United States
I" Environmental Protection
mh I m * Agency
Climate Resilience Evaluation and Awareness Tool
Version 3.1 Methodology Guide
GREAT Methodology Guide
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Disclaimer
The Climate Resilience Evaluation and Awareness Tool (CREAT) 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. CREAT 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 CREAT 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.
Changes are periodically made to the information herein that may be incorporated in new
editions of this document EPA may make improvements or changes to CREAT at any time.
Office of Water (4608-T) EPA 817-B-21-001
March 2021
CREAT Methodology Guide
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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
2.1 Framework 3
2.2 Streamlined Analysis Option 3
2.3 CREAT Reports 4
Chapter 3. Climate Awareness: Module 1 5
3.1 Climate Change Concerns in CREAT 5
Chapter 4. Scenario Development: Module 2 7
4.1 Climate Change Threats in CREAT 7
4.2 Climate Change Assessments in CREAT 8
4.3 Baseline Scenario 9
4.3.1 Historical Climate Conditions 9
4.3.2 Historical Extreme Events 10
4.3.3 Historical Streamflow 11
4.3.4 Coastal Data 11
4.4 Time Period 12
4.5 Projected Scenarios 12
4.5.1 Projected Changes in Temperature and Precipitation 13
4.5.2 Projected Extreme Events 14
4.5.3 Projected Extreme Flows 15
4.5.4 Sea Level Rise Projections 17
4.6 Threat Definition 19
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Chapter 5. Consequences and Assets: Module 3 21
5.1 Economic Consequence Categories 21
5.2 Default Economic Consequences Matrix 22
5.2.1 Utility Business Impacts 24
5.2.2 Utility Equipment Damage 26
5.2.3 Source/Receiving Water Impacts 27
5.2.4 Environmental Impacts 29
5.3 Regional Economic Consequence Assessment 30
5.4 Public Health Consequence Assessment 32
Chapter 6. Adaptation Planning: Module 4 33
6.1 Asset Identification and Assignment 33
6.2 Adaptation Plan Selection and Use in Assessments 33
Chapter 7. Risk Assessment: Module 5 36
7.1 Consequence Assessment Process 37
7.2 Risk Assessment Results 37
7.3 Scenario Likelihood Sensitivity Analysis 38
7.4 Plan Comparison 39
Chapter 8. References 40
8.1 Climate Data Sources 40
8.2 Adaptive Measure Cost Sources 42
Appendices 45
A-l: Models Used in Developing Climate Data 45
A-2: Default Threat Definitions 46
A-3: Examples of Economic Consequences Matrices 49
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List of Figures
Figure 1. CREAT 3.1 Home Screen 1
Figure 2. CREAT Module Overview 3
Figure 3. Climate Awareness Interactive Map 9
Figure 4. Illustration of Ensemble-informed Selection of Model Projections to Define Potential
Future Conditions 14
Figure 5. Illustration of Ensemble-informed Selection of Model Projections to Define Potential
Future Storm Conditions 15
Figure 6. Three Scenarios of Eustatic Sea Level Change Relative to 1992 (solid lines) and 2016
(dashed lines) 19
Figure 7. CREAT Results Showing Monetized Risk Reduction 36
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List of Tables
Table 1. Default Definitions for Consequence Category Levels Used for All System Types 23
Table 2. CREAT Financial Condition by System Type 24
Table 3. Total Operating Expenses by System Type based on AWWA (2015) Benchmark Data.25
Table 4. Debt Coverage Ratio Values for CREAT Consequence Values 26
Table 5. Baseline Cash Reserve Days by System Type from AWWA (2015) 27
Table 6. Per Capita Historical System Expansion Cost Outlays by System Ownership from CWSS
(2009) 28
Table 7. Per Capita Historical Regulatory Compliance Cost Outlays by System Ownership from
CWSS (2009) 30
Table 8. Default Costs for Selected Adaptive Measures in CREAT Adaptation Library 34
Table 9. Models Used in Developing Climate Data 45
Table 10. Default Definitions for CREAT-provided Economic Consequences Matrix (all users) .49
Table 11. Default Economic Consequence Matrix for Drinking Water Assets of a Public
Combined Water System Serving 25,000 Customers with 5 MGD Service in Good Financial
Condition 50
Table 12. Default Economic Consequence Matrix for Drinking Water Assets of a Public
Combined Water System Serving 1,000,000 Customers with 150 MGD Service in Strong
Financial Condition 50
Table 13. Default Economic Consequence Matrix for Wastewater Assets of a Public Combined
System Serving 25,000 Customers with 5 MGD Service in Good Financial Condition 50
Table 14. Default Economic Consequence Matrix for Wastewater Assets of a Public Combined
System Serving 1,000,000 Customers with 150 MGD Service in Strong Financial Condition
51
Table 15. Current Measures Assessment for Drinking Water Assets of a Public Combined System
Serving 25,000 Customers with 5 MGD Service in Good Financial Condition 51
Table 16. DW Adaptation Plan Assessment for Drinking Water Assets of a Public Combined
System Serving 25,000 Customers with 5 MGD Service in Good Financial Condition 52
Table 17. Monetized Risk Reduction for Combined Water System DW Adaptation Plan 52
Table 18. Current Measures Assessment for Wastewater Assets of a Public Combined System
Serving 1,000,000 Customers with 150 MGD Service in Strong Financial Condition 53
Table 19. WW Adaptation Plan Assessment for Wastewater Assets of a Public Combined System
Serving 1,000,000 Customers with 150 MGD Service in Strong Financial Condition 53
Table 20. Monetized Risk Reduction for Combined Water System WW Adaptation Plan 54
CREAT Methodology Guide vi
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Acronyms
A WW A - American Water Works Association
CMIP5 - Coupled Model Intercomparison Project,1 Phase 5
CREAT - 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
1 World Climate Research Programme Coupled Model Intercomparison Project, https://www.wcrp-
climate.org/wgcm-cmip
2 U.S. Environmental Protection Agency 2006 Community Water System Survey, Volume II: Detailed Tables and
Survey Methodology. EPA 815-R-09-002.
3 National Climate Assessment, https://www.globalchange.gov/what-we-do/assessment
4 PRISM Climate Group, Oregon State University, https://www.prism.oregonstate.edu
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Chapter 1. Background
The U.S. Environmental Protection Agency (EPA] developed the Climate Resilience Evaluation and
Awareness Tool (CREAT) 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. CREAT was developed under EPA's Creating Resilient Water Utilities
initiative.
CREAT 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. CREAT (Figure 1) leverages the most current scientific information
available at the time of development. Data provided within CREAT are updated and augmented, as
appropriate.
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Study and Information Exchange Map Thismap provides links to brief stories*# planning efforts being
twiducleti by vvaLeryLiliLiesacioss (he Uni.ed Stales.1 hess JliliLies haveshaied tfer expenencesaiid
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Figure 1. CREAT 3.1 Home Screen
The results generated by CREAT 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 and can be revisited for future risk analyses. The fundamental goals of CREAT are to:
I In CREAT, 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.
CREAT Methodology Guide
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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;
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
2.1 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 GREAT modules align with the overall workflow of the
application and the chapters of this guide.
O CLIMATE
W AWARENESS
MODULE 1:
Input basic utility information and review a regional map for
building climate awareness. Chapter3
SCENARIO
-O^J DEVELOPMENT
MODULE 2:
Select and define threat scenarios based on available climate data
at your location. Chapter4
CONSEQUENCES
& ASSETS
MODULE 3:
Review economic values provided based on your utility location.
Define critical assets that provide value to your system. Chapter 5
ADAPTATION
PLANNING
7^1 RISK
-r1 ASSESSMENT
MODULE 4:
Define adaptation plans that include potential measures that
would reduce consequences of threats. Chapter 6
MODULE 5:
Select economic consequence levels for each asset/threat pair
and review risk assessments. Chapter 7
Figure 2. GREAT Module Overview
2.2 Streamlined Analysis Option
GREAT offers a streamlined analysis option that guides decisions for the analysis, provides default
values, and requires only basic data entry. This workflow allows users to progress through CREAT
quickly by reducing the scope of analysis and focusing on priority concerns. Selecting the
streamlined option can help users to become familiar with the risk assessment process before
conducting more in-depth analyses.
With the streamlined path, users still must proceed through the Climate Awareness Module for
basic utility information entry and views additional material on climate change and current
concerns for awareness purposes. One default threat and one 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, CREAT will define
CREAT Methodology Guide
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one adaptation plan including all potential adaptive measures previously entered for consideration
during risk assessment.
Given that the number of assessments increases if 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. The streamlined option in CREAT produces a more
focused assessment requiring fewer inputs. The outputs describe a concise and focused result for
users who are in the early stages of risk assessment and adaptation.
2,3 CREAT 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 in the
assessment;
The Consequences and Assets Report includes the economic consequences matrix, a list of the
assets defined, and summary information on the regional economic and public health
consequences (if included); and
The Adaptation Planning Report details each adaptation plan with the cost of each adaptive
measure included in the plans.
The 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. The reports help to build confidence that the utility is being proactive or identifying areas
where additional funding may be needed to bolster climate readiness.
The final report generated in the Risk Assessment module is the Plan Report, which includes the
results of the risk assessment for each specific adaptation plan. 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 Awareness: Module 1
This module begins the risk assessment process with a review of climate science and climate
change impacts. Users first identify the utility location6 for their assessment, as well as basic utility
information, including population served, total flow, and financial condition. The financial condition
indicates the utility's strength to endure operating revenue loss or capacity by expending funds to
repair and replace equipment Financial condition can be based on debt coverage and operating
ratios. CREAT also requires 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.
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 the Climate
Awareness module, users identify these concerns, which help organize information to identify
climate change threats, as well as assets7 to consider during the assessment
3,1 Climate Change Concerns in CREAT
CREAT provides climate change information to help identify the utility's current concerns and
consider how these concerns may be exacerbated as a result of a changing climate. The process is
designed to help organize information and identify the threats and assets to consider in the risk
assessment.
