AEPA
United States
Environmental
Protection Agency
EPA600/R-23/175 | July 2023 | www.epa.gov/research
Handbook on Indicators of
Community Vulnerability to
Extreme Events: Considering
Sites and Waste Management
Facilities
Office of Research and Development
Center for Public Health and Environmental Assessment
-------
EPA/600/R-23/175
July 2023
www.epa.gov/research
Handbook on Indicators of Community
Vulnerability to Extreme Events:
Considering Sites and Waste
Management Facilities
Center for Public Health and Environmental Assessment
Office of Research & Development
U.S. Environmental Protection Agency
1200 Pennsylvania Ave, NW
Washington, DC 20460
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Contents
Executive Summary 1
1 Introduction 1
1.1 Context and Purpose 1
1.2 Scope 2
1.3 Applying the Handbook 3
1.3.1 Who should use this handbook? 3
1.3.2 What should the handbook be used for? 4
1.3.3 How to use the handbook 4
2 Indicator Framework and Approach 6
2.1 Framework 6
2.2 Indicators 8
2.2.1 Extreme Events 9
2.2.2 Sites and Waste Facilities 15
2.2.3 Fate and Transport 20
2.2.4 Sensitivity 27
3 Steps for Implementing Approach 31
3.1 Step 1: Define the Target Audience, Goals of the Analysis, and Scope 31
3.2 Step 2: Identify the Vulnerability Factors of Interest and the Indicators to Represent the
Factors 32
3.3 Step 3: Measure (Calculate Metrics) 32
3.4 Step 4: Analyze and Communicate Results 33
4 Flooding Example 36
4.1 Step 1: Define the Target Audience, Goals of the Analysis, and Scope 36
4.2 Step 2: Identify the Vulnerability Factors of Interest and the Indicators to Represent the
Factors 36
4.3 Step 3: Measure (Calculate Metrics) 39
4.4 Step 4: Analyze and Communicate Results 40
5 Checklists for Developing and Applying Indicators 41
References 234
Appendix A. Indicator Descriptions, Potential Contaminants and Brownfield Sites and
Vulnerability of Superfund Remediation Technology 235
Appendix B. Equations 249
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Figures
Figure 1. Vulnerability Scope: Impacts on Sites/Waste Facilities and Surrounding Communities 2
Figure 2. Conceptual Framework 7
Figure 3. Potential Wind and Surface Water Transport of Contaminants from Sites/Waste Facilities to
Downwind and Downstream Communities 21
Figure 4. Conceptual Model of Waterbody and Overland Flow from Sites/Waste Facilities to Surface
Waters and Nearby Populated Block Groups 23
Figure 5. Conceptual Model of Wind Flow from Sites/Waste Facilities to Populated Block Groups and
Use of Wind Rose 25
Figure 6. Four-step Process for Implementing Indicator Approach 31
Figure 7. Flooding Scenario: Questions the Indicators Aim to Inform (Examples) 38
Tables
Table 1. Potential Impacts of Extreme Events on Sites/Waste Facilities 8
Table 2. Overview of Vulnerability Sources and Indicator Information 9
Table 3. Exposure: Extreme Events 13
Table 4. Exposure: Sites/Waste Facilities 17
Table 5. Application of Waste Hazard Basis Codes to Assess Impacts of Extreme Events on Hazardous
Waste 18
Table 6. Exposure: Transport and Fate 22
Table 7. Sensitivity: Household Characteristics 29
ii
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Checklists
Vulnerability Source 1.1. Exposure: Extreme Events 42
Indicator 1.1.1. Checklist for Extreme Heat Indicator 42
Indicators 1.1.2 & 1.1.3. Checklist for Threshold-Based Extreme Heat Indicator 46
Indicator 1.1.4. Checklist for Wildfire Indicator 51
Indicator 1.1.5. Checklist for Floodplain-Based Flood Indicator 55
Indicator 1.1.6. Checklist for Precipitation-Based Flood Indicator 58
Indicator 1.1.7. Checklist for Threshold-Based Flood Indicator 64
Indicator 1.1.8. Checklist for Physically Based Flood Indicator 69
Indicators 1.1.9 & 1.1.11. Checklist for Drought Indicator 72
Indicators 1.1.10 & 1.1.12. Checklist for Threshold-Based Drought Indicator 77
Vulnerability Source 1.2. Exposure: Sites/Waste Facilities 82
Indicator 1.2.1. Checklist for Total Count of Sites/Waste Facilities Indicator 82
Indicator 1.2.2. Checklist for Count of Sites/Waste Facilities per Square Kilometer Indicator 85
Indicator 1.2.3. Checklist for Sites/Waste Facilities Count by Type Indicator 88
Indicator 1.2.4. Checklist for Tons of Hazardous Waste Indicator 91
Indicator 1.2.5. Checklist for Sites/Waste Facilities Count (by Hazard Type) Indicator 95
Indicator 1.2.6. Checklist for Waste Tonnage (by Hazard Type) Indicator 98
Indicator 1.2.7. Checklist for Brownfield Count with Contaminant; Cleanup Unknown (by
Contaminant) Indicator 102
Indicator 1.2.8. Checklist for Superfund Count with Vulnerable Remedy Technology (by Extreme
Event) Indicator 105
Indicator 1.2.9. Checklist for Count of Specific Type of Tank (UST/AST) Indicator 108
Indicator 1.2.10. Checklist for Total Tank Capacity (UST/AST) Indicator Ill
Vulnerability Source 1.3. Exposure: Transport and Fate 114
Indicator 1.3.1. Checklist for Count of Sites/Waste Facilities in a Floodplain Indicator 114
Indicator 1.3.2. Checklist for Count of Sites/Waste Facilities Within a Specific Hydrologic Distance of
a Flowline Indicator 117
Indicator 1.3.3. Checklist for Shortest Hydrologic Distance Upstream to a Site/Waste Facility
Indicator 121
Indicator 1.3.4. Checklist for Count of Upstream Sites/Waste Facilities within a Specific Hydrologic
Distance of a Community Indicator 125
Indicator 1.3.5. Checklist for Shortest Distance to a Site/Waste Facility Upwind [Season] Indicator...
129
Indicator 1.3.6. Checklist for Count of Sites/Waste Facilities "Upwind" within a Specific Season and
Distance of a Community Indicator 134
Indicator 1.3.7. Checklist for Minimum Response Time, [by Season] Indicator 140
Indicator 1.3.8. Checklist for Count of Sites/Waste Facilities That Are within Specific Response Time
Ranges, [by Season] Indicator 145
Vulnerability Source 2.1. Sensitivity: Household/Receptor Characteristics 150
Indicator 2.1.1. Checklist for Total Population Indicator 150
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.2. Checklist for Count of Households/Occupied Housing Units Indicator 153
Indicator 2.1.3. Checklist for Median Household Income Indicator 156
Indicator 2.1.4. Checklist for Percent of Population with Ratio of Income to Poverty Level Less Than
0.5 Indicator 159
Indicator 2.1.5. Checklist for Percent of Population with Ratio of Income to Poverty Level Between
0.5 and 1 Indicator 162
Indicator 2.1.6. Checklist for Percent of Households with Self-Employment Income Indicator 165
Indicator 2.1.7. Checklist for Percent of Civilian Employed Population 16 Years and over Who Work
Outdoors Indicator 168
Indicator 2.1.8. Checklist for Percent of Households That Are Renters Indicator 171
Indicator 2.1.9. Checklist for Percent of Households Living in a Mobile Home/Boat/RV/Van Indicator
174
Indicator 2.1.10. Checklist for Percent of Households without Telephone Service Indicator 177
Indicator 2.1.11. Checklist for Percent of Households with No Internet Access Indicator 180
Indicator 2.1.12. Checklist for Percent of Households Who Do Not Have a Vehicle Indicator 183
Indicator 2.1.13. Checklist for Percent of Population with No High School Degree Indicator 186
Indicator 2.1.14. Checklist for Percent of Population with No Health Insurance Indicator 189
Indicator 2.1.15. Checklist for Percent of Households with at Least 1 Person That Has a Disability
Indicator 192
Indicator 2.1.16. Checklist for Percent of Population under the Age of 18 Indicator 195
Indicator 2.1.17. Checklist for Percent of Population Who Are 65 or Over Indicator 198
Indicator 2.1.18. Checklist for Percent of Households with Single Members Who Are 65 or Over
Indicator 201
Indicator 2.1.19. Checklist for Percent of Population with Female Household Heads Indicator 204
Indicator 2.1.20. Checklist for Percent of Population That Is Black or African American Alone
Indicator 207
Indicator 2.1.21. Checklist for Percent of Population That Are Native Hawaiian or Other Pacific
Islander Alone Indicator 210
Indicator 2.1.22. Checklist for Percent of Population That Are American Indian or Alaska Native
Alone Indicator 213
Indicator 2.1.23. Checklist for Percent of Population That Are Asian Alone Indicator 216
Indicator 2.1.24. Checklist for Percent of Population That Belongs to Other Non-White Races
Indicator 219
Indicator 2.1.25. Checklist for Percent of Population That Are Hispanic or Latino Indicator 222
Indicator 2.1.26. Checklist for Percent of Households That Have Limited English Speaking Ability
Indicator 225
Indicator 2.1.27. Checklist for Percent of the Population Who Are over 18 and Non-U.S. Citizens
Indicator 228
Indicator 2.1.28. Checklist for Percent of Households That Moved within the Last 3 Years Indicator..
231
Indicator 1.1.1. Equations for Extreme Heat Indicator 249
Indicators 1.1.2 & 1.1.3. Equations for Threshold-Based Extreme Heat Indicator 249
Indicators 1.1.4. Equations for Wildfire Indicator 250
Indicator 1.1.5. Equation for Floodplain-Based Flood Indicator 251
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.1.6. Equations for Precipitation-Based Flood Indicator 251
Indicator 1.1.7. Equation for Threshold-Based Flood Indicator 252
Indicator 1.1.8. Equation for Physically Based Flood Indicator 252
Indicators 1.1.9 & 1.1.11. Equation for Drought Indicator 253
Indicators 1.1.10 & 1.1.12. Equation for Threshold-Based Drought Indicator 253
Indicator 1.2.1. Equation for Total Count of Sites/Waste Facilities Indicator 253
Indicator 1.2.2. Equation for Count of Sites/Waste Facilities per Square Kilometer Indicator 253
Indicator 1.2.3. Equation for Sites/Waste Facilities Count by Type Indicator 254
Indicator 1.2.4. Equation for Tons of Hazardous Waste Indicator 254
Indicator 1.2.5. Equation for Sites/Waste Facilities Count (by Hazard Type) Indicator 254
Indicator 1.2.6. Equation for Waste Tonnage (by Hazard Type) Indicator 254
Indicator 1.2.7. Equation for Brownfield Count with Contaminant; Cleanup Unknown (by
Contaminant) Indicator 255
Indicator 1.2.8. Equation for Superfund Count with Vulnerable Remedy Technology (by Extreme
Event) 255
Indicator 1.2.9. Equation for Count of Specific Type of Tank (UST/AST) 255
Indicator 1.2.10. Equation for Total Tank Capacity (UST/AST) 255
Indicator 2.1.1. Equation for Total Population 256
Indicator 2.1.2. Equation for Count of Households/Occupied Housing Units 256
Indicator 2.1.3. Equation for Median Household Income 256
Indicator 2.1.4. Equation for Percent of Population with Ratio of Income to Poverty Level Less Than
0.5 256
Indicator 2.1.5. Equation for Percent of Population with Ratio of Income to Poverty Level Between
0.5 and 1 256
Indicator 2.1.6. Equation for Percent of Households with Self-Employment Income 257
Indicator 2.1.7. Equation for Percent of Civilian Employed Population 16 Years and over Who Work
Outdoors 257
Indicator 2.1.8. Equation for Percent of Households That Are Renters 257
Indicator 2.1.9. Equation for Percent of Households Living in a Mobile Home/Boat/RV/Van 257
Indicator 2.1.10. Equation for Percent of Households without Telephone Service 258
Indicator 2.1.11. Equation for Percent of Households with No Internet Access 258
Indicator 2.1.12. Equation for Percent of Households Who Do Not Have a Vehicle 258
Indicator 2.1.13. Equation for Percent of Population with No High School Degree 259
Indicator 2.1.14. Equation for Percent of Population with No Health Insurance 259
Indicator 2.1.15. Equation for Percent of Households with at Least 1 Person That Has a Disability260
Indicator 2.1.16. Equation for Percent of Population under Age of 18 260
Indicator 2.1.17. Equation for Percent of Population Who Are 65 or Over 261
Indicator 2.1.18. Equation for Percent of Households with Single Members Who Are 65 or Over. 261
Indicator 2.1.19. Equation for Percent of Population with Female Household Heads 261
Indicator 2.1.20. Equation for Percent of Population That Is Black or African American Alone 262
Indicator 2.1.21. Equation for Percent of Population That Are Native Hawaiian or Other Pacific
Islander Alone 262
Indicator 2.1.22. Equation for Percent of Population That Are American Indian or Alaska Native
Alone 262
v
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.23. Equation for Percent of Population That Are Asian Alone 262
Indicator 2.1.24. Equation for Percent of Population That Belongs to Other Non-White Races 263
Indicator 2.1.25. Equation for Percent of Population That Are Hispanic or Latino 263
Indicator 2.1.26. Equation for Percent of Households That Have Limited English Speaking Ability 263
Indicator 2.1.27. Equation for Percent of the Population Who Are over 18 and Non-U.S. Citizens 264
Indicator 2.1.28. Equation for Percent of Households That Moved within the Last 3 Years 264
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
ACRONYMS AND ABBREVIATIONS
3-D
three-dimensional
3DEP
USGS 3-D Elevation Program
ACRES
Assessment, Cleanup and Redevelopment Exchange System
ACS
American Community Survey
ADEQ
Arizona Department of Environmental Quality
API
Application Programming Interface
AST
Aboveground storage tank
AZ
Arizona
AZDEQ
Arizona Department of Environmental Quality
BG
Block Group
BRAC
Base Realignment and Closure
BRS
Biennial Reporting Service
CESQG
Conditionally Exempt Small Quantity Generators
CIMC
Cleanups in My Community
CIRA
Climate Change Impacts and Risk Analysis
CMIP3
Coupled Model Intercomparison Project Phase 3
CMIP5
Coupled Model Intercomparison Project Phase 5
CONUS
Continental United States
CRAN
Comprehensive R Archive Network
CSV
Comma Separated Value
CT
Connecticut
DEM
Digital Elevation Model
EPA
U.S. Environmental Protection Agency
FEMA
Federal Emergency Management Agency
FRP
Facility Response Plan
FRS
Facility Registry Service
FTP
File Transfer Protocol
GCRP
Global Change Research Program
GHGRP
Greenhouse Gas Reporting Program
GIS
Geographic Information Systems
GRIB2
General Regularly-distributed Information in Binary form; commonly also called as Gridded
Binary
HAND
Height Above Nearest Drainage
HUC6
6-digit Hydrologic Unit Code
IPUMS
Integrated Public Use Microdata Series
JSON
JavaScript Object Notation
LMOP
Landfill Methane Outreach Program
LOCA
Localized Constructed Analogs
LQG
Large Quantity Generators
LUST-ARRA
Leaking Underground Storage Tank - American Recovery and Reinvestment Act
MNA
Monitored natural attenuation
NA
Not applicable
vii
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
NAM
North American Mesoscale Forecast System
NAM-ANL
North American Mesoscale Forecast System Analyses
NCEI
NOAA National Centers for Environmental Information
NCEP
National Centers for Environmental Prediction
NE
Northeast
NetCDF
Network Common Data Form
NFHL
National Flood Hazard Layer
NHD
National Hydrography Dataset
NIH
National Institutes of Health
NOAA
National Oceanic and Atmospheric Administration
NPL
National Priorities List
NW
Northwest
OEM
EPA Office of Emergency Management
ORCR
EPA Office of Resource Conservation and Recovery
ORD
EPA Office of Research and Development
ORNL
Oak Ridge National Laboratory
OSC
On-Scene Coordinator
OSRREPRB
Office of Site Remediation and Restoration Emergency Planning and Response Branch
PET
Potential evapotranspiration
R1
U.S. EPA Region 1
R9
U.S. EPA Region 9
RCP
Representative Concentration Pathway (Climate Scenario)
RCRA
Resource Conservation and Recovery Act
RV
Recreational vehicle
SAA
Superfund Alternative Approach
SE
Southeast
SEMS
Superfund Enterprise Management System
SHC
Sustainable and Healthy Communities
SNAP
Supplemental Nutrition Assistance Program
SPCC
Spill Prevention, Control, and Countermeasure
SPEI
Standardized Precipitation-Evapotranspiration Index
SPI
Standardized Precipitation Index
SQG
Small Quantity Generators
SW
Southwest
TC
Toxicity characteristic
TCLP
Toxicity Characteristic Leaching Procedure
TSDF
Treatment, Storage, and Disposal Facilities
UGRD
wind vector component, u
USDA
U.S. Department of Agriculture
USGS
U.S. Geological Survey
UST
Underground storage tank
UTC
Coordinated Universal Time
VGRD
wind vector component, v
VOIP
Voice over internet protocol
viii
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
WHO
World Health Organization
ix
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
PREFACE
This handbook was prepared by the U.S. Environmental Protection Agency's (EPA's) Sustainable and
Healthy Communities (SHC) research program, located within the Office of Research and
Development, with support from RTI International. The SHC research program provides scientific
information and tools that integrate public health, physical, natural, and social sciences, toxicology,
engineering, and ecosystems research to support Agency priorities and empower communities to
make scientifically informed decisions. Research is done with and for communities to improve their
access to clean air, water, and land for increased health and well-being where people live, learn,
work, and play. Across the U.S., there are thousands of sites contaminated by releases of chemicals
and toxins. These sites detract from human health and well-being, disrupt ecosystem services, and
limit productive use of the land. This handbook provides a conceptual framework and indicators to
assess the indirect impacts of extreme events such as extreme heat, floods, drought, and wildfires
on communities through potential exposure to contaminant releases from nearby contaminated
sites and waste management facilities. The indicator approach assists in identifying and prioritizing
communities that may be impacted the most and focusing preparedness, response, recovery, and
climate adaptation planning on areas that are the least resilient to extreme events.
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
AUTHORS, CONTRIBUTORS, AND REVIEWERS
The Sustainable and Healthy Communities (SHC) research program of EPA's Office of Research and
Development was responsible for producing this report. The report was prepared by Research Triangle
Institute (RTI) International in Research Triangle Park, NC, under EPA Contract No. 68HERD20A0004,
Task Order 68HERH20F0422. Philip Morefield served as the Task Order Contracting Officer's
Representative providing overall direction, and Meridith Fry served as the Alternate Task Order
Contracting Officer's Representative, providing technical assistance and was a contributing author.
AUTHORS:
Paramita Sinha, RTI International
Robert Truesdale, RTI International
Meridith Fry, U.S. EPA, Office of Research and Development
Susan Julius, U.S. EPA, Office of Research and Development
James Cajka, RTI International
Michele Eddy, RTI International
Donna Womack, RTI International
Prakash Doraiswamy, RTI International
Emily Decker, RTI International
Chandler Cowell, RTI International
Brian Lim, RTI International
Maggie O'Neal, RTI International
Jennifer Richkus, RTI International
John Buckley, RTI International
Ryan Brown, RTI International
Nathan Yates (Editor)
Nunzio Landi (Graphics Designer)
COLLABORATORS AND PARTNERS:
Ann Carroll, U.S. EPA Office of Land and Emergency Management
Laurie Amaro, U.S. EPA Region 9
xi
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Robin Thomas, Arizona Department of Environmental Quality
Rosanne Albright, Julie Riemenschneider, Matthew Potzler, City of Phoenix, Arizona
Jessica Dominguez, U.S. EPA Region 1
City of Waterbury, Connecticut
INTERNAL REVIEWERS:
Marissa Liang, U.S. EPA Office of Chemical Safety and Pollution Prevention
Alexander Hall, U.S. EPA Office of Research and Development
Britta Bierwagen, U.S. EPA Office of Research and Development
Steve Dutton, U.S. EPA Office of Research and Development
EXTERNAL REVIEWERS:
Susan Aragon-Long, Senior Science Advisor, U.S. Geological Survey
Ann Stapleton, National Institute for Food and Agriculture, U.S. Department of Agriculture
Jessica Blunden, National Oceanic and Atmospheric Administration
ACKNOWLEDGEMENTS:
We would like to thank the partners and collaborators for their participation and contributions to this
project. We also thank our internal and external reviewers for their insightful comments and suggestions
for improving this report.
xii
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Executive Summary
When extreme events (extreme heat, floods, droughts, wildfires) happen, sites and waste management facilities
have the potential to release contaminants, possibly impacting nearby communities. This handbook describes
how to select, develop, map, and analyze indicators as a screening approach to identify community sensitivities
and potential vulnerabilities. This approach was developed as a collaborative effort between RTI international,
the U.S. Environmental Protection Agency (EPA), and regional, state, and local partners. It has been refined and
demonstrated using two case studies (Waterbury, CT and Maricopa County, AZ).
The screening approach described herein involves four key steps:
1) Define the scope and spatial/temporal extent of the analysis in collaboration with partners
2) Identify key vulnerability factors and indicators to represent them
3) Develop and calculate indicators (identified above) for key determinants of impacts of extreme events
4) Use spatial mapping techniques to analyze and communicate results
Throughout the process, close coordination with local partners (e.g., community representatives and members,
technical experts, decision-makers) is crucial to ensure that the analysis is customized to suit specific community
needs and clearly communicated. (Note: This handbook uses the terms "partners" and "target audience"
interchangeably.) In the first two steps, the spatial area and vulnerability factors of interest to the communities
are defined and indicators representing these factors are selected. Developing indicators in the third step entails
gathering and vetting publicly available geospatial data on indicators that represent historical baseline and
projected extreme events, different types of waste facilities and potentially contaminated sites, pathways of
contaminant release and transport (via air and water), and characteristics of community populations. Mapping
the geospatial data in the fourth step aims to represent and communicate the information in a way that is most
useful to the target audience.
This handbook offers a transparent and replicable method of developing vulnerability indicators that teams of
planners, decision-makers, and technical advisors (for localities, cities, tribes, states, and regions), scientific
researchers, environmental advocates, and community organizations may use to screen and assess where the
most vulnerable communities/areas are in order to communicate and focus resources most effectively. More
specifically, it provides a way to (1) identify areas that are potentially vulnerable and identify the source(s) of
vulnerabilities, (2) track and monitor sites/waste facilities, including those of local interest, and (3) evaluate and
communicate how extreme events may impact the sites/waste facilities and the nearby community. Decision-
makers can use these results to develop and prioritize targeted strategies (e.g., adaptation, mitigation,
resilience, response) to prepare for and prevent potentially deleterious health and environmental impacts from
contaminant releases.
ES-1
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
1 Introduction
1.1 Context and Purpose
Extreme events, including excessive heat,
prolonged droughts, floods, and wildfires, are
projected to become more frequent and intense
under future climate scenarios. In addition to
their direct effects, extreme events also have
indirect effects on people through their impacts
to infrastructure and the surrounding
communities. Indirect consequences of these
extreme events may include higher exposures to
contaminants accidentally released from sites
and facilities that are either actively or have a
history of managing or storing hazardous
substances, wastes, or potential contamination
(henceforth referred to as "sites/waste
facilities"). This creates further hazards for
surrounding communities. Understanding the
risks to communities from potential exposures to
water- and airborne contaminants underlies
preparedness, emergency response, and
mitigation planning.
To better understand and communicate what is
known and unknown about such risks, this
handbook describes how to select, develop, map,
and analyze indicators. Indicators are repeatedly
tracked observations or modeled outcomes that
describe the state and trends of key variables
which can be used to understand changes and to
inform decision/policy making, (adapted from
Janetos and Kenney 2016). Example applications
of the indicators include:
Box 1. Indicator Approach
Why develop vulnerability indicators?
To support state and local decision-makers in
developing and prioritizing targeted mitigation,
adaptation, resilience, and response strategies to
prepare for and prevent potentially negative
health and environmental outcomes from
accidental contaminant releases from sites/waste
facilities.
Why indicators, why not site-specific
modeling?
In-depth modeling activities provide detailed
information but are typically:
• Complex, time and resource intensive
• In need of visualization and communication
expertise to support decision-making.
Indicators can be helpful as a screening tool for
prioritizing areas and resources where more in-
depth analysis is needed since they are:
• Easier and quicker to implement
• Simpler to visualize and communicate.
Has the method described in this handbook
been applied?
The community vulnerability indicators and maps
developed for the Maricopa County case study in
collaboration with EPA, Maricopa County, and
Arizona Department of Environmental Quality
(ADEQ) have been used to communicate, plan, and
take action to address community vulnerabilities in
the Phoenix Climate Action Plan
(www.phoenix.gov/oep/cap).
• Identifying communities that are vulnerable and the sources of their vulnerability
• Tracking sites/waste facilities, including those of local interest
• Communicating how extreme events may impact such facilities and the surrounding community.
1
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
This handbook provides a transparent and replicable approach that regional, state, and local managers
may apply to screen and assess vulnerable communities in order to communicate and focus resources
most effectively.
The indicators in this handbook were developed in close collaboration with the U.S. Environmental
Protection Agency (EPA) and regional, state, and local partners. They were refined and demonstrated
using two case studies (Waterbury, CT and Maricopa County, AZ). Local partners were engaged
throughout the process of developing the approach, defining the indicators, selecting, and vetting
datasets and mapping the indicators. Quantitative results and maps from the case studies were not
included in this document since this is meant to be a handbook for future users.1
1.2 Scope
The method presented in this handbook (Figure 1) covers the following measures (indicators):
• Historical and projected spatial information on the magnitude and frequency of extreme heat,
floods, droughts, and wildfires
• Characteristics of sites/waste facilities that are typically regulated, managed, or supported by the
EPA's Office of Land and Emergency Management or its state and Tribal counterparts
• Wind and hydrological patterns that determine how contaminants may be transported from sites/
waste facilities to communities
• Socioeconomic, demographic, and health characteristics of community populations
Figure 1. Vulnerability Scope: Impacts on Sites/Waste Facilities and Surrounding Communities
1 For the interested reader, results for Maricopa County are available in a journal article. Box 2 on page 4 provides example applications
of the results in Maricopa County/ Phoenix City to illustrate how the indicators in this handbook can be used.
2
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
While there are numerous risks to communities from worsening extreme events, this
handbook focuses on indicators of the indirect impacts of extreme heat, floods, droughts,
wildfires on communities through potential contaminant releases from sites/waste
facilities. Communities that are downstream and downwind of sites/waste facilities are
identified. Socioeconomic, demographic and health characteristics of community
populations identify individuals who are predisposed to be impacted more.
The indicators method presented in this handbook does not cover:
• Extreme events relevant to coastal areas such as sea level rise and hurricanes.
• Site-specific analysis and in-depth modeling activities that can be extremely resource and time
intensive. Instead, this method provides physically based indicators informed by models. These
indicators can be applied for screening purposes, so resources for modeling can be focused on the
most vulnerable communities.2 Compared to in-depth modeling, indicators can often effectively
convey the impacts of climate change to policymakers and the public in a simpler framework that is
easier to implement and understand.
• Direct impacts on human health (e.g., heat stroke, heat exhaustion). Rather, the method focuses on
the potential for contaminant releases from sites/waste facilities during the four types of extreme
events listed that may indirectly impact downstream or downwind communities.
• Indicators aggregated into an index or score. Keeping the indicators separate allows for each
source of vulnerability to be identified and for actions to be targeted toward specific sources.
1.3 Applying the Handbook
This handbook summarizes the steps and key considerations for developing vulnerability indicators to
screen, assess, and communicate potential extreme event impacts on sites/waste facilities and
surrounding communities.
1.3.1 Who should use this handbook?
This handbook is designed for teams of planners, decision-makers, and technical advisors (for localities,
cities, tribes, states, and regions), scientific researchers, environmental advocates, and community
organizations. Those new to using indicators and a vulnerability framework for assessing extreme
event impacts on communities can use this handbook independently or in consultation with others
who have preliminary experience with database management and geographic information systems
(GIS) techniques. Extensive expertise in fields such as environmental, health, or social science are not
required to use this handbook, since it provides a basic framework and step-by-step process for
applying the approach.
2 While in-depth modeling can provide more detailed and accurate information, communities may not have the resources to undertake
such efforts and screening indicators may provide crucial information for protecting the most vulnerable communities.
3
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
1.3.2 What should the handbook be used for?
This handbook provides steps for developing indicators to identify local areas and communities that
may be vulnerable to accidental releases of hazardous substances during extreme events. By mapping
sites/waste facilities, potential extreme events, local environmental conditions, and characteristics of
the population in the surrounding community, this information may be used to improve local decisions
and planning (e.g., adaptation, mitigation, resilience, response). A few examples of opportunities and
decision points where the method may be used include:
1) Citywide and neighborhood land planning and management for improving emergency
preparedness
2) Transportation, housing, and community development planning
3) Communication and building understanding of environmental assessment and cleanup needs
Box 2. Application of City of Phoenix and Maricopa County Results: Illustrative Example
The community vulnerability indicators and maps developed for the City of Phoenix and Maricopa County
case study were beneficial to Phoenix by:
1) Providing a greater understanding of the type and magnitude of sites/waste facilities in the
project area
2) Mapping sites/waste facilities that could be used for emergency preparedness and response
3) Providing maps of future climate scenarios to assess potential impacts of extreme heat drought,
wildfire, and flooding events
4) Providing the spatial distribution of population characteristics, particularly those most vulnerable
Initially, Phoenix used the extreme heat mapping scenarios as part of the city's 2021 Climate Action Plan and
related presentations to the public to illustrate the effects of temperature increases. As the plan progresses,
additional data and indicators for heat and drought will likely be presented and discussed. The social
vulnerability indicators may be used to assist in equitable decision-making as it relates to heat and water
resilience in the plan. This information will be shared with the city's Office of Homeland Security and
Emergency Response to be used, as applicable, in hazard mitigation planning and with city departments
focused on equity and environmental justice.
1.3.3 How to use the handbook
This handbook provides an indicator framework and approach for developing vulnerability indicators to
screen, assess, and communicate potential extreme event impacts on waste facilities and surrounding
communities (Section 2). It then describes how to implement a four-step approach for developing the
vulnerability indicators (Section 3). The steps outlined here also can be customized for specific
community needs and interests. Such customization is crucial to ensure that the analysis is relevant to
the community and can be clearly communicated to the public. The indicator lists provided in this
4
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
handbook are meant to provide options so that communities can choose what is relevant for them.
Further, considering selected indicators in conjunction with each other would allow for a more holistic
assessment than considering each indicator in isolation. It is not necessary to use every single indicator
presented here. An illustrative example for applying this approach in the context of flooding is
provided (Section 4). Later in this handbook, detailed checklists summarize how to develop, analyze,
and map each indicator. Users can adapt these checklists as needed for their purposes. Each checklist
(accompanied by appendices as needed) is designed to be stand-alone so that users can focus on only
those indicators that are relevant or of interest (Section 5).
5
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
2 Indicator Framework and Approach
2.1 Framework
Climate vulnerability refers to the susceptibility to, or level of inability to cope with, the adverse
impacts of climate change. This framework and approach specifically address the potential impacts of
extreme events (i.e., floods, drought, wildfire, extreme heat) that may be experienced by a community
due to a change in climatic conditions. The community impacts assessed include potential exposure to
contaminants that may be released from sites/waste facilities due to an extreme event and
transported by air and water to communities. The degree to which a community may be affected by
exposures resulting from an extreme event or changing climate depends on the sensitivity
characteristics of individuals in the community. Within this handbook, community adaptive capacity is
the ability to be resilient and adaptable to the risks and impacts of extreme events.
Figure 2 provides a conceptual framework that shows the connections between four sources of
vulnerability: extreme events [1], potential contaminant sources [2], exposure pathways [3], and
communities [4]. Extreme heat, floods, droughts, and wildfire [1] impact sites/waste facilities in
different ways (Table 1). Potential impacted sites/waste facilities include hazardous waste facilities,
Brownfields sites, contaminated sites subject to response and cleanup under the Resource
Conservation and Recovery Act (RCRA) (Corrective Action sites), federal and state Superfund sites,
removal or emergency response sites, and underground storage tank (UST) or aboveground storage
tank (AST) sites [2]. Contaminants can be released from such sites/waste facilities exposing people in
surrounding communities via numerous pathways [3]. The indicators in this handbook focus on
transport through air and surface water.3
Contaminant exposures can occur through different routes including inhalation, direct ingestion,
incidental ingestion, dermal contact, and indirect ingestion through the food chain. Not all people
experience the same severity and nature of impacts—some individuals and population groups are
predisposed to be more affected by exposures. In addition, communities have a range of
socioeconomic, demographic, and biological susceptibility characteristics [4]. Locational or community
conditions (e.g., economy, institutions, governance, natural and built environments, cultural norms,
social networks, legacies) interact with individual characteristics resulting in increased vulnerability for
some sensitive population groups (Maxwell, 2018). Such sensitive groups experience
disproportionately greater impacts because they are more likely to face higher exposures, they
experience greater impacts even for the same exposure, or they have more difficulty avoiding and/or
recovering from impacts.
3 Other important exposure pathways may include soil to groundwater; groundwater to surface water; vapor intrusion; for soil, direct and
incidental ingestion and dermal contact; and plant uptake. These pathways need to be considered in future research.
6
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Figure 2. Conceptual Framework
1. Extreme Events
Q ©
Extreme Heat Wildfire
Flood
Drought
©
1
. Sites & Waste Facilities
Types
• Hazardous waste operators
• Brownfields and Cleanup Sites
• Other sites and waste facilities
v. y
Release Mechanisms
I
I
V
3. Fate & Transport
Pathways
• Airtransport
• Surface water, Groundwater
• Soil
I
Y
Routes
4. Community
Sensitivity
• Socioeconomics
• Demographics
• Medical conditions
Adaptive Capacity
Planning/actions developed at the local level
Extreme events [1] increase the risk of contaminant release from sites/waste facilities [2], which are then transported
via air and surface water [3] potentially exposing nearby communities. Transport via groundwater and soil (shown in
gray) will be considered in future research. Impacts may be higher for community populations who are more sensitive
[4], Information on each of these four vulnerability sources can support planning/actions at the local level. Adaptive
capacity (in purple) is the ability of the community to address impacts on the four sources. Well-informed planning/
actions can help improve adaptative capacity. Note that direct impacts such as heat stroke and indirect impacts on fate
and transport such as change in wind patterns (shown by dashed arrows) are not considered in the handbook.
7
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Table 1. Potential Impacts of Extreme Events on Sites/Waste Facilities
Extreme Heat
¦ Increased fire hazards
¦ Off-gassing
¦ High pressures in closed vessels/tanks
¦ Changes to remediation effectiveness
¦ Overheating of equipment
¦ Power fluctuations/outages
Wildfires
¦ Catastrophic damage to facilities, ignitable ¦ Increased hazardous/non-hazardous waste
wastes; high pressure in closed vessels generation
¦ Overheating of equipment ¦ Power fluctuations/outages
¦ Incident waste facility closures ¦ Acute air quality incident/emergency:
hazardous smoke plume
Drought
¦ Increased fire hazards, fugitive dust
¦ Reduction in remediation effectiveness
¦ Changes in groundwater plume dynamics
and quality due to lower water table
Floods
¦ Erosion may be more likely
¦ Ponding/stormwater management difficult
¦ Water damage (corrosion, water logging)
¦ Power fluctuations/outages
¦ Groundwater plume changes and higher
water table
¦ Spreading/migration of contamination
¦ Catastrophic events destroy structures
¦ Releases from overwash, infiltration, and
leaching
Note: The table above provides example impacts via soil and groundwater that are not the focus of this handbook but are
included in this table for completeness.
2.2 Indicators
The indicators in this handbook represent each of the four vulnerability sources shown in Figure 2 and
Table 2. Exposure indicators represent potential exposure due to extreme events (heat, floods,
drought, and wildfire), specific sources of contaminant releases (the different types of sites/waste
facilities), and contaminant fate and transport (through water and wind). The fourth type of indicator
represents population sensitivity characteristics (demographics, socioeconomic conditions, existing
health conditions) that indicate which individuals in the community may be impacted more by extreme
events. The indicators are provided at the Block Group level (U.S. Census Bureau, 2022), and each
Block Group is considered to be a "community". Other spatial scales can be chosen as relevant.
Water use restrictions impacts
Increased scrutiny of groundwater extraction
systems
Damage to vegetative covers
Incident waste facility closures
Increased hazardous/non-hazardous waste
generation
Groundwater pump-and-treat remedies may
not be allowed to discharge
Increased potential for flooding of treatment
systems
Dislodged debris from treatment/containment
systems or contained wastes
Reactive wastes
8
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Table 2. Overview of Vulnerability Sources and Indicator Information
Sources of Vulnerabilities
What Information Do Vulnerability
Indicators Provide?
