&EPA United States Environmental Protection Agency Climate Resilience Evaluation and Awareness Tool Version 3.0 Methodology Guide CLIMATE READY WATER UTILITIES ------- Disclaimer The Climate Resilience Evaluation and Awareness Tool (GREAT) was prepared by the U.S. Environmental Protection Agency (EPA) as an informational tool to assist drinking water, wastewater and stormwater utility owners and operators in understanding and addressing climate change risks. GREAT does not purport to provide a comprehensive or exhaustive list of all impacts and potential risks from climate change or any other threats. The information contained in GREAT was developed in accordance with best industry practices. It should not be relied on exclusively when conducting risk assessments or developing response plans. This information is also not a substitute for the professional advice of an attorney or environmental or climate change professional. This information is provided without warranty of any kind and EPA hereby disclaims any liability for damages arising from use of this tool, including without limitation, direct, indirect or consequential damages including personal injury, property loss, loss of revenue, loss of profit, loss of opportunity or other loss. This document may include technical inaccuracies or typographical errors. Changes are periodically made to the information herein that may be incorporated in new editions of this document EPA may make improvements in or changes to GREAT at any time. Office of Water (4608-T) EPA815-B-16-004 May 2016 GREAT Methodology Guide ------- Table of Contents Disclaimer ii Table of Contents iii List of Figures v List of Tables vi Acronyms vii Chapter 1. Background 1 Chapter 2. CREAT Overview 3 Tool Framework 3 Streamlined Analysis Option 5 CREAT Reports 6 Chapter 3. Climate Change Scenarios and Data 7 Climate Change Assessments in CREAT 8 Overview of Climate Data in CREAT 8 Historical Climate Conditions 8 Historical Extreme Events 9 Vertical Land Movement 9 Time Period 10 Projected Climate Conditions 10 Projected Extreme Events 11 Sea Level Rise Projections 12 Scenario Selection and Customization 14 Threat Definition 14 Chapter 4. Economic and Public Health Consequences 15 Economic Consequence Categories 15 Default Economic Consequences Matrix 15 Utility Business Impacts 17 Utility Equipment Damage 19 CREAT Methodology Guide iii ------- Source/Receiving Water Impacts 20 Environmental Impacts 22 Public Health Consequence Assessment 23 Chapter 5. Assets and Adaptation Planning 24 Asset Identification and Assignment 24 Adaptation Plan Selection and Use in Assessments 24 Chapter 6. Risk Assessment and Results 27 Consequence Assessment Process 27 Risk Assessment Results 28 Scenario Likelihood Sensitivity Analysis 29 Plan Comparison 29 Chapter 7. References 31 Adaptive Measure Cost Sources 33 Appendix 35 Models Used in Developing Climate Data 35 Default Threat Definitions 36 Examples of Economic Consequences Matrices 39 GREAT Methodology Guide iv ------- List of Figures Figure 1. GREAT 3.0 Home Screen 1 Figure 2. Alignment of GREAT Modules with Methodology Guide Chapters 3 Figure 3. Summary of Module Modification in GREAT Streamlined Analysis 6 Figure 4. Climate Awareness Interactive Map 8 Figure 5. Illustration of Ensemble-informed Selection of Model Projections to Define Potential Future Conditions 11 Figure 6. Illustration of Ensemble-informed Selection of Model Projections to Define Potential Future Storm Conditions 12 Figure 7.Three Scenarios of Eustatic Sea Level Change Relative to 1992 (solid lines) and 2016 (dashedlines) 13 Figure 8. GREAT Results Showing Monetized Risk Reduction 27 GREAT Methodology Guide ------- List of Tables Table 1. Default Definitions for Consequence Category Levels Used for All System Types 16 Table 2. GREAT Financial Condition by System Type 17 Table 3. Total Operating Expenses by System Type based on AWWA (2015) Benchmark Data 18 Table 4. Debt Coverage Ratio Values for GREAT Consequence Values 19 Table 5. Baseline Cash Reserve Days by System Type from AWWA (2015) 20 Table 6. Per Capita Historical System Expansion Cost Outlays by System Ownership from CWSS (2009) 21 Table 7. Per Capita Historical Regulatory Compliance Cost Outlays by System Ownership from CWSS (2009) 23 Table 8. Default Costs for Selected Adaptive Measures in GREAT Adaptation Library 25 Table 9. Default Definitions for CREAT-provided Economic Consequences Matrix (all users)..39 Table 10. Default Economic Consequence Matrix for Drinking Water Assets of a Public Combined System Serving 25,000 Customers (5 MGD) in Good Financial Condition ..39 Table 11. Default Economic Consequence Matrix for Drinking Water Assets of a Public Combined System Serving 1,000,000 Customers (150 MGD) in Strong Financial Condition 40 Table 12. Default Economic Consequence Matrix for Wastewater Assets of a Public Combined System Serving 25,000 Customers (5 MGD) in Good Financial Condition 40 Table 13. Default Economic Consequence Matrix for Wastewater Assets of a Public Combined System Serving 1,000,000 Customers (150 MGD) in Strong Financial Condition 40 GREAT Methodology Guide vi ------- Acronyms AWWA - American Water Works Association CMIP5 - Coupled Model Intercomparison Project,1 Phase 5 GREAT - Climate Resilience Evaluation and Awareness Tool CWSS - Community Water System Survey2 DCR - Debt coverage ratio EPA - U.S. Environmental Protection Agency GCM - Global Climate Model (or general circulation model) GEV - Generalized extreme value curve IPCC - Intergovernmental Panel on Climate Change MGD - Millions of gallons per day MRR - Monetized risk reduction NCA - National Climate Assessment3 NOAA - National Oceanic and Atmospheric Administration O&M - Operations and maintenance costs PRISM - Parameter-elevation Regressions on Independent Slopes Model4 SLR - Sea level rise VLM - Vertical land movement VSI - Value of a Statistical Injury VSL - Value of a Statistical Life i World Climate Research Programme Coupled Model Intercomparison Project. Available online at: http://cmip- pcmdi.llnl.gov/cmip5/ 2 U.S. Environmental Protection Agency 2006 Community Water System Survey, Volume II: Detailed Tables and Survey Methodology. EPA 815-R-09-002. 3 2014 National Climate Assessment. Available online at: http://www.globalchange.gov/what-we-do/assessment/ 4 PRISM Climate Group, Oregon State University. Available online at: http://www.prism.oregonstate.edu/ GREAT Methodology Guide vii ------- This page left intentionally blank. GREAT Methodology Guide viii ------- Chapter 1. Background The U.S. Environmental Protection Agency (EPA) developed the Climate Resilience Evaluation and Awareness Tool (GREAT) to assist drinking water, wastewater and stormwater utility owners and operators in understanding potential climate change threats5 and assessing the related risks at their individual utilities. GREAT was developed under EPA's Climate Ready Water Utilities initiative. GREAT was designed in consultation with a working group that helped to provide key feedback on features and functionality. The working group was composed of representatives from drinking water and wastewater utilities, water sector associations, climate science experts, risk assessment experts and federal partners. The first two versions of GREAT were released as downloadable software. The most recent iteration, GREAT 3.0, is a web-based application (Figure 1). Each version of GREAT leverages the most current scientific information available at the time of development Data provided within GREAT will be updated and augmented, as appropriate. Dorovei - -. . - - :,. Share :*> - Figure 1. GREAT 3.0 Home Screen The results generated by GREAT provide decision-support outputs to assist in the selection and justification of investments in climate change adaptation. The risk assessment process is designed to be iterative; it can be revisited for future risk analyses. The fundamental goals of GREAT are to: Increase drinking water, wastewater and stormwater operator awareness of potential climate change impacts on utility operations and missions; Assist utilities in the determination of threshold levels for asset failures and resulting consequences of an asset's inability to perform its designed function; 5 In GREAT, "climate change threats" are climatic, hydrologic, geophysical and geochemical changes in terrestrial and aquatic ecosystems that alter the operating environment of utility facilities and operations. GREAT Methodology Guide ------- Guide utilities through the risk assessment process to quantify potential consequences from climate-related or other threats; Inform adaptation decision-making by identifying and considering adaptation options that address identified threats and reduce associated impacts; and Examine the cost of these different adaptation options in comparison to the economic losses associated with the consequences of climate change threats. GREAT Methodology Guide ------- Chapter 2. GREAT Overview Tool Framework GREAT guides users through five modules designed to help them complete a climate change risk assessment These modules employ a systematic process for evaluating the potential risks that may be incurred from changing climate conditions. Each module assists users to meet specific goals, such as building awareness of the latest climate science, and builds on inputs from previous modules. Figure 2 illustrates how the chapters in this guide align with the workflow of the five GREAT modules. V : Development Adaptation Planning RlSk ASSeSSmeflt Chapters. Climate Change Scenarios and Data Chapters. Climate Change Scenarios and Data ChapterA. Economicand Public Health Consequences Chapter 5. Assets and Adaptation Planning Chapters. Assets and Adaptation Planning Chapters. Risk Assessment and Results Figure 2. Alignment of GREAT Modules with Methodology Guide Chapters The Climate Awareness module starts the risk assessment process with a review of climate science and climate change impacts. Users identify the analysis location6 for their assessment, as well as basic utility information, such as population served, total flow and financial condition. This condition indicates a utility's strength to endure operating revenue loss or capacity to expend funds to repair and replace equipment This condition can be based on debt coverage and operating ratios. GREAT also prompts users to identify a system type for the utility from the following choices: Water only system: a utility that provides drinking water services; Wastewater only system: a utility that provides wastewater or stormwater services; Combined Water: a combined utility with a focus on drinking water assets; and Combined Wastewater: a combined utility with a focus on wastewater assets. 6 GREAT will provide climate data, such as temperature and precipitation, for the analysis location selected. Sea level rise is also provided for coastal locations, which are those near tidal water bodies. GREAT Methodology Guide ------- A critical first step in the identification of potential climate-related risk for any utility is the recognition of known current concerns that are presently being addressed. In this module, users identify these concerns, which help organize information to identify climate change threats, as well as assets7 to consider during the assessment The Scenario Development module helps users consider CREAT-provided climate data as scenarios8 that represent a range of possible future climate conditions and the potential threats these conditions could generate. Based on the current concerns that were identified in the Climate Awareness module, GREAT provides default threat selections9 for the user's review. To explore and assess their current risk, users establish a Baseline Scenario based on data that are used for planning decisions and other assessments, such as historical data provided within GREAT or from records kept by the user. GREAT is flexible in its approach and users can easily override GREAT provided data with their own. The Baseline Scenario is compared to other scenarios in the Risk Assessment module or is used to help identify "no regrets" options, which are options that have benefit with or without changes in climate. Historical data provided by GREAT includes average annual and monthly temperature, total annual and monthly precipitation, storm precipitation totals for several event return intervals and the average number of "hot days" in a year, which are those days with high temperatures over 100 degrees Fahrenheit. Additional scenarios for risk assessment can be based on any of the CREAT-provided projections of changes in climate conditions. These projections are based on averages of climate model outputs to provide a representative range of how temperature and precipitation could change in the future. Once selected, the threats associated with these projections provide a range of possible conditions for consideration in the risk assessment. The Consequences and Assets module provides guidance for users to define the potential economic, environmental and public health consequences of their threats. In this module, users define the consequences that could occur if a critical asset were to be destroyed, damaged or rendered inoperable for a period of time. An asset/threat pair is the unit of analysis for a climate change risk assessment; the focus is on the consequences to the critical asset if the threat were to occur across a number of scenarios. GREAT provides an economic consequences matrix10 to help users make decisions systematically; this matrix includes consequence categories, which were developed in collaboration with federal and state partners, water associations and utility personnel. The consequence categories in GREAT classify the types of economic consequences that would be incurred if a threat were to impact an asset For each category, users are asked to define the monetary range for each level of 7 In GREAT, an asset can be anything of value that contributes to a utility's ability to meet its mission, including physical infrastructure, entire facilities or natural resources that provide services or water to the utility regardless of its ownership or the parties responsible for its management. 8 In GREAT, scenarios refer to groups of threats that are defined by the user based on available historical or projected climate data, as well as any other relevant data, such as demand forecasts. 