Current concerns available in CREAT are related to potential threats that can be defined and
assessed using CREAT, which are as follows:
Water Supply Management: drought, seasonal demand, snowpack, reservoir storage, and low
streamflow conditions;
Peak Service Challenges: stormwater runoff, seasonal demand, and discharge under low
receiving water flow conditions;
Water Quality Management: runoff, treatment, violations, saltwater intrusion, source water
turbidity, and algal blooms;
Natural Disasters: fires, floods, tornadoes, and ice storms;
6 CREAT provides climate data, such as temperature, precipitation, and surface water flow data, for the analysis location
selected. Coastal data including vertical land movement, sea level rise, and number of days with tidal flooding is also
provided for coastal locations, which are those near tidal water bodies.
7 In CREAT, 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.
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Ecosystem/Landscape Management: coastal erosion, wetland loss, and endangered species
protection;
Population/Demographic Changes: customer base, land use, and workforce availability;
Sector Water/Service Needs: agriculture, energy sector, health services, and local industries;
Interdependent Sector Reliability: power sector, transportation, and chemical suppliers; and
Sea Level Rise (SLR): saltwater intrusion, and coastal storm surge.
These concerns are assessed based on an understanding of climate change and other projected
trends that may impact utility operations or infrastructure. CREAT provides climate data for use in
prioritizing these concerns and defining related threats in the risk assessment process.
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Chapter 4. Scenario Development: Module 2
This module assists users consider CREAT-provided historical and projected climate data as
scenarios8 that represent a range of possible future climate conditions and the potential threats
these conditions could generate. CREAT provides default threat selections9 based on the current
concerns identified in the Climate Awareness module.
To explore and assess their current risk, users establish a Baseline Scenario for planning decisions
and other assessments, by including historical data provided within CREAT or custom data records.
CREAT is flexible in its approach and users can replace CREAT provided data with custom values.
Historical data provided by CREAT include:
average annual and monthly temperature;
average number of days exceeding 90, 95, and 100 degrees Fahrenheit in a year;
total annual and monthly precipitation;
storm precipitation totals over 24 hours and 72 hours for several event return intervals;
streamflow measures for mean, minimum, and maximum flow conditions; and
coastal data for vertical land movement and the number of days with tidal flooding for several
increments of sea level rise.
If available, custom data measurements may also be added by users to track additional conditions
such as population trends, alternate temperature or precipitation thresholds, or other metrics.
Once a Baseline Scenario has been established, additional Projected 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, precipitation, surface water, and coastal data could change. Once selected, the
threats associated with these projections provide a range of possible conditions for consideration in
the risk assessment
In the Risk Assessment module (Module 5), the Baseline Scenario is compared to other scenarios
and is used to help identify "no regrets" options, which are options that have benefit with or
without changes in climate.
4,1 Climate Change Threats in CREAT
Threats are assessed based on an understanding of climate change and other projected trends that
may impact utility operations or infrastructure. CREAT provides climate data for use in prioritizing
these concerns and defining related threats in the risk assessment process.
8 In CREAT, scenarios refer to groups of threats that are defined by users based on available historical or projected
climate data, as well as any other relevant data, such as demand forecasts.
9 The default threats in CREAT 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.
CREAT Methodology Guide
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In the Scenario Development Module, CREAT 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.
These threats are considered starting with a Baseline Scenario consisting of climate conditions
based on historical or observed data to enable comparison of current climate conditions and
associated threats with how they could change in the future. This scenario helps utilities evaluate
their current resilience based on threat magnitudes and timing that are already used in planning
decisions and other assessments.
The climate information available in CREAT provides a snapshot of how changes in climate might
exacerbate current concerns. In addition to the national and international assessments synthesized
in CREAT, historical observations and model projections are organized for users to review and
select as part of their scenarios.
In the Scenario Development Module, users establish Baseline and Projected Scenarios, based on p
historical and projected changes in climate conditions. Scenarios are defined based on data
provided in CREAT or from the utility's 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.
4.2 Climate Change Assessments in CREAT
In the Climate Awareness Module, an interactive map (Figure 3) provides the ability to focus on
regional impacts or impacts to specific sectors with information from the most recent National
Climate Assessment10 (NCA). CREAT 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.
10 National Climate Assessment: https://www.globalchange.gov/what-we-do/assessment
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national or coastal climate impacts and learn about how climate change is expected to impact a specific sector by
clicking on the Topic Links.
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Figure 3. Climate Awareness Interactive Map
4.3 Baseline Scenario
The data used to define the Baseline Scenario should be based on event magnitudes and timing to
assist in planning decisions and other assessments, such as historical data provided within CREAT
or from records kept by the utility. Default data provided by CREAT for the Baseline Scenario
include average temperature, total precipitation, intense precipitation, extreme temperature days
(hot days), high and low streamflow, and coastal data.
In the Baseline Scenario Dashboard, users can review the default selected measurements and select
or deselect additional measurements to be included in the Baseline Scenario. Users can also add
custom data, such as natural resource and socioeconomic data, to provide a more robust Baseline
Scenario. Once the measurements are selected, users can review and choose to accept the default
data or replace default values with custom data.
4.3.1 Historical Climate Conditions
CREAT 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 Model11 (PRISM) data set based
11 PRISM Climate Group, Oregon State University, https://www.prism.oregonstate.edu
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on observations from 1981 to 2010. Data available from the Climate Research Unit12 are used in
places where PRISM data were unavailable, such as in Alaska, Hawaii, and Puerto Rico. The
resultant data set covers all 50 states and Puerto Rico at a 0.5-degree resolution in latitude and
longitude.
4,3,2 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.13 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.
Historical hot days, those days with daily maximum temperature exceeding 90, 95, and 100 degrees
Fahrenheit, were calculated using historical daily maximum temperature data from 8,150 stations.
These stations were selected 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 data set), zero days in the record qualified as hot days.
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 and 72-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.14 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-, ^,(7,0 = exp{-[ 1 + ^((x - ju)/ct)]a((-1) / O }, where
x is the event magnitude; ^ is the shape parameter; a is the scale parameter; and is the location
parameter. The three parameters (^ a, and |i) were used to fit the curve. The peak magnitudes of
24-hour and 72-hour rainfall events were calculated for storms with return intervals of 5,10,15,
30, 50, and 100 years.
12 University of East Anglia Climatic Research Unit; Jones, P.D.; I. Harris. (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, April 2015.
https://catalogue.ceda.ac.uk/uuid/2949a8a25b375c9e323c53f6b6cb2a3a
13 For more information on NOAA climate stations, see: https://www.ncdc.noaa.gov/data-access/land-based-station-data
14 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.
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4.3.3 Historical Streaiiiflow
Historical flow data in CREAT are from approximately 8,200 U.S. Geological Survey (USGS) stream
gaging sites across the United States with daily discharge information covering the period of record
(USGS, 2017).15 The time-series data were compiled to provide the following for each site:
start and end years of the record;
years in the record with data for at least 95% of the days ("complete" years);
possible influence of tides in flow record (these gage sites were excluded);16
average, minimum and maximum daily flows for each complete year; and
minimum 7-day flow for each complete year.
Using the annual flow metrics above, the following data are provided in CREAT at each gage:
average daily flow;
average annual minimum daily flow;
average annual maximum daily flow;
the 10th percentile of annual 7-day low flows from complete years (7Q2); and
the 50th percentile of annual 7-day low flows from complete years (7Q2).
Note that the period of record in CREAT may include more than 30 years, where data were
available, because longer periods of record are more useful for identifying infrequent extreme
conditions. Users should select the USGS gage that is most appropriate for their use. For example,
an appropriate gage could be located along the same stream reach or stream network as their
utility assets.
4.3.4 Coastal Data
CREAT provides projections of future flood frequency under various projected sea level rise
scenarios to help users assess short-term and long-term risk of coastal flooding. Projected sea level
rise and flooding scenarios are derived from models produced by the National Oceanographic and
Atmospheric Administration (NOAA) and published in a series of two reports which report sea level
rise scenarios and flood inundation frequency at select locations.
For assessing risk of coastal flooding for current global mean sea level (GMSL), NOAA employed
methods17 to account for regional considerations, such as earth's gravitation field and rotation,
shifts in oceanographic circulation, and vertical land movement (VLM), to produce relative sea level
(RSL) to compare with calculated flooding thresholds at tide gauge locations. These thresholds
were developed by NOAA to provide a national definition of coastal flooding and quantification of
15 USGS, 2017. Surface-Water Daily Data for the Nation. U.S. Geological Survey, National Water Information System
(NWIS]. Available: https://waterdata.usgs.gov/nwis/dv/?referred_module=sw
16 Gages in coastal areas that have flows influenced by tides typically have flow heading upstream as tides are rising,
resulting in negative minimum annual flow values.
17 NOAA Technical Report NOS CO-OPS 083: Global and Regional Sea Level Rise Scenarios for the United States
CREAT Methodology Guide
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flood impacts. Based on these thresholds, flood frequency was estimated using empirical (kernel)
probability estimates from 1998-2016 at gauge locations in all States and Territories, excluding
Alaska18.
Vertical land movement is the rate of land moving up or down due to several processes, such as
tectonics, subsidence, and ground water extraction. In a place where VLM is upward, local SLR is
slower than the rate of global SLR. When VLM is downward, local SLR is faster than global SLR.
CREAT includes estimates from NOAA19 using 30 to 60 years of data.
