄 − 𝑄𝑄𝑃𝑃)𝑑𝑑 𝑉𝑉 − 𝑣𝑣𝑡𝑡𝐴𝐴𝑠𝑠𝑑𝑑 Where vp is a first order settling loss, assumed to be 10 meters per year. The loading is a constant value per month based on export coefficients and per capita loadings. Wpl and Wpa are the loadings from land surface runoff and from air deposition on the lake surface. 1d. N itrogen model The purpose of the nitrogen model is twofold. One goal is to track total nitrogen in the system. For this reduced form process, nitrogen is treated as a conserved substance with a certain “settling” rate (assumed to be 5 meters per year). The assumption that nitrogen is conserved is a simplification, but approximates the true system on longer time scales. As such, nitrogen is treated in an identical fashion to phosphorus. The second purpose of nitrogen modeling is to evaluate the implications of nitrification on dissolved oxygen concentrations. 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 = 𝐺𝐺𝑖𝑖𝑝𝑝 + 𝐺𝐺𝑖𝑖𝑚𝑚 𝑉𝑉 − (𝑄𝑄 − 𝑄𝑄𝑃𝑃)𝑑𝑑 𝑉𝑉 − 𝑣𝑣𝑖𝑖𝐴𝐴𝑠𝑠𝑑𝑑 Final – March 31, 2018 19 1e. D isso lved oxygen model The three driving forces of dissolved oxygen evaluated in this model are nitrification, oxidation of carbonaceous biochemical oxygen demand (BOD), and reaeration.2 The oxygen model is therefore: ð�‘‘ð�‘‘ð�‘‘ð�‘‘ ð�‘‘ð�‘‘ð�‘‘ð�‘‘ = ð��¾ð��¾ð�‘�ð�‘�ð��´ð��´ð�‘ ð�‘ (ð�‘‘ð�‘‘ð�‘ ð�‘ ð�‘šð�‘šð�‘¡ð�‘¡ − ð�‘‘ð�‘‘) − ð�‘Ÿð�‘Ÿð�‘œð�‘œð�‘�ð�‘�ð��¶ð��¶ð��¶ð��¶ð��¶ð��¶ð��¶ð��¶ − ð�‘Ÿð�‘Ÿð�‘œð�‘œð�‘–ð�‘–ð�‘‘ð�‘‘ð��¼ð��¼ð��¼ð��¼ Where Kl is the oxygen mass transfer coefficient (from Chapra 1997) and driven by windspeed. Osat, or oxygen saturation, is adjusted automatically for temperature and elevation, and roc and ron are the stoichiometric coefficients for the oxidation of CBOD and nitrification (2.67 and 4.57, respectively). nIO is the inorganic nitrogen provided by export coefficients (Chapra 1997), assumed to be entirely made up of ammonia. CBOD is also provided in the form of per capita loadings of sewage. PART 2. LAKE KYOGA EXAMPLE: INPUT PARAMETERS AND MODEL OUTPUTS For the current study, the LBWQ tool was applied to evaluate water quality implications of land use and management interventions on the four major lakes of Uganda: Victoria, Edwards, Kyoga, and Albert (Figure 4B-2). This section provides an overview of the input parameters and model outputs for the Uganda LBWQ model. For each of the four lakes, there are three investment scenarios and two future years (2020 and 2040), for 24 future models in total. There is also one 2015 run per lake. Each of these 28 runs is processed for a total of 10 representative years with a common set of climate data for each lake, to allow steady state conditions to develop. FIGURE 4B-2 MAJOR LAKES OF UGANDA 3 2 The effects of photosynthesis and respiration are not evaluated in this model. As a result, a eutrophic lake will have no effect on oxygen concentrations. 3 Source: Accessed on May 5, 2016 from https://upload.wikimedia.org/wikipedia/commons/thumb/d/df/Rivers and lakes of Uganda.png/473px Rivers and lakes of Uganda.png Final – March 31, 2018 20 2a. Model ing inputs and assumpt ions The next several tables provide the model inputs and outputs used for Lake Kyoga. TABLE 4B-1 PARAMETER INPUTS PARAMETER VALUE UNITS Volume (V) 7,900 millions of m3 Surface area (A) 1,820 km2 Initial water temperature (TW0) 24.13 degC Initial phos. concentration (cp0) 200 ug/L Initial nitr. concentration (cn0) 4,000 ug/L Initial DO concentration (do0) 5.00 mg/L Settling rate for P (vsp) 10.0 m/yr Settling rate for N (vsn) 5.0 m/yr Latitude of basin centroid (lat) 0.5 deg Elevation of basin centroid (elev) 618.00 m Table 4B-2 provides export coefficients for loading of nitrogen and phosphorus in kilograms per hectare per year. In the model, it is assumed that all of this loading reaches the lake (i.e., no reduction in loading during transport). For the purposes of the Uganda model, agriculture was assumed to be evenly divided between maize and small grain. TABLE 4B-2 EXPORT COEFFICIENTS CONSTITUENT LAND USE F O R E ST C O R N C O T T O N SO Y B E A N S SM A LL G R A IN P A ST U R E F E E D LO T D A IR Y ID LE R E SI D E N T IA L B U SI N E SS IN D U ST R IA L Nitrogen 1.8 11.1 10 12.5 5.3 3.1 2,900 3.4 7.5 13.8 4.4 Phosphorus 0.1 2.0 4.3 4.6 1.5 0.1 220 0.1 1.2 3.0 3.8 Source: NCSU Table 4B-3 shows the export coefficients of the atmosphere on the lake. Although atmospheric deposition of nitrogen and phosphorus also occurs on land, it was assumed that these were included in the export coefficients presented above. TABLE 4B-3 EXPORT COEFFICIENTS AND LOADINGS OF ATMOSPHERE ON LAKE PARAMETER N P ATMOSPHERIC (KG/HA/YR) 24.0 1.0 WATM (KG/YR) 1,200 50 Source: Chapra 1997 Per capita loading estimates are provided in Table 4B-4. Note that inorganic nitrogen is conservatively assumed to be entirely ammonia, which consumes oxygen through nitrification. Final – March 31, 2018 21 CBOD is also assumed to deplete oxygen when it enters the lake. Both scenarios draw on population projections stating that 32,126,000 people live within the basin by 2040. TABLE 4B-4 PER CAPITA LOADING AND RATES NUTRIENT RATE (KG/CAP/YR) Nitrogen-O 3.1 Nitrogen-IO 5.2 P with det 1.6 CBOD (dev.) 21.9 Source of rates: Chapra 1997 Table 4B-5 displays the land use characteristics under scenarios 1 and 2. The high investment scenario has higher nitrogen and phosphorus concentrations than the BAU growth scenario because the overall amount of other land uses is projected to remain unchanged. Note that the approximately 80 percent of both nitrogen and phosphorus loading are attributable to sewage. TABLE 4B-5 LAND CHARACTERISTICS LAND USE SCENARIO 1: BAU GROWTH 2040 SCENARIO 2: HIGH INVESTMENT 2040 AREA (HA) WN (KG/YR) % OF N LOAD WP (KG/YR) % OF P LOAD AREA (HA) WN (KG/YR) % OF N LOAD WP (KG/YR) % OF P LOAD TOTAL 4,567,559 132,700 100% 26,500 100% 5,548,345 134,100 100% 26,600 100% Forest 419,103 700 0.6% 46.1 0.2% 1,186,234 2,100 1.6% 130 0.5% Corn 1,394,471 15,500 11.7% 2,800 10.5% 1,394,471 15,500 11.5% 2,800 10.5% Small Grain 1,394,471 7,400 5.6% 2,100 7.9% 1,394,471 7,400 5.5% 2,100 7.9% Sewage 0 109,100 82.2% 21,600 81.4% 0 109,100 81.4% 21,600 81.2% Changes in wetland and forest cover will affect the removal efficiency of BOD, and as a result affect DO concentrations in the lakes. According to Karathanasis et al. (2003), unplanted wetlands remove approximately 10 g/m2/day of BOD. To be conservative, we assume 1 percent of this removal efficiency, and that forests remove one-quarter of what wetlands remove. Removal is assumed to be applied to any increases or decreases in cover relative to historical conditions. In addition to changes in land use, supplying livestock with water will keep the animals out of lake tributaries, and as a result, reduce the introduction of BOD. In the high 2040 scenario, 73 percent of livestock are supplied with piped water, whereas in the BAU growth 2040 scenario, only 52 percent are on piped water. The effect of this is fairly limited, however, as BOD contribution of animals that use rivers and streams directly for water supply is only 10 percent higher. Table 4B-6 shows the breakdown of BOD reduction and total BOD loading across the two scenarios. Not considering removal of BOD by wetlands and forests, total loading is 499,000 tons/yr under the BAU growth 2040 scenario, and 491,000 tons/yr under the High 2040 scenario. The difference between these is the effect of supplying water to livestock. Removal by wetlands and forests produces a much higher effect, reducing total loadings down to 496,000 tons/ha in BAU growth 2040, and 343,000 tons/ha in High 2040. Final – March 31, 2018 22 TABLE 4B-6 TOTAL BOD CONTRIBUTION, WITH BREAKDOWN INTO FORESTS/WETLANDS CHARACTERISTIC BAU 2040 HIGH 2040 WETLANDS FORESTS WETLANDS FORESTS Additional cover (km2) 393 -1,246 2,529 6,425 BOD removal (ton/kg/yr) 36 9 36 9 Removal (tons) 14,040 -11,140 90,470 57,460 Total removal (tons) 2,900 147,930 BOD loading w/o removal (tons/yr) 499,000 491,000 TOTAL BOD loading (tons/yr) 496,000 343,000 4.B.2 STORAGE YIELD CURVES The reliability of storage is captured in the concept of storage yield, which is a measure of the annually reliable water supply from a reservoir. Much of the water available in a river basin during a given year is lost if not stored, so storage in a basin can greatly increase its reliable supply, or yield. Storage yield is a useful broad indicator of the climate risk to basin-level water resources. In the context of this study, these curves are used to determine the volume of storage yield needed to supply villages with sustainable yield in each of the 23 Ugandan catchments. The storage yield curve has been developed as a means of relating yield to storage, and is a measure of the volume of storage needed to achieve a given level of reliable yield. Figure 4B-3 provides an example of a storage yield curve for the Nile River at Aswan, and illustrates the information that the curve provides. The maximum yield on the curve indicates the mean annual runoff in the basin upstream of the reservoir, and the minimum yield indicates the lowest flow in the runoff time series for the basin in question. That is, in a basin with no storage, the basin yield is assumed to be the lowest recorded annual flow. The shape of the storage yield curve is determined by the historical variability of basin runoff, where a steeper curve indicates a more stable system and a flatter curve a more variable one. So all else equal, a basin with more variable flows would require more storage to achieve the same level of reliable yield. Final – March 31, 2018 23 FIGURE 4B-3 STORAGE YIELD CURVE FOR THE NILE RIVER AT ASWAN Source: Strzepek et al. (2011) To construct storage yield curves, a standard approach is the sequent peak algorithm (Wiberg and Strzepek 2005, modified from Thomas and Fiering 1962), which is an iterative procedure that identifies the minimum storage volume needed to generate various levels of reliable yield, given a basin inflow time series. In this formulation, the elements include reservoir storage, releases, evaporation and precipitation over the reservoir, and inflow, all at a monthly time step. For small reservoirs and associated drainage basins, a daily time series of runoff data may be more appropriate. St = {Rt + Et−1 − Pt−1 − Qt + St−1 | if positive; otherwise, St = 0} Where: St = reservoir storage requirement Rt = releases Et = evaporation above the reservoir Pt = precipitation above the reservoir Qt = inflow t = time step In the case of Uganda, given a known amount of reliable water supply yield that is needed for a village, the above procedure can be used to estimate upstream storage requirements. 0 10 20 30 40 50 60 70 80 90 0 50 100 150 200 Storage (109 m3) Y ie ld ( 10 9 m 3 ) Final – March 31, 2018 24 REFERENCES Allen, R., P. Droogers and G. Hargreaves. 2002. Predicting Reference Crop Evapotranspiration with Arid Weather Data. ICID. Chapra, S. 1997. Surface Water-Quality Modeling. Waveland Press: Long Grove, Illinois. FAO (Food and Agricultural Organization). 1998. Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper. Accessed on May 6, 2012 from http://www.fao.org/docrep/X0490E/x0490e07.htm#radiation. Karathanasis, A. C. Potter, M. Coyne. 2003. Vegetation effects on fecal bacteria, BOD, and suspended solid removal in constructed wetlands treating domestic wastewater. Ecological Engineering. 20:157-169 LMNO Engineering. 2003. Rational Equation Calculator. Accessed on May 4, 2012 from http://www.lmnoeng.com/Hydrology/rational.htm NCSU Water Quality Group. 2012. Pollutant Budget Estimation Form. Accessed on May 4, 2012 from http://www.water.ncsu.edu/watershedss/dss/spread1.html. Strzepek, K., A. McCluskey, B. Boehlert, M. Jacobsen, C. Fant. 2011. Climate variability and change: a basin scale indicator approach to understanding the risk to water resources development and management. World Bank, Washington, DC. Ward, P., Strzepek, K. Pauw, W., Brander, L. Hughes, G., Aerts, J. 2010. Partial costs of global climate change adaptation for the supply of raw industrial and municipal water: a methodology and application. Environmental Research Letters. 5(4). Wiberg, D. and Strzepek. K.M. 2005.Development of Regional Economic Supply Curves for Surface Water Resources and Climate Change Assessments: A Case Study of China. IIASA Research Report RR-05-001. November. http://www.lmnoeng.com/Hydrology/rational.htm Final – March 31, 2018 25 ANNEX 4C. INDICATOR DETAILS This annex provides further information about each of the 24 indicators. Details include key assumptions, unit estimation methods, and cost estimation methods. 1. VILLAGE WATER SUPPLY INDICATOR INTERPRETATION ASSUMPTIONS • Only one water source per village is required for coverage under this indicator o Source can be any type (i.e. Shallow well, deep borehole, rainwater tank, piped system, and to some extent protected springs—although these are phased out) o Each improved source (i.e. not a piped scheme) is assumed to cover 300 people • Each piped system is assumed to cover 500 people TABLE 4C-1. V ILLAGE WATER SUPPLY UNIT ESTIMATION Units Village Base Year National Estimate 2017 National number of villages from 2017 SPR Base Year Catchment Estimates Base year National estimate is divided into catchments using the proportion of small towns per catchment4 Projection to 2030 Number of villages assumed to increase at rate of population growth (3.05%) TABLE 4C-2. V ILLAGE WATER SUPPLY INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Water Supply-Basic Average cost of water source (per capita costs from SSIP 2009 inflated to 2017 dollars) provision assuming a changing mix of sources over time O&M: Water Supply-Basic 2% of capital cost annually Replacement: Water Supply-Basic Replacement of existing infrastructure assumed lifespan of 25 years. Follows replacement schedule for sample of piped water supply (see Figure A-1). Replacement cost is equal to capital cost. Capital: Water Supply-Piped Average cost of piped water (per capita costs from SSIP 20095 inflated to 2017 dollars) 4 Small town locations from a GIS file provided by Aurecon. 5 SSIP 2009 Final – March 31, 2018 26 Investment Cost Description O&M: Water Supply-Piped 2% of capital cost annually Replacement: Water Supply-Piped Replacement of existing infrastructure assumed lifespan of 25 years. See replacement schedule in Figure A-16. Replacement cost is equal to capital cost. F IGURE A-1. PERCENT OF EXISTING WATER SUPPLY INFRASTRUCTURE REPLACED BY YEAR 2. FUNCTIONAL RURAL WATER SOURCES INDICATOR INTERPRETATION ASSUMPTIONS • For water sources to be designated as functional, they should be inspected annually. • A proportion of facilities inspected each year will need repair (beyond normal O&M) to remain functional TABLE 4C-3. FUNCTIONAL RURAL WATER SOURCES UNIT ESTIMATION Units Village Base Year National Estimate Rural water sources from the 2016 Water Supply Atlas Base Year Catchment Estimates Water Supply Atlas provides district level information which is spatially aggregated to the 23 catchments Projection to 2030 Number of sources is assumed to increase at rate of population growth (3.05%)7 6 Replacement schedule information provided by MWE. 7 UBOS 2014 Population Projections Final – March 31, 2018 27 TABLE 4C-4. FUNCTIONAL RURAL WATER SOURCES INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Source Repair Cost per repair facility that includes field staff time, training, and travel expenses; and the cost of repairing 1/6 of facilities checked 3. IMPROVED DRINKING WATER INDICATOR INTERPRETATION ASSUMPTIONS • Improved drinking water sources include: protected springs, shallow wells, deep boreholes, rainwater harvesting tasks, kiosks, as well as the tap stands and household connections • New investment focuses on shallow wells, boreholes, and rainwater harvesting tanks (i.e. reduced protected spring investment) • Piped water is not required to meet this target but it does contribute to overall water coverage. • TABLE 4C-5. IMPROVED DRINKING WATER UNIT ESTIMATION Units People Base Year National Estimate Total population from the 2016 Water Supply Atlas Base Year Catchment Estimates Water Supply Atlas provides district level information which is spatially aggregated to the 23 catchments Projection to 2030 Population increased by UBOS expected population growth rate (3.05%) TABLE 4C-6. IMPROVED DRINKING WATER INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Water Supply-Basic Average cost of water source (per capita costs from SSIP 2009 inflated to 2017 dollars) provision assuming a changing mix of sources over time O&M: Water Supply-Basic 2% of capital cost annually Replacement: Water Supply-Basic Replacement of existing infrastructure assumed lifespan of 25 years. Follows replacement schedule for sample of piped water supply (see Figure A-1). Replacement cost is equal to capital cost. Capital: Water Supply-Piped Average cost of piped water (per capita costs from SSIP 2009 inflated to 2017 dollars) Final – March 31, 2018 28 Investment Cost Description O&M: Water Supply-Piped 2% of capital cost annually Replacement: Water Supply-Piped Replacement of existing infrastructure assumed lifespan of 25 years. See replacement schedule in Figure A-1. Replacement cost is equal to capital cost. 4. SAFELY MANAGED WATER INDICATOR INTERPRETATION ASSUMPTIONS • Safely managed drinking water is defined as piped water supply TABLE 4C-7. SAFELY MANAGED WATER UNIT ESTIMATION Units People Base Year National Estimate Total population from the 2016 Water Supply Atlas Base Year Catchment Estimates Water Supply Atlas provides district level information which is spatially aggregated to the 23 catchments Projection to 2030 Population increased by UBOS expected population growth rate (3.05%) TABLE 4C-8. SAFELY MANAGED WATER INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Water Supply-Piped Average cost of piped water (per capita costs from SSIP 2009 inflated to 2017 dollars) O&M: Water Supply-Piped 2% of capital cost annually Replacement: Water Supply-Piped Replacement of existing infrastructure assumed lifespan of 25 years. See replacement schedule in Figure A-1. Replacement cost is equal to capital cost. 5. COST PER CAPITA • This indicator is only used for reporting purposes. It is calculated based on the costs used in the improved water coverage indicator and will vary over time as supply investments trend towards more expensive supply technologies. Final – March 31, 2018 29 6. URBAN WATER SERVICE FUNCTIONALITY INDICATOR INTERPRETATION ASSUMPTIONS • For water sources to be designated as functional, they should be inspected annually. • A proportion of facilities inspected each year will need repair (beyond normal O&M) to remain functional TABLE 4C-9. FUNCTIONAL URBAN WATER SOURCES UNIT ESTIMATION Units Sources Base Year National Estimate Urban water sources from the 2016 Water Supply Atlas Base Year Catchment Estimates Water Supply Atlas provides district level information which is spatially aggregated to the 23 catchments Projection to 2030 Number of sources is assumed to increase at rate of population growth (3.05%) TABLE 4C-10. FUNCTIONAL URBAN WATER SOURCES INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Source Repair Cost per repair facility that includes field staff time, training, and travel expenses; and the cost of repairing 1/6 of facilities checked 7. SOLID WASTE DISPOSAL INDICATOR INTERPRETATION ASSUMPTIONS • Indicator covers the waste generated by residents of the twelve largest cities and towns in Uganda, as stated in the 2014 Census (UBOS 2014). TABLE 4C-11. SOLID WASTE DISPOSAL UNIT ESTIMATION Units Tonnes of solid waste Base Year National Estimate Population of twelve largest towns (UBOS 2014) multiplied by the per capita waste generation estimate from the KCCA Project Teaser solid waste management pamphlet8 Base Year Catchment Estimates Same as above, with catchment-specific large town population estimates. Projection to 2030 Large town population increased by UBOS population growth rate (3.05%) and per capita waste generation increased linearly from 8 KCCA Project Teaser (2017): https://www.kcca.go.ug/uDocs/kampala-waste-treatment-and-disposal-ppp.pdf Final – March 31, 2018 30 current KCCA estimates to World Bank estimate for average middle income country in 20309 TABLE 4C-12. SOLID WASTE DISPOSAL INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Solid waste disposal Costs per tonne of solid waste annually are estimated in a 2012 World Bank report (Hoornweg and Bhada-Tata, 2012)10. This figure includes the costs of solid waste collection and landfilling. 8. IMRPROVED SANITATION INDICATOR INTERPRETATION ASSUMPTIONS • Improved sanitation refers to access to sanitation facilities not shared with other households • Household education is the only required investment for this indicator, although coverage with a faecal sludge management system (for Indicator 9. Safely Managed Sanitation) will also achieve this goal. TABLE 4C-13. IMPROVED SANITATION UNIT ESTIMATION Units People Base Year National Estimate Total population from the 2016 Water Supply Atlas Base Year Catchment Estimates Water Supply Atlas provides district level information which is spatially aggregated to the 23 catchments Projection to 2030 Population increased by UBOS expected population growth rate (3.05%) TABLE 4C-14. IMPROVED SANITATION INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Faecal Sludge Management Costs of fecal sludge management per capita were drawn from the MWE’s 2015 draft report, “Concept Note of Uganda´s Proposal to Finance Uganda Small Towns and Rural Growth Centres Faecal Sludge and Sanitation Project”. Includes some software components. O&M: Faecal Sludge Management 2% of capital costs 9 What A Waste: https://siteresources.worldbank.org/INTURBANDEVELOPMENT/Resources/336387 1334852610766/What a Waste2012 Final.pdf 10 Ibid. Final – March 31, 2018 31 Software: Household education campaigns Assumes a ten day intervention reaching 100 people (both directly and through training community members). Effectiveness of the intervention is assumed to be 85%. 9. SAFELY MANAGED SANITATION INDICATOR INTERPRETATION ASSUMPTIONS • Safely managed sanitation refers to proper maintenance and treatment of sanitation facilities and waste, either in situ or offsite. • This investment requires both a capital investment and a training component. TABLE 4C-15. IMPROVED SANITATION UNIT ESTIMATION Units People Base Year National Estimate Total population from the 2016 Water Supply Atlas Base Year Catchment Estimates Water Supply Atlas provides district level information which is spatially aggregated to the 23 catchments Projection to 2030 Population increased by UBOS expected population growth rate (3.05%) TABLE 4C-16. IMPROVED SANITATION INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Faecal Sludge Management Costs of fecal sludge management per capita were drawn from the MWE’s 2015 draft report, “Concept Note of Uganda´s Proposal to Finance Uganda Small Towns and Rural Growth Centres Faecal Sludge and Sanitation Project”. Includes some software components. O&M: Faecal Sludge Management 2% of capital costs Software: Household education campaigns Assumes a 21 day intervention reaching 100 people (both directly and through training community members). Effectiveness of the intervention is assumed to be 85%. 10. HANDWASHING AT HOME INDICATOR INTERPRETATION ASSUMPTIONS • The Sector’s contribution to this indicator is assumed to include only household education campaigns. Final – March 31, 2018 32 TABLE 4C-17. HANDWASHING AT HOME UNIT ESTIMATION Units People Base Year National Estimate Total population from the 2016 Water Supply Atlas Base Year Catchment Estimates Water Supply Atlas provides district level information which is spatially aggregated to the 23 catchments Projection to 2030 Population increased by UBOS expected population growth rate (3.05%) TABLE 4C-18. HANDWASHING AT HOME INVESTMENTS AND COST ESTIMATION Investment Cost Description Software: Household education campaigns Assumes a 14 day intervention reaching 100 people (both directly and through training community members). Effectiveness of the intervention is assumed to be 85%. 11. HANDWASHING AT SCHOOL INDICATOR INTERPRETATION ASSUMPTIONS • The Sector’s contribution to this indicator is assumed to include only school education campaigns. Ministry of Sports and Education is assumed to be responsible for providing soap and other handwashing supplies. • The indicator is assumed to apply to primary and secondary schools. TABLE 4C-19. HANDWASHING AT SCHOOL UNIT ESTIMATION Units Students Base Year National Estimate Total population from the 2016 Water Supply Atlas multiplied by the percent of population in school. This second estimate comes from the 2014 Uganda Census (population age 5-19) and a 2014 Education Policy Data Center report (percentage of ages 6-18 in school). Base Year Catchment Estimates Water Supply Atlas provides district level population information which is spatially aggregated to the 23 catchments. School attendance rates are calculated separately by urban and rural areas but assumed otherwise constant across space. Projection to 2030 Population increased by UBOS expected population growth rate (3.05%) Final – March 31, 2018 33 TABLE 4C-20. HANDWASHING AT SCHOOL INVESTMENTS COST ESTIMATION Investment Cost Description Software: School education campaigns Assumes a 14 day intervention reaching 100 people (both directly and through training community members). Effectiveness of the intervention is assumed to be 85%. 12. IRR IGATION INDICATOR INTERPRETATION ASSUMPTIONS • Irrigation O&M costs are assumed to be borne by the farmers benefiting from the irrigation projects • Ministry of Agriculture, Animal Industry and Fisheries (MAAIF) is assumed to contribute to on farm infrastructure TABLE 4C-21. IRRIGATION UNIT ESTIMATION Units Potentially irrigable hectares Base Year National Estimate From the report “Assessment of the Irrigation Potential in Burundi, Eastern DRC, Kenya, Rwanda, Southern Sudan, Tanzania and Uganda: Uganda Report Phase 2” Base Year Catchment Estimates The report came with an associated shapefile that allowed for catchment disaggregation Projection to 2030 Potentially irrigable area assumed to remain constant over time TABLE 4C-22. IRRIGATION INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Irrigation Includes the cost headworks, primary canals from river to secondary system (concrete lined), secondary canals, and the cost of setting up a management system. Replacement: Irrigation Replacement cost is equal to initial capital cost. Replacement schedule assumes even age distribution with an expected useful life of 20 years. 