Exposure
Extreme
Events
¦ Extreme heat
¦ Floods
¦ Droughts
¦ Wildfires
Historical and projected information on
the magnitude and frequency that
indicate which sites/waste facilities and
communities face higher risks from
specific events and how these risks may
change under changing climates
Sites/Waste
Facilities
¦ Hazardous waste
operators
¦ Sites and cleanup
facilities
¦ Other sites/waste
facilities
Potential sources of contaminant
releases such as site/waste facilities
locations, contaminants/hazardous
waste present, and remediation
technologies
Fate and
Transport
¦ Surface water
¦ Wind
How far contaminants may be
transported due to local hydrological and
wind patterns
Sensitivity
Population
¦ Demographics
¦ Socioeconomics
¦ Existing health
conditions
Which individuals are least able to
prepare for and respond to contaminant
exposures due to extreme events
Note: Transport via groundwater and soil will be considered in future research.
2.2.1 Extreme Events
Information on the magnitude and frequency of extreme events can provide useful indicators of
potential risks for sites/waste facilities. The heat, wildfire, flood, and drought indicators (Table 3)4
capture the following:
• Scenarios: Historical trends and future projections represent past and changing environmental
conditions. See Box 3 on Climate Change Terms and Concepts (page 11) for additional context.
o To encompass a wide range of possible futures, multiple climate scenarios can be used. For
example, Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 represent moderate and
more extreme conditions, respectively. Other climate scenarios/models can be used as needed.
• Time period: Averages over a 20-year period are provided to avoid short-term weather fluctuations
or modeling uncertainties and to represent long-term climatic conditions and patterns.
o In this handbook, historical baseline conditions are represented by averages over 1986-2005,
and future conditions by averages over the mid-century (2040-2059). Climate projections in this
4 Appendix Table A1 provides more detailed descriptions of the indicators.
9
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
handbook are calibrated using data for 1986-2005; therefore, these years were selected as the
baseline period. The Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report
also used 1986-2005 as the baseline period. A 30-year period can also be used to represent
longer-term climatic conditions as needed.5 Other time periods can be used as needed.
• Metric type: Climate projections vary across time and location and alternate types of metrics that
represent extremes (e.g., maximum summer temperature) and differences (e.g., difference
between maximum summer temperature in mid-century and historical) are provided.
o Planners may find different metrics useful depending on their needs. For example, they may
want to assess which communities face increased risks over time compared to others.
Differences in projected and historical values may be useful either in absolute or in percentage
terms.
• Thresholds: To assess what would be considered an "extreme" event in the context of sites/waste
facilities, a "threshold" approach is provided. Ideally, site-specific information is needed to capture
thresholds beyond which sites/waste facilities are at an increased risk of failure. However, because
this handbook aims to provide a simpler, screening indicator- approach, the indicators provided use
historical baseline extreme conditions as thresholds. The assumption is that existing sites/waste
facilities are typically built to withstand historical baseline average conditions. Anything beyond
historical baseline extremes is potentially riskier for sites/waste facilities with (active or legacy)
contaminants and technologies that are sensitive to temperature or precipitation.
o The handbook uses 99th percentile measures for temperature and precipitation to define
extremes to represent high-end risks and be most protective. For example, extremely hot days
occur when the maximum daily temperature is greater than the 99th percentile of maximum
daily temperatures. Different percentiles can be used depending on community needs and local
conditions.
o The handbook provides measures of drought that use a time scale of at least six months. This
provides a more likely condition of drought that impacts a site/waste facility (Table 1) due to
lack of water availability. At shorter time scales, drought conditions can often be managed
through alternate water supplies or demand management (e.g., restricted landscape watering).
Measures based on 6 months and 12 months are provided as potential indicator options for
community use.
• Physically based measures: For flooding, an indicator based on the terrain (elevation) of the area is
provided because precipitation is not the only factor that determines flood risk. Low-lying areas are
often more likely to flood.
5 Traditionally, 30-year timeframes were considered most appropriate for capturing climate conditions and patterns. However, shorter
timeframes are now also being considered to reflect more recent changing conditions. For example, NOAA now produces 15-year climate
normals in addition to the 30-year normals. Our choice of 20 years was driven by inputs from local partners during the case studies.
10
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Box 3. Climate Change Terms and Concepts
Climate refers to how the atmosphere behaves over long periods of time, while weather refers to short-
term changes in the atmosphere. Climate is usually defined as average weather patterns for a particular
region and time period. Climate reflects typical expectations, while weather reflects what happens.
• Climate change reflects changes in long term averages of daily weather.
• A baseline period serves as the reference point from which to calculate changes in climate. Due to
climate variability, a single year may not be a useful reference point for measuring climate change,
since it can be unusually warm, cold, dry, or wet. It is more common to use the average climate
over a certain period to define the baseline climate.
• Traditionally, 30-year time frames are considered most appropriate for capturing climate
conditions and patterns. However, shorter time frames are used to reflect more recent changing
conditions. For example, NOAA now produces 15-year climate normals in addition to the 30-year
normals. The IPCC 5th Assessment Report uses a 20-year average for 1986-2005 for their analysis
(Collins et al., 2013).
To understand how climate may change in the future, researchers use computer models to simulate Earth's
climate system. The simulated results, or climate projections are uncertain due to uncertainty in how
human and natural systems may evolve and the responses of the Earth's climate system to the complex
interaction of different natural and human systems.
• Scenarios provide climate researchers with a common set of plausible descriptions of how the
future may evolve with respect to a range of variables. These variables may include socio-
economic change, technological change, energy and land use, and emissions of greenhouse gases
and air pollutants.
• The development of scenarios has evolved over time and may continue to do so based on new
information and research needs. Currently, representative concentration pathways (RCPs) are the
most used scenarios in climate research (e.g., in the IPCC 5th Assessment Report). They provide
projections of how concentrations of greenhouse gases in the atmosphere will change in future as
a result of human activities. RCPs capture future trends in technology, economies, lifestyle, and
policy including climate mitigation and adaptation options.
• Scenarios assist in the evaluation of uncertainty in human contributions to climate change, the
response of the Earth system to human activities, the impacts of a range of future climates, and
the implications of different approaches to mitigation (measures to reduce net emissions) and
adaptation (actions that facilitate response to new climate conditions). Rather than predict the
future, scenarios help understand a range of possible futures. The four RCPs (2.6, 4.5, 6.0, and 8.5)
range from very high (RCP8.5) to very low (RCP2.6) future concentrations of greenhouse gases.
For more information, please see:
NASA—What's the difference between weather and climate?
https://www.nasa.gov/mission pages/noaa-n/climate/climate weather.html
NOAA NCEI—What's the difference between weather and climate?
https://www.ncei.noaa.gov/news/weather-vs-climate
11
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
NOAA—Understanding climate normals, https://www.noaa.gov/explainers/understanding-climate-
normals
Collins, M., et al. (2013): Long-term Climate Change: Projections, Commitments and Irreversibility. In:
Climate Change 2013: The Physical Science Basis. ICPP
https://www.ipcc.ch/site/assets/uploads/2018/02/WGlAR5 Chapterl2 FINAL.pdf
Moss, R.H., et al. (2010). The next generation of scenarios for climate change research and
assessment. Nature, 463(7282), 747-756. http://dx.doi.org/10.1038/nature08823
van Vuuren, D.P., et al. (2011). The representative concentration pathways: An overview. Climatic
Change, 109(1-2), 5-31. http://dx.doi.org/10.1007/slQ584-011-0148-z
12
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Table 3. Exposure: Extreme Events
ID*
Indicator Definition**
Parameter Options
Extreme Heat
1.1.1
Extreme heat: Maximum summer temperature [for
selected time period, scenario]
¦ 1986-2005/2040-2059
¦ RCP 4.5/8.5
¦ Absolute historical and projected
¦ Difference or percent difference between projected and historical mean
1.1.2
Threshold-based extreme heat: Annual maximum
temperature for "extreme heat days" [for selected time
period, scenario]
¦ 1986-2005/2040-2059
¦ RCP 4.5/8.5
¦ Absolute historical and projected
1.1.3
Threshold-based extreme heat: Change in the annual
count of "extreme heat days" between [selected time
period, scenario] and historical
¦ 1986-2005/2040-2059
¦ RCP 4.5/8.5
¦ Difference between projected and historical count
Wildfire
1.1.4
Wildfire: Fraction of Block Group area burned for
[selected time period, scenario]
¦ 1986-2005/2040-2059
¦ RCP 4.5/8.5
¦ Absolute historical and projected
¦ Difference or percent difference between projected and historical mean
Flood
1.1.5
Percent of Block Group within a [selected degree of
flood] floodplain
¦ 100/500-year
1.1.6
Precipitation-based flood: Annual % of precipitation depth
falling during "heavy events" for [selected time period,
scenario]
¦ 1986-2005/2040-2059
¦ RCP 4.5/8.5
¦ Absolute historical and projected
1.1.7
Threshold-based flood: Change in the average annual
percent of precipitation depth falling during "heavy
events" between [selected time period, scenario] and
historical
¦ 1986-2005/2040-2059
¦ RCP 4.5/8.5
¦ Difference between projected and historical mean
1.1.8
Physically based flood: Mean height above the nearest
drainage
N/A
13
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
ID*
Indicator Definition**
Parameter Options
Drought
1.1.9
Drought: Count of drought (defined by SPEI-6) months
for [selected time period, scenario]
¦ 1986-2005/2040-2059
¦ RCP 4.5/8.5
¦ Absolute historical and projected
1.1.10
Threshold-based drought: Change in the count of drought
(defined by SPEI-6) months between [selected time
period, scenario] and historical
¦ 1986-2005/2040-2059
¦ RCP 4.5/8.5
¦ Difference between projected and historical count
1.1.11
Drought: Count of drought (defined by SPEI-12) months
for [selected time period, scenario]
¦ 1986-2005/2040-2059
¦ RCP 4.5/8.5
¦ Absolute historical and projected
1.1.12
Threshold-based drought: Change in the count of drought
(defined by SPEI-12) months between [selected time
period, scenario] and historical
¦ 1986-2005/2040-2059
¦ RCP 4.5/8.5
¦ Difference between projected and historical count
* ID num
bering X.Y.Z: X denotes exposure/sensitivity (exposure: 1; sensitivity: 2); Y denotes 3 sources of exposure (extreme events:!, sites/
waste facilities: 2, fate/transport: 3, and 1 source of sensitivity (population characteristics: 1), and Z denotes the indicator (numbered
sequentially)
** All indicators are by Block Group
14
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
2.2.2 Sites and Waste Facilities
Information on the sites/waste facilities can provide useful indicators of potential risks for contaminant
releases. Expertise in the types of sites/waste facilities and their attributes may be valuable in
reviewing and selecting which of these indicators to consider. The site/waste facility indicators in Table
4 capture the following:
• Spatial attributes: Location of sites/waste facilities provide the simplest indicators of potential
contaminant releases.
o The handbook provides indicators for counts and density of sites/waste facilities. Counts are
simpler to use and communicate, but density accounts for variation in the community/spatial
unit size and may provide more information to decision-makers, depending on their needs.
• Broad physical/environmental/regulatory attributes: Types of facilities provide information on the
types of contaminants that may be present, the physical structures themselves, and how they are
managed or regulated.
o The handbook provides indicators for 15 types of sites/waste facilities. Communities may be
interested in one or more types. The density of each facility type may also be useful.
• Specific hazardous environmental attributes: Types of waste present at each facility handling
hazardous wastes provides more information on the different hazards present and what types of
extreme events could most impact these sites/waste facilities (Table 5). An EPA listing of hazardous
waste codes indicates why the waste was listed as hazardous and crosswalks hazardous waste
codes with six hazardous waste characteristics: ignitable, corrosive, reactive, toxicity characteristic,
and acute hazardous and toxic (U.S. EPA, 2012a, U.S. EPA, 2022).6
o The handbook provides counts of hazardous waste facilities and quantity of the hazardous
waste. Simple counts of hazardous waste facilities may fail to reflect the total amount of waste
stored or processed at these sites/waste facilities. The waste tonnage indicator helps address
that shortcoming.
o The handbook provides indicators that map the types of waste present to one or more of the six
hazard types. Both counts of facilities with each type of hazard and tons of each type of
hazardous waste are included. The count of hazardous waste facilities with a specific type of
hazardous waste present can help identify community vulnerabilities to specific waste types. For
example, a Block Group with a high number of facilities with ignitable waste may pose a higher
risk in extreme heat or wildfires, while a Block Group with reactive wastes may pose a higher risk
during flooding. Tonnage of specific types of hazardous waste provides even more detail.
6 Note that more detailed definitions of hazards (e.g., carcinogenicity, genotoxicity mutagenicity, endocrine disruption, bioaccumulation
potential) could be used. However, the EPA listing does not provide crosswalks of such characteristics with waste codes. Also, a detailed
breakdown of hazards was not considered for this simple screening approach.
15
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
o The handbook provides indicators for the counts and capacity of aboveground and underground
storage tanks containing potentially hazardous substances (e.g., petroleum, solvents, hazardous
wastes). A high number of tanks does not always translate to a high volume of stored
substances. A high number of low-capacity tanks may pose less risk than a small number of high-
capacity tanks. Capacity indicators are included to provide information on the volume of
substances that can potentially be stored in the tanks.
• Specific environmental/regulatory attributes: Cleanup status and type of contaminants that may
potentially be present at Brownfield sites provide more information than simple counts of
Brownfields sites. Sites/waste facilities that have been assessed and contaminants found are likely
to be riskier unless they have been cleaned up. However, the information in publicly available
Brownfield datasets may not be updated regularly and may be inaccurate as a result. Information
such as cleanup status should be checked for accuracy with local regulators and other partners
prior to use in this application.
o The handbook provides indicators with information on the potential contaminants that may be
present at the site/waste facility. Appendix Table A.2 provides a list of contaminants that may be
found in Brownfields data sources. However, caution must be used because datasets with this
information are based on voluntary reporting, and assessment and cleanup status available
publicly may not be reflective of the most current status at a site/waste facility.
• Specific physical and technological attributes: Sites/waste facilities can have remediation
technologies in place that may pose a risk during specific types of extreme events.
o The handbook provides indicators with information on the counts of Superfund sites with any
remediation technology vulnerable to any extreme event. Appendix Table A.3 provides a
crosswalk of vulnerability of 27 possible Superfund remediation technologies to drought, fire,
flooding, or extreme heat.7
7 Methods provided by U.S. EPA (2012) was adapted and expanded to develop this mapping.
16
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Table 4. Exposure: Sites/Waste Facilities
ID*
Indicator Definition**
Parameter Options
Counts
1.2.1
Total count of sites/waste facilities
N/A
1.2.2
Count of sites/waste facilities per square km
N/A
1.2.3
Sites/waste facilities count by type
. fieneratnrq " SiteS listed in the SuPerfund
_ , , 0, . Enterprise Management System
¦ Treatment, Storage, and , ,. , . . 3 ..
Disposal facilities (TSDFs) jfpELMS>< bul n0>lncluded on lhe
¦ Transporters ,,
¦ Transfer facilities " Removal/emergency response
¦ Other operators
¦ Resource Conservation and " Nonhazardous landfills
Recovery Act (RCRA) 1 Pefroieum storage tanks
_ ¦¦ a 1- -1 ¦ Incident waste facilities
Corrective Action sites ... . ,.
0 ,. .. ¦ Oil spill response/prevention
¦ Brownfields ^ ^ ^
sites
¦ Federal and State Superfund , „ . . ,.c . .. .
.. ... .. Mx-, ¦ Other locally identified sites/
sites listed on the National . f ..... . . ...
n ¦ x- i ¦ x/Mm x waste facilities not included
Priorities List (NPL) . . «
v ' above (optional)
Hazardous Waste
1.2.4
Tons of hazardous waste
N/A
1.2.5
Sites/waste facilities count (by hazard type***)
¦ Ignitable
¦ Corrosive
¦ Reactive
¦ Toxicity characteristic
¦ Acute hazardous
¦ T oxic
1.2.6
Waste tonnage (by hazard type***)
Brownfields and Superfund
1.2.7
Brownfield count with contaminant; cleanup unknown (by
contaminant)
¦ Contaminant list in Appendix Table A.2
1.2.8
Superfund count with vulnerable remedy technology (by
extreme event)
¦ Remediation technology list in Appendix Table A.3
17
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
ID*
Indicator Definition**
Parameter Options
Tanks
1.2.9
Count of specific type of tank (UST/AST)
¦ Underground storage tanks/aboveground storage tanks
1.2.10
Total tank capacity (UST/AST)
N/A
* ID num
bering X.Y.Z: X denotes exposure/sensitivity (exposure: 1; sensitivity: 2); Y denotes 3 sources of exposure (extreme events:!, sites/
waste facilities: 2, fate/transport: 3, and 1 source of sensitivity (population characteristics: 1), and Z denotes the indicator (numbered
sequentially)
** All indicators are by Block Group
*** The six hazard types (list under Parameter Options) are defined in Table 5.
Table 5. Application of Waste Hazard Basis Codes to Assess Impacts of Extreme Events on Hazardous Waste
Hazard
Code*
Hazard Code
Description
Application to Extreme Events Impacts
1
Ignitable Waste
Sensitive to high temperatures (heat waves, wildfire). Includes liquids with flash points below 60°C, nonliquids
that cause fire through specific conditions, ignitable compressed gases, and oxidizers
C
Corrosive Waste
Aqueous wastes with a pH < 2 or> 12.5; hazards due to flood washout or based on the liquid's ability to
corrode steel
R
Reactive Waste
Unstable under normal conditions; reacts with water (floods), may give off toxic gases and may be capable of
detonation or explosion under normal conditions or when heated (heat waves, wildfire)
E
T oxicity
Characteristic
Waste
Harmful when ingested or absorbed. Hazardous through leaching to groundwater (flooding); Determined
through Toxicity Characteristic Leaching Procedure (TCLP) (extract leachate and identify when concentrations
are greater than regulatory toxicity characteristic [TC] limits)
H
Acute Hazardous
Waste
Acutely hazardous waste if it is fatal to humans or animals at low doses, is beyond specified toxicity limits, or is
otherwise capable of causing or significantly contributing to an increase in serious irreversible, or incapacitating
reversible, illness; may require emergency response if released through water or air
T
Toxic Waste
Waste listed because of toxicity to humans or environment; hazardous exposures through soil, water, or air
exposure pathways
18
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
* The first four hazard codes apply to wastes that have been listed because they typically exhibit one of the four regulatory characteristics of
hazardous waste. The last two hazard codes apply to listed wastes with constituents that pose additional threat to human health and the
environment (U.S. EPA, 2012a, U.S. EPA, 2022).
19
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
2.2.3 Fate and Transport
Information on how far contaminants travel can provide useful indicators for identifying areas and
communities that may potentially face high exposure risks. Once contaminants are released, they
move through the environment by naturally occurring processes. As an initial step to identifying areas
most vulnerable to impacts, the distance of communities from a site/waste facility can be considered.
However, as illustrated in Figure 3, using simple radial distances may not provide accurate measures of
how far contaminants can be transported. To more accurately identify communities impacted, this
handbook provides indicators based on fate and transport concepts that consider water and air
pathways through which contaminants may reach communities. This approach integrates information
on site/waste facility count and elevation, hydrology, and meteorology to represent movement
processes in the environment.8 Expertise in hydrologic flow and wind patterns would be valuable for
calculating the fate and transport indicators (Table 6), which capture the following:
• Hydrology: Water flowing from a site/waste facility toward a community increases the potential risk
of exposure to waterborne contaminants (Figure 4). Increases in risk are not only due to the flowing
waters of streams and rivers that may receive contaminants after a release, but also due to erosion
and overland flow or runoff generated from flooding events that spread the contamination from a
facility.
o The handbook provides indicators with information on the count of facilities within floodplains,
upstream distances (in terms of overland flow) between sites/waste facilities and communities,
proximity of facilities to the hydrologic network and counts of facilities within certain upstream
"raindrop" distances. Decision-makers have the flexibility of selecting different distance cutoffs
for the latter indicator.
• Wind patterns: Wind blowing from a site/waste facility toward a community increases the potential
risk of exposure to airborne contaminants (Figure 5). Wind speed and wind direction inform the
potential location and estimated time it takes for contaminants to reach communities. However,
wind patterns vary significantly across seasons, and both wind speed and wind direction can change
within minutes. The method applied here is to use wind rose (e.g., see USDA, 2022) information to
identify the predominant wind direction, which refers to the direction from which the wind blows
for most of the time during the season, and is calculated based on historical wind patterns. It would
be important to also monitor for long-term changes in wind patterns that might impact which
communities are vulnerable.
o The handbook provides indicators with information on upwind distances between sites/waste
facilities and communities, proximity of communities to facilities in the direction of wind, the
time it may take for contaminants to be transported to communities and counts of facilities
within certain upwind distances. Decision-makers have the flexibility of selecting different
distance cutoffs for the latter indicator. Each indicator is provided for each of the four seasons.
8 Future research can consider including characteristics of sites/waste facilities (e.g., contaminants) and population characteristics (e.g.,
those below the poverty line) in fate and transport indicators.
20
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Figure 3. Potential Wind and Surface Water Transport of Contaminants from Sites/Waste Facilities to Downwind and
Downstream Communities
21
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Table 6. Exposure: Transport and Fate
ID*
Indicator Definition**
Parameter Options
Surface Water
1.3.1
Count of sites/waste facilities in a floodplain [100-year
and 500-year]
¦ 100/500-year
1.3.2
Count of sites/waste facilities within a specific hydrologic
distance of a flowline
¦ 500 m/1 km/2 km/5 km
1.3.3
Shortest hydrologic distance (m) upstream to a site/waste
facility
N/A
1.3.4
Count of upstream sites/waste facilities within a specific
hydrologic distance of a community
¦ 500 m/1 km/2 km/5 km
Air
1.3.5
Shortest distance to a site/waste facility upwind [season]
¦ Spring/summer/fall/winter
1.3.6
Count of sites/waste facilities in predominant wind
direction "upwind" within a specific season and distance
of a community
¦ Spring/summer/fall/winter
¦ 5 km/15 km/25 km/40 km
1.3.7
Minimum response time, [by season]
¦ Spring/summer/fall/winter
1.3.8
Count of sites/waste facilities that are within specific
response time ranges, [by season]
¦ Spring/summer/fall/winter
¦ 2 min/5 min/10 min/15 min/20 min
* ID num
bering X.Y.Z: X denotes exposure/sensitivity (exposure: 1; sensitivity: 2); Y denotes 3 sources of exposure (extreme events:!, sites/
waste facilities: 2, fate/transport: 3, and 1 source of sensitivity (population characteristics: 1), and Z denotes the indicator (numbered
sequentially)
** All indicators are by Block Group
22
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Figure 4. Conceptual Model of Waterbody and Overland Flow from Sites/Waste Facilities to Surface Waters and Nearby
Populated Block Groups
[ 1 Block group
Stream reach catchment
(Surface area contributing runoff
to the specific stream reach)
Distance upstream
from a block group
rRJIq Facility with a release through
Jjffij surface flow into local stream
£i Community within a block group
23
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
A (Indicator 1.3.1. Count of sites/waste facilities within a floodplain): Block Group A contains both housing and sites/
waste facilities within the floodplain area (green dashed lines surrounding the waterway). In this case, the Block Group
contains two sites/waste facilities within the floodplain area.
B (Indicator 1.3.2. Count of sites/waste facilities within a certain distance of a flowline): Block Group B contains two
sites/waste facilities. The lower site/waste facility in the figure is closer, using the overland distance flow measure (red
lines) than the upper site/waste facility. In this case, we may set a threshold for the distance to the flowline (i.e., stream)
that counts the lower site/waste facility but does not count the upper site/waste facility as it is farther away and poses
less risk of an overland release through heavy rain reaching the stream channel on which the Block Group lies.
C (Indicator 1.3.3. Shortest hydrologic distance upstream to a site/waste facility): Block Group C illustrates how a release
from a site/waste facility due to heavy rain may reach a Block Group as it flows downslope to a waterway. For this Block
Group there is one upstream site/waste facility where the distance reported is shown by the red line, which measures
the overland flow distance that a raindrop would flow from the point of release at the facility to the boundary of the Block
Group. Additionally, the shortest hydrologic distance upstream to a site/waste facility is illustrated by the other example
Block Groups where both A and B would have a value of 0 because they contain sites/ waste facilities, and D would
report the distance from the site/waste facility on the right, which is closer in overland flow path.
D (Indicator 1.3.4. Count of upstream facilities within a certain distance of the community): For Block Group D there are
two sites/waste facilities that are upstream or upgradient of the Block Group. The distance from the site/waste facility to
the community is measured as the overland flow distance to the boundary (red line). In this case, we may set a threshold
for that distance that would count the closer site/waste facility on the right, while the site/waste facility on the left is
farther away and therefore poses less risk.
Note: The accompanying fate and transport processes within the soil and groundwater that could likely accompany the
depicted pathways are not included within this initial screening-level process and are instead referred for future research.
24
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Figure 5. Conceptual Model of Wind Flow from Sites/Waste Facilities to Populated Block Groups and Use of Wind Rose
,Ju£
Quadrant centered on
block group centroid
Radial distances from the
\ block group centroid
Block group
Wind rose
Straight line distance of
facility from block group
a
Grouping of facilities
impacting block group
25
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Schematic showing a community Block Group (black polygon) impacted by sites/waste facilities F1 and F2 that are
upwind of the community. The wind rose indicated by the violet spokes shows the direction from which the wind blows.
The length of each spoke indicates the percent of time that the wind is blowing from a specific direction. The longer the
spoke, the more frequent is the wind from that respective direction. The gradient in the color (shades of violet in this
example) refers to the different speeds at which the wind is blowing from that specific direction, resulting in an average
wind speed of Ws. In this example, the wind blows predominantly from the northwest at an average wind speed of Ws.
Indicator 1.3.5. Shortest distance to an upwind site/waste facility: D1 and D2 indicate the distance of Sites/waste facilities
F1 and F2 respectively from the centroid of the Block Group. In this example, D2 is the shortest distance to the nearest
upwind facility F2 in the predominant wind direction.
Indicator 1.3.6. Count of sites/waste facilities in predominant wind direction ("upwind") within a specified distances (5,
15, 25 and 40 km) of community: Sites/waste facilities F1 and F2 (indicated by the red enclosure) impact the community,
while F3 does not under northwest wind conditions shown in this example. Thus, a total of 2 sites are upwind; this
indicator presents this a cumulative count within specified radial distances from the community. The distances D1 and
D2 will determine the counts within each distance. If for example, D1 = 6 km and D2 = 3 km, we would have one site
within 5 km and 2 sites within 15 km.
Indicator 1.3.7. Minimum response time: The time for a site/waste facility's emissions to impact the community is equal
to the distance D1 or D2 divided by the wind speed Ws. The shortest distance to a site/waste facility in the predominant
(i.e., northwest) direction of the community determines the minimum response time. In this example, the minimum
response time for the community to take action is D2/Ws.
Indicator 1.3.8. Count of sites/waste facilities that are within specified response time ranges (2, 5, 10, 15 and 20 min):
Similar to indicator 1.3.6, this indicator is a cumulative count of facilities within specified response time ranges. The
response times for the two facilities, D1/Ws and D2/Ws, determine the counts within different time ranges.
26
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
2.2.4 Sensitivity
Information on household characteristics can provide useful indicators for identifying which population
groups may be impacted most. Sensitive households experience disproportionately higher negative
impacts because: 1) they are more likely to face higher exposures given current circumstances (e.g.,
those who work outdoors), 2) they experience greater impacts even for the same exposure (e.g.,
children/elderly/asthmatics have more respiratory issues), and 3) they may have more difficulty
avoiding or recovering from negative impacts (e.g., those with insufficient community networks). A key
point to note is that a population group may represent vulnerability due to multiple reasons. For
example, elderly may have health concerns, restricted mobility, be less connected to support
networks, and have limited resources. Further, communities at greatest risk of exposure and least able
to prepare for and respond are often those that have a high proportion of households that are already
disadvantaged or face other environmental injustices. The sensitivity indicators (Table 7) capture the
following:
• Overall size of community: Population size provides the simplest measure of how many people may
be impacted.
o This handbook provides indicators for total population size and count of households. Total
population size includes those living in "group quarters" (U.S. Census Bureau, 2019). Group
quarters include such places as college residence halls, residential treatment centers, skilled
nursing facilities, group homes, military barracks, correctional facilities, workers' dormitories,
and facilities for people experiencing homelessness. Decision-makers may also need information
on number of families that need to be sent communications materials before or during an event.
The count of households indicator includes only those residences that are occupied.
• Under-resourced households: Households with low income and wealth may not have resources to
take preventive measures or recover quickly (e.g., due to low home or medical insurance
coverage). They may also live and work in higher exposure areas (e.g., close to sites/waste facilities
or in flood-prone areas), which historically have lower property values. Households may also have
jobs that are likely to be more locally dependent (e.g., those who work in agriculture/natural
resource-based industries or small businesses may be impacted by extreme events). Renters are
often underinsured, live in less protected structures, and receive less disaster assistance than
homeowners.
o This handbook provides indicators for household income, self-employed people (who may have
less backup and supportive resources), outdoors workers, renters (who may be earning less
income (on average), rental units tend to be less maintained and protected, and they often
receive less disaster relief (Hamel et al., 2017)), those who live in mobile structures, low
education, and minority populations. Decision-makers may also need to identify not just low-
income households, but those who have the least resources. The Census Bureau defines the
poverty line to measure economic well-being and assess the poverty status of households. These
27
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
poverty line data are used to assess the need for assistance and are included in federal
allocation formulas for many government programs (U.S. Census Bureau, 2019). The handbook
provides indicators for two measures using poverty lines that may be relevant depending on the
local contexts.
• Health and safety concerns: Individuals in certain age groups (children, elderly) or those who are
disabled may be predisposed to certain health impacts, have mobility issues, and be dependent on
caregivers. Those who lack insurance coverage would have difficulties getting medical care.
o This handbook provides indicators for the children and elderly, those with at least one disabled
person, and households with no health insurance.
• Marginalized or isolated: Households with limited family or community networks may face
difficulties coping or recovering from impacts. Those who have less access to information or
transportation or have cultural or language barriers will also face challenges with warnings,
evacuation orders, and recovery efforts. Certain population groups may also face discriminatory or
prejudicial practices, which result in isolation.
o This handbook provides indicators for households without telephone, internet, and vehicle
access. Indicators also include elderly individuals living alone, households with female household
heads, minority and ethnic groups, and immigrants and recent migrants.
28
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Table 7. Sensitivity: Household Characteristics
ID*
Indicator Definition**
Under-resourced
Health or safety
concerns
Marginalized or
isolated
2.1.1
Total population***
X
X
X
2.1.2
Count of households/occupied housing units***
X
X
X
2.1.3
Median household Income
X
2.1.4
Percent of population with ratio of income to poverty level less than 0.5
X
2.1.5
Percent of population with ratio of income to poverty level between 0.5
and 1
X
2.1.6
Percent of households with self-employment income
X
X
2.1.7
Percent of civilian employed population 16 years and over who work
outdoors
X
2.1.8
Percent of households that are renters
X
X
2.1.9
Percent of households living in a mobile home/boat/RV/van
X
X
X
2.1.10
Percent of households without telephone service
X
X
2.1.11
Percent of households with no internet access
X
X
2.1.12
Percent of households who do not have a vehicle
X
X
X
2.1.13
Percent of population over 25 with no high school degree
X
X
2.1.14
Percent of population with no health insurance
X
X
X
2.1.15
Percent of households with at least 1 person that has a disability
X
2.1.16
Percent of population under the age of 18
X
2.1.17
Percent of population who are 65 or over
X
X
2.1.18
Percent of households with single members who are 65 or over
X
2.1.19
Percent of population with female household heads
X
29
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
ID*
Indicator Definition**
Under-resourced
Health or safety
concerns
Marginalized or
isolated
2.1.20
Percent of population that is Black or African American alone
X
X
2.1.21
Percent of population that are Native Hawaiian or Other Pacific Islander
alone
X
2.1.22
Percent of population that are American Indian or Alaska Native alone
X
2.1.23
Percent of population that are Asian alone
X
2.1.24
Percent of population that belongs to other non-White races
X
2.1.25
Percent of population that are Hispanic or Latino
X
X
2.1.26
Percent of households that have limited English speaking ability
X
2.1.27
Percent of the population who are over 18 and non-U.S. citizens
X
2.1.28
Percent of households that moved within the last 3 years
X
* ID numbering X.Y.Z: X denotes exposure/sensitivity (exposure: 1; sensitivity: 2); Y denotes 3 sources of exposure (extreme events:l, sites/
waste facilities: 2, fate/transport: 3, and 1 source of sensitivity (population characteristics: 1), and Z denotes the indicator (numbered
sequentially)
** All indicators are by Block Group
***Total population and households represent overall impacts rather than specific reasons for sensitivity
Note: Alternative options for sensitivity indicators were presented to case study partners. The handbook includes only those that were
determined to be most suitable for case study partner's objectives and for which data was publicly available. The table provides an indication of
why each group is "sensitive", illustrating that certain groups face multiple types of difficulties.
30
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
3 Steps for implementing Approach
This handbook presents a four-step process to implementing the framework described in Section 2
(Figure 6). Close coordination with partners throughout the process is crucial to ensure that the
indicators reflect community needs and local knowledge and can also be communicated in a
meaningful way.
Figure 6, Four-step Process for Implementing Indicator Approach
• Define target audience, the
goals and intended uses of
the indicators
¦ Determine the scope and
spatial coverage
Role of Partners
Determine scope and spatial
coverage or relevance to the
local community
¦Apply the conceptual
framework to trace pathway
and identify key
vulnerabilities
¦ Identify appropriate
indicators from the lists
provided to represent the
key vulnerabilities
Role of Partners
Determine key vulnerability
factors and indicators of
interest to local partners and
community
.J
>
¦ Determine spatial unit of
analysis
¦ Compile data using
checklists provided
¦ Refine indicators based on
data availability at the
chosen scale
¦ Calculate indicator using
checklists provided
)
Role of Partners
Vet methods and data with
the partners so the selected
indicators are community
relevant
w
Communicate
• Map geospatial indicators
• Develop communications
products to convey key
results and caveats
Role of Partners
Incorporate feedback on
terminology and visuals so
they clearly communicate
key results to the community
3.1 Step 1: Define the Target Audience, Goals of the Analysis, and Scope
1) Determine the target audience for scoping the
analysis and for developing appropriate interim and
final products.
2) Define the goals and intended uses to ensure that the
indicators are developed, analyzed, and
communicated to meet the specific needs and
concerns of the community.
3) Define the scope of the analysis to ensure best use of
resources.
4) Determine the spatial coverage of the study to ensure
that key areas of the community's vulnerability
concerns are incorporated.
Box 4. Examples of Considerations:
Step 1
• Who will be using the indicators?
• What is the intended use of the
indicators? Is the goal to support
long-term adaptation? Is it for
emergency response?
• Are there specific concerns?
• Are there specific locations that
are of interest?
31
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
3.2 Step 2: Identify the Vulnerability Factors of Interest and the Indicators to
Represent the Factors
1) Apply framework (Figure 2) to identify vulnerability
factors of interest.
2) Select the indicators to represent the identified factors
from the full list of indicators (Tables 3, 4, 6 and 7). The
checklists in Section 5 provides illustrative uses of
indicators. These lists are meant to provide options so
that decision-makers can choose what is most relevant
for their community.
3) Determine the variants and representations that are
most useful (e.g., difference versus percent difference
in baseline and projected).
4) Determine combinations of indicators that are
informative and useful.
3.3 Step 3: Measure (Calculate Metrics)
1) Determine spatial unit of analysis. For the purposes of
visual display and analysis, this method defines a
community as a Block Group, which is a census data
collection unit. Decision-makers may choose to use
Census Tracts or other spatial units if Block Groups
are too difficult to interpret or communicate.9
Box 5. Examples of Considerations:
Step 2
• What have been historical
challenges? Are there emerging
challenges from recent trends
(e.g., more impervious cover from
increasing development or urban
sprawl)?
• Is the goal to compare changes in
risk of extreme events? Is the goal
to prioritize communities
vulnerable under historical
baseline conditions or who may
become vulnerable in the future,
or both? Do you want to consider
the most extreme scenarios to be
most protective or do you want to
focus on more moderate
scenarios?
• Are there specific types of sites/
waste facilities or communities
that are of greatest interest?
• Are there combinations of
indicators that are most
informative?
2) Compile data for indicators using publicly available
sources identified in the checklists provided in Section 4. Vetting of methods and data by
community partners is needed to ensure that the most accurate information is represented to
the extent feasible, and that the information is relevant to the community.
a. Use U.S. EPA databases to compile data for indicators representing sites/waste facilities.
Consult with state and local agencies for further detail or timely information that is regularly
collected and available. Seek local knowledge to identify or confirm site/waste facility
locations at risk.
i. Use location information shared by your community planners, environmental
agencies, and responders to support collaboration.
9 Note that the community serves as the unit of analysis in this method and thus also represents the resolution of the maps.
32
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
ii. Remove duplicates after reviewing name
and location information since the same
facility may come from different data
sources.
iii. Decide which sites/waste facilities to
include or exclude.
b. Leverage publicly available datasets for data on
extreme events, wind and hydrological patterns,
and population characteristics.
c. For data on sensitivity characteristics, use Census
data as described in the checklists. If a different
alternative is desired for the community than
what is in the checklists, care must be taken to
ensure that the correct population or calculation
is being used.
3) Indicators/metrics may need to be refined based on
data availability.
4) Calculate metrics for each Block Group or community
as described in the checklists. This involves overlaying
raw data with Block Group boundary files and
calculating metrics (as defined by the indicator) for
data found within the Block Group. Equations are also
provided in Appendix B for communities who might
find them useful.