4.4 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 selected for each assessment file and
constitutes the range of years being considered for the analysis. 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. CREAT provides climate data based on the End Year of the user-defined time period to
support the climate change risk assessment
4.5 Projected Scenarios
CREAT provides projected changes from Global Climate Models20 (GCMs) as available from the
Coupled Model Intercomparison Project, Phase 5 (CMIP5),21 which are the same data used to
support the IPCC Fifth Assessment Report Data provided in CREAT 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, CREAT provides averages from model projections that
represent a range of potential future climate conditions. Generally, all models project warming but
projections for precipitation vary more widely. Users may choose to apply all, or part of the
projection data provided, along with custom data projections for climate or other parameters, to
enhance their scenarios. For example, users may want to incorporate data collected by the utility,
in-house models, projected changes in population, demand, or energy costs.
Different approaches were used to estimate changes in different climate conditions, as described
below. These differences were necessitated by the availability of data and the goal of providing
CREAT users with the range of projections to select from rather than a few scenarios narrowly
18 NOAA Technical Report NOS CO-OPS 086
19 NOAA, 2013. Estimating Vertical Lane Motion from Long-Term Tide Gauge Records. Technical Report NOS CO-OPS 065.
20 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.
21 World Climate Research Programme Coupled Model Intercomparison Project, https://www.wcrp-climate.org/wgcm-
cmip
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defined by a few models or model runs. Due to the differences in data sources across different data
types, users should not assume the conditions are linked and that a scenario represents a potential
future derived by consistent model projections. Instead, scenarios represent a combination of
potential conditions, artificially combined to present challenges that may necessitate changes to
withstand if they were to occur.
4,5,1 Projected Changes In Temperature and Precipitation
CREAT uses an ensemble-informed approach to derive meaningful choices from the results of 38
model runs22 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 4). 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;
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. CREAT 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 appropriate CREAT-provided time period is based on the End Year
defined by users on the time period page. If the End Year is 2049 or earlier, the 2035 data are
selected; otherwise, the 2060 data set is selected.
22 List of models used in analyses provided in Appendix A-l.
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Modelilhifl. protect
hatter anddr*r
conditions
Figure 4. Illustration of Ensemble-informed Selection of Model Projections to Define Potential Future Conditions
4.5.2 Projected Extreme Events
CREAT also provides projections of extreme heat in terms of total number of days exceeding 90ฐF,
95ฐF, and 100ฐF following the projected increases in temperature. The projected changes in hot
days were linked to the models selected for projected changes in average monthly 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 days exceeding 90ฐF, 95ฐF, and 100ฐF were then
calculated using the same method employed for historical hot days to generate projected number of
days exceeding 90ฐF, 95ฐF, and 100ฐF.
Similar to the development of model projections of changes in average temperatures and
precipitation, CREAT 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,23
or changes in precipitation per degree of warming, for storm events of the same return intervals as
the historical storms provided in CREAT. Each model provides a different scalar for each return
inteival 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
23 This set of spatially explicit scalars was collected in cooperation with ClimSystems: https://www.climsystems.com
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case, these models were averaged to provide two model projections available to users, as shown in
Figure 5.
Models that project
more stormy conditions
s
~
~
19
1=
Projected
Changes in
Storm
Precipitation
by 2060
13
El
HI
ID
~
\D
'Models that project less stormy
conditions
~ Individual climate model
result for this location
Models Ranked in
Increasing Change
Figure 5. 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(RI, Proj) = Intense Precip(Rl, Hist) * (l + A Intense Precip(Rl, Proj)), where
where 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. It is recommended that the same scalars be used to
estimate changes in 24- and 72-hour intense precipitation events.24 For utilities concerned with
intense precipitation, this approach will define a wider range of values for projected storm events
from the available models.
4.5.3 Projected Extreme Flows
CREAT uses projections of change in extreme low and high streamflow. The flow projections are
from downscaled climate and hydrologic modeling developed by a collaborative effort between the
24 Analysis of observations and model projections of changes in 24-hour and 72-hour intense precipitation events found
no significant difference in the observations or model projections. That is, the increase in intensity of 24-hour and 72-
hour precipitation events does not appear to be significantly different. It was concluded that it most prudent to use the
same scalars for single day and multi-day precipitation events.
A Intense Precip(RI, Proj) = Scalar(RI, Proj) * ATemp(Proj)
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U.S. Bureau of Reclamation (USBR), National Center for Atmospheric Research (NCAR), U.S. Army
Corps of Engineers (USACE), U.S. Geological Survey (USGS), and a number of universities.25 The
modeling used RCP 8.5 (the same forcing scenario as used for the extreme temperature analysis)
but used Bias-Corrected Spatially Disaggregated (BCSD)26 methodology to provide higher
resolution climate change projections than from the GCMs. These high resolutions climate
projections were translated into runoff using the Variable Infiltration Capacity (VIC) hydrologic
model,27 and then routed through a nationwide stream network28 of 57,000 stream reaches.
Five global climate models were used in this analysis:
National Center for Atmospheric Research (CCSM4);
NASA Goddard Institute for Space Studies (GISS-E2-R);
Canadian Centre for Climate Modeling and Analysis (CanESM2);
Met Office Hadley Centre (HadGEM2-ES); and
Atmosphere and Ocean Research Institute, National Institute for Environmental Studies, and
Japan Agency for Marine-Earth Science and Technology (MIROC5).
These models are the same five GCMs used in the EPA's Climate Change Impacts and Risk Analysis
(CIRA) project29 and selected with the intent of capturing a wide range of climate projections for
the continental United States.
Using the climate model data, CREAT estimates relative change for several metrics of interest to
water utilities dependent on streamflow patterns for water supply and discharge. Projections for
each metric were calculated for each stream reach, for two time periods: 2001-2030 ("Baseline")
and 2046-2075 ("Mid-Century").
CREAT provides changes in flow as a ratio of Mid-Century projections versus baseline estimates
from the model for the downstream location (node) of each stream reach for the following
variables:
Min Flow Ratio: change in average annual minimum flows in Mid-Century vs. Baseline periods;
25 Reclamation, 2014. Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections: Release of Hydrology
Projections, Comparison with preceding Information, and Summary of User Needs. U.S. Department of the Interior,
Bureau of Reclamation, Technical Services Center, Denver, Colorado. 110 pp.
26 Maurer, E. P., L. Brekke, T. Pruitt, and P. B. Duffy. 2007. "Fine-resolution climate projections enhance regional climate
change impact studies." Eos Transactions. 88(47]: 504.
27 https://vic.readthedocs.io/en/master/Overview/ModelOverview/
28 Full documentation of the raw data is available here: https://gdo-
dcp.ucllnl.org/downscaled_cmip_projections/techmemo/BCSD5HydrologyMemo.pdf
29 U.S. EPA, 2015. Climate Change in the United States: Benefits of Global Action. EPA 420-R-15-001. U.S. Environmental
Protection Agency Office of Atmospheric Programs, Washington, DC.
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Max Flow Ratio: change average annual maximum flows in Mid-Century vs. Baseline periods;
Mean Flow Ratio: change in average annual mean flows in Mid-Century vs. Baseline periods;
7Q10 Ratio: change in 7Q10 flow in Mid-Century vs. Baseline period; and
7Q2 Ratio: change in 7Q2 flow in Mid-Century vs. Baseline period.
Since only five downscaled GCM hydrologic projections were used in the Bureau of Reclamation
analysis, CREAT presents the highest and lowest model changes for each flow metric by node. For
example, CREAT presents the value from the model that simulates the largest decrease or smallest
increase in low (minimum) flow and the model that estimates the smallest decrease or largest
increase in high (maximum) flow. The high and low models for each flow metric may not be the
same models, as they are selected independently. In addition, the selection of high and low models
for the extreme flows varies with the geographic variance of the hydrologic projections. Thus,
nodes near each other might provide values from different climate models.
CREAT users should be aware that the approach used to select models for the low and high flow
analysis across the United States is a different approach from the cell-by-cell selection of models
used for other climate variables in CREAT. In those other applications, models were selected from a
larger suite of models for individual cells based on analysis of cell-by-cell climate projections. That
means the projections of changes in extreme temperature should not be combined with projections
of change in low and high flow conditions.
The projections of change in conditions can be combined with the observations to estimate how
absolute flow conditions can change near locations of interest. While the stream gage locations are
distinct from the locations of the future flow projections, the data are mapped along with stream
reaches of future projections to enable the end user to combine data from both observed and
projection data sets.
For outputs expressed as ratios, the changes in conditions should be multiplied by the appropriate
metric from the observations. In other words, if minimum flow is projected to fall by 20% (a ratio of
0.8), then the value of 0.8 should be multiplied by observed low flow values to estimate projected
low flows.
4,5,4 Sea Level Rise Projections
Global mean sea level (GMSL) scenarios by 2100 are based on specific scientific assumptions,
including future greenhouse gas emissions, ocean-atmospheric warming, and land-ice loss. The Sea
Level Rise and Coastal Flood Hazard Scenarios and Tools Interagency Task Force produced six
scenarios on a decadal frequency from 2000 to 2100 (Figure x). These six GMSL scenarios included
the following projections: Low (0.3 m), Intermediate-Low (0.5 m), Intermediate (1.0 m),
Intermediate-High (1.5 m), High (2.0 m), and Extreme (2.5 m).
These GMSL projections are then used to produce relative sea level (RSL) projections onto a 1-
degree grid for the US shoreline, as done for the Baseline Scenario with zero GMSL.
CREAT reports the results of these flood frequency estimates for approximately 100 coastal tide
gauge stations across the US. For each of these coastal gauges, CREAT presents the predicted annual
number of flood days for the following projections:
0.0m GMSL rise (baseline);
0.5m GMSL rise;
1.0m GMSL rise; and
2.0m GMSL rise.
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CREAT provides SLR projections to facilitate climate risk assessment and climate change adaptation
for coastal regions of the United States. 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
United States Army Corps of Engineers (USACE) and NOAA have developed tools that are publicly
accessible and can be used calculate local sea level in the future. SLR projections in CREAT 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 CREAT. The lowest
projection of 0.2 m, which is an extrapolation of the historical trend, was excluded since it adds
little benefit to the analysis of risk by coastal water utilities.