13. WATER FOR PRODUCTION FUNCTIONALITY INDICATOR INTERPRETATION ASSUMPTIONS • For water sources to be designated as functional, they should be inspected annually. Final – March 31, 2018 34 • A proportion of facilities inspected each year will need repair (beyond normal O&M) to remain functional TABLE 4C-23. FUNCTIONAL WATER FOR PRODUCTION SOURCES UNIT ESTIMATION Units Sources Base Year National Estimate Reported in 2017 Sector Performance Report Base Year Catchment Estimates Sources are distributed to catchments based on total population share. Projection to 2030 Number of sources is assumed to increase at rate of population growth (3.05%) TABLE 4C-24. FUNCTIONAL WATER FOR PRODUCTION SOURCES INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Source Repair Cost per repair facility that includes field staff time, training, and travel expenses; and the cost of repair, assuming a 20% failure rate Software: Management Planning Software costs cover the expenses of revitalizing one Water User Committee (WUC) 14. STORAGE CAPACITY INDICATOR INTERPRETATION ASSUMPTIONS • Includes the total volume of water storage TABLE 4C-25. FUNCTIONAL URBAN WATER SOURCES UNIT ESTIMATION Units Million cubic meters (MCM) Base Year National Estimate Reported in 2017 Sector Performance Report Base Year Catchment Estimates Sources are distributed to catchments based on total population share. Projection to 2030 Base units represent the target storage volume, which remains constant over time. TABLE 4C-26. FUNCTIONAL URBAN WATER SOURCES INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Storage Includes the cost of either communal valley tank or earth dam storage (including management) or individual facility cost, weighted by prevalence of each storage type nationally. Final – March 31, 2018 35 Catchment costs are differentiated based on the relative costs derived using average basin slope and the volume of a typical small scale reservoir. These are routed through the Wiberg and Strzepek formulation (see Annex 3C on storage yield curves for details). O&M: Storage 2% of capital costs 15. COMPLIANCE WITH DRINKING WATER STANDARDS INDICATOR INTERPRETATION ASSUMPTIONS • Assumed to only pertain to drinking water samples • Growth in samples taken may be an underestimate in the future as more sampling efforts are undertaken TABLE 4C-27. COMPLIANCE WITH DRINKING WATER STANDARDS UNIT ESTIMATION Units Number of drinking water source samples per year Base Year National Estimate Reported in 2017 Sector Performance Report Base Year Catchment Estimates Sources are distributed to catchments based on total population share. Projection to 2030 Number of sources is assumed to increase at rate of population growth (3.05%) TABLE 4C-28. COMPLIANCE WITH DRINKING WATER STANDARDS COST ESTIMATION Investment Cost Description Capital: Drinking water samples • For rural samples, a weighted average of source replacement and repair is used to estimate the cost to get one source into compliance. This is then divided by the sampling rate (# samples/# sources) and multiplied by the inverse of the compliance rate to get the cost of bringing a probabilistic sample into compliance. • For urban samples, a sample is assumed to represent one facility day, where the number of facilities is estimated based on typical daily capacities and daily water use rates. Costs are generated on a m3 basis. The cost per m3 of wastewater treatment is found in Dore et al. (2013) and is used a proxy cost for getting that unit of water in compliance, which is then multiplied by the inverse of the compliance rate. • The final multiplier is a weighted average of the urban and rural multiplier values, weighted by the number of samples taken from each source category. Final – March 31, 2018 36 16. PERMIT COMPLIANCE INDICATOR INTERPRETATION ASSUMPTIONS • The estimated number of permits may be an underestimate of future permits processed as the Sector continues to bring illegal operators under permit • The permit cycle is five years; therefore the average permit would run through two cycles within the timeframe of the SSIP. Therefore costs per permit are estimated to cover two issuances. TABLE 4C-29. PERMIT COMPLIANCE UNIT ESTIMATION Units Permits Base Year National Estimate Reported in 2017 Sector Performance Report Base Year Catchment Estimates Permits are distributed to catchments based on total population share. Projection to 2030 Number of permits is assumed to increase at rate of population growth (3.05%) TABLE 4C-30. PERMIT COMPLIANCE INVESTMENTS AND COST ESTIMATION Investment Cost Description Software: Permit Compliance Software costs cover the expenses of an initial permit assessment, printing of the permit and advertising it in a newspaper, monitoring for compliance (field work, labor, and supplies), as well as a shared portion of monitoring equipment 17. WASTEWATER TREATMENT INDICATOR INTERPRETATION ASSUMPTIONS • Treatment is assumed to include conventional treatment plant methods. TABLE 4C-31. WASTEWATER TREATMENT UNIT ESTIMATION Units People Base Year National Estimate Total population from the 2016 Water Supply Atlas Base Year Catchment Estimates Water Supply Atlas provides district level information which is spatially aggregated to the 23 catchments Final – March 31, 2018 37 Projection to 2030 Population increased by UBOS expected population growth rate (3.05%) TABLE 4C-32. WASTEWATER TREATMENT INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Wastewater treatment Capital costs are based on the recent construction of a 800,000 hectoliters per year treatment plant in Uganda11 O&M: Wastewater treatment 2% of capital costs Replacement: Wastewater treatment Replacement cost is assumed to be equal to capital costs. Replacement schedule is based on an assumed even age distribution of existing infrastructure, assuming a 50 year useful life.12 18. AMBIENT WATER QUALITY INDICATOR INTERPRETATION ASSUMPTIONS • BOD concentration less than 2 ppm is assumed to be a proxy for indicator definition (“proportion of water bodies with good ambient water quality”) • BOD is removed via wetlands (1073 kg/HA/year) and forest (268 kg/HA/year) restoration and increased wastewater treatment (21.66 kg/cap/year for moving to treated wastewater) TABLE 4C-33. AMBIENT WATER QUALITY UNIT ESTIMATION Units Tonnes of BOD (to be removed to meet standard) Base Year National Estimate Base loadings are based on total population and livestock by catchment and associated loadings rate. Base concentration is calculated by dividing these loadings by the volume of water in Uganda (note: only half of the volume of Lake Victoria is considered). The total BOD loading representing a 2 ppm concentration is subtracted from this estimate to define the base 11 https://ugandabreweries.com/uganda-breweries-limiteds-shs20b-water-treatment-plant-saving-lake-victoria/ 12 US EPA. 2002. The Clean Water and Drinking Water Infrastructure Gap Analysis. https://nepis.epa.gov/Exe/ZyNET.exe/901R0200.txt?ZyActionD=ZyDocument&Client=EPA&Index=2000%20Thru%202005&Docs=&Query=&Time =&EndTime=&SearchMethod=1&TocRestrict=n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFieldDay=&UseQField=&IntQFieldOp =0&ExtQFieldOp=0&XmlQuery=&File=D%3A%5CZYFILES%5CINDEX%20DATA%5C00THRU05%5CTXT%5C00000011%5C901R0200.txt&User=ANONYMO US&Password=anonymous&SortMethod=h%7C &MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i425&Display=hpfr&DefSeekPage=x&SearchBack=ZyAction L&Back=ZyActionS&BackDesc=Results%20page&MaximumPages=1&ZyEntry=2# Final – March 31, 2018 38 year unit estimate. Base Year Catchment Estimates Tonnes of BOD are split between catchments based on the surface area of water in each catchment (Census 2014) Projection to 2030 BOD level is assumed to stay constant over time. TABLE 4C-34. AMBIENT WATER QUALITY INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Wetlands rehabilitation Conversion-specific estimates of wetland rehabilitation costs plus demarcation costs O&M: Wetlands rehabilitation 2% of capital costs (includes monitoring and enforcement) Capital: Forest rehabilitation Ecosystem-specific rehabilitation costs O&M: Forest rehabilitation 2% of capital costs (includes monitoring and enforcement) Capital: Wastewater treatment Capital costs are based on the recent construction of a 800,000 hectoliters per year treatment plant in Uganda13 O&M: Wastewater treatment 2% of capital costs Replacement: Wastewater treatment Replacement cost is assumed to be equal to capital costs. Replacement schedule is based on an assumed even age distribution of existing infrastructure, assuming a 50 year useful life.14 19. WETLANDS COVERAGE INDICATOR INTERPRETATION ASSUMPTIONS • Wetlands are assumed to degrade over time in absence of any investment (2.5% per year) TABLE 4C-35. WETLANDS UNIT ESTIMATION Units Hectares of Wetland (to be restored) Base Year National Estimate Target number of hectares minus existing number of hectares of 13 https://ugandabreweries.com/uganda-breweries-limiteds-shs20b-water-treatment-plant-saving-lake-victoria/ 14 US EPA. 2002. The Clean Water and Drinking Water Infrastructure Gap Analysis. https://nepis.epa.gov/Exe/ZyNET.exe/901R0200.txt?ZyActionD=ZyDocument&Client=EPA&Index=2000%20Thru%202005&Docs=&Query=&Time =&EndTime=&SearchMethod=1&TocRestrict=n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFieldDay=&UseQField=&IntQFieldOp =0&ExtQFieldOp=0&XmlQuery=&File=D%3A%5CZYFILES%5CINDEX%20DATA%5C00THRU05%5CTXT%5C00000011%5C901R0200.txt&User=ANONYMO US&Password=anonymous&SortMethod=h%7C &MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i425&Display=hpfr&DefSeekPage=x&SearchBack=ZyAction L&Back=ZyActionS&BackDesc=Results%20page&MaximumPages=1&ZyEntry=2# Final – March 31, 2018 39 wetlands Base Year Catchment Estimates Distribution of existing hectares by catchment is known and targets are assumed to be proportional to existing hectares by catchment Projection to 2030 The number of hectares to be restored is assumed to grow at 2.5% per year, signifying a base case degradation of 2.5% per year. TABLE 4C-36. WETLANDS INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Wetlands rehabilitation Conversion-specific estimates of wetland rehabilitation costs plus demarcation costs O&M: Wetlands rehabilitation 2% of capital costs (includes monitoring and enforcement) 20. FOREST COVERAGE INDICATOR INTERPRETATION ASSUMPTIONS • Wetlands are assumed to degrade over time in absence of any investment (2.5% per year) TABLE 4C-37. FOREST UNIT ESTIMATION Units Hectares of Forest(to be restored) Base Year National Estimate Target number of hectares minus existing number of hectares of forest Base Year Catchment Estimates Distribution of existing hectares by catchment is known and targets are assumed to be proportional to existing hectares by catchment Projection to 2030 The number of hectares to be restored is assumed to grow at 2.5% per year, signifying a base case degradation of 2.5% per year. TABLE 4C-38. FOREST INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Forest rehabilitation Ecosystem-specific rehabilitation costs O&M: Forest rehabilitation 2% of capital costs (includes monitoring and enforcement) Final – March 31, 2018 40 21. GREENHOUSE GAS EMISSIONS INDICATOR INTERPRETATION ASSUMPTIONS • The Sector is assumed to improve GHG emissions by reforestation • Each hectare of forest is assumed to reduce GHG emissions by 26 tonnes per year15 TABLE 4C-39. GREENHOUSE GAS UNIT ESTIMATION Units Tonnes of CO2 Base Year National Estimate Current population multiplied by the per capita emissions rate for Uganda from the World Bank database16 Base Year Catchment Estimates Catchment populations multiplied by the per capita emissions rate for Uganda from the World Bank database Projection to 2030 Population, grown at 3.05% annually, multiplied by a trajectory of per capita emissions from current Uganda levels to average low middle income country levels by 2030.17 TABLE 4C-40. GREENHOUSE GAS INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Forest rehabilitation Ecosystem-specific rehabilitation costs O&M: Forest rehabilitation 2% of capital costs (includes monitoring and enforcement) 22. CLIMATE VULERNABILITY INDEX • This indicator is a reporting indicator only, meaning no investments are made to specifically improve the indicator performance; however the index is improved by investments in water supply, sanitation, water storage, and irrigation. The index used in ND-GAIN (https://gain.nd.edu/). 23. OPERATIONAL WEATHER STATIONS INDICATOR INTERPRETATION ASSUMPTIONS • Weather stations is assumed to include synoptic, agromet, hydromet, rainfall, upper air, RADARs, and automatic weather stations (AWS). • Each hectare of forest is assumed to reduce GHG emissions by 26 tonnes per year18 15 IPCC: http://www.ipcc.ch/ipccreports/sres/land use/index.php?idp=253 16 Source: https://data.worldbank.org/indicator/EN.ATM.CO2E.PC 17 Ibid. Final – March 31, 2018 41 TABLE 4C-41. WEATHER STATAION UNIT ESTIMATION Units Weather Stations Base Year National Estimate Base number of weather stations (by type) provided by UNMA. Base Year Catchment Estimates Spatial distribution of weather stations by catchment proportional to the distribution of weather stations in GIS shapefile19 Projection to 2030 Target number of stations in 2030 provided by UNMA. Assumes a linear growth from current levels to 2030. TABLE 4C-42. WEATHER STATION INVESTMENTS AND COST ESTIMATION Investment Cost Description Capital: Weather Station Ecosystem-specific rehabilitation costs O&M: Weather Station 2% of capital costs (includes monitoring and enforcement) Replacement: Weather Station Replacement cost is assumed to be equal to capital costs. The replacement schedule assumes an even age distribution and a 15 year useful life. 18 IPCC: http://www.ipcc.ch/ipccreports/sres/land use/index.php?idp=253 19 Weather station shapefile provided by Aurecon. Final – March 31, 2018 42 REFERENCES Castro, A., Maoulidi, Moumie, and MCI. 2009. A water and sanitation needs assessment for Mekelle City, Ethiopia, New York, NY: Millennium Cities Initiative. Connor, Richard. 2016. The United Nations World Water Development Report 2016: Water and Jobs, chapter 2: The Global Perspective on Water. Paris: UNESCO. p. 26. ISBN 978-92-3-100155-0. Crocker, J., Bartram, B. (2014) 'Comparison and cost analysis of drinking water quality monitoring requirements versus practice in seven developing countries', International Journal of Environmental Research and Public Health , 11(7), pp. 7333-7346. Dore, M.H., Singh, R.G., Khaleghi-Moghadam, A. and Achari, G., 2013. Cost differentials and scale for newer water treatment technologies. International Journal of Water Resources and Environmental Engineering, 5(2), pp.100-109. Hoornweg, D. and Bhada-Tata, P. 2012. What a waste: A global review of solid waste management. Urban Development Series Knowledge Papers. No. 15. The World Bank. March 2012. Intergovernmental Panel on Climate Change (IPCC). 2000. Land Use, Land Use Change and Forestry. Robert T. Watson, Ian R. Noble, Bert Bolin, N. H. Ravindranath, David J. Verardo and David J. Dokken (Eds.). Cambridge University Press, UK. pp 375 http://www.ipcc.ch/ipccreports/sres/land use/index.php?idp=253 Kampala Capital City Authority (KCCA). 2017. Project Teaser: Kampala waste treatment and disposal PPP. International Finance Corporation. October 2017. King, D. and Bohlen, C., 1994. Estimating the costs of restoration. National Wetlands Newsletter, 16(3), pp.3-5. Kollert, W. (n.d.) Forest Restoration and Rehabilitation, Rome, Italy: Food and Agricultural Organization of the United Nations. Lopez, J., Maoulidi, M., and MCI. 2009 Education needs assessment for Mekelle City, Ethiopia, New York, NY: Millennium Cities Initiative. Ministry of Public Service (n.d.) Salary structure for financial year 2015/2016, Kampala, Uganda: Ministry of Public Service. Ministry of Water and Environment. 2015. Concept note of Uganda’s proposal to finance “Uganda small towns and rural growth centres faecal sludge and sanitation project”. Urban Sanitation Division, Urban Water and Sewerage Services Department. September 2015. MWE. 2016. Sector Performance Report. Kampala, Uganda: Ministry of Water and Environment, Government of Uganda. Accessed on March 27, 2017 from http://www.mwe.go.ug/index.php?option=com docman&task=cat view&gid=15&Itemid= 55. Ministry of Water and Environment. 2017. Water and environment sector performance report 2017, Kampala, Uganda: Government of Uganda. http://www.mwe.go.ug/index.php?option=com docman&task=cat view&gid=15&Itemid=55 http://www.mwe.go.ug/index.php?option=com docman&task=cat view&gid=15&Itemid=55 Final – March 31, 2018 43 OECD EAP Task Force Secretariat. 2005. Rural cost functions for water supply and sanitation, Paris, France: Organisation for Economic Co-operation and Development. Office of Director-General (n.d.) Case study: Uganda, Rome, Italy: Food and Agriculture Organization of the United Nations. Sutton, W., et al. (2013) Reducing the Vulnerability of Albania’s agricultural systems to climate change, Washington, DC: International Bank for Reconstruction and Development / The World Bank. TAHMO (n.d.) About TAHMO, Available at: http://tahmo.org/about-tahmo-2/ (Accessed: 13th November 2017). Uganda Bureau of Statistics (UBOS). 2016. The National Population and Housing Census 2014 – Main Report, Kampala, Uganda. Venkataramanan, V. 2015 CLTS learning series: Uganda country report, Chapel Hill, USA: The Water Institute at UNC. Water for People. 2017. District investment planning, Every household every health clinic every school. Government of Uganda. May 2017. The World Bank (2007) Rural productivity in Moldova – managing natural vulnerability , Washington, DC: The World Bank. The World Bank (2017) International bank for reconstruction and development project appraisal document on proposed grants ... to the Democratic Republic of Congo for a strengthening hydro-meteorological and climate services project, Washington, DC: International Bank for Reconstruction and Development / The World Bank. Whinnery, J., et al. 2016. Handwashing with a water-efficient tap and low-cost foaming soap: the Povu Poa ‘cool foam’ system in Kenya, Global Health: Science and Practice, 4(2), pp. 336–341. Zhang, C. et al. 2013. 'Promoting clean hands among children in Uganda: a school-based intervention using ‘tippy-taps', Public Health, 127(6), pp. 586–589. Final – March 31, 2018 44 ANNEX 4D. DETAILED INDICATOR PERFORMANCE TABLES FIGURE 4D-1 INDICATOR PERFORMANCE TOWARD TARGETS:BAU Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 Vi lla ge w at er s up pl y 0 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 1 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 2 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 3 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 4 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 5 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 6 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 7 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 8 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 9 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 10 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 11 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 12 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 13 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 14 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 15 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 16 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 17 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 18 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 19 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 20 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 21 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 22 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% 23 66.0% 73.0% 78.8% 83.1% 86.0% 88.3% 89.9% 91.0% 91.6% 89.9% Fu nc tio na l ru ra l w at er so ur ce s 0 85.0% 88.9% 90.7% 91.5% 91.7% 91.8% 91.7% 91.5% 91.1% 87.9% 1 80.3% 86.3% 89.2% 90.6% 91.1% 91.5% 91.5% 91.3% 91.0% 87.9% Final – March 31, 2018 45 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 2 85.0% 88.9% 90.7% 91.5% 91.7% 91.8% 91.7% 91.5% 91.1% 87.9% 3 82.4% 87.4% 89.9% 91.0% 91.4% 91.6% 91.6% 91.4% 91.1% 87.9% 4 73.3% 82.5% 87.0% 89.3% 90.3% 90.9% 91.1% 91.1% 90.9% 87.9% 5 86.5% 89.7% 91.1% 91.8% 91.8% 91.9% 91.8% 91.5% 91.2% 87.9% 6 82.9% 87.7% 90.0% 91.1% 91.4% 91.7% 91.6% 91.4% 91.1% 87.9% 7 89.5% 91.3% 92.1% 92.3% 92.2% 92.1% 91.9% 91.6% 91.2% 88.0% 8 81.0% 86.7% 89.4% 90.8% 91.2% 91.5% 91.5% 91.3% 91.0% 87.9% 9 68.9% 80.1% 85.7% 88.5% 89.8% 90.6% 90.9% 91.0% 90.8% 87.8% 10 81.2% 86.8% 89.5% 90.8% 91.2% 91.5% 91.5% 91.4% 91.0% 87.9% 11 83.8% 88.2% 90.3% 91.3% 91.5% 91.7% 91.6% 91.4% 91.1% 87.9% 12 83.7% 88.2% 90.3% 91.3% 91.5% 91.7% 91.6% 91.4% 91.1% 87.9% 13 88.8% 90.9% 91.9% 92.2% 92.1% 92.1% 91.9% 91.6% 91.2% 88.0% 14 93.0% 93.2% 93.2% 93.0% 92.6% 92.4% 92.1% 91.7% 91.3% 88.0% 15 89.6% 91.4% 92.1% 92.4% 92.2% 92.1% 91.9% 91.6% 91.2% 88.0% 16 91.0% 92.1% 92.6% 92.6% 92.4% 92.2% 92.0% 91.7% 91.3% 88.0% 17 79.0% 85.6% 88.8% 90.4% 91.0% 91.4% 91.4% 91.3% 91.0% 87.9% 18 67.2% 79.2% 85.2% 88.2% 89.6% 90.5% 90.8% 90.9% 90.7% 87.8% 19 89.3% 91.2% 92.0% 92.3% 92.2% 92.1% 91.9% 91.6% 91.2% 88.0% 20 86.1% 89.4% 91.0% 91.7% 91.8% 91.9% 91.7% 91.5% 91.2% 87.9% 21 82.3% 87.4% 89.8% 91.0% 91.4% 91.6% 91.6% 91.4% 91.1% 87.9% 22 86.4% 89.6% 91.1% 91.8% 91.8% 91.9% 91.8% 91.5% 91.2% 87.9% 23 81.1% 86.7% 89.5% 90.8% 91.2% 91.5% 91.5% 91.3% 91.0% 87.9% Im pr ov ed d ri nk in g w at er 0 70.0% 72.3% 74.1% 75.2% 75.7% 76.0% 76.0% 75.7% 75.2% 70.7% 1 75.6% 76.9% 77.9% 78.5% 78.5% 78.4% 78.1% 77.6% 76.9% 71.8% 2 82.2% 82.4% 82.4% 82.3% 81.8% 81.3% 80.6% 79.8% 78.9% 73.1% 3 78.8% 79.6% 80.1% 80.3% 80.1% 79.8% 79.3% 78.7% 77.9% 72.5% 4 92.4% 90.8% 89.4% 88.1% 86.8% 85.6% 84.4% 83.2% 82.0% 75.2% 5 73.6% 75.3% 76.5% 77.3% 77.5% 77.6% 77.3% 76.9% 76.3% 71.4% 6 64.0% 67.4% 70.0% 71.8% 72.8% 73.5% 73.7% 73.7% 73.4% 69.5% 7 65.4% 68.5% 70.9% 72.6% 73.4% 74.0% 74.2% 74.2% 73.8% 69.8% Final – March 31, 2018 46 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 8 59.0% 63.2% 66.5% 68.9% 70.3% 71.3% 71.8% 72.0% 71.9% 68.5% 9 86.4% 85.8% 85.3% 84.7% 83.9% 83.1% 82.2% 81.2% 80.2% 74.0% 10 73.1% 74.8% 76.2% 77.0% 77.2% 77.3% 77.1% 76.7% 76.1% 71.3% 11 66.2% 69.2% 71.5% 73.0% 73.8% 74.4% 74.5% 74.4% 74.1% 69.9% 12 61.5% 65.3% 68.3% 70.4% 71.5% 72.4% 72.8% 72.9% 72.7% 69.0% 13 70.8% 73.0% 74.7% 75.7% 76.2% 76.4% 76.3% 76.0% 75.5% 70.9% 14 72.4% 74.3% 75.7% 76.6% 76.9% 77.0% 76.9% 76.5% 75.9% 71.2% 15 72.4% 74.2% 75.7% 76.6% 76.9% 77.0% 76.9% 76.5% 75.9% 71.2% 16 69.1% 71.6% 73.5% 74.7% 75.3% 75.6% 75.6% 75.4% 74.9% 70.5% 17 82.1% 82.3% 82.3% 82.2% 81.7% 81.2% 80.5% 79.8% 78.9% 73.1% 18 77.6% 78.5% 79.3% 79.6% 79.5% 79.3% 78.8% 78.2% 77.5% 72.2% 19 58.6% 62.9% 66.3% 68.7% 70.1% 71.2% 71.7% 71.9% 71.8% 68.4% 20 68.5% 71.1% 73.1% 74.4% 75.0% 75.4% 75.4% 75.2% 74.8% 70.4% 21 65.8% 68.8% 71.2% 72.8% 73.6% 74.2% 74.4% 74.3% 73.9% 69.8% 22 71.1% 73.2% 74.8% 75.9% 76.3% 76.5% 76.4% 76.1% 75.5% 70.9% 23 80.3% 80.8% 81.1% 81.1% 80.8% 80.4% 79.8% 79.1% 78.3% 72.7% Sa fe ly m an ag ed d ri nk in g w at er 0 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 1 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 2 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 3 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 4 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 5 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 6 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 7 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 8 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 9 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 10 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 11 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 12 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 13 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% Final – March 31, 2018 47 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 14 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 15 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 16 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 17 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 18 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 19 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 20 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 21 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 22 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% 23 6.6% 7.0% 7.4% 7.8% 8.2% 8.6% 8.9% 9.2% 9.5% 10.1% Co st p er C ap ita 0 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 2 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 3 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 4 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 5 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 6 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 7 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 8 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 9 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 10 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 11 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 12 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 13 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 14 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 15 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 16 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 17 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 18 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 19 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Final – March 31, 2018 48 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 20 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 21 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 22 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 23 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% U rb an w at er s er vi ce fu nc tio na lit y 0 92.0% 94.6% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 98.7% 1 94.1% 95.2% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 98.6% 2 30.0% 75.4% 93.4% 97.4% 97.8% 98.4% 98.6% 99.0% 99.1% 100.0% 3 84.4% 92.2% 95.9% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 99.0% 4 87.9% 93.3% 96.1% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 98.9% 5 87.6% 93.2% 96.0% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 98.9% 6 97.7% 96.3% 96.5% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 98.5% 7 98.8% 96.7% 96.6% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 98.5% 8 84.5% 92.3% 95.9% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 99.0% 9 89.4% 93.8% 96.1% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 98.8% 10 100.0% 100.0% 97.0% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 98.0% 11 100.0% 99.1% 96.9% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 98.1% 12 91.1% 94.3% 96.2% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 98.8% 13 95.3% 95.6% 96.4% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 98.6% 14 100.0% 100.0% 98.5% 97.1% 97.9% 98.3% 98.7% 98.9% 99.2% 97.2% 15 100.0% 97.5% 96.7% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 98.4% 16 100.0% 97.1% 96.6% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 98.4% 17 74.1% 89.0% 95.4% 97.3% 97.9% 98.3% 98.7% 98.9% 99.1% 99.4% 18 83.3% 91.9% 95.8% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 99.1% 19 94.0% 95.2% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 98.6% 20 99.4% 96.9% 96.6% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 98.4% 21 82.7% 91.7% 95.8% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 99.1% 22 92.7% 94.8% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 98.7% 23 88.0% 93.3% 96.1% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 98.9% So lid w as te di sp os al 0 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% 1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Final – March 31, 2018 49 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 2 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% 4 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% 5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 6 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% 7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 8 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% 9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 11 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 12 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% 13 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% 14 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 15 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 16 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% 17 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 19 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% 20 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% 21 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% 22 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 23 67.5% 63.6% 62.6% 62.3% 62.8% 64.0% 65.5% 67.3% 68.9% 74.5% Im pr ov ed s an ita tio n 0 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 1 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 2 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 3 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 4 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 5 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 6 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 7 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% Final – March 31, 2018 50 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 8 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 9 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 10 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 11 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 12 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 13 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 14 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 15 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 16 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 17 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 18 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 19 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 20 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 21 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 22 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% 23 19.2% 25.4% 31.2% 36.2% 40.0% 43.3% 45.8% 47.7% 49.2% 50.4% Sa fe ly m an ag ed s an ita tio n 0 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 1 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 2 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 3 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 