9 The default threats in GREAT are derived from a combination of changes in climatic conditions that may result in impacts to assets, including drought, floods, ecosystem changes, service demand and use, and water quality degradation. 10 See Chapter 4. Economic and Public Health Consequences for matrix development method. GREAT Methodology Guide ------- consequences, on a scale from low to very high, by either accepting the default values or providing their own values. The resultant matrix is used during risk assessment to gauge potential loss for every combination of scenario, threat and asset. The Adaptation Planning module prompts users to define adaptive measures and adaptation plans. Adaptive measures are physical infrastructure or actions and strategies that a utility can use to protect their assets and mitigate the impacts of threats. These measures include those already implemented measures that provide resilience now, as well as potential measures that could increase resilience when implemented as part of adaptation plans. Each measure is defined based on the cost of implementation and whether or not the measure is expected to be effective in reducing consequences from each threat that is defined. These adaptation plans could be based on several goals, such as protecting critical assets, addressing specific threats or exploring options as part of broader utility planning decisions. Each assessment considers the implementation of a specific adaptation plan and compares those results with results if no additional adaptation was implemented, called the Current Measures plan. The Risk Assessment module is the last module in the climate change risk assessment process. GREAT provides monetized risk from assessments to support adaptation planning decisions and characterize current and potential risks to utility assets and resources. Monetized risk refers to the anticipated financial impact of a threat if it occurs, which is based on those consequences assessed for each critical asset. Users assess risk for each asset/threat pair across scenarios and plans to generate results that can be compared in terms of their cost and potential risk reduction to identify those that would be most effective. Streamlined Analysis Option GREAT provides a streamlined analysis option that guides decisions for the analysis, provides default values and requires less customization (Figure 3). This workflow allows users to progress through GREAT quickly by reducing the scope of analysis and focusing on priority concerns. These users become familiar with the risk assessment process before conducting more in-depth analyses. No streamlined path is offered within the Climate Awareness module, which is provided for informational and awareness building purposes. A single default threat and scenario are provided in the Scenario Development module to ensure a manageable scope in the assessment. In the Consequences and Assets module, limited asset selection is encouraged. For Adaptation Planning, GREAT will define a single plan including all potential adaptive measures for consideration during risk assessment Given that the number of assessments rapidly multiplies when using GREAT as additional assets and threats are selected, the streamlined analysis path encourages users to assess the risk for a single asset/threat pair instead of having multiple combinations to consider. CREAT's streamlined process produces a more focused assessment requiring fewer inputs. The outputs describe a concise and focused result for users who are beginning their work on risk assessment and adaptation. GREAT Methodology Guide ------- Climate A\X/ar£ri£SS Basic utility inputsand an interactive mapfor building climate awareness. SCGnarJO DOVGlODITlGnt Threat and historical climate data provided based on location. Projected data provided for threat selected. /** COnS6QU6nCGS & ASSGtS Economic values provided based on utility information. Adaptation Planning Adaptation plan that includes all defined potential measures. RiSk ASSGSSm0nt Focused assessment due to streamlined inputs throughout previous modules. Figure 3. Summary of Module Modification in GREAT Streamlined Analysis GREAT Reports At the conclusion of each of the first four modules, users may generate interim reports to inform utility planning and decision-making as described below: The Climate Awareness Report summarizes potential future climate conditions and impacts to the water sector and local communities; The Scenario Development Report lists each scenario and the associated threats as defined by the user; The Consequences and Assets Report includes the user's economic consequences matrix and a list of the assets defined; and The Adaptation Planning Report details each adaptation plan defined by the user with the cost of each adaptive measure included in these plans. These high-level summary reports document progress through the overall risk assessment process, communicate key information and provide a basis for additional work to be conducted within the tool. These reports help to build confidence that the user is being appropriately proactive or identifying areas where additional funding may be needed to bolster climate readiness. The final report generated by GREAT, in the Risk Assessment module, is the Plan Report, which includes the results of the risk assessment for a specific adaptation plan selected by the user. The Plan Report is a summary of the risk reduction possible that can be compared with the cost of implementing the adaptation plan. This report can be used as decision support to inform adaptation planning or to determine if there is a need for further assessment. GREAT Methodology Guide ------- Chapter 3. Climate Change Scenarios and Data GREAT provides climate change information to help users identify their current concerns and consider how these concerns may be exacerbated as a result of a changing climate. The process is designed to help users organize information and identify the threats and assets to consider in the risk assessment. Several of the current concerns available in GREAT are related to potential threats that can be defined and assessed using GREAT, such as water supply management, peak service challenges, water quality management, natural disasters, ecosystem/landscape management and sea level rise (SLR). These threats are assessed based on an understanding of climate change and other projected trends that may impact utility operations or infrastructure. GREAT provides climate data for use in prioritizing these concerns and defining related threats in the risk assessment process. To simplify and expedite the process, GREAT provides five general threats related to climate conditions for use in the risk assessment, which are as follows: Drought: changing water levels in aquifers and reservoirs, loss of snowpack and reductions in surface water flows; Ecosystem Changes: altered status, structure or functionality of an ecosystem, such as loss of coastal systems, increases in wildfires or altered vegetation; Floods: high flows from intense precipitation events or surges associated with coastal storms in combination with SLR; Service Demand and Use: altered volume and temperature of influent or challenges meeting the needs of agricultural and energy sectors; and Water Quality Degradation: saline intrusion into aquifers and contaminated or negatively altered surface water quality. In GREAT, these threats are defined across scenarios starting with a Baseline Scenario, which is comprised of the climate conditions, often based on historical or observed data, that serve as a baseline for comparing how climate and associated threats behave now and how they could change in the future given changing climate conditions. This scenario helps users evaluate their current resilience based on threat magnitudes and timing that are already used in planning decisions and other assessments. The data used to define this baseline should be based on event magnitudes and timing to assist in planning decisions and other assessments, such as historical data provided within GREAT or from records kept by the user. Default data provided by GREAT for the Baseline Scenario includes average annual temperature, total annual precipitation, intense precipitation for a 100-year storm event and the annual number of days over 100 degrees Fahrenheit, also called hot days. The user can choose to accept the default data or augment it with other available data. There is the opportunity to review and confirm the user-defined data included in the Baseline Scenario. Users can include natural resource and socioeconomic data, such as projected population or economic growth, to provide a more robust Baseline Scenario. Users establish Projected Scenarios, based on projected changes in climate with respect to historical climate conditions, for comparison with their Baseline Scenario. These Projected Scenarios are defined by the user based on data provided in GREAT or from their own sources or models. Each scenario describes different changes in climate conditions that may present different threats. Considering multiple scenarios increases the range of possible future climate conditions included in the risk assessment GREAT Methodology Guide ------- Climate Change Assessments in GREAT An interactive map (Figure 4) in GREAT provides the ability to focus on regional impacts or impacts to specific sectors with information from the most recent11 National Climate Assessment (NCA). Users can also browse regional overviews from EPA's Adaptation Strategies Guide, which summarize data and impacts from the NCA and the Intergovernmental Panel on Climate Change (IPCC) by geographic climate region. GREAT provides climate information by defined geographic regions including the Northeast, Southeast, Midwest, Great Plains, Southwest, Northwest, Alaska, Islands and Coasts, with particular emphasis on how climate may impact the water sector. O Curate Awareness utllrty Location Climate cringe Bacicc- Current Concerns * O Scenario Development ^ (Jon^Ht jiltinCfW Ki O Adaptation Panning . RKk Assessment Climate Change Basicy Cuck on iiti'r i^-yiun HI Lliw rnjp Lx.jlow Lu Ivjrn jiwul ulnrult; tf JIIAJ Yyu I.UM uAjU n.-du-w notKjrwl C* COysUrt cUinaU- irnptK-'U jnd Lwtn jtwJt ngw *. p^f.'^-rl in mpflc.t a "pecifc rn^trir ny r.iir.kinrj on rile [Qpir. I inkr. Note MoponaTop»cunksop*nina new wmdow or tao m yowr **o orowser Figure 4. Climate Awareness Interactive Map Overview of Climate Data in GREAT The climate information available in GREAT provides a snapshot of how changes in climate might exacerbate current concerns. In addition to the national and international assessments synthesized in GREAT, historical observations and model projections are organized for users to review and select as part of their scenarios. Historical Climate Conditions GREAT provides historical climate data for temperature and precipitation to help users assess current risk as part of their Baseline Scenario. Average annual and monthly conditions are sourced from the Parameter-elevation Regressions on Independent Slopes Model12 (PRISM) dataset based on observations from 1981 to 2010. Data available from the Climate Research Unit13 are used in 11 2014 National Climate Assessment available at: http://www.globalchange.gov/what-we-do/assessment 12 PRISM Climate Group, Oregon State University. Available online at: http://www.prism.oregonstate.edu/ « University of East Anglia Climatic Research Unit; Jones, P.O.; Harris, I. (2013): CRU TS3.20: Climatic Research Unit (CRU) Time-Series (TS) Version 3.20 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901 - Dec. 2011). NCAS British Atmospheric Data Centre,April2015. Available: http://catalogue.ceda.ac.uk/uuid/2949a8a25b375c9e323c53f6b6cb2a3a GREAT Methodology Guide ------- places where PRISM data were unavailable, such as in Alaska, Hawaii and Puerto Rico. The resultant dataset covers all 50 U.S. states and Puerto Rico at a 0.5-degree resolution in latitude and longitude. Historical Extreme Events Historical data on extreme events, including both temperature and precipitation, are based on time- series analysis of the data available from the National Oceanic and Atmospheric Administration (NOAA) National Climate Data Center climate stations.14 Data for historical extreme events are representative of each station. Users have the flexibility to select a station independent of the location used for historical average conditions. For intense precipitation events, time series of historical daily precipitation data from 11,010 stations were reviewed and converted into annual maxima time series for 24-hour precipitation. Any station with data available during 1981 through 2010 was included. This time series was then used to develop the historical generalized extreme value (GEV) curve for each station that describes the maximum amount of precipitation observed over 24 hours for several event return intervals.15 Curves were calculated using the exceedance probabilities, which are fractions of observations over a series of event magnitudes on an annual basis, from observed daily total precipitation fit to the following cumulative distribution function: F(x; [L, a, 0 = exp f- [l + f (^)] 1 where x is the event magnitude; ^ is the shape parameter; a is the scale parameter; and [i is the location parameter. The three parameters (^, a, and [i) were used to fit the curve. The peak magnitudes of 24-hour rainfall events were calculated for storms with return intervals of 5, 10, 15, 30, 50 and 100 years. Historical hot days, those days with daily maximum temperature over 100 degrees Fahrenheit, were calculated using historical daily maximum temperature data from 8,150 stations. These stations were selected from the same stations used for intense precipitation based on a minimum of 95% completeness for April through October daily observations from at least one calendar year in the period of observation. For 1,825 stations (22% of dataset), zero days in the record qualified as hot days. Vertical Land Movement For coastal locations, GREAT provides the ability to enter local vertical land movement (VLM) to account for uplift or subsidence in any projections of local sea level. VLM is the rate of land moving up or down due to several processes, such as tectonics, subsidence and ground water extraction. VLM can either add to or counteract SLR. In a place where VLM is upward, local SLR is slower. When VLM is downward, local SLR is effectively faster. NOAA has developed a method16 to estimate VLM at tide stations with 30 to 60 years of data. Their report also includes VLM estimates for a 14 For more information on NOAA climate stations, see: http://www.ncdc.noaa.gov/data-access/land-based-station-data 15 A storm event with a return interval of 100 years is an event that has a 1% chance of being observed or exceeded in any year, based on the historical record. This event is sometimes called the 100-year storm. The return interval does not strictly define a frequency for the event; it is possible that historically rare events could occur more frequently in periods of the record. 