To estimate future sea level, CREAT uses the equation and constants provided by the NCA:
SLRiyear, 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 m by 2100, relative to 1992); and
0.0271 mm per year2 for low curve (0.5 m by 2100, relative to 1992).
Curves were calculated in5-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 etal., 2012). In CREAT, eustatic sea level change is adjusted relative to the reference year
2016 (Figure 6) by subtracting the calculated SLR, relative to 1992. Finally, if users enter a non-
zero VLM, the curve is corrected for the influence of land movement on the relative projected SLR:
SLRiyear, level) = a *Y + b(leveV) * Y2 SLR (2 016, level) VLM * (year 2016)
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Sea Level Rise (SLR) Projections with 3 Scenarios
Year
Figure 6. Three Scenarios of Eustatic Sea Level Change Relative to 1992 (solid lines) and 2016 (dashed lines)
4.6 Threat Definition
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 utility conducting a CREAT assessment. For coastal
locations, users will have the ability to select a CREAT projected value for total sea level rise
corresponding to potential scenarios of lower, moderate, or higher rate of SLR. Users may also
choose to specify a custom value for meters of SLR. 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.
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 delineate the
scenario. The threat definition includes any important aspects of the threat that would affect risk
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assessments, including historical trends, quantitative threat metrics, links to scenario data and
assumptions.
Since threat definition is often a challenging step for utilities, CREAT supports this step by
providing default threat definitions as a starting point for users.30 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 CREAT, entered into CREAT, 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.
30 List of default threat definitions is provided in Appendix A-2.
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Chapter 5. Consequences and Assets: Module 3
This module provides guidance for users to define the potential economic, environmental, regional
economic, 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. An asset/threat (A/T) 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
the user-defined scenarios.
CREAT provides an economic consequence matrix to help users make systemic decisions. This
matrix includes consequence categories, which were developed in collaboration with federal and
state partners, water associations, and utility personnel. The consequence categories in CREAT
classify the types of economic consequences that would be incurred if a threat were to impact an
asset. For each category, users can review the monetary range for each level of consequences based
on a scale from low to very high, by either accepting the default values or providing custom
monetary values. The matrix is used during risk assessment to gauge potential loss for every
combination of scenario, threat, and asset.
CREAT provides methods for assessing the impacts to a region from service interruptions; see
Section 5.3. To determine regional economic consequences, CREAT employs a method based on
EPA's Water Health and Economic Analysis Tool (WHEAT], which is used in EPA's Vulnerability
Self Assessment Tool (VSAT). Calculations are based on the type of utility, population served, and
the State in which the utility is located. These factors determine the economic loss per capita, per
day, when service is not available. If a user includes regional economic consequences in the
assessment, the number of days of service outage and the percent of customers affected by outages
will be entered for each asset-threat combination in the Risk Assessment module (Module 5).
Public health consequences may also be assessed in CREAT. These calculations employ a value for
statistical life (VSL) and statistical injury (VSI) that would result from the occurrence of a threat.
These values can be adjusted and the inclusion of this method for public health consequences in the
assessment is optional; see Section 5.4.
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. CREAT 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.
5,1 Economic Consequence Categories
CREAT provides categories that users can incorporate for gauging potential economic
consequences to assets. Users have the opportunity to refine the categories or add custom
categories for additional consequences. The most important part of this step is for users to
determine if monetary values should be assigned to the levels of each consequence category. 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:
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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.
5,2 Default Economic Consequences Matrix
CREAT 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. CREAT provides default definitions for the levels of
consequences in each category to use in the assessment of each asset/threat pair (Table 1).
For each level, there is a monetary range that is used in the risk calculation. The default values for
this matrix are based on the assessment inputs in the Climate Awareness module that include: 1)
system type;31 2) population served; 3) total flow in millions of gallons per day (MGD);
4) ownership (public or private); and 5) financial condition (adequate, good, or strong.) These
inputs are used to obtain default values from available benchmark utility survey data.32 33
31 The system type may be water only, wastewater only, or combined. For combined systems, users differentiate which
portion of the specific 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 CREAT.
32 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.
33 American Water Works Association (AWWA], 2015. Benchmarking Performance Indicators for Water and Wastewater
Utilities 2013, Survey Data and Analyses Report.
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Table 1. Default Definitions for Consequence Category Levels Used for All System Types
Consequence
Category
Low
Medium
High
Very High
Utility Business
Impacts
Minimal potential
for loss of revenue
or operating
income
Minor and short-
term reductions in
expected revenue
Seasonal or episodic
compromise of
revenue or
operating income
Long-term or
significant loss of
revenue or
operating income
Utility Equipment
Damage
Minimal damage to
equipment
Minor damage to
equipment
Significant damage
to equipment
Complete loss of
asset
Environmental
Impacts
No impact or
environmental
damage
Short-term
environmental
damage,
compliance can be
quickly restored
Persistent
environmental
damage - may incur
regulatory action
Significant
environmental
damage - may
incur regulatory
action
Source/Receiving
Water Impacts
No more than
minimal changes to
water quality
Temporary impact
on source water
quality or quantity
Seasonal or episodic
compromise of
source water quality
or quantity
Long-term
compromise of
source water
quality or quantity
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
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 utility might characterize the dollar 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. Users can
review and accept descriptions and values. Alternatively, users can provide the monetary values
that estimate their utility-specific consequence levels, if custom values are known. All saved values
will then be applied in assessment calculations of monetized risk and risk reduction.
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Table 2. CREAT Financial Condition by System Type
System Type
Financial Condition
Baseline DCR
Baseline
Operating Ratio
Water Only System
Top Quartile34
Strong
2.62
0.50
Median
Good
1.45
0.69
Bottom Quartile
Adequate
0.47
0.86
Wastewater Only System
Top Quartile
Strong
2.39
0.42
Median
Good
1.43
0.51
Bottom Quartile
Adequate
0.41
0.82
Combined Water
Top Quartile
Strong
3.39
0.46
Median
Good
1.67
0.57
Bottom Quartile
Adequate
1.24
0.73
Combined Wastewater
Top Quartile
Strong
1.93
0.47
Median
Good
1.25
0.61
Bottom Quartile
Adequate
0.67
0.73
5.2.1 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
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 inputs for system type and financial
condition;
34 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 data set. The top quartile is defined as the midpoint
between the median and the highest number in the data set.
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2. Estimate annual operating expenses for the utility. 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
Total O&M Cost in Dollars per Million Gallons
Water Only System
$2,176
Wastewater Only System
$1,945
Combined Water
$2,240
Combined Wastewater
$2,233
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, CREAT 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 CREAT, as outlined below:
ฆ Target 1, the threshold between Low and Medium impacts, is equal to a 25% decrease in the
baseline DCR;
ฆ Target 2, the threshold between Medium and High impacts, is equal to a 50% decrease in
the baseline DCR; and
ฆ 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
move to each of the three target values specified above. These values become the new upper
and lower bounds for the individual CREAT consequence levels, from Low to Very High:
Revenue Losst
= (% decrease in DCR)t
* (Annual Operating Revenue Annual Operating Expenses)
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Table 4. Debt Coverage Ratio Values for CREAT Consequence Values
System Type
Baseline DCR
Target 1
Medium
Target 2
High
Target 3
Very High
Water Only System
Strong
2.62
2.0
1.3
0.7
Good
1.45
1.1
0.7
0.4
Adequate
0.47
0.4
0.2
0.1
Wastewater Only System
Strong
2.39
1.8
1.2
0.6
Good
1.43
1.1
0.7
0.4
Adequate
0.41
0.3
0.2
0.1
Combined Water
Strong
3.39
2.5
1.7
0.8
Good
1.67
1.3
0.8
0.4
Adequate
1.24
0.9
0.6
0.3
Combined Wastewater
Strong
1.93
1.4
1.0
0.5
Good
1.25
0.9
0.6
0.3
Adequate
0.67
0.5
0.3
0.2
5.2.2 Utility Equipment Damage
The Utility Equipment Damage category refers to the cost required to replace or repair damaged
assets. The associated costs would occur as 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:
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. CREAT considers different percentage thresholds of cash reserve
utilization for association with the consequence levels, as outlined below:
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ฆ Target 1, the threshold between Low and Medium impacts, is equal to 10% of undesignated
cash reserves;
ฆ T arget 2, the threshold between Medium and High impacts, is equal to 25% of undesignated
cash reserves; and
ฆ 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 CREAT consequence levels,
from Low to Very High:
Cash Reserves Losst
= (% decrease in Available Cash Reserves)i * Undesignated Cash Reserves
Table 5. Baseline Cash Reserve Days by System Type from AWWA (2015)
System Type
Baseline Cash Reserve Days by Financial Condition
Strong
Good
Adequate
Drinking Water Only
517
258
139
Drinking Water component of
Combined Utility
656
238
126
Wastewater Only
515
141
109
Wastewater component of
Combined Utility
536
305
133
5.2.3 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 CREAT 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
utility population size ranges.35 The default economic consequences matrix estimates for the
Source/Receiving Water Impacts category are developed using the following four steps:
35 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.
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1. Assign the utility a baseline for per capita historical system expansion cost outlays based
on population served bin. The appropriate population range bin36 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 system expansion cost outlays based on actual population served. The
estimate for baseline expansion cost outlays for the utility 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)
Per capita Historical Expansion Cost Outlay
Public Systems
Private Systems
100 or Less
$350.30
$132.03
101-500
$378.26
$28.95
501 -3,300
$103.67
$30.16
3,301- 10,000
$40.91
$42.41
10,001-50,000
$42.80
$37.87
50,001 -100,000
$21.96
$35.08
100,001-500,000
$38.05
$4.69
Greater than 500,000
$32.44
$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.