3.4 Step 4: Analyze and Communicate Results
1) Map indicator data. The purpose of mapping
indicator data is to present data geographically,
allowing the audience to see spatial relationships and
trends not obvious when examining tabular data alone.
Box 6. Examples of Considerations:
Step 3
• Do I want to look at the most
detailed resolution possible or is
my area too large for assessing
and communicating such fine-
scaled results? Are the indicators
being used for regional, state, city,
or community planning?
• When identifying sites/waste
facilities, consider:
o Current numbers of sites and
types of facilities governed by
federal, state, or local
environmental regulatory
authorities can be in flux as
new sites are identified, their
boundaries change, rules and
policies change, science
improves detection, and sites
or facilities are cleaned up and
revitalized for a new reuse.
o An unrecognized or well-
regulated site/waste facility
may become unsafe, as a
result of extreme events,
natural or manmade hazards,
and changes in environmental
conditions.
o Prior to "the event" these
locations may not be
recognized or considered in
preparedness planning.
a. A major part of mapping is symbology.10 The first decision when choosing a symbology is to
determine whether the same symbology is to be applied to different data sets. Doing so
allows the map reader to compare the maps more easily, since the same color represents
the same category (or bin) on each map. An example of this would be generating multiple
maps to compare maximum values across climate scenarios, decades, or locations. The full
10 Symbology is how maps communicate tabular data without words and is done with a combination of symbols and colors. In this case,
we are referring to the colors used to represent values on the maps.
33
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Box 7. Examples of Considerations:
Step 4
communities over time? Or to look
at a certain snapshot in time?
range of values for all scenarios/decades/
locations needs be used to create a single unique
symbology to apply to all maps. Therefore, the
same color will represent the same value across * ls t'10 goa'to comPare
the maps, making direct comparisons much
easier. Browse the data across years and
4. -j 4.-x . i i r , .. • Is the goal to compare absolute
scenarios to identify the number of observations, b ^
values or percentages?
the maximum, the minimum, and the distribution
of values. This will help decide how many bins to * Has there been proper translation
I, . i £' j.i ^ • .u of scientific jargon into accurate
use, and how to define them. Data covering the
but easy-to-interpret language that
entire study area and all relevant time periods wj|| be understood within the
need to be used to create a symbology. community? Avoid confusion, lack
of clarity, and misunderstandings.
Do the visuals clearly communicate
key results to the community?
b. Using 5-7 bins/categories is recommended so
that the map reader can readily distinguish
between color categories and visually match
. .1 i , ,, i . . i • £ ji • Are there caveats specific to the
them to the legend. Use equal intervals if the
community?
data are equally distributed throughout the
range, which create equal steps between categories. For example, 0-10, 11-20, etc.
However, all categories may not necessarily contain values on the map since there might not
be data values for each bin. The alternative to equal intervals is quantiles, deciles, or any
percentile of choice, which attempts to create the same number of observations per bin
(e.g., 20% of the Block Groups are in each category/bin when quantiles are chosen. This
method should be used when the distribution of the data is concentrated in a few small
ranges, since it will create smaller bins and provide additional clarity to the map in these
concentrated ranges. Using quantiles also assures that each color in the symbology will
appear on the map., but it will make comparisons across different time periods and scenarios
more challenging. Allowing the data distribution to drive the map bin generation will
produce the optimal results. However, using this method causes each bin to have a different
interval and requires careful interpretation and communication.
Indicator data are likely to be continuous (spanning a range of values), rather than
categorical (comprised of unique non-sequential values). If data are continuous, use a
graduated color ramp progressing from light to dark, depicting less vulnerability to more
vulnerability. In some cases, higher values indicate higher vulnerability (e.g., percent of
household heads over the age of 65) and in some cases lower values indicate higher
vulnerability (e.g., household income). For diverging data (e.g., positive through negative
values with an inflection point in the middle), use a diverging color scheme characterized by
darker colors at both extremes and lighter in the middle). This may be relevant, for example,
when displaying percentage changes across time periods that encompass both positive and
negative values. For categorical data, choose colors that are as distinct from each other as
34
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
possible. A good source of symbology choices can be found on the ColorBrewer website
(www.colorbrewer2.org). A variety of options exist that are colorblind safe and appropriate
for continuous, categorical, and divergent data.
2) Determine key elements to include in a legend, so that consistency is maintained across maps.
For example, each color variation should be matched to its color ramp in the legend.
3) Summarize key results, lessons learned, and caveats considering the target audience. For
example, indicators at a high spatial resolution can be provided to a technical audience as
tabular data. Summarized, categorized information (i.e., high, medium, or low risk) may be
developed from the true values and presented only for specific areas of interest in a color-
coded map for non-technical audiences. The user is encouraged to think about ways to
understand and clearly present the data to suit their audience once the assessment is
completed.
35
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
4 Flooding Example
In this section, we provide a hypothetical example illustrating how to implement the four-step process
described in Section 3 in the context of flooding. It is not meant to be a comprehensive list of all
possible considerations. We demonstrate how the conceptual framework, list of indicators, and
detailed checklists may be applied in practice.
4.1 Step 1: Define the Target Audience,
Goals of the Analysis, and Scope
Consider a scenario where the mayor of a small city is in
the process of revising emergency response plans for
different types of hazards. The city has an industrial
heritage and multiple rivers running through it. While
the city has experienced floods before, they have not
been very localized and not very severe. However,
other cities in the same region have experienced more
severe flooding than usual over the past year. These
floods impacted multiple sites/waste facilities resulting
in adverse environmental and health consequences for
surrounding communities. To prevent similar outcomes
in the city, the mayor wants to develop long-term
adaptation plans for the city to prepare for worsening
extreme events.
To make the best use of their limited resources, the mayor must identify and prioritize communities to
focus on. The mayor is interested in information for the entire county within which the city is located
since people commute from outside the city for work. She is especially concerned about the eastern
border of the city which has a lot of sites/waste facilities and has also been flooded in the past. The
technical advisory committee to the mayor will be conducting an indicators-based vulnerability
assessment to help prioritize long-term adaptation and formulate more immediate emergency
response plans for the city and county.
4.2 Step 2: Identify the Vulnerability Factors of Interest and the Indicators to
Represent the Factors
The technical advisory committee used Figure 2 to identify the vulnerability factors of interest. Among
the four extreme events in [1], the committee selected flooding based on earlier conversations with
the mayor and their knowledge of the recent issues in the region. After a closer look at the count and
type of sites/waste facilities in the area, they selected hazardous waste facilities from [2] as their
primary focus. To identify which communities may be impacted by contaminant releases from the
hazardous waste sites, they wanted to understand where these contaminants could travel to. Given
Box 8. Examples of Considerations:
Step 1
• The mayor and technical advisory
committee will be using the
indicators.
• The intended use of the indicators
is to support long-term adaptation
and emergency response.
• The scope of the assessment
covers the entire county.
• There are specific concerns about
the eastern side of the town where
there is a concentration of sites/
waste facilities.
36
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
that they had limited resources, they prioritized on considering different pathways of flowing waters
rather than wind direction from [3]. For [4], the committee selected factors that would be important
for both long-term adaptation as well as immediate emergency response planning.
After identifying the vulnerability factors, the committee
met with the community representatives in the mayor's
office to ensure that their concerns were incorporated in the
assessment. The mayor agreed with the prioritized list of
vulnerability factors. The mayor also added that due to
increases in congestion and rising prices in the city center,
there were new low-income housing developments
(comprising mostly of rental apartments) in the eastern side.
There were preliminary ongoing discussions about
developing a new assisted care facility near that area and
the mayor wanted to know whether the location was
appropriate prior to finalizing the decision and engaging a
developer.
The mayor was interested in learning more about the ways
in which they could measure and track each of these
vulnerability factors. The mayor asked questions about each
of the factors and the committee used these questions to
select appropriate indicators from the indicator checklist.
Figure 7 shows example questions and four sets of indicators
to represent vulnerability factors described above.
Box 9. Examples of Considerations:
Step 2
• Flooding has been an issue
historically and regional flood
events have been more severe
recently. Growth of low-income
housing and assisted care living
facilities in the eastern part of the
city is an emerging trend.
• The goal to understand baseline
vulnerabilities and future trends in
vulnerabilities under changing
climate conditions. More extreme
climate scenarios were used to
prepare for the worst possible
conditions.
• To get a holistic view of the
vulnerabilities, indicators on
heavy precipitation, low-lying
areas, reactive waste tonnage,
proximity of sites/waste facilities
to flowing waters and
downstream communities and
households who have difficulty
evacuating, getting medical help
or other necessities are
considered in conjunction with
each other.
To answer the question of where the risks of inundation
across a given area are during a flood event, a floodplain
indicator was chosen. Both 100-year and 500-year
floodplains were considered to incorporate smaller, but
more frequently inundated areas and larger, but less
frequently inundated areas. Given the recent flood events in
other areas in the region, the mayor was interested in knowing about the changing patterns of
precipitation across the city and county. The indicator showing the percentage of rain that falls as a
heavy event (the daily depth is higher than a high percentile of historical daily values) was selected as a
surrogate for likely flood-inducing events. To support emergency response planning, historical values
of the heavy precipitation indicator were selected. To prepare for changing frequency and intensity of
precipitation, the difference between projected and historical time periods was selected. A mid-
century time period was considered as a first step to understanding future vulnerabilities. To identify
low-lying areas within a community that have the potential for riverine flooding and receiving overland
flow during high-depth precipitation events, the Height Above Nearest Drainage (HAND) indicator was
selected.
37
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
To understand what the sources of contaminant releases and the size of the overall risk may be,
indicators showing simple counts by types of sites/waste facilities were considered. To prioritize
monitoring activities, the indicator showing the number of hazardous sites with reactive waste was
selected. Tonnage of reactive waste was selected to provide additional information on the magnitude
of the risk.
Figure 7. Flooding Scenario: Questions the Indicators Aim to Inform (Examples)
Where may floods occur? What about intensity of floods? Frequency? How may these change over time?
Floodplains
Heavy precipitation events, climate scenarios
Low lying areas
*Unstable under normal conditions; reacts with water (floods) or when heated (heat waves, wildfire).
To identify communities most at risk, information on where potential contaminants can flow to was
needed. The mayor and the committee decided that to get a complete picture, rivers that may receive
contaminants after a release and overland flow or runoff that may spread the contamination from a
facility needed to be considered. The count of upstream facilities indicator was selected to identify
communities that may face the highest contamination risk. Count of sites/waste facilities close to a
flowline (stream/river) was used to understand potential risks to human and ecological health through
direct ingestion, incidental ingestion, dermal contact, or secondary exposure. The closest upstream
facility indicator was selected to prioritize and plan for emergency containment methods during an
event. Distances of 500 meters were selected as a cutoff for determining "close" facilities to be most
38
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
protective, considering that the stormwater infrastructure
within the city would likely intercept overland releases from
facilities beyond that distance.
For emergency response planning, the mayor wanted
information on who may face mobility issues that prevents
them from evacuating. Indicators providing information on
households without private vehicles and elderly people living
alone was selected to prioritize evacuation measures. Also,
people without health insurance who would have difficulty
getting care in the event of any flood-related injuries were
identified. Information on household income and highest
poverty levels were used to identify those who may not be
able to afford bottled water and other necessities in the
event of contamination.
Box 10. Examples of
Considerations: Step 3
• Block Group was chosen
because the goal was to look at
the finest resolution possible in
the dense city center and use
less resolution for the outskirts.
• Aggregate tract levels were
selected for communications
purposes.
• Recently cleaned up Brownfield
sites were excluded from the
analysis.
4.3 Step 3: Measure (Calculate Metrics)
The mayor wanted to conduct the assessment for the city, which had a dense population center, and
the surrounding county, which was more sparsely populated. To get more detailed information in the
city center, the mayor requested finer resolution in the more populated areas and coarser resolution
outside the city limits. A Block Group was accordingly chosen as the spatial unit of analysis. To allow for
easier communication to city residents, the mayor elected to roll up the information to the tract level
when presenting results.
Data were compiled by the committee using the checklists in this handbook. The indicator showing
counts of sites/waste facilities by type showed a large number of Brownfield sites in the study area. To
ensure that the data reflected the most recent information, the mayor was consulted and two sites
that had been recently cleaned up (after the data release) were dropped from the dataset. The mayor
was also interested in tracking a hazardous waste facility with long-standing community concerns.
The data was overlaid with Block Group boundary files. The metrics were calculated using the methods
detailed in the checklists. The equations provided in the Appendix were used to ensure that the correct
calculations were done. The Block Group-level information was then rolled up to the tract level for
communicating the results to a is a wide audience at town hall meetings. Caveats associated with using
the screening indicators were also documented.
39
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
4.4 Step 4: Analyze and Communicate Results
The indicators were mapped for the entire city and the county. The data intervals and color scheme
were selected to allow for clear communications. A single symbology/color scheme was used for the
heavy precipitation indicator to compare across scenarios. For most of the other indicators, the data
were divided into intervals using quantiles to ensure that each bin had the same number of
observations and all Block Groups on the map were shaded. Custom intervals were chosen for the
HAND indicator based on the scientific judgement of the committee. The hazardous waste facility that
was of interest to the community was identified and included on the legend of the extreme event
maps.
The mayor also wanted to understand where the locations of the sites/waste facilities were relative to
where the flood risks were. Also, the mayor wanted to gain an understanding of how close the sites/
waste facilities were to environmental justice communities. The committee, therefore, suggested
overlaying site/waste facility locations with the flood and sensitivity indicators. The maps showed that
although historical flood risks were more in the eastern part of the city, the northern boundary may
experience much more heavy precipitation in the future. Although there were not many hazardous
waste facilities with reactive waste in that area, there was a large Superfund site that used remediation
technology that was vulnerable to flooding. The indicator showing counts of Superfund sites with
remedy technology vulnerable to floods was added to the list of indicators to consider in the next
assessment.
The results also highlighted the importance of considering where the contaminants could flow to
rather than where sites are located because there were some Block Groups that did not contain any
sites/waste facilities but had sites/waste facilities located
500 meters upstream. The planned location for the new
assisted care facility was also downstream of hazardous
waste facilities.
The tract-level maps were shown at the monthly town hall
meeting. It was emphasized that the sites/waste facilities
should be viewed as potential sources of contaminant
releases and the indicators should not be interpreted as
portraying a certain impact. Rather they are meant to be
used for screening to prioritize planning and resources. The
committee also mentioned that there were ongoing
cleanup activities and data releases planned in the county,
which may alter results. In this hypothetical example, the
mayor, committee, potential developers, and town hall
attendees discussed whether the location of the assisted
care facility should be reconsidered given the indicator screening results.
Box 11. Examples of
Considerations: Step 4
• To compare communities over
time, a single symbology was
selected for flood indicators and
quantiles for other indicators.
• The hazardous waste facility of
interest was identified on the
heavy precipitation map to place it
in context with flood risks.
• The aggregated results were used
for communications. Upcoming
cleanup and data release
information was shared.
40
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
5 Checklists for Developing and Applying Indicators
This handbook summarizes the steps and key considerations for developing vulnerability indicators to
screen, assess, and communicate potential extreme event impacts on sites/waste facilities and
surrounding communities. Again, these indicators are not intended to replace site-specific analysis and
in-depth modeling activities (e.g., risk assessments). Rather, potential users can apply indicators for
screening and prioritization purposes, so resources for more in-depth analysis can be focused on the
most vulnerable communities. Potential users include, but are not limited to, planners, decision-
makers, and technical advisors (for localities, cities, tribes, states, and regions), technical advisors to
decision-makers, scientific researchers, environmental advocates, and scientific and community
organizations.
This section provides detailed descriptions of how to develop, analyze, and map each indicator. These
descriptions are not intended to be seen as the only way to develop the indicators. Users can adapt
these steps as needed for their purposes and based on their expertise. For example, more advanced
users familiar with datasets or methods may not require all the detailed descriptions. Rather the
descriptions should be seen as checklists or a "did I consider this" type of document. Each checklist
(accompanied by appendices as needed) is designed to be stand-alone so that users can focus on only
those indicators that are relevant or of interest. Considering a set of indicators in conjunction with
each other would allow for a more holistic assessment than considering each indicator in isolation.
41
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Vulnerability Source 1.1. Exposure: Extreme Events
Indicator 1.1.1. Checklist for Extreme Heat Indicator
Potential impacts of extreme heat on sites and waste facilities include increased fire hazards, high
pressures in closed vessels/tanks, overheating of equipment, off-gassing, changes to remediation
effectiveness, and power fluctuations/outages.
Extreme Heat Indicator
Definition of the indicator
~
Definition
Maximum summer temperature for the user-selected time period and climate
scenario/model.
Maximum temperatures for each day during summer (defined as June through
August) are assembled and the maximum value of this time series is selected for
each year in the selected time period. These maximum values are then averaged
over the time period.
Variants of the indicator that can be considered include (1) absolute historical and
projected values for the user-selected time period and (2) difference or percent
difference between the projected and historical values.
~
Interpretation
This indicator provides an overall intensity measure based on the highest
temperatures expected on average for each community across the summer.
Comparing projected values from historic conditions, the indicator shows, for
example, how much hotter on average extreme summer temperatures can be
expected to get. The indicator also shows variations in extreme heat conditions
across and between communities.
Data source
~
Data Source
The LOCA (Localized Constructed Analogs) downscaled climate data for the
historical and future periods for Representative Concentration Pathway (RCP)
scenarios provides the necessary temperature inputs.
• Time periods can be selected to represent historical conditions (e.g., 1986-
2005), mid-century (2040-2059), or late century (2080-2099) scenarios.
Other time periods/timeframes can be selected depending on user needs.
• RCP 4.5 and 8.5 are recommended to represent moderate and more
extreme conditions but other scenarios can be selected depending on user
needs.
• Outputs from the CanESM2 climate model can be selected to consider
more extreme conditions under a given emissions scenario. Other climate
models or a combination of climate models can be used based on user
needs.
~
Temporal
Resolution
Daily time series of maximum temperatures (historical and projected)
42
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Spatial
Resolution
LOCA data are available for raster cells, which are 1/16th of a degree of latitude and
1/16th of a degree of longitude in size
Block Group (BG) shapefile
~
Data Format
Shapefile (BG); NetCDF (LOCA)
Decisions needed for calculation
~
Summer Defined
as June through
August
Based on long-term climatic conditions, it is assumed that the highest daily
temperatures will occur in the months of June-August (defined as the summer
season). There is the potential for sporadic heat waves outside of this period, but on
average this period is captures the most extreme heat conditions in any study area.
~
Use of Daily
Maximum
Temperatures
To calculate extreme heat measures, series of average, minimum, or maximum
temperatures for each day could be used. Daily maximum, and then the seasonal
maximum is used to be represent the worst possible conditions that communities
can prepare for.
~
Choice of
Appropriate Time
Period to
Represent Long-
term Climatic
Conditions/
Average Value
for Final Indicator
Averages across a time period covering several years are recommended to avoid
short-term weather fluctuations or modeling uncertainties and to capture represent
long-term climatic conditions and patterns. Time periods covering 20 years are
recommended. An alternate number of years can be used depending on local
needs. For example, a 30-year time period represents longer-term climatic
conditions and shorter timeframes (e.g., 15 years) reflect more recent changing
conditions.
Calculation steps and assumptions
~
Compile Block
Group Maximum
Temperature
Time Series
Inputs: LOCA data and BG shapefile
Calculations:
• Extract all grid cells that touch any BGs within the study area.
• Resample the extracted raster by a factor of 100, to produce smaller grid
cells, each with the same value as the original raster. This needs to be
done if the BGs are small compared to the grid cells to ensure that even the
smallest BGs have at least one grid cell with data fall within it.
• If there are multiple grid cells overlapping with a BG, use the zonal
statistical function to take the average value of the indexed cells to report a
temperature value for each BG.
Outputs: Daily time series of maximum temperature per BG
~
Limit Time Series
to Summer
Months
Input: Time series of maximum temperature per BG
Calculation: Select values where Day >152 AND Day < 243 (Note: LOCA data do
not include leap years)
Output: Time series of maximum temperatures for summer months per BG
43
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Average Daily
Maximum
Summer
Temperature
(Indicator 1)
Inputs: Time series of maximum temperatures for summer months per BG
Calculation: Take the maximum of all values per BG
Outputs: Maximum of the daily maximum summer temperature per BG
~
Repetition for
Time Periods and
Summary Value
for Time Period
Repeat all steps for each year in the selected time period (historical and future) and
average over the time period.
~
Calculate Percent
Difference in
Summer
Maximum
Temperature
(Variant of
Indicator)
Inputs:
• Average daily maximum summer temperature per BG for historic period
• Average daily maximum summer temperature for future scenario period
Calculation: See Appendix Equation EH-1a and EH-1b for more details.
Find the difference between the average daily maximum summer temperature from
the historic period and the future scenario period. Then divide by the average daily
maximum summer temperature from the historic period to get the percent change.
Output: Percent difference in summer maximum temperature per BG for future
scenario.
~
Repetition for
Future Scenarios
Repeat the above step for any additional future scenarios.
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have temperature values.
~
Choosing a
Symbology
A single unique symbology that spans climate scenarios is recommended. This will
result in the same color representing the same value across the maps, making
direct comparisons easier. To build this, find the minimum and maximum values
across climate scenarios, and then use the full range of values to create a single
unique symbology to apply to all maps.
~
Binning the Data
by Block Groups
Using 5-7 bins/categories is recommended so that the map reader can readily
distinguish between color categories and visually match them to the legend. Use
equal intervals, if possible, which create equal steps between categories (e.g., 0-
10, 11-20). Not all categories may necessarily contain values on the map.
An alternative to equal intervals is quantiles, which attempts to create the same
number of observations per bin. This causes each bin to have a different interval
and requires careful interpretation and communication. Using quantiles ensures that
each color in the symbology will appear on the map, and it will add more detail to
the map if the data are concentrated in a few small ranges. Deciles or other
percentiles can also be used depending on user needs.
44
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Choosing Colors
For maps with sequential data, use a symboiogy that becomes darker as the
vulnerability increases.
For maps with diverging data, such as the variants of the indicator showing percent
change, use a diverging color scheme characterized by darker colors at both
extremes and lighter in the middle.
Examples of how the indicator can be useful
~
Impacts of
Summer
Maximum
Temperatures on
Sites/Waste
Facilities
Given the potential impacts of extreme heat on sites and waste facilities, this
indicator (in combination with sites/waste facilities) can be used for identifying which
sites/waste facilities and communities may face higher risks of contamination
release due to heat. If expressed in differences or percent differences, the indicator
can be used to assess how these spatial patterns in risks may change over time.
Key caveats/limitations
~
Spatial
Differentiation
Based on Climate
Data Source
When interpreting the maps, it is important to remember that the resolution of the
LOCA modeled data is fairly coarse compared to BGs. Each raster cell is 1/16th of a
degree of latitude and 1/16th of a degree of longitude in size. This equates to
approximately 5 km x 7 km at temperate latitudes such as the United States. There
is the potential that this indicator may not provide enough variation across BGs
consisting of small spatial areas. The other aspect to be aware of is that difference
between BGs may be smaller than the errors in the modeled data. Therefore,
differences displayed cartographically, may be within the range of model error. This
limitation will apply to any indicators developed with the LOCA data as its input
source.
Citations
~
Dataset/Tool
Pierce, D.W. (2016). LOCA (Local Constructed Analogs) statistical downscaling
version 40. Scripps Institute of Oceanography, Division of Climate, Atmospheric
Sciences, and Physical Oceanoaraohv. http://loca.ucsd.edu/
~
Additional
Resources
Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections. (2021).
htto.Y/ado-dco.ucllnl.ora/downscaled cmio oroiections/
BG: Block Group, LOCA: Localized Constructed Analogs, RCP: Representative Concentration Pathway
45
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicators 1.1.2 & 1.1.3. Checklist for Threshold-Based Extreme Heat Indicator
Potential impacts of extreme heat on sites and waste facilities include increased fire hazards, high pressures in
closed vessels/tanks, overheating of equipment, off-gassing, changes to remediation effectiveness, and power
fluctuations/outages. Our assumption is that sites and waste facilities are built to withstand the environmental
conditions typical of its surroundings. Temperature values beyond historical baseline extremes, are therefore
riskier for sites and waste facilities designed for historical average conditions. Historical baseline extremes
therefore can be viewed as a "threshold."
Threshold-Based Extreme Heat Indicator
Definition of the indicator
~
Definition
(1) The change in the annual number of days with maximum daily temperature
above the 99th percentile (extreme heat days), where the 99th percentile is
calculated for the historical period and the difference is between the historical
period and the future scenario period.
(2) The average of maximum temperatures for extreme heat days (averaged across
all the years in the relevant time period).
~
Interpretation
(1) This frequency-based indicator provides a measure of the change, likely an
increase, in the number of extreme heat days a community may experience in the
future. Presenting the change as an annual average, the community can easily
assess how much of their year may experience extreme heat and therefore pose
increased risks for the sites/waste facilities.
(2) This intensity-based indicator provides the average of the maximum
temperatures for extreme heat days. So, whether there are few or many extreme
heat days in a year, the maximum temperature for those days are included in the
overall average for this indicator providing a measure of how hot it may get during
these extreme heat episodes for each time period or scenario.
Data source
~
Data Source
The LOCA (Localized Constructed Analogs) downscaled climate data for the
historical and future periods for Representative Concentration Pathway (RCP)
scenarios provides the necessary temperature inputs.
• Time periods can be selected to represent historical conditions (e.g., 1986-
2005), mid- century (2040-2059), or late century (2080-2099) scenarios.
Other time periods/timeframes can be selected depending on user needs.
• RCP 4.5 and 8.5 are recommended to represent moderate and more
extreme conditions but other scenarios can be selected depending on user
needs.
• Outputs from the CanESM2 climate model can be selected to consider
more extreme conditions under a given emissions scenario. Other climate
models or a combination of climate models can be used based on user
needs.
~
Temporal
Resolution
Daily time series of maximum temperatures
46
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Spatial
Resolution
LOCA data is available for raster cells which are 1/16th of degree of latitude and
1/16th of a degree of longitude in size
Block Group (BG) shapefile
~
Data Format
Shapefile (BG); NetCDF (LOCA data)
Decisions needed for calculation
~
Use of Daily
Maximum
Temperatures
To calculate the percentiles, series of daily average, minimum, or maximum
temperatures could be used. Daily maximum is used to be consistent with the other
extreme heat indicators.
~
Percentile Value
from Historic
Period
Determine a percentile value of the daily maximum from the historic period and
using that to assess extreme heat days during the future periods. Using a historical
threshold provides a means to compare how extreme heat days are changing in
number and intensity into the future as compared to the historic conditions to which
sites/waste facilities are accustomed.
~
Extreme Heat
Days Defined as
Top 1%of
Maximum
Temperatures
l/l/e propose using the 99th to capture the most extreme conditions (similar to our
logic of using maximum temperature previously). Alternate percentiles such as 85th
(as used by U.S. Global Change Research Program [GCRP] for heat waves for
assessing health effects), 90th, 95th, 98th, and/or 99th can be used depending on
user needs and local conditions.
~
Choice of
Appropriate Time
Period to
Represent Long-
term Climatic
Conditions/
Average Value
for Final Indicator
Averages across a time period covering several years are recommended to avoid
short-term weather fluctuations or modeling uncertainties and to capture represent
long-term climatic conditions and patterns. Time periods covering 20 years are
recommended. An alternate number of years can be used depending on local
needs. For example, a 30-year time period represents longer term climatic
conditions and shorter timeframes (e.g., 15 years) reflect more recent changing
conditions.
Calculation steps and assumptions
~
Compile Block
Group Maximum
Temperature
Time Series
Inputs: LOCA data and BG shapefile.
Calculations:
• Extract all grid cells that touch any BGs within the study area.
• Resample the extracted raster by a factor of 100, to produce smaller grid
cells, each with the same value as the original raster. This needs to be
done if the BGs are small compared to the grid cells to ensure that even the
smallest BGs would have at least one grid cell with data fall within it.
• If there are multiple grid cells overlapping with a BG, use the zonal
statistical function, take the average value of the indexed cells to report a
temperature value for each BG.
Outputs: Daily time series of maximum temperature per BG.
47
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Find 99th
Percentile Value
for Historic Period
Input: Time series of maximum temperature from historic period per BG.
Calculation: Compute the 99th percentile value of maximum temperature across
the entire 20-year historic period by BG.
Output: 99th percentile value per BG.
~
Indicate Extreme
Heat Days with
Maximum
Temperature
Time Series
Inputs:
• 99th percentile value.
• Time series of maximum temperature per BG.
Calculation: See Appendix Equation TBEH-1.
Using the 99th percentile value, indicate days from the daily time series that are
greater than the 99th percentile value for each period by BG with a 1.
Outputs: Time series of extreme heat days per BG.
~
Calculate Total
Number of
Extreme Heat
Days Each Year
Inputs: Time series of extreme heat days per BG.
Calculation: See Appendix Equation TBEH-2.
Sum the extreme heat day indicators within each year to find the total number of
extreme heat days each year.
Output: Total number of extreme heat days per year per BG.
~
Calculate Total
Number of
Extreme Heat
Days per Period
Inputs: Time series of extreme heat days per year per BG.
Calculation: See Appendix Equation TBEH-3.
Sum the yearly total of extreme heat days to find the total number of extreme heat
days for the assessment period.
Output: Total number of extreme heat days per BG.
~
Calculate the
Annual Average
Maximum
Temperature for
Extreme Heat
Days
Inputs:
• Time series of extreme heat days per year per BG.
• Total number of extreme heat days each year per BG.
Calculation: See Appendix Equation TBEH-4.
For each year, sum the maximum temperature for days classified as extreme heat
days then divide by the number of extreme heat days for the corresponding year.
Output: Average maximum temperature for extreme heat days per year per BG.
~
Calculate
Average Annual
Average
Maximum
Temperature for
Extreme Heat
Days (Indicator 2)
Inputs: Average maximum temperature for extreme heat days per year per BG.
Calculation: See Appendix Equation TBEH-5.
Sum the average maximum temperature for extreme heat days per year per BG
and divide by 20 (years in time period).
Output: Average annual average of maximum temperature for extreme heat days
per BG.
~
Repetition for
Time Periods and
Summary Value
for Time Period
Repeat all steps except for the calculation of the 99th percentile value for the future
time periods. Use the value from the historic period for the 99th percentile for all
time periods.
48
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Calculate Change
in Total Number
of Annual
Extreme Heat
Days (Indicator 1)
Inputs:
• Total number of extreme heat days per BG for historic period.
• Total number of extreme heat days per BG for future scenario period.
Calculation: See Appendix Equation TBEH-6.
Subtract the number of extreme heat days during the historic period from the
number of extreme heat days during the future scenario period. Divide by 20 to get
the annual estimates.
Output: Change in total number of annual extreme heat days per BG for future
scenario.
~
Repetition for
Future Scenarios
Repeat the above step for any additional future scenarios.
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have change values.
~
Choosing a
Symbology
The recommended symbology for this indicator is a single unique symbology that
spans climate scenarios. This will result in the same color representing the same
value across the maps, making direct comparisons much easier. To build this, find
the minimum and maximum values across climate scenarios, and then use the full
range of values to create a single unique symbology to apply to all maps.
~
Binning the Data
by Block Groups
Using 5-7 bins/categories is recommended so that the map reader can readily
distinguish between color categories and visually match them to the legend. Use
equal intervals, if possible, which create equal steps between categories (e.g., 0-
10, 11-20). Not all categories may necessarily contain values on the map.
The alternative to equal intervals is quantiles, which attempts to create the same
number of observations per bin. This causes each bin to have a different interval
and requires careful interpretation and communication. Using quantiles ensures that
each color in the symbology will appear on the map, and it will add more detail to
the map if the data is concentrated in a few small ranges. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
For maps with sequential data, use a symbology that becomes darker as the
vulnerability increases.
For maps with diverging data, such as the variants of the indicator showing percent
change, use a diverging color scheme characterized by darker colors at both
extremes and lighter in the middle.
Examples of how the indicator can be useful
~
Tracking of Days
with Possible
Emergency
Declarations or
Health Warnings
The change in number of annual extreme heat days could provide information on
how many times in a year could there be failures for sites/waste facilities.
49
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Average
Maximum
Temperature for
Extreme Heat
Days for
Resiliency
Planning
This indicator provides a measure of the highest temperatures that will be
experienced occasionally during any year. This indicator (in combination with sites/
waste facilities) can be used for assessing whether facilities, equipment, and
operations that are built to historical average conditions may face higher risks under
future scenarios.
Key caveats/limitations
~
Calculations
Completed within
R
A custom program such as R needs to be created to input the processed LOCA-
based climate data indexed by BG unique identifier and complete all calculations. If
using the custom R program developed by RTI, a user need only supply the links to
the input temperature datasets for the program to complete all calculation steps.
~
Spatial
Differentiation
Based on Climate
Data Source
When interpreting the maps, it is important to remember that the resolution of the
LOCA modeled data is fairly coarse compared to BGs. Each raster cell is 1/16th of a
degree of latitude and 1/16th of a degree of longitude in size. This equates to
approximately 5 km x 7 km at temperate latitudes such as the United States. There
is the potential that this indicator may not provide enough variation across BGs
consisting of small spatial areas. The other aspect to be aware of is that difference
between BGs may be smaller than the errors in the modeled data. Therefore,
differences displayed cartographically, may be within the range of model error. This
limitation will apply to any indicators developed with the LOCA data as its input
source.
Citations
~
Dataset/Tool
Pierce, D. I/I/. (2016). LOCA (Local Constructed Analogs) statistical downscaling
version 40. Scripps Institute of Oceanography, Division of Climate, Atmospheric
Sciences, and Physical Oceanoaraohv. htto:/Aoca. ucsd.edu/
~
Additional
Resources
Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections. (2021).
http://ado-dcp.ucllnl.ora/downscaled cmip projections/
BG: Block Group, GCRP: U.S. Global Change Research Program; LOCA: Localized Constructed Analogs, RCP: Representative Concentration
Pathway
50
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.1.4. Checklist for Wildfire Indicator
The potential impacts of wildfires on sites and waste facilities include catastrophic damage to facilities and
ignitable wastes, high pressure in closed vessels, overheating of equipment, incident waste facility closures,
increased hazardous/non-hazardous waste generation, power fluctuations/outages, and acute air quality
incidents/emergency due to hazardous smoke plumes.
Wildfire Indicator
Definition of the indicator
~
Definition
The fraction of Block Group (BG) burned for the user-selected time period and
climate scenario/model.
Percent values for each year are assembled in the selected time period. These
values are then averaged over the time period.
Variants of the indicator that can be considered include (1) absolute historical and
projected values for the user selected time period and (2) difference or percent
difference between the projected and historical values.
~
Interpretation
The percent value would indicate the average area that may burn in a given BG
during the specified time period.
Data source
~
Data Source
The CIRA II data for the historical and future periods for Representative
Concentration Pathway (RCP) scenarios provides the necessary wildfire inputs.
• Time periods can be selected to represent historical conditions (e.g., 1986-
2005), mid-century (2040-2059), or late century (2080-2099) scenarios.
Other time periods/timeframes can be selected depending on user needs.
• RCP 4.5 and 8.5 are recommended to represent moderate and more
extreme conditions but other scenarios can be selected depending on user
needs.
• Outputs from the CanESM2 climate model can be selected to consider
more extreme conditions under a given emissions scenario. Other climate
models or a combination of climate models can be used based on user
needs.
~
Temporal
Resolution
Time series of percent of grid cell burned for each year
~
Spatial
Resolution
CIRA data are available for raster cells which are 1/16th of a degree of latitude and
1/16th of a degree of longitude in size
BG shapefile
~
Data Format
Shapefile (BG); NetCDF (CIRA data available on request from EPA)
51
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Decisions needed for calculation
~
Use of Mean
Across Grid Cells
To calculate wildfire measures, total, average, minimum, or maximum fractions
across grid cells within a BG could be used. Average values are recommended to
avoid modeling uncertainties at such fine scales. Maximum may also be used to be
represent the worst possible conditions that the communities can prepare for.
~
Choice of
Appropriate Time
Period to
Represent Long-
term Climatic
Conditions/
Average Value for
Final Indicator
Averages across a time period covering several years are recommended to avoid
short-term weather fluctuations or modeling uncertainties and to capture represent
long-term climatic conditions and patterns. Time periods covering 20 years are
recommended. An alternate number of years can be used depending on local
needs. For example, a 30-year time period represents longer term climatic
conditions and shorter timeframes (e.g., 15 years) reflect more recent changing
conditions.
Calculation steps and assumptions
~
Compile Block
Group Fraction
Burned Time
Series
Inputs: CIRA data and BG shape file
Calculations:
• Extract all grid cells that touch any BGs within the study area.
• Resample the extracted raster by a factor of 100, to produce smaller grid
cells, each with the same value as the original raster. This needs to be
done if the BGs are small compared to the grid cells to ensure that even the
smallest BGs would have at least one grid cell with data fall within it.
• If there are multiple grid cells overlapping with a BG, use the zonal
statistical function, take the average value of the indexed cells to report a
wildfire value for each BG.
Outputs: Annual time series of fraction of BG area burned
~
Summary Value
for Time Period
Calculation: See Appendix Equation WF-1.
Average value over the time period (historical and future).