16 NOAA, 2013. Estimating Vertical Lane Motion from Long-Term Tide Gauge Records. Technical Report NOS CO-OPS 065. GREAT Methodology Guide ------- number of U.S. tide gauges; users can review these values in GREAT when defining their Baseline Scenario. If a non-zero VLM is used in the Baseline Scenario, this same rate is included in SLR projections for use in Projected Scenarios. Time Period To effectively apply risk assessment results to planning efforts, users must identify a time period for use in developing Projected Scenarios. This time period is the range of years being considered for the analysis, as defined by the user. The period selected, from Start Year to End Year, may be based on planning horizons for asset or water resource management, improvement schedules or climate action plans. The End Year defines the target for planning when adaptation plans would be completed and the conditions in Projected Scenarios may be experienced. GREAT provides climate data based on the End year of the user-defined time period to support the climate change risk assessment. Projected Climate Conditions GREAT provides projected changes from Global Climate Models17 (GCMs) as available from the Coupled Model Intercomparison Project, Phase 5 (CMIP5),18 which is the same data used to support the IPCC Fifth Assessment Report Data provided in GREAT were from model simulations employing Representative Concentration Pathway 8.5, a higher trajectory for projected greenhouse gas concentrations to support assessments looking at higher potential risk futures. Because the outputs from GCMs vary, GREAT provides averages from model projections that represent a range of potential future climate conditions. Generally, all models project warming but projections for precipitation varies more widely. Users may choose to apply all or part of the projection data provided, along with their own projections for climate or other parameters to customize their scenarios. For example, they may want to incorporate data collected by the utility, in-house models, projected changes in population, demand or energy costs. GREAT uses an ensemble-informed approach to derive meaningful choices from the results of 38 model runs19 for each 0.5 by 0.5 degree location. This approach involves generating a scatter plot of normalized, projected changes in annual temperature and precipitation by 2060 for all models. Statistical targets were calculated based on the distribution of these model results and the five models closest to those targets were averaged to generate each projection (Figure 5). The targets were designed to capture a majority of the range in model projections of changes in annual temperature and precipitation, as follows: Warmer and wetter future conditions: average of five individual models that are nearest to the 95th percentile of precipitation and 5th percentile of temperature projections; 17 Global Climate Models are mathematical models that model the physical processes of earth's atmosphere, ocean, cryosphere and land surfaces. These models are used to simulate the response to increasing greenhouse gas concentrations. The outcomes of different GCMs vary because the feedback mechanisms of various processes that are incorporated differ from model to model. 18 World Climate Research Programme Coupled Model Intercomparison Project available at: http://cmip- pcmdi.llnl.gov/cmip5/ 19 List of models used in analyses provided in Appendix. GREAT Methodology Guide 10 ------- Moderate future conditions: average of five individual models that are nearest to the median (50th percentile) of both precipitation and temperature projections; and Hotter and drier future conditions: average of five individual models that are nearest to the 5th percentile of precipitation and 95th percentile of temperature projections. Once the models for each projection were selected, these models were ensemble-averaged to calculate annual and monthly changes for temperature and precipitation. GREAT selects the most appropriate data to match the defined planning horizon from two available data sets - one for 2035, which is based on projection data for 2025-2045, and one for 2060, which is based on projection data for 2050-2070. The selection of the appropriate CREAT-provided time period is based on the End Year defined by the user during the time period selection. If the End Year is 2049 or earlier, the 2035 data are selected; otherwise, GREAT selects the 2060 data set. Models that project wetter and \warmerconditions Projected Changesin Precipitation by 2060 Models that project hotter and drier onditions Individual climate model resultforthis location Projected Changesin Temperature by 2060 Figure 5. Illustration of Ensemble-informed Selection of Model Projections to Define Potential Future Conditions Projected Extreme Events GREAT also provides projections of extreme heat in terms of the new total number of hot days following the projected shift in temperature. The projected changes in hot days were linked to the models selected for projected changes in average temperature and precipitation. Changes in monthly average temperatures from each projection were used as an estimate of how the historical daily maximum temperature time series would shift for each of the model projections selected. The change in monthly average temperature for April through October for the analysis location was added to the daily time series from that station to generate a new time series for each projection. The number of hot days was then calculated using the same method employed for historical hot days to generate projected number of hot days. Similar to the development of model projections of changes in average temperatures and precipitation, GREAT uses an ensemble-based approach to identify a range of possible changes in total storm precipitation. A subset of the GCMs used earlier (22 of the 38 models) provide scalars,20 20 This set of spatially explicit scalars was collected in cooperation with ClimSystems (company website: http://www.climsystems.com/). GREAT Methodology Guide 11 ------- or changes in precipitation per degree of warming, for storm events of the same return intervals as the historical storms provided in GREAT. Each model provides a different scalar for each return interval based on model-projected daily precipitation patterns. The scalars from these models were ranked based on the scalars for the storm events with a 5-year return interval. The use of 5-year storm events to rank the models was based on the assumption that water sector utilities dealing with intense storm events are often more concerned with more frequent storm events. Ensembles of five models were selected as describing a "Stormy Future," which are the highest models and a "Not as Stormy Future," which are the lowest models. In each case, these models were averaged to provide two model projections available to users, as shown in Figure 6. Projected Changes In storm Precipitation by 2060 Models that project more stormy conditions els that project less stormv conditions Individual climate model result for this location Models Ranked in Increasing Change Figure 6. Illustration of Ensemble-informed Selection of Model Projections to Define Potential Future Storm Conditions The selected models were used to provide ensemble average scalars for changes in precipitation per degree of warming for all the return intervals provided for historical data including 5-year, 10- year, 15-year, 30-year, 50-year and 100-year. Projected changes in event magnitudes were calculated using the scalars, generating a new GEV curve for each future time period, as follows: Intense Precip(PJ, Pro/) = Intense Precip(Rl,Hist) * (l + klnten.se Predp(PJ, Pro/)), where AIntense Precip(RI, Pro/) = Scalar(PJ, Pro/) * ATemp(Proj), and ATemp is the change in global mean temperature from the same model. This method provides more detailed information than simply using the values from the models identified for the average conditions. Selecting different models for storms decouples changes in storm events from changes in average events. For utilities concerned with intense precipitation, this approach will define a wider range of values for projected storm events from the available models. Sea Level Rise Projections GREAT provides SLR projections to facilitate climate risk assessment and climate change adaptation for coastal regions of the U.S. The approach incorporates recent developments in understanding the mechanisms of SLR and the models that provide projections, as documented in peer-reviewed studies and the IPCC Fifth Assessment Report. Other federal agencies, such as the U.S. Army Corps GREAT Methodology Guide 12 ------- of Engineers and NOAA have developed tools that are publicly accessible which can be used calculate local sea level in the future. SLR projections in GREAT are based on current scientific understanding and approaches to avoid duplicating existing efforts from other federal agencies and eliminate possible discrepancies. SLR projections consist of two parts: eustatic sea level change and local VLM. Eustatic sea level represents the level of the ocean independent of land movement and is often estimated based on historical tide gauge records over the globe and satellite altimeter data. The NCA considered four SLR scenarios: 0.2 meters (lowest), 0.5 meters (intermediate-low), 1.2 meters (intermediate-high) and 2.0 meters (highest) by 2100 (relative to 1992). The three highest NCA scenarios of eustatic sea level change (0.5 meters, 1.2 meters and 2.0 meters) were incorporated in GREAT. The lowest projection of 0.2 m, which is an extrapolation of the historic trend, was excluded since it adds little benefit to the analysis of risk by coastal water utilities. To estimate future sea level, GREAT uses the equation and constants provided by the NCA: SLR(year, level) = a *Y + b(level) * Y2, where Y is the number of years since 1992, a is an estimated global sea level trend of 1.7mm per year and b is a curvature for each SLR curve: 0.156 mm per year2 for high curve (2.0 m by 2100, relative to 1992); 0.0871 mm per year2 for medium curve (1.2 mby 2100, relative to 1992); and 0.0271 mm per year2 for low curve (0.5 m by 2100, relative to 1992). Curves were calculated in 5-year increments through 2100. Itshouldbe emphasized that this straightforward quadratic approach to the time evolution is chosen in part for its simplicity; there is no scientific reason or evidence to assume that SLR will evolve in precisely this smooth manner (Parris et al, 2012). In GREAT, eustatic sea level change is adjusted relative to the reference year 2016 (Figure 7) by subtracting the calculated SLR, relative to 1992. Finally, if the user enters a non- zero VLM, the curve is corrected for the influence of land movement on the relative projected SLR: SLR(year, level} = a * Y + b(level) *Y2- SLR(2016, level) - VLM * (year - 2016) 72 GO "? 48 -Intermeifiate (Low) Inteimecfcate (H^h) 201O 204O 2070 21OO Year Figure 7.Three Scenarios of Eustatic Sea Level Change Relative to 1992 (solid lines) and 2016 (dashed lines) GREAT Methodology Guide 13 ------- Scenario Selection and Customization The process for scenario definition involves the review and selection of available data. Any or all of the data can be revised to meet the needs of the user conducting a GREAT assessment For coastal locations, users will have the ability to select the projected sea level from any value along the three curves between 2020 and 2100. This flexibility allows users to find the amount of SLR that concerns them based on the range possible over time. This process differs for users conducting a streamlined analysis. In that case, the single threat selected determines which of the model projections are provided as a default: Drought: Hotter and drier future conditions combined with the Stormy projection; Ecosystem Changes: Hotter and drier future conditions combined with the Stormy projection; Floods: Warmer and wetter future conditions combined with the Stormy projection; Service Demand and Use: Hotter and drier future conditions combined with the Stormy projection; and Water Quality Degradation: Warmer and wetter future conditions combined with the Stormy projection. Streamlined users in coastal locations also receive a default value for SLR based on the high SLR curve for the year closest to their End Year. Threat Definition Translating climate change impacts into utility-specific threats requires additional understanding of the changes that would imperil water sector assets. For their Baseline Scenario and each Projected Scenario, users are encouraged to define the selected threats in terms of their frequency, duration or magnitude based on the appropriate data for each scenario. The same threats are used in all scenarios; however, the specific threat definitions will differ based on the data used to define the scenario. The threat definition includes any important aspects of the threat that would affect risk assessments, including historical trends, quantitative threat metrics, links to scenario data and assumptions. Since threat definition is often a challenging step for utilities, GREAT supports this step by providing default threat definitions as a starting point for users.21 Assessment of risk from each threat needs to be considered with respect to a "threshold" condition for asset damage or failure. These thresholds can be based on information provided by GREAT, entered into GREAT or already known by users. Thresholds can be defined in terms of threat magnitude, location, frequency or any other metric that represents potential damage to assets. Where possible, users should define these thresholds carefully and in detail. During assessments, these thresholds are compared with projected