CREAT considers different percentage thresholds of outlays for association with the
consequence levels:
ฆ Target 1, the threshold between Low and Medium impacts, is equal to 10% of historical
expansion costs;
ฆ T arget 2, the threshold between Medium and High impacts, is equal to 25% of historical
expansion costs; and
ฆ Target 3, the threshold between High and Very High impacts, is equal to 60% of historical
expansion costs.
36 Although these population bins may be more refined than the average utility operator is accustomed to, they allow
CREAT to provide the best default values based on utility size. The data selection based on these categories is not visible
to users.
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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 boundary
values that separate the different CREAT consequence levels:
Water Resource Losst
= (% of Historical Expansion Costs)i * Baseline System Expansion Costs
5,2,4 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 CREAT 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.37 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. CREAT selects the appropriate population range bin38 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 compliance cost outlays based on actual population served. The
estimate for baseline compliance cost outlays for the utility 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.
CREAT considers different percentage thresholds of outlays for association with the
consequence levels:
ฆ Target 1, the threshold between Low and Medium impacts, is equal to 10% of baseline
compliance costs;
ฆ Target 2, the threshold between Medium and High impacts, is equal to 25% of baseline
compliance costs; and
ฆ Target 3, the threshold between High and Very High impacts, is equal to 60% of baseline
compliance costs.
37 The corresponding data specific to wastewater systems were not available in either the CWSS or AWWA sources.
Drinking water system data are used as a proxy to develop default values for all system types as reasonable estimates.
38 Although these population bins may be more refined than the average utility operator is accustomed to, they allow
CREAT to provide the best default values based on utility size. The data selection based on these categories is not visible
to users.
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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 CREAT consequence levels, from Low to Very High:
Environmental Losst = (% of Historical Compliance Costs)i * Baseline Compliance Costs
Table 7. Per Capita Historical Regulatory Compliance Cost Outlays by System Ownership from CWSS (2009)
Population Served (bins)
Per Capita Historical Cost Outlay
Public Systems
Private Systems
100 or Less
$212.02
$20.31
101-500
$11.57
$46.58
501 -3,300
$21.64
$5.28
3,301 -10,000
$9.54
$36.55
10,001-50,000
$6.31
$0.47
50,001 -100,000
$10.78
$10.78*
100,001 - 500,000
$6.66
$11.01
Greater than 500,000
$5.02
$5.02*
* Value based on public system data due to missing data for this population bin
5.3 Regional Economic Consequence Assessment
Regional economic consequence estimates in CREAT include lost revenue from businesses and
industries in the utility's area that cannot operate due to water or wastewater service disruptions.
For each asset/threat pair, CREAT estimates state-level economic consequences for business
activity in the utility's seivice area that are impacted by a disruption and allows for the possibility
that only a portion of the utility's service may be impacted by a disruption from any give
asset/threat pair. The magnitude of regional economic consequences is linked to the duration and
extent of the disruption in normal services. These consequences are estimating using a multi-sector,
inter-industry framework contained in CREAT.
Regional economic consequences are estimated using a combination of inputs previously specified
in the assessment for the utilitylocation, utility type (water, wastewater), and population
servedalong with additional databases included in CREAT, described below:
Baseline regional economic activity data. To characterize economic activity in the region
served by the utility, CREAT includes a database of state-level economic activity data compiled
from the U.S. 2012 Economic and Agricultural Census for 84 industries. For each economic
sector, the database describes economic activity based on the annual dollar value of economic
output (i.e., industry revenues).
Fraction of economic activity served by the utility. Since any single utility does not service
the entirety of the businesses, or population, in a given state, CREAT estimates the fraction of
total state-level economic activity that is served by the utility based on the proportion of the
utility's population served to the total population in the state using the calculation below.
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CREAT includes a database of state-level population values from the Census' 2017 Annual
Estimates of the Resident Population.39
Utility Population
Fraction of Business Activity Served = -
State Population
Economic input-output multipliers, by economic sector. One factor that strongly influences
the magnitude of regional economic consequences is the interdependence of economic sectors.
Inter-industry links in the economy are specified using final-demand multipliers from the U.S.
Bureau of Economic Analysis' 2016 Regional Input-Output Modeling System (RIMS II). CREAT
includes a database of state-level, final-demand input-output multipliers for 64 industries.
These values are then mapped to the 84 industries in the baseline Census economic activity
data.
Service loss economic impact factors. All businesses are not affected to the same degree as a
result of a loss in water or wastewater service. For example, businesses in some industries can
more easily find ways to continuing working in whole or in part. To account for this concept,
CREAT includes a database of water and wastewater service "economic loss factors" (ELF) for
each economic industry. These factors are used to account for the varying resilience of
industries under conditions where services are not available. Each service loss economic impact
factor indicates the proportion of business activity in an industry that is lost due to a loss in
water or wastewater service. These inputs are based on values in the literature from Rose and
Liao (2005) and the American Technology Council (1991) for water and wastewater,
respectively.
Service loss profile. Lastly, the regional economic consequences also require input from users
to specify the service loss profile. The service loss profile describes the extent and duration of
the loss in water or wastewater services based on inputs for:
ฆ The duration of the service outage in days; and
ฆ The percentage of customers without service during this period (%).
Using the above inputs, CREAT calculates direct and total regional economic consequences. Direct
business revenue impacts are those associated with businesses directly served by the water or
wastewater system. This is estimated by industry using the calculation below:
Direct Business Consequences
= Industry Revenue * Fraction of Industry Served * ELF
* Percent of Customers without Service * (Days without Service/365.25)
Individual industry-level estimates are then aggregated across all industries to produce an estimate
of all direct economic business consequences (i.e., revenue loss) in the utility's service territory.
Total regional consequences in the state refer to the direct and indirect economic effects, a measure
that captures the additional output losses among other businesses that are linked economically to
businesses directly affected by the disruption. Total business revenue impacts for a service
https://www.census.gov/data/tables/time-series/demo/popest/2010s-national-total.html
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disruption are determined based on the direct impacts and economic input-output multipliers from
the U.S. Bureau of Economic Analysis, referenced above. This is calculated as:
Total Business Consequences = Direct Business Consequences * 10 Multiplier
Again, the estimates are calculated for each individual industry, since direct consequences vary by
industry and the input-output multipliers vary by industry. Total business consequences are then
aggregated across all industries to produce an overall estimate of total economic business
consequences (i.e., revenue loss) in the utility's service territory.
5,4 Public Health Consequence Assessment
In CREAT, 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 human fatalities or injuries for each
asset/threat pair. CREAT assists users by providing default values for the Value of a Statistical Life
(VSL),40 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),41 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 CREAT provides default values for VSL and VSI that can be used in these calculations, users
may edit these values if desired. 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 users who do not wish to monetize public health consequences,
public health impacts can be considered by ranking their adaptation plans on a qualitative impact
scale.
40 VSL is the value attributed to each fatality assessed due to the occurrence of a threat to a particular asset. A VSL value of
$7,400,000 is in 2006 dollars is 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
(https://www.epa.gov/environmental-economics/mortality-risk-valuation]. This approach was vetted and endorsed by
the Agency when the Guidelines for Preparing Economic Analyses and remains EPA's default guidance for valuing mortality
risk changes. EPA is currently reviewing this guidance through a Science Advisory Board Environmental Economics
Advisory Committee (SAB-EEAC] expert panel and commissioned reports on the various approaches used in the literature
to estimate the value of mortality risk reductions (Alberini 2004, Black et al. 2003, and Blomquist 2004]. EPA has
prepared a white paper on Valuing Mortality Risk Reductions in Environmental Policy
(https://www.epa.gov/environmental-economics/valuing-mortality-risk-reductions-environmental-policy-white-paper-
2010] featuring EPA's latest review of important issues surrounding how to value the reductions in risk to human health
from environmental regulations and other Agency decisions. EPA has submitted the whitepaper to its Science Advisory
Board for feedback and recommendations. Among the potential forthcoming revisions is a change to the often
misunderstood term "value of statistical life" with the more accurate term "value of mortality risk reduction.".
41 VSI is the value attributed to each injury assessed due to the occurrence ofa threat to a particular asset. VSI of $74,000
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 ofthe 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.
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Chapter 6. Adaptation Planning: Module 4
This 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 currently implemented measures that
provide resilience now (Existing Measures), 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 the measure is expected to be effective in reducing
consequences from each defined threat.
Adaptation plans can 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 the
Current Measures plan, which contains results if no additional adaptation was implemented.
After considering consequence criteria, CREAT guides users to identify assets at risk from each
previously defined threat. Users are encouraged to focus on these critical assets rather than
attempting to define all of their assets. CREAT 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.
6.1 Asset Identification and Assignment
Users can choose from assets provided in a CREAT library or add custom assets. After assets are
defined, users select those that are critical for the risk assessment. In CREAT, 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 historical knowledge and experience.
Asset definition includes a description and assignment of relevant threats. This selection is the
basis for asset/threat pairs in CREAT. 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, a pump station is selected as a critical asset; for this assessment, the
utility may be concerned about only potential utility business impacts and utility equipment
damage. Only those categories selected for an asset will be available during the risk assessment.
6.2 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
CREAT 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 that were 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.
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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 CREAT. 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 available adaptive measure cost information, and the preferred approach of the
assessment To assist users in gauging the potential cost of implementation, CREAT provides
default unit costs for several adaptive measures within the CREAT library (Table 8). Unit-cost
values refer to the cost associated with implementing a specific adaptive measure, 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,42
including available case-study reports for projects implemented at utilities.43 Users can choose to
adopt the default ranges or provide their own estimated cost.