~
Calculate Percent
Difference in
Fraction of BG
Area Burned
(Variant of
Indicator)
Inputs:
• Average fraction of BG area burned for historic period
• Average fraction of BG area burned for future scenario period
Calculation: See Appendix Equation WF-2.
Take the difference between the fraction of BG area burned from the historic period
to the future scenario period. Then divide by the average fraction of BG area burned
from the historic period to get the percent change.
Output: Percent difference in fraction of BG area burned for future scenario.
~
Repetition for
Future Scenarios
Repeat the above step for any additional future scenarios.
52
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have percent of grid cell burned values.
~
Choosing a
Symbology
A single unique symbology that spans climate scenarios is recommended. This will
result in the same color representing the same value across the maps, making
direct comparisons easier. To build this, find the minimum and maximum values
across climate scenarios, and then use the full range of values to create a single
unique symbology to apply to all maps.
~
Binning the Data
by Block Groups
Using 5-7 bins/categories is recommended so that the map reader can readily
distinguish between color categories and visually match them to the legend. Use
equal intervals, if possible, which create equal steps between categories (e.g., 0-
10, 11-20). Not all categories may necessarily contain values on the map.
An alternative to equal intervals is quantiles, which attempts to create the same
number of observations per bin. This causes each bin to have a different interval
and requires careful interpretation and communication. Using quantiles ensures that
each color in the symbology will appear on the map, and it will add more detail to
the map if the data are concentrated in a few small ranges. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
For maps with sequential data, use a symbology that becomes darker as the
vulnerability increases.
For maps with diverging data, such as the variants of the indicator showing percent
change, use a diverging color scheme characterized by darker colors at both
extremes and lighter in the middle.
Examples of how the indicator can be useful
~
Wildfire Indicator
Given the potential impacts of wildfire on sites and waste facilities, this indicator (in
combination with sites/waste facilities) can be used for identifying which sites/waste
facilities and communities may face higher risks of contamination release due to
wildfires. If expressed in differences or percent differences, the indicator can be
used to assess how these spatial patterns in risks may change over time. If
expressed in differences, the indicator can be used to assess how these spatial
patterns in risks may change over time.
Key caveats/limitations
~
Spatial
Differentiation
Based on Data
Source
When interpreting the maps, it is important to remember that the resolution of the
CIRA modeled data is fairly coarse compared to BGs. Each raster cell is 1/16th of a
degree of latitude and 1/16th of a degree of longitude in size. This equates to
approximately 5 km x 7 km at temperate latitudes such as the United States.
Therefore, for very small BGs, the number of cells that comprise the mean might
only be one or two. The other aspect to be aware of is that difference in projected
areas between BGs might only be fractions of a percent, which may be smaller than
the errors in the modeled data. Therefore, differences displayed cartographically,
may be within the range of model error.
53
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Citations
Dataset/Tool
(Available upon
request from
EPA)
U.S. Environmental Protection Agency (U.S. EPA). 2017. Multi-model framework for
quantitative sectoral impacts analysis: A technical report for the Fourth National
Climate Assessment. EPA 430-R-17-001.
https://cfDub.eDa.aov/si/si Dublic record ReDort.cfm?dirEntrvld=335095
Additional
Resources
Mills, D., Jones, R., Wobus, C., Ekstrom, J., Jantarasami, L., St. Juliana, A.,
Crimmins, A. (2018). Projecting age-stratified risk of exposure to inland flooding and
wildfire smoke in the United States under two climate scenarios. Environmental
Health Persoectives. 126(4). httDs://doi.ora/10.1289/EHP2594
BG: Block Group, EPA: U.S. Environmental Protection Agency, RCP: Representative Concentration Pathway
54
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.1.5. Checklist for Floodplain-Based Flood Indicator
Potential impacts of floods on sites and waste facilities include more likely erosion, difficult ponding/stormwater
management, water damage (corrosion, water logging), power fluctuations/outages, groundwater plume
changes and higher water table, spreading/migration of contamination, catastrophic events destroy structures,
releases from overwash, infiltration, and leaching, incident waste facility closures, increased hazardous/non-
hazardous waste generation, groundwater pump-and-treat remedies may not be allowed to discharge,
increased potential for flooding of treatment systems, and dislodged debris from treatment/containment
systems or contained wastes and reactive wastes.
Floodplain-Based Flood Indicator
Definition of the indicator
~
Definition
Percent of Block Group (BG) area contained within a designated 100-year/500-year
floodplain.
~
Interpretation
Floodplains as defined by the National Flood Hazard Layer (NFHL) show the land
area along a watercourse likely to be inundated during storm events of
corresponding return intervals. By determining the percentage of the BG area
containing a floodplain, the relative risk of flood inundation can be determined for
the BG and across BGs depending on the recurrence interval/storm probability
chosen for the floodplain (i.e., 100- or 500-year).
Data source
~
Data Source
The NFHL can be accessed at https://msc.fema.aov/Dortal/advanceSearch. Data for
the necessary location is obtained through searching by state and county. A zip file
is downloaded from the search result.
~
Temporal
Resolution
NA - This indicator is a static measure without a time component. The measure
represents the information available at the time of download.
~
Spatial
Resolution
NFHL resolution varies depending on location but is generally accurate at a scale
<1 meter.
BG shapefile
~
Data Format
Shapefile (BG and NFHL)
Decisions needed for calculation
~
100-year or 500-
year Floodplain
Indicator
The 100-year floodplain defines the area more likely to be inundated by rainfall
events with a 1% annual chance of occurrence, while the 500-year floodplain
defines the area more likely to be inundated by events with a 0.2% annual chance
of occurrence A community can choose which version of the indicator to use. The
choice between whether one looks at the 100-year or 500-year floodplain is a
choice between defining smaller, but more frequent inundated areas versus large,
but less frequently inundated areas.
55
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Calculation steps and assumptions
~
Extract 100- and
500-year Flood
Hazard Limits
Inputs: NFHL
Calculations:
• Use shapefile S_FLD_HAZ_AR.shp.
• The field "FLD_ZONE" is used to separate the 100-year and 500-year flood
hazard extents.
• Values of either "A" or "AE" were combined to define the 100-year extent,
while a value of "X" was used to define the 500-year extent.11
Outputs: Shapefile containing polygon extents for the 100-year and 500-year
floodplains
~
Clip the Block
Groups by the
Floodplains
Input:
• Shapefile containing polygon extents for the 100-year and 500-year
floodplains
• BG shapefile
Calculation:
• Within ArcGIS, use the "Clip" tool within the "Extract" toolset, within the
"Analysis Tools" toolbox
• Include area field during clip function
• Complete twice, once for each floodplain extent
Output: Shapefile with BG - floodplain intersection for 100- and 500-year extents
~
Calculate the
Floodplain-Based
Flood Indicator
Input: Shapefile with BG - floodplain intersection for 100- and 500-year extents
Calculation: See Appendix Equation FBF-1.
Using the area field for the clip and the area field for the original BG, determine the
percent area (area-clip/area-original).
Output: Percent area within the floodplain per BG per recurrence interval (i.e., 100-
and 500-year)
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have count values. Zero is a valid value.
~
Choosing a
Symbology
The recommended symbology for this indicator is equal intervals. The percent of
BG within the flood plain will range between 0 to 100%. In this case dividing the
data into equal intervals will present the data in a way that makes comparisons
between categories (or bins) easier. Alternatively, it is also possible to use
quantiles. This will create an equal number of observations per category, but it
makes comparison between categories less intuitive.
11 Definitions of the values contained within this field can be found at
https://www.fedl.ore/metadata/metadata archive/fedl html/dfirm fldhaz apr08.htm.
56
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Binning the Data
by Block Groups
Using 5-7 bins/categories is recommended so that the map reader can readily
distinguish between color categories and visually match them to the legend. Use
equal intervals, if possible, which create equal steps between categories (e.g., 0-
10, 11-20). Not all categories may necessarily contain values on the map.
An alternative to equal intervals is quantiles, which attempts to create the same
number of observations per bin. This causes each bin to have a different interval
and requires careful interpretation and communication. Using quantiles ensures that
each color in the symbology will appear on the map, and it will add more detail to
the map if the data are concentrated in a few small ranges. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
For maps with sequential data, use a symbology that becomes darker as the
vulnerability increases.
For maps with diverging data, such as the variants of the indicator showing percent
change, use a diverging color scheme characterized by darker colors at both
extremes and lighter in the middle.
Examples of how the indicator can be useful
~
Community
Vulnerability to
Flooding
This indicator provides a spatial representation of vulnerability of a community to
flooding. Combining this indicator with information on sites/waste facilities can
provide information on direct or indirect risks (e.g., due to compromised
infrastructure such as power disruptions).
Key caveats/limitations
~
NFHL Validity
FEMA maintains the NFHL on a schedule that is set by region. Before beginning
any analysis, a user should check for updates to the NFHL polygons. For layers that
have not been updated within the last few years, there is a greater probability that
the flood extents are out of date due to either land development or changes in
precipitation frequencies.
~
NFHL
Applicability
Actual inundation during any flooding event will vary depending on preceding
hydrologic conditions and the characteristics of the storm event itself. Therefore,
these defined flood plains are the best estimates of inundated area based on historic
storm events. As extreme precipitation events change into the future, these
floodplain boundaries will change and will likely expand to cover larger areas.
Further, this indicator does not provide any information on the severity or frequency
of flood events. Floodplains data also face other issues such as incomplete
coverage and coarse resolution (Wing et at, 2018).
Citations
~
Dataset/Tool
FEMA. (2021). National Flood Hazard Laver. htto://www.fema.aov/national-flood-
hazard-laver-nfhl.
~
Additional
Resources
Wing, O.E.J., Bates, P.D., Smith, A.M., Sampson, C. C., Johnson, K.A., Fargione,
J., & Morefield, P. (2018). Estimates of present and future flood risk in the
conterminous United States. Environmental Research Letters, 13, 034023
httDs://doi.ora/10.1088/1748-9326/aaac65
BG: Block Group, FEMA: U.S. Federal Emergency Management Agency, NFHL: National Flood Hazard Layer
57
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.1.6. Checklist for Precipitation-Based Flood Indicator
Potential impacts of floods on sites and waste facilities include more likely erosion, difficult ponding/stormwater
management, water damage (corrosion, water logging), power fluctuations/outages, groundwater plume
changes and higher water table, spreading/migration of contamination, catastrophic events destroy structures,
releases from overwash, infiltration, and leaching, incident waste facility closures, increased hazardous/non-
hazardous waste generation, groundwater pump-and-treat remedies may not be allowed to discharge,
increased potential for flooding of treatment systems, and dislodged debris from treatment/containment
systems or contained wastes and reactive wastes.
Precipitation-Based Flood Indicator
Definition of the indicator
~
Definition
Average annual percent of precipitation depth falling during "heavy" events where
"heavy" events are those with a daily precipitation depth greater than the 99th
percentile of daily precipitation for the user selected time period and climate
scenario/model. Alternate percentiles can be used to define heavy events.
Variants of the indicator that can be considered include (1) absolute historical and
projected values for the user selected time period and (2) difference or percent
difference between the projected and historical values.
~
Interpretation
The average annual proportion of precipitation that falls during the heaviest 1% of
precipitation events is a surrogate for likely flood-inducing events. Averaging across
the annual values allows for the influence of extreme years on the indicator value
(i. e., one year with a smaller number of extremely intense events will lead to a
higher indicator value). By using the 1% value from the historic period when
calculating the proportions for future periods, the increase or decrease in likely
flooding can easily be discerned. The indicator also shows variations in flood risks
across and between communities. While the actual flood-causing runoff and
streamflow generated from heavy events will depend on a number of factors,
including local land cover and antecedent moisture conditions, this indicator is
meant to be used for screening purposes that will identify priority areas for more in-
depth modeling.
58
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Data source
~
Data Source
The LOCA (Localized Constructed Analogs) downscaled climate data for the
historical and future periods for Representative Concentration Pathway (RCP)
scenarios provides the necessary temperature inputs.
• Time periods can be selected to represent historical conditions (e.g., 1986-
2005), mid-century (2040-2059), or late century (2080-2099) scenarios.
Other time periods/timeframes can be selected depending on user needs.
• RCP 4.5 and 8.5 are recommended to represent moderate and more
extreme conditions but other scenarios can be selected depending on user
needs.
• Outputs from the CanESM2 climate model can be selected to consider
more extreme conditions under a given emissions scenario. Other climate
models or a combination of climate models can be used based on user
needs.
~
Temporal
Resolution
Daily time series of precipitation
~
Spatial
Resolution
LOCA data are available for raster cells which are 1/16th of a degree of latitude and
1/16th of a degree of longitude in size.
Block Group (BG) shapefile
~
Data Format
Shapefile (BG); NetCDF (LOCA)
Decisions needed for calculation
~
Percentile Value
from Historic
Period
Determine a percentile value from the historic period and use that to assess heavy
events during the future periods. Using a historical threshold provides a means to
compare how heavy events are changing into the future as compared to the historic
conditions to which sites/waste facilities are accustomed.
~
Heavy Events
Defined as Top
1% Events
By using events that fall in the top 1% across the whole period, this analysis
captures the most extreme conditions, which therefore keeps annual depth
percentages tied to long-term heavy events. For example, USEPA (2016) and
USGCRP (2018) define "heavy event indicator" as the top 1% of precipitation
events. Alternate percentiles can be used depending on user needs and local
conditions.
~
Choice of
Appropriate Time
Period to
Represent Long-
term Climatic
Conditions/
Average Annual
Value for Final
Indicator
By averaging the annual percentage of heavy event precipitation across all the
years in the relevant time period, the indicator avoids short-term weather
fluctuations or modeling uncertainties. It considers the climatic variation of dry and
wet years and any patterns in the heavy event precipitation. To capture long-term
climatic conditions, time periods covering 20 years are recommended. An alternate
number of years can be used depending on local needs. For example, averages
over a 30-year time period represent longer term climatic conditions and shorter
timeframes (e.g., 15 years) reflect more recent changing conditions.
59
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Calculation steps and assumptions
~
Compile Block
Group
Precipitation
Time Series
Inputs: LOCA data and BG shapefile
Calculations:
• Extract all grid cells that touch any BGs within the study area.
• Resample the extracted raster by a factor of 100 to produce smaller grid
cells, each with the same value as the original raster.
• This needs to be done if the BGs are small compared to the grid cells to
ensure that even the smallest BGs would have at least one grid cell with
data fall within it.
• Using the zonal statistical function, take the average value of the indexed
cells to report a precipitation value for each BG.
Outputs: Daily time series of precipitation per BG
~
Create a Time
Series of Wet
Days
Input: Daily time series of precipitation per BG
Calculation: For each time period for each BG, select days where precipitation > 0
Output: Time series limited to days with precipitation per BG
~
Find Percentile
Value for Historic
Period
Input: Time series of wet days from historic period per BG
Calculation: Compute the selected percentile value of precipitation across the wet
days within the entire 20-year historic period by BG. For illustration we use the 99th
percentile to define heavy events as the top 1% of precipitation events.
Output: Selected percentile value per BG
~
Select Heavy
Precipitation
Days
Inputs:
• Selected percentile value
• Time series of wet days per BG
Calculation: Using the 99th percentile value, select days from the time series of
wet days that are greater than the selected percentile value for each period by BG.
Outputs: Time series of heavy precipitation days per BG
~
Calculate Total
Heavy Event
Precipitation
Each Year
Inputs: Time series of heavy precipitation days per BG
Calculation: See Appendix Equation PBF-1.
Sum the precipitation depth on heavy precipitation days within each year to find the
total precipitation due to heavy events each year.
Output: Total heavy precipitation per year per BG
~
Calculate Total
Precipitation
Each Year
Inputs: Time series of wet days per BG
Calculation: See Appendix Equation PBF-2.
Sum daily precipitation depths within each year.
Output: Total precipitation per year per BG
60
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Calculate
Percentage of
Precipitation
Falling during
Heavy Events
Each Year
Inputs:
• Total heavy precipitation per year per BG
• Total precipitation per year per B G
Calculation: See Appendix Equation PBF-3.
For each year divide the total heavy precipitation per year by the total precipitation
per year. Complete this calculation for each year and each BG.
Output: Percent of precipitation falling as heavy events per year per BG
~
Calculate the
Precipitation-
Based Flood
Indicator
Inputs: Percent of precipitation falling as heavy events per year per BG
Calculation: See Appendix Equation PBF-4.
Sum the percent of precipitation falling as heavy events from each year per BG and
divide by 20 (years in time period).
Output: Average annual percent of precipitation falling as heavy events per BG
~
Repetition for
Time Periods/
Scenarios and
Summary Value
for Time Period
Repeat all steps except for the calculation of the selected percentile value for the
selected time periods (historical and future) and average over the time period. Use
the value from the historic period for the selected percentile for all time periods.
Repeat the above step for any additional future scenarios.
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have percent values.
~
Choosing a
Symbology
A single unique symbology that spans climate scenarios is recommended. This will
result in the same color representing the same value across the maps, making
direct comparisons easier. To build this, find the minimum and maximum values
across climate scenarios, and then use the full range of values to create a single
unique symbology to apply to all maps.
~
Binning the Data
by Block Groups
Using 5-7 bins/categories is recommended so that the map reader can readily
distinguish between color categories and visually match them to the legend. Use
equal intervals, if possible, which create equal steps between categories (e.g., 0-
10, 11-20). Not all categories may necessarily contain values on the map.
An alternative to equal intervals is quantiles, which attempts to create the same
number of observations per bin. This causes each bin to have a different interval
and requires careful interpretation and communication. Using quantiles ensures that
each color in the symbology will appear on the map, and it will add more detail to
the map if the data are concentrated in a few small ranges. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
For maps with sequential data, use a symbology that becomes darker as the
vulnerability increases.
For maps with diverging data, such as the variants of the indicator showing percent
change, use a diverging color scheme characterized by darker colors at both
extremes and lighter in the middle.
61
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Planning for
Stormwater
Management and
Site/Waste
Facility Breaches
This indicator provides a spatial representation of vulnerability of sites/waste
facilities to flooding due to extreme precipitation events. These events may
represent more frequent storms during wet years or fewer but larger events during
dry years. Areas with higher percentages will face greater risks of flooding and risks
of site/waste facility breaches. Therefore, the areas with higher indicator values are
more vulnerable and should be targeted for adaptation.
Using this indicator in conjunction with the mean Height Above Nearest Drainage
(HAND) per BG (Indicator 1.1.8) provides even more information on flood risk.
The high percentage areas also face challenges in dealing with the on-the-ground
stormwater resulting from these events, whether the event results in flooding or not.
Therefore, the areas with higher indicator values should be targeted for stormwater
management planning as well.
Key caveats/limitations
~
Calculations
Completed within
R
A custom program such as R needs to be created to input the processed LOCA-
based climate data indexed by BG unique identifier and complete all calculations. If
using the custom R program developed by RTI, a user need only supply the links to
the input precipitation datasets for the program to complete all calculation steps.
~
Spatial
Differentiation
Based on Climate
Data Source
When interpreting the maps, it is important to remember that the resolution of the
LOCA modeled data is fairly coarse compared to BGs. Each raster cell is 1/16th of a
degree of latitude and 1/16th of a degree of longitude in size. This equates to
approximately 5 km x 7 km at temperate latitudes such as the United States. There
is the potential that this indicator may not provide enough variation across BGs
consisting of small spatial areas. The other aspect to be aware of is that difference
between BGs may be smaller than the errors in the modeled data. Therefore,
differences displayed cartographically, may be within the range of model error. This
limitation will apply to any indicators developed with the LOCA data as its input
source.
Citations
~
Dataset/Tool
Pierce, D. I/I/. (2016). LOCA (Local Constructed Analogs) statistical downscaling
version 40. Scripps Institute of Oceanography, Division of Climate, Atmospheric
Sciences, and Physical Oceanoaraohv. http://loca.ucsd.edu/
62
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections. (2021).
httoJ/gdo-dcD.ucllnl.org/downscaled cmio projections/
Groisman, P.Y., Knight, R.W., Karl, T.R., Easterling, D.R., Sun, B., andLawrimore,
J.H. (2004). Contemporary Changes of the Hydrological Cycle over the Contiguous
United States: Trends Derived from In Situ Observations. Journal of
Hydrometeorology 5, 64-85.
U.S. Environmental Protection Agency. (2016). Climate change indicators in the
United States, 2016. Fourth edition. EPA 430-R-16-004. www.epa.gov/climate-
indicators.
U.S. Global Change Research Program. (2018). Impacts, Risks, and Adaptation in
the United States: Fourth National Climate Assessment, Volume II [Reidmiller, D.R.,
C.W. Avery, D.R. Easterling, KE. Kunkel, K.L.M. Lewis, T.K Maycock, and B.C.
Stewart (eds.)]. U.S. Global Change Research Program, Washington, DC, USA,
1515pp. doi: 10.7930/NCA4.2018.
BG: Block Group, LOCA: Localized Constructed Analogs, RCP: Representative Concentration Pathway
63
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.1.7. Checklist for Threshold-Based Flood Indicator
Potential impacts of floods on sites and waste facilities include more likely erosion, difficult ponding/stormwater
management, water damage (corrosion, water logging), power fluctuations/outages, groundwater plume
changes and higher water table, spreading/migration of contamination, catastrophic events destroy structures,
releases from overwash, infiltration, and leaching, incident waste facility closures, increased hazardous/non-
hazardous waste generation, groundwater pump-and-treat remedies may not be allowed to discharge,
increased potential for flooding of treatment systems, and dislodged debris from treatment/containment
systems or contained wastes and reactive wastes.
Threshold-Based Flood Indicator
Definition of the indicator
~
Definition
The percent change in the depth of precipitation falling during extreme events (i.e.,
change in total depth over the 20-year period) from the historic period to the user
selected future assessment period and climate scenario/model.
~
Interpretation
The percent change in depth due to heavy precipitation from the historic to the
future (future total heavy precipitation depth minus historic total heavy precipitation
depth as a percent of the historic total heavy precipitation depth) provides a
threshold value for the increased risk from heavy precipitation events in the future
as compared to the heavy precipitation expected and/or experienced and
accounted for by facility operations in the more recent, historic period. A positive
value for this indicator means that more of the precipitation that falls over the long-
term will be falling as part of heavy precipitation events. It measures the long-term
percent change in heavy precipitation depth.
Data source
~
Data Source
The LOCA (Localized Constructed Analogs) downscaled climate data for the
historical and future periods for Representative Concentration Pathway (RCP)
scenarios provides the necessary temperature inputs.
• Time periods can be selected to represent historical conditions (e.g., 1986-
2005), mid-century (2040-2059), or late century (2080-2099) scenarios.
Other time periods/timeframes can be selected depending on user needs.
• RCP 4.5 and 8.5 are recommended to represent moderate and more
extreme conditions but other scenarios can be selected depending on user
needs.
• Outputs from the CanESM2 climate model can be selected to consider
more extreme conditions under a given emissions scenario. Other climate
models or a combination of climate models can be used based on user
needs.
~
Temporal
Resolution
Daily time series of precipitation
~
Spatial
Resolution
LOCA data are available for raster cells, which are 1/16th of a degree of latitude and
1/16th of a degree of longitude in size
Block Group (BG) shapefile
64
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Data Format
Shapefile (BG); NetCDF (LOCA)
Decisions needed for calculation
~
Percentile Value
from Historic
Period
Determine a percentile value from the historic period and using that to assess
heavy events during the future periods. Using a historical threshold provides a
means to compare how heavy events are changing into the future as compared to
the historic conditions to which sites/waste facilities are accustomed.
~
Heavy Events
Defined as Top
1% Events
By using events that fall in the top 1% across the whole period, this analysis is
limited to truly large events, which therefore keeps annual depth percentages tied to
long-term heavy events. Alternate percentiles such as can be used depending on
user needs and local conditions.
~
Choice of
Appropriate Time
Period to
Represent Long-
term Climatic
Conditions
Considering a time period covering several years is recommended to avoid short-
term weather fluctuations or modeling uncertainties and to capture representative
long-term climatic conditions and patterns. Time periods covering 20 years are
recommended. An alternate number of years can be used depending on local
needs. For example, a 30-year time period represents longer term climatic
conditions and shorter timeframes (e.g., 15 years) reflect more recent changing
conditions.
Calculation steps and assumptions
~
Compile Block
Group
Precipitation
Time Series
Inputs: LOCA data and BG shapefile
Calculations:
• Extract all grid cells the touch any BGs within the study area.
• Resample the extracted raster by a factor of 100 to produce smaller grid
cells, each with the same value as the original raster. This needs to be
done if the BGs are small compared to the grid cells to ensure that even the
smallest BGs would have at least one grid cell with data fall within it.
• If there are multiple grid cells overlapping with a BG, use the zonal
statistical function, take the average value of the indexed cells to report a
precipitation value for each BG.
Outputs: Daily time series of precipitation per BG
~
Create a Time
Series of Wet
Days
Input: Daily time series of precipitation per BG
Calculation: For each time period for each BG, select days where precipitation is
greater than 0.
Output: Time series limited to days with precipitation per BG
~
Find Percentile
Value for Historic
Period
Input: Time series of wet days from historic period per BG
Calculation: Compute the selected percentile value of precipitation across the wet
days within the entire 20-year historic period by BG. For illustration we use the 99th
percentile to define heavy events as the top 1% of precipitation events.
Output: Selected percentile value per BG
65
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Select Heavy
Precipitation
Days
Inputs:
• Selected percentile value
• Time series of wet days per BG
Calculation: Using the selected percentile value, select days from the time series
of wet days that are greater than the selected percentile value for each period by
BG.
Outputs: Time series of heavy precipitation days per BG
~
Calculate Total
Heavy Event
Precipitation
Each Year
Inputs: Time series of heavy precipitation days per BG
Calculation: See Appendix Equation PBF-1.
Sum precipitation depth on the heavy precipitation days within each year to find the
total precipitation depth due to heavy events each year.
Output: Total heavy precipitation per year per BG
~
Repetition for
Time Periods
Repeat all steps except for the calculation of the 99th percentile value for the future
time periods. Use the value from the historic period for the 99th percentile for all
time periods.
~
Calculate the
Threshold-Based
Flood Indicator
for RCP4.5
Scenario
Inputs: Total depth of precipitation from heavy events during the historic period and
total depth of precipitation from heavy events during the future RCP4.5 scenario,
per year per BG.
Calculation: See Appendix Equation TBF-1.
Take the difference between the total depth of precipitation from heavy events from
the historic period to the future scenario period. Then divide by the total depth of
precipitation during heavy events during the historic period to get the percent
change.
Output: Percent change in heavy event precipitation from historic conditions for the
future scenario per BG
~
Repetition for
RCP8.5 Scenario
Repeat the above calculation using the total depth of heavy event precipitation for
future RCP8.5 scenario.
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have change values.
~
Choosing a
Symbology
A single unique symbology that spans climate scenarios is recommended. This will
result in the same color representing the same value across the maps, making
direct comparisons easier. To build this, find the minimum and maximum values
across climate scenarios, and then use the full range of values to create a single
unique symbology to apply to all maps.
66
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Binning the Data
by Block Groups
Using 5-7 bins/categories is recommended so that the map reader can readily
distinguish between color categories and visually match them to the legend. Use
equal intervals, if possible, which create equal steps between categories (e.g., 0-
10, 11-20). Not all categories may necessarily contain values on the map.
An alternative to equal intervals is quantiles, which attempts to create the same
number of observations per bin. This causes each bin to have a different interval
and requires careful interpretation and communication. Using quantiles ensures that
each color in the symbology will appear on the map, and it will add more detail to
the map if the data are concentrated in a few small ranges. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
For maps with sequential data, use a symbology that becomes darker as the
vulnerability increases.
For maps with diverging data, such as the variants of the indicator showing percent
change, use a diverging color scheme characterized by darker colors at both
extremes and lighter in the middle.
Examples of how the indicator can be useful
~
Resiliency
Planning for
Heavy
Precipitation
Events
Resiliency planning for sites/waste facilities should consider an increased frequency
and/or intensity of heavy events when large values for this indicator are projected.
Resiliency could examine efforts to protect from flash floods or to provide
stormwater storage, routing, or treatment, for example.
Using this indicator in conjunction with the mean Height Above Nearest Drainage
(HAND) per BG (Indicator 1.1.8) provides even more information on flood risk.
Key caveats/limitations
~
Calculations
Completed within
R
A custom program such as R needs to be created to input the processed LOCA-
based climate data indexed by BG unique identifier and complete all calculations. If
using the custom R program developed by RTI, a user need only supply the links to
the input precipitation datasets for the program to complete all calculation steps.
~
Spatial
Differentiation
Based on Climate
Data Source
When interpreting the maps, it is important to remember that the resolution of the
LOCA modeled data is fairly coarse compared to BGs. Each raster cell is 1/16th of a
degree of latitude and 1/16th of a degree of longitude in size. This equates to
approximately 5 km x 7 km at temperate latitudes such as the United States. There
is the potential that this indicator may not provide enough variation across BGs
consisting of small spatial areas. The other aspect to be aware of is that difference
between BGs may be smaller than the errors in the modeled data. Therefore,
differences displayed cartographically, may be within the range of model error. This
limitation will apply to any indicators developed with the LOCA data as its input
source.
Citations
~
Dataset/Tool
Pierce, D. I/I/. (2016). LOCA (Local Constructed Analogs) statistical downscaling
version 40. Scripps Institute of Oceanography, Division of Climate, Atmospheric
Sciences, and Physical Oceanoaraohv. http://loca.ucsd.edu/
67
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections. (2021).
httoJ/gdo-dcD.ucllnl.org/downscaled cmio projections/
Groisman, P.Y., Knight, R.W., Karl, T.R., Easterling, D.R., Sun, B., and Lawrimore,
J.H. (2004). Contemporary Changes of the Hydrological Cycle over the Contiguous
United States: Trends Derived from In Situ Observations. Journal of
Hydrometeorology 5, 64-85.
U.S. Global Change Research Program. (2018) Impacts, Risks, and Adaptation in
the United States: Fourth National Climate Assessment, Volume II [Reidmiller, D.R.,
C.W. Avery, D.R. Easterling, K.E. Kunkel, KL.M. Lewis, T.K Maycock, and B.C.
Stewart (eds.)]. U.S. Global Change Research Program, Washington, DC, USA,
1515pp. doi: 10.7930/NCA4.2018.
BG: Block Group, HAND: Height Above Nearest Drainage, LOCA: Localized Constructed Analogs, RCP: Representative Concentration
Pathway
68
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.1.8. Checklist for Physically Based Flood Indicator
Potential impacts of floods on sites and waste facilities include more likely erosion, difficult ponding/stormwater
management, water damage (corrosion, water logging), power fluctuations/outages, groundwater plume
changes and higher water table, spreading/migration of contamination, catastrophic events destroy structures,
releases from overwash, infiltration, and leaching, incident waste facility closures, increased hazardous/non-
hazardous waste generation, groundwater pump-and-treat remedies may not be allowed to discharge,
increased potential for flooding of treatment systems, and dislodged debris from treatment/containment
systems or contained wastes and reactive wastes.
The Height Above Nearest Drainage (HAND) dataset is a hydrological terrain raster available for the
conterminous United States. The national dataset was created using the 10m Digital Elevation Model (DEM) data
produced by the U.S. Geological Survey (USGS) 3DEP (the 3-D Elevation Program) and the NHDPIus hydrography
dataset produced by USGS and EPA. The value of each raster cell in HAND is an approximation of the relative
elevation between the cell and its nearest waterbody. The physically based flood indicator is defined as the
mean HAND value per Block Group.
Physically Based Flood Indicator
Definition of the indicator
~
Definition
The mean Height Above Nearest Drainage (HAND) value per Block Group (BG)
approximates the average difference in elevation within the BG from its nearest
waterbody.
~
Interpretation
The idea behind using HAND values is that areas with a lower elevation distance
between the land and water surface are more likely to experience flooding than
higher grounds. Low-lying areas within a study area or community have the
potential for both riverine flooding and receiving overland flow during high-depth
precipitation events.
Data source
~
Data Source
The HAND dataset is a hydrological terrain raster available for the conterminous
United States from Oak Ridge National Lab (ORNL).
~
Spatial
Resolution
• 10-meter HAND raster for all 6-digit Hydrologic Unit Code (HUC6) crossed
by the study area
• BG shapefile
~ET
Data Format
Shapefile (BG); raster in .tif(HAND)
69
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Decisions needed for calculation
~
Mean HAND
versus other
statistics
The mean HAND value was chosen as the indicator as opposed to another statistic
such as the minimum, maximum, or a percentile. Due to the fine-scale raster
gradation of the dataset, it is likely the choice of a single value such as a minimum
could identify an outlier cell that is not representative of the BG as a whole.
Similarly, choosing a percentile (i.e., 10th percentile) could still define a small outlier
area rather than a distributed low-lying area within the BG confusing the flooding
risk. By taking an average value the indicator provides a higher likelihood in
identifying a range of comparable vulnerabilities due to flooding across the study
area. Median values could also be used instead of means.
Calculation steps and assumptions
~
Combine HUC6
HAND Rasters as
Needed
Inputs: 10-meter HAND raster for all HUC6 crossed by the study area
Calculations:
• Use the Mosaic to New Raster command within the Raster Dataset toolset
within the Data Management Toolbox within ArcGIS to combine all the
extracted rasters into a single raster.
• These rasters likely extend beyond the limits of the study area.
Outputs: A single raster mosaic with the same raster cell size as the inputs
~
Create HAND
Raster Specific to
Study Area
Input: The single raster mosaic from the previous step and the shapefile of the
study area
Calculation: Use the Extract By Mask command within the ArcGIS Spatial Analyst
Toolbox to extract the portion of the HAND raster mosaic that falls within the study
area boundary.
Output: Raster coverage that contains 10 m cells with HAND values covering the
whole study area
~
Calculate the
Physically Based
Flood Indicator
Input: HAND raster specific to study area and BG layer
Calculation: Use Zonal Stats as Table within the ArcGIS Spatial Analyst toolbox to
calculate the mean HAND raster value for each BG.
Output: Mean HAND value per BG
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have elevation values.
~
Choosing a
Symbology
The recommended symbology for this indicator is one specific to the current area of
interest. It is not necessary to create one that can be compared across scenarios,
time periods, or locations.
70
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Binning the Data
by Block Groups
The distribution of HAND values by BG may indicate that either equal intervals or
quantiles display the data best. However, the areas of greatest concern are those
with the lowest HAND values. Therefore, to highlight these areas use bins that
provide differentiation in the lowest values and group the highest values together.
This will focus attention on the areas of greatest concern. Use a maximum of 5-7
categories so that the map reader can readily distinguish between color categories
and visually match them to the legend.
~
Choosing Colors
Use a color gradation that becomes lighter as the HAND values increase. This will
draw the map reader's focus to the areas of greatest concern. Avoid using a
divergent color scheme (darker at both extremes and lighter in the middle), which
implies an inflection point such as 0 in a dataset containing positive and negative
values.
Examples of how the indicator can be useful
~
Localized and
Riverine Flood
Risk Assessment
As opposed to delineated floodplains, which are mapped only for certain storm
recurrence intervals, the HAND-based flood indicator shows the relative elevation
above the nearest waterbody for each BG. Because this indicator takes into
account elevation and relief, it provides a more robust and comparable measure of
potential flood impacts from large storms. Using this indicator in conjunction with the
heavy precipitation indicators (1.1.5/1.1.6) provides even more information on flood
risk.
Key caveats/limitations
~
Future Scenarios
The HAND dataset, being based on land elevation, will not change into the future.
~
Spatial
Differentiation
Based on HAND
Data Source
The HAND data are distributed by HUC6 watersheds with grid cells at 10 m
resolution. For study areas where communities span more than one HUC6
watershed, multiple HAND datasets will need to be combined to create a full
coverage of the area. Given that the data are included within a national repository,
the formatting of data should be consistent, allowing for simple joining methods.
The 10 m resolution ensures that each BG can be characterized by multiple cells
allowing for the calculation of an average value for the indicator.
Citations
~
Dataset/Tool
Liu, Y.Y. (2020). Height Above Nearest Drainage (HAND) and Hydraulic Property
Table for CONUS - Version 0.2. Oak Ridge Leadership Computing Facility. (2020).
DOI: httDs://doi. ora/10.13139/ORNLNCCS/1608331
~
Additional
Resources
Liu, Y.Y., Maidment, D.R., Tarboton, D.G., Zheng, X. & Wang, S. (2018). A
CyberGIS integration and computation framework for high-resolution continental-
scale flood inundation mapping. Journal of the American Water Resources
Association, 54(4), 770-784. DOI: 10.1111/1752-1688.12660
Nobre, A.D., Cuartas, L.A., Momo, M.R., Severn, D.L., Pinheiro, A. & Nobre, C.A.,
(2016). HAND contour: a new proxy predictor of inundation extent. Hydrological
Processes, 30(2), 320-333.
3DEP: USGS 3-D Elevation Program, BG: Block Group, DEM: Digital Elevation Model, EPA: U.S. Environmental Protection Agency, HAND:
Height Above Nearest Drainage, HUC6: 6-digit Hydrologic Unit, ORNL: Oak Ridge National Laboratory, USGS: U.S. Geological Survey
71
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicators 1.1.9 & 1.1.11. Checklist for Drought Indicator
Potential impacts of droughts on sites and waste facilities include increased fire hazards and fugitive dust,
reduction in remediation effectiveness, changes in groundwater plume dynamics due to lower water table,
water use restrictions impacts, increased scrutiny of groundwater extraction systems, and damage to vegetative
covers. To summarize, most of the impacts of drought on sites and waste facilities are related to water
availability.