conditions to estimate how likely it is that the threshold will be exceeded, such as the threat occurring, and what the level of consequence will be to each asset. List of default threat definitions is provided in the Appendix. GREAT Methodology Guide 14 ------- Chapter 4. Economic and Public Health Consequences When assessing risk, users will need to consider consequences that could occur if an asset were to be destroyed, damaged or rendered inoperable for some period of time. In this context, consequences generally describe dollar values that would constitute low, medium, high or very high impacts to the utility if climate change threat(s) occur. These consequences may include loss of revenue, partial or complete loss of an asset, impacts to source and receiving water, environmental damage and public health impacts. GREAT does not assign or assess the extent of damage or consequences for each individual threat because this decision is dependent on the specific characteristics of the utility. Economic Consequence Categories GREAT provides categories that users can incorporate for gauging potential economic consequences to assets. Users have the opportunity to refine these categories or add any custom categories for consequences they feel may not be adequately represented. The most important part of this step is for users to determine if they would like to assign monetary values to the levels of each consequence category by choosing to monetize them. Some categories may be important to users even though monetary impacts would be too difficult to determine. These categories can be deferred for use in the comparison of plans rather than in the assessment of risk. Users can use these deferred categories to rate the performance of each plan with respect to the categories. The default economic consequence categories are defined as follows: Utility Business Impact - Operating revenue loss evaluated in terms of the magnitude and recurrence of service interruptions. Consequences range from long-term loss of expected operating revenue to minimal potential for any loss; Utility Equipment Damage - Cost of replacing the service equivalent provided by a utility or piece of equipment evaluated in terms of the magnitude of damage and financial impacts. Consequences range from complete loss of the asset to minimal damage to the equipment; Source/Receiving Water Impacts - Degradation or loss of source or receiving water quality or quantity evaluated in terms of recurrence. Consequences range from long-term compromise to no more than minimal changes to water quality or quantity; and Environmental Impacts - Evaluated in terms of environmental damage or loss, aside from damage to water resources, and compliance with environmental regulations. Consequences range from significant environmental damage to minimal impact or damage. Default Economic Consequences Matrix GREAT provides an economic consequences matrix defining the monetary scales of potential loss within these consequence categories. This matrix identifies different levels of consequences that may be experienced for each consequence category as related to a given threat occurring to a specific asset This matrix supports systematic and comparable decisions during consequence assessments across multiple assets and threats. GREAT provides default definitions for the levels of consequences in each category to use in the assessment of each asset/threat pair (Table 1). GREAT Methodology Guide 15 ------- For each level, there is a monetary range that is used in the risk calculation. The default values for this matrix are based on user inputs that include: 1) system type;22 2) population served; 3) total flow in millions of gallons per day (MGD); 4) public or private ownership; and 5) financial condition, such as adequate, good or strong. These inputs are used to obtain default values from available benchmark utility survey data.23'24 Table 1. Default Definitions for Consequence Category Levels Used for All System Types Level Very High High Medium Low Utility Business Long-term or significant loss of revenue or operating income Seasonal or episodic compromise of revenue or operating income Minor and short- term reductions in expected revenue Minimal potential for loss of revenue or operating income Utility Equipment Damage Complete loss of asset Significant damage to equipment Minor damage to equipment Minimal damage to equipment Source/Receiving Water Impacts Long-term compromise of source water quality or quantity Seasonal or episodic compromise of source water quality or quantity Temporary impact on source water quality or quantity No more than minimal changes to water quality Environmental Impacts Significant environmental damage - may incur regulatory action Persistent environmental damage - may incur regulatory action Short-term environmental damage, compliance can be quickly restored No impact or environmental damage Users are advised to select the most appropriate financial condition based on their understanding of their system finances, including the debt coverage ratio (DCR) and operating ratio. Utilities that can calculate their ratios may elect to use Table 2 to select the most appropriate financial condition for their analysis. DCR is the ratio of net operating income to total debt service: (Total Operating Revenue Total Operating Expenses) Debt Coverage Ratio = Total Debt Service Higher DCR values indicate more cash flow is available to meet interest, principal and sinking fund payments. DCR ratios less than 1 indicate a negative cash flow, meaning a utility is not generating enough income to pay its debt obligations strictly through operations. The operating ratio is a 22 The system type may be water only, wastewater or combined. For combined systems, users differentiate which portion of your system (drinking water or wastewater) is the focus of their analysis so the relevant monetary ranges can be provided. Stormwater utilities are advised to use the wastewater option in GREAT. 23 U.S. Environmental Protection Agency (EPA), 2009. 2006 Community Water System Survey (CWSS), Volume II: Detailed Tables and Survey Methodology. EPA 815-R-09-002. 24 American Water Works Association (AWWA), 2015. Benchmarking Performance Indicators for Water and Wastewater Utilities 2013, Survey Data and Analyses Report. GREAT Methodology Guide 16 ------- utility's total operating expenses divided by its total operating revenue and takes into account expansion or debt repayment (or net sales): Total Operating Expenses Operating Ratio = Total Operating Revenue This chapter provides an explanation of how these baseline values are used for the default economic consequences matrix value calculations, by category. The ranges associated with each consequence level are indicative of how the user might characterize the do liar value of impact associated with each consequence level. The range assigned to each consequence level is used as a proxy for the "cost" of doing nothing to protect an asset, assuming the threat occurs. The user can review and accept descriptions and values. Alternatively, a user can provide the monetary values that estimate their utility-specific consequence levels, if they are known. All saved values will then be applied in assessment calculations of monetized risk and risk reduction. Table 2. GREAT Financial Condition by System Type System Type Financial Condition Baseline OCR Baseline Operating Ratio Water Only System Top Quartile25 Median Bottom Quartile Strong Good Adequate 2.62 1.45 0.47 0.50 0.69 0.86 Wastewater Only System Top Quartile Median Bottom Quartile Strong Good Adequate 2.39 1.43 0.41 0.42 0.51 0.82 Combined Water Top Quartile Median Bottom Quartile Strong Good Adequate 3.39 1.67 1.24 0.46 0.57 0.73 Combined Wastewater Top Quartile Median Bottom Quartile Strong Good Adequate 1.93 1.25 0.67 0.47 0.61 0.73 Utility Business Impacts The Utility Business Impacts category refers to revenue loss, which would manifest to the utility as an operating statement effect. Consequence levels are estimated as the loss in utility operating 25 The terms top and bottom quartile refer to the distribution within the total data set. The bottom quartile is defined as the midpoint between the median and the lowest number in the dataset. The top quartile is defined as the midpoint between the median and the highest number in the dataset. GREAT Methodology Guide 17 ------- revenue that would cause financial changes in its baseline operating condition. The overall strength of the utility's baseline operating condition and subsequent changes due to operating revenue loss is modeled by observing changes in the baseline DCR, which is an overall indicator of operating condition. The default economic consequences matrix estimates for the Utility Business Impacts category are developed using the following five steps: 1. Assign the utility a baseline debt coverage ratio and operating ratio value. The utility being assessed was assigned a baseline DCR and operating ratio values from one of twelve possible model utility baseline values (Table 2) based on user inputs for system type and financial condition; 2. Estimate annual operating expenses for the user's system. To calculate estimated annual operating expenses, the median total operations and maintenance costs (O&M) per million gallons for the system type (Table 3) was multiplied by the system total flow, in MGD over 365 days; Annual Operating Expenses = Total 0&.M per million gallons * MGD * 365 Table 3. Total Operating Expenses by System Type based on AWWA (2015) Benchmark Data System Type Water Only System Wastewater Only System Combined Water Combined Wastewater Total O&M per Million Gallons $2,176 $1,945 $2,240 $2,233 3. Estimate annual operating revenues and annual debt service. Annual operating revenues and debt service were estimated using the baseline ratios for the utility and annual operating expenses as follows; Annual Operating Expenses Annual Operating Revenue = Baseline Operating Ratio and (Annual Operating Revenue Annual Operating Expenses) Annual Debt Service = Baseline DCR 4. Specify DCR threshold values associated with each consequence level. For each model baseline condition, GREAT provides the loss in revenue that produces each of three possible threshold changes in DCR (Table 4). These threshold changes align with increases to higher consequence levels in GREAT, as outlined in the following; Target 1, the threshold between Low and Medium impacts, is equal to a 25% decrease in the baseline DCR; Target 2, the threshold between Medium and High impacts, is equal to a 50% decrease in the baseline DCR; and Target 3, the threshold between High and Very High impacts, is equal to a 75% decrease in the baseline DCR. 5. Estimate default values for each consequence level boundary. The last step of this process was to estimate the value of operating revenue loss that would cause the baseline DCR value to GREAT Methodology Guide 18 ------- move to each of the three target values specified above. These values become the new upper and lower bounds for the individual GREAT consequence levels, from Low to Very High: Revenue = (% decrease in DCR)t * (Annual Operating Revenue Annual Operating Expenses^) Table 4. Debt Coverage Ratio Values for GREAT Consequence Values System Type Baseline OCR Target 1 Medium Target 2 High Target 3 Very High Water Only System Strong Good Adequate 2.62 1.45 0.47 2.0 1.1 0.4 1.3 0.7 0.2 0.7 0.4 0.1 Wastewater Only System Strong Good Adequate 2.39 1.43 0.41 1.8 1.1 0.3 1.2 0.7 0.2 0.6 0.4 0.1 Combined Water Strong Good Adequate 3.39 1.67 1.24 2.5 1.3 0.9 1.7 0.8 0.6 0.8 0.4 0.3 Combined Wastewater Strong Good Adequate 1.93 1.25 0.67 1.4 0.9 0.5 1.0 0.6 0.3 0.5 0.3 0.2 Utility Equipment Damage The Utility Equipment Damage category refers to the cost required to replace or repair damaged assets. The associated costs would incur unplanned capital outlays for the asset repair or replacement. The approach for this category estimates consequence level thresholds based on changes in estimated cash reserves. This indicator quantifies the number of days of available cash reserves as a measure of financial liquidity. Days of cash reserves are calculated using the amount of undesignated reserves and the average daily cost of ongoing operations. The default economic consequences matrix estimates for the Utility Equipment Damage category are developed using the following four steps: 1. Assign a baseline cash reserve days value. A baseline cash reserve days value was assigned (Table 5) based on system type and financial condition. 2. Estimate the value of undesignated cash reserves. The value of undesignated cash reserves was estimated based on annual operating expenses, which was calculated using the methodology outlined for the Utility Business Impacts category, and the baseline cash reserve days value using the following equation: GREAT Methodology Guide 19 ------- Undesignated Cash Reserves = Baseline Cash Reserve Days * Annual Operating Expenses^ 365 3. Specify losses in available cash reserves as threshold values associated with each consequence level. GREAT considers different percentage thresholds of cash reserve utilization for association with the consequence levels, as outlined in the following: Target 1, the threshold between Low and Medium impacts, is equal to 10% of undesignated cash reserves; Target 2, the threshold between Medium and High impacts, is equal to 25% of undesignated cash reserves; and Target 3, the threshold between High and Very High impacts, is equal to 60% of undesignated cash reserves. 