Table 8. Default Costs for Selected Adaptive Measures in CREAT Adaptation Library
Adaptive Measure
Default Unit-Cost Range
Construct
Back-up power
$250 to $800 per kilowatt of capacity
Levee
$80 to $220 per linear foot
Low-head dam
$3,411 to $29,333 per linear foot
Sea wall
$350 to $760 per linear foot
Temporary flood barrier
$63 to $750 per linear foot
Ecosystem / Land Use
Erosion and sediment control
$12 to $1750 per linear foot
Fire management
$660 to $1,500 per acre treated
Wetlands for flood protection
$4,700 to $154,300 per acre-foot of stormwater captured
Green infrastructure
Bioretention facilities
$7 to $26 per square foot of bioretention infrastructure
Green roofs
$8 to $40 per square foot of green roof
Permeable pavement
$10 to $22 per square foot of permeable pavement
New Supplies and Demand Management
Demand management
$465 to $980 per acre-foot
Desalination - inland
$375 to $1,290 per acre-foot
Desalination - seawater
$1,600 to $3,250 per acre-foot
42 For more information, visit: https://www.rsmeans.com/
43 See subsection (adaptive measure cost sources] in Chapter 7, References, for the sources of cost estimates.
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Adaptive Measure
Default Unit-Cost Range
Groundwater / aquifer recharge with
possible conjunctive use
$90 to $1,100 per acre-foot
Increased storage
$0,005 to $4 per gallon of storage
Interconnections
$95 to $1,250 per linear foot
Municipal water reuse system - non-
potable
$300 to $2,000 per acre-foot
Municipal water reuse system -
potable
$800 to $2,000 per acre-foot
Rainwater collection / use - rain
barrels
$70 to $300 per residential rain barrel system (or
household)
Repair/Retrofit
Altered treatment - total dissolved
solids
$2.7M to $3.8M per MGD
Distributed treatment
$600,000 to $10.4M per MGD
Infiltration reduction
$1,000 to $5,000 per number of laterals
Leakage reduction
$100 to $200 per acre-foot
Retrofit intakes
$450,000 to $3.1M per MGD
Retrofit intakes - Invasive species
$18,000 to $76,000 per MGD
Silt removal
$5 to $20 per cubic yard
Sewage separation
$240 to $300 per linear feet of pipe being separated
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,
users are prompted to define the number of units needed to implement the adaptive measure. This
approach enables CREAT 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. CREAT calculates a
total cost based on the cost of all included measures and indicates the relevance to threats for each
plan based on the relevance entered 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 that plans apply to all identified threats of concern and
that no gaps remain when all plans are defined. For streamlined users, CREAT assembles an "All
Potential Measures" plan that contains all potential measures defined in this module for
consideration in risk assessment.
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Chapter 7. Risk Assessment: Module 5
This module is the last module in the climate change risk assessment process and provides
monetized risk results 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.
The monetized risk results overview also provides an opportunity to view the regional economic
consequences and public health consequences if selected for assessment and specified during asset
selection and asset definition.
CREAT guides users 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 7 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.
Monetized
Risk {$) or
Total
Impact
Without
Adaptation
Without
Adaptation
| Current risk profile
| Future risk profile
| Reduced risk profile
With
Adaptation
Monetized Risk
Reduction
Current Conditions
(today's climate)
Projected Climate
Scenario (2035 or 2060)
Figure 7. CREAT Results Showing Monetized Risk Reduction
CREAT 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: CREAT 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;
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2. Vulnerability: Vulnerability refers to the degree to which assets are susceptible to, and unable
to cope with, adverse impacts. CREAT does not directly support the ability to consider how
adaptation may reduce asset vulnerability; and
3. Likelihood: In CREAT, users consider threats assuming the threats have a 100% chance of
occurring in the given time period. The tool provides an option to explore the effect of differing
percentages of scenario likelihood on risk reduction to potentially further inform adaptation
planning and decision making.
7.1 Consequence Assessment Process
To assess risk, CREAT guides users through an assessment of the consequences following
implementation of each adaptation plan for all scenarios as described below:
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;
Users select a level of consequence in each category relevant to the asset and for each scenario
where the threat is defined. CREAT retrieves the monetary value ranges for each assessed level
from the consequence matrix; and
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 CREAT 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 regional economic consequences and public health impacts, provides a
measure of risk, expressed as a range from minimum to maximum overall impact:
Minimum Overall Impact
X(Win Impact Economic Consequence categ0ries) Impact Regional Economic
+ iTTipCLCtpu^frHc. Health
Maximum Overall Impact
Y,{Max Impact Economic Consequence Categories) Impact Regional Economic
+ ImpactPublic Health
7.2 Risk Assessment Results
The difference between the consequences following implementation of an adaptation plan and the
consequences without adaptation is reported as MRR in CREAT. This reduction could be considered
as a benefit from adapting that can be directly compared to the cost of implementing the plan.
CREAT calculates the MRR by summing the difference in consequence level in each category, rather
than the difference in the overall consequence.44 Therefore, the MRR for each category is calculated
as follows:
44 See example calculations in Appendix A-4.
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Monetized Risk Reduction = (Max ImpactPL category - Min ImpactCM,category ) to
(Max IiTijJcici f .v/xY/1 cijory ~ Min ImpactPL Categ0ry) ,
where the risk based on Current Measures in place for this consequence category is the range:
(Min Impact CM,categoiy to Max Impact CM,categoiy) and,
the risk following implementation of an Adaptation Plan for the same category is the range:
(Min Impact pL,categoiy to Max Impact pL,categoiy )ฆ
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 = j(Min Monetized Risk ReductionConsequence categories)
Max Risk Reduction = ฃ(Max Monetized Risk ReductionConsequence categories)
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 CREAT 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;
Regional economic consequences for both Current Measures and the selected Adaptation Plan;
and
Public health impacts for both Current Measures and the selected Adaptation Plan.
7,3 Scenario Likelihood Sensitivity Analysis
Up to this point, users have considered threats as if the threats are 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, users are 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, users 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.
CREAT calculates three ranges of scenario likelihood where the comparison of cost with risk
reduction would support different decisions:
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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.
7,4 Plan Comparison
In the final step of the tool, CREAT 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 consequence assessment earlier in this module. These impacts
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 were 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 CREAT and are designed to support adaptation planning based on
assessment results.
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Chapter 8. References
3,1 Climate Data Sources
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Brekke, L.D., J.E. Kiang, J.R Olsen, etal. 2009. Climate Change and Water Resources Management - A
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Bureau of Reclamation. 2008. Sensitivity of Future CVP/SWP operations to potential climate change
and associated sea level rise. Appendix R in CVP/SWP OCAP Biological Assessment. Bureau of
Reclamation, U.S. Department of the Interior.
Carter, T.R 2007. General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation
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Climate Change Science Program (CCSP). 2008. Our changing planet - The U.S. Climate Change
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Food and Agriculture Organization of the United Nations (FAO). 2011. Climate change, water and
food security. http://www.fao.Org/3/i2096e/i2096e.pdf
Fowler, H.J., S. Blenkinsop, and C. Tebaldi. 2007. Review: Linking climate change modeling to
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International Journal of Climatology 27: 1547-1578.
Groisman, P.Y., RW. Knight, D.R. Easterling, etal. 2005. Trends in Intense Precipitation in the
Climate Record. Journal of Climate 18: 1326-1350.
Hansen, J.E. 2007. Scientific reticence and sea level rise. Environ. Res. Lett 2, doi: 10.1088/1748-
9326/2/2/024002, 6 pp.
Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: The Physical
Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change (Solomon, S., D. Qin, M. Manning, et al., Eds.).
Cambridge University Press, Cambridge, UK, and New York, NY, USA, 996 pp.
Maurer, E.P. 2007. Uncertainty in hydrologic impacts of climate change in the Sierra Nevada,
California, under two emissions scenarios, Climatic Change, 82(3-4), 309-325;
D01:10.1007/sl 0584-006-9180-9.
CREAT Methodology Guide
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Maurer, E.P., A.W. Wood, J.C. Adam, et al. 2002. A long-term hydrologically-based data set of land
surface fluxes and states for the coterminous United States. Journal of Climate 15: 3237-3251.
Milly, P.C.D., K.A Dunne, and A.V. Veccia. 2005. Global pattern of trends in streamflow and water
availability in a changing climate. Nature 438: 347-350.
National Association of Clean Water Agencies and American Metropolitan Water Authority. 2009.
Confronting Climate Change: An Early Analysis of Water and Wastewater Adaptation Costs.
https://www.nacwa.org/docs/default-source/news-publications/White-Papers/2009-10-
28ccreportpdf?sfvrsn=2
National Climatic Data Center (NCDC). 2002. Daily documentation for dataset9101, global daily
climatology network, version 1.0. 26 pp.
http://citeseerx.istpsu.edu/viewdoc/download?doi=10.1.1.195.95 80&rep=repl&type=pdf
N0AA. 2013. Estimating Vertical Lane Motion from Long-Term Tide Gauge Records. Technical
ReportNOS CO-OPS 065 Silver Spring, Maryland. Table 1: NOAATide Station Relative Sea Level
Trends and Estimated Rates of Vertical Land Movement.
Parris, A., P. Bromirski, V. Burkett, D. Cayan, M. Culver, J. Hall, R. Horton, K. Knuuti, R. Moss, J.
Obeysekera, A. Sallenger, and J. Weiss. 2012. Global Sea Level Rise Scenarios for the United States
National Climate Assessment. NOAA Tech Memo OAR CPO-1, 37 pp., National Oceanic and
Atmospheric Administration, Silver Spring, MD.
https://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf
Rahmstorf, S. 2007. A Semi-Empirical Approach to Projecting Future Sea level Rise. Science
315(5810), 368-370.
Randall, D.A., RA. Wood, S. Bony, etal. 2007. Climate Models and Their Evaluation. In: Climate
Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change (Solomon, S., D. Qin, M.
Manning, et al., Eds.). Cambridge University Press, Cambridge, UK, and New York, NY, USA.
Rose, A., and S. Liao. 2005. Modeling Regional Economic Resilience to Disasters: A Computable
General Equilibrium Analysis of Water Service Disruptions. Journal of Regional Science, v.
45(1):75-112.