4 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 5 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 6 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 7 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 8 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 9 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 10 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 11 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 12 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 13 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% Final – March 31, 2018 51 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 14 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 15 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 16 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 17 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 18 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 19 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 20 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 21 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 22 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% 23 9.6% 10.0% 10.4% 10.8% 11.2% 11.5% 11.9% 12.2% 12.4% 12.7% H an dw as hi ng a t h om e 0 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 1 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 2 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 3 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 4 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 5 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 6 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 7 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 8 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 9 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 10 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 11 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 12 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 13 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 14 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 15 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 16 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 17 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 18 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 19 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% Final – March 31, 2018 52 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 20 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 21 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 22 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% 23 37.0% 36.2% 35.6% 35.1% 34.9% 34.9% 35.0% 35.3% 35.7% 38.2% H an dw as hi ng a t s ch oo l 0 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 1 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 2 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 3 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 4 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 5 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 6 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 7 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 8 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 9 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 10 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 11 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 12 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 13 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 14 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 15 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 16 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 17 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 18 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 19 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 20 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 21 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 22 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% 23 35.0% 34.5% 34.6% 35.4% 36.6% 38.3% 40.3% 42.6% 44.8% 53.4% Ir ri ga tio n 0 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 1 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% Final – March 31, 2018 53 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 2 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 3 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 4 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 5 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 6 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 7 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 8 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 9 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 10 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 11 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 12 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 13 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 14 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 15 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 16 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 17 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 18 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 19 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 20 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 21 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 22 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% 23 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6% 0.7% 0.8% W fP fu nc tio na lit y 0 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 1 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 2 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 3 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 4 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 5 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 6 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 7 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% Final – March 31, 2018 54 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 8 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 9 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 10 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 11 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 12 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 13 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 14 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 15 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 16 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 17 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 18 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 19 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 20 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 21 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 22 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% 23 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.4% 98.5% 98.5% 98.5% St or ag e Ca pa ci ty 0 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 1 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 2 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 3 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 4 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 5 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 6 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 7 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 8 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 9 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 10 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 11 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 12 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 13 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% Final – March 31, 2018 55 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 14 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 15 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 16 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 17 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 18 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 19 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 20 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 21 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 22 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% 23 23.7% 24.0% 24.4% 24.9% 25.6% 26.4% 27.4% 28.5% 29.7% 36.4% Co m pl ia nc e w ith w at er s ta nd ar ds 0 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 1 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 2 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 3 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 4 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 5 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 6 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 7 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 8 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 9 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 10 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 11 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 12 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 13 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 14 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 15 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 16 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 17 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 18 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 19 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% Final – March 31, 2018 56 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 20 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 21 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 22 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% 23 64.4% 63.9% 64.0% 64.7% 65.6% 66.8% 68.1% 69.3% 70.3% 73.4% Pe rm it co m pl ia nc e 0 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 1 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 2 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 3 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 4 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 5 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 6 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 7 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 8 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 9 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 10 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 11 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 12 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 13 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 14 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 15 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 16 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 17 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 18 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 19 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 20 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 21 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 22 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% 23 71.0% 79.5% 84.1% 85.8% 86.2% 86.7% 87.0% 87.2% 87.3% 87.7% W as te w at er tr ea tm en t 0 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 1 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% Final – March 31, 2018 57 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 2 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 3 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 4 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 5 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 6 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 7 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 8 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 9 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 10 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 11 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 12 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 13 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 14 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 15 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 16 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 17 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 18 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 19 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 20 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 21 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 22 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% 23 20.0% 19.5% 19.1% 18.7% 18.3% 17.9% 17.6% 17.2% 16.9% 15.5% A m bi en t w at er q ua lit y 0 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 1 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 2 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 3 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 4 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 5 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 6 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 7 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% Final – March 31, 2018 58 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 8 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 11 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 12 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 13 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 14 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 15 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 16 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 17 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 19 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 20 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 21 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 22 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% 23 0.0% 0.4% 0.9% 1.4% 1.9% 2.4% 3.0% 3.7% 4.3% 7.6% W at er S tr es s 0 2.4% 2.5% 2.6% 2.7% 2.8% 2.9% 3.0% 3.1% 3.2% 3.6% 1 34.1% 35.7% 37.3% 38.9% 40.5% 42.1% 43.6% 45.2% 46.7% 53.5% 2 39.2% 40.9% 42.6% 44.4% 46.1% 47.8% 49.5% 51.2% 52.9% 60.2% 3 3.4% 3.5% 3.6% 3.8% 3.9% 4.0% 4.1% 4.2% 4.3% 4.9% 4 2.7% 2.8% 2.9% 3.0% 3.1% 3.3% 3.4% 3.5% 3.6% 4.1% 5 4.4% 4.6% 4.8% 5.1% 5.3% 5.5% 5.7% 5.9% 6.1% 7.0% 6 5.6% 5.8% 6.0% 6.2% 6.5% 6.7% 6.9% 7.2% 7.4% 8.5% 7 4.8% 4.9% 5.1% 5.2% 5.4% 5.5% 5.7% 5.8% 6.0% 6.7% 8 29.7% 30.7% 31.7% 32.7% 33.7% 34.6% 35.6% 36.6% 37.6% 42.2% 9 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 10 0.2% 0.2% 0.2% 0.2% 0.3% 0.3% 0.3% 0.4% 0.4% 0.5% 11 7.4% 7.9% 8.3% 8.7% 9.2% 9.6% 10.0% 10.4% 10.8% 12.5% 12 0.5% 0.6% 0.6% 0.6% 0.6% 0.6% 0.7% 0.7% 0.7% 0.8% 13 0.8% 0.9% 0.9% 0.9% 1.0% 1.0% 1.0% 1.0% 1.1% 1.2% Final – March 31, 2018 59 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 14 12.3% 12.7% 13.1% 13.6% 14.0% 14.4% 14.8% 15.2% 15.6% 17.5% 15 79.1% 81.4% 83.8% 86.3% 88.7% 91.1% 93.5% 95.9% 98.3% 100.0% 16 7.3% 7.5% 7.8% 8.0% 8.3% 8.5% 8.8% 9.0% 9.3% 10.4% 17 10.2% 10.8% 11.4% 12.1% 12.7% 13.4% 14.0% 14.6% 15.2% 17.7% 18 8.4% 8.8% 9.3% 9.8% 10.3% 10.7% 11.2% 11.6% 12.1% 14.0% 19 9.0% 9.3% 9.6% 9.9% 10.3% 10.6% 10.9% 11.3% 11.6% 13.2% 20 3.2% 3.4% 3.5% 3.6% 3.7% 3.8% 4.0% 4.1% 4.2% 4.8% 21 16.1% 16.6% 17.2% 17.8% 18.5% 19.1% 19.7% 20.3% 21.0% 24.1% 22 6.5% 6.7% 6.9% 7.2% 7.4% 7.6% 7.9% 8.1% 8.3% 9.4% 23 7.2% 7.4% 7.7% 7.9% 8.2% 8.4% 8.7% 9.0% 9.2% 10.4% W et la nd s co ve ra ge 0 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 1 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 2 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 3 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 4 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 5 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 6 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 7 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 8 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 9 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 10 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 11 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 12 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 13 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 14 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 15 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 16 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 17 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 18 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 19 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% Final – March 31, 2018 60 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 20 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 21 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 22 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% 23 0.0% 0.6% 1.2% 1.9% 2.5% 3.2% 3.8% 4.5% 5.2% 8.1% Fo re st C ov er ag e 0 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 1 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 2 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 3 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 4 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 5 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 6 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 7 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 8 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 9 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 10 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 11 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 12 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 13 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 14 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 15 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 16 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 17 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 18 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 19 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 20 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 21 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 22 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% 23 0.0% 0.3% 0.5% 0.8% 1.1% 1.4% 1.7% 2.0% 2.3% 3.5% G H G em is si on s 0 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 1 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% Final – March 31, 2018 61 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 2 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 3 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 4 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 5 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 6 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 7 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 8 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 9 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 10 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 11 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 12 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 13 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 14 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 15 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 16 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 17 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 18 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 19 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 20 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 21 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 22 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% 23 0.0% 1.1% 1.9% 2.6% 3.0% 3.4% 3.7% 3.9% 4.1% 4.3% Cl im at e Ch an ge V ul ne ra bi lit y 0 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 2 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 3 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 4 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 5 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 6 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 7 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Final – March 31, 2018 62 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 8 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 9 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 10 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 11 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 12 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 13 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 14 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 15 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 16 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 17 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 18 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 19 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 20 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 21 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 22 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 23 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% O pe ra tio na l w ea th er s ta tio ns 0 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 3 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 4 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 5 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 6 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 7 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 8 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 11 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 12 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 13 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% Final – March 31, 2018 63 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 14 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 15 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 16 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 17 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 19 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 20 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 21 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 22 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% 23 43.0% 43.9% 49.8% 55.5% 59.4% 63.3% 66.0% 68.2% 69.9% 74.0% Final – March 31, 2018 64 FIGURE 4D-2 INDICATOR PERFORMANCE TOWARD TARGETS: LOW-MODERATE Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 Vi lla ge w at er s up pl y 0 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 1 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 2 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 3 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 97.6% 99.2% 4 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 98.5% 100.0% 5 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 6 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 97.3% 100.0% 7 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 8 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 98.9% 100.0% 100.0% 9 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 10 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 11 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 12 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 99.3% 100.0% 100.0% 13 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 97.8% 100.0% 99.4% 14 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 15 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 97.2% 16 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 99.8% 98.5% 100.0% 17 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 98.3% 18 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 19 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 97.9% 97.6% 20 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 21 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 99.9% 100.0% 100.0% 100.0% 22 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 23 66.0% 79.9% 89.3% 95.4% 99.2% 100.0% 100.0% 100.0% 98.9% 100.0% Fu nc tio na l r ur al w at er so ur ce s 0 85.0% 91.2% 94.4% 95.0% 95.1% 95.1% 95.0% 94.8% 94.6% 92.6% 1 80.3% 89.4% 93.9% 94.8% 95.0% 95.1% 95.0% 94.8% 94.6% 92.6% 2 85.0% 91.3% 94.4% 95.0% 95.1% 95.1% 95.0% 94.8% 94.6% 92.6% 3 82.4% 90.2% 94.1% 94.9% 95.1% 95.1% 95.0% 94.8% 94.6% 92.6% Final – March 31, 2018 65 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 4 73.3% 86.7% 93.0% 94.5% 94.9% 95.1% 95.0% 94.8% 94.6% 92.6% 5 86.5% 91.8% 94.6% 95.1% 95.1% 95.2% 95.0% 94.8% 94.6% 92.6% 6 82.9% 90.4% 94.2% 94.9% 95.1% 95.1% 95.0% 94.8% 94.6% 92.6% 7 89.5% 93.0% 94.9% 95.2% 95.2% 95.2% 95.0% 94.9% 94.6% 92.6% 8 81.0% 89.7% 93.9% 94.9% 95.0% 95.1% 95.0% 94.8% 94.6% 92.6% 9 68.9% 85.0% 92.5% 94.4% 94.8% 95.0% 95.0% 94.8% 94.6% 92.6% 10 81.2% 89.8% 94.0% 94.9% 95.0% 95.1% 95.0% 94.8% 94.6% 92.6% 11 83.8% 90.8% 94.3% 95.0% 95.1% 95.1% 95.0% 94.8% 94.6% 92.6% 12 83.7% 90.7% 94.3% 95.0% 95.1% 95.1% 95.0% 94.8% 94.6% 92.6% 13 88.8% 92.7% 94.8% 95.2% 95.2% 95.2% 95.0% 94.9% 94.6% 92.6% 14 93.0% 94.3% 95.3% 95.4% 95.2% 95.2% 95.0% 94.9% 94.6% 92.6% 15 89.6% 93.0% 94.9% 95.2% 95.2% 95.2% 95.0% 94.9% 94.6% 92.6% 16 91.0% 93.6% 95.1% 95.3% 95.2% 95.2% 95.0% 94.9% 94.6% 92.6% 17 79.0% 88.9% 93.7% 94.8% 95.0% 95.1% 95.0% 94.8% 94.6% 92.6% 18 67.2% 84.4% 92.3% 94.3% 94.8% 95.0% 95.0% 94.8% 94.6% 92.6% 19 89.3% 92.9% 94.9% 95.2% 95.2% 95.2% 95.0% 94.9% 94.6% 92.6% 20 86.1% 91.7% 94.5% 95.1% 95.1% 95.2% 95.0% 94.8% 94.6% 92.6% 21 82.3% 90.2% 94.1% 94.9% 95.1% 95.1% 95.0% 94.8% 94.6% 92.6% 22 86.4% 91.8% 94.6% 95.1% 95.1% 95.2% 95.0% 94.8% 94.6% 92.6% 23 81.1% 89.7% 94.0% 94.9% 95.0% 95.1% 95.0% 94.8% 94.6% 92.6% Im pr ov ed d ri nk in g w at er 0 70.0% 75.7% 79.3% 81.6% 82.8% 83.5% 83.8% 83.9% 83.7% 80.3% 1 75.6% 79.7% 82.2% 83.7% 84.4% 84.8% 84.9% 84.7% 84.4% 80.7% 2 82.2% 84.4% 85.7% 86.3% 86.4% 86.4% 86.1% 85.8% 85.2% 81.1% 3 78.8% 81.9% 83.9% 85.0% 85.4% 85.6% 85.5% 85.2% 84.8% 80.9% 4 92.4% 91.6% 91.0% 90.3% 89.5% 88.8% 88.1% 87.3% 86.5% 81.7% 5 73.6% 78.2% 81.2% 83.0% 83.8% 84.4% 84.5% 84.4% 84.1% 80.6% 6 64.0% 71.4% 76.2% 79.3% 81.0% 82.1% 82.7% 83.0% 82.9% 80.0% 7 65.4% 72.4% 76.9% 79.8% 81.4% 82.4% 83.0% 83.2% 83.1% 80.0% 8 59.0% 67.8% 73.6% 77.3% 79.4% 80.9% 81.7% 82.2% 82.3% 79.6% 9 86.4% 87.4% 87.8% 87.9% 87.7% 87.4% 86.9% 86.4% 85.8% 81.4% Final – March 31, 2018 66 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 73.1% 77.8% 80.9% 82.7% 83.7% 84.2% 84.4% 84.3% 84.1% 80.5% 11 66.2% 72.9% 77.3% 80.1% 81.6% 82.6% 83.1% 83.3% 83.2% 80.1% 12 61.5% 69.6% 74.9% 78.3% 80.2% 81.5% 82.2% 82.6% 82.6% 79.8% 13 70.8% 76.3% 79.8% 81.9% 83.0% 83.7% 84.0% 84.0% 83.8% 80.4% 14 72.4% 77.3% 80.6% 82.5% 83.5% 84.1% 84.3% 84.2% 84.0% 80.5% 15 72.4% 77.3% 80.6% 82.5% 83.5% 84.1% 84.3% 84.2% 84.0% 80.5% 16 69.1% 75.0% 78.9% 81.2% 82.5% 83.3% 83.7% 83.7% 83.6% 80.3% 17 82.1% 84.3% 85.6% 86.3% 86.4% 86.4% 86.1% 85.7% 85.2% 81.1% 18 77.6% 81.1% 83.3% 84.5% 85.0% 85.3% 85.3% 85.0% 84.6% 80.8% 19 58.6% 67.6% 73.4% 77.2% 79.3% 80.8% 81.7% 82.1% 82.2% 79.6% 20 68.5% 74.6% 78.6% 81.0% 82.3% 83.2% 83.6% 83.6% 83.5% 80.2% 21 65.8% 72.6% 77.1% 79.9% 81.5% 82.5% 83.0% 83.2% 83.1% 80.1% 22 71.1% 76.4% 79.9% 82.0% 83.1% 83.8% 84.0% 84.0% 83.8% 80.4% 23 80.3% 83.0% 84.7% 85.6% 85.9% 85.9% 85.8% 85.5% 85.0% 81.0% Sa fe ly m an ag ed d ri nk in g w at er 0 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 1 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 2 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 3 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 4 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 5 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 6 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 7 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 8 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 9 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 10 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 11 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 12 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 13 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 14 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 15 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% Final – March 31, 2018 67 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 17 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 18 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 19 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 20 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 21 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 22 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% 23 6.6% 7.4% 8.4% 9.4% 10.4% 11.3% 12.2% 13.0% 13.7% 15.8% Co st p er C ap ita 0 