Drought Indicator
Definition of the indicator
~
Definition
The count of months within the 20-year assessment period having a Standardized
Precipitation-Evapotranspiration Index (SPEI) within the drought range (as defined
by the U.S. Drought Monitor as less than -0.8), where the SPEI may be calculated
at either the six-month (SPEI-6) or 12-month (SPEI-12) time scale depending on
the community's hydrologic concerns.
~
Interpretation
The SPEI is calculated based on a climatic water balance using monthly
precipitation and temperature, making it suitable for assessing differences due to
changes in the climate between historic and future periods. The resulting monthly
SPEI time series shows the fluctuation overtime of drought, dry, normal, wet, and
abnormally wet conditions through the SPEI values, which are comparable in space
and time due to the standardization process embedded within the SPEI calculation.
Positive values indicate wet conditions, while negative values indicate dry
conditions; the magnitude of the SPEI represents the severity of a condition. The
time scale chosen for SPEI indicates the length of preceding months considered in
the calculation.
Data source
~
Data Source
The LOCA (Localized Constructed Analogs) downscaled climate data for the
historical and future periods for Representative Concentration Pathway (RCP)
scenarios provides the necessary temperature inputs.
• Time periods can be selected to represent historical conditions (e.g., 1986-
2005), mid-century (2040-2059), or late century (2080-2099) scenarios.
Other time periods/timeframes can be selected depending on user needs.
• RCP 4.5 and 8.5 are recommended to represent moderate and more
extreme conditions but other scenarios can be selected depending on user
needs.
• Outputs from the CanESM2 climate model can be selected to consider
more extreme conditions under a given emissions scenario. Other climate
models or a combination of climate models can be used based on user
needs.
~
Temporal
Resolution
Time series of precipitation and potential evapotranspiration (PET) by Block Group
(BG). PET can be calculated from LOCA data (described below).
72
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Spatial Resolution
LOCA is available for raster cells which are 1/16th of a degree of latitude and
1/16th of a degree of longitude in size
BG shape file
~
Data Format
Shapefile (BG); NetCDF (LOCA)
Decisions needed for calculation
~
PET Calculation
Method
Data on PET is needed for SPEI and must be calculated. The SPEI formulation is
not tied to any one PET method so different methods can be used depending on
data availability. The Hargreaves method is recommended and requires minimum
and maximum temperature as well as latitude for the location.
~
Time Scale for
SPEI
The valid range for calculation is 1 to 48 months for SPEI. Selection of the time
scale will depend on the type of drought a user wishes to assess and will therefore
be community dependent, l/l/e recommend using 6 months (SPEI-6) as a
conservative indicator of drought conditions beginning to impact soils and water use
and 12 months to represent drought conditions that are more likely to impact water
availability. (SPEI-12 follows the similar and well-documented Standardized
Precipitation Index at a 12-month time scale [SPI-12] used by the EPA's Climate
Change Division)
~
Definition of
Drought using SPEI
There are several ranges for defining drought severity using the continuous SPEI
values across the months of the 20-year assessment period. The recommendation
of this study is to use -0.8 as the upper threshold for drought conditions based on
the U.S. Drought Monitor's ranges for drought using the related Standardized
Precipitation Index (SPI). All months with SPEI values less than or equal to -0.8 are
considered drought conditions.
~
Choice of
Appropriate Time
Period to
Represent Long-
term Climatic
Conditions/Annual
Average Value for
Final Indicator
By averaging the count of drought months across all the years in the relevant time
period, the indicator avoids short-term weather fluctuations or modeling
uncertainties. It considers the climatic variation of cool and hot years and any
patterns in the episodes of drought. To capture long-term climatic conditions, time
periods covering 20 years are recommended. An alternate number of years can be
used depending on local needs. For example, averages over a 30-year time period
represent longer-term climatic conditions and shorter timeframes (e.g., 15 years)
reflect more recent changing conditions.
73
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Calculation steps and assumptions
~
Compile Block
Group
Precipitation and
Temperature
Time Series
Inputs: LOCA data and BG shapefile
Calculations:
• Extract all grid cells that touch any BGs within the study area.
• Resample the extracted raster by a factor of 100, to produce smaller grid
cells, each with the same value as the original raster. This needs to be
done if the BGs are small compared to the grid cells to ensure that even
the smallest BGs would have at least one grid cell with data fall within it.
• If there are multiple grid cells overlapping with a BG, use the zonal
statistical function, take the average value of the indexed cells to report a
climate value for each BG.
Output: Daily time series for each climate parameter per BG
~
Determine Block
Group Latitude
Input: BG shapefile
Calculation: Using the centroid of each BG, assign a latitude value using the
Calculate Geometry geospatial function.
Output: Single latitude value per BG
~
Compile Monthly
Time Series for
Temperature and
Precipitation
Inputs: Daily time series for each climate parameter per BG
Calculations:
• Minimum temperature: take the minimum of the minimum daily values for
each month
• Maximum temperature: take the maximum of the maximum daily values for
each month
• Precipitation: sum daily values across each month
Outputs: Monthly time series of minimum and maximum temperature and
precipitation per BG
~
Compute PET
Using the SPEI R Package, compute monthly PET time series based on minimum
and maximum temperature and latitude by BG.
~
Compute Climatic
Water Balance
Inputs: Monthly precipitation time series and monthly PET time series by BG
Calculation: Precipitation - PET
Output: Time series of monthly climatic water balance
~
Compute SPEI
Values
Using the SPEI R Package, compute the SPEI values for each selected time scale
(6 and 12 months) using Climatic Water Balance time series.
• Two function calls used, one for each time scale. SPEI package computes
values for each individual BG.
• Default selections are accepted within the package for SPEI calculation
other than time scale selection.
74
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Calculate Drought
Indicator for each
Time Period
Inputs: Monthly time series of SPEI values by BG
Calculation: Count all SPEI values less than or equal to -0.8 (i.e., drought
conditions) over the relevant time period for each BG
Output: Count of drought months by BG
~
Repetition for
Time Periods/
Scenarios
Repeat all steps for the selected time periods (historical and future). Repeat the
above step for any additional future scenarios.
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA BGs will all have percent values.
~
Choosing a
Symbology
A single unique symbology that spans climate scenarios is recommended. This will
result in the same color representing the same value across the maps, making
direct comparisons easier. To build this, find the minimum and maximum values
across climate scenarios, and then use the full range of values to create a single
unique symbology to apply to all maps.
~
Binning the Data
by Block Groups
Using 5-7 bins/categories is recommended so that the map reader can readily
distinguish between color categories and visually match them to the legend. Use
equal intervals, if possible, which create equal steps between categories (e.g., 0-
10, 11-20). Not all categories may necessarily contain values on the map.
An alternative to equal intervals is quantiles, which attempts to create the same
number of observations per bin. This causes each bin to have a different interval
and requires careful interpretation and communication. Using quantiles ensures
that each color in the symbology will appear on the map, and it will add more detail
to the map if the data are concentrated in a few small ranges. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
For maps with sequential data, use a symbology that becomes darker as the
vulnerability increases.
For maps with diverging data, such as the variants of the indicator showing percent
change, use a diverging color scheme characterized by darker colors at both
extremes and lighter in the middle.
Examples of how the indicator can be useful
~
SPEI-6 Drought
Indicator
SPEI-6 is an indicator of drought conditions where the impact to soils and water use
is likely to become influential. By comparing the indicator values across
communities, the relative vulnerability to water resources is visible spatially and can
be used to assess the vulnerability of sites/waste facilities dependent on a readily
available water supply.
~
SPEI-12 Drought
Indicator
SPEI-12 represents drought conditions that are more likely to impact water
availability and sustained dry conditions. By comparing the indicator values across
communities, the relative vulnerability to water supply or source waters is visible
spatially and can be compared to specific water supply locations or water supply
watersheds for assessment. This can be used to assess the vulnerability of sites/
waste facilities dependent on a readily available water supply.
75
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Key caveats/limitations
~
Calculations
Completed within
R using SPEI
Package
A custom R program needs to be created to input the processed LOCA-based
climate data indexed by BG unique identifier. The SPEI package available from
CRAN (httos://cran.r-Droiect.ora/web/Dackaaes/SPEI/index.html) can be used as a
starting point. The Hargreaves PET method was used based on minimum and
maximum temperatures. If using the custom R program developed by RTI, a user
need only supply the links to the input climate and latitude datasets for the program
to complete all calculation steps.
~
Spatial
Differentiation
Based on Climate
Data Source
When interpreting the maps, it is important to remember that the resolution of the
LOCA modeled data is fairly coarse compared to BGs. Each raster cell is 1/16th of a
degree of latitude and 1/16th of a degree of longitude in size. This equates to
approximately 5 km x 7 km at temperate latitudes such as the United States. There
is the potential that this indicator may not provide enough variation across BGs
consisting of small spatial areas. The other aspect to be aware of is that difference
between BGs maybe smaller than the errors in the modeled data. Therefore,
differences displayed cartographically, may be within the range of model error. This
limitation will apply to any indicators developed with the LOCA data as its input
source.
Citations
Dataset/Tool
Pierce, D.W. (2016). LOCA (Local Constructed Analogs) statistical downscaling
version 40. Scripps Institute of Oceanography, Division of Climate, Atmospheric
Sciences, and Physical Oceanoaraohv. http://loca.ucsd.edu/
Additional
Resources
Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections. (2021).
htto.Y/ado-dco.ucllnl.ora/downscaled cmio oroiections/
Begueria, S., Vicente-Serrano, S.M., Reig, F. & Latorre, B. (2014). Standardized
precipitation evapotranspiration index (SPEI) revisited: parameter fitting,
evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol.,
34, 3001-3023. doi:10.1002/joc.3887
Vicente-Serrano, S.M., Begueria, S., & Lopez-Moreno, J.I. (2010). A multi-scalar
drought index sensitive to global warming: The Standardized Precipitation
Evapotranspiration Index - SPEI. Journal of Climate, 23, 1696, DOI:
10.1175/2009JCLI2909.1.
Naumann, G., Alfieri, L., Wyser, K., Mentaschi, L., Betts, R. A., Carrao, H., et al.
(2018). Global changes in drought conditions under different levels of warming.
Geophysical Research Letters, 45, 3285-3296.
httosJ/doi. ora/10.1002/2017GL076521
Schwalm, C.R., Anderegg, W.R., Michalak, A.M., Fisher, J.B., Biondi, F., Koch, G.,
Litvak, M., Ogle, K., Shaw, J.D., Wolf, A. & Huntzinger, D.N., 2017. Global patterns
of drought recovery. Nature, 548(7666), 202-205.
BG: Block Group, CRAN: Comprehensive R Archive Network, EPA: U.S. Environmental Protection Agency, LOCA: Localized Constructed
Analogs, PET: Potential Evapotranspiration, RCP: Representative Concentration Pathway, SPEI: Standardized Precipitation-
Evapotranspiration Index, SPI: Standardized Precipitation Index
76
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicators 1.1.10 & 1.1.12. Checklist for Threshold-Based Drought Indicator
Potential impacts of droughts on sites and waste facilities include increased fire hazards and fugitive dust,
reduction in remediation effectiveness, changes in groundwater plume dynamics due to lower water table,
water use restrictions impacts, increased scrutiny of groundwater extraction systems, and damage to vegetative
covers. To summarize, most of the impacts of drought on sites and waste facilities are related to water
availability.
Threshold-Based Drought Indicator
Definition of the indicator
~
Definition
The change in the count of drought months between the historic and future period,
where drought is determined by the Standardized Precipitation-Evapotranspiration
Index (SPEI) as less than -0.8. SPEI may be calculated at either the 6-month
(SPEI-6) or 12-month (SPEI-12) time scale depending on the community's
hydrologic concerns.
~
Interpretation
The count of drought months indicates how many months out of the selected period
are expected to experience drought conditions. By calculating the change in the
count of drought months between the historic and future period, the increase or
decrease in length of time of expected drought conditions in the future can be
examined.
Data source
~
Data Source
The LOCA (Localized Constructed Analogs) downscaled climate data for the
historical and future periods for Representative Concentration Pathway (RCP)
scenarios provides the necessary temperature inputs.
• Time periods can be selected to represent historical conditions (e.g., 1986-
2005), mid-century (2040-2059), or late century (2080-2099) scenarios.
Other time periods/timeframes can be selected depending on user needs.
• RCP 4.5 and 8.5 are recommended to represent moderate and more
extreme conditions but other scenarios can be selected depending on user
needs.
• Outputs from the CanESM2 climate model can be selected to consider
more extreme conditions under a given emissions scenario. Other climate
models or a combination of climate models can be used based on user
needs.
~
Temporal
Resolution
Time series of precipitation and potential evapotranspiration (PET) by Block Group
(BG). PET can be calculated from LOCA data (described below).
~
Spatial
Resolution
LOCA is available for raster cells, that are 1/16th of a degree of latitude and 1/16th
of a degree of longitude in size
BG shapefile
~
Data Format
Shapefile (BG); NetCDF (LOCA)
77
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Additional Inputs
While the precipitation input required for SPEI is typically readily available, PET
often is not and must be calculated. The SPEI formulation is not tied to any one
PET method, the methods used depend on data availability. The Hargreaves
method is recommended and requires minimum and maximum temperature as well
as latitude for the location.
Decisions needed for calculation
~
Time Scale for
SPEI
The valid range for calculation is 1 to 48 months for SPEI. Selection of the time
scale will depend on the type of drought a user wishes to assess and will therefore
be community dependent, l/l/e recommend using 6 months (SPEI-6) as a
conservative indicator of drought conditions beginning to impact soils and
vegetation and 12 months (SPEI-12 following the similar and well-documented
Standardized Precipitation Index at a 12-month time scale [SPI-12] used by the
EPA's Climate Change Division) to represent drought conditions that are more likely
to impact water availability
~
Definition of
Drought using
SPEI
There are several ranges for defining drought severity using the continuous SPEI
values across the months of the selected assessment period. The recommendation
of this study is to use -0.8 as the upper threshold for drought conditions based on
the U.S. Drought Monitor's ranges for drought using the related Standardized
Precipitation Index (SPI). All months with SPEI values less than or equal to -0.8 are
considered drought conditions.
~
Choice of
Appropriate Time
Period to
Represent Long-
term Climatic
Conditions
Considering a time period covering several years is recommended to avoid short-
term weather fluctuations or modeling uncertainties and to capture representative
long-term climatic conditions and patterns. Time periods covering 20 years are
recommended. An alternate number of years can be used depending on local
needs. For example, a 30-year time period represents longer-term climatic
conditions and shorter timeframes (e.g., 15 years) reflect more recent changing
conditions.
Calculation steps and assumptions
~
Compile Block
Group
Precipitation and
Temperature
Time Series
Inputs: LOCA data and BG shapefile
Calculations:
• Extract all grid cells the touch any BGs within the study area.
• Resample the extracted raster by a factor of 100, to produce smaller grid
cells, each with the same value as the original raster. This needs to be
done if the BGs are small compared to the grid cells to ensure that even the
smallest BGs would have at least one grid cell with data fall within it.
• If there are multiple grid cells overlapping with a BG, use the zonal
statistical function, take the average value of the indexed cells to report a
climate value for each BG.
Output: Daily time series for each climate parameter per BG
~
Determine Block
Group Latitude
Input: BG shapefile
Calculation: Using the centroid of each BG, assign a latitude value using the
Calculate Geometry geospatial function.
Output: Single latitude value per BG
78
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Compile Monthly
Time Series for
Temperature and
Precipitation
Inputs: Daily time series for each climate parameter per BG
Calculations:
• Minimum temperature: take the minimum of the minimum daily values for
each month
• Maximum temperature: take the maximum of the maximum daily values for
each month
• Precipitation: sum daily values across each month
Outputs: Monthly time series of minimum and maximum temperature and
precipitation per BG
~
Compute PET
Using the SPEI R Package, compute monthly PET time series based on minimum
and maximum temperature and latitude by BG.
~
Compute Climatic
Water Balance
Inputs: Monthly precipitation time series and monthly PET time series by BG
Calculation: Precipitation - PET
Output: Time series of monthly climatic water balance
~
Compute SPEI
Values
Using the SPEI R Package, compute the SPEI values for each selected time scale
(6 and 12 months) using the Climatic Water Balance time series.
• Two function calls used, one for each time scale. SPEI package computes
values for each individual BG.
• Default selections are accepted within the package for SPEI calculation
other than time scale selection.
~
Calculate
Drought Indicator
Inputs: Monthly time series of SPEI values by BG
Calculation: Count all SPEI values less than or equal to -0.8 (i.e., drought
conditions) over the assessment period for each BG
Output: Count of drought months by BG
~
Repetition for
Time Periods
Repeat all steps for each assessment period as needed (i.e., historic, mid-century
RCP 4.5, and mid-century RCP 8.5)
~
Calculate the
Threshold-Based
Drought Indicator
Inputs: Count of drought months by BG for historic period and for future scenario
Calculation: Monthsputure - MonthSHistonc
Output: Change in count of drought months from historic period to future scenario
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have count values. Zero is a valid value.
~
Choosing a
Symbology
A single unique symbology that spans climate scenarios is recommended. This will
result in the same color representing the same value across the maps, making
direct comparisons easier. To build this, find the minimum and maximum values
across climate scenarios, and then use the full range of values to create a single
unique symbology to apply to all maps.
79
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Binning the Data
by Block Groups
Using 5-7 bins/categories is recommended so that the map reader can readily
distinguish between color categories and visually match them to the legend. Use
equal intervals, if possible, which create equal steps between categories (e.g., 0-
10, 11-20). Not all categories may necessarily contain values on the map.
An alternative to equal intervals is quantiles, which attempts to create the same
number of observations per bin. This causes each bin to have a different interval
and requires careful interpretation and communication. Using quantiles ensures that
each color in the symbology will appear on the map, and it will add more detail to
the map if the data are concentrated in a few small ranges. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
For maps with sequential data, use a symbology that becomes darker as the
vulnerability increases.
For maps with diverging data, such as the variants of the indicator showing percent
change, use a diverging color scheme characterized by darker colors at both
extremes and lighter in the middle.
Examples of how the indicator can be useful
~
SPEI-6 Drought
Indicator
SPEI-6 is an indicator of drought conditions where the impact to soils influential. As
the number of months with SPEI-6 below -0.8 increases, the impact on water uses
for sites and waste facilities increases.
~
SPEI-12 Drought
Indicator
SPEI-12 represents drought conditions that are more likely to impact water
availability and sustained dry conditions. An increase in months experiencing
drought conditions according to SPEI-12 should lead a community to examine the
vulnerabilities of their water supplies, particularly surface water supplies, as well as
water uses for maintaining sites/waste facilities.
Key caveats/limitations
~
Calculations
Completed within
R using SPEI
Package
A custom R program was created to input the processed LOCA-based climate data
A custom R program needs to be created to input the processed LOCA-based
climate data indexed by BG unique identifier. The SPEI package available from
CRAN (https://cran.r-proiect.ora/web/packaaes/SPEI/index.html) can be used as a
starting point. The Hargreaves PET method was used based on minimum and
maximum temperatures. If using the custom R program developed by RTI, a user
need only supply the links to the input climate and latitude datasets for the program
to complete all calculation steps.
~
Spatial
Differentiation
Based on Climate
Data Source
When interpreting the maps, it is important to remember that the resolution of the
LOCA modeled data is fairly coarse compared to BGs. Each raster cell is 1/16th of a
degree of latitude and 1/16th of a degree of longitude in size. This equates to
approximately 5 km x 7 km at temperate latitudes such as the United States. There
is the potential that this indicator may not provide enough variation across BGs
consisting of small spatial areas. The other aspect to be aware of is that difference
between BGs may be smaller than the errors in the modeled data. Therefore,
differences displayed cartographically, may be within the range of model error. This
limitation will apply to any indicators developed with the LOCA data as its input
source.
80
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Citations
~
Dataset/Tool
Pierce, D. 1/1/. (2016). LOCA (Local Constructed Analogs) statistical downscaling
version 40. Scripps Institute of Oceanography, Division of Climate, Atmospheric
Sciences, and Physical Oceanoaraohv. htto:/Aoca. ucsd.edu/
~
Additional
Resources
Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections. (2021).
http://ado-dcD.ucllnl.ora/downscaled cmio projections/
U.S. Environmental Protection Agency. (2015). Climate Change in the United
States: Benefits of Global Action. United States Environmental Protection Agency,
Office of Atmospheric Programs, EPA 430-R-15-001.
BG: Block Group, CRAN: Comprehensive R Archive Network, EPA: U.S. Environmental Protection Agency, LOCA: Localized Constructed
Analogs, PET: Potential Evapotranspiration, RCP: Representative Concentration Pathway, SPEI: Standardized Precipitation-
Evapotranspiration Index, SPI: Standardized Precipitation Index
81
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Vulnerability Source 1.2. Exposure: Sites/Waste Facilities
Indicator 1.2.1. Checklist for Total Count of Sites/Waste Facilities Indicator
Total Count of Sites/Waste Facilities Indicator
Definition of the indicator
~
Definition
A count of all sites/waste facilities in a given Block Group (BG).
~
Interpretation
This indicator includes the total number of the following types of sites/waste
facilities:
• Hazardous waste operators: Resource Conservation and Recovery Act
(RCRA) Subtitle C
o Hazardous waste generators
o Waste treatment, storage, and disposal facilities and units
o Waste transporters
o Hazardous waste transfer facilities
o Other hazardous waste operators
• Sites and cleanup facilities:
o RCRA Corrective Action
o Brown fields
o Federal and state Superfund sites
o Removal/emergency response sites
o Other cleanup sites (not on the National Priorities List [NPL])
• Other sites and waste facilities
o Fuel terminals and other sites subject to Spill Prevention, Control,
and Countermeasure (SPCC) and Facility Response Plan (FRP)
regulations to prevent and respond to oil spills
o Incident waste facilities
o Solid waste landfills (nonhazardous)
o Petroleum storage tanks (underground and aboveground)
o Any other sites/waste facilities identified by local decision-makers
Data source
~
Data Source
Facility Registry Service (FRS)
Additional data sources should also be consulted to identify sites/waste facilities of
interest that are not listed in the FRS data. These data sources are listed in Table
A. 4.
~
Temporal
Resolution
FRS data represents the information available at the time of download
82
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Spatial
Resolution
BG shapefile, coordinates for each site/waste facility
~
Data Format
Two options available for users to select from:
• National and state CSV files
• File geodatabase
Decisions needed for calculation
~
Size of Hazardous
Waste Generators
Excluding Small Quantity Generators (SQGs), Conditionally-Exempt Small Quantity
Generators (CESQGs), and facilities with no Generator Status is recommended
since these are not likely to pose significant risks. However, users can choose to
include these based on their needs.
~
Compiling and
Vetting of Data
FRS is recommended as the primary source of coordinate data since it provides
consistent spatial information for different types of sites/waste facilities. Any sites/
waste facilities found in additional data sources (Table A. 4) but not included in the
FRS dataset should be added to the list of sites/waste facilities used to create the
indicator. This combined list should be reviewed by local partners to ensure that the
facilities most relevant to the community are included.
~
Matching Method
for Cross-Data
Source
Comparisons
Comparing the sites/waste facilities listed in FRS and non-FRS data sources should
primarily rely on matching unique facility identification numbers.
• The most used identification number is the EPA ID. EPA IDs are 12-
character unique identifiers.
• The first two characters of a facility's EPA ID are the state abbreviation for
the state in which the facility is located.
Facilities from datasets that do not include EPA IDs will need to be compared
manually using facility names. If there are many facilities without EPA IDs, fuzzy
lookup tools can be used in place of manual comparisons.
Calculation steps and assumptions
~
Identify the Block
Group Where
Each Site/Waste
Facility Is
Located
Inputs: BG shapefile, site/waste facility coordinate data.
Calculations: Determine the boundaries of each BG. Uses these boundaries to
determine the BG where each site/waste facility is located.
Output: Site/waste facility dataset with a BG identifier tied to each site/waste
facility.
~
Count the Sites/
Waste Facilities
in Each Block
Group
Inputs: Site/waste facility dataset with a BG identifier tied to each site/waste facility
Calculations: Count the total number of sites/waste facilities within each BG. See
Appendix Equation SWF-1
Output: Total count of sites/waste facilities
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
BGs that do not contain any facilities can be shown a zero or as a separate layer
that grays out the polygons.
83
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Choosing a
Symbology
Maps showing the distribution of counts of sites/waste facilities will generally not be
compared across scenarios, time periods, or locations so a single symbology will
not be necessary. The symbology will be specific to the data for current study area.
~
Binning the Data
by Block Groups
The distribution of counts of sites/waste facilities will likely be skewed towards
values closer to zero. For this situation, using quantiles (equal number of
observations per bin) is recommended. Using a maximum of 5-7 categories is also
recommended so that the map reader can readily distinguish between color
categories and visually match them to the legend. However, deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the count values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Risks of
Contaminant
Releases
A total count of sites/waste facilities in a study area can provide the simplest
measures of potential contaminant releases and can be used to identify BGs that
may face the highest risks in the event of a release. It can be used to identify
priorities for cleanup, maintenance, and adaptation/response strategies.
~
Counts vs.
Density
Counts are simpler to use and communicate, but density may provide more
information to decision-makers, depending on their needs.
Key caveats/limitations
~
Data Sources are
Not Updated with
Uniform
Frequency
• Not all data sources listed are updated at the same time and compiling
different sources may result in discrepancies.
• Data sources are also not frequently updated. As a result, the sites/waste
facilities included in these data sources may no longer be a concern. More
recently identified sites/waste facilities may also be left out.
Tracking when each dataset was accessed and having local experts review the final
list of sites are crucial to ensure the list of sites includes the most concerning sites
for the study area and does not include sites that are no longer an issue.
Citations
Dataset/Tool
U.S. EPA. (2022). Geo spatial data download service.
https://www.eDa.aov/frs/aeosoatial-data-download-service
Additional
Resources
U.S. EPA. (2021). Facility Reaistrv Service (FRS). https://www.eDa.aov/frs
BG: Block Group, CESQC: Conditionally-Exempt Small Quantity Generators, FRP: Facility Response Plan, FRS: Facility Registry Service, NPL:
National Priorities List, RCRA: Resource Conservation and Recovery Act, SPCC: Spill Prevention, Control, and Countermeasure, SQG: Small
Quantity Generators
84
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.2.2. Checklist for Count of Sites/Waste Facilities per Square Kilometer Indicator
Count of Sites/Waste Facilities per Square Kilometer Indicator
Definition of the indicator
~
Definition
A count of sites/waste facilities per square kilometer in a given Block Group (BG).
~
Interpretation
This indicator includes the density of the following types of sites/waste facilities:
• Hazardous waste operators: RCRA (Resource Conservation and Recovery
Act) Subtitle C
o Hazardous Waste Generators
o Waste Treatment, Storage, and Disposal Facilities & Units
o Waste Transporters
o Hazardous Waste Transfer Facilities
o Other Hazardous Waste Operators
• Sites and cleanup facilities:
o RCRA Corrective Action
o Brown fields
o Federal and state Superfund sites
o Removal/emergency response sites
o Other cleanup sites (not on the National Priorities List (NPL)
• Other sites and waste facilities
o Fuel terminals and other sites subject to SPCC and FRP
regulations to prevent and respond to oil spills
o Incident Waste Facilities
o Solid Waste Landfills (Nonhazardous)
o Petroleum Storage Tanks
o Any other sites/waste facilities identified by local decision-makers
Data source
~
Data Source
Facility Registry Service (FRS)
Additional data sources should also be consulted to identify sites/waste facilities of
interest that are not listed in the FRS data. These data sources are listed in Table
A. 4.
~
Temporal
Resolution
FRS data represents the information available at the time of download
~
Spatial
Resolution
BG shapefile, Coordinates for each site/waste facility
85
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Data Format
Two options available for users to select from:
• National and State Comma Separated Value (CSV) files
• File Geodatabase
Decisions needed for calculation
~
Size of Hazardous
Waste Generators
Excluding Small Quantity Generators (SQGs), Conditionally Small Quantity
Generators (CESQGs), and facilities with no Generator Status is recommended
since these are not likely to pose significant risks. However, users can choose to
include these based on their needs.
~
Compiling and
Vetting of Data
FRS is recommended as the primary source of coordinate data since it provides
consistent spatial information for different types of sites/waste facilities. Any sites/
waste facilities found in additional data sources (Table A. 4) but not included in the
FRS dataset should be added to the list of sites/waste facilities used to create the
indicator. This combined list should be reviewed by local partners to ensure that the
facilities most relevant to the community are included.
~
Matching Method
for Cross-Data
Source
Comparisons
Comparing the sites/waste facilities listed in FRS and non-FRS data sources should
primarily rely on matching unique facility identification numbers.
• The most used identification number is the EPA ID. EPA IDs are 12-
character unique identifiers.
• The first two characters of a facility's EPA ID are the state abbreviation for
the state in which the facility is located.
Facilities from datasets that do not include EPA IDs will need to be compared
manually using facility names. If there are many facilities without EPA IDs, fuzzy
lookup tools can be used in place of manual comparisons.
Calculation steps and assumptions
~
Calculate the
Count of Sites/
Waste Facilities
per square km
Inputs: BG Shapefile, Total Count of Sites/Waste Facilities Indicator (1.2.1)
Calculations: Determine the total square km for each BG. This can be done by
using ArcGIS to add a field to hold the square km value, such as "square_km".
Open the attribute table and right click on the newly added field and choose
"Calculate Geometry". Within this tool choose "Area" as the property, and "Square
Kilometers" as the area unit. For the coordinate system, choose an equal area
projection such as Albers USGS
(USA_Contiguous_Albers_Equal_Area_Conic_USGS_version). The tool calculates
the area in square kilometers for each BG.
Divide the Total Count of SitesAA/aste Facilities Indicator for each BG by the total
square km for the given BG.
See Appendix Equation SWF-2.
Output: Count of Sites/Waste Facilities per square km
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
BGs that do not contain any facilities can be shown as a separate layer that grays
out the polygons.
86
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Choosing
Symbology
Maps showing the distribution of density of sites/waste facilities will generally not be
compared across scenarios, time periods, or locations so a single symbology will
not be necessary. The symbology will be specific to the data for current study area
~
Binning the Data
by Block Group
For this indicator using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the density values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Density of
Facilities Within
Each Block
Group/Risks of
Contaminant
Releases
The count of facilities per square kilometer gives a sense of the density of facilities
within each BG. Smaller BGs with a certain number of sites/waste facilities may be
considered to be riskier than larger BGs with the same number of sites/waste
facilities since they are concentrated in a smaller area. BGs with a higher density of
facilities may be more vulnerable to extreme events.
The density of sites/waste facilities in a study area can be used to identify BGs that
may face the highest risks in the event of a release. It can be used to identify
priorities for cleanup, maintenance, and adaptation/response strategies.
~
Counts vs.
Density
Counts are simpler to use and communicate, but density may provide more
information to decision-makers, depending on their needs.
Key caveats/limitations
~
Data Sources are
Not Updated with
Uniform
Frequency
• Not all data sources listed are updated at the same time and compiling
different sources may result in discrepancies.
• Data sources are also not frequently updated. As a result, the sites/waste
facilities included in these data sources may no longer be a concern. More
recently identified sites/waste facilities may also be left out.
Tracking when each dataset was accessed and having local experts review the final
list of sites are crucial to ensure the list of sites includes the most concerning sites
for the study area and does not include sites that are no longer an issue.
Citations
Dataset/Tool
U.S. EPA. (2022). Geo spatial data download service.
https://www.eDa.aov/frs/aeosoatial-data-download-service
Additional
Resources
U.S. EPA. (2021). Facility Reaistrv Service (FRS). https://www.eDa.aov/frs
BG: Block Group, FRS: Facility Registry Service, RCRAInfo or RCRA: Resource Conservation and Recovery Act Info, API: Application
Programming Interface, BRS: Biennial Reporting System, ACRES: Assessment Cleanup and Redevelopment Exchange System, CIMC:
Cleanups in My Community, SEMS: Superfund Enterprise Management System, l-Waste: Incident Waste Assessment and Tonnage
Estimator, GHGRP: Greenhouse Gas Reporting Program, OSRR EPRB: Office of Site Remediation and Restoration Emergency Planning and
Response Branch.
87
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.2.3. Checklist for Sites/Waste Facilities Count by Type Indicator
Sites/Waste Facilities Count by Type Indicator
Definition of the indicator
~
Definition
Count of [Fill in facility type] by Block Group (BG)
~
Interpretation
This indicator includes the number of each of the following types of sites/waste
facilities:
• Hazardous waste operators: RCRA (Resource Conservation and Recovery
Act) Subtitle C
o Hazardous Waste Generators
o Waste Treatment, Storage, and Disposal Facilities & Units
o Waste Transporters
o Hazardous Waste Transfer Facilities
o Other Hazardous Waste Operators
• Sites and cleanup facilities:
o RCRA Corrective Action
o Brown fields
o Federal and state Superfund sites
o Removal/emergency response sites
o Other cleanup sites (not on the National Priorities List (NPL)
• Other sites and waste facilities
o Fuel terminals and other sites subject to SPCC and FRP
regulations to prevent and respond to oil spills
o Incident Waste Facilities
o Solid Waste Landfills (Nonhazardous)
o Petroleum Storage Tanks
o Any other sites/waste facilities identified by local decision-makers
Data source
~
Data Source
Facility Registry Service (FRS)
Additional data sources should also be consulted to identify sites/waste facilities of
interest that are not listed in the FRS data. These data sources are listed in Table
A.4.
~
Temporal
Resolution
FRS data represents the information available at the time of download
~
Spatial
Resolution
BG shapefile, Coordinates for each site/waste facility.
88
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Required Site/
Facility
Characteristic
Inputs
Sites/waste facilities can be categorized based on the data source or based on the
data fields included in the data source. Table A.4 shows the various types of sites/
waste facilities and how to determine if a site/waste facility falls within a given type.
~
Data Format
Two options available for users to select from:
• National and state CSV files
• File geodatabase
Decisions needed for calculation
~
Size of Hazardous
Waste Generators
Excluding Small Quantity Generators (SQGs), Conditionally Small Quantity
Generators (CESQGs), and facilities with no Generator Status is recommended
since these are not likely to pose significant risks. However, users can choose to
include these based on their needs.
~
Compiling and
Vetting of Data
FRS is recommended as the primary source of coordinate data since it provides
consistent spatial information for different types of sites/waste facilities. Any sites/
waste facilities found in additional data sources (Table A.4) but not included in the
FRS dataset should be added to the list of sites/waste facilities used to create the
indicator. This combined list should be reviewed by local partners to ensure that the
facilities most relevant to the community are included.
~
Matching Method
for Cross-Data
Source
Comparisons
Comparing the sites/waste facilities listed in FRS and non-FRS data sources should
primarily rely on matching unique facility identification numbers.
• The most used identification number is the EPA ID. EPA IDs are 12-
character unique identifiers.
• The first two characters of a facility's EPA ID are the state abbreviation for
the state in which the facility is located.
Facilities from datasets that do not include EPA IDs will need to be compared
manually using facility names. If there are many facilities without EPA IDs, fuzzy
lookup tools can be used in place of manual comparisons.
Calculation steps and assumptions
~
Calculate the
Sites/Waste
Facilities Count
by Type Indicator
Inputs: BG Shapefile, coordinates for each site/facility, site/waste facility
characteristics
Calculations: Identify the facility type for each site/waste facility. Count the total
number of sites of a given facility type within a given BG. See Appendix Equation
SWF-3.
Output: Sites/waste facilities count by type
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
BGs that do not contain any facilities of any type can be shown as a separate layer
that grays out the polygons.
89
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Choosing
Symbology
Maps showing the distribution of counts of sites/waste facilities by type will
generally not be compared across scenarios, time periods, or locations so a single
symbology will not be necessary The symbology will be specific to the data for
current study area
~
Binning the Data
by Block Group
The distribution of counts of sites will likely be skewed towards values closer to
zero. For this situation, using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the count values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Broad Physical/
Environmental/
Regulatory
Attributes
Types of facilities provide information indicative of the types of contaminants that
may be present, physical structures and how they are managed or regulated. It can
be used to identify priorities for cleanup, maintenance, and adaptation/response
strategies.
The density of each site/waste facility type may also be useful.
Key caveats/limitations
~
Data Sources are
Not Updated with
Uniform
Frequency
• Not all data sources listed are updated at the same time and compiling
different sources may result in discrepancies.
• Data sources are also not frequently updated. As a result, the sites/waste
facilities included in these data sources may no longer be a concern. More
recently identified sites/waste facilities may also be left out.
Tracking when each dataset was accessed and having local experts review the final
list of sites are crucial to ensure the list of sites includes the most concerning sites
for the study area and does not include sites that are no longer an issue.