Slack, J.R, A.L. Lumb, and J.M. Landwehr. 1993. Hydro-Climatic Data Network (HCDN): Streamflow
Data Set, 1874-1988. USGS Water Resources Investigations Report 93-4076.
https://pubs.usgs.gov/wri/wri934076/lst_page.html
U.S. Environmental Protection Agency (EPA). 2009. 2006 Community Water System Survey, Volume
II: Detailed Tables and Survey Methodology. EPA 815-R-09-002.
U.S. Department of the Interior, Bureau of Reclamation, Technical Memorandum 86-68210-2010-
01, Climate Change and Hydrology Scenarios for Oklahoma Yield Studies.
U.S. Global Change Research Program. 2009. Global Climate Change Impacts in the United States.
(Karl, T. R J. M. Melillo, and T. C. Peterson, Eds.) ISBN 978-0-521-14407-0.
Water UK. 2007. A Climate Change Adaptation Approach for Asset Management Planning.
https://www.water.org.uk/guidance/asset-management-planning-climate-change/
Water Utility Climate Alliance (WUCA). 2010. Evaluating Decision Support Methods for
Incorporating Climate Change Uncertainties into Water Planning.
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Wigley, T.M.L. 2008. "Model for the Assessment of Greenhouse-gas Induced Climate
Change/SCENGEN 5.3." Boulder, Colorado: National Center for Atmospheric Research.
https://www.cgd.ucar.edu/cas/wigley/magicc/ and http://www.magicc.org
Wood, A.W., E.P. Maurer, A. Kumar, and D.P. Lettenmaier. 2002. Long-range experimental
hydrologic forecasting for the Eastern United States. Journal of Geophysical Research 107(D20):
p. 4429.
Wood, A.W., L.R. Leung, V. Sridhar, and D.P. Lettenmaier. 2004. Hydrologic implications of
dynamical and statistical approaches to downscaling climate model outputs. Climatic Change 15:
189-216.
Woodhouse, C.A., and J.J. Lucas. 2006. Multi-century tree-ring reconstructions of Colorado
streamflow for water resource planning. Climatic Change 78: 293-315.
World Climate Research Programme. 2013. Coupled Model Intercomparison Project, Phase 5
(CMIP5). https://www.wcrp-climate.org/wgcm-cmip
Xu, C.-Y., and V.P. Singh. 2004. Review on Regional Water Resources Assessment Models Under
Stationary and Changing Climate. Water Resources Management 18(6): 591-612.
Zwally H.J., W. Abdalati, T. Herring, etal. 2002. Surface melt-induced acceleration of Greenland ice-
sheet flow. Science 297 218-2.
8.2 Adaptive Measure Cost Sources
Apex Green Roofs. Technical Info FAQ. http://www.apexgreenroofs.com/faqs
Arroyo, J., and S. Shirazi. 2012. Innovative Water Technologies, Texas Water Development Board.
Cost of Brackish Groundwater Desalination in Texas.
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Barr Engineering Company. 2011. Best Management Practices Construction Costs, Maintenance
Costs, and Land Requirements. Prepared for Minnesota Pollution Control Agency.
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California Coastal Commission. 2015. Comments on Independent Scientific and Technical Advisory
Panel Draft Phase 2 Report on Feasibility of Alternative Intake Designs for Proposed Huntington
Beach Desalination Facility, https://documents.coastal.ca.gov/assets/press-releases/huntington-
beach-desal/CCC-Poseidon_ISTAP_Draft_Phase_2_Report_for_Public_Review_8-14-15.pdf
CH2M Hill, Inc. 2011. Green Infrastructure Plan, prepared for the City of Lancaster, PA
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ullReportpdf
CH2M Hill, Inc. 2011. Green Infrastructure Plan: Appendix A, Green Infrastructure Technology Fact
Sheets, prepared for the City of Lancaster, PA. https://cityoflancasterpa.com/wp-
content/uploads/2014/01/cityo flancaster_giplan_fullreport_april2011_final_0.pdf
CTC & Associates LLC. 2012. Comparison of Permeable Pavement Types: Hydrology, Design,
Installation, Maintenance and Cost Prepared for the Wisconsin Department of Transportation,
Southeast Region, https://rosap.ntl.bts.gov/view/dot/23787
DC Green Works. Green Roof Toolkit 2009.
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it.pdf
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Delta Institute. 2015. Green Infrastructure Designs: Bioswale/Hybrid Ditch. https://delta-
institute.org/wp-content/uploads/2020/04/Green-Infrastructure-Toolkit-September-17-l.pdf
Federal Emergency Management Agency (FEMA). 2007. Selecting Appropriate Mitigation Measures
for Floodprone Structures, https://www.fema.gov/sites/default/files/2020-08/fema_551.pdf
Jaber, F., D. Woodson, C. LaChance, and C. York. 2013. Texas A&M AgriLife Extension, Stormwater
Management: Rain Gardens, http://agrilifecdn.tamu.edu/water/files/2013/02/
stormwater-management-rain-gardens.pdf
Low Impact Development (LID) Center. Urban Design Tools, Low Impact Development: Rain Barrels
and Cisterns, http://www.lid-stormwater.net/raincist_cost.htm
Mason, L., B. Lippke, andE. Oneil. 2007. Future of Washington's Forest and Forest Industries Study,
Discussion Paper 10: Benefits/Avoided Costs of Reducing Fire Risk on Eastside, Final Report
Mitchell, D. 2012. Review of Unit Cost Ranges for CWF Water Efficiency Strategies. Technical
Memorandum prepared by M-Cubed for Mike Wyatt, California Water Foundation, Oakland, CA.
Northeast Ohio Regional Sewer District (NEORSD). 2012. Green Infrastructure Plan.
https://www.neorsd.org/I_Library.php?a=download_file&LIBRARY_RECORD_ID=5526
Pacific Institute, H. Cooley, and N. Ajami. 2012. Key Issues for Desalination in California: Cost and
Financing, https://pacinst.org/publication/costs-and-financing-of-seawater-desalination-in-
california/
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WateReuse Research Foundation, https://www.watereuse.org/watereuse-research/
the-opportunities-and-economics-of-direct-potable-reuse/
Rhode, M. 2014. Stanford Woods Institute for the Environment Water in the West, Understanding
California's Groundwater: Recharge.
https://waterintheweststanford.edu/groundwater/recharge/
RSMeans. 2014. Facilities Construction Cost Data, 29th Annual Edition. Division 33 16 Water Utility
Storage Tanks. Norwell, MA: Reed Construction Data.
RSMeans. 2009. Assemblies Cost Data. Division 5090 210. Norwell, MA: Reed Construction Data.
State of California, Department of Water Resources (CADWR). Water Audit and Leak Detection.
https://water.ca.gov/Programs/Water-Use-And-Efficiency/Urban-Water-Use-
Efficiency/Validated-Water-Loss-Reporting
The Nature Conservancy, Rio Grande Water Fund (RGWF). 2014. Comprehensive Plan for Wildfire
and Water Source Protection, http://riograndewaterfund.org/wp-
content/uploads/2 017/01 /rgwf_compplan.pdf
U.S. Environmental Protection Agency (EPA). 1999. Combined Sewer Overflow Management Fact
Sheet Sewer Separation. EPA 832-F-99-041. https://www3.epa.gov/npdes/pubs/sepa.pdf
U.S. Environmental Protection Agency (EPA). 2015. Water Best Management Practices: Stormwater
Wetland, https://www.epa.gov/npdes/national-menu-best-management-practices-bmps-
stormwater-documents
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Appendices
A-l: Models Used in Developing Climate Data
Table 9. Models Used in Developing Climate Data
Model Name (Year)
Storm
Scalars
Source / Institution
ACCESS1 0
Australia, Commonwealth Scientific and Industrial Research Organization (CSIRO) and
Bureau of Meteorology (BOM)
ACCESS1-3
X
BCC-CSM1 1
China, Beijing Climate Center, China Meteorological Administration
BCC CSM1 1 M
BNU ESM
China, College of Global Change and Earth System Science, Beijing Normal University
CANESM2
X
Canada, Canadian Centre for Climate Modelling and Analysis
CCSM4
X
USA, National Center for Atmospheric Research (NCAR)
CESM1 BGC
X
USA, Community Earth System Model Contributors
CESM1 CAM5
CMCC CM
X
Italy, Centro Euro-Mediterraneo per i Cambiamenti Climatici
CMCC CMS
X
CNRM_CM5
X
France, Centre National de Recherches Meteorologiques / Centre Europeen de
Recherche et Formation Avancee en Calcul Scientifique
CSIRO_Mk_3_6
X
Australia, Commonwealth Scientific and Industrial Research Organization in
collaboration with Queensland Climate Change Centre of Excellence
EC EARTH
EC-EARTH consortium
FGOALS_G2
China, LASC, Institute of Atmospheric Physics, Chinese Academy of Sciences and CESS,
Tsinghua University
FGOALS S2
China, LASC, Institute of Atmospheric Physics, Chinese Academy of Sciences
GFDL CM3
USA, NOAA General Fluid Dynamics Lab
GFDL ESM2G
X
GFDL ESM2M
X
GISS E2 H
USA, NASA Goddard Institute for Space Studies
GISS E2 H CC
GISS E2 R
GISS E2 R CC
HADGEM2_AO
Korea, National Institute of Meteorological research/Korea Meteorological
Administration
HADGEM2 CC
UK, Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by
Instituto Nacional de Pesquisas Espaciais)
HadGEM2 ES
X
INMCM4
X
Russia, Institute for Numerical Mathematics
IPSL CM5A LR
X
France, Institute Pierre Simon Laplace
IPSL CM5A MR
X
IPSL CM5B LR
X
MIROC ESM
X
Japan, Japan Agency for Marine-Earth Science and Technology, Atmosphere and
Ocean Research Institute (The University of Tokyo), and National Institute for
Environmental Studies
MIROC ESM CHEM
X
MIROC5
X
MPI ESM LR
X
Germany, Max-Planck-lnstitut fur Meteorologie (Max Planck Institute for Meteorology)
MPI ESM MR
X
MRI CGCM3
X
Japan, Meteorological Research Institute
NorESMl M
X
Norway, Norwegian Climate Center
NORESMl_ME
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A-.'; Default Threat Definitions
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 CREAT 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 CREAT 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 the events 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 CREAT 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 should 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 CREAT 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, without additional treatment or relocation of supply, the relative depths of
saline and freshwater tables will drive the interface past wells and limit production.