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 2 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 3 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 4 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 5 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 6 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 7 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 8 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 9 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 10 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 11 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 12 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 13 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 14 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 15 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 16 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 17 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 18 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 19 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 20 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 21 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Final – March 31, 2018 68 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 22 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 23 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% U rb an w at er s er vi ce fu nc tio na lit y 0 92.0% 94.6% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 1 94.1% 95.2% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 97.4% 2 30.0% 75.4% 93.4% 97.4% 97.8% 98.4% 98.6% 99.0% 99.1% 99.2% 3 84.4% 92.2% 95.9% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 4 87.9% 93.3% 96.1% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 5 87.6% 93.2% 96.0% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 6 97.7% 96.3% 96.5% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 98.9% 7 98.8% 96.7% 96.6% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 99.4% 8 84.5% 92.3% 95.9% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 9 89.4% 93.8% 96.1% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 10 100.0% 100.0% 97.0% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 11 100.0% 99.1% 96.9% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 12 91.1% 94.3% 96.2% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 13 95.3% 95.6% 96.4% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 97.5% 14 100.0% 100.0% 98.5% 97.1% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 15 100.0% 97.5% 96.7% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 16 100.0% 97.1% 96.6% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 17 74.1% 89.0% 95.4% 97.3% 97.9% 98.3% 98.7% 98.9% 99.1% 100.0% 18 83.3% 91.9% 95.8% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 19 94.0% 95.2% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 97.4% 20 99.4% 96.9% 96.6% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 99.7% 21 82.7% 91.7% 95.8% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 22 92.7% 94.8% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 98.8% 23 88.0% 93.3% 96.1% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% So lid w as te d is po sa l 0 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% 1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% Final – March 31, 2018 69 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 4 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% 5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 6 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% 7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 8 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% 9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 11 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 12 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% 13 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% 14 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 15 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 16 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% 17 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 19 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% 20 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% 21 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% 22 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 23 67.5% 63.9% 63.7% 64.9% 67.0% 69.7% 72.2% 74.5% 76.3% 81.2% Im pr ov ed s an ita tio n 0 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 1 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 2 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 3 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 4 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 5 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 6 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 7 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 8 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 9 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% Final – March 31, 2018 70 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 11 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 12 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 13 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 14 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 15 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 16 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 17 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 18 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 19 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 20 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 21 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 22 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% 23 19.2% 30.6% 40.8% 48.8% 54.6% 59.0% 62.3% 64.7% 66.3% 68.2% Sa fe ly m an ag ed s an ita tio n 0 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 1 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 2 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 3 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 4 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 5 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 6 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 7 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 8 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 9 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 10 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 11 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 12 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 13 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 14 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 15 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% Final – March 31, 2018 71 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 17 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 18 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 19 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 20 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 21 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 22 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% 23 9.6% 10.5% 11.5% 12.6% 13.6% 14.6% 15.5% 16.3% 17.0% 19.0% H an dw as hi ng a t h om e 0 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 1 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 2 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 3 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 4 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 5 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 6 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 7 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 8 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 9 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 10 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 11 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 12 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 13 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 14 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 15 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 16 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 17 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 18 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 19 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 20 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 21 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% Final – March 31, 2018 72 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 22 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% 23 37.0% 36.4% 36.2% 36.4% 36.9% 37.7% 38.9% 40.2% 41.6% 48.9% H an dw as hi ng a t s ch oo l 0 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 1 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 2 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 3 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 4 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 5 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 6 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 7 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 8 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 9 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 10 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 11 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 12 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 13 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 14 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 15 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 16 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 17 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 18 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 19 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 20 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 21 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 22 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% 23 35.0% 35.0% 36.1% 38.4% 41.4% 45.1% 48.8% 52.4% 55.7% 66.4% Ir ri ga tio n 0 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 1 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 2 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 3 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% Final – March 31, 2018 73 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 4 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 5 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 6 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 7 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 8 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 9 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 10 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 11 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 12 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 13 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 14 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 15 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 16 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 17 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 18 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 19 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 20 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 21 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 22 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% 23 0.5% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.8% 0.8% 1.2% W fP fu nc tio na lit y 0 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 1 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 2 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 3 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 4 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 5 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 6 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 7 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 8 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 9 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% Final – March 31, 2018 74 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 11 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 12 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 13 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 14 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 15 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 16 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 17 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 18 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 19 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 20 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 21 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 22 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 23 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% St or ag e Ca pa ci ty 0 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 1 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 2 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 3 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 4 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 5 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 6 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 7 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 8 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 9 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 10 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 11 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 12 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 13 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 14 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 15 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% Final – March 31, 2018 75 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 17 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 18 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 19 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 20 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 21 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 22 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% 23 23.7% 24.2% 24.9% 26.0% 27.3% 29.0% 30.9% 33.0% 35.3% 47.7% Co m pl ia nc e w ith w at er s ta nd ar ds 0 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 1 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 2 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 3 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 4 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 5 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 6 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 7 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 8 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 9 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 10 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 11 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 12 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 13 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 14 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 15 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 16 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 17 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 18 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 19 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 20 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 21 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% Final – March 31, 2018 76 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 22 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% 23 64.4% 64.9% 66.6% 68.9% 71.0% 73.1% 74.8% 76.2% 77.3% 80.2% Pe rm it co m pl ia nc e 0 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 1 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 2 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 3 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 4 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 5 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 6 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 7 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 8 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 9 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 10 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 11 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 12 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 13 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 14 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 15 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 16 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 17 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 18 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 19 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 20 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 21 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 22 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% 23 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.1% 89.5% W as te w at er tr ea tm en t 0 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 1 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 2 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 3 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% Final – March 31, 2018 77 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 4 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 5 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 6 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 7 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 8 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 9 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 10 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 11 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 12 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 13 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 14 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 15 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 16 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 17 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 18 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 19 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 20 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 21 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 22 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% 23 20.0% 19.6% 19.3% 19.0% 18.8% 18.6% 18.4% 18.3% 18.2% 17.7% A m bi en t w at er q ua lit y 0 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 1 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 2 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 3 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 4 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 5 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 6 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 7 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 8 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Final – March 31, 2018 78 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 11 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 12 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 13 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 14 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 15 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 16 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 17 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 19 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 20 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 21 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 22 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% 23 0.0% 0.8% 1.7% 2.7% 3.7% 4.9% 6.1% 7.4% 8.7% 15.7% W at er S tr es s 0 2.4% 2.5% 2.6% 2.7% 2.8% 2.9% 3.0% 3.1% 3.2% 3.6% 1 34.1% 35.7% 37.3% 38.9% 40.5% 42.1% 43.6% 45.2% 46.7% 53.5% 2 39.2% 40.9% 42.6% 44.4% 46.1% 47.8% 49.5% 51.2% 52.9% 60.2% 3 3.4% 3.5% 3.6% 3.8% 3.9% 4.0% 4.1% 4.2% 4.3% 4.9% 4 2.7% 2.8% 2.9% 3.0% 3.1% 3.3% 3.4% 3.5% 3.6% 4.1% 5 4.4% 4.6% 4.8% 5.1% 5.3% 5.5% 5.7% 5.9% 6.1% 7.0% 6 5.6% 5.8% 6.0% 6.2% 6.5% 6.7% 6.9% 7.2% 7.4% 8.5% 7 4.8% 4.9% 5.1% 5.2% 5.4% 5.5% 5.7% 5.8% 6.0% 6.7% 8 29.7% 30.7% 31.7% 32.7% 33.7% 34.6% 35.6% 36.6% 37.6% 42.2% 9 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 10 0.2% 0.2% 0.2% 0.2% 0.3% 0.3% 0.3% 0.4% 0.4% 0.5% 11 7.4% 7.9% 8.3% 8.7% 9.2% 9.6% 10.0% 10.4% 10.8% 12.5% 12 0.5% 0.6% 0.6% 0.6% 0.6% 0.6% 0.7% 0.7% 0.7% 0.8% 13 0.8% 0.9% 0.9% 0.9% 1.0% 1.0% 1.0% 1.0% 1.1% 1.2% 14 12.3% 12.7% 13.1% 13.6% 14.0% 14.4% 14.8% 15.2% 15.6% 17.5% 15 79.1% 81.4% 83.8% 86.3% 88.7% 91.1% 93.5% 95.9% 98.3% 100.0% Final – March 31, 2018 79 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 7.3% 7.5% 7.8% 8.0% 8.3% 8.5% 8.8% 9.0% 9.3% 10.4% 17 10.2% 10.8% 11.4% 12.1% 12.7% 13.4% 14.0% 14.6% 15.2% 17.7% 18 8.4% 8.8% 9.3% 9.8% 10.3% 10.7% 11.2% 11.6% 12.1% 14.0% 19 9.0% 9.3% 9.6% 9.9% 10.3% 10.6% 10.9% 11.3% 11.6% 13.2% 20 3.2% 3.4% 3.5% 3.6% 3.7% 3.8% 4.0% 4.1% 4.2% 4.8% 21 16.1% 16.6% 17.2% 17.8% 18.5% 19.1% 19.7% 20.3% 21.0% 24.1% 22 6.5% 6.7% 6.9% 7.2% 7.4% 7.6% 7.9% 8.1% 8.3% 9.4% 23 7.2% 7.4% 7.7% 7.9% 8.2% 8.4% 8.7% 9.0% 9.2% 10.4% W et la nd s co ve ra ge 0 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 1 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 2 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 3 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 4 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 5 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 6 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 7 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 8 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 9 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 10 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 11 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 12 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 13 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 14 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 15 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 16 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 17 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 18 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 19 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 20 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 21 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% Final – March 31, 2018 80 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 22 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% 23 0.0% 1.2% 2.4% 3.7% 5.0% 6.3% 7.7% 9.1% 10.4% 16.6% Fo re st C ov er ag e 0 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 1 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 2 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 3 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 4 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 5 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 6 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 7 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 8 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 9 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 10 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 11 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 12 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 13 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 14 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 15 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 16 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 17 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 18 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 19 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 20 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 21 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 22 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% 23 0.0% 0.5% 1.0% 1.6% 2.2% 2.8% 3.3% 3.9% 4.5% 7.1% G H G e m is si on s 0 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 1 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 2 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 3 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% Final – March 31, 2018 81 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 4 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 5 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 6 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 7 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 8 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 9 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 10 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 11 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 12 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 13 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 14 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 15 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 16 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 17 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 18 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 19 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 20 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 21 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 22 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% 23 0.0% 2.1% 3.7% 5.0% 5.9% 6.7% 7.3% 7.8% 8.1% 8.8% Cl im at e Ch an ge V ul ne ra bi lit y 0 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 2 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 3 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 4 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 5 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 6 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 7 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 8 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 9 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Final – March 31, 2018 82 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 11 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 12 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 13 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 14 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 15 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 16 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 17 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 18 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 19 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 20 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 21 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 22 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 23 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% O pe ra tio na l w ea th er s ta tio ns 0 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 3 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 4 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 5 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 6 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 7 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 8 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 11 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 12 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 13 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 14 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 15 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Final – March 31, 2018 83 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 17 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 19 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 20 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 21 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 22 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% 23 43.0% 54.5% 65.4% 71.8% 75.0% 77.7% 79.4% 80.8% 81.8% 85.1% Final – March 31, 2018 84 FIGURE 4D-3 INDICATOR PERFORMANCE TOWARD TARGETS: MODERATE-HIGH Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 Vi lla ge w at er s up pl y 0 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 2 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 3 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 97.6% 100.0% 100.0% 100.0% 4 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 97.1% 100.0% 100.0% 5 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 6 66.0% 100.0% 100.0% 100.0% 100.0% 99.9% 98.6% 100.0% 100.0% 100.0% 7 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 98.5% 100.0% 8 66.0% 100.0% 100.0% 100.0% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 9 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 10 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 11 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 12 66.0% 100.0% 100.0% 100.0% 100.0% 97.3% 100.0% 100.0% 100.0% 100.0% 13 66.0% 100.0% 100.0% 99.2% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 14 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 15 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 16 66.0% 100.0% 100.0% 100.0% 100.0% 98.7% 100.0% 100.0% 100.0% 98.2% 17 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 18 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 19 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 98.4% 100.0% 100.0% 100.0% 20 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 99.4% 100.0% 21 66.0% 100.0% 99.9% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 98.9% 22 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 23 66.