Citations
Dataset/Tool
U.S. EPA. (2022). Geo spatial data download service.
https://www.eDa.aov/frs/aeosoatial-data-download-service
Additional
Resources
U.S. EPA. (2021). Facility Reaistrv Service (FRS). https://www.eDa.aov/frs
BG: Block Group, FRS: Facility Registry Service, RCRAInfo or RCRA: Resource Conservation and Recovery Act Info, API: Application
Programming Interface, BRS: Biennial Reporting System, ACRES: Assessment Cleanup and Redevelopment Exchange System, CIMC:
Cleanups in My Community, SEMS: Superfund Enterprise Management System, l-Waste: Incident Waste Assessment and Tonnage
Estimator, GHGRP: Greenhouse Gas Reporting Program, OSRR EPRB: Office of Site Remediation and Restoration Emergency Planning and
Response Branch.
90
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.2.4. Checklist for Tons of Hazardous Waste Indicator
Tons of Hazardous Waste Indicator
Definition of the indicator
~
Definition
The total amount of hazardous waste contained at hazardous waste facilities within
a Block Group (BG).
~
Interpretation
This waste tonnage indicator reflects the total amount of waste stored or processed
at hazardous waste facilities.
Hazards can be one of six types—ignitable (1), corrosive (C), reactive (R), toxicity
characteristic (E), acutely hazardous (H), and toxic (T)—and are identified through
an EPA listing (EPA 2012).
Data source
~
Data Source
• Facility Registry Service (FRS)
• RCRAInfo (recommend using the RCRAInfo API to access these data)
• BRS
~
Temporal
Resolution
• FRS data, RCRAInfo, and BRS data represent the information available at
the time of download
• BRS data are available every two years
~
Spatial
Resolution
BG shapefile, coordinates for each waste facility that are required to file a BRS
report
~
Facility
Characteristic
Inputs
This indicator only uses waste facilities that are considered hazardous waste
generators. This includes hazardous waste generators; hazardous waste treatment,
storage, and disposal facilities (TSDFs); transporters; transfer facilities, and other
hazardous waste operators. Table A. 4 includes details on how to identify these
facility types.
The indicator includes all waste facilities downloaded using the RCRAInfo API that
were labeled as "Large Quantity Generators" and were required to file a BRS report.
No other waste facilities have waste tonnage data available.
~
Required Waste
Tonnage Data
BRS data contain data on the amount of hazardous waste stored at select RCRA
waste facilities that are labeled "Large Quantity Generators." (See "BRS Data
Availability" below)
~
Data Format
• FRS has two options available for users to select from:
o National and state CSV files
o File geodatabase
• RCRAInfo: The default output is in XML; however, output options of JSON,
CSV, or Excel can be requested in the URL during the API query
• BRS data is available in fixed format and can be opened in either Excel or
any file editor
91
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Decisions needed for calculation
~
Compiling and
Vetting of Data
FRS is recommended as the primary source of coordinate data. Any waste facilities
found in RCRAinfo or BRS (Table A.4) but not included in the FRS dataset should
be added to the list of facilities used to create the indicator. This combined list
should be reviewed by local partners to ensure that the facilities most relevant to
the community are included.
~
Matching Method
for Cross-Data
Source
Comparisons
Waste facilities included in this indicator can be linked using the EPA ID (sometimes
referred to as RCRA ID) variable that is common to the FRS, RCRAinfo, and BRS
data.
Calculation steps and assumptions
~
Calculate the
Tons of
Hazardous Waste
Indicator
Inputs: BG shapefile, Coordinates for each waste facility, Waste facility
characteristics, Waste tonnage data
Calculations:
• Identify the facility type for each waste facility.
• Filter to exclude any facility that is not a Hazardous Waste Generator
required to file a BRS report.
• Sum the tons of waste stored at all Hazardous Waste Generators in a given
BG.
• See Appendix Equation 1.2.4.
Output: Tons of Hazardous Waste
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
BGs that do not contain any facilities at all can be shown as a separate layer that
grays out the polygons.
~
Choosing
Symbology
Maps showing the distribution of tons of waste will generally not be compared
across scenarios, time periods, or locations so a single symbology will not be
necessary. The symbology will be specific to the data for current study area
~
Binning the Data
into Block Groups
For this indicator using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the indicator values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
92
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Community
Vulnerability to
High Quantities of
Hazardous Waste
This indicator shows which BGs contain hazardous waste and help identify BGs
with large amounts of waste This tonnage indicator provides information on risk of
release of contaminants that may be most harmful to communities. It can be used to
identify priorities for cleanup, maintenance, and adaptation/response strategies.
~
Counts vs.
Tonnage
Simple count of hazardous waste facilities (Indicator 1.2.3) provides information on
how many facilities to monitor or prepare for.
The tonnage (this indicator) reflects the magnitude of the risk, which is determined
by the total amount of waste stored or processed at these facilities.
Key caveats/limitations
~
Data Sources Are
Not Updated with
Uniform
Frequency
• Not all data sources listed are updated at the same time and compiling
different sources may result in discrepancies.
• Data sources are also not frequently updated. As a result, the waste
facilities included in these data sources may no longer be a concern. More
recently identified waste facilities may also be left out.
Tracking when each dataset was accessed and having local experts review the final
list of waste facilities are crucial to ensure the list includes the most concerning
facilities for the study area and does not include those that are no longer an issue.
~
BRS Data
Availability
BRS data are generated every two years through mandatory reporting by facilities
satisfying the definition of LQGs in any calendar month (See "DETERMINING WHO
MUST FILE" in the "Excerpt of RCRA Subtitle C Reporting Forms and Instruction."
~
BRS Waste
Tonnage Data Do
Not Distinguish
Between Specific
Wastes
BRS waste tonnage data only provides a total waste tonnage for each facility. The
data do not provide disaggregated waste tonnage data showing the amount of each
specific type of hazardous waste as defined by the EPA listing. However, certain
types of waste may be more vulnerable to specific events.
Citations
Dataset/Tool
• FRS: U.S. EPA. (2022). Geospatial data download service.
https://www.eDa.aov/frs/aeosoatial-data-download-service
• RCRAInfo (available through API queries on Envirofacts): U.S. EPA.
(2022). Envirofacts data service API.
httosJ/www. eoa. aov/enviro/envirofacts-data-service-aoi
• BRS: U.S. EPA. (2022). RCRAInfo public extract.
httosJ/rcraoublic. eoa.aov/rcra-Dublic-exDort/
93
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
• FRS: U.S. EPA. (2021). Facility Registry Service (FRS).
httos://www. eoa. aov/frs
• RCRAinfo: U.S. EPA. (2021). RCRAinfo overview.
https://www.eDa.aov/enviro/rcrainfo-overview
• BRS
• Data dictionary and file specifications guide: U.S. EPA. (n.d.).
RCRAinfo introduction. https://rcrainfo.epa.aov/rcrainfo-
help/appHcation/publicHelp/index.htm
Additional
Resources
• BRS general information: U.S. EPA. (2021). Biennial Hazardous Waste
Report. https://www.epa.aov/hwaenerators/biennial-hazardous-waste-
report
• BRS reporting form: U.S. EPA. (n.d.). RCRA Subtitle C Reporting
Instructions and Forms, EPA Forms 8700-12, 8700-13 A/B, 8700-23.
https://www.epa.aov/sites/default/files/2021-
05/documents/excerpt biennial report rcra subtitlec forms and instru
ction 5 12 2021.pdf
• U.S. EPA. (2012). Hazardous Waste Listings.
https://www.epa.aov/sites/default/files/2016-
01/documents/hw listref sep2012.pdf.
API: Application Programming Interface, BRS: Biennial Reporting System, BG: Block Group, FRS: Facility Registry Service, RCRA: Resource
Conservation and Recovery Act, TSDF: Treatment Storage, and Disposal Facility.
94
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.2.5. Checklist for Sites/Waste Facilities Count (by Hazard Type) Indicator
Sites/Waste Facilities Count (by Hazard Type) Indicator
Definition of the indicator
~
Definition
A count of hazardous waste facilities with a specific type of hazardous waste
present.
~
Interpretation
This indicator provides the number of hazardous waste facilities by each hazard
type (Section 3.2, Table 5). Hazard types are identified as ignitable (1), corrosive
(C), reactive (R), toxicity characteristic (E), acutely hazardous (H), and toxic (T)
through an EPA listing (EPA 2012).
Data source
~
Data Source
• Facility Registry Service (FRS)
• RCRAInfo (recommend using the RCRAInfo API to access this data)
• BRS
~
Temporal
Resolution
• FRS data, RCRAInfo and BRS data represent the information available at
the time of download
BRS data are available every two years
~
Spatial
Resolution
Block Group (BG) shapefile, coordinates for each waste facility that are required to
file a BRS report
~
Facility
Characteristic
Inputs
This indicator only uses waste facilities that are considered hazardous waste
generators. This includes hazardous waste generators; hazardous waste treatment,
storage, and disposal facilities (TSDFs); transporters; transfer facilities, and other
hazardous waste operators. Table A. 4 includes details on how to identify these
facility types.
The indicator includes all facilities downloaded using the RCRAInfo API that were
labeled as "Large Quantity Generators."
~
Required Hazard
Type Inputs
The RCRAInfo data downloaded using the RCRAInfo API includes waste codes for
the types of waste present at each facility. An EPA listing of hazard codes indicates
why the waste was listed as hazardous and crosswalks the classes or types of
wastes (see Interpretation).
~
Data Format
• FRS has two options available for users to select from:
o National and state CSV files
o File geodatabase
• RCRAInfo: The default output is in XML; however, output options of JSON,
CSV, or Excel can be requested in the URL during the API query
95
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Decisions needed for calculation
~
Compiling and
Vetting of Data
FRS is recommended as the primary source of coordinate data. Any waste facilities
found in RCRAinfo (Table A.4) but not included in the FRS dataset should be added
to the list of facilities used to create the indicator. This combined list should be
reviewed by local partners to ensure that the facilities most relevant to the
community are included.
~
Matching Method
for Cross-Data
Source
Comparisons
Waste facilities included in this indicator can be linked using the EPA ID (sometimes
referred to as RCRA ID) variable that is common to the FRS, RCRAinfo, and BRS
data.
Calculation steps and assumptions
~
Identify the
Hazard Types
Associated with
Each Waste
Code
Inputs: RCRAinfo data, waste codes & hazard codes (EPA, 2012)
Calculations: Use the EPA listing to identify each of the six hazard types
associated with each waste code for each facility listed in the Facility Characteristic
Inputs section above.
Output: RCRAinfo data with hazard type identified
~
Calculate the
Sites/Waste
Facilities Count
(by Hazard Type)
Indicator
Inputs: BG shapefile, coordinates for each waste facility, RCRAinfo data with
hazard type identified
Calculations: Count the number of facilities with a given hazard type present within
a given BG. See Appendix Equation SWF-5.
Output: Sites/waste facilities count (by hazard type)
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
BGs that do not contain any facilities at all should be shown as a separate layer that
grays out the polygons.
~
Choosing
Symbology
Maps showing the distribution of this indicator will generally not be compared
across scenarios, time periods, or locations so a single symbology will not be
necessary. The symbology will be specific to the data for current study area
~
Binning the Data
by Block Group
The distribution of this indicator will likely be skewed towards values closer to zero.
For this situation using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the indicator values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
96
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Community
Vulnerability to
Specific Types of
Hazardous Waste
This indicator helps identify community vulnerabilities to specific waste types and
events. Certain types of hazardous waste may be more or less concerning under
different extreme events. For example, a BG with a high number of facilities with
ignitable waste may be at higher risk in extreme heat or wildfires, while a BG with
hazardous wastes that exhibit the reactivity characteristic (e.g., forms potentially
explosive mixtures with water) may be at higher risk during flooding.
This indicator can provide more information on how many facilities need to be
considered when planning for specific extreme event impacts than simple counts of
all facilities or the total number of hazardous waste facilities. (See Section 3.2,
Table 5 for more details.)
~
Counts vs.
Tonnage
Count of hazardous waste facilities for each hazard type (this indicator) provides
information on how many facilities to monitor or prepare for in the context of specific
events.
The tonnage for each hazard type (Indicator 1.2.6) reflects the magnitude of the
risk, which is determined by the total amount of waste stored or processed at these
facilities in the context of specific events.
Key caveats/limitations
~
Data Sources Are
Not Updated with
Uniform
Frequency
• Not all data sources listed are updated at the same time and compiling
different sources may result in discrepancies.
• Data sources are also not frequently updated. As a result, the waste
facilities included in these data sources may no longer be a concern. More
recently identified waste facilities may also be left out.
Tracking when each dataset was accessed and having local experts review the final
list of waste facilities are crucial to ensure the list includes the most concerning
facilities for the study area and does not include those that are no longer an issue.
Citations
Dataset/Tool
• FRS: U.S. EPA. (2022). Geospatial data download service.
httDsJ/www.eoa.aov/frs/aeosDatial-data-download-service
• RCRAInfo (available through API queries on Envirofacts): U.S. EPA.
(2022). Envirofacts data service API.
httos://www.eoa.aov/enviro/envirofacts-data-service-aDi
• U.S. EPA. (2012). Hazardous Waste Listings.
httos://www.eoa.aov/sites/default/files/2016-
01/documents/hw listref sep2012.pdf
Additional
Resources
• FRS: U.S. EPA. (2021). Facility Registry Service (FRS).
httosJ/www. eoa. a ov/frs
• RCRAInfo: U.S. EPA. (2021). RCRAInfo overview.
httos.Y/www.eoa.aov/enviro/rcrainfo-overview
• U.S. EPA. (2012). Hazardous Waste Listings.
httos://www.eoa.aov/sites/default/files/2016-
01/documents/hw listref seo2012.odf.
API: Application Programming Interface, BRS: Biennial Reporting System, BG: Block Group, FRS: Facility Registry Service, RCRA: Resource
Conservation and Recovery Act, TSDF: Treatment Storage, and Disposal Facility.
97
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.2.6. Checklist for Waste Tonnage (by Hazard Type) Indicator
Waste Tonnage (by Hazard Type) Indicator
Definition of the indicator
~
Definition
Total tons of a given type (hazard type) of hazardous waste present at a hazardous
waste facility. Hazard types include ignitable, corrosive, reactive, toxic, acutely
hazardous, and toxicity characteristic.
~
Interpretation
This waste tonnage indicator provides the total amount of waste stored or processed at
hazardous waste facilities by each hazard type. Hazard types are identified as ignitable
(1), corrosive (C), reactive (R), toxicity characteristic (E), acutely hazardous (H), and toxic
(T) through an EPA listing (EPA, 2012)
Data source
~
Data Source
• Facility Registry Service (FRS)
• RCRAInfo (Recommend using the RCRAInfo API to access this data)
• BRS
~
Temporal
Resolution
• FRS data, RCRAInfo and BRS data represents the information available at the
time of download
BRS data are available every two years
~
Spatial
Resolution
Block Group (BG) shapefile, coordinates for each waste facility that are required to file a
BRS report.
~
Facility
Characteristic
Inputs
This indicator only uses waste facilities that are considered hazardous waste generators.
This includes hazardous waste generators; hazardous waste treatment, storage, and
disposal facilities (TSDFs); transporters; transfer facilities, and other hazardous waste
operators. Table A.4 includes details on how to identify these facility types.
The indicator includes all facilities downloaded using the RCRAInfo API that were
labeled as "Large Quantity Generators" and were required to file a BRS report. No other
sites have waste tonnage data available.
~
Required
Waste
Tonnage
Data
BRS data includes data on the amount of hazardous waste stored at select RCRA sites
that are labeled "Large Quantity Generators." (See "BRS Data Availability" below.)
~
Required
Hazard Type
Inputs
The RCRAInfo data downloaded using the RCRAInfo API includes waste codes for the
types of waste present at each facility. An EPA listing of hazard codes indicates why the
waste was listed as hazardous and crosswalks the classes or types of wastes (see
Interpretation).
98
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Data Format
• FRS has two options available for users to select from:
o National and state CSV files
o File geodatabase
• RCRAInfo: The default output is in XML; however, output options ofJSON, CSV,
or Excel can be requested in the URL during the API query
• BRS data are available in fixed format and can be opened in either Excel or any
file editor
Decisions needed for calculation
~
Compiling
and Vetting
of Data
FRS is recommended as the primary source of coordinate data. Any facilities found in
RCRAInfo or BRS (Table A. 4) but not included in the FRS dataset should be added to
the list of facilities used to create the indicator. This combined list should be reviewed by
local partners to ensure that the facilities most relevant to the community are included.
~
Matching
Method for
Cross-Data
Source
Comparisons
Waste facilities included in this indicator can be linked using the EPA ID (sometimes
referred to as RCRA ID) variable that is common to the FRS, RCRAInfo, and BRS data.
Calculation steps and assumptions
~
Identify the
Hazard Type
Associated
with Each
Waste Code
Inputs: RCRAInfo data, waste codes & hazard codes (EPA, 2012)
Calculations: Use the EPA listing to identify each of the six hazard types associated
with each waste code for each facility listed in the Facility Characteristic Inputs section
above.
Output: RCRAInfo data with hazard type identified
~
Calculate the
Waste
Tonnage (by
Hazard Type)
Indicator
Inputs: BG shapefile, coordinates for each facility, facility characteristics, waste tonnage
data, RCRAInfo data with hazard codes
Calculations:
• Identify the facility type for each waste facility.
• Filter to exclude any facility that is not a Hazardous Waste Generator required to
file a BRS report.
• Sum the tons of waste stored at all Hazardous Waste Generators for a given
hazard type in a given BG.
• See Appendix Equation SWF-6.
Output: Waste tonnage (by hazard type)
Decisions needed for mapping and interpretation
~
Mapping
Limited to
Block Groups
Containing
Data
BGs that do not contain any facilities should be shown as a separate layer with gray
polygons.
99
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Choosing
Symbology
Maps showing the distribution of tons of waste by hazard type will generally not be
compared across scenarios, time periods, or locations so a single symbology will not be
necessary The symbology will be specific to the data for current study area
~
Binning the
Data into
Block Groups
For this indicator using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that the
map reader can readily distinguish between color categories and visually match them to
the legend. However, deciles or other percentiles can also be used depending on user
needs.
~
Choosing
Colors
Use a color gradation that becomes darker as the indicator values increase. Avoid using
a divergent color scheme (darker at both extremes and lighter in the middle), which
implies an inflection point such as 0 in a dataset containing positive and negative values.
Examples of how the indicator can be useful
~
Community
Vulnerability
to Specific
Types of
Hazardous
Waste
This indicator shows which BGs contain hazardous waste and help identify BG with
large amounts of specific types of hazardous waste. This tonnage indicator provides
information on risk of release of contaminants that may be most harmful to communities.
Moreover, it reflects the fact that certain types of hazardous waste may be more or less
concerning under different extreme events. For example, a BG with a high number of
facilities with ignitable waste may be at higher risk in extreme heat or wildfires, while a
BG with hazardous wastes that exhibit the reactivity characteristic (e.g., forms potentially
explosive mixtures with water) may be at higher risk during flooding.
This indicator can be used to identify priorities for cleanup, maintenance, and
adaptation/response strategies.
~
Counts vs.
Tonnage
Simple count of hazardous waste facilities for each hazard type (Indicator 1.2.5)
provides information on how many facilities to monitor or prepare for in the context of
specific events.
The tonnage indicator for each hazard type (this indicator) reflects the magnitude of the
risk, which is determined by the total amount of waste stored or processed at these
facilities in the context of specific events.
Key caveats/limitations
~
Data Sources
are Not
Updated with
Uniform
Frequency
• Not all data sources listed are updated at the same time and compiling different
sources may result in discrepancies.
• Data sources are also not frequently updated. As a result, the waste facilities
included in these data sources may no longer be a concern. More recently
identified waste facilities may also be left out.
Tracking when each dataset was accessed and having local experts review the final list
of waste facilities are crucial to ensure the list includes the most concerning facilities for
the study area and does not include those that are no longer an issue.
100
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Citations
Dataset/Tool
• FRS: U.S. EPA. (2022). Geospatial data download service.
https://www.epa.aov/frs/aeospatial-data-download-service
• RCRAInfo (available through API queries on Envirofacts): U.S. EPA. (2022).
Envirofacts data service API. https://www.epa.aov/enviro/envirofacts-data-
service-api
• U.S. EPA. (2012). Hazardous Waste Listings.
https://www.epa.aov/sites/default/files/2016-
01/documents/hw listref sep2012.pdf
Additional
Resources
• FRS: U.S. EPA. (2021). Facility Reaistrv Service (FRS). https://www.epa.aov/frs
• RCRAInfo: U.S. EPA. (2021). RCRAInfo overview.
https://www. epa. aov/enviro/rcrainfo-overview
• BRS
• Data dictionary and file specifications guide: U.S. EPA. (n.d.). RCRAInfo
introduction, https://rcrainfo.epa.aov/rcrainfo-
help/application/publicHelp/index.htm
• BRS general information: U.S. EPA. (2021). Biennial Hazardous Waste
Report, https://www.epa.aov/hwaenerators/biennial-hazardous-waste-report
• BRS reporting form: U.S. EPA. (n.d.). RCRA Subtitle C Reporting
Instructions and Forms, EPA Forms 8700-12, 8700-13 A/B, 8700-23.
https://www.epa.aov/sites/default/files/2021-
05/documents/excerpt biennial report rcra subtitlec forms and instructio
n 5 12 2021.pdf
• U.S. EPA. (2012). Hazardous Waste Listings.
https://www.epa.aov/sites/default/files/2016-
01/documents/hw listref sep2012.pdf.
API: Application Programming Interface, BRS: Biennial Reporting System, BG: Block Group, FRS: Facility Registry Service, RCRA: Resou rce
Conservation and Recovery Act, TSDF: Treatment Storage, and Disposal Facility.
101
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.2.7. Checklist for Brownfield Count with Contaminant; Cleanup Unknown (by
Contaminant) Indicator
Count of Brownfield Count with Contaminant; Cleanup Unknown (by
Contaminant) Indicator
Definition of the indicator
~
Definition
Count of Brownfield sites with a specific contaminant found and no information on
cleanup of that contaminant available.
~
Interpretation
This indicator provides insights on type of contaminants that may potentially be
present at Brownfield sites at a particular time. It also indicates if the contaminants
found have been cleaned up (if information was available at the time of data
download). Brownfields sites where assessment or cleanup was complete, and no
further action was indicated are not included in this indicator.
Data source
~
Data Source
• Facility Registry Service (FRS)
• ACRES-CIMC: Brownfields data are reported by grant recipients via the
ACRES database and updated and stored in Envirofacts monthly and can
be accessed through the CIMC web service
~
Temporal
Resolution
• FRS data and ACRES-CIMC data represents the information available at the
time of download
~
Spatial
Resolution
Block Group (BG) shapefile, coordinates for each site.
~
Required
Brownfield
Characteristics
Inputs
ACRES-CIMC includes data on the contaminants found at each site and the
contaminants cleaned up at each site. The list of contaminants included in the
ACRES-CIMC data is included in Table A.2 in Appendix A.
~
Data Format
Spreadsheet
Decisions needed for calculation
~
Compiling and
Vetting of Data
FRS is recommended as the primary source of coordinate data. Any sites found in
ACRES-CIMC (Table A.4) but not included in the FRS dataset should be added to
the list of sites used to create the indicator. This combined list should be reviewed
by local partners to ensure that the sites most relevant to the community are
included.
Brownfield datasets may not be updated regularly and may be inaccurate as a
result. Information such as cleanup status should be checked for accuracy with
local regulators and other stakeholders partners prior to use in this application.
102
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Matching Method
for Cross-Data
Source
Comparisons
Sites included in this indicator can be linked using the EPA ID variable that is
common to the FRS and ACRES-CIMC data.
Calculation steps and assumptions
~
Identifying Sites
with No Cleanup
Information
Inputs: The ACRES-CIMC data contain two variables for each potential
contaminant (for a list of contaminants see Table A. 2 in Appendix A).
• The first variable is called Contaminant Found and indicates whether the
specified contaminant was found at the site.
• The second variable is called Contaminant Cleaned Up and refers to
whether a cleanup action occurred.
Calculations: To identify sites where a contaminant was found but not cleaned up,
match the contaminant found variable to the corresponding contaminant cleaned up
variable.
• If Contaminant Found for a given site is marked "Y" and Contaminant
Cleaned Up is marked "N" or left blank, count the site as a site with the
contaminant found but no information on cleanup status.
o Blank entries reflect missing information.
o "N" reflects information at the time of data update.
• Count of sites described above.
• See Appendix Equation SWF-7.
Output: Count of Brownfield sites where a contaminant was found but not cleaned
up in each BG
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
BGs that do not contain any facilities should be shown as a separate layer that
grays out the polygons.
~
Choosing
Symbology
Maps showing the distribution of counts of sites will generally not be compared
across scenarios, time periods, or locations so a single symbology will not be
necessary. The symbology will be specific to the data for current study area
~
Binning the Data
by Block Group
The distribution of counts of sites will likely be skewed towards values closer to
zero. For this situation using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the indicator values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
103
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Community
Vulnerability to
Specific Types of
Contaminants
Cleanup status and type of contaminants that may potentially be present at
Brownfield sites may provide more information than simple counts of Brownfields
sites. Sites that have been assessed and contaminants found are likely to be riskier
unless they have been cleaned up. However, the information needs to be vetted
with local partners and decision-makers prior to use in this application (See
"Compiling and Vetting of Data" above)
Key caveats/limitations
~
Data Sources Are
Not Updated with
Uniform
Frequency
• Not all data sources listed are updated at the same time and compiling
different sources may result in discrepancies.
• Data sources are also not frequently updated. As a result, the sites included
in these data sources may no longer be a concern. More recently identified
sites may also be left out.
Tracking when each dataset was accessed and having local experts review the final
list of sites are crucial to ensure the list of sites includes the most relevant sites for
the study area and does not include sites that are no longer an issue.
Caution must be used because datasets (e.g., ACRES-CIMC) with this information
are based on voluntary reporting, and assessment and cleanup status available
publicly may not be reflective of the most current status at a site.
~
Lack of Cleanup
Information Is Not
Necessarily
Concerning
Sites with a contaminant found but no information on cleanup are not necessarily a
source of high risk.
• It is possible that a cleanup did occur at a given site, but updated
information was not entered into ACRES-CIMC at the time of data
download.
• The ACRES-CIMC data on contaminants is only a Yes or No indicator.
There is no data on the amount of a contaminant found at a site. A
contaminant could be present in very small amounts so that even without
cleanup action, the site may not pose high levels of risk.
• Further, all contaminants do not pose the same risks.
Citations
Dataset/Tool
• U.S. EPA. (2022). Cleanups In My Community - Create a table.
https://ordspub.epa.aov/ords/cimc/f?p=cimc:createtable:0
Additional
Resources
• U.S. EPA. (2022). Cleanups In My Community.
https://www. epa. aov/cleanups/cleanups-mv-communitv
ACRES: Assessment Cleanup and Redevelopment Exchange System, BG: Block Group, CIMC: Cleanups in My Community, FRS: Facility
Registry Service,
104
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.2.8. Checklist for Superfund Count with Vulnerable Remedy Technology (by
Extreme Event) Indicator
Superfund Count with Vulnerable Remedy Technology (by Extreme Event)
Indicator
Definition of the indicator
~
Definition
A count of NPL Superfund sites with a remedy vulnerable to a specific type of
extreme event in each Block Group (BG).
~
Interpretation
This indicator provides information on Superfund sites that employ remedy
technologies that are vulnerable to each of the four extreme events (extreme heat,
wildfire, floods, drought).
Data source
~
Data Source
• Facility Registry Service (FRS)
• SEMS data are stored in Envirofacts.
~
Temporal
Resolution
FRS and SEMS data represents the information available at the time of download
~
Spatial
Resolution
BG shapefile, Coordinates for each site
~
Superfund
Characteristic
Inputs
SEMS data contain information about
• NPL status of each site
Remedies present at each site
~
Remedy
Vulnerability
Inputs
Table A. 3 in Appendix A includes a list of potential remedy technologies found at
Superfund sites and their vulnerability to specific extreme events. This vulnerability
mapping (developed by RTI) is an adapted version of findings from EPA (2012)
~
Data Format
Superfund sites must be searched individually in SEMS to manually copy their
remedy information.
Decisions needed for calculation
~
Compiling and
Vetting of Data
FRS is recommended as the primary source of coordinate data since it provides
consistent spatial information for different types of sites/waste facilities. Any sites
found in SEMS (Table A.4) but not included in the FRS dataset should be added to
the list of sites used to create the indicator. This combined list should be reviewed
by local partners to ensure that the facilities most relevant to the community are
included.
~
Matching Method
for Cross-Data
Source
Comparisons
Sites included in this indicator can be linked using the EPA ID variable that is
common to the FRS and SEMS data.
105
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Calculation steps and assumptions
~
Identifying NPL
Superfund Sites
SEMS data can be filtered along the NPL status variable to exclude sites not on the
NPL if desired by local partners/decision-makers.
~
Identifying
Remedies
Vulnerable to
Certain
Technologies
Inputs: Table A. 3 identifying which events each remedy technology is vulnerable to
and remedy technologies being employed at each site from SEMS
Calculations:
• Table A. 3 can be used to look up whether the remedies being used at each
Superfund site (available from SEMS) is vulnerable to each of the four
events. This will allow you to identify which sites are vulnerable to specific
extreme events based on the remedies present at those sites.
• A count of sites that employ remediation technologies vulnerable to each
extreme event can be calculated.
• Repeat the previous step for all events.
• See Appendix Equation SWF-8.
Output: Count of Superfund sites in each BG with any remedy technology
vulnerable to each of the four extreme events (extreme heat, wildfire, floods and
drought).
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
BGs that do not contain any facilities should be shown as a separate layer that
grays out the polygons.
~
Choosing
Symbology
Maps showing the distribution of counts of sites will generally not be compared
across scenarios, time periods, or locations so a single symbology will not be
necessary. The symbology will be specific to the data for current study area.
~
Binning the Data
by Block Group
The distribution of counts of sites will likely be skewed towards values closer to
zero. For this situation using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the indicator values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Superfund Site
Vulnerability to
Various Extreme
Events
Sites can have remediation technologies in place that may pose a risk during
specific types of extreme events. This indicator can identify specific BGs where a
Superfund site could present a risk during a certain extreme event. This could help
with disaster planning for climate resiliency, for example, flood risk during
hurricanes (U.S. EPA, 2018).
106
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Key caveats/limitations
~
Data Sources are
Not Updated with
Uniform
Frequency
• Not all data sources listed are updated at the same time and compiling
different sources may result in discrepancies.
• Data sources are also not frequently updated. As a result, the sites included
in these data sources may no longer be a concern. More recently identified
sites facilities may also be left out.
Tracking when each dataset was accessed and having local experts review the final
list of sites are crucial to ensure the list of sites includes the most relevant sites for
the study area and does not include sites that are no longer an issue.
~
Sensitivity around
Non-NPL
Superfund Sites
Consult with local partners to determine ifnon-NPL sites are a concern for them
and filter the data accordingly.
Citations
Dataset/Tool
U.S. EPA. (2021). SEMS search. https://www.eDa.aov/enviro/sems-search
Additional
Resources
U.S. EPA. (2012). Adaptation of Superfund Remediation to Climate Change, Table
1.
U.S. EPA. (2018). Evaluation of remedy resilience at Superfund NPL and SA4
sites. Final Report, https://www.epa.aov/sites/default/files/2019-
02/documents/evaluation-of-remedv-resilience-at-superfund-npl-and-saa-sites.pdf
BG: Block Group, FRS: Facility Registry Service, NPL: National Priorities List, SEMS: Superfund Enterprise Management System
107
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.2.9. Checklist for Count of Specific Type of Tank (UST/AST) Indicator
Count of Specific Type of Tank (UST/AST) Indicator
Definition of the indicator
~
Definition
A count of specific type of tank (UST or AST) by Block Group (BG)
~
Interpretation
This indicator includes the total number of the following types of sites:
• Underground Storage Tank (UST)
• Aboveground Storage Tank (AST)
Data source
~
Data Source
• Facility Registry Service (FRS)
• State databases
Additional data sources should also be consulted to identify sites of interest that are
not listed in the FRS data. These data sources are listed in Table A.4.
~
Temporal
Resolution
FRS data represent the information available at the time of download
~
Spatial
Resolution
BG shapefile, coordinates for each site
~
Data Format
Varies across data sources
Decisions needed for calculation
~
Compiling and
Vetting of Data
FRS is recommended as the primary source of coordinate data since it provides
consistent spatial information for different types of sites. Any sites found in
additional data sources (Table A.4) but not included in the FRS dataset should be
added to the list of sites used to create the indicator. This combined list should be
reviewed by local partners to ensure that the facilities most relevant to the
community are included.
~
Matching Method
for Cross-Data
Source
Comparisons
State/regional UST and AST databases do not usually include EPA ID so a manual
comparison or fuzzy lookup is necessary to identify any duplicate observations
between the state/regional databases and FRS data.
Calculation steps and assumptions
~
Exclude Cross
Dataset Duplicate
Observations
Once duplicate observations between FRS and state/regional databases have been
identified, duplicate observations should be excluded from the mapping data.
108
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Count Number of
ASTs/USTs
Inputs: FRS and state/regional databases
Calculation steps:
• USTs: Count the number of open and temporarily closed tanks.
• ASTs: Count the number ofASTs.
• See Appendix Equation SWF-9.
Outputs: Counts of tanks of each type (ASTs/USTs) by BG
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
BGs that do not contain any facilities should be shown as a separate layer that
grays out the polygons.
~
Choosing
Symbology
Maps showing the distribution of counts of sites will generally not be compared
across scenarios, time periods, or locations so a single symbology will not be
necessary. The symbology will be specific to the data for current study area
~
Binning the Data
by Block Group
The distribution of counts of sites will likely be skewed towards values closer to
zero. For this situation using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the count values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Highlighting Block
Groups with
Large Numbers
of Tanks
This indicator helps identify BGs with the highest number of tanks containing
potentially hazardous substances (e.g., petroleum, solvents, hazardous wastes).
Such tanks may be susceptible to damage during extreme events, and this may
lead to a release of their contents. Certain BGs will likely contain a larger number of
USTs/ASTs while others may contain only a handful. BGs with a heavy
concentration of tanks may potentially face higher risks. This indicator can be used
to identify priorities for cleanup, maintenance, and adaptation/response strategies.
Key caveats/limitations
~
State/Regional
Data Quality
Varies
There is no single national database for USTs/ASTs and state/regional database
quality can vary significantly.
109
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Data Sources Are
Not Updated with
Uniform
Frequency
• Not all data sources listed are updated at the same time and compiling
different sources may result in discrepancies.
• Data sources are also not frequently updated. As a result, the sites included
in these data sources may no longer be a concern. More recently identified
sites may also be left out.
Tracking when each dataset was accessed and having local experts review the final
list of sites are crucial to ensure the list of sites includes the most relevant sites for
the study area and does not include sites that are no longer an issue.
~
Limited
Information
This indicator does not include information on the quantity or type of contents in the
tanks. For example, a low volume of leaded gasoline stored in a UST poses a
greater human health risk than a UST storing a more inert substance in larger
capacities. It also does not provide any information on the age and condition of the
tank, which can indicate how likely a tank is to be damaged.
Citations
Dataset/Tool
• State datasets—e.g., Arizona Department of Environmental Quality, (n.d.).
Underground storage tank (UST) database search.
http://leaacv.azdea.aov/databases/ustsearch drupal.html
• U.S. Environmental Protection Agency. (2021). UST Finder.
httosJ/www. eoa. aov/ust/ust-finder or
httDs://eDa.maDs.arcais.com/aDDs/webaoDviewer/index.html?id=b03763d3f
2754461adf86f121345d7bc
AST: Aboveground Storage Tank, BG: Block Group, FRS: Facility Registry Service, LUST-ARRA: Leaking Underground Storage Tank-
American Recovery and Reinvestment Act, UST: Underground Storage Tank
110
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.2.10. Checklist for Total Tank Capacity (UST/AST) Indicator
Total Tank Capacity (UST/AST) Indicator
Definition of the indicator
~
Definition
The total tank capacity of each type of tank (either UST or AST) within a Block
Group (BG).
~
Interpretation
This indicator provides information on the volume of substances that can be
potentially stored in underground and aboveground storage tanks (USTs and
ASTs).
Data source
~
Data Source
• Facility Registry Service (FRS)
• State databases
Additional data sources should also be consulted to identify sites of interest that are
not listed in the FRS data. These data sources are listed in Table A.4.
~
Temporal
Resolution
FRS data represents the information available at the time of download
~
Spatial
Resolution
BG shapefile, coordinates for each site/waste facility
~
Data Format
Varies across data sources
~
Required Tank
Characteristic
Inputs
Some state/regional databases on tanks include data on the total capacity of each
tank. This capacity information will allow you to map the total capacity of tanks
within a BG.
Decisions needed for calculation
~
Compiling and
Vetting of Data
FRS is recommended as the primary source of coordinate data since it provides
consistent spatial information for different types of sites. Any sites found in
additional data sources (Table A.4) but not included in the FRS dataset should be
added to the list of sites used to create the indicator. This combined list should be
reviewed by local partners to ensure that the facilities most relevant to the
community are included.
~
Matching Method
for Cross-Data
Source
Comparisons
State/regional UST and AST databases do not usually include EPA ID so a manual
comparison or fuzzy lookup is necessary to identify any duplicate observations
between the state/regional databases and FRS data.