Default definitions for water quality degradation threats provided in CREAT 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.
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A-3: Examples of Economic Consequences Matrices
The default economic consequences matrix includes definitions and impacts for each level within
each consequence category (Table 10). The standardized definitions define the basis for the
monetary impact values provided by CREAT and serve as a starting point for users to revise the
levels based on their own assessment priorities.
Table 10. Default Definitions for CREAT-provided Economic Consequences Matrix (all users)
Category
Low
Medium
High
Very High
Utility Business
Impacts
Minimal potential
for loss of revenue
or operating
income
Minor and short-
term reductions in
expected revenue
Seasonal or
episodic
compromise of
revenue or
operating income
Long-term or
significant loss
of revenue or
operating
income
Utility Equipment
Damage
Minimal damage
to equipment
Minor damage to
equipment
Significant
damage to
equipment
Complete loss of
asset
Environmental
Impacts
No impact or
environmental
damage
Short-term
environmental
damage,
compliance can be
quickly restored
Persistent
environmental
damage - may
incur regulatory
action
Significant
environmental
damage - may
incur regulatory
action
Source/Receiving
Water Impacts
No more than
minimal changes
to water quality
Temporary impact
on source water
quality or quantity
Seasonal or
episodic
compromise of
source water
quality or quantity
Long-term
compromise of
source water
quality or
quantity
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 5,
Consequences and Assets: Module 3. These default values provided by CREAT 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 11 through 14 provide examples of default consequence
matrices based on hypothetical utilities.
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Table 11. Default Economic Consequence Matrix for Drinking Water Assets of a Public Combined Water System Serving
25,000 Customers with 5 MGD Service in Good Financial Condition
Category
Low
Medium
High
Very High
Utility Business
Impacts
Up to $800,000
$800,000 -$1.6M
$1.6M -$2.4M
Greater than
$2.4M
Utility
Equipment
Damage
Up to $275,000
$275,000 -
$690,000
$690,000 -
$1.66M
Greater than
$1.66M
Environmental
Impacts
Up to $15,750
$15,750-$39,500
$39,500 -
$94,500
Greater than
$94,500
Source/Receiving
Water Impacts
Up to $107,000
$107,000 -
$267,500
$267,500 -
$642,000
Greater than
$642,000
Table 12. Default Economic Consequence Matrix for Drinking Water Assets of a Public Combined Water System Serving
1,000,000 Customers with 150 MGD Service in Strong Financial Condition
Category
Low
Medium
High
Very High
Utility Business
Impacts
Up to $37.35M
$37.35M -$74.55M
$74.55M -
$111.9M
Greater than
$111.9M
Utility
Equipment
Damage
Up to $22.8M
$22.8M - $57.15M
$57.15M -
$137.1M
Greater than
$137.1M
Environmental
Impacts
Up to $500,000
$500,000 -$1.26M
$1.26M - $3.01M
Greater than
$3.01M
Source/Receiving
Water Impacts
Up to $3.24M
$3.24M - $8.11M
$8.11M -
$19.47M
Greater than
$19.47M
Table 13. Default Economic Consequence Matrix for Wastewater Assets of a Public Combined System Serving 25,000
Customers with 5 MGD Service in Good Financial Condition
Category
Low
Medium
High
Very High
Utility Business
Impacts
Up to $675,000
$675,000 -$1.35M
$1.35M - $2.03M
Greater than
$2.03M
Utility
Equipment
Damage
Up to $355,000
$355,000 -
$885,000
$885,000 -
$2.12M
Greater than
$2.12M
Environmental
Impacts
Up to $15,750
$15,750 -$39,500
$39,500 -
$94,500
Greater than
$94,500
Source/Receiving
Water Impacts
Up to $107,000
$107,000 -
$267,500
$267,500 -
$642,000
Greater than
$642,000
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Table 14. Default Economic Consequence Matrix for Wastewater Assets of a Public Combined System Serving 1,000,000
Customers with 150 MGD Service in Strong Financial Condition
Category
Low
Medium
High
Very High
Utility Business
Impacts
Up to $35.7M
$35.7M - $71.4M
$71.4M -
$107.25M
Greater than
$107.25M
Utility
Equipment
Damage
Up to $18.6M
$18.6M - $46.5M
$46.5M -
$111.6M
Greater than
$111.6M
Environmental
Impacts
Up to $500,000
$500,000 -$1.26M
$1.26M - $3.01M
Greater than
$3.01M
Source/Receiving
Water Impacts
Up to $3.24M
$3.24M - $8.11M
$8.11M -
$19.47M
Greater than
$19.47M
A-4: 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 7, Risk Assessment: Module 5. The following sections provide examples
from two hypothetical utilities and the results based on their entries.
A.4.1 Combined Water Example
This analysis is based on the default matrix of economic consequences, provided by CREAT, for the
drinking water assets of a public combined water system serving 25,000 customers with 5 MGD
service, and good financial condition. See Table 11 to review their Economic Consequences matrix.
This example assessment pursues 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 were assessed: Baseline and Projected. Upon considering
current resilience, which is based on a consideration of existing measures, the following assessment
was selected:
Table 15. Current Measures Assessment for Drinking Water Assets of a Public Combined System Serving 25,000 Customers
with 5 MGD Service in Good Financial Condition
Current Measures
Scenarios
Baseline
Projected
Utility Business Impacts
Medium
$800,000 -$1.6M
High
$1.6M - $2.4M
Utility Equipment Damage
n/a
n/a
Environmental Impacts
n/a
n/a
Source/Receiving Water Impacts
Low
Up to $107,000
High
$267,500 -$642,000
Overall Consequence
$800,000 -$1.71M
$1.87M - $3.04M
Previously, the utility 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
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they named "DW Adaptation Plan." Next, the levels of consequence were considered following the
implementation of the DW Adaptation Plan:
Table 16. DW Adaptation Plan Assessment for Drinking Water Assets of a Public Combined System Serving 25,000 Customers
with 5 MGD Service in Good Financial Condition
DW Adaptation Plan
Scenarios
Baseline
Projected
Utility Business Impacts
Medium
$800,000 -$1.6M
Medium
$800,000 -$1.6M
Utility Equipment Damage
n/a
n/a
Environmental Impacts
n/a
n/a
Source/Receiving Water Impacts
Low
Up to $107,000
Low
Up to $107,000
Overall Consequence
$800,000 -$1.71M
$800,000 -$1.71M
The overall consequence from the second assessment is the same for the Baseline Scenario and is
lower than the overall impact without adaptation for the Projected Scenario.
The difference in the two assessments was calculated by CREAT using the movement of
consequence level in each category, rather than the difference in the overall consequence:
Table 17. Monetized Risk Reduction for Combined Water System DW Adaptation Plan
DW Adaptation Plan
Scenarios
Baseline
Projected
Utility Business Impacts
$0
$0 -$1.6M
Utility Equipment Damage
n/a
n/a
Environmental Impacts
n/a
n/a
Source/Receiving Water Impacts
$0
$160,500 -$642,000
Monetized Risk Reduction
$0
$160,500 -$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).
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A.4.2 Combined Wastewater Example
This analysis is based on the default matrix of economic consequences, provided by CREAT, for the
wastewater assets of a public combined system serving 1,000,000 customers with 150 MGD service
and strong financial condition. See Table 12 to review the consequences matrix. This assessment
example pursues 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, the following assessment was selected:
Table 18. Current Measures Assessment for Wastewater Assets of a Public Combined System Serving 1,000,000 Customers
with 150 MGD Service in Strong Financial Condition
Current Measures
Scenarios
Baseline
Projected
Utility Business Impacts
n/a
n/a
Utility Equipment Damage
Medium
$18.6M - $46.5M
Very High
Greater than $111.6M
Environmental Impacts
Low
Up to $500,000
Medium
$500,000 -$1.3M
Source/Receiving Water Impacts
n/a
n/a
Overall Consequence
$18.6M - $47M
Greater than $112.9M
The utility 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 was named "WW Adaptation Plan."
Next, the levels of consequence were considered following the implementation of the WW
Adaptation Plan:
Table 19. WW Adaptation Plan Assessment for Wastewater Assets of a Public Combined System Serving 1,000,000
Customers with 150 MGD Service in Strong Financial Condition
WW Adaptation Plan
Scenarios
Baseline
Projected
Utility Business Impacts
n/a
n/a
Utility Equipment Damage
Low
Up to $18.6M
Low
Up to $18.6M
Environmental Impacts
Low
Up to $500,000
Low
Up to $500,000
Source/Receiving Water Impacts
n/a
n/a
Overall Consequence
Up to $19.1M
Up to $19.1M
The overall consequence from the second assessment is lower than the overall impact without
adaptation. The difference in the two assessments is calculated by CREAT using the movement of
consequence level in each category, rather than the difference in the overall consequence:
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Table 20. Monetized Risk Reduction for Combined Water System WW Adaptation Plan
WW Adaptation Plan
Scenarios
Baseline
Projected
Utility Business Impacts
n/a
n/a
Utility Equipment
Damage
$0 -$46.5M
Greater than $93.1M
Environmental Impacts
$0
$0 -$1.3M
Source/Receiving Water
Impacts
n/a
n/a
Monetized Risk
Reduction
$0 -$46.5M
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).
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Office of Water (4608-T) EPA 817-B-21-001
March 2021
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