&EPA
United States
Environmental Protection
Agency
Climate Resilience Evaluation and Awareness Tool
Version 3.0 Methodology Guide
CLIMATE READY
WATER UTILITIES
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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
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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
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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
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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
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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
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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/
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GREAT Methodology Guide viii
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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
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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.
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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
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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/).
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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
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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
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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.
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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
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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.
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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
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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
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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:
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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
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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.
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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;
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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
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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
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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.
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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.
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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
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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
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Office of Water (4608-T) EPA 815-B-16-004 May 2016
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