Calculation steps and assumptions
~
Match Cross
Dataset Duplicate
Observations
Once duplicate observations between FRS and state/regional databases have been
identified, duplicate observations should be matched together but not deleted. For
this indicator you will need to use FRS coordinates for duplicates alongside the tank
capacity data from state/regional databases to map total capacity.
ill
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Calculate the
Total Capacity of
ASTs/USTs
Indicator
Inputs: FRS and state/regional databases
Calculation steps:
• USTs: Identify the number of open and temporarily closed tanks and add
the capacity across all such tanks.
• ASTs: Add the capacity of all ASTs.
• See Appendix Equation SWF-10.
Outputs: Total capacity of tanks of each type (ASTs/USTs) by BG
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
BGs that do not contain any facilities should be shown as a separate layer that
grays out the polygons.
~
Choosing
Symbology
Maps showing the distribution of capacity will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current study area
~
Binning the Data
into Block Groups
For this indicator using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the indicator values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Highlighting Block
Groups with High
Tank Capacity
A high number of tanks does not always translate to a high amount of stored
substances. It is possible that a BG with a high number of low-capacity tanks may
be less at risk than a BG with a small number of tanks but high capacity. This
indicator helps provide more detailed information on risk by showing capacity
instead of raw number of tanks. This indicator can be used to identify priorities for
cleanup, maintenance, and adaptation/response strategies.
Key caveats/limitations
~
State/Regional
Data Quality
Varies
There is no single national database for USTs/ASTs and state/regional database
quality can vary significantly. Noy all state/regional tank data will include data on
capacity.
112
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Data Sources Are
Not Updated with
Uniform
Frequency
• Not all data sources listed are updated at the same time and compiling
different sources may result in discrepancies.
• Data sources are also not frequently updated. As a result, the sites included
in these data sources may no longer be a concern. More recently identified
sites may also be left out.
Tracking when each dataset was accessed and having local experts review the final
list of sites are crucial to ensure the list of sites includes the most relevant sites for
the study area and does not include sites that are no longer an issue.
~
Limited
Information
This indicator does not include information on the actual quantity or type of contents
in the tanks. For example, a low volume of leaded gasoline stored in a UST poses a
greater human health risk than a UST storing a more inert substance in larger
capacities. It also does not provide any information on the age and condition of the
tank, which can indicate how likely a tank is to be damaged.
Citations
Dataset/Tool
State datasets—e.g., Arizona Department of Environmental Quality, (n.d.).
Underground storage tank (UST) database search.
http://leaacv.azdea.aov/databases/ustsearch drupal.html
AST: Aboveground Storage Tank, BG: Block Group, FRS: Facility Registry Service, LUST-ARRA: Leaking Underground Storage Tank-
American Recovery and Reinvestment Act, UST: Underground Storage Tank
113
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Vulnerability Source 1.3. Exposure: Transport and Fate
Indicator 1.3.1. Checklist for Count of Sites/Waste Facilities in a Floodplain Indicator
Count of Sites/Waste Facilities in a Floodplain Indicator
Definition of the indicator
~
Definition
Count of site/waste facilities in a floodplain [ 100-year and 500-year] by Block Group
(BG)
~
Interpretation
The identification and count of facilities within the floodplain provides a simple
measure of risk to the surrounding, local BGs and the BGs downstream that access
the river/stream channel due to the potential transport of hazardous substances
released from a site/waste facility during a flooding event. This indicator measures
the risk of sites/waste facilities getting flooded and contaminants being released.
Further, note the delineated land areas inundated during events with a 1% annual
chance of flooding (100-year floodplain) or a 0.2% annual chance of flooding (500-
year floodplain) are not meant to exactly designate the frequency or the extent of
hypothetical flood events. Rather, they should be used to indicate a relative risk of
inundation across a given area.
Data source*2
~
Data Source
The National Flood Hazard Layer (NFHL). Data for the necessary location is
obtained by searching by state and county. A zip file is downloaded from the search
result.
~
Temporal
Resolution
This indicator is a static measure without a time component. The measure
represents the information available at the time of download.
~
Spatial
Resolution
• BG shapefile
• NFHL resolution varies depending on location but is generally accurate at a
scale <1 meter
~
Data Format
Shapefile (BG and NFHL)
Decisions needed for calculation
~
100-year or 500-
year Floodplain
Indicator
The 100-year floodplain defines the area more likely to be inundated by rainfall
events with a 1% annual chance of occurrence, while the 500-year floodplain
defines the area more likely to be inundated by events with a 0.2% annual chance
of occurrence. Therefore, the 500-year floodplain is more expansive in land area
but delineates areas that have lower risk of flooding in terms of frequency. A
community must choose which version of the indicator to use.
12 For further details on site/waste facility data (data source, decisions for calculations and vetting), see Indicator 1.2.1.
114
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Calculation steps and assumptions
~
Extract 100- and
500-year Flood
Hazard Limits
Inputs: National Flood Hazard Layer (NFHL)
Calculations:
• Use shapefile S_FLD_HAZ_AR.shp.
• The field "FLD_ZONE" is used to separate the 100-year and 500-year flood
hazard extents.
• Values of either 'A' or 'AE' were combined to define the 100-year extent,
while a value of 'X' was used to define the 500-year extent.13
Outputs: Shapefile containing polygon extents for the 100-year and 500-year
floodplains
~
Clip the Block
Groups by the
Floodplains
Input:
• Shapefile containing polygon extents for the 100-year and 500-year
floodplains
• BG shapefile
Calculation:
• Within ArcGIS, use the "Clip" tool within the "Extract" toolset, within the
"Analysis Tools" toolbox
• Complete twice, once for each floodplain extent
Output: Shapefile with BG - floodplain intersection for 100- and 500-year extents
~
Identify and
Count Facilities
within Floodplain
Portions of Block
Groups
(Indicator)
Input:
• Facilities shapefile
• Shapefile with BG - floodplain intersection
Calculation:
• Intersect the facilities shapefile with the BG-floodplain intersection shapefile
to identify all facilities falling within floodplain areas by BG
• Using the BG unique identifier, count the facilities identified
• Complete for each floodplain extent
Output: Count of facilities within the floodplain (100- or 500-year) per BG
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
BGs that are partly or wholly within a floodplain and have zero facilities within the
floodplain boundary should be appropriately shown with a value of zero. BGs that
do not contain any area of the floodplain should be shown separately and not
included in the zero-count category as these BGs are not considered at risk from
this flooding indicator.
~
Choosing
Symbology
Maps showing the distribution of counts of sites within a floodplain will generally not
be compared across scenarios, time periods, or locations so a single symbology will
not be necessary. The symbology will be specific to the data for study area.
13 Definitions of the values contained within this field can be found at
https://www.fedl.ore/metadata/metadata archive/fedl html/dfirm fldhaz apr08.htm
115
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Binning in the
Data by Block
Group
The distribution of counts of sites within a floodplain will likely be skewed towards
values closer to zero. For this situation using quantiles (equal number of
observations per bin) is recommended. Use a maximum of 5-7 categories so that
the map reader can readily distinguish between color categories and visually match
them to the legend. Deciles or other percentiles can also be used depending on
user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the indicator values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Community Risk
Due to Releases
During Floods
For each BG with a non-zero count for this indicator, there will be a risk of
contamination to land within the floodplain (or more specially, within the inundated
area) during a flooding event with a release.
~
Emergency
Preparedness for
Specific
Contaminants
By characterizing the different facilities captured within the counts for a BG and
combining with other indicators (e.g., Indicators 1.2.6), specific preparedness
actions can be taken if there were to be a release of a contaminants from one of
these facilities into the floodplain.
Key caveats/limitations
~
NFHL Validity
FEMA maintains the NFHL on a schedule that is set by region. Before beginning
any analyses, a user should check for updates to the NFHL polygons. For layers
that have not been updated within the last few years, there is a greater probability
that the flood extents are out of date due to either land development or changes in
precipitation frequencies.
~
NFHL
Applicability
Actual inundation during any flooding event will vary depending on preceding
hydrologic conditions and the characteristics of the storm event itself. Therefore,
these defined flood plains are the best estimates of inundated area based on historic
storm events. As extreme precipitation events change into the future, these
floodplain boundaries will change and will likely expand to cover larger areas.
Floodplains data also face other issues such as incomplete coverage and coarse
resolution (Wing et al., 2018).
Citations
Dataset/Tool
FEMA. (2021). National Flood Hazard Laver. www.fema.aov/national-flood-hazard-
laver-nfhl. Alternate data portal: https://msc.fema.aov/Dortal/advanceSearch
Additional
Resources
Wing, 0. E. J., Bates, P. D., Smith, A. M., Sampson, C. C., Johnson, K. A.,
Fargione, J., & Morefield, P. (2018). Estimates of present and future flood risk in the
conterminous United States. Environmental Research Letters, 13, 034023
httos://doi.ora/10.1088/1748-9326/aaac65
BG: Block Group, FEMA: Federal Emergency Management Agency, NFHL: National Flood Hazard Layer
116
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.3.2. Checklist for Count of Sites/Waste Facilities Within a Specific Hydrologic
Distance of a Flowline Indicator
Count of Sites/Waste Facilities Within a Specific Hydrologic Distance of a
Flowline Indicator
Definition of the indicator
~
Definition
Count of facilities within a certain (500 m, 1 km, 2km, or 5 km) "raindrop" distances
to streams/rivers (NHD flowline) or lake/reservoir (shoreline).
~
Interpretation
This indicator first determines the overland flow distance that a raindrop falling at a
facility takes to reach the nearest flowline (i.e., stream/river or waterbody),
representing the path water on the ground surface at a facility would take as it
moves away from a facility.
Once each facility has an associated distance, the count of facilities within a certain
distance (selected by the decision-makers) can be calculated to provide the relative
likelihood of contaminants reaching flowing water.
A limitation for this indicator is that the raindrop analysis does not take into account
stormwater infrastructure that may intercept an overland flow release. However, this
limitation is countered with the assumption that a storm event of a magnitude high
enough to trigger a release from a facility also has a high likelihood of overwhelming
a typical stormwater infrastructure. In a full risk assessment, indicator values should
be reviewed closely in urban areas with stormwater infrastructure.
Data source™
~
Data Source
The enhanced National Hydrography Dataset (NHDPIus) medium resolution data
~
Temporal
Resolution
This indicator is a static measure without a time component. The measure
represents the information available at the time of download.
~
Spatial
Resolution
• Block Group (BG) shapefile
• NHDPIus; Medium resolution
o Flow Direction Grid raster (30m x 30m resolution)
o Flowlines (1:100,000 scale)
~
Processing Tool
• The "Raindrop Tool" is used to conduct the overland navigation analysis.
This online tool includes the NHDPIus Flow Direction Grid raster; therefore,
when using this method, a user does not need to provide that spatial input
separately.
• Alternatively, a user may create custom code following the methods
provided by RTI (see Bergenroth, 2009) to conduct the raindrop navigation
in the absence of the online tool. In that case, the user is required to
provide the NHDPIus Flow Direction Grid raster.
14 For further details on site/waste facility data (data source, decisions for calculations and vetting), see Indicator 1.2.1.
117
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Data Format
• Spreadsheet (SiteAA/aste Facility)
• Shapefile (BG, NHDPIus)
• Raster (Flow Direction Grid)
Decisions needed for calculation
~
Distance Limit for
Counting
There is a continuous range of values among the calculated distances from each
facility to a flowline. Therefore, depending on the local study area conditions (e.g.,
relief, density of streams/rivers, presence of stormwater infrastructure), different
limits on the distance from a facility to a flowline are applicable to assess a risk of
contamination from overland flow. Suggested limits for distance counts include 500
m, 1 km, 2 km, and 5 km. The smallest distance limit, 500 m, may be applicable for
densely populated areas, whereas larger distance limits may be applicable in higher
relief areas with lower populations.
Calculation steps and assumptions
~
Identify the
Applicable Block
Group for Each
Facility
Inputs:
• Facility shapefile
• BG shapefile
Calculation: Intersect the facility shapefile with the BG shapefile to determine the
specific BG in which each facility is located.
Outputs: Shapefile containing facility points with unique BG identifier
~
Determine
Distance to
Nearest Flowline
for each Facility
Input: Shapefile containing facility points with unique BG identifier
Calculation:
• Using the "Raindrop Tool" provided as part of the U.S. EPA's EnviroAtlas,
determine the distance from each facility to the nearest flowline.
• Inputs to the tool include the facility shapefile and two parameters
o Maximum raindrop distance (km): suggested value of 10 km as the
maximum distance the raindrop process will navigate before exiting
its process
o Maximum snap distance (km): suggested value of 0.005 km
distance from the end of the navigation line to the flowline to exit
the navigation and return a distance value
• Modify the input shapefile to include the distance returned for each facility
Output: Shapefile containing facility points with unique BG identifier and distance to
nearest flowline (in km)
118
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Count Facilities
within Specified
Distance
(Indicator)
Input: Shapefile containing facility points with unique BG identifier and distance to
nearest fiowline (in km)
Calculation:
• Using the unique BG identifier, select all facilities within the specified
distance (500 m, 1 km, 2 km, or 5 km) that fall within the BG.
• Count the selected features and report by BG identifier.
• For any BG without at least one facility, return a -1 value to use in mapping.
Output: Count of facilities within the specified distance (500 m, 1 km, 2 km, or 5
km) per BG
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing
Counts
BGs that are not found to have any sites or waste facilities within their boundaries
should be shown separately (i.e., in gray) and not included in the distance category
as these BGs are not considered at risk from this indicator.
~
Choosing
Symbology
Maps showing the distribution of counts of sites will generally not be compared
across scenarios, time periods, or locations so a single symbology will not be
necessary. The symbology will be specific to the data for study area.
~
Binning in the
Data by Block
Group
The distribution of counts of sites will likely be skewed towards values closer to
zero. For this situation using quantiles (equal number of observations per bin) is
recommended. Use a maximum of 5-7 categories so that the map reader can
readily distinguish between color categories and visually match them to the legend.
Deciles or other percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the count values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Vulnerability due
to Contaminate
Transport to
Waterways
This indicator measures both the closeness and density of facilities to the
hydrologic network. The more facilities there are and the closer they are to flowing
waters, the greater the likelihood that an accidental release from a facility or a
heavy precipitation event acting on surface contaminants at a facility will produce
contamination of the nearby flowing waters. Contaminated water can cause risk to
human and ecological health through direct exposure or secondary exposure
through drinking water supplies. Therefore, the selection of distance limit can
impact the type of vulnerabilities and risks assessed.
Key caveats/limitations
~
Raindrop
Navigation
This navigation does not take into consideration any sewers or stormwater
infrastructure. Rather it assumes that any stormwater infrastructure either drains to
the same closest waterway or becomes inundated during events that would cause a
facility to have substantial enough surface runoff reaching a waterbody.
119
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Processing of
Raindrop
Distances
There are two options for implementing the raindrop navigation, either through the
U.S. EPA's EnviroAtias Interactive Mapping tool or through creation of custom
programming against the NHDPIus Flow Direction Grid. Use of the available online
tool is subject to the limitations and set-up described within the tool; however, it is a
service requiring minimal input or technical expertise. Creating custom
programming given the underlying navigation process allows a user greater
flexibility for setting limits or including complex hydrologic situations, but it also
requires technical expertise around geospatial processing and the NHDPIus
dataset.
~
Identified
Waterbodies
NHDPIus resolution is 1:100,000, which means it captures the majority of flowing
waters in the United States, as well as some ephemeral streams. Therefore, the
distances calculated will be from the facility to readily identifiable streams/rivers,
which may not account for small drainage channels and ditches that convey
stormwater.
Citations
Dataset/Tool
U.S. EPA. (2021). Get NHDPIus (National Hydrography Dataset Plus) Data.
httosJ/www. eoa.aov/waterdata/aet-nhdolus-national-h vdroaraoh v-dataset-olus-data
Bergenroth, B. (2009). Combining vector and raster data for hydro flow analysis:
The Raindrop Tool. Oracle Spatial User Conference. Tampa, FL.
httos://www.oracle.comfiechnetwork/database/entemrise-edition/osuc2009-
raindroo-beraenroth-134405.odf.
U.S. EPA. (2022). EnviroAtias Interactive Map.
https://www.eDa.aov/enviroatlas/enviroatlas-interactive-maD
BG: Block Group, EPA: U.S. Environmental Protection Agency; NHD: National Hydrography Dataset
120
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.3.3. Checklist for Shortest Hydrologic Distance Upstream to a Site/Waste Facility
Indicator
Shortest Hydrologic Distance Upstream to a Site/Waste Facility
Definition of the indicator
~
Definition
Shortest hydrologic distance (overland) to an upstream facility
~
Interpretation
This indicator represents the shortest navigated upstream hydrologic distance
overland from a Block Group (BG) boundary to a facility. Each facility surrounding a
BG is examined to find the closest facility in terms of how runoff from a facility would
travel to the BG lands. For BGs that contain facilities, the distance is set to zero.
This indicator examines all facilities upstream of BGs using the "raindrop" distance
and finds the closest facility. This means that the indicator reports the shortest
hydrologic distance from a facility downstream, overland to a BG. If a BG contains
one or more facilities, this distance is set to zero. The distance represents the most
likely facility and distance traveled during an overland flow event (i.e., flooding,
heavy precipitation, release with wash off), which could contribute contaminants to
the BG lands.
A limitation for this indicator is that the raindrop analysis does not take into account
stormwater infrastructure that may intercept an overland flow release. However, this
limitation is countered with the assumption that a storm event of a magnitude high
enough to trigger a release from a facility also has a high likelihood of overwhelming
a typical stormwater infrastructure. In a full risk assessment, indicator values should
be reviewed closely in urban areas with stormwater infrastructure.
Data source"
~
Data Source
The enhanced National Hydrography Dataset (NHDPIus) medium resolution data
~
Temporal
Resolution
This indicator is a static measure without a time component. The measure
represents the information available at the time of download.
~
Spatial
Resolution
• BG shapefile
• NHDPIus; Medium resolution
o Flow Direction Grid raster (30m x 30m resolution)
o Flowlines (1:100,000 scale)
~
Processing Tool
• For this indicator, custom code following the methods provided by RTI (see
Bergenroth, 2009) to conduct the raindrop navigation was created so that
the raindrop navigation could be intersected with BG boundaries rather than
an NHDPIus flowline. The code requires the NHDPIus Flow Direction Grid
raster.
• The "Raindrop Tool" provides the basic overland navigation analysis but
does not allow for navigation to boundaries other than NHD flowlines. The
tool could be explored for the calculation of the indicator but would require
additional processing.
15 For further details on site/waste facility data (data source, decisions for calculations and vetting), see Indicator 1.2.1.
121
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Data Format
• Spreadsheet (SiteAA/aste Facility)
• Shapefile (BG, NHDPIus)
• Raster (Flow Direction Grid)
Decisions needed for calculation
~
N/A
Calculation steps and assumptions
~
Identify the
Applicable Block
Group for each
Facility
Inputs:
• Facility shapefile
• BG shapefile
Calculation: Intersect the facility shapefile with the BG shapefile to determine the
specific BG in which each facility is located
Outputs: Shapefile containing facility points with unique BG identifier
~
Determine Block
Groups
Containing
Facilities
Input: Shapefile containing facility points with unique BG identifier
Calculation: For all BGs containing a facility, set the indicator value to 0
Output: BG attribute table with field for indicator where value set to 0 for BGs the
contain facilities
~
Navigation from
Facilities
Inputs:
• Shapefile containing facility points with unique BG identifier
• BG shapefile
Calculation: For all facilities
• Using the raindrop navigation technique, navigate downstream from each
facility to create a flow path per facility
• Intersect each flow path with the BG boundaries
• Measure length of raindrop flow path line between facility and intersection
of BG boundary
• Report BG identifier, distance, and facility identifier for each facility
Output: Facility attribute table of downstream BGs and distances
~
Identify Upstream
Facilities per
Block Group
Inputs:
• BG attribute table with field for indicator where value set to count of facilities
within the BG
• Facility attribute table of downstream BGs and distances
Calculation: Join BG attribute table and Facility attribute table on unique BG
identifier
Output: BG attribute table containing fields for indicator value, upstream facilities,
upstream facility distances
122
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Find Shortest
Upstream
Distance to a
Facility (Indicator)
Input: BG attribute table containing fields for indicator value, upstream facilities,
upstream facility distances
Calculation:
• For each BG where indicator value is not 0
o Select all corresponding facilities within the table that correspond to
the BG
o Take the minimum facility distance from the selected facilities
o Enter this distance as the value for the indicator field for the BG
• Leave each BG with an indicator value of 0 alone
Output: BG attribute table containing field for indicator value (either 0 or shortest
hydrologic distance upstream)
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing or
Downstream of
Sites/Waste
Facilities
BGs that are not found to have any sites or waste facilities within their boundaries
or upstream of their boundaries should be shown separately (i.e., in gray) and not
included in the distance category as these BGs are not considered at risk from this
indicator.
~
Choosing
Symbology
Maps showing the distribution of shortest hydrologic distance to an upstream facility
will generally not be compared across scenarios, time periods, or locations so a
single symbology will not be necessary. The symbology will be specific to the data
for study area.
~
Binning in the
Data by Block
Group
The distribution of shortest hydrologic distances will likely be skewed towards
values closer to zero, and it is likely that some BGs will have a value of zero since
the closest upstream facility will be in the same BG. For this indicator, create a
separate bin for a value of zero, and then for the remainder of the data range using
quantiles (equal number of observations per bin) is recommended. Use a maximum
of 5-7 categories so that the map reader can readily distinguish between color
categories and visually match them to the legend. Deciles or other percentiles can
also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes lighter as the distance values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Vulnerability due
to Overland
Contaminant
Releases
This indicator provides the closest upstream hydrologic distance (in terms of
overland flow) from a facility to the BG boundary, where the indicator value is zero if
at least one facility exists within the BG. When a BG does not contain a facility
knowing the closest upstream facility identifies the facility that could contribute
contaminants to the lands of a BG during a release event. Therefore, in addition to
any facilities within the BG, a community can plan for the types and levels of action
needed to mitigate the impacts of potential contaminant releases during an overland
flow event.
123
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Planning for
Emergency
Containment
Methods During a
Release
Understanding how the overland flow from a facility could travel to reach a
downstream receiving area (i.e., a BG), provides information on where potential
emergency containment methods could be deployed to mitigate releases from a
facility to a community. This indicator provides the shortest flow path (and therefore
the contributing facility) that could be prioritized for action in planning.
Key caveats/limitations
~
Processing of
Raindrop
Distances
There are two options for implementing the raindrop navigation, either through the
U.S. EPA's EnviroAtlas Interactive Mapping Raindrop Tool or through creation of
custom programming against the NHDPIus Flow Direction Grid. Use of the available
online tool is subject to the limitations and set-up described within the tool; however,
it is a service requiring minimal input or technical expertise. Creating custom
programming given the underlying navigation process allows a user greater
flexibility for setting limits or including complex hydrologic situations, but it also
requires technical expertise around geospatial processing and the NHDPIus
dataset.
~
Geopolitical and
Hydrologic
Boundary
Mismatches
BGs are political boundaries that do not often correspond to elevation contours like
the hydrologic boundaries and flowpaths. Because of this mismatch, when trying to
determine hydrologically based attributes for a BG there can be results that appear
confounding to the casual viewer. For instance, a facility that looks close to a BG
boundary may not actually be hydrologically close. That is the surface runoff from
that facility may run in the opposite direction, away from the closer political
boundary. Results from this indicator should be viewed giving consideration to the
local hydrology and surface relief.
Citations
Dataset/Tool
U.S. EPA. (2021). Get NHDPIus (National Hydrography Dataset Plus) Data.
httosJ/www. eoa.aov/waterdata/aet-nhdolus-national-h vdroaraoh v-dataset-olus-data
Bergenroth, B. (2009). Combining vector and raster data for hydro flow analysis:
The Raindrop Tool. Oracle Spatial User Conference. Tampa, FL.
httos://www.oracle.comfiechnetwork/database/entemrise-edition/osuc2009-
raindroo-beraenroth-134405.odf.
U.S. EPA. (2022). Enviro Atlas Interactive Map.
https://www.eDa.aov/enviroatlas/enviroatlas-interactive-maD
BG: Block Group, EPA: U.S. Environmental Protection Agency; NHDPIus: Enhanced National Hydrography Dataset
124
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.3.4. Checklist for Count of Upstream Sites/Waste Facilities within a Specific
Hydrologic Distance of a Community Indicator
Count of Upstream Sites/Waste Facilities Within a Specific Hydrologic
Distance of Community Indicator
Definition of the indicator
~
Definition
Count of facilities within a certain (500 m, 1 km, 3 km, or 5 km) upstream "raindrop"
distance to a Block Group (BG) boundary, including any facilities within the BG.
~
Interpretation
This indicator examines all facilities within a specified upstream "raindrop" distance,
meaning that it reports the count of facilities that have an overland flow path
downstream to a BG within a distance relevant to the community and study area.
This count represents the number of facilities that could contribute contaminants
during an overland flow event (i.e., flooding, heavy precipitation, release with wash
off).
The greater number of facilities upstream (in terms of overland flow) of a BG, the
greater the risk of a quantifiable release reaching the community/BG. This indicator
is a proxy for size of contamination.
Data source*6
~
Data Source
The enhanced National Hydrography Dataset (NHDPIus) medium resolution data
~
Temporal
Resolution
This indicator is a static measure without a time component. The measure
represents the information available at the time of download.
~
Spatial
Resolution
• BG shapefile
• NHDPIus; Medium resolution
o Flow Direction Grid raster (30m x 30m resolution)
o Flowlines (1:100,000 scale)
~
Processing Tool
• For this indicator, custom code following the methods provided by RTI (see
Bergenroth, 2009) to conduct the raindrop navigation was created so that
the raindrop navigation could be intersected with BG boundaries rather than
an NHDPIus flowline. The code requires the NHDPIus Flow Direction Grid
raster.
• The "Raindrop Tool" provides the basic overland navigation analysis but
does not allow for navigation to boundaries other than NHD flowlines. The
tool could be explored for the calculation of the indicator but would require
additional processing.
~
Data Format
• Spreadsheet (SiteAA/aste Facility)
• Shapefile (BG, NHDPIus)
• Raster (Flow Direction Grid)
16 For further details on site/waste facility data (data source, decisions for calculations and vetting), see Indicator 1.2.1.
125
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Decisions needed for calculation
~
Distance Limit for
Counting
There is a continuous range of values among the calculated distances upslope from
each BG boundary. Therefore, depending on the local study area conditions (e.g.,
relief, stormwater infrastructure, soil hydrologic group) different limits on the
distance upstream from a BG are applicable to assess a risk of contamination from
overland flow. Suggested limits for distance counts include 500 m, 1 km, 3 km, and
5 km. The smallest distance limit, 500 m, may be applicable for densely populated
areas, whereas larger distance limits may be applicable in areas with less
development.
Calculation steps and assumptions
~
Identify the
Applicable Block
Group for Each
Facility
Inputs:
• Facility shapefile
• BG shapefile
Calculation: Intersect the facility shapefile with the BG shapefile to determine the
specific BG in which each facility is located
Outputs: Shapefile containing facility points with unique BG identifier
~
Determine
Number of
Facilities within
Each Block
Group
Input: Shapefile containing facility points with unique BG identifier
Calculation: For all BGs, count the number of facilities within the BG
Output: BG attribute table with field for indicator where value set to count of
facilities within the BG
~
Navigation from
Facilities
Inputs:
• Shapefile containing facility points with unique BG identifier
• BG shapefile
Calculation: For all facilities
• Using the raindrop navigation technique, navigate downstream from each
facility to create a flow path per facility
• Intersect each flow path with the BG boundaries
• Measure length of raindrop flow path line between facility and intersection
of BG boundary
• Report BG identifier, distance, and facility identifier for each facility
Output: Facility attribute table of downstream BGs and distances
~
Identify Upstream
Facilities per BG
Inputs:
• BG attribute table with field for indicator where value set to count of facilities
within the BG
• Facility attribute table of downstream BGs and distances
Calculation: Join BG attribute table and Facility attribute table on unique BG
identifier
Output: BG attribute table containing fields for indicator value, upstream facilities,
upstream facility distances
126
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Count Facilities
within Specified
Upstream
Distance
(Indicator)
Input: BG attribute table containing fields for indicator value, upstream facilities,
upstream facility distances
Calculation:
• For all BGs, select all corresponding facilities within the table that have a
downstream distance less than or equal to the specified upstream distance
(500 m, 1 km, 3 km, or 5 km)
• Count the selected facilities by BG identifier
• Add this count to the value within the indicator column of the attribute table
Output: Count of facilities within the specified upstream distance (500 m, 1 km, 3
km, or 5 km) per BG
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing
Counts
All BGs will have valid values, including zero.
~
Choosing
Symbology
Maps showing the distribution of counts of sites will generally not be compared
across scenarios, time periods, or locations so a single symbology will not be
necessary. The symbology will be specific to the data for study area.
~
Binning in the
Data by Block
Group
The distribution of counts of sites will likely be skewed towards values closer to
zero. For this situation using quantiles (equal number of observations per bin) is
recommended. Use a maximum of 5-7 categories so that the map reader can
readily distinguish between color categories and visually match them to the legend.
Deciles or other percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the count values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Vulnerability due
to Overland
Contaminant
Releases
This indicator provides the number of facilities within a specified upstream distance
(in terms of overland flow) of a BG, taking into account only facilities that could
contribute releases within a surface runoff event either without or outside of the BG
boundary. By understanding the potential number of facilities that could contribute
contaminants to the lands of a BG during a release event, a community can plan for
the types and levels of action needed to mitigate the impacts.
~
Planning for
Emergency
Containment
Methods During a
Release
Understanding how the overland flow from a facility could travel to reach a
downstream receiving area (i.e., a BG), provides information on where potential
emergency containment methods could be deployed to mitigate releases from a
facility to a community. This indicator provides a count of a how many facilities and
corresponding flow paths should be considered in planning.
127
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Key caveats/limitations
~
Processing of
Raindrop
Distances
There are two options for implementing the raindrop navigation, either through the
U.S. EPA's EnviroAtias Interactive Mapping Raindrop Tool or through creation of
custom programming against the NHDPIus Flow Direction Grid. Use of the available
online tool is subject to the limitations and set-up described within the tool; however,
it is a service requiring minimal input or technical expertise. Creating custom
programming given the underlying navigation process allows a user greater
flexibility for setting limits or including complex hydrologic situations, but it also
requires technical expertise around geospatial processing and the NHDPIus
dataset.
~
Geopolitical and
Hydrologic
Boundary
Mismatches
BGs are political boundaries that do not often correspond to elevation contours like
the hydrologic boundaries and flowpaths. Because of this mismatch, when trying to
determine hydrologically based attributes for a BG there can be results that appear
confounding to the casual viewer. For instance, a facility that looks close to a BG
boundary may not actually be hydrologically close. That is the surface runoff from
that facility may run in the opposite direction, away from the closer political
boundary. Results from this indicator should be viewed giving consideration to the
local hydrology and surface relief.
Citations
Dataset/Tool
U.S. EPA. (2021). Get NHDPIus (National Hydrography Dataset Plus) Data.
httosJ/www. eoa.aov/waterdata/aet-nhdolus-national-h vdroaraoh v-dataset-olus-data
Bergenroth, B. (2009). Combining vector and raster data for hydro flow analysis:
The Raindrop Tool. Oracle Spatial User Conference. Tampa, FL.
httos://www.oracle.comfiechnetwork/database/entemrise-edition/osuc2009-
raindroo-beraenroth-134405. odf.
U.S. EPA. (2022). EnviroAtias Interactive Map.
httos://www.eoa.aov/enviroatlas/enviroatlas-interactive-maD
BG: Block Group, EPA: U.S. Environmental Protection Agency; NHDPIus: Enhanced National Hydrography Dataset
128
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.3.5. Checklist for Shortest Distance to a Site/Waste Facility Upwind [Season]
Indicator
Shortest Distance to a Site/Waste Facility Upwind [Season] Indicator
Definition of the indicator
~
Definition
The shortest distance between a community and a nearby facility that is in the
predominant upwind direction. Predominant wind direction refers to the direction
from which the wind blows for most of the time during the season, calculated based
on historical wind patterns.
~
Interpretation
The shorter the distance, the greater the risk of the community being impacted by
emissions from the facility.
Since the predominant wind direction may change by season, this indicator is
developed for each season. This implies that the list of facilities that pose a risk to
the community may be different in each season, l/l/e define seasons as follows:
winter (December-February), spring (March-May), summer (June-August), and
fall (September-November).
Data source*7
~
Data Source
The National Oceanic and Atmospheric Administration (NOAA)'s North American
Mesoscale Forecast System (NAM) Analyses (NAM-ANL) model data. The NAM
model is run by the National Centers for Environmental Prediction (NCEP) for
forecasting weather on daily basis.
~
Temporal
Resolution
Model simulations were available at four time periods per day corresponding to the
start of each forecast simulation: 0000 hours UTC time, 0600 hours UTC, 1200
hours UTC, and 1800 hours UTC. These are normally referred to as OOz, 06z, 12z,
and 18z model cycles, respectively.
Data are available from 2006 to present.
~
Spatial
Resolution
12-km grid resolution, continental United States (CONUS)
~
Data Format
GRIB2
17 For further details on site/waste facility data (data source, decisions for calculations and vetting), see Indicator 1.2.1.
129
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Decisions needed for calculation
~
Model Cycles and
Variables
Incorporating higher time resolution data would enable capturing diurnal variability.
However, the choice is also dependent on the computational resources available,
and the study timeframe and objectives. At least two model cycles are
recommended. Model cycles OOz and 12z are generally considered more reliable
due to the extent of assimilation of observational data used for forecast simulations
and are recommended for the development of the indicators.
The wind vector components (UGRD, u wind [m/s] and VGRD, v wind [m/s]) are
available at multiple heights (or atmospheric pressure equivalents), l/l/e recommend
using 10 m height to be consistent with the height at which anemometer
measurements are made and to represent wind patterns closer to the surface.
The "UGRD: 10 m above gnd" and "VGRD: 10 m above gnd" variables
representing, respectively, the U and V vector components of wind at 10 m above
ground were extracted.
~
Wind Direction
Sectors
Choose how fine a resolution you want to resolve the wind direction (e.g., 36 bins of
10° each, or 12 bins of 30° each). The finer the resolution, the more specific and
narrow the risk profile will be. For climatological indicators, and given the inherent
uncertainty in the forecast simulations, it is recommended to choose a larger wind
sector. It would also be easier to interpret.
l/l/e recommend using four sectors of 90° each aligning with the common wind
direction terminologies: northeast (NE: 0-90°), southeast (SE: 90°-180°), southwest
(SW: 180°-270°) and northwest (NW: 270-360°).
Calculation steps and assumptions
~
Assumptions and
Calculations
The overall approach involves the following steps:
• Overlay the site/waste facility and Block Group (BG) information on the
model data to define a common grid ID
• Analyze the wind data to understand the frequency of wind speeds and
wind directions by season
• Identify the quadrant (NE, SE, SI/I/, NW) a facility is in relative to the
community BG
• For each grid ID, summarize the calculated wind distribution data for each
quadrant to list predominant wind direction and the maximum wind speed in
that direction
• Calculate the time by dividing the straight-line distance from the facility to
the centroid of the BG by the wind speed
• Calculate the relevant metrics
The shortest distance to a facility is obtained by calculating for each season the
minimum value of the distance between the BG and the different facilities.
130
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Import Model
Data into
Common Grid
System and
Extract Model
Data
Inputs:
• NAM model data file
• Shapefile with facilities and BG
Calculation:
• Superimpose the NAM model grid on facilities and BG using ArcGIS.
Develop a mapping linking each facility and centroid of BG and the NAM
model grid and create a common mapping grid with a unique grid ID.
• Calculate the straight-line distance between each combination of facility
and census BG.
Outputs:
• Text file (csv format) with the mapping of facility, BG, unique grid ID and
distance between facility and BG
• Text file (csv format) of extracted UGRD and VGRD values with the
matched unique grid ID
~
Read Model Data
and Calculate
Wind Speed and
Wind Direction
Input: Text file with extracted UGRD and VGRD values
Calculations:
• Read "u" and "v" component values and calculate scalar wind speed and
wind direction.
• Plot windrose (USDA, 2022) by season. Choose wind speed cutoffs of
2 m/s, 4 m/s, 6 m/s, 10 m/s, and >10 m/s
• Calculate the cumulative percent of time the wind is blowing from each wind
direction sector (e.g., NE, SE) at wind speeds within each cutoff (< 2m/s,
< 4m/s, etc.).
• Calculate percent of time in each speed bin (e.g., 0-2 m/s, 2-4 m/s), and
normalize it to calculate relative fractions in each speed bin (e.g., % time in
2-4 m/s bin as a fraction of total cumulative time in that wind direction). This
estimate is used to calculate a weighted average wind speed in each wind
direction sector for each season.
• Calculate a weighted wind speed by adding the product of the relative
fraction in each speed bin by the top end of the speed bin. For the >10 m/s
wind speed bin, use the maximum wind speed. Using the maximum speed
in each bin results in a conservative estimate.
Output:
• Text file (csv format) summarizing the calculated cumulative percent time,
the relative fraction within each speed bin, and the weighted wind speed for
each wind direction sector.
~
Identify Quadrant
that the Facility Is
Located in
Relative to the
Block Group
Input: Text file (csv format) with the mapping of facility, BG, unique grid ID, and
distance between facility and BG
Calculation: Determine where the facility coordinates (latitude and longitude) fall in
relation to the BG centroid coordinates. Accordingly categorize the quadrant that
the facility is in relative to the BG as NE, SE, SI/I/, and NW.