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 97.9% 100.0% 100.0% Fu nc tio na l r ur al w at er so ur ce s 0 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 1 80.3% 89.4% 94.3% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 2 85.0% 91.3% 94.8% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 3 82.4% 90.2% 94.5% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% Final – March 31, 2018 85 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 4 73.3% 86.7% 93.6% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 5 86.5% 91.8% 94.9% 96.7% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 6 82.9% 90.4% 94.5% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 7 89.5% 93.0% 95.2% 96.7% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 8 81.0% 89.7% 94.3% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 9 68.9% 85.0% 93.1% 96.5% 97.7% 98.2% 98.5% 98.4% 98.4% 97.8% 10 81.2% 89.8% 94.4% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 11 83.8% 90.8% 94.6% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 12 83.7% 90.7% 94.6% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 13 88.8% 92.7% 95.1% 96.7% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 14 93.0% 94.3% 95.6% 96.7% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 15 89.6% 93.0% 95.2% 96.7% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 16 91.0% 93.6% 95.4% 96.7% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 17 79.0% 88.9% 94.1% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 18 67.2% 84.4% 93.0% 96.5% 97.7% 98.2% 98.5% 98.4% 98.4% 97.8% 19 89.3% 92.9% 95.2% 96.7% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 20 86.1% 91.7% 94.9% 96.7% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 21 82.3% 90.2% 94.5% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 22 86.4% 91.8% 94.9% 96.7% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% 23 81.1% 89.7% 94.4% 96.6% 97.7% 98.3% 98.5% 98.4% 98.4% 97.8% Im pr ov ed d ri nk in g w at er 0 70.0% 85.7% 90.9% 93.9% 95.7% 96.9% 97.7% 98.1% 98.3% 97.2% 1 75.6% 87.9% 91.8% 94.2% 95.8% 96.9% 97.7% 98.1% 98.3% 97.2% 2 82.2% 90.3% 92.9% 94.6% 95.9% 96.9% 97.7% 98.1% 98.3% 97.2% 3 78.8% 89.1% 92.3% 94.4% 95.8% 96.9% 97.7% 98.1% 98.3% 97.2% 4 92.4% 94.2% 94.5% 95.2% 96.0% 96.9% 97.7% 98.1% 98.3% 97.2% 5 73.6% 87.1% 91.5% 94.1% 95.8% 96.9% 97.7% 98.1% 98.3% 97.2% 6 64.0% 83.5% 89.9% 93.6% 95.6% 96.9% 97.7% 98.1% 98.3% 97.2% 7 65.4% 84.0% 90.1% 93.6% 95.7% 96.9% 97.7% 98.1% 98.3% 97.2% 8 59.0% 81.6% 89.1% 93.3% 95.6% 96.9% 97.7% 98.1% 98.3% 97.2% 9 86.4% 91.9% 93.5% 94.8% 95.9% 96.9% 97.7% 98.1% 98.3% 97.2% Final – March 31, 2018 86 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 73.1% 86.9% 91.4% 94.1% 95.8% 96.9% 97.7% 98.1% 98.3% 97.2% 11 66.2% 84.3% 90.2% 93.7% 95.7% 96.9% 97.7% 98.1% 98.3% 97.2% 12 61.5% 82.6% 89.5% 93.4% 95.6% 96.9% 97.7% 98.1% 98.3% 97.2% 13 70.8% 86.1% 91.0% 93.9% 95.7% 96.9% 97.7% 98.1% 98.3% 97.2% 14 72.4% 86.6% 91.3% 94.0% 95.7% 96.9% 97.7% 98.1% 98.3% 97.2% 15 72.4% 86.6% 91.2% 94.0% 95.7% 96.9% 97.7% 98.1% 98.3% 97.2% 16 69.1% 85.4% 90.7% 93.8% 95.7% 96.9% 97.7% 98.1% 98.3% 97.2% 17 82.1% 90.3% 92.8% 94.6% 95.9% 96.9% 97.7% 98.1% 98.3% 97.2% 18 77.6% 88.6% 92.1% 94.3% 95.8% 96.9% 97.7% 98.1% 98.3% 97.2% 19 58.6% 81.5% 89.0% 93.3% 95.6% 96.9% 97.7% 98.1% 98.3% 97.2% 20 68.5% 85.2% 90.6% 93.8% 95.7% 96.9% 97.7% 98.1% 98.3% 97.2% 21 65.8% 84.2% 90.2% 93.7% 95.7% 96.9% 97.7% 98.1% 98.3% 97.2% 22 71.1% 86.2% 91.0% 94.0% 95.7% 96.9% 97.7% 98.1% 98.3% 97.2% 23 80.3% 89.6% 92.5% 94.5% 95.8% 96.9% 97.7% 98.1% 98.3% 97.2% Sa fe ly m an ag ed d ri nk in g w at er 0 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 1 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 2 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 3 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 4 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 5 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 6 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 7 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 8 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 9 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 10 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 11 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 12 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 13 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 14 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 15 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% Final – March 31, 2018 87 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 17 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 18 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 19 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 20 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 21 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 22 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% 23 6.6% 8.9% 11.9% 15.0% 17.9% 20.7% 23.2% 25.4% 27.5% 35.2% Co st p er C ap ita 0 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 2 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 3 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 4 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 5 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 6 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 7 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 8 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 9 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 10 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 11 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 12 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 13 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 14 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 15 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 16 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 17 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 18 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 19 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 20 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 21 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Final – March 31, 2018 88 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 22 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 23 0.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% U rb an w at er s er vi ce fu nc tio na lit y 0 92.0% 94.6% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 1 94.1% 95.2% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 97.4% 2 30.0% 75.4% 93.4% 97.4% 97.8% 98.4% 98.6% 99.0% 99.1% 99.2% 3 84.4% 92.2% 95.9% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 4 87.9% 93.3% 96.1% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 5 87.6% 93.2% 96.0% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 6 97.7% 96.3% 96.5% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 98.9% 7 98.8% 96.7% 96.6% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 99.4% 8 84.5% 92.3% 95.9% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 9 89.4% 93.8% 96.1% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 10 100.0% 100.0% 97.0% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 11 100.0% 99.1% 96.9% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 12 91.1% 94.3% 96.2% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 13 95.3% 95.6% 96.4% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 97.5% 14 100.0% 100.0% 98.5% 97.1% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 15 100.0% 97.5% 96.7% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 16 100.0% 97.1% 96.6% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 17 74.1% 89.0% 95.4% 97.3% 97.9% 98.3% 98.7% 98.9% 99.1% 100.0% 18 83.3% 91.9% 95.8% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 19 94.0% 95.2% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 97.4% 20 99.4% 96.9% 96.6% 97.2% 97.9% 98.3% 98.7% 98.9% 99.2% 99.7% 21 82.7% 91.7% 95.8% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% 22 92.7% 94.8% 96.3% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 98.8% 23 88.0% 93.3% 96.1% 97.3% 97.9% 98.3% 98.7% 98.9% 99.2% 100.0% So lid w as te d is po sa l 0 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% 1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% Final – March 31, 2018 89 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 4 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% 5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 6 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% 7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 8 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% 9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 11 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 12 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% 13 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% 14 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 15 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 16 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% 17 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 19 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% 20 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% 21 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% 22 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 23 67.5% 64.7% 67.4% 72.3% 76.7% 80.0% 82.1% 83.6% 84.6% 87.4% Im pr ov ed s an ita tio n 0 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 1 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 2 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 3 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 4 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 5 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 6 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 7 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 8 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 9 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% Final – March 31, 2018 90 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 11 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 12 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 13 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 14 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 15 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 16 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 17 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 18 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 19 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 20 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 21 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 22 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% 23 19.2% 46.2% 65.3% 75.8% 82.1% 86.4% 89.4% 91.5% 93.0% 95.5% Sa fe ly m an ag ed s an ita tio n 0 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 1 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 2 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 3 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 4 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 5 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 6 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 7 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 8 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 9 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 10 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 11 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 12 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 13 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 14 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 15 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% Final – March 31, 2018 91 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 17 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 18 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 19 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 20 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 21 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 22 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% 23 9.6% 12.1% 15.4% 18.8% 21.9% 24.8% 27.4% 29.7% 31.8% 39.2% H an dw as hi ng a t h om e 0 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 1 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 2 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 3 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 4 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 5 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 6 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 7 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 8 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 9 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 10 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 11 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 12 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 13 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 14 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 15 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 16 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 17 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 18 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 19 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 20 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 21 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% Final – March 31, 2018 92 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 22 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% 23 37.0% 37.1% 38.4% 40.6% 43.5% 46.9% 50.5% 54.1% 57.5% 70.0% H an dw as hi ng a t s ch oo l 0 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 1 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 2 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 3 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 4 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 5 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 6 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 7 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 8 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 9 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 10 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 11 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 12 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 13 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 14 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 15 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 16 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 17 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 18 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 19 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 20 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 21 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 22 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% 23 35.0% 36.3% 40.9% 47.8% 55.0% 61.5% 66.7% 70.7% 73.7% 81.2% Ir ri ga tio n 0 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 1 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 2 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 3 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% Final – March 31, 2018 93 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 4 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 5 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 6 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 7 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 8 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 9 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 10 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 11 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 12 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 13 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 14 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 15 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 16 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 17 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 18 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 19 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 20 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 21 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 22 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% 23 0.5% 0.5% 0.6% 0.7% 0.8% 1.0% 1.1% 1.2% 1.4% 2.2% W fP fu nc tio na lit y 0 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 1 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 2 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 3 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 4 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 5 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 6 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 7 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 8 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 9 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% Final – March 31, 2018 94 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 11 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 12 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 13 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 14 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 15 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 16 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 17 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 18 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 19 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 20 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 21 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 22 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% 23 85.0% 91.2% 94.8% 96.6% 97.7% 98.3% 98.6% 98.9% 99.2% 100.0% St or ag e Ca pa ci ty 0 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 1 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 2 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 3 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 4 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 5 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 6 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 7 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 8 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 9 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 10 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 11 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 12 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 13 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 14 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 15 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% Final – March 31, 2018 95 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 17 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 18 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 19 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 20 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 21 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 22 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% 23 23.7% 24.9% 27.0% 29.9% 33.5% 37.8% 42.4% 47.4% 52.4% 73.7% Co m pl ia nc e w ith w at er s ta nd ar ds 0 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 1 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 2 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 3 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 4 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 5 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 6 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 7 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 8 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 9 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 10 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 11 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 12 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 13 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 14 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 15 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 16 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 17 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 18 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 19 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 20 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 21 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% Final – March 31, 2018 96 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 22 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% 23 64.4% 68.1% 73.8% 78.0% 80.5% 82.2% 83.4% 84.2% 84.8% 87.0% Pe rm it co m pl ia nc e 0 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 1 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 2 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 3 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 4 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 5 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 6 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 7 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 8 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 9 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 10 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 11 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 12 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 13 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 14 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 15 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 16 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 17 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 18 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 19 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 20 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 21 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 22 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% 23 71.0% 79.5% 84.1% 86.5% 87.7% 88.4% 88.8% 89.0% 89.3% 90.0% W as te w at er tr ea tm en t 0 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 1 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 2 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 3 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% Final – March 31, 2018 97 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 4 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 5 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 6 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 7 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 8 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 9 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 10 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 11 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 12 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 13 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 14 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 15 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 16 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 17 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 18 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 19 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 20 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 21 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 22 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% 23 20.0% 20.0% 20.1% 20.4% 20.7% 21.2% 21.6% 22.2% 22.7% 26.2% A m bi en t w at er q ua lit y 0 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 1 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 2 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 3 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 4 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 5 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 6 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 7 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 8 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Final – March 31, 2018 98 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 11 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 12 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 13 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 14 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 15 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 16 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 17 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 19 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 20 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 21 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 22 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% 23 0.0% 2.1% 4.7% 7.5% 10.4% 13.6% 16.8% 20.2% 23.8% 43.3% W at er S tr es s 0 2.4% 2.5% 2.6% 2.7% 2.8% 2.9% 3.0% 3.1% 3.2% 3.6% 1 34.1% 35.7% 37.3% 38.9% 40.5% 42.1% 43.6% 45.2% 46.7% 53.5% 2 39.2% 40.9% 42.6% 44.4% 46.1% 47.8% 49.5% 51.2% 52.9% 60.2% 3 3.4% 3.5% 3.6% 3.8% 3.9% 4.0% 4.1% 4.2% 4.3% 4.9% 4 2.7% 2.8% 2.9% 3.0% 3.1% 3.3% 3.4% 3.5% 3.6% 4.1% 5 4.4% 4.6% 4.8% 5.1% 5.3% 5.5% 5.7% 5.9% 6.1% 7.0% 6 5.6% 5.8% 6.0% 6.2% 6.5% 6.7% 6.9% 7.2% 7.4% 8.5% 7 4.8% 4.9% 5.1% 5.2% 5.4% 5.5% 5.7% 5.8% 6.0% 6.7% 8 29.7% 30.7% 31.7% 32.7% 33.7% 34.6% 35.6% 36.6% 37.6% 42.2% 9 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 10 0.2% 0.2% 0.2% 0.2% 0.3% 0.3% 0.3% 0.4% 0.4% 0.5% 11 7.4% 7.9% 8.3% 8.7% 9.2% 9.6% 10.0% 10.4% 10.8% 12.5% 12 0.5% 0.6% 0.6% 0.6% 0.6% 0.6% 0.7% 0.7% 0.7% 0.8% 13 0.8% 0.9% 0.9% 0.9% 1.0% 1.0% 1.0% 1.0% 1.1% 1.2% 14 12.3% 12.7% 13.1% 13.6% 14.0% 14.4% 14.8% 15.2% 15.6% 17.5% 15 79.1% 81.4% 83.8% 86.3% 88.7% 91.1% 93.5% 95.9% 98.3% 100.0% Final – March 31, 2018 99 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 7.3% 7.5% 7.8% 8.0% 8.3% 8.5% 8.8% 9.0% 9.3% 10.4% 17 10.2% 10.8% 11.4% 12.1% 12.7% 13.4% 14.0% 14.6% 15.2% 17.7% 18 8.4% 8.8% 9.3% 9.8% 10.3% 10.7% 11.2% 11.6% 12.1% 14.0% 19 9.0% 9.3% 9.6% 9.9% 10.3% 10.6% 10.9% 11.3% 11.6% 13.2% 20 3.2% 3.4% 3.5% 3.6% 3.7% 3.8% 4.0% 4.1% 4.2% 4.8% 21 16.1% 16.6% 17.2% 17.8% 18.5% 19.1% 19.7% 20.3% 21.0% 24.1% 22 6.5% 6.7% 6.9% 7.2% 7.4% 7.6% 7.9% 8.1% 8.3% 9.4% 23 7.2% 7.4% 7.7% 7.9% 8.2% 8.4% 8.7% 9.0% 9.2% 10.4% W et la nd s co ve ra ge 0 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 1 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 2 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 3 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 4 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 5 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 6 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 7 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 8 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 9 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 10 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 11 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 12 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 13 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 14 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 15 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 16 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 17 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 18 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 19 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 20 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 21 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% Final – March 31, 2018 100 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 22 