4. Estimate default values for each consequence level boundary. The last step was to estimate the loss of available cash reserves that would exceed the thresholds specified above. These values become the new upper and lower bounds for the individual GREAT consequence levels, from Low to Very High: Cash Reserves Losst = (% decrease in Available Cash Reserves^ * Undesignated Cash Reserves Table 5. Baseline Cash Reserve Days by System Type from AWWA (2015) System Type Drinking Water Only Drinking Water component of Combined Utility Wastewater Only Wastewater component of Combined Utility Baseline Cash Reserve Days by Financial Condition Strong 517 656 515 536 Good 258 238 141 305 Adequate 139 126 109 133 Source/Receiving Water Impacts The Source/Receiving Water Impacts category refers to the cost associated with the degradation or loss of source water or receiving water quality or quantity, which would manifest as additional capital outlays for source and receiving water enhancement. The approach for this category relies on threshold levels of water resource spending, relative to historical levels of spending for system expansion, which align with the GREAT consequence levels. Historical expansion outlays are used as a proxy for the cost to access or acquire new resources if current source or receiving water resources are degraded or lost These levels are based on those reported in EPA's CWSS as per-capita historical systems expansion cost outlays differentiated by CREAT Methodology Guide 20 ------- utility population size ranges.26 The default economic consequences matrix estimates for the Source/Receiving Water Impacts category are developed using the following four steps: 1. Assign the utility a baseline for per-capita historical system expansion cost outlays based on population served bin. The appropriate population range bin27 from those used to report data in the CWSS was selected to estimate per-capita historical system expansion cost outlays (Table 6) based on system ownership, either public or private, and population served. 2. Calculate baseline for system expansion cost outlays based on actual population served. The estimate for baseline expansion cost outlays for the user's system was estimated based on per-capita historical system expansion cost derived from the population bin and the population served: Baseline System Expansion Costs = Per capita Historical Cost Outlays * Population Served Table 6. Per Capita Historical System Expansion Cost Outlays by System Ownership from CWSS (2009) Population Served (bins) 100 or Less 101-500 501-3,300 3,301-10,000 10,001-50,000 50,001 - 100,000 100,001-500,000 Over 500,000 Per capita Historical Cost Outlay Public Systems $350.30 $378.26 $103.67 $40.91 $42.80 $21.96 $38.05 $32.44 Private Systems $132.03 $28.95 $30.16 $42.41 $37.87 $35.08 $4.69 $32.44* * Value based on public system data due to missing data for this population bin 3. Specify levels of spending as threshold values associated with each consequence level. GREAT considers different percentage thresholds of outlays for association with the consequence levels: Target 1, the threshold between Low and Medium impacts, is equal to 10% of historical expansion costs; Target 2, the threshold between Medium and High impacts, is equal to 25% of historical expansion costs; and 26 The corresponding data specific to wastewater systems were not available in either the CWSS or AWWA sources. Drinking water system data is used as a proxy to develop default values for all system types as reasonable estimates. 27 Although these population bins may be more refined than the average utility operator is accustomed to, they allow GREAT to provide the best default values based on utility size. The data selection based on these categories is not visible to the user. CREAT Methodology Guide 21 ------- Target 3, the threshold between High and Very High impacts, is equal to 60% of historical expansion costs. 4. Estimate default values for each consequence level boundary. The last step was to estimate the loss that would exceed the thresholds specified above. These values become the boundary values that separate the different GREAT consequence levels: Water Resource = (% of Historical Expansion Costs') t * Baseline System Expansion Costs Environmental Impacts The Environmental Impacts category refers to the cost associated with environmental damage or loss (aside from water or other resources) and compliance with environmental regulations, which would manifest to the utility as additional costs for environmental and regulatory compliance. The approach for this category relies on threshold levels of cost for regulatory compliance, relative to historical levels of spending that align with the GREAT consequence levels. Historical levels are based on those reported in EPA's CWSS as per-capita historical regulatory compliance cost outlays differentiated by utility population size ranges.28 The default matrix estimates for the Environmental Impacts category are developed using the following four steps: 1. Assign a baseline for per-capita historical regulatory compliance cost outlays based on population served bin. GREAT selects the appropriate population range bin29 from the bin used CWSS data to select for per-capita historical regulatory compliance cost outlays (Table 7) based on system ownership, either public or private, and population served. 2. Calculate baseline for compliance cost outlays based on actual population served. The estimate for baseline compliance cost outlays for the user's system was estimated based on per- capita historical compliance costs derived from the population bin and the population served. Baseline Compliance Costs = Per capita Historical Cost Outlays * Population Served 3. Specify levels of spending as threshold values associated with each consequence level. GREAT considers different percentage thresholds of outlays for association with the consequence levels: Target 1, the threshold between Low and Medium impacts, is equal to 10% of baseline compliance costs; Target 2, the threshold between Medium and High impacts, is equal to 25% of baseline compliance costs; and Target 3, the threshold between High and Very High impacts, is equal to 60% of baseline compliance costs. 28 The corresponding data specific to wastewater systems were not available in either the CWSS or AWWA sources. Drinking water system data is used as a proxy to develop default values for all system types as reasonable estimates. 29 Although these population bins may be more refined than the average utility operator is accustomed to, they allow GREAT to provide the best default values based on utility size. The data selection based on these categories is not visible to the user. GREAT Methodology Guide 22 ------- 4. Estimate default values for each consequence level boundary. The last step was to estimate the loss that would exceed the thresholds specified above. These values become the new upper and lower bounds for the individual GREAT consequence levels, from Low to Very High: Environmental Losst = (% of Historical Compliance Costs); * Baseline Compliance Costs Table 7. Per Capita Historical Regulatory Compliance Cost Outlays by System Ownership from CWSS (2009) Population Served (bins) 100 or Less 101-500 501-3,300 3,301 - 10,000 10,001-50,000 50,001 - 100,000 100,001-500,000 Over 500,000 Per capita Historical Cost Outlay Public Systems $212.02 $11.57 $21.64 $9.54 $6.31 $10.78 $6.66 $5.02 Private Systems $20.31 $46.58 $5.28 $36.55 $0.47 $10.78* $11.01 $5.02* * Value based on public system data due to missing data for this population bin Public Health Consequence Assessment In GREAT, public health impacts are evaluated in terms of the number of fatalities and injuries expected or used in ranking the effectiveness of different adaptation plans. This quantitative approach to public health impacts is based on the estimate of person-related fatalities or injuries for each asset/threat pair. GREAT assists the user by providing default values the Value of a Statistical Life (VSL),30 which is the value attributed to each fatality assessed due to the occurrence of a threat to a particular asset, and the Value of a Statistical Injury (VSI),31 or the value attributed to each injury assessed due to the occurrence of a threat to a particular asset The tool uses the following calculation to monetize public health consequences: Public Health Impact = (# fatalities * VSL) + (# injuries * VSI] While GREAT provides default values for VSL and VSI that can be used in these calculations, users may edit these values if they choose. When monetized, the public health impacts are added to the economic impacts calculated based on the selected levels of consequence across all the categories used in the risk assessment. For those users who do not wish to monetize public health consequences, they can consider public health impacts by ranking their adaptation plans on a descriptive impact scale. 30 VSL of $7,900,000 is in 2008 dollars and based on EPA's "Guidelines for Preparing Economic Analyses," dated December 17,2010 and updated in May 2014. This value was recommended to be used in all benefits analyses that seek to quantify mortality risk reduction benefits regardless of the age, income or other characteristics of the affected population. This value is based on a distribution fitted to twenty-six published VSL estimates that EPA reviewed. si VSI of $79,000 based on 1% of the default VSL. This fraction of the VSL was selected based on the range of possible values and injuries characterized in the "Department of Transportation. Revised Departmental Guidance 2013: Treatment of the Value of Preventing Fatalities and Injuries in Preparing Economic Analyses" and literature cited therein for the severity of injuries that would characterize those for water sector asset loss and damage. GREAT Methodology Guide 23 ------- Chapter 5. Assets and Adaptation Planning After considering consequence criteria, GREAT guides users to identify at-risk assets from each previously defined threat. Users are encouraged to focus on these critical assets rather than attempt to define all of their assets. GREAT also provides an opportunity to review adaptation options that may protect vulnerable assets, as well as the ability to consider the potential cost of implementing these adaptation options. Asset Identification and Assignment Users can choose from assets provided in a GREAT library or identify custom assets. As users define their assets, they have the opportunity to identify those that are critical for the risk assessment. In GREAT, critical assets are those assets that have the potential for loss from damage or destruction due to the occurrence of threats. In some cases, critical status could be influenced by asset location, elevation, age or may simply be based on the user's historical knowledge and experience. Asset definition includes a description and assignment of relevant threats. This selection is the basis for asset/threat pairs in GREAT. An asset/threat pair is the unit of analysis for a climate change risk assessment; the focus is on the consequences to the asset if the threat were to occur across a number of scenarios. Users are prompted to consider whether all consequence categories apply to each asset included in their assessment For example, if the user selected a pump station as a critical asset, they may only be concerned about potential utility business impacts and utility equipment damage. Only those categories selected for an asset will be available during the risk assessment. Adaptation Plan Selection and Use in Assessments Adaptation plans may be designed to protect specific assets, meet utility goals for resilience and sustainability or address specific threats or vulnerabilities. Typically, these plans are composed of various strategies capable of reducing risk associated with climate-related or other threats. Users begin their adaptation planning by identifying existing adaptive measures either from the GREAT Adaptation Library or by defining custom adaptive measures. Existing adaptive measures are actions or strategies a utility has already implemented to protect critical assets. A Current Measures plan is generated within the tool for users and includes all of the existing adaptive measures they identified and defined. This plan represents the current capacity of a utility to address threat-related impacts today without any further action being taken or strategies being implemented. The Current Measures plan is used as part of risk assessment for comparison with the same results following the implementation of adaptation plans. The process of selecting and defining adaptive measures is repeated for potential adaptive measures, which are those measures being considered for future implementation as part of adaptation plans. Some potential adaptive measures can be defined by improving existing adaptive measures already entered into GREAT. The ability to improve current capabilities reflects the practice of identifying opportunities to incrementally improve protection rather than develop new projects to adapt to climate change. For each measure, cost data and threat relevance must be entered to support calculations following the risk assessment Cost of a measure is defined either as a monetary range or as a single value depending on the user's preference. To assist users in gauging the potential cost of implementation, GREAT provides default unit-costs for certain adaptive measures within the GREAT library (Table 8). Unit-cost values refer to the cost associated with implementing a specific adaptive measure, GREAT Methodology Guide 24 ------- such as the amount it would cost for each kilowatt of capacity of back-up power or the cost of a gallon of storage. Default unit cost values for each measure were developed using data from publicly available sources, such as EPA, the Federal Emergency Management Agency and RSMeans,32 including available case-study reports for projects implemented at utilities.33 Users can choose to adopt the default ranges or provide their own estimated cost. Table 8. Default Costs for Selected Adaptive Measures in GREAT Adaptation Library Adaptive Measure Default Unit-Cost Range Repair /Retrofit Leakage reduction Sewage separation Silt removal $100 to $200 per acre-foot reduced $240 to $300 per linear feet of pipe being separated $5 to $20 per cubic yard removed Construct Back-up power Levee Sea wall Temporary flood barrier $250 to $800 per kilowatt of capacity $80 to $220 per linear foot $350 to $760 per linear foot $63 to $750 per linear foot New Supplies and Demand Management Demand management Desalination - inland Desalination - seawater Groundwater / aquifer recharge with possible conjunctive use Increased storage Municipal water reuse system - non- potable Municipal water reuse system - potable Rainwater collection / use - rain barrels $465 to $980 per acre-foot $375 to $1,290 per acre-foot $1,600 to $3,250 per acre-foot $90 to $1,100 per acre-foot $0.005 to $4 per gallon of storage $300 to $2,000 per acre-foot $800 to $2,000 per acre-foot $70 to $300 per residential rain barrel system (or household) Green infrastructure Bioretention facilities Green roofs Permeable pavement $7 to $26 per square foot of bioretention infrastructure $8 to $40 per square foot of green roof $10 to $22 per square foot of permeable pavement Ecosystem / Land Use Fire management Wetlands for flood protection $660 to $1,500 per acre treated $4,700 to $154,300 per acre-foot of stormwater captured 32 For more information, visit: http://www.rsmeans.com/ 33 See subsection (adaptive measure cost sources) in Chapter 7, References, for the sources of cost estimates. GREAT Methodology Guide 25 ------- The default range presented for an adaptive measure generally reflects a range of approaches for implementing the measure. When default unit-costs are available for a selected adaptive measure, the user will be prompted to define the number of units needed to implement the adaptive measure. This approach enables GREAT to scale the default cost values according to specific conditions or criteria, rather than using a one-size-fits-all costing approach. In addition to defining costs, users also select threat relevance for each measure. For example, some adaptive measures, such as a sea wall, have a high capacity to deal with a threat like coastal flooding but may not be relevant to other threats like drought. By default, adaptive measures are "Relevant" to all threats and users can either accept this default setting or switch any of them to "Not Relevant" Users develop adaptation plans by grouping their potential measures together. GREAT calculates a total cost based on the cost of all included measures and indicates the relevance to threats for each plan based on user-entered relevance for the included adaptive measures. If a selected measure for a plan is relevant to a threat, then the plan is also relevant to the same threat Users are encouraged to review these relevance results to ensure plans apply to all their threats of concern and that no gaps remain when all plans are defined. For streamlined users, GREAT assembles an "All Potential Measures" plan that contains all potential measures defined in this module for consideration in risk assessment GREAT Methodology Guide 26 ------- Chapter 6. Risk Assessment and Results GREAT guides the user through an assessment of risk for each asset/threat pair across all defined scenarios. Each assessment considers the implementation of a specific adaptation plan; these results can be compared with the results from the Current Measures plan, or a "no-action" alternative, where no potential adaptive measures are implemented. Figure 8 depicts the risk reduction that can be achieved through the implementation of adaptation strategies. Monetized risk reduction (MRR) is the change in assessed risk based on the increased capabilities of assets to withstand impacts of threats, following the implementation of an adaptation plan. Results from the implementation of each adaptation plan, compared to Current Measures, can help to inform adaptation planning and decision-making. Current risk profile Future risk profile Reduced risk profile Monetized Risk($)or Total Impact Without Adaptation Monetized Risk Reduction Current Conditions (today's climate) Projected Climate Scenario (2035 or 2060} Figure 8. GREAT Results Showing Monetized Risk Reduction GREAT provides MRR from assessments to support adaptation planning decisions and characterization of current and potential risk to utility assets and resources. Ideally, a risk assessment would consider three components: 1. Consequences: GREAT focuses on the assessment of monetary consequences for each scenario with Current Measures and the adjustment of these consequences when considering the implementation of potential adaptation plans; 2. Vulnerability: Vulnerability refers to the degree to which assets are susceptible to, and unable to cope with, adverse impacts. GREAT does not directly support the ability to consider how adaptation may reduce asset vulnerability; and 3. Likelihood: In GREAT, users consider threats as if they were 100% likely to occur in the given time period. The tool provides an option to explore the effect of scenario likelihood on risk reduction to potentially further inform adaptation planning and decision-making. Consequence Assessment Process To assess risk, GREAT walks the user through an assessment of the consequences following implementation of each adaptation plan for all scenarios as described below: GREAT Methodology Guide 27 ------- Each assessment begins with the Current Measures plan to establish current risk in the Baseline Scenario and the potential risk if no additional adaptation actions are implemented; The user selects a level of consequence in each category relevant to the asset and for each scenario where the threat is defined. GREAT retrieves the monetary value ranges for each assessed level from the consequences matrix; and Then this assessment is repeated for each plan, where each consequence level assessed for the plan is either the same or reduced when compared to the same assessment with only Current Measures in place. The final outputs from GREAT are based on a standard risk assessment process where consequences are assessed as monetary impacts. The sum of these impacts for a specific asset/threat pair, including public health impacts, provides a measure of risk, expressed as a range from minimum to maximum overall impact: Minimum Overall Impact = ^l(MinImpactEconomic consequence Categories) + ImPact Public Health Maximum Overall Impact = ^(Max Impact Economic consequence Categories) + I rnPac^ public Health Risk Assessment Results The difference between the consequences following implementation of an adaptation plan and the consequences without adaptation is reported as MRR in GREAT. This reduction could be considered as a benefit from adapting that can be directly compared to the cost of implementing the plan. GREAT calculates the MRR by summing the difference in consequence level in each category, rather than the difference in the overall consequence.34 Therefore, the MRR for each category is calculated as follows: Monetized Risk Reduction = (Max ImpactPL category - Min Impact CMCategory ) to (Max Impact CM :Category - Min ImpactpL>Category} , where the risk based on Current Measures in place for this consequence category is the range: ( Min Impact cM,categoiyto Max Impact cM.category} and the risk following implementation of an Adaptation Plan for the same category is the range: ( Min Impact PL,Category tO MaX Impact PL,Category ) The sum of these reductions provides the final result for the risk reduction attributable to the adaptation plan for a single asset/threat pair: Min Risk Reduction = ^(Min Monetized Risk ReductionConsequence categories} Max Risk Reduction = ^(Max Monetized Risk ReductionConsequence categories} 1 See example calculations in Appendix. GREAT Methodology Guide 28 ------- Finally, all of these ranges are summed for all asset/threat pairs to provide the total risk reduction that can be achieved; these results can be filtered within GREAT to focus on a specific scenario, asset or adaptation plan. As the assessments are completed, the results dashboard is updated to provide users with tabular and graphical comparisons of overall results: Monetized risk with Current Measures; Monetized risk with the Adaptation Plan implemented; Monetized risk reduction; Adaptation Plan cost; and Public health impacts for both Current Measures and the selected Adaptation Plan. Scenario Likelihood Sensitivity Analysis Up to this point, users have considered threats as if they were 100% likely to occur in the given time period. This assumption allows the risk assessment to be more straightforward and helps prevent difficulties among users that are unfamiliar with the process of assessing likelihood or are unable to determine likelihood for any or all scenarios. Once the risk assessment has been completed, the user is provided with an opportunity to review the data and consider how different likelihood values may influence their decisions. Each adaptation plan has a cost for implementation and a range of MRR for each scenario. When the risk reduction for a conditional threat is less than the implementation cost of a plan, the user can clearly see that the plan does not provide a return on investment that supports an implementation decision. Alternatively, MRR in excess of the implementation cost would indicate that the benefit of taking action would exceed the cost for some range of scenario likelihood. GREAT calculates three ranges of scenario likelihood where the comparison of cost with risk reduction would support different decisions: Wait and See: The range of implementation cost of the selected plan exceeds the entire range of possible risk reduction for the threats in the selected scenario. Based on the current assessment, there would be a negative return on investment. It is possible that based on additional experience and improved data, a later assessment may reduce this range of likelihood and support implementation; Consider Implementing Plan: The range of implementation cost of the selected plan overlap with the range of possible risk reduction for the threats in the selected scenario. Based on the current assessment, there would be an uncertain return on investment. Consider additional benefits from implementing this plan or return to conduct another assessment to support the decision regarding implementation of this plan; and Implement Plan: The entire range of implementation costs of this selected plan is below the entire range of possible risk reduction for the threats in the selected scenario. Based on the current assessment, there would be a positive return on investment. The MRR alone provides adequate benefit to support the decision regarding implementation of this plan. Plan Comparison In the final step of the tool, GREAT provides a table of adaptation plans that were considered during the risk assessment. Users are asked to consider additional impacts for the adaptation plans that were not considered as part of the consequences assessment earlier in this module. These impacts GREAT Methodology Guide 29 ------- may relate to or influence utility planning priorities, such as energy and socioeconomic impacts. Each impact is rated as a change relative to the Current Measures plan where no new actions are taken. Energy impacts reflect the net change in energy use due to adaptation and plans may be rated as Energy Saving, Neutral, or increasing energy use to a Low, Medium or High degree. Socioeconomic impacts are rated on a similar scale, with the potential to recognize plans that are beneficial versus those that may impact public or ecosystem services. At this point, users also revisit consequence categories that they previously deferred for consideration. Plan reports detailing the results of the assessment are available for download as well. These reports are the final output from GREAT and are designed to support adaptation planning based on assessment results. GREAT Methodology Guide 30 ------- Chapter 7. References Alexander, L.V., X. Zhang, T.C. Peterson, etal. 2006. Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research, Vol. Ill, D05109, doi: 10:1029/2005JD006290, 22 pp. American Water Works Association (AWWA). 2006. Climate Change and Water Resources: A Primer for Municipal Water Providers. K. Miller and D. Yates, National Center for Atmospheric Research (NCAR), AWWA Research Foundation Report #91120. American Water Works Association (AWWA). 2015. Benchmarking Performance Indicators for Water and Waste water Utilities 2013, Survey Data and Analyses Report. Brekke, L.D., J.E. Kiang, J.R. Olsen, et al. 2009. Climate Change and Water Resources Management - A Federal Perspective. Reston, VA: U.S. Department of the Interior, U.S. Geological Survey Circular 1331, 65 pp. Available online at: http://pubs.usgs.gov/circ/1331/ Bureau of Reclamation. 2008. Sensitivity of Future CVP/SWP operations to potential climate change and associated sea level rise. Appendix R in CVP/SWP OCAP Biological Assessment Bureau of Reclamation, U.S. Department of the Interior. Carter, T. R. 2007. General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment Task Group on Data and Scenario Support for Impact and Climate Assessment (TGICA), Intergovernmental Panel on Climate Change. Version 2, June 2007. Climate Change Science Program (CCSP). 2008. Our changing planet - The U.S. Climate Change Science Program for Fiscal Year 2008. Climate Change Science Program and Subcommittee on Global Climate Change. Food and Agriculture Organization of the United Nations (FAO). 2011. Climate change, water and food security. Available online at: http://www.fao.org/docrep/014/i2096e/i2096e.pdf Fowler, H. J., S. Blenkinsop and C. Tebaldi. 2007. Review: Linking climate change modeling to impacts studies - Recent advances in downscaling techniques for hydrological modeling. International Journal of Climatology 27: 1547-1578. Groisman, P. Ya., R.W. Knight, D.R. Easterling, et al. 2005. Trends in Intense Precipitation in the Climate Record. Journal of Climate 18: 1326-1350. Hansen, J. E. 2007. Scientific reticence and sea level rise. Environ. Res. Lett. 2, doi: 10.1088/1748- 9326/2/2/024002, 6pp. Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Solomon, S., D. Qin, M. Manning, etal., eds.). Cambridge University Press, Cambridge, UK and New York, NY, USA, 996 pp. Maurer, E. P. 2007. Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California, under two emissions scenarios, Climatic Change, 82(3-4), 309-325; DOI:10.1007/sl0584-006-9180-9. Maurer, E.P., AW. Wood, J.C. Adam, et al. 2002. A long-term hydrologically-based data set of land surface fluxes and states for the coterminous United States. Journal of Climate 15: 3237-3251. GREAT Methodology Guide 31 ------- Milly, P.C.D., K.A Dunne and A.V. Veccia. 2005. Global pattern of trends in streamflow and water availability in a changing climate. Nature 438: 347-350. National Association of Clean Water Agencies and American Metropolitan Water Authority. 2009. Confronting Climate Change: An Early Analysis of Water and Wastewater Adaptation Costs. Available online at: http://www.amwa.net/galleries/climate- change/ConfrontingClimateChangeOct09.pdf National Climatic Data Center (NCDC). 2002. Daily documentation for dataset 9101, global daily climatology network, version 1.0. 26 pp. Available online at: https://data.noaa.gov/dataset/global-historical-climatology-network-daily-ghcn-daily-version-2- version-superseded/resource/9c6c785d-f562-4a8b-a24a-67212633d683 NOAA. 2013. Estimating Vertical Lane Motion from Long-Term Tide Gauge Records. Technical Report NOS CO-OPS 065 Silver Spring, Maryland. Table 1: NOAA Tide Station Relative Sea Level Trends and Estimated Rates of Vertical Land Movement. Parris, A., P. Bromirski, V. Burkett, D. Cayan, M. Culver, J. Hall, R. Horton, K. Knuuti, R. Moss, J. Obeysekera, A. Sallenger and J. Weiss. 2012. Global Sea Level Rise Scenarios for the United States National Climate Assessment. NOAA Tech Memo OAR CPO-1, 37 pp., National Oceanic and Atmospheric Administration, Silver Spring, MD. Available online at: http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf Rahmstorf, S. 2007. A Semi-Empirical Approach to Projecting Future Sea level Rise. Science 315(5810), 368-370. Randall, D.A., R.A. Wood, S. Bony, etal. 2007. Climate Models and Their Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, et al. (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. Slack, J.R., A.L. Lumb and J.M. Landwehr. 1993. Hydro-Climatic Data Network (HCDN): Streamflow Data Set, 1874-1988. USGS Water Resources Investigations Report 93-4076. Available online at: http://pubs.usgs.gov/wri/wri934076/lst_page.html U.S. Environmental Protection Agency (EPA). 2009. 2006 Community Water System Survey, Volume II: Detailed Tables and Survey Methodology. EPA 815-R-09-002. U.S. Department of the Interior, Bureau of Reclamation, Technical Memorandum 86-68210-2010- 01, Climate Change and Hydrology Scenarios for Oklahoma Yield Studies. U.S. Global Change Research Program. 2009. Global Climate Change Impacts in the United States. (Karl, T. R., J. M. Melillo and T. C. Peterson, Eds.) ISBN 978-0-521-14407-0. Water UK (2007) A Climate Change Adaptation Approach for Asset Management Planning. Available online at: http://www.water.org.uk/home/policy/climate-change/cc-update/asset-mgt- planning-tool Water Utility Climate Alliance (WUCA). 2010. Evaluating Decision Support Methods for Incorporating Climate Change Uncertainties into Water Planning. Wigley T.M.L. 2008. "Model for the Assessment of Greenhouse-gas Induced Climate Change/SCENGEN 5.3." Boulder, Colorado: National Center for Atmospheric Research. Available online at: http://www.cgd.ucar.edu/cas/wigley/magicc/ GREAT Methodology Guide 32 ------- Wood, A.W., L.R. Leung, V. Sridhar and D.P. Lettenmaier. 2004. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change 15: 189-216. Woodhouse, C.A. andJ.J. Lucas. 2006. Multi-century tree-ring reconstructions of Colorado streamflow for water resource planning. Climatic Change 78: 293-315. World Climate Research Programme. 2013. Coupled Model Intercomparison Project, Phase 5 (CMIP5). Available online at: http://cmip-pcmdi.llnl.gov/cmip5/ Xu, C.-Y. and V.P. Singh. 2004. Review on Regional Water Resources Assessment Models Under Stationary and Changing Climate. Water Resources Management 18(6): 591-612. Zwally H.J., Abdalati W., Herring T., et al. 2002. Surface melt-induced acceleration of Greenland ice- sheet flow Science 297 218-2. Apex Green Roofs. Technical Info FAQ. Available online at: http://www.apexgreenroofs.com/faq/ Arroyo, J. and Shirazi, S. 2012. Innovative Water Technologies, Texas Water Development Board. Cost of Brackish Groundwater Desalination in Texas. Available online at: http://www. twdb.texas.gov/innovativewate r/desal/doc/Cost_of_Desalination_in_Texas_rev.pdf Barr Engineering Company. 2011. Best Management Practices Construction Costs, Maintenance Costs, and Land Requirements. Prepared for Minnesota Pollution Control Agency. Available online at: http://www.pca.state.mn.us/index.php/view-document.html?gid=17134 California Coastal Commission. 2015. Comments on Independent Scientific and Technical Advisory Panel Draft Phase 2 Report on Feasibility of Alternative Intake Designs for Proposed Huntington Beach Desalination Facility. Available online at: http://documents.coastal.ca.gov/assets/press- releases/huntington-beach-desal/CCC- Poseidon_ISTAP_Draft_Phase_2_Report_for_Public_Review_8-14-15.pdf CH2M Hill, Inc. 2011. Green Infrastructure Plan, prepared for the City of Lancaster, PA. Available online at: http://cityoflancaste rpa.com/sites/default/files/documents/cityo flancaster_giplan_fullreport_ap ril2011_final_0.pdf CH2M Hill, Inc. 2011. Green Infrastructure Plan: Appendix A, Green Infrastructure Technology Fact Sheets, prepared for the City of Lancaster, PA. Available online at: http://www.dcnr.state.pa.us/cs/groups/public/documents/document/dcnr_004822.pdf CTC & Associates LLC. 2012. Comparison of Permeable Pavement Types: Hydrology, Design, Installation, Maintenance and Cost Prepared for the Wisconsin Department of Transportation, Southeast Region. Available online at: http://ntl.bts.gov/lib/43000/43500/43570/TSR-2011- permeable-pavements.pdf DC Green Works. Green Roof Toolkit 2009. Available online at: http://dcgreenworks.org/wp- content/uploads/2012/08/2009.05.04_Green_Roof_Toolkitpdf Delta Institute. 2015. Green Infrastructure Designs: Bioswale/Hybrid Ditch. Available online at: http://delta-institute.org/delta/wp-content/uploads/GI-Toolkit-Bioswale-Section.pdf Federal Emergency Management Agency (FEMA). 2007. Selecting Appropriate Mitigation Measures for Floodprone Structures. Available online at: http://www.fema.gov/media- library/assets/documents/10618 GREAT Methodology Guide 33 ------- Federal Emergency Management Agency (FEMA). 2007. Selecting Appropriate Mitigation Measures for Floodprone Structures. Available online at: http://www.fema.gov/media- library/assets/documents/10618 Jaber, F., Woodson, D., LaChance, C., York, C. 2013. Texas A&M AgriLife Extension, Stormwater Management: Rain Gardens. Available online at: http://water.tamu.edu/files/2013/02/stormwater-management-rain-gardens.pdf Low Impact Development (LID) Center. Urban Design Tools, Low Impact Development: Rain Barrels and Cisterns. http://www.lid-stormwater.net/raincist_cost.htm Mason, L., Lippke, B., Oneil, E. 2007. Future of Washington's Forest and Forest Industries Study, Discussion Paper 10: Benefits/Avoided Costs of Reducing Fire Risk on Eastside, Final Report Available online at: https://www.ruraltech.org/projects/fwaf/final_report/pdfs/16_Discussion_Paper_10.pdf Mitchell, D. 2012. Review of Unit Cost Ranges for CWF Water Efficiency Strategies. Technical Memorandum prepared by M-Cubed for Mike Wyatt, California Water Foundation, Oakland, CA. Northeast Ohio Regional Sewer District (NEORSD). 2012. Green Infrastructure Plan. Available online at: https://www.neorsd.org/I_Library.php?a=download_file&LIBRARY_RECORD_ID=5526 Pacific Institute, Cooley, H. and N. Ajami. 2012. Key Issues for Desalination in California: Cost and Financing. Available online at: http://pacinst.org/publication/costs-and-financing-of-seawater- desalination-in-california/ Raucher, B. and Tchobanoglous, G. 2014. The Opportunities and Economics of Direct Potable Reuse. WateReuse Research Foundation. Available online at: https://www.watereuse.org/watereuse- research/the-opportunities-and-economics-of-direct-potable-reuse/ Rhode, Melissa. 2014. Stanford Woods Institute for the Environment. Water in the West, Understanding California's Groundwater: Recharge. http://waterinthewest.stanford.edu/groundwater/recharge/ RS Means. 2014. Facilities Construction Cost Data, 29th Annual Edition. Division 3316 Water Utility Storage Tanks. Norwell, MA: Reed Construction Data. RS Means. 2009. Assemblies Cost Data. Division 5090 210. Norwell, MA: Reed Construction Data. State of California, Department of Water Resources (CADWR). Water Audit and Leak Detection. http://www.water.ca.gov/wateruseefficiency/leak/ The Challenge for Sustainability. Temporary Flood Barrier, Inside the Floodplain. Boston, MA. http://challengeforsustainability.org/resiliency-toolkit/temporary-flood-barrier/ The Nature Conservancy, Rio Grande Water Fund (RGWF). 2014. Comprehensive Plan for Wildfire and Water Source Protection. Available online at: http://www.nmconservation.org/RGWF/RGWF_CompPlan.pdf U.S. Environmental Protection Agency (EPA). 1999. Combined Sewer Overflow Management Fact Sheet Sewer Separation. EPA 832-F-99-041. Available online at: https://www3.epa.gov/npdes/pubs/sepa.pdf U.S. Environmental Protection Agency (EPA). 2015. Water Best Management Practices: Stormwater Wetland. http://water.epa.gov/polwaste/npdes/swbmp/Stormwater-Wetland.cfm GREAT Methodology Guide 34 ------- Appendix Models Used in Developing Climate Data Model Name (Year) ACCESS1 0 ACCESS1-3 BCC-CSM1 1 BCC CSM1 1 M BNU ESM CANESM2 CCSM4 CESM1_BGC CESM1_CAM5 CMCC_CM CMCC CMS CNRM_CM5 CSIRO_Mk_3_6 EC EARTH FGOALS_G2 FGOALS_S2 GFDL_CM3 GFDL_ESM2G GFDL_ESM2M GISS_E2_H GISS E2 H CC GISS E2 R GISS E2 R CC HADGEM2_AO HADGEM2 CC HadGEM2 ES INMCM4 IPSL_CM5A_LR IPSL_CM5A_MR IPSL_CM5B_LR MIROC_ESM MIROC_ESM_CHEM MIROC5 MPI ESM LR MPI ESM MR MRI CGCM3 NorESMl M NORESM1_ME Storm Scalars X X X X X X X X X X X X X X X X X X X X X X Source / Institution Australia, Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM) China, Beijing Climate Center, China Meteorological Administration China, College of Global Change and Earth System Science, Beijing Normal University Canada, Canadian Centre for Climate Modelling and Analysis USA, National Center for Atmospheric Research (NCAR) USA, Community Earth System Model Contributors Italy, Centra Euro-Mediterraneo per i Cambiamenti Climatici France, Centre National de Recherches Meteorologiques / Centre Europeen de Recherche et Formation Avancee en Calcul Scientifique Australia, Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence EC-EARTH consortium China, LASC, Institute of Atmospheric Physics, Chinese Academy of Sciences and CESS, Tsinghua University China, LASC, Institute of Atmospheric Physics, Chinese Academy of Sciences USA, NOAA General Fluid Dynamics Lab USA, NASA Goddard Institute for Space Studies Korea, National Institute of Meteorological research/Korea Meteorological Administration UK, Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by Institute Nacional de Pesquisas Espaciais) Russia, Institute for Numerical Mathematics France, Institute Pierre Simon Laplace Japan, Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies Germany, Max-Planck-lnstitut fur Meteorologie (Max Planck Institute for Meteorology) Japan, Meteorological Research Institute Norway, Norwegian Climate Center GREAT Methodology Guide 35 ------- Drought: Increasing temperature and changing precipitation patterns could result in lower lake and reservoir levels, as well as reduced groundwater recharge and reduced snowpack. Through evaporation and insufficient inflows following precipitation events, declines in reservoir levels would jeopardize supply and other resources dependent on sufficient inflows. Lower soil moisture, total precipitation and a greater fraction of precipitation during intense events all act to restrict percolation into aquifers to maintain the water table and well production. Changes in precipitation timing, rain rather than snow, and earlier snowmelt will change the amount and timing of water supply, as well as impact receiving water quality in downstream waterways. Default definitions for drought threats provided in GREAT are as follows: Lower lake and reservoir levels: Decreases in annual precipitation will lead to lower lake and reservoir levels that utilities rely on for surface water supplies. In addition, evaporation rates and water loss from vegetation will be higher due to increasing temperatures. These lower levels may make it difficult to meet water demands, especially in summer months and may drop water levels below intake infrastructure; Reduced groundwater recharge: Decreases in annual precipitation will decrease surface water supplies and groundwater recharge, especially impacting utilities that rely on groundwater supplies. In addition, evaporation rates and water loss from vegetation will be higher due to increasing temperatures; and Reduced snowpack: Increasing temperature and changing precipitation patterns combine to decrease the depth and extent of snowpack; often considered a reservoir of source water. Changes in precipitation timing, rain rather than snow, and earlier snowmelt will change the amount and timing of water supply, as well as impact receiving water quality in downstream waterways. Ecosystem Changes: Increasing temperature and changing precipitation patterns may shift environmental conditions in a way that alters the dominant species of vegetation or persistence of pests or disease that impact current vegetation. Shifts in biodiversity and potentially drier conditions may also increase the risks of wildfire. Water resources and facilities can be damaged by these shifts, depending on the rate of change, extent of impacted ecosystems and frequency of fire events. In addition, intense storms, coupled with rising sea level, are capable of eroding coastal landforms and compromising the flood protection and ecological value provided by them. These climate drivers may impact the inflow and retention of water in current wetlands and damage wetland vegetation through salinity changes. Storm damage and shifts in the sediment balance through erosion or accretion could change wetland coverage along a shoreline. Default definitions for ecosystem change threats provided in GREAT are as follows: Altered vegetation / wildfire risk: Increasing temperature and changing precipitation patterns can contribute to vegetation changes or persistence of pests or disease. Shifts in biodiversity and potentially drier conditions also increase the risks of wildfire. Water resources and facilities can be damaged by these shifts, depending on the rate of change, extent of impacted ecosystems and frequency of fire events; Loss of coastal landforms: Sea level rise and increasing frequency of damaging tropical storms can lead to losses of coastal and stream ecosystems. Loss of these landforms can reduce the buffer against coastal storms, which may damage coastal treatment plants and infrastructure, leading to service disruptions; and GREAT Methodology Guide 36 ------- Loss of wetlands: Increasing temperature, changing precipitation patterns and rising sea level will impact wetland habitats. These climate drivers have the potential to alter the inflow and retention of water in current wetlands and damage wetland vegetation through salinity changes. Storm damage and shifts in the sediment balance through erosion or accretion could change wetland coverage along a shoreline. Floods: Changes in precipitation patterns, particularly greater storm intensities, may generate additional floods associated with high flow events. Intense storms, coupled with rising sea level in coastal locations, are capable of generating floods associated with coastal storm surges. Several factors can influence extent and depth of flooding, requiring some knowledge of how storms generate floods under current and future sea levels. Increasing floods and high flow events are most problematic when they occur in areas with little previous experience with flooding and knowledge of connecting precipitation to potential extent and depth of flooding is limited. Default definitions for flood threats provided in GREAT are as follows: Coastal storm surges: Increases in storm frequency or intensity may increase the frequency and extent of coastal storm surges, especially when combined with sea level rise. This combination results in inundation of coastal areas, disruption of service and damage to infrastructure such as treatment plants, intake facilities, water conveyance and distribution systems, pump stations and sewer infrastructure; and High flow events: Changes in precipitation patterns, particularly greater storm intensities, may generate additional floods associated with high flow events. These flooding events may challenge current infrastructure for water management and flood control. When these protections fail, inundation may damage infrastructure such as water treatment plants, intake facilities and water conveyance and distribution systems. More extreme events can lead to combined sewer overflows and reduce the capacity of sewer systems already impacted by inflow and infiltration. Service Demand and Use: Increasing temperature and changing precipitation patterns combine to change the demand for water used in agriculture and irrigation, as well as impact the generation of and demand for energy. Increased demand for water related to agriculture and irrigation results from decreased precipitation and increased evaporative losses from soil and crops. The consumption of energy is strongly linked to seasonal temperatures, such as indoor climate control and the energy needs of water utilities. Residential demand for water, such as bathing and drinking water, is also strongly linked to seasonal temperatures. Additionally, changes in temperature and flow may have important ramifications on influent conditions, altering the effectiveness of treatment and capacity of the system, as well as challenge the ability of utilities to provide adequate wastewater and stormwater services. Each municipality needs to critically evaluate historical demand for their systems and any link to climate conditions to project changes in demand. Default definitions for service demand and use threats provided in GREAT are as follows: Changes in agricultural practice and outdoor use: Increasing temperature and changing precipitation patterns combine to increase evaporative losses from soil and crops. A change in agricultural demand could impact the ability of drinking water utilities to provide sufficient supply for their ratepayers; Changes in energy sector water needs: Increasing temperature and changing precipitation patterns combine to change the demand for water used in the generation of energy. The consumption of energy is strongly linked to seasonal temperatures and the energy needs of water utilities; GREAT Methodology Guide 37 ------- Changes in influent flow and temperature: Increasing temperature and changing precipitation patterns both alter influent conditions. Changes in temperature and flow may have important ramifications on the effectiveness of treatment and capacity of the system; and Changes in residential use: Residential demand for water is strongly linked to seasonal temperatures. Changes in future temperatures will challenge the ability of utilities to provide adequate levels of wastewater and stormwater services. Water Quality Degradation: For surface waters, water quality will be affected by increasing temperature, changing precipitation patterns and rising sea level. All drivers have the potential to degrade water quality in ways that limit or prohibit the use of the water resource as either a source or receiving water. Examples of water quality degradation include harmful algal blooms, nutrient or sediment runoff from storm events and saline intrusion into historically freshwater bodies. For coastal aquifers, both changing precipitation patterns and rising sea level have the potential to generate favorable groundwater conditions for the intrusion of saline waters into freshwater aquifers. Through time, the relative depths of saline and fresh water tables will drive the interface past wells and limit production without additional treatment or relocation of supply. Default definitions for water quality degradation threats provided in GREAT are as follows: Altered surface water quality: Surface water quality is affected by changes in temperature, precipitation patterns and the number of extreme hot days. Examples of water quality degradation include harmful algal blooms, nutrient or sediment runoff from storm events and saline intrusion into historically freshwater bodies; and Saline intrusion into aquifers: Projected sea level rise, combined with higher water demand from coastal communities, can lead to saltwater intrusion in both coastal groundwater aquifers and estuaries. This combination may reduce water quality and increase treatment costs for water treatment facilities. GREAT Methodology Guide 38 ------- Examples of Economic Consequences Matrices The default economic consequences matrix for each user includes definitions and impacts for each level within each consequence category. The definitions are the same for all users (Table 9). These definitions define the basis for the monetary impact values provided by GREAT and serve as a starting point for users to revise the levels based on their own assessment priorities. Table 9. Default Definitions for CREAT-provided Economic Consequences Matrix (all users) i i«.:i:«... Dn»:nn»» . Utility DU9IIIC99 Level . y Impacts Very High High Medium Low Long-term or significant loss of revenue or operating income Seasonal or episodic compromise of revenue or operating income Minor and short- term reductions in expected revenue Minimal potential for loss of revenue or operating income Utility Equipment Source/Receiving Environmental Damage Water Impacts Impacts Complete loss of asset Significant damage to equipment Minor damage to equipment Minimal damage to equipment Long-term compromise of source water quality or quantity Seasonal or episodic compromise of source water quality or quantity Temporary impact on source water quality or quantity No more than minimal changes to water quality Significant environmental damage - may incur regulatory action Persistent environmental damage - may incur regulatory action Short-term environmental damage, compliance can be quickly restored No impact or environmental damage The default values in the consequences matrix vary based on utility system type, population served, service volume, financial condition and ownership; this method is described in Chapter 4, Economic and Public Health Consequences. These default values provided by GREAT serve as a starting point for users to revise based on their experience and known thresholds for significant impacts from asset loss or damage. Tables 10 through Table 13 provide examples of default consequence matrices based on hypothetical utilities. Table 10. Default Economic Consequence Matrix for Drinking Water Assets of a Public Combined System Serving 25,000 Customers (5 MGD) in Good Financial Condition Very High High Medium Low ility Business Impacts Greater than $2.4M $1.6M - $2.4M $800,000 -$1.6M Up to $800,000 ftility Equipment Damage Greater than $1.6M $700,000 -$1.6M $300,000 -$700,000 Up to $300,000 rrce/Receiving ater Impacts Greater than $640,000 $270,000 - $640,000 $110,000 - $270,000 Up to $110,000 Environmental Impacts Greater than $95,000 $39,000 -$95,000 $16,000 -$39,000 Up to $16,000 GREAT Methodology Guide 39 ------- Table 11. Default Economic Consequence Matrix for Drinking Water Assets of a Public Combined System Serving 1,000,000 Customers (150 MGD) in Strong Financial Condition Level Very High High Medium Low Utility Business Utility Equipment Greater than $111.9M $74.6M-$111.9M $37.3M-$74.6M Upto$37.3M LSdllldgC Greater than $137. 1M $57.1M-$137.1M $22.9M-$57.1M Upto$22.9M Source/Receiving Water Impacts Greater than $19M $8.1M-$19M $3.2M-$8.1M Upto$3.2M Environmental Impacts Greater than $3M $1.3M-$3M $500,000 -$1.3M Up to $500,000 Table 12. Default Economic Consequence Matrix for Wastewater Assets of a Public Combined System Serving 25,000 Customers (5 MGD) in Good Financial Condition , Utility Business Level Impacts Very 1 , Greater than $2.0M High High $1.3M-$2.0M Medium Low $700,000 -$1.3M Up to $700,000 Utility Equipment Source/Receiving Damage Water Impacts Greater than $2. 1M $0.9M-$2.1M $400,000 -$0.9M Up to $400,000 Greater than $640,000 $270,000 - $640,000 $110,000 - $270,000 Up to $110,000 Environmental Impacts Greater than $95,000 $39,000 -$95,000 $16,000 -$39,000 Up to $16,000 Table 13. Default Economic Consequence Matrix for Wastewater Assets of a Public Combined System Serving 1,000,000 Customers (150 MGD) in Strong Financial Condition Level Very High High Medium Low Utility Business Utility Equipment Greater than $107.2M $71.5M-$107.2M $35.7M-$71.5M Upto$35.7M isaiiiagc Greater than $111.7M $46.5M-$111.7M $18.6M-$46.5M Upto$18.6M Source/Receiving Water Impacts Greater than $19M $8.1M-$19M $3.2M-$8.1M Upto$3.2M Environmental Impacts Greater than $3M $1.3M-$3M $500,000 -$1.3M Up to $500,000 Examples of Monetized Risk Reduction Calculation The assessment process utilizes the decisions made by users related to levels of consequences and their matrix of monetary impacts for each level within the consequence categories; this method is described in Chapter 6, Risk Assessment and Results. The following sections provide examples from two hypothetical utilities and the results based on their entries. GREAT Methodology Guide 40 ------- Combined Water Example This analysis is based on the default matrix of economic consequences, provided by GREAT, for the drinking water assets of a public combined system serving 25,000 customers (5 MGD); see Table 10 to review their consequences matrix. This user is pursuing a single asset/threat pair: loss of water in their only aquifer source, a well. For this asset, only Utility Business and Source/Receiving Water Impacts are expected. Two scenarios of the threat are being assessed: Baseline and Projected. Upon considering their current resilience, which is based on a consideration of existing measures, this user made the following assessment: Current Measures Baseline Medium $800,000 - $1.6M Projected High $1.6M-$2.4M n/a n/a Source/Receiving Water Im Low Up to $110,000 High $270,000 - $640,000 Environr n/a n/a Overall Consequence $800,000-$1.71M $1.87M-$3.04M Previously, they identified a set of potential adaptive measures that would cost $300,000 to $550,000 to implement. These measures were selected for inclusion in their adaptation plan, which they named 'DW Adaptation Plan.' Next, this user considered the levels of consequence following the implementation of the DW Adaptation Plan: Scenarios Baseline Medium $800,000 -$1.6M n/a Low Up to $110,000 Projected Medium $800,000-$1.6M n/a Low Up to $110,000 n/a n/a $800,000-$1.71M $800,000-$1.71M The overall consequence from this second assessment is the same for the Baseline Scenario and is lower than the overall impact without adaptation for the Projected Scenario. CREAT Methodology Guide 41 ------- The difference in these two assessments was calculated by GREAT using the movement of consequence level in each category, rather than the difference in the overall consequence: DW Adaptation Plan Utility Business Impac Utility Equipment Damage Source/Receiving Wat^ Environmental Impac Monetized Risk Reduc Scenarios Baseline $0 n/a $0 n/a $0 Projected $0-$1.6M n/a $160,000-$640,000 n/a $160,000-$2.24M This final range, the MRR, for the Baseline Scenario is negligible. For the Projected Scenario, the risk reduction overlaps the range of implementation cost of the DW Adaptation Plan ($300,000 to $550,000). Combined Wastewater Example This analysis is based on the default matrix of economic consequences, provided by GREAT, for the wastewater assets of a public combined system serving 1,000,000 customers (150 MGD). See Table 13 to review their consequences matrix. This user is pursuing a single asset/threat pair: flooding at their wastewater treatment plant. For this asset, only Utility Equipment and Environmental Impacts are expected. Two scenarios of the threat are being assessed: Baseline and Projected. Upon considering their current resilience, based on existing measures, this user made the following assessment: Medium $18.6M-$46.5M Projected n/a Very High Greater than $111.7M n/a n/a Low Up to $500,000 Medium $500,000-$1.3M $18.6M-$47M Greater than $112.2M They have identified a set of potential adaptive measures that would cost $10,000,000 to $20,000,000 to implement. These measures were selected for inclusion in their adaptation plan, which they named 'WW Adaptation Plan.' GREAT Methodology Guide 42 ------- Next, this user considered the levels of consequence following the implementation of the WW Adaptation Plan: WW Adaptation Plan Utility Business Impac Utility Equipment Dam Scenarios Baseline n/a Low Upto$18.6M n/a Projected n/a Low Upto$18.6M n/a Low Up to $500,000 Low Up to $500,000 Overall Consequence Up to $19.1M Up to $19.1M The overall consequence from this second assessment is lower than the overall impact without adaptation. The difference in these two assessments is calculated by GREAT using the movement of consequence level in each category, rather than the difference in the overall consequence: WW Adaptation Plan Utility Business Impacts Utility Equipment Damage Source/" Impact Scenarios Baseline Environme Monetized R Reduction $0-$46.5M $0 $0-$46.5M Projected n/a Greater than $93.1M n/a $0-$1.3M Greater than $93.1M This final range, the MRR, for both scenarios either overlaps or exceeds the implementation cost of the WW Adaptation Plan ($10,000,000 to $20,000,000). GREAT Methodology Guide 43 ------- Office of Water (4608-T) EPA 815-B-16-004 May 2016 ------- |