Output: Quadrant information for each facility and BG combination.
131
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Calculate Metric
Inputs:
• Text file (csv format) summarizing the calculated cumulative percent time,
the relative fraction within each speed bin, and the weighted wind speed for
each wind direction sector
• Quadrant information for each facility and BG combination
Calculation:
• Link the two datasets using the common grid ID
• In the wind data file, assign quadrant based on the wind direction
• For each quadrant (e.g., NE, SE) relative to the BG under consideration,
assign the weighted average wind speed and the cumulative percent time
that wind blows from that quadrant by season
• Take the minimum of the distance between all facility and BG combinations
in that quadrant for each season. This gives the shortest distance.
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have distance values
~
Choosing a
Symbology
The recommended symbology for this indicator is a single unique symbology that
spans seasons. This will result in the same color representing the same value
across the maps, making direct comparisons much easier. To build this, find the
minimum and maximum values across seasons, and then use the full range of
values to create a single unique symbology to apply to all maps.
~
Binning in the
Data by Block
Group
For this indicator, using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs.
~
Choosing Colors
Use a color gradation that becomes lighter as the distance values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Adaptation
Planning/
Emergency
Response
This indicator provides an estimate of how close the community is to sites/waste
facilities with potentially hazardous emissions during extreme events. The indicator
provides valuable information on which communities are at risk in the event of an
emergency and how to prioritize response. For example, BGs with shorter distances
(darkest color) may need to be evacuated or addressed first in the event of an
emergency.
132
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Key caveats/limitations
~
Representative-
ness
• Since the indicators are developed for adaptation planning and
preparedness for extreme events, the indicators represent a worst-case
estimate based on nearest facilities, wind speed and wind direction, without
air dispersion modeling. This approach does not account for geographical
features (e.g., mountains) that may impact the movement of wind.
• The values are based on analysis of wind patterns for one year. This
analysis will need to be performed over a longer period (e.g., 10 years) to
capture long-term climatological patterns.
• The underlying data are based on weather forecasts and not actual point
measurements or modeled retrospective reanalysis meteorological fields.
While the forecasts are typically reliable and representative, there may be
minor differences between actual meteorology and weather forecasts.
• For certain locations such as coastal communities, the wind direction may
change frequently, and the predominant wind direction may not necessarily
be representative every year. This could be mitigated by using multiple
years to get a true climatological profile as noted above.
Citations
Dataset/T ool
NOAA NCEI. (n.d.) North American Mesoscale Forecast System.
httos://www.ncei.noaa.aov/Droducts/weather-climate-models/north-american-
mesoscale. Recent model data available at https://www.ncei.noaa.aov/data/north-
american-mesoscale-model/access/. Historical data are available at
https://www.ncei.noaa.aov/data/north-american-mesoscale-
model/access/historical/analvsis/.
Additional
Resources
Grange, S. K. (2014). Technical note: Averaging wind speeds and directions. DOI:
10.13140/RG.2.1.3349.2006.
USDA. (2022). Wind rose resources.
https://www.nrcs.usda.aov/wps/portal/wcc/home/climateSupport/windRoseResourc
es/
BG: Block Group, CONUS: Continental United States, NAM-ANL: North American Mesoscale Forecast System Analyses, NCEP: National
Centers for Environmental Prediction, NOAA: National Oceanic and Atmospheric Administration
133
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.3.6. Checklist for Count of Sites/Waste Facilities "Upwind" within a Specific Season
and Distance of a Community Indicator
Count of Sites/Waste Facilities "Upwind" within a Specific Season and
Distance of a Community Indicator
Definition of the indicator
~
Definition
The number of facilities that are in the predominant upwind direction with specified
distances from the community. Predominant wind direction refers to the direction
from which the wind blows for most of the time during the season, calculated based
on historical wind patterns.
~
Interpretation
The larger the number of facilities, the greater the risk for a potential aggregated
impact if all facilities were to fail at the same time during an extreme event. Since
the predominant wind direction may change by season, this indicator is developed
for each season. This implies that the list of facilities that pose a risk to the
community may be different in each season, l/l/e define seasons as follows: winter
(December-February), spring (March-May), summer (June-August), and
fall (September-November).
Data source"
~
Data Source
The National Oceanic and Atmospheric Administration (NOAA)'s North American
Mesoscale Forecast System (NAM) Analyses (NAM-ANL) model data. The NAM
model is run by the National Centers for Environmental Prediction (NCEP) for
forecasting weather on daily basis.
~
Temporal
Resolution
Model simulations were available at four time periods per day corresponding to the
start of each forecast simulation: 0000 hours UTC time, 0600 hours UTC, 1200
hours UTC, and 1800 hours UTC. These are normally referred to as OOz, 06z, 12z,
and 18z model cycles, respectively.
Data are available from 2006 to present.
~
Spatial
Resolution
12-km grid resolution, continental United States (CONUS)
~
Data Format
GRIB2
18 For further details on site/waste facility data (data source, decisions for calculations and vetting), see Indicator 1.2.1.
134
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Decisions needed for calculation
~
Model Cycles and
Variables
Incorporating higher time resolution data would enable capturing diurnal variability.
However, the choice is also dependent on the computational resources available,
and the study timeframe and objectives. At least two model cycles are
recommended. Model cycles OOz and 12z are generally considered more reliable
due to the extent of assimilation of observational data used for forecast simulations
and are recommended for the development of the indicators.
The wind vector components (UGRD, u wind [m/s] and VGRD, v wind [m/s]) are
available at multiple heights (or atmospheric pressure equivalents), l/l/e recommend
using 10 m height to be consistent with the height at which anemometer
measurements are made and to represent wind patterns closer to the surface.
The "UGRD: 10 m above gnd" and "VGRD: 10 m above gnd" variables
representing, respectively, the U and V vector components of wind at 10 m above
ground were extracted.
~
Wind Direction
Sectors
Choose how fine a resolution you want to resolve the wind direction (e.g., 36 bins of
10° each, or 12 bins of 30° each). The finer the resolution, the more specific and
narrow the risk profile will be. For climatological indicators, and given the inherent
uncertainty in the forecast simulations, it is recommended to choose a larger wind
sector. It would also be easier to interpret.
l/l/e recommend using four sectors of 90° each aligning with the common wind
direction terminologies: northeast (NE: 0-90°), southeast (SE: 90°-180°), southwest
(SW: 180°-270°) and northwest (NW: 270-360°).
~
Distance Limit for
Counting
There is a continuous range of values among the calculated distances upwind from
each Block Group (BG). Therefore, depending on the local study area conditions
(e.g., size of counties /BG, population distribution, density of facilities, typical wind
conditions) different limits on the distance upwind from a BG are applicable to
assess a risk of exposure from the facilities. Look at the percentile distribution of
distances to come up with suitable distance limits. Suggested limits for distance
counts include 1 km, 2 km, 4 km and 5 km for short distance ranges, and 5 km, 15
km, 25 km and 40 km for large distance ranges. The smaller distance limits may be
applicable for densely populated areas, whereas larger distance limits may be
applicable in areas with less development.
135
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Calculation steps and assumptions
~
Assumptions and
Calculations
The overall approach involves the following steps:
• Overlay the site/waste facility and BG information on the model data to
define a common grid ID
• Analyze the wind data to understand the frequency of wind speeds and
wind directions by season
• Identify the quadrant (NE, SE, SI/1/, NW) a facility is in relative to the
community BG
• For each grid ID, summarize the calculated wind distribution data for each
quadrant to list predominant wind direction and the maximum wind speed in
that direction
• Calculate the time by dividing the straight-line distance from the facility to
the centroid of the BG by the wind speed
• Calculate the relevant metrics
The number of facilities for each specified distance is obtained by calculating for
each season the total count of facilities in the predominant wind direction.
~
Import Model
Data into
Common Grid
System and
Extract Model
Data
Inputs:
• NAM model data file
• Shapefile with facilities and BG
Calculation:
• Superimpose the NAM model grid on facilities and BG using ArcGIS.
Develop a mapping linking each facility and centroid of BG and the NAM
model grid and create a common mapping grid with a unique grid ID.
• Calculate the straight-line distance between each combination of facility
and the centroid of the census BG.
Outputs:
• Text file (csv format) with the mapping of facility, BG, unique grid ID and
distance between facility and BG
• Text file (csv format) of extracted UGRD and VGRD values with the
matched unique grid ID
136
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Read Model Data
and Calculate
Wind Speed and
Wind Direction
Input: Text file with extracted UGRD and VGRD values
Calculations:
• Read "u" and "v" component values and calculate scalar wind speed and
wind direction.
• Plot windrose (USDA, 2022) by season. Choose wind speed cutoffs of
2 m/s, 4 m/s, 6 m/s, 10 m/s and >10 m/s
• Calculate the cumulative percent of time the wind is blowing from each wind
direction sector (e.g., NE, SE) at wind speeds within each cutoff (< 2m/s,
< 4m/s, etc.).
• Calculate percent of time in each speed bin (e.g., 0-2 m/s, 2-4 m/s), and
normalize it to calculate relative fractions in each speed bin (e.g., % time in
2-4 m/s bin as a fraction of total cumulative time in that wind direction). This
estimate is used to calculate a weighted average wind speed in each wind
direction sector for each season.
Output:
• Text file (csv format) summarizing the calculated cumulative percent time
and the relative fraction within each speed bin for each season.
~
Identify Quadrant
that the Facility Is
Located in
Relative to the
Block Group
Input: Text file (csv format) with the mapping of facility, BG, unique grid ID and
distance between facility and BG
Calculation: Determine where the facility coordinates (latitude and longitude) fall in
relation to the BG centroid coordinates. Accordingly categorize the quadrant that
the facility is in relative to the BG as NE, SE, SI/I/, and NW.
Output: Quadrant information for each facility and BG combination.
~
Calculate Metric
Inputs:
• Text file (csv format) summarizing the calculated cumulative percent time,
and the relative fraction within each speed bin for each season.
• Quadrant information for each facility and BG combination
Calculation
• Link the two datasets using the common grid ID.
• In the wind data file, assign quadrant based on the wind direction.
• For each quadrant (e.g., NE, SE) relative to the BG under consideration,
assign the cumulative percent time that wind blows from that quadrant by
season.
• Count the number of facilities that fall within specified distance limits for
each season.
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have count values. Zero is a valid value.
137
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Choosing a
Symbology
The recommended symbology for this indicator is a single unique symbology that
spans seasons for any given distance. This will result in the same color
representing the same value across the maps, making direct comparisons much
easier. To build this, find the minimum and maximum values across seasons for
each distance, and then use the full range of values to create a single unique
symbology to apply to all maps.
~
Binning in the
Data by Block
Group
For this indicator using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the count values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Adaptation
Planning/
Emergency
Response
This indicator provides an estimate of how many facilities have the potential to
impact the community during extreme events. The indicator provides valuable
information on which communities are at risk in the event of an emergency and how
to prioritize response. By understanding the potential number of facilities whose
emissions could impact the BG during a release event, a community can plan for
the types and levels of action needed to mitigate the impacts.
Key caveats/limitations
~
Representative-
ness
• Since the indicators are developed for adaptation planning and
preparedness for extreme events, the indicators represent a worst-case
estimate based on nearest facilities, wind speed and wind direction, without
air dispersion modeling. This approach does not account for geographical
features (e.g., mountains) that may impact the movement of wind.
• The values are based on analysis of wind patterns for one year. This
analysis will need to be performed over a longer period (e.g., 10 years) to
capture long-term climatological patterns.
• The underlying data are based on weather forecasts and not actual point
measurements or modeled retrospective reanalysis meteorological fields.
While the forecasts are typically reliable and representative, there may be
minor differences between actual meteorology and weather forecasts. The
indicator does not categorize by the nature of the facility or by the potential
risk of air emissions.
• For certain locations such as coastal communities, the wind direction may
change frequently, and the predominant wind direction may not necessarily
be representative every year. This could be mitigated by using multiple
years to get a true climatological profile as noted above.
138
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Citations
Dataset/T ool
NOAA NCEI. (n.d.) North American Mesoscale Forecast System.
https://www.ncei.noaa.aov/products/weather-climate-models/north-american-
mesoscale. Recent model data available at https://www.ncei.noaa.aov/data/north-
american-mesoscale-model/access/. Historical data are available at
https://www.ncei.noaa.aov/data/north-american-mesoscale-
model/access/historical/analvsis/
Additional
Resources
Grange, S. K. (2014). Technical note: Averaging wind speeds and directions. DOI:
10.13140/RG.2.1.3349.2006.
USDA. (2022). Wind rose resources.
https://www.nrcs.usda.aov/wps/portal/wcc/home/climateSupport/windRoseResourc
es/
Reference
Technical note: Averaging wind speeds and directions, June 2014. DOI: 10.13140/
RG.2.1.3349.2006.
BG: Block Group, CONUS: Continental United States, NAM-ANL: North American Mesoscale Forecast System Analyses, NCEP: National
Centers for Environmental Prediction, NOAA: National Oceanic and Atmospheric Administration
139
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.3.7. Checklist for Minimum Response Time, [by Season] Indicator
Minimum Response Time, [by Season] Indicator
Definition of the indicator
~
Definition
The minimum time that a community has to respond before being impacted by
emissions from nearby faciiity(ies) during extreme events.
~
Interpretation
The lower the response time, the greater the risk to the community. Communities
with very low response time are at higher risk compared to communities that have
large response times.
Since the predominant wind direction may change by season, this indicator is
developed for each season. This implies that the list of facilities that pose a risk to
the community may be different in each season, l/l/e define seasons as follows:
winter (December-February), spring (March-May), summer (June-August), and
fall (September-November).
Data source"
~
Source
The National Oceanic and Atmospheric Administration (NOAA)'s North American
Mesoscale Forecast System (NAM) Analyses (NAM-ANL) model data. The NAM
model is run by the National Centers for Environmental Prediction (NCEP) for
forecasting weather on daily basis.
~
Temporal
resolution
Model simulations were available at four time periods per day corresponding to the
start of each forecast simulation: 0000 hours UTC time, 0600 hours UTC, 1200
hours UTC, and 1800 hours UTC. These are normally referred to as OOz, 06z, 12z,
and 18z model cycles, respectively.
Data are available from 2006 to present.
~
Spatial resolution
12-km grid resolution, continental United States (CONUS)
~
Data format
GRIB2
19 For further details on site/waste facility data (data source, decisions for calculations and vetting), see Indicator 1.2.1.
140
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Decisions needed for calculation
~
Model Cycles and
Variables
Incorporating higher time resolution data would enable capturing diurnal variability.
However, the choice is also dependent on the computational resources available,
and the study timeframe and objectives. At least two model cycles are
recommended. Model cycles OOz and 12z are generally considered more reliable
due to the extent of assimilation of observational data used for forecast simulations
and are recommended for the development of the indicators.
The wind vector components (UGRD, u wind [m/s] and VGRD, v wind [m/s]) are
available at multiple heights (or atmospheric pressure equivalents), l/l/e recommend
using 10 m height to be consistent with the height at which anemometer
measurements are made and to represent wind patterns closer to the surface.
The "UGRD: 10 m above gnd" and "VGRD: 10 m above gnd" variables
representing, respectively, the U and V vector components of wind at 10 m above
ground were extracted.
Calculation steps and assumptions
~
Assumptions and
Calculations
The overall approach involves the following steps:
• Overlay the site/waste facility and Block Group (BG) information on the
model data to define a common grid ID
• Analyze the wind data to understand the frequency of wind speeds and
wind directions by season
• Identify the quadrant (NE, SE, SI/I/, NW) a facility is in relative to the
community BG
• For each grid ID, summarize the calculated wind distribution data for each
quadrant to list predominant wind direction and the maximum wind speed in
that direction
• Calculate the time by dividing the straight-line distance from the facility to
the centroid of the BG by the wind speed
• Calculate the relevant metrics
The minimum response time is obtained by calculating for each season the
minimum value of the travel time for the air based on distance and wind speed.
~
Import Model
Data into
Common Grid
System and
Extract Model
Data
Inputs:
• NAM model data file
• Shapefile with facilities and BG
Calculation:
• Superimpose the NAM model grid on facilities and BG using ArcGIS.
Develop mapping linking each facility and centroid of the BG and the NAM
model grid and create a common mapping grid with a unique grid ID.
• Calculate the straight-line distance between each combination of facility
and census BG.
Outputs:
• Text file (csv format) with the mapping of facility, BG, unique grid ID and
distance between facility and BG
• Text file (csv format) of extracted UGRD and VGRD values with the
matched unique grid ID
141
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Read Model Data
and Calculate
Wind Speed and
Wind Direction
Input: Text file with extracted UGRD and VGRD values
Calculations:
• Read "u" and "v" component values and calculate scalar wind speed and
wind direction.
• Plot windrose (USDA, 2022) by season. Choose wind speed cutoffs of
2 m/s, 4 m/s, 6 m/s, 10 m/s, and >10 m/s
• Calculate the cumulative percent of time the wind is blowing from each wind
direction sector (e.g., NE, SE) at wind speeds within each cutoff (< 2m/s,
< 4m/s, etc.).
• Calculate percent of time in each speed bin (e.g., 0-2 m/s, 2-4 m/s), and
normalize it to calculate relative fractions in each speed bin (e.g., % time in
2-4 m/s bin as a fraction of total cumulative time in that wind direction). This
estimate is used to calculate a weighted average wind speed in each wind
direction sector for each season.
• Calculate a weighted wind speed by adding the product of the relative
fraction in each speed bin by the top end of the speed bin. For the >10 m/s
wind speed bin, use the maximum wind speed. Using the maximum speed
in each bin results in a conservative estimate.
Output:
• Text file (csv format) summarizing the calculated cumulative percent time,
the relative fraction within each speed bin, and the weighted wind speed for
each wind direction sector.
~
Identify Quadrant
that the Facility Is
Located in
Relative to the
Block Group
Input: Text file (csv format) with the mapping of facility, BG, unique grid ID, and
distance between facility and BG
Calculation: Determine where the facility coordinates (latitude and longitude) fall in
relation to the BG centroid coordinates. Accordingly categorize the quadrant that
the facility is in relative to the BG as NE, SE, SI/I/, and NW.
Output: Quadrant information for each facility and BG combination.
~
Calculate Metric
Inputs:
• Text file (csv format) summarizing the calculated cumulative percent time,
the relative fraction within each speed bin, and the weighted wind speed for
each wind direction sector
• Quadrant information for each facility and BG combination
Calculation:
• Link the two datasets using the common grid ID
• In the wind data file, assign quadrant based on the wind direction
• For each quadrant (e.g., NE, SE) relative to the BG under consideration,
assign the weighted average wind speed and the cumulative percent time
that wind blows from that quadrant by season
• For each BG, calculate the response time for impact from each facility by
dividing its distance from the BG by the weighted average wind speed for
each season. Take the minimum of all the response times across all
facilities for each BG by season.
142
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have response time values.
~
Choosing a
Symbology
The recommended symbology for this indicator is a single unique symbology that
spans seasons. This will result in the same color representing the same value
across the maps, making direct comparisons much easier. To build this, find the
minimum and maximum values across seasons, and then use the full range of
values to create a single unique symbology to apply to all maps.
~
Binning in the
Data by Block
Group
For this indicator using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs. An alternative would be to use equal intervals.
~
Choosing Colors
Use a color gradation that becomes lighter as the minimum response time values
increase. Avoid using a divergent color scheme (darker at both extremes and lighter
in the middle), which implies an inflection point such as 0 in a dataset containing
positive and negative values.
Examples of how the indicator can be useful
~
Adaptation
Planning/
Emergency
Response
This indicator provides an estimate of how much time a community has to respond
before being impacted by potentially hazardous emissions from nearby site/waste
facilities during extreme events. The indicator provides valuable information on
which communities are at risk during an extreme event and how to prioritize
response. For example, BGs with shorter response times (darkest color) may need
to be evacuated or addressed first in the event of an emergency. If the response
time is on the order of a few seconds, it also implies that those communities will
need to develop and maintain as part of its disaster response plans, clearly defined
actions that can be implemented swiftly during an emergency. In other words, the
community will not have time to develop response strategies during an extreme
event emergency.
The indicator can be used to identify priorities for preparedness activities such as
cleanup and maintenance of sites/waste facilities that are within short response
times. It can also inform longer term adaptation planning. For example, new
assisted care facilities should not be built in BGs with shorter response times.
Depending on how different the response times are across seasons, the strategy
may also need to differ by season.
143
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Key caveats/limitations
~
Representative-
ness
• Since the indicators are developed for adaptation planning and
preparedness for extreme events, the indicators represent a worst-case
estimate based on nearest facilities, wind speed and wind direction, without
air dispersion modeling. This approach does not account for geographical
features (e.g., mountains) that may impact the movement of wind.
• The values are based on analysis of wind patterns for one year. This
analysis will need to be performed over a longer period (e.g., 10 years) to
capture long-term climatological patterns.
• The underlying data are based on weather forecasts and not actual point
measurements or modeled retrospective reanalysis meteorological fields.
While the forecasts are typically reliable and representative, there may be
minor differences between actual meteorology and weather forecasts.
• For certain locations such as coastal communities, the wind direction may
change frequently, and the predominant wind direction may not necessarily
be representative every year. This could be mitigated by using multiple
years to get a true climatological profile as noted above.
Citations
Dataset/T ool
NOAA NCEI. (n.d.) North American Mesoscale Forecast System.
httos://www.ncei.noaa.aov/Droducts/weather-climate-models/north-american-
mesoscale. Recent model data available at https://www.ncei.noaa.aov/data/north-
american-mesoscale-model/access/. Historical data are available at
https://www.ncei.noaa.aov/data/north-american-mesoscale-
model/access/historical/analvsis/
Additional
Resources
Grange, S. K. (2014). Technical note: Averaging wind speeds and directions. DOI:
10.13140/RG.2.1.3349.2006.
USDA. (2022). Wind rose resources.
https://www.nrcs.usda.aov/wps/portal/wcc/home/climateSupport/windRoseResourc
es/
BG: Block Group, CONUS: Continental United States, NAM-ANL: North American Mesoscale Forecast System Analyses, NCEP: National
Centers for Environmental Prediction, NOAA: National Oceanic and Atmospheric Administration
144
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.3.8. Checklist for Count of Sites/Waste Facilities That Are within Specific Response
Time Ranges, [by Season] Indicator
Count of Sites/Waste Sites/Waste Facilities That Are within Specific
Response Time Ranges, [by Season] Indicator
Definition of the indicator
~
Definition
The number of facilities that have the potential to impact a community within a
specified response time.
~
Interpretation
The larger the number of facilities within a short response time window from the BG,
the greater the risk for a potential aggregated impact if all facilities were to fail at the
same time during an extreme event.
Data source20
~
Data Source
The National Oceanic and Atmospheric Administration (NOAA)'s North American
Mesoscale Forecast System (NAM) Analyses (NAM-ANL) model data. The NAM
model is run by the National Centers for Environmental Prediction (NCEP) for
forecasting weather on daily basis.
~
Temporal
Resolution
Model simulations were available at four time periods per day corresponding to the
start of each forecast simulation: 0000 hours UTC time, 0600 hours UTC, 1200
hours UTC, and 1800 hours UTC. These are normally referred to as OOz, 06z, 12z,
and 18z model cycles, respectively.
Data are available from 2006 to present.
~
Spatial
Resolution
12-km grid resolution, continental United States (CONUS)
Data Format
GRIB2
Decisions needed for calculation
~
Model Cycles and
Variables
Incorporating higher time resolution data would enable capturing diurnal variability.
However, the choice is also dependent on the computational resources available,
and the study timeframe and objectives. At least two model cycles are
recommended. Model cycles OOz and 12z are generally considered more reliable
due to the extent of assimilation of observational data used for forecast simulations
and are recommended for the development of the indicators.
The wind vector components (UGRD, u wind [m/s] and VGRD, v wind [m/s]) are
available at multiple heights (or atmospheric pressure equivalents), l/l/e recommend
using 10 m height to be consistent with the height at which anemometer
measurements are made and to represent wind patterns closer to the surface.
The "UGRD: 10 m above gnd" and "VGRD: 10 m above gnd" variables
representing, respectively, the U and V vector components of wind at 10 m above
ground were extracted.
20 For further details on site/waste facility data (data source, decisions for calculations and vetting), see Indicator 1.2.1.
145
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Time Limits for
Counting
There is a continuous range of values among the calculated response times for
each BG. Therefore, depending on the local study area conditions (e.g., size of
counties /BG, population distribution, density of facilities, typical wind conditions)
different limits on the response time for a BG are applicable to assess a risk of
exposure from the facilities. Look at the percentile distribution of response times to
come up with suitable response time limits, including some lower end time limits on
the order of seconds or a few minutes to capture facilities that pose the greatest
risk. Suggested limits for response times include 2 min, 5 min, 10 min, 15 min and
20 min.
Calculation steps and assumptions
~
Assumptions and
Calculations
The overall approach involves the following steps:
• Overlay the site/waste facility and BG information on the model data to
define a common grid ID
• Analyze the wind data to understand the frequency of wind speeds and
wind directions by season
• Identify the quadrant (NE, SE, SI/I/, NW) a facility is in relative to the
community BG
• For each grid ID, summarize the calculated wind distribution data for each
quadrant to list predominant wind direction and the maximum wind speed in
that direction
• Calculate the time by straight-line distance from the facility by the wind
speed
• Calculate the relevant metrics
The number of facilities is obtained by calculating for each season the total count of
facilities within a specified travel time from the community. The travel time for the air
from the facility to the community is the response time that the community has to
take action.
~
Import Model
Data into
Common Grid
System and
Extract Model
Data
Inputs:
• NAM model data file
• Shapefile with facilities and BG
Calculation:
• Superimpose the NAM model grid on facilities and BG using ArcGIS.
Develop mapping linking each facility and centroid of the BG and the NAM
model grid and create a common mapping grid with a unique grid ID.
• Calculate the straight-line distance between each combination of facility
and census BG.
Outputs:
• Text file (csv format) with the mapping of facility, BG, unique grid ID and
distance between facility and BG
• Text file (csv format) of extracted UGRD and VGRD values with the
matched unique grid ID
146
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Read Model Data
and Calculate
Wind Speed and
Wind Direction
Input: Text file with extracted UGRD and VGRD values
Calculations:
• Read "u" and "v" component values and calculate scalar wind speed and
wind direction.
• Plot windrose (USDA, 2022) by season. Choose wind speed cutoffs of
2 m/s, 4 m/s, 6 m/s, 10 m/s, and >10 m/s
• Calculate the cumulative percent of time the wind is blowing from each wind
direction sector (e.g., NE, SE) at wind speeds within each cutoff (< 2m/s,
< 4m/s, etc.).
• Calculate percent of time in each speed bin (e.g., 0-2 m/s, 2-4 m/s), and
normalize it to calculate relative fractions in each speed bin (e.g., % time in
2-4 m/s bin as a fraction of total cumulative time in that wind direction). This
estimate is used to calculate a weighted average wind speed in each wind
direction sector for each season.
• Calculate a weighted wind speed by adding the product of the relative
fraction in each speed bin by the top end of the speed bin. For the >10 m/s
wind speed bin, use the maximum wind speed. Using the maximum speed
in each bin results in a conservative estimate.
Output:
• Text file (csv format) summarizing the calculated cumulative percent time,
the relative fraction within each speed bin, and the weighted wind speed for
each wind direction sector.
~
Identify Quadrant
that the Facility Is
Located in
Relative to the
Block Group
Input: Text file (csv format) with the mapping of facility, BG, unique grid ID, and
distance between facility and BG
Calculation: Determine where the facility coordinates (latitude and longitude) fall in
relation to the BG centroid coordinates. Accordingly categorize the quadrant that
the facility is in relative to the BG as NE, SE, SI/I/, and NW.
Output: Quadrant information for each facility and BG combination.
~
Calculate Metric
Inputs:
• Text file (csv format) summarizing the calculated cumulative percent time,
the relative fraction within each speed bin, and the weighted wind speed for
each wind direction sector
• Quadrant information for each facility and BG combination
Calculation:
• Link the two datasets using the common grid ID.
• In the wind data file, assign quadrant based on the wind direction.
• For each quadrant (e.g., NE, SE) relative to the BG under consideration,
assign the cumulative percent time that wind blows from that quadrant by
season.
• For each BG, calculate the response time for impact from each facility by
dividing its distance from BG by the weighted average wind speed for each
season.
• Count the number of facilities that fall within specified response time limits
for each season.
147
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - BGs will all have count values. Zero is a valid value.
~
Choosing a
Symbology
The recommended symbology for this indicator is a single unique symbology that
spans seasons for any given response time. This will result in the same color
representing the same value across the maps, making direct comparisons much
easier. To build this, find the minimum and maximum values across seasons for
each response time, and then use the full range of values to create a single unique
symbology to apply to all maps.
~
Binning in the
Data by Block
Group
For this indicator using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is also recommended so that
the map reader can readily distinguish between color categories and visually match
them to the legend. However, deciles or other percentiles can also be used
depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the count values increase. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Adaptation
Planning /
Emergency
Response
This indicator provides an estimate of how many site/waste facilities have the
potential to impact the community within a specified response time. The indicator
provides valuable information on which communities are at risk in the event of an
emergency and how to prioritize response. The larger the number of facilities, the
greater the risk for a potential aggregated impact if all facilities were to fail at the
same time during an extreme event. For example, BGs with largest number of
facilities (darkest color) may need to be addressed first in the event of an
emergency. This is highly critical for situations with shorter response times. By
combining response time and number of facilities, this indicator helps with advance
planning on clearly defined actions for each community based on the risk.
Depending on how different the response times and facility counts are across
seasons, the strategy may also need to differ by season.
The indicator can be used to identify priorities for preparedness activities such as
cleanup and maintenance of sites/waste facilities that are within small response
times. It can also inform longer term planning. For example, new assisted care
facilities should not be built in BGs with shorter response times.
148
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Key caveats/limitations
~
Representative-
ness
• Since the indicators are developed for preparedness for extreme events,
the indicators represent a worst-case estimate based on nearest facilities,
wind speed, and wind direction, without air dispersion modeling. This
approach does not account for geographical features (e.g., mountains) that
may impact the movement of wind.
• The values are based on analysis of wind patterns for one year. This
analysis will need to be performed over a longer period (e.g., 10 years) to
capture long-term climatological patterns.
• The underlying data are based on weather forecasts and not actual point
measurements or modeled retrospective reanalysis meteorological fields.
While the forecasts are typically reliable and representative, there may be
minor differences between actual meteorology and weather forecasts. The
indicator does not categorize by the nature of the facility or by the potential
risk of air emissions.
• For certain locations such as coastal communities, the wind direction may
change frequently, and the predominant wind direction may not necessarily
be representative every year. This could be mitigated by using multiple
years to get a true climatological profile as noted above.
Citations
Dataset/T ool
NOAA NCEI. (n.d.) North American Mesoscale Forecast System.
https://www.ncei.noaa.aov/products/weather-climate-models/north-american-
mesoscale. Recent model data available at https://www.ncei.noaa.aov/data/north-
american-mesoscale-model/access/. Historical data are available at
https://www.ncei.noaa.aov/data/north-american-mesoscale-
model/access/historical/analvsis/
Additional
Resources
Grange, S. K. (2014). Technical note: Averaging wind speeds and directions. DOI:
10.13140/RG.2.1.3349.2006.
USDA. (2022). Wind rose resources.
https://www.nrcs.usda.aov/wps/portal/wcc/home/climateSupport/windRoseResourc
es/
BG: Block Group, CONUS: Continental United States, NAM-ANL: North American Mesoscale Forecast System Analyses, NCEP: National
Centers for Environmental Prediction, NOAA: National Oceanic and Atmospheric Administration
149
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Vulnerability Source 2.1. Sensitivity: Household/Receptor Characteristics
Indicator 2.1.1. Checklist for Total Population Indicator
Total Population Indicator
Definition of the indicator
~
Definition
The total population in each Block Group (BG)
~
Interpretation
This indicator provides the simplest measure of how many people may be impacted
by contaminant exposures due to extreme events.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or
a more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B01003. "Total Population"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
BG
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (e.g., 2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "Total population"
under the population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B01003.
"Total Population").
150
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Selection
Once the table of interest (Table Number B01003) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculations
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data needs a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use as is.
Calculation steps and assumptions
~
Calculation on
Data
No calculations are required. Data for Variable AJWME001- "Estimates: Total
Population" need to be used "as is".
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of this indicator will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for study.
~
Binning the Data
by Block Group
The distribution of this indicator by BG will not likely be equal across the data range.
For this situation, using quantiles (equal number of observations per bin) is
recommended. Using a maximum of 5-7 categories is recommended so that the map
reader can readily distinguish between color categories and visually match them to
the legend. Deciles or other percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability or risk increases.
Avoid using a divergent color scheme (darker at both extremes and lighter in the
middle), which implies an inflection point such as 0 in a dataset containing positive
and negative values.
Examples of how the indicator can be useful
~
Emergency
Response/
This indicator can help assess the potential magnitude and distribution of impact of
contaminant releases. It could be used to anticipate the number of individuals that
151
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Adaptation
Planning
need to be evacuated from each BG in the event of an emergency or the maximum
number of people potentially exposed to a release from a site/waste facility.
Key caveats/limitations
~
Aggregate
Variable
This variable is an aggregated measure and does not provide information on
characteristics of people.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS
estimates, especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosV/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
152
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.2. Checklist for Count of Households/Occupied Housing Units Indicator
Count of Households/Occupied Housing Units Indicator
Definition of the indicator
~
Definition
The number of households within each Block Group (BG).
~
Interpretation
This indicator provides a measure of the number of occupied residences.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B25003. "TENURE"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
BG
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (e.g., 2014-2018).
~
Variable/Topic
Select the topic "OccupancyA/acancy and Use" under the Housing tab
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. The page needs to be searched for the specific table number (Table
Number B25003. "TENURE").
~
Selection
Once the table of interest (Table Number B25003) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
153
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use as is.
Calculation steps and assumptions
~
Calculation on
Data
No calculations are required. Variable AJ1UE001 - "Estimates: Total Households"
needs to be used "as is."
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values.
~
Choosing
Symbology
Maps showing the distribution of this indicator will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for the current area of interest and should
become darker as the count increases.
~
Binning the Data
by Block Group
The distribution of this indicator by BG will not likely be equal across the data range.
For this situation, using quantiles (equal number of observations per bin) is
recommended. Use a maximum of 5-7 categories so that the map reader can readily
distinguish between color categories and visually match them to the legend. Deciles
or other percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the count values increase. Avoid using
a divergent color scheme (darker at both extremes and lighter in the middle), which
implies an inflection point such as 0 in a dataset containing positive and negative
values.
Examples of how the indicator can be useful
~
Emergency
Response/Adapt
ion Planning
The count of households indicator includes only those residences that are occupied.
This indicator can be used to assess the magnitude and distribution of risk and thus
prioritize outreach, communications, and response in the event of an emergency. For
example, the indicator provides information on the number of families that need to be
sent communications materials before or during an event.
154
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Key caveats/limitations
~
Aggregate
Variable
This variable is an aggregated measure and does not provide information on
characteristics of households.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosV/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
155
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.3. Checklist for Median Household Income Indicator
Median Household Income Indicator
Definition of the indicator
~
Definition
The median household income for each Block Group (BG).
~
Interpretation
This indicator provides information on the extent and distribution of under-resourced
households in the BG who may not have resources to take preventive measures or
recover quickly (e.g., due to low home or medical insurance coverage).
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or
a more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B19013. "Median Household Income In The Past 12 Months (In 2018
Inflation-Adjusted Dollars)"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
BG
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2. nhais. ora/main
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
Spatial Scale
After logging in, select the Geographic Level of Block Group
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (e.g., 2014-2018)
Variable/Topic
Select the topic "Household and Family Income" under the Population tab
156
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B19013-
"Median Household Income In The Past 12 Months (In 2018 Inflation-Adjusted
Dollars)").
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Selection
Once the table of interest (Table Number B19013) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
~
Calculations
Use as is.
Calculation steps and assumptions
~
Calculation on
Data
No calculations required. Variable AJZAE001 - "Estimates: Median Income" needs
to be used "as is."
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values.
~
Choosing
Symbology
Maps showing the distribution of this indicator will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for study.
~
Binning the Data
by Block Group
The distribution of this indicator by BG will not likely be equal across the data range.
For this situation using quantiles (equal number of observations per bin) is
recommended. Use a maximum of 5-7 categories so that the map reader can readily
distinguish between color categories and visually match them to the legend. Deciles
or other percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability or risk increases.
Avoid using a divergent color scheme (darker at both extremes and lighter in the
middle), which implies an inflection point such as 0 in a dataset containing positive
and negative values.
157
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
Lower income residents may not have resources to take preventive measures or
respond and recover when an incident occurs, and they may live and work in higher
exposure areas. This indicator is useful for assessing the income and poverty status
of BG residents, which in turn describes their level of exposure, access to resources,
and ability to respond to an emergency. This indicator will help assess the need for
assistance.
Key caveats/limitations
~
Variable
Represents a
Central Income
Value
This variable is an overall measure of central tendency of the income distribution in
the BG and does not provide information on households at the lower end of the
distribution or the poverty status of BGs.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS