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% 23 0.0% 3.2% 6.8% 10.5% 14.1% 17.7% 21.3% 24.8% 28.2% 43.6% Fo re st C ov er ag e 0 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 1 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 2 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 3 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 4 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 5 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 6 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 7 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 8 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 9 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 10 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 11 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 12 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 13 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 14 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 15 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 16 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 17 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 18 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 19 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 20 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 21 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 22 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% 23 0.0% 1.3% 2.8% 4.4% 6.0% 7.5% 9.1% 10.7% 12.3% 20.3% G H G e m is si on s 0 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 25.3% 1 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 20.3% 2 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 20.9% 3 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 29.6% Final – March 31, 2018 101 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 4 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 22.3% 5 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 21.9% 6 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 24.8% 7 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 32.3% 8 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 24.9% 9 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 88.7% 10 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 100.0% 11 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 23.5% 12 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 23.3% 13 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 20.3% 14 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 26.6% 15 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 22.8% 16 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 26.5% 17 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 21.4% 18 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 26.7% 19 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 23.9% 20 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 21.7% 21 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 32.8% 22 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 24.7% 23 0.0% 5.4% 10.2% 13.7% 16.3% 18.4% 20.0% 21.3% 22.3% 23.4% Cl im at e Ch an ge V ul ne ra bi lit y 0 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 1 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 2 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 3 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 4 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 5 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 6 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 7 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 8 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 9 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Final – March 31, 2018 102 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 11 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 12 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 13 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 14 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 15 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 16 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 17 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 18 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 19 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 20 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 21 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 22 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 23 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% O pe ra tio na l w ea th er s ta tio ns 0 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 3 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 4 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 5 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 6 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 7 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 8 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 10 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 11 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 12 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 13 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 14 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 15 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Final – March 31, 2018 103 Indicator Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 17 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 19 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 20 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 21 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 22 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% 23 43.0% 65.0% 77.1% 84.4% 89.1% 92.3% 93.7% 94.1% 94.6% 96.5% Final – March 31, 2018 104 ANNEX 4E. DETAILED SIM INPUT TABLES TABLE 4E-1. COST PER UNIT (MILLION UGX) Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 Vi lla ge w at er s up pl y W at er S up pl y Ba si c 0 23.1 24.6 26.1 27.7 29.2 30.7 32.2 33.7 35.2 35.2 1 25.8 26.7 27.7 28.7 29.6 30.6 31.5 32.5 33.5 33.5 2 29.4 29.9 30.5 31.0 31.6 32.2 32.7 33.3 33.8 33.8 3 26.0 27.1 28.3 29.4 30.6 31.7 32.9 34.0 35.2 35.2 4 31.7 32.1 32.6 33.0 33.4 33.8 34.2 34.7 35.1 35.1 5 23.2 24.6 26.0 27.4 28.8 30.2 31.6 33.0 34.4 34.4 6 21.9 23.2 24.5 25.8 27.1 28.4 29.7 31.0 32.2 32.2 7 29.3 31.6 33.9 36.1 38.4 40.7 42.9 45.2 47.4 47.4 8 23.6 24.9 26.2 27.6 28.9 30.2 31.6 32.9 34.2 34.2 9 35.7 35.8 35.9 36.0 36.0 36.1 36.2 36.2 36.3 36.3 10 21.1 22.4 23.7 25.0 26.3 27.6 29.0 30.3 31.6 31.6 11 32.5 32.8 33.1 33.4 33.8 34.1 34.4 34.7 35.0 35.0 12 18.3 20.0 21.6 23.3 24.9 26.5 28.2 29.8 31.5 31.5 13 14.9 17.6 20.4 23.1 25.8 28.6 31.3 34.1 36.8 36.8 14 26.6 27.4 28.2 28.9 29.7 30.5 31.3 32.1 32.8 32.8 15 25.0 26.1 27.2 28.3 29.4 30.5 31.6 32.7 33.8 33.8 16 25.8 26.9 28.1 29.3 30.4 31.6 32.7 33.9 35.1 35.1 17 33.6 33.8 34.0 34.2 34.4 34.6 34.7 34.9 35.1 35.1 18 35.0 35.1 35.2 35.3 35.4 35.4 35.5 35.6 35.7 35.7 19 39.9 41.7 43.4 45.2 47.0 48.8 50.6 52.4 54.2 54.2 20 21.0 22.5 24.0 25.5 27.0 28.5 30.0 31.5 33.1 33.1 21 19.8 21.4 23.0 24.6 26.3 27.9 29.5 31.1 32.7 32.7 22 24.8 25.7 26.6 27.5 28.4 29.4 30.3 31.2 32.1 32.1 23 25.2 26.2 27.2 28.3 29.3 30.3 31.3 32.3 33.4 33.4 Final – March 31, 2018 105 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 Vi lla ge w at er s up pl y W at er S up pl y Pi pe d 0 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 1 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 2 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 3 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 4 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 6 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 7 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 8 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 9 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 10 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 11 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 12 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 13 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 14 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 15 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 16 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 17 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 18 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 19 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 20 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 21 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 22 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 23 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 189.5 Vi lla ge w at er s up pl y W at er S up pl y Ba si c 0 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 1 0.5 0.5 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.7 2 0.6 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 3 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 4 0.6 0.6 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 Final – March 31, 2018 106 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 5 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.7 6 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.6 7 0.6 0.6 0.7 0.7 0.8 0.8 0.9 0.9 0.9 0.9 8 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 9 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 10 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.6 11 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 12 0.4 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.6 13 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0.7 0.7 0.7 14 0.5 0.5 0.6 0.6 0.6 0.6 0.6 0.6 0.7 0.7 15 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 16 0.5 0.5 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 17 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 18 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 19 0.8 0.8 0.9 0.9 0.9 1.0 1.0 1.0 1.1 1.1 20 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 21 0.4 0.4 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 22 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.6 0.6 0.6 23 0.5 0.5 0.5 0.6 0.6 0.6 0.6 0.6 0.7 0.7 Vi lla ge w at er s up pl y W at er S up pl y Pi pe d 0 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 1 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 2 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 4 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 5 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 6 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 7 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 9 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 Final – March 31, 2018 107 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 11 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 12 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 13 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 14 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 15 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 16 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 17 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 18 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 19 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 20 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 21 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 22 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 23 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 Fu nc tio na l r ur al w at er s ou rc es W at er S ou rc e Re pa ir R ur al 0 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 1 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 2 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 3 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 4 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 6 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 7 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 8 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 9 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 10 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 11 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 12 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 13 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 14 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 Final – March 31, 2018 108 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 15 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 16 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 17 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 18 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 19 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 20 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 21 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 22 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 23 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 Im pr ov ed d ri nk in g w at er W at er S up pl y Ba si c 0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 3 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 4 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 5 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 6 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 7 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 8 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 9 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 10 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 11 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 12 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 13 0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 14 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 15 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 16 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 17 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 18 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 19 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 Final – March 31, 2018 109 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 20 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 21 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 22 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 23 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Im pr ov ed d ri nk in g w at er W at er S up pl y Pi pe d 0 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 1 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 2 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 7 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 9 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 10 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 11 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 12 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 13 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 14 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 15 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 16 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 17 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 18 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 19 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 20 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 21 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 22 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 23 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 w at e r Su pp ly Ba si c 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 110 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Im pr ov ed d ri nk in g w at er W at er S up pl y Pi pe d 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 111 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Sa fe ly m an ag ed d ri nk in g w at er W at er S up pl y Pi pe d 0 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 1 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 2 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 7 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 9 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 10 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 Final – March 31, 2018 112 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 11 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 12 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 13 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 14 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 15 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 16 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 17 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 18 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 19 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 20 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 21 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 22 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 23 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 Sa fe ly m an ag ed d ri nk in g w at er W at er S up pl y Pi pe d 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 113 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 U rb an w at er s er vi ce fu nc tio na lit y W at er S ou rc e Re pa ir U rb an 0 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 1 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 2 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 4 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 5 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 6 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 7 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 9 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 10 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 11 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 12 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 13 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 14 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 15 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 16 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 17 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 18 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 19 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 20 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 Final – March 31, 2018 114 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 21 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 22 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 23 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 So lid w as te d is po sa l So lid W as te D is po sa l 0 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 1 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 6 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 7 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 8 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 9 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 10 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 11 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 12 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 13 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 14 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 15 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 16 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 17 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 18 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 19 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 20 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 21 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 22 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 23 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 Im pr ov ed sa ni ta tio n Se w er ag e 0 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Final – March 31, 2018 115 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 3 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 4 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 5 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 6 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 7 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 8 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 9 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 10 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 11 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 12 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 13 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 14 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 15 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 16 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 17 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 18 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 19 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 20 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 21 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 22 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 23 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Im pr ov ed s an ita tio n Se w er ag e 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 116 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Im pr ov ed s an ita tio n Ba si c Sa ni ta tio n CB 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 117 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Sa fe ly m an ag ed s an ita tio n Sa fe S an ita tio n CB 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 118 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Sa fe ly m an ag ed s an ita tio n Se w er ag e 0 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 3 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 4 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 5 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 6 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 7 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 8 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 9 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 10 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 11 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 12 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 13 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 14 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 15 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 16 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 17 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 18 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 19 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 20 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 21 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Final – March 31, 2018 119 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 22 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 23 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Sa fe ly m an ag ed s an ita tio n Se w er ag e 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 H an dw as hi ng a t ho m e H om e H an dw as hi ng CB 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 120 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 H an dw as hi ng a t s ch oo l Sc ho ol H an dw as hi ng C B 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 121 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Ir ri ga tio n Ir ri ga tio n 0 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 1 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 2 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 3 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 4 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 6 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 7 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 8 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 9 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 10 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 11 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 12 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 Final – March 31, 2018 122 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 13 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 14 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 15 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 16 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 17 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 18 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 19 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 20 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 21 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 22 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 23 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 43.5 Ir ri ga tio n Ir ri ga tio n O & M 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 123 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 W fP fu nc tio na lit y W fP S ou rc e Re pa ir 0 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 2 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 3 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 4 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 5 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 6 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 7 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 8 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 9 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 10 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 11 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 12 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 13 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 14 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 15 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 16 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 17 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 18 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 19 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 21 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 22 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 Final – March 31, 2018 124 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 23 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 20.1 W fP fu nc tio na lit y W fP S ou rc e CB 0 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 1 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 2 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 3 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 4 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 5 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 6 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 7 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 9 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 10 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 11 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 13 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 14 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 15 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 16 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 17 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 18 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 19 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 20 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 21 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 22 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 23 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 St or ag e Ca pa ci ty St or ag e 0 19194.8 19194.8 19194.8 19194.8 19194.8 19194.8 19194.8 19194.8 19194.8 19194.8 1 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 2 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 3 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 Final – March 31, 2018 125 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 6 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 7 31463.9 31463.9 31463.9 31463.9 31463.9 31463.9 31463.9 31463.9 31463.9 31463.9 8 22342.0 22342.0 22342.0 22342.0 22342.0 22342.0 22342.0 22342.0 22342.0 22342.0 9 36842.5 36842.5 36842.5 36842.5 36842.5 36842.5 36842.5 36842.5 36842.5 36842.5 10 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 11 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 13 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 14 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 16 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 17 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18598.7 18 31463.9 31463.9 31463.9 31463.9 31463.9 31463.9 31463.9 31463.9 31463.9 31463.9 19 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 26630.4 20 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 21 8058.7 8058.7 8058.7 8058.7 8058.7 8058.7 8058.7 8058.7 8058.7 8058.7 22 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 12747.4 23 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 15400.5 St or ag e Ca pa ci ty St or ag e O & M 0 383.9 383.9 383.9 383.9 383.9 383.9 383.9 383.9 383.9 383.9 1 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 2 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 3 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 4 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 5 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 6 372.0 372.0 372.0 372.0 372.0 372.0 372.0 372.0 372.0 372.0 7 629.3 629.3 629.3 629.3 629.3 629.3 629.3 629.3 629.3 629.3 8 446.8 446.8 446.8 446.8 446.8 446.8 446.8 446.8 446.8 446.8 Final – March 31, 2018 126 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 9 736.9 736.9 736.9 736.9 736.9 736.9 736.9 736.9 736.9 736.9 10 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 11 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 12 372.0 372.0 372.0 372.0 372.0 372.0 372.0 372.0 372.0 372.0 13 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 14 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 15 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 16 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 17 372.0 372.0 372.0 372.0 372.0 372.0 372.0 372.0 372.0 372.0 18 629.3 629.3 629.3 629.3 629.3 629.3 629.3 629.3 629.3 629.3 19 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 532.6 20 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 21 161.2 161.2 161.2 161.2 161.2 161.2 161.2 161.2 161.2 161.2 22 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 254.9 23 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 308.0 Co m pl ia nc e w ith w at er s ta nd ar ds D ri nk in g w at er s am pl es 0 495.9 495.9 495.9 495.9 495.9 495.9 495.9 495.9 495.9 495.9 1 366.3 366.3 366.3 366.3 366.3 366.3 366.3 366.3 366.3 366.3 2 202.7 202.7 202.7 202.7 202.7 202.7 202.7 202.7 202.7 202.7 3 437.0 437.0 437.0 437.0 437.0 437.0 437.0 437.0 437.0 437.0 4 300.0 300.0 300.0 300.0 300.0 300.0 300.0 300.0 300.0 300.0 5 594.5 594.5 594.5 594.5 594.5 594.5 594.5 594.5 594.5 594.5 6 408.4 408.4 408.4 408.4 408.4 408.4 408.4 408.4 408.4 408.4 7 598.7 598.7 598.7 598.7 598.7 598.7 598.7 598.7 598.7 598.7 8 609.5 609.5 609.5 609.5 609.5 609.5 609.5 609.5 609.5 609.5 9 295.5 295.5 295.5 295.5 295.5 295.5 295.5 295.5 295.5 295.5 10 307.8 307.8 307.8 307.8 307.8 307.8 307.8 307.8 307.8 307.8 11 457.1 457.1 457.1 457.1 457.1 457.1 457.1 457.1 457.1 457.1 12 434.6 434.6 434.6 434.6 434.6 434.6 434.6 434.6 434.6 434.6 13 641.7 641.7 641.7 641.7 641.7 641.7 641.7 641.7 641.7 641.7 Final – March 31, 2018 127 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 14 405.9 405.9 405.9 405.9 405.9 405.9 405.9 405.9 405.9 405.9 15 476.3 476.3 476.3 476.3 476.3 476.3 476.3 476.3 476.3 476.3 16 507.3 507.3 507.3 507.3 507.3 507.3 507.3 507.3 507.3 507.3 17 219.4 219.4 219.4 219.4 219.4 219.4 219.4 219.4 219.4 219.4 18 488.3 488.3 488.3 488.3 488.3 488.3 488.3 488.3 488.3 488.3 19 631.8 631.8 631.8 631.8 631.8 631.8 631.8 631.8 631.8 631.8 20 414.9 414.9 414.9 414.9 414.9 414.9 414.9 414.9 414.9 414.9 21 380.5 380.5 380.5 380.5 380.5 380.5 380.5 380.5 380.5 380.5 22 617.7 617.7 617.7 617.7 617.7 617.7 617.7 617.7 617.7 617.7 23 251.7 251.7 251.7 251.7 251.7 251.7 251.7 251.7 251.7 251.7 Pe rm it co m pl ia nc e Pe rm it Co m pl ia nc e CB 0 32.7 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 1 64.4 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 3 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 4 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 5 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 6 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 7 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 8 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 9 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 10 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 11 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 12 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 13 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 14 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 15 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 16 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 17 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 18 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 Final – March 31, 2018 128 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 19 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 20 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 21 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 22 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 23 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.2 W as te w at er tr ea tm en t W as te w at er T re at m en t 0 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 1 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 2 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 7 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 9 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 10 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 11 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 12 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 13 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 14 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 15 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 16 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 17 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 18 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 19 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 20 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 21 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 22 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 23 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 Final – March 31, 2018 129 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 W as te w at er tr ea tm en t W as te w at er T re at m en t O & M 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 A m bi en t w at er q ua lit y W et la nd s Re st or at io n 0 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 12.8 1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 2 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 3 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 4 8.4 8.4 8.4 8.4 8.4 8.4 8.4 8.4 8.4 8.4 Final – March 31, 2018 130 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 6 17.2 17.2 17.2 17.2 17.2 17.2 17.2 17.2 17.2 17.2 7 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 8 18.1 18.1 18.1 18.1 18.1 18.1 18.1 18.1 18.1 18.1 9 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 10 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 11 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 12 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 13 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 14 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 15 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 16 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 17 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 18 8.2 8.2 8.2 8.2 8.2 8.2 8.2 8.2 8.2 8.2 19 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 8.9 20 21.7 21.7 21.7 21.7 21.7 21.7 21.7 21.7 21.7 21.7 21 17.1 17.1 17.1 17.1 17.1 17.1 17.1 17.1 17.1 17.1 22 8.1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 23 8.1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 8.1 A m bi en t w at er q ua lit y Fo re st R eh ab ili ta tio n 0 22.2 22.2 22.2 22.2 22.2 22.2 22.2 22.2 22.2 22.2 1 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 2 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 3 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 4 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 5 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 6 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2 7 23.6 23.6 23.6 23.6 23.6 23.6 23.6 23.6 23.6 23.6 8 21.0 21.0 21.0 21.0 21.0 21.0 21.0 21.0 21.0 21.0 9 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 Final – March 31, 2018 131 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 10 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 11 23.6 23.6 23.6 23.6 23.6 23.6 23.6 23.6 23.6 23.6 12 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 13 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 14 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 15 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 16 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 17 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 18 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 19 22.6 22.6 22.6 22.6 22.6 22.6 22.6 22.6 22.6 22.6 20 20.4 20.4 20.4 20.4 20.4 20.4 20.4 20.4 20.4 20.4 21 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2 22 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 23 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 A m bi en t w at er q ua lit y W et la nd s Re gu la tio n O & M 0 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 4 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 5 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 6 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 7 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 9 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 132 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 18 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 19 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 20 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 21 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 22 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 23 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 A m bi en t w at er q ua lit y Fo re st R eh ab ili ta tio n 0 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 2 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 7 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 9 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 10 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 11 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 12 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 13 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 14 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 15 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 16 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 17 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 18 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 19 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Final – March 31, 2018 133 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 20 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 21 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 22 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 23 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 A m bi en t w at er q ua lit y W as te w at er T re at m en t 0 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 1 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 2 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 3 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 4 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 6 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 7 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 8 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 9 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 10 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 11 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 12 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 13 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 14 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 15 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 16 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 18 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 19 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 20 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 21 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 22 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 23 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 17.5 qu al i ty er Tr ea t m en t 0 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 Final – March 31, 2018 134 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 1 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 2 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 7 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 9 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 10 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 11 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 12 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 13 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 14 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 15 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 16 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 17 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 18 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 19 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 20 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 21 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 22 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 23 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 W et la nd s co ve ra ge W et la nd s Re st or at io n 0 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 1 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 2 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 3 9.3 9.3 9.3 9.3 9.3 9.3 9.3 9.3 9.3 9.3 4 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 5 4.8 4.8 4.8 4.8 4.8 4.8 4.8 4.8 4.8 4.8 Final – March 31, 2018 135 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 6 18.4 18.4 18.4 18.4 18.4 18.4 18.4 18.4 18.4 18.4 7 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 8 19.4 19.4 19.4 19.4 19.4 19.4 19.4 19.4 19.4 19.4 9 9.3 9.3 9.3 9.3 9.3 9.3 9.3 9.3 9.3 9.3 10 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 11 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 10.2 12 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 13 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 14 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 15 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 16 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 17 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 7.5 18 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 19 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 20 23.3 23.3 23.3 23.3 23.3 23.3 23.3 23.3 23.3 23.3 21 18.4 18.4 18.4 18.4 18.4 18.4 18.4 18.4 18.4 18.4 22 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 23 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 8.7 W et la nd s co ve ra ge W et la nd s Re gu la tio n O & M 0 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 4 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 5 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 7 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 8 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 9 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 136 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 11 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 18 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 19 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 20 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 21 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 22 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 23 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 Fo re st C ov er ag e Fo re st R eh ab ili ta tio n 0 6.2 6.2 6.2 6.2 6.2 6.2 6.2 6.2 6.2 6.2 1 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 2 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 4 7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 5 6.8 6.8 6.8 6.8 6.8 6.8 6.8 6.8 6.8 6.8 6 5.7 5.7 5.7 5.7 5.7 5.7 5.7 5.7 5.7 5.7 7 6.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3 8 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 9 7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 7.3 10 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 11 6.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3 12 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 13 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 14 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 15 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 Final – March 31, 2018 137 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 16 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 17 7.1 7.1 7.1 7.1 7.1 7.1 7.1 7.1 7.1 7.1 18 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 19 6.1 6.1 6.1 6.1 6.1 6.1 6.1 6.1 6.1 6.1 20 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 5.5 21 5.7 5.7 5.7 5.7 5.7 5.7 5.7 5.7 5.7 5.7 22 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 23 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 Fo re st C ov er ag e Fo re st R eh ab ili ta tio n 0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 3 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 4 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 5 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 6 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 7 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 8 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 9 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 10 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 11 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 12 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 13 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 14 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 15 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 16 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 17 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 18 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 19 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 20 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Final – March 31, 2018 138 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 21 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 22 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 23 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 G H G e m is si on s Fo re st R eh ab ili ta tio n 0 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 4 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 5 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 6 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 7 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 8 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 9 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 10 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 11 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 12 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 13 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 14 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 15 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 16 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 17 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 18 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 19 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 20 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 21 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 22 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 23 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 G H G em is si on s Fo re st Re ha bi lit at io n 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Final – March 31, 2018 139 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 O pe ra tio na l w ea th er s ta tio ns W ea th er S ta tio ns 0 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 2 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 3 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 4 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 5 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 6 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 Final – March 31, 2018 140 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 7 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 8 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 9 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 10 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 11 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 12 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 13 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 14 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 15 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 16 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 17 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 18 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 19 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 20 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 21 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 22 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 23 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 215.1 O pe ra tio na l w ea th er s ta tio ns W ea th er S ta tio ns 0 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 1 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 2 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 4 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 5 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 6 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 7 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 9 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 10 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 11 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 Final – March 31, 2018 141 Ind Inv Catch Year 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 30 12 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 13 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 14 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 15 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 16 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 17 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 18 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 19 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 20 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 21 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 22 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 23 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 FIGURE 4E-2 LEVEL OF UNITS FOR EACH INDICATOR OVER TIME Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 Vi lla ge w at er s up pl y 0 57950 59715 61534 63408 65339 67330 69380 71494 73671 85596 1 1001 1031 1062 1095 1128 1163 1198 1234 1272 1478 2 772 795 820 845 870 897 924 952 981 1140 3 2745 2828 2914 3003 3095 3189 3286 3386 3489 4054 4 2259 2327 2398 2471 2547 2624 2704 2786 2871 3336 5 1258 1296 1336 1376 1418 1462 1506 1552 1599 1858 6 2916 3005 3096 3191 3288 3388 3491 3598 3707 4307 7 1687 1738 1791 1846 1902 1960 2019 2081 2144 2491 8 4403 4537 4675 4817 4964 5115 5271 5432 5597 6503 9 57 59 61 63 64 66 68 71 73 84 10 172 177 182 188 193 199 205 212 218 253 Final – March 31, 2018 142 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 11 1401 1444 1487 1533 1579 1628 1677 1728 1781 2069 12 3860 3977 4098 4223 4352 4484 4621 4762 4907 5701 13 7347 7571 7802 8039 8284 8537 8797 9065 9341 10853 14 686 707 729 751 774 797 821 846 872 1013 15 600 619 637 657 677 698 719 741 763 887 16 3288 3388 3491 3597 3707 3820 3936 4056 4180 4856 17 972 1002 1032 1064 1096 1129 1164 1199 1236 1436 18 314 324 334 344 355 365 377 388 400 465 19 2573 2651 2732 2815 2901 2989 3081 3174 3271 3800 20 1601 1650 1700 1752 1805 1860 1917 1975 2035 2365 21 14781 15231 15695 16173 16665 17173 17696 18235 18790 21832 22 1144 1178 1214 1251 1289 1329 1369 1411 1454 1689 23 2116 2180 2246 2315 2385 2458 2533 2610 2690 3125 Fu nc tio na l r ur al w at er s ou rc es 0 109695 113036 116479 120027 123683 127450 131332 135332 139454 162026 1 1667 1718 1770 1824 1880 1937 1996 2057 2119 2462 2 1430 1473 1518 1564 1612 1661 1712 1764 1818 2112 3 9552 9843 10143 10452 10771 11099 11437 11785 12144 14110 4 7007 7221 7441 7667 7901 8142 8389 8645 8908 10350 5 5296 5457 5624 5795 5971 6153 6341 6534 6733 7823 6 6074 6259 6449 6646 6848 7057 7272 7493 7721 8971 7 5842 6019 6203 6392 6586 6787 6994 7207 7426 8628 8 6057 6242 6432 6628 6830 7038 7252 7473 7701 8947 9 193 199 205 211 217 224 231 238 245 285 10 79 81 84 86 89 92 94 97 100 117 11 2037 2099 2163 2229 2297 2367 2439 2513 2590 3009 12 6833 7041 7255 7476 7704 7939 8181 8430 8687 10093 13 18385 18945 19522 20117 20730 21361 22012 22682 23373 27156 14 1445 1488 1534 1581 1629 1678 1729 1782 1836 2134 15 1434 1478 1523 1569 1617 1666 1717 1769 1823 2118 16 9726 10022 10327 10642 10966 11300 11644 11999 12364 14365 Final – March 31, 2018 143 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 17 1326 1366 1408 1450 1495 1540 1587 1635 1685 1958 18 711 732 754 777 801 826 851 877 903 1050 19 7805 8042 8287 8540 8800 9068 9344 9629 9922 11528 20 3163 3259 3359 3461 3566 3675 3787 3902 4021 4672 21 6556 6756 6961 7173 7392 7617 7849 8088 8334 9683 22 3023 3115 3210 3308 3409 3512 3619 3730 3843 4465 23 4055 4179 4306 4437 4572 4711 4855 5003 5155 5990 Im pr ov ed d ri nk in g w at er 0 3703778 1 3816590 3 3932838 6 4052627 6 4176065 3 4303262 7 4434334 4 4569398 3 4708576 2 5470695 0 1 527696 543769 560332 577399 594985 613108 631782 651025 670855 779438 2 485488 500275 515513 531215 547395 564068 581249 598953 617196 717094 3 3147370 3243234 3342019 3443812 3548706 3656795 3768176 3882950 4001220 4648847 4 1703313 1755193 1808654 1863743 1920510 1979007 2039284 2101398 2165404 2515891 5 1646528 1696679 1748358 1801610 1856485 1913031 1971300 2031343 2093215 2432017 6 3273639 3373350 3476098 3581975 3691077 3803502 3919352 4038730 4161745 4835355 7 1011250 1042052 1073791 1106497 1140200 1174929 1210716 1247592 1285592 1493675 8 2145069 2210405 2277731 2347107 2418597 2492264 2568175 2646398 2727004 3168391 9 46292 47702 49155 50653 52195 53785 55423 57112 58851 68377 10 24644 25395 26168 26965 27786 28633 29505 30404 31330 36401 11 829135 854390 880413 907229 934862 963337 992679 1022915 1054071 1224681 12 2695474 2777575 2862176 2949354 3039187 3131757 3227146 3325441 3426729 3981371 13 4303636 4434719 4569794 4708984 4852414 5000212 5152512 5309450 5471169 6356719 14 601492 619813 638691 658145 678191 698848 720134 742068 764671 888439 15 564908 582115 599845 618116 636943 656343 676334 696935 718162 834402 16 3921729 4041180 4164269 4291107 4421808 4556491 4695275 4838287 4985655 5792621 17 453292 467098 481326 495986 511093 526660 542702 559232 576265 669538 18 266618 274738 283107 291730 300615 309772 319207 328930 338948 393810 19 1418976 1462196 1506732 1552626 1599916 1648648 1698863 1750609 1803930 2095909 20 1631417 1681107 1732312 1785076 1839447 1895474 1953207 2012699 2074004 2409697 21 3659647 3771115 3885978 4004340 4126307 4251989 4381499 4514953 4652473 5405511 Final – March 31, 2018 144 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 22 1256740 1295019 1334463 1375109 1416993 1460153 1504627 1550456 1597681 1856278 23 1423430 1466785 1511462 1557499 1604938 1653823 1704196 1756103 1809592 2102488 Sa fe ly m an ag ed d ri nk in g w at er 0 3703778 1 3816590 3 3932838 6 4052627 6 4176065 3 4303262 7 4434334 4 4569398 3 4708576 2 5470695 0 1 527696 543769 560332 577399 594985 613108 631782 651025 670855 779438 2 485488 500275 515513 531215 547395 564068 581249 598953 617196 717094 3 3147370 3243234 3342019 3443812 3548706 3656795 3768176 3882950 4001220 4648847 4 1703313 1755193 1808654 1863743 1920510 1979007 2039284 2101398 2165404 2515891 5 1646528 1696679 1748358 1801610 1856485 1913031 1971300 2031343 2093215 2432017 6 3273639 3373350 3476098 3581975 3691077 3803502 3919352 4038730 4161745 4835355 7 1011250 1042052 1073791 1106497 1140200 1174929 1210716 1247592 1285592 1493675 8 2145069 2210405 2277731 2347107 2418597 2492264 2568175 2646398 2727004 3168391 9 46292 47702 49155 50653 52195 53785 55423 57112 58851 68377 10 24644 25395 26168 26965 27786 28633 29505 30404 31330 36401 11 829135 854390 880413 907229 934862 963337 992679 1022915 1054071 1224681 12 2695474 2777575 2862176 2949354 3039187 3131757 3227146 3325441 3426729 3981371 13 4303636 4434719 4569794 4708984 4852414 5000212 5152512 5309450 5471169 6356719 14 601492 619813 638691 658145 678191 698848 720134 742068 764671 888439 15 564908 582115 599845 618116 636943 656343 676334 696935 718162 834402 16 3921729 4041180 4164269 4291107 4421808 4556491 4695275 4838287 4985655 5792621 17 453292 467098 481326 495986 511093 526660 542702 559232 576265 669538 18 266618 274738 283107 291730 300615 309772 319207 328930 338948 393810 19 1418976 1462196 1506732 1552626 1599916 1648648 1698863 1750609 1803930 2095909 20 1631417 1681107 1732312 1785076 1839447 1895474 1953207 2012699 2074004 2409697 21 3659647 3771115 3885978 4004340 4126307 4251989 4381499 4514953 4652473 5405511 22 1256740 1295019 1334463 1375109 1416993 1460153 1504627 1550456 1597681 1856278 23 1423430 1466785 1511462 1557499 1604938 1653823 1704196 1756103 1809592 2102488 Co st p er C ap ita 0 3703778 1 3816590 3 3932838 6 4052627 6 4176065 3 4303262 7 4434334 4 4569398 3 4708576 2 5470695 0 1 527696 543769 560332 577399 594985 613108 631782 651025 670855 779438 2 485488 500275 515513 531215 547395 564068 581249 598953 617196 717094 Final – March 31, 2018 145 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 3 3147370 3243234 3342019 3443812 3548706 3656795 3768176 3882950 4001220 4648847 4 1703313 1755193 1808654 1863743 1920510 1979007 2039284 2101398 2165404 2515891 5 1646528 1696679 1748358 1801610 1856485 1913031 1971300 2031343 2093215 2432017 6 3273639 3373350 3476098 3581975 3691077 3803502 3919352 4038730 4161745 4835355 7 1011250 1042052 1073791 1106497 1140200 1174929 1210716 1247592 1285592 1493675 8 2145069 2210405 2277731 2347107 2418597 2492264 2568175 2646398 2727004 3168391 9 46292 47702 49155 50653 52195 53785 55423 57112 58851 68377 10 24644 25395 26168 26965 27786 28633 29505 30404 31330 36401 11 829135 854390 880413 907229 934862 963337 992679 1022915 1054071 1224681 12 2695474 2777575 2862176 2949354 3039187 3131757 3227146 3325441 3426729 3981371 13 4303636 4434719 4569794 4708984 4852414 5000212 5152512 5309450 5471169 6356719 14 601492 619813 638691 658145 678191 698848 720134 742068 764671 888439 15 564908 582115 599845 618116 636943 656343 676334 696935 718162 834402 16 3921729 4041180 4164269 4291107 4421808 4556491 4695275 4838287 4985655 5792621 17 453292 467098 481326 495986 511093 526660 542702 559232 576265 669538 18 266618 274738 283107 291730 300615 309772 319207 328930 338948 393810 19 1418976 1462196 1506732 1552626 1599916 1648648 1698863 1750609 1803930 2095909 20 1631417 1681107 1732312 1785076 1839447 1895474 1953207 2012699 2074004 2409697 21 3659647 3771115 3885978 4004340 4126307 4251989 4381499 4514953 4652473 5405511 22 1256740 1295019 1334463 1375109 1416993 1460153 1504627 1550456 1597681 1856278 23 1423430 1466785 1511462 1557499 1604938 1653823 1704196 1756103 1809592 2102488 U rb an w at er s er vi ce fu nc tio na lit y 0 18756 19327 19916 20523 21148 21792 22456 23140 23844 27704 1 262 270 278 286 295 304 313 323 333 387 2 86 88 91 94 96 99 102 106 109 126 3 1407 1450 1494 1540 1587 1635 1685 1736 1789 2079 4 1048 1080 1113 1146 1181 1217 1254 1293 1332 1548 5 568 585 603 621 640 659 680 700 722 838 6 1810 1865 1922 1980 2041 2103 2167 2233 2301 2673 7 622 641 660 680 701 722 744 767 790 918 8 1567 1614 1663 1714 1766 1820 1876 1933 1992 2314 Final – March 31, 2018 146 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 9 25 26 27 28 29 29 30 31 32 37 10 21 22 22 23 24 24 25 26 27 31 11 561 578 595 614 632 651 671 692 713 828 12 1592 1641 1691 1742 1795 1850 1906 1964 2024 2352 13 4232 4361 4494 4631 4772 4917 5067 5221 5380 6251 14 96 99 102 105 109 112 115 119 122 142 15 154 159 163 168 174 179 184 190 196 227 16 842 868 894 921 949 978 1008 1039 1070 1244 17 152 157 162 167 172 177 183 188 194 225 18 121 125 129 133 137 141 145 149 154 179 19 1217 1254 1293 1332 1372 1414 1457 1502 1547 1798 20 584 601 620 639 658 678 699 720 742 862 21 1052 1084 1117 1151 1186 1222 1259 1297 1337 1553 22 271 279 288 296 305 315 324 334 344 400 23 467 482 496 511 527 543 560 577 594 690 So lid w as te d is po sa l 0 912143 972461 1004997 1037533 1070069 1102605 1135141 1167676 1200212 1362892 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 3 32782 34950 36119 37289 38458 39627 40797 41966 43135 48982 4 16543 17637 18227 18817 19407 19997 20587 21177 21767 24718 5 0 0 0 0 0 0 0 0 0 0 6 100730 107391 110984 114577 118170 121763 125356 128949 132542 150507 7 0 0 0 0 0 0 0 0 0 0 8 64510 68776 71077 73378 75679 77980 80281 82582 84883 96388 9 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 12 36975 39420 40739 42058 43377 44696 46015 47333 48652 55247 13 22224 23694 24487 25280 26072 26865 27658 28451 29243 33207 14 0 0 0 0 0 0 0 0 0 0 Final – March 31, 2018 147 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 15 0 0 0 0 0 0 0 0 0 0 16 20322 21666 22391 23116 23840 24565 25290 26015 26740 30364 17 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 19 42708 45533 47056 48579 50103 51626 53150 54673 56196 63813 20 130063 138664 143303 147942 152582 157221 161860 166499 171139 194335 21 423509 451514 466621 481727 496834 511940 527047 542153 557260 632792 22 0 0 0 0 0 0 0 0 0 0 23 21777 23217 23994 24770 25547 26324 27101 27877 28654 32538 Im pr ov ed s an ita tio n 0 3703778 1 3816590 3 3932838 6 4052627 6 4176065 3 4303262 7 4434334 4 4569398 3 4708576 2 5470695 0 1 527696 543769 560332 577399 594985 613108 631782 651025 670855 779438 2 485488 500275 515513 531215 547395 564068 581249 598953 617196 717094 3 3147370 3243234 3342019 3443812 3548706 3656795 3768176 3882950 4001220 4648847 4 1703313 1755193 1808654 1863743 1920510 1979007 2039284 2101398 2165404 2515891 5 1646528 1696679 1748358 1801610 1856485 1913031 1971300 2031343 2093215 2432017 6 3273639 3373350 3476098 3581975 3691077 3803502 3919352 4038730 4161745 4835355 7 1011250 1042052 1073791 1106497 1140200 1174929 1210716 1247592 1285592 1493675 8 2145069 2210405 2277731 2347107 2418597 2492264 2568175 2646398 2727004 3168391 9 46292 47702 49155 50653 52195 53785 55423 57112 58851 68377 10 24644 25395 26168 26965 27786 28633 29505 30404 31330 36401 11 829135 854390 880413 907229 934862 963337 992679 1022915 1054071 1224681 12 2695474 2777575 2862176 2949354 3039187 3131757 3227146 3325441 3426729 3981371 13 4303636 4434719 4569794 4708984 4852414 5000212 5152512 5309450 5471169 6356719 14 601492 619813 638691 658145 678191 698848 720134 742068 764671 888439 15 564908 582115 599845 618116 636943 656343 676334 696935 718162 834402 16 3921729 4041180 4164269 4291107 4421808 4556491 4695275 4838287 4985655 5792621 17 453292 467098 481326 495986 511093 526660 542702 559232 576265 669538 18 266618 274738 283107 291730 300615 309772 319207 328930 338948 393810 19 1418976 1462196 1506732 1552626 1599916 1648648 1698863 1750609 1803930 2095909 Final – March 31, 2018 148 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 20 1631417 1681107 1732312 1785076 1839447 1895474 1953207 2012699 2074004 2409697 21 3659647 3771115 3885978 4004340 4126307 4251989 4381499 4514953 4652473 5405511 22 1256740 1295019 1334463 1375109 1416993 1460153 1504627 1550456 1597681 1856278 23 1423430 1466785 1511462 1557499 1604938 1653823 1704196 1756103 1809592 2102488 Sa fe ly m an ag ed s an ita tio n 0 3703778 1 3816590 3 3932838 6 4052627 6 4176065 3 4303262 7 4434334 4 4569398 3 4708576 2 5470695 0 1 527696 543769 560332 577399 594985 613108 631782 651025 670855 779438 2 485488 500275 515513 531215 547395 564068 581249 598953 617196 717094 3 3147370 3243234 3342019 3443812 3548706 3656795 3768176 3882950 4001220 4648847 4 1703313 1755193 1808654 1863743 1920510 1979007 2039284 2101398 2165404 2515891 5 1646528 1696679 1748358 1801610 1856485 1913031 1971300 2031343 2093215 2432017 6 3273639 3373350 3476098 3581975 3691077 3803502 3919352 4038730 4161745 4835355 7 1011250 1042052 1073791 1106497 1140200 1174929 1210716 1247592 1285592 1493675 8 2145069 2210405 2277731 2347107 2418597 2492264 2568175 2646398 2727004 3168391 9 46292 47702 49155 50653 52195 53785 55423 57112 58851 68377 10 24644 25395 26168 26965 27786 28633 29505 30404 31330 36401 11 829135 854390 880413 907229 934862 963337 992679 1022915 1054071 1224681 12 2695474 2777575 2862176 2949354 3039187 3131757 3227146 3325441 3426729 3981371 13 4303636 4434719 4569794 4708984 4852414 5000212 5152512 5309450 5471169 6356719 14 601492 619813 638691 658145 678191 698848 720134 742068 764671 888439 15 564908 582115 599845 618116 636943 656343 676334 696935 718162 834402 16 3921729 4041180 4164269 4291107 4421808 4556491 4695275 4838287 4985655 5792621 17 453292 467098 481326 495986 511093 526660 542702 559232 576265 669538 18 266618 274738 283107 291730 300615 309772 319207 328930 338948 393810 19 1418976 1462196 1506732 1552626 1599916 1648648 1698863 1750609 1803930 2095909 20 1631417 1681107 1732312 1785076 1839447 1895474 1953207 2012699 2074004 2409697 21 3659647 3771115 3885978 4004340 4126307 4251989 4381499 4514953 4652473 5405511 22 1256740 1295019 1334463 1375109 1416993 1460153 1504627 1550456 1597681 1856278 23 1423430 1466785 1511462 1557499 1604938 1653823 1704196 1756103 1809592 2102488 H an dw as hi ng a t ho m e 0 3703778 1 3816590 3 3932838 6 4052627 6 4176065 3 4303262 7 4434334 4 4569398 3 4708576 2 5470695 0 Final – March 31, 2018 149 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 1 527696 543769 560332 577399 594985 613108 631782 651025 670855 779438 2 485488 500275 515513 531215 547395 564068 581249 598953 617196 717094 3 3147370 3243234 3342019 3443812 3548706 3656795 3768176 3882950 4001220 4648847 4 1703313 1755193 1808654 1863743 1920510 1979007 2039284 2101398 2165404 2515891 5 1646528 1696679 1748358 1801610 1856485 1913031 1971300 2031343 2093215 2432017 6 3273639 3373350 3476098 3581975 3691077 3803502 3919352 4038730 4161745 4835355 7 1011250 1042052 1073791 1106497 1140200 1174929 1210716 1247592 1285592 1493675 8 2145069 2210405 2277731 2347107 2418597 2492264 2568175 2646398 2727004 3168391 9 46292 47702 49155 50653 52195 53785 55423 57112 58851 68377 10 24644 25395 26168 26965 27786 28633 29505 30404 31330 36401 11 829135 854390 880413 907229 934862 963337 992679 1022915 1054071 1224681 12 2695474 2777575 2862176 2949354 3039187 3131757 3227146 3325441 3426729 3981371 13 4303636 4434719 4569794 4708984 4852414 5000212 5152512 5309450 5471169 6356719 14 601492 619813 638691 658145 678191 698848 720134 742068 764671 888439 15 564908 582115 599845 618116 636943 656343 676334 696935 718162 834402 16 3921729 4041180 4164269 4291107 4421808 4556491 4695275 4838287 4985655 5792621 17 453292 467098 481326 495986 511093 526660 542702 559232 576265 669538 18 266618 274738 283107 291730 300615 309772 319207 328930 338948 393810 19 1418976 1462196 1506732 1552626 1599916 1648648 1698863 1750609 1803930 2095909 20 1631417 1681107 1732312 1785076 1839447 1895474 1953207 2012699 2074004 2409697 21 3659647 3771115 3885978 4004340 4126307 4251989 4381499 4514953 4652473 5405511 22 1256740 1295019 1334463 1375109 1416993 1460153 1504627 1550456 1597681 1856278 23 1423430 1466785 1511462 1557499 1604938 1653823 1704196 1756103 1809592 2102488 H an dw as hi ng a t s ch oo l 0 1255590 4 1293834 0 1333242 5 1373851 3 1415697 0 1458817 3 1503250 9 1549037 9 1596219 6 1854579 8 1 178943 184393 190010 195797 201761 207906 214239 220764 227489 264309 2 164653 169668 174836 180162 185649 191304 197131 203135 209322 243202 3 1067253 1099760 1133258 1167775 1203344 1239996 1277765 1316684 1356788 1576395 4 577555 595147 613274 631954 651202 671037 691476 712537 734240 853082 5 558412 575420 592947 611007 629618 648795 668556 688920 709903 824806 Final – March 31, 2018 150 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 6 1108946 1142723 1177529 1213395 1250353 1288437 1327681 1368121 1409792 1637977 7 342888 353332 364094 375183 386611 398387 410521 423025 435910 506465 8 727392 749547 772377 795903 820145 845126 870867 897392 924726 1074400 9 15697 16175 16668 17176 17699 18238 18793 19366 19956 23185 10 8354 8609 8871 9141 9419 9706 10002 10307 10620 12339 11 281195 289760 298586 307680 317052 326709 336660 346914 357480 415341 12 913897 941733 970417 999974 1030432 1061818 1094159 1127486 1161828 1349878 13 1459069 1503511 1549305 1596495 1645122 1695231 1746865 1800072 1854900 2155130 14 203996 210209 216612 223210 230009 237014 244233 251672 259338 301314 15 191592 197428 203441 209638 216023 222603 229383 236370 243569 282993 16 1329827 1370332 1412070 1455080 1499400 1545070 1592130 1640625 1690596 1964232 17 153738 158420 163246 168218 173342 178621 184062 189668 195445 227079 18 90426 93180 96018 98943 101957 105062 108262 111560 114958 133564 19 481031 495683 510780 526338 542370 558889 575913 593454 611530 710511 20 552694 569529 586876 604751 623171 642152 661711 681866 702635 816362 21 1239598 1277355 1316261 1356353 1397666 1440237 1484104 1529308 1575889 1830958 22 426085 439063 452437 466217 480418 495050 510129 525667 541678 629353 23 482663 497364 512513 528123 544209 560785 577866 595467 613604 712921 Ir ri ga tio n 0 3027788 3027788 3027788 3027788 3027788 3027788 3027788 3027788 3027788 3027788 1 105295 105295 105295 105295 105295 105295 105295 105295 105295 105295 2 86274 86274 86274 86274 86274 86274 86274 86274 86274 86274 3 168795 168795 168795 168795 168795 168795 168795 168795 168795 168795 4 203252 203252 203252 203252 203252 203252 203252 203252 203252 203252 5 356880 356880 356880 356880 356880 356880 356880 356880 356880 356880 6 252321 252321 252321 252321 252321 252321 252321 252321 252321 252321 7 28478 28478 28478 28478 28478 28478 28478 28478 28478 28478 8 98962 98962 98962 98962 98962 98962 98962 98962 98962 98962 9 2489 2489 2489 2489 2489 2489 2489 2489 2489 2489 10 56967 56967 56967 56967 56967 56967 56967 56967 56967 56967 11 279187 279187 279187 279187 279187 279187 279187 279187 279187 279187 Final – March 31, 2018 151 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 12 162548 162548 162548 162548 162548 162548 162548 162548 162548 162548 13 178177 178177 178177 178177 178177 178177 178177 178177 178177 178177 14 35591 35591 35591 35591 35591 35591 35591 35591 35591 35591 15 14333 14333 14333 14333 14333 14333 14333 14333 14333 14333 16 212246 212246 212246 212246 212246 212246 212246 212246 212246 212246 17 175585 175585 175585 175585 175585 175585 175585 175585 175585 175585 18 86548 86548 86548 86548 86548 86548 86548 86548 86548 86548 19 104096 104096 104096 104096 104096 104096 104096 104096 104096 104096 20 72763 72763 72763 72763 72763 72763 72763 72763 72763 72763 21 152300 152300 152300 152300 152300 152300 152300 152300 152300 152300 22 86177 86177 86177 86177 86177 86177 86177 86177 86177 86177 23 108521 108521 108521 108521 108521 108521 108521 108521 108521 108521 W fP fu nc tio na lit y 0 1206 1243 1281 1320 1360 1401 1444 1488 1533 1781 1 17 18 18 19 19 20 21 21 22 25 2 16 16 17 17 18 18 19 20 20 23 3 102 106 109 112 116 119 123 126 130 151 4 55 57 59 61 63 64 66 68 71 82 5 54 55 57 59 60 62 64 66 68 79 6 107 110 113 117 120 124 128 132 136 157 7 33 34 35 36 37 38 39 41 42 49 8 70 72 74 76 79 81 84 86 89 103 9 2 2 2 2 2 2 2 2 2 2 10 1 1 1 1 1 1 1 1 1 1 11 27 28 29 30 30 31 32 33 34 40 12 88 90 93 96 99 102 105 108 112 130 13 140 144 149 153 158 163 168 173 178 207 14 20 20 21 21 22 23 23 24 25 29 15 18 19 20 20 21 21 22 23 23 27 16 128 132 136 140 144 148 153 158 162 189 17 15 15 16 16 17 17 18 18 19 22 Final – March 31, 2018 152 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 18 9 9 9 9 10 10 10 11 11 13 19 46 48 49 51 52 54 55 57 59 68 20 53 55 56 58 60 62 64 66 68 78 21 119 123 127 130 134 138 143 147 151 176 22 41 42 43 45 46 48 49 50 52 60 23 46 48 49 51 52 54 55 57 59 68 St or ag e Ca pa ci ty 0 164 164 164 164 164 164 164 164 164 164 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 14 14 14 14 14 14 14 14 14 14 4 8 8 8 8 8 8 8 8 8 8 5 7 7 7 7 7 7 7 7 7 7 6 14 14 14 14 14 14 14 14 14 14 7 4 4 4 4 4 4 4 4 4 4 8 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 11 4 4 4 4 4 4 4 4 4 4 12 12 12 12 12 12 12 12 12 12 12 13 19 19 19 19 19 19 19 19 19 19 14 3 3 3 3 3 3 3 3 3 3 15 2 2 2 2 2 2 2 2 2 2 16 17 17 17 17 17 17 17 17 17 17 17 2 2 2 2 2 2 2 2 2 2 18 1 1 1 1 1 1 1 1 1 1 19 6 6 6 6 6 6 6 6 6 6 20 7 7 7 7 7 7 7 7 7 7 21 16 16 16 16 16 16 16 16 16 16 22 6 6 6 6 6 6 6 6 6 6 23 6 6 6 6 6 6 6 6 6 6 Final – March 31, 2018 153 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 Co m pl ia nc e w ith w at er s ta nd ar ds 0 939 968 997 1027 1059 1091 1124 1158 1194 1387 1 14 15 15 15 16 16 17 17 18 21 2 11 11 12 12 12 13 13 14 14 16 3 80 83 85 88 90 93 96 99 102 118 4 59 61 63 64 66 68 70 73 75 87 5 43 44 46 47 48 50 51 53 54 63 6 58 59 61 63 65 67 69 71 73 85 7 47 49 50 52 53 55 57 58 60 70 8 56 57 59 61 63 65 67 69 71 82 9 2 2 2 2 2 2 2 2 2 2 10 1 1 1 1 1 1 1 1 1 1 11 19 20 20 21 21 22 23 23 24 28 12 62 63 65 67 69 72 74 76 78 91 13 165 170 176 181 186 192 198 204 210 244 14 11 12 12 12 13 13 13 14 14 17 15 12 12 12 13 13 13 14 14 15 17 16 77 80 82 85 87 90 92 95 98 114 17 11 11 11 12 12 13 13 13 14 16 18 6 6 6 7 7 7 7 8 8 9 19 66 68 70 72 74 77 79 81 84 97 20 27 28 29 30 31 32 33 34 35 40 21 56 57 59 61 63 65 67 69 71 82 22 24 25 26 26 27 28 29 30 31 36 23 33 34 35 36 37 38 40 41 42 49 Pe rm it co m pl ia nc e 0 1130 1164 1200 1236 1274 1313 1353 1394 1437 1669 1 16 17 17 18 18 19 19 20 20 24 2 15 15 16 16 17 17 18 18 19 22 3 96 99 102 105 108 112 115 118 122 142 4 52 54 55 57 59 60 62 64 66 77 5 50 52 53 55 57 58 60 62 64 74 Final – March 31, 2018 154 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 6 100 103 106 109 113 116 120 123 127 148 7 31 32 33 34 35 36 37 38 39 46 8 65 67 69 72 74 76 78 81 83 97 9 1 1 1 2 2 2 2 2 2 2 10 1 1 1 1 1 1 1 1 1 1 11 25 26 27 28 29 29 30 31 32 37 12 82 85 87 90 93 96 98 101 105 121 13 131 135 139 144 148 153 157 162 167 194 14 18 19 19 20 21 21 22 23 23 27 15 17 18 18 19 19 20 21 21 22 25 16 120 123 127 131 135 139 143 148 152 177 17 14 14 15 15 16 16 17 17 18 20 18 8 8 9 9 9 9 10 10 10 12 19 43 45 46 47 49 50 52 53 55 64 20 50 51 53 54 56 58 60 61 63 74 21 112 115 119 122 126 130 134 138 142 165 22 38 40 41 42 43 45 46 47 49 57 23 43 45 46 48 49 50 52 54 55 64 W as te w at er tr ea tm en t 0 3703778 1 3816590 3 3932838 6 4052627 6 4176065 3 4303262 7 4434334 4 4569398 3 4708576 2 5470695 0 1 527696 543769 560332 577399 594985 613108 631782 651025 670855 779438 2 485488 500275 515513 531215 547395 564068 581249 598953 617196 717094 3 3147370 3243234 3342019 3443812 3548706 3656795 3768176 3882950 4001220 4648847 4 1703313 1755193 1808654 1863743 1920510 1979007 2039284 2101398 2165404 2515891 5 1646528 1696679 1748358 1801610 1856485 1913031 1971300 2031343 2093215 2432017 6 3273639 3373350 3476098 3581975 3691077 3803502 3919352 4038730 4161745 4835355 7 1011250 1042052 1073791 1106497 1140200 1174929 1210716 1247592 1285592 1493675 8 2145069 2210405 2277731 2347107 2418597 2492264 2568175 2646398 2727004 3168391 9 46292 47702 49155 50653 52195 53785 55423 57112 58851 68377 10 24644 25395 26168 26965 27786 28633 29505 30404 31330 36401 Final – March 31, 2018 155 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 11 829135 854390 880413 907229 934862 963337 992679 1022915 1054071 1224681 12 2695474 2777575 2862176 2949354 3039187 3131757 3227146 3325441 3426729 3981371 13 4303636 4434719 4569794 4708984 4852414 5000212 5152512 5309450 5471169 6356719 14 601492 619813 638691 658145 678191 698848 720134 742068 764671 888439 15 564908 582115 599845 618116 636943 656343 676334 696935 718162 834402 16 3921729 4041180 4164269 4291107 4421808 4556491 4695275 4838287 4985655 5792621 17 453292 467098 481326 495986 511093 526660 542702 559232 576265 669538 18 266618 274738 283107 291730 300615 309772 319207 328930 338948 393810 19 1418976 1462196 1506732 1552626 1599916 1648648 1698863 1750609 1803930 2095909 20 1631417 1681107 1732312 1785076 1839447 1895474 1953207 2012699 2074004 2409697 21 3659647 3771115 3885978 4004340 4126307 4251989 4381499 4514953 4652473 5405511 22 1256740 1295019 1334463 1375109 1416993 1460153 1504627 1550456 1597681 1856278 23 1423430 1466785 1511462 1557499 1604938 1653823 1704196 1756103 1809592 2102488 A m bi en t w at er q ua lit y 0 1823421 1823421 1823421 1823421 1823421 1823421 1823421 1823421 1823421 1823421 1 31693 31693 31693 31693 31693 31693 31693 31693 31693 31693 2 21658 21658 21658 21658 21658 21658 21658 21658 21658 21658 3 19437 19437 19437 19437 19437 19437 19437 19437 19437 19437 4 86 86 86 86 86 86 86 86 86 86 5 10772 10772 10772 10772 10772 10772 10772 10772 10772 10772 6 38 38 38 38 38 38 38 38 38 38 7 366 366 366 366 366 366 366 366 366 366 8 8290 8290 8290 8290 8290 8290 8290 8290 8290 8290 9 0 0 0 0 0 0 0 0 0 0 10 6017 6017 6017 6017 6017 6017 6017 6017 6017 6017 11 191559 191559 191559 191559 191559 191559 191559 191559 191559 191559 12 245478 245478 245478 245478 245478 245478 245478 245478 245478 245478 13 87877 87877 87877 87877 87877 87877 87877 87877 87877 87877 14 1218 1218 1218 1218 1218 1218 1218 1218 1218 1218 15 3573 3573 3573 3573 3573 3573 3573 3573 3573 3573 16 3044 3044 3044 3044 3044 3044 3044 3044 3044 3044 Final – March 31, 2018 156 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 17 322 322 322 322 322 322 322 322 322 322 18 0 0 0 0 0 0 0 0 0 0 19 11045 11045 11045 11045 11045 11045 11045 11045 11045 11045 20 1497 1497 1497 1497 1497 1497 1497 1497 1497 1497 21 1167287 1167287 1167287 1167287 1167287 1167287 1167287 1167287 1167287 1167287 22 5075 5075 5075 5075 5075 5075 5075 5075 5075 5075 23 7088 7088 7088 7088 7088 7088 7088 7088 7088 7088 W at er S tr es s 0 31233 31233 31233 31233 31233 31233 31233 31233 31233 31233 1 32 32 32 32 32 32 32 32 32 32 2 26 26 26 26 26 26 26 26 26 26 3 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 4 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 5 777 777 777 777 777 777 777 777 777 777 6 1210 1210 1210 1210 1210 1210 1210 1210 1210 1210 7 435 435 435 435 435 435 435 435 435 435 8 148 148 148 148 148 148 148 148 148 148 9 0 0 0 0 0 0 0 0 0 0 10 400 400 400 400 400 400 400 400 400 400 11 235 235 235 235 235 235 235 235 235 235 12 10328 10328 10328 10328 10328 10328 10328 10328 10328 10328 13 10416 10416 10416 10416 10416 10416 10416 10416 10416 10416 14 101 101 101 101 101 101 101 101 101 101 15 15 15 15 15 15 15 15 15 15 15 16 1107 1107 1107 1107 1107 1107 1107 1107 1107 1107 17 94 94 94 94 94 94 94 94 94 94 18 67 67 67 67 67 67 67 67 67 67 19 327 327 327 327 327 327 327 327 327 327 20 1036 1036 1036 1036 1036 1036 1036 1036 1036 1036 21 468 468 468 468 468 468 468 468 468 468 22 400 400 400 400 400 400 400 400 400 400 Final – March 31, 2018 157 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 23 410 410 410 410 410 410 410 410 410 410 W et la nd s co ve ra ge 0 884228 904123 924466 945267 966535 988282 1010518 1033255 1056503 1180830 1 19272 19706 20149 20603 21066 21540 22025 22520 23027 25737 2 19482 19920 20368 20827 21295 21774 22264 22765 23277 26017 3 38064 38920 39796 40691 41607 42543 43500 44479 45479 50831 4 56233 57498 58792 60114 61467 62850 64264 65710 67189 75095 5 74869 76554 78276 80037 81838 83680 85562 87488 89456 99983 6 102923 105238 107606 110027 112503 115034 117623 120269 122975 137447 7 12248 12524 12806 13094 13388 13690 13998 14313 14635 16357 8 83282 85156 87072 89031 91034 93082 95177 97318 99508 111218 9 1285 1314 1344 1374 1405 1437 1469 1502 1536 1717 10 10260 10491 10727 10968 11215 11467 11725 11989 12259 13701 11 56535 57807 59107 60437 61797 63188 64609 66063 67549 75498 12 46497 47543 48613 49706 50825 51968 53138 54333 55556 62093 13 31418 32125 32847 33587 34342 35115 35905 36713 37539 41956 14 10443 10677 10918 11163 11415 11671 11934 12202 12477 13945 15 10302 10534 10771 11013 11261 11514 11773 12038 12309 13758 16 65264 66733 68234 69770 71339 72945 74586 76264 77980 87156 17 47739 48813 49912 51035 52183 53357 54558 55785 57040 63753 18 9222 9430 9642 9859 10080 10307 10539 10776 11019 12315 19 41659 42596 43555 44535 45537 46561 47609 48680 49775 55633 20 34759 35541 36341 37159 37995 38849 39724 40617 41531 46419 21 49700 50818 51961 53131 54326 55548 56798 58076 59383 66371 22 27639 28261 28897 29547 30212 30891 31587 32297 33024 36910 23 35134 35924 36733 37559 38404 39268 40152 41055 41979 46919 Fo re st C ov er ag e 0 3014414 3082239 3151589 3222500 3295006 3369144 3444949 3522461 3601716 4025558 1 3219 3291 3365 3441 3519 3598 3679 3761 3846 4299 2 20532 20994 21467 21950 22443 22948 23465 23993 24532 27419 3 94911 97047 99230 101463 103746 106080 108467 110907 113403 126748 4 36808 37637 38483 39349 40235 41140 42066 43012 43980 49155 Final – March 31, 2018 158 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 5 112719 115255 117848 120500 123211 125983 128818 131716 134680 150529 6 430314 439997 449896 460019 470370 480953 491774 502839 514153 574658 7 13438 13740 14049 14365 14689 15019 15357 15703 16056 17945 8 148275 151611 155023 158511 162077 165724 169453 173265 177164 198012 9 12107 12380 12658 12943 13234 13532 13836 14148 14466 16168 10 85863 87795 89770 91790 93855 95967 98126 100334 102592 114664 11 88996 90999 93046 95140 97280 99469 101707 103995 106335 118849 12 611653 625415 639487 653876 668588 683631 699013 714741 730822 816824 13 369188 377495 385988 394673 403553 412633 421917 431411 441117 493027 14 42242 43193 44165 45158 46174 47213 48276 49362 50472 56412 15 53319 54519 55745 56999 58282 59593 60934 62305 63707 71204 16 266169 272158 278282 284543 290945 297492 304185 311029 318027 355452 17 29910 30583 31271 31974 32694 33429 34182 34951 35737 39942 18 2072 2119 2166 2215 2265 2316 2368 2421 2476 2767 19 14263 14584 14912 15248 15591 15942 16300 16667 17042 19047 20 183079 187198 191410 195717 200121 204623 209227 213935 218749 244490 21 202722 207283 211947 216716 221592 226578 231676 236889 242219 270722 22 113182 115729 118333 120995 123717 126501 129347 132258 135233 151147 23 79432 81219 83046 84915 86825 88779 90776 92819 94907 106076 G H G e m is si on s 0 1447945 8 1817651 1 2208534 7 2621543 5 3057662 8 3517917 3 4003372 5 4515136 7 5054362 2 8206042 4 1 206296 258970 314661 373505 435641 501216 570381 643295 720121 1169157 2 189795 238256 289493 343630 400796 461125 524758 591840 662521 1075641 3 1230425 1544590 1876752 2227716 2598318 2989430 3401957 3836840 4295059 6973271 4 665889 835911 1015672 1205609 1406174 1617838 1841092 2076444 2324426 3773837 5 643690 808043 981812 1165417 1359295 1563903 1779714 2007220 2246935 3648026 6 1279788 1606558 1952046 2317090 2702560 3109363 3538440 3990770 4467373 7253032 7 395336 496277 603001 715765 834840 960504 1093049 1232777 1380003 2240513 8 838588 1052705 1279088 1518285 1770866 2037426 2318581 2614973 2927269 4752586 9 18097 22718 27604 32766 38217 43969 50037 56433 63173 102565 Final – March 31, 2018 159 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 10 9634 12094 14695 17443 20345 23407 26637 30043 33630 54601 11 324140 406903 494407 586864 684494 787528 896203 1010768 1131480 1837021 12 1053762 1322820 1607291 1907863 2225255 2560211 2913508 3285951 3678380 5972057 13 1682453 2112035 2566225 3046124 3552877 4087673 4651752 5246401 5872958 9535079 14 235146 295186 358665 425738 496563 571308 650146 733256 820826 1332658 15 220844 277232 336851 399844 466361 536560 610603 688659 770902 1251604 16 1533151 1924612 2338497 2775810 3237593 3724931 4238953 4780832 5351789 8688932 17 177209 222456 270294 320841 374216 430545 489958 552591 618585 1004307 18 104231 130844 158982 188713 220107 253238 288184 325024 363840 590715 19 554731 696371 846124 1004355 1171439 1347770 1533755 1729820 1936406 3143864 20 637782 800627 972801 1154721 1346820 1549550 1763380 1988799 2226313 3614545 21 1430693 1795994 2182220 2590307 3021230 3476001 3955672 4461338 4994139 8108266 22 491307 616753 749384 889524 1037505 1193675 1358396 1532044 1715011 2784417 23 556472 698557 848780 1007507 1175116 1352000 1538569 1735250 1942484 3153732 Cl im at e Ch an ge V ul ne ra bi lit y 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 0 Final – March 31, 2018 160 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 16 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 O pe ra tio na l w ea th er s ta tio ns 0 207 296 386 475 565 654 743 833 922 1369 1 0 0 0 0 0 0 0 0 0 0 2 3 4 5 6 7 8 10 11 12 18 3 13 19 25 31 37 42 48 54 60 89 4 3 4 5 6 7 8 10 11 12 18 5 19 27 35 43 51 59 68 76 84 124 6 19 27 35 43 51 59 68 76 84 124 7 3 4 5 6 7 8 10 11 12 18 8 11 15 20 25 29 34 39 43 48 71 9 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 11 16 23 30 37 44 51 58 65 72 107 12 11 15 20 25 29 34 39 43 48 71 13 30 42 55 68 81 93 106 119 132 196 14 5 8 10 12 15 17 19 22 24 36 15 0 0 0 0 0 0 0 0 0 0 16 24 35 45 56 66 76 87 97 108 160 17 3 4 5 6 7 8 10 11 12 18 18 0 0 0 0 0 0 0 0 0 0 19 11 15 20 25 29 34 39 43 48 71 20 3 4 5 6 7 8 10 11 12 18 21 19 27 35 43 51 59 68 76 84 124 Final – March 31, 2018 161 Ind Cat Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2030 22 11 15 20 25 29 34 39 43 48 71 23 5 8 10 12 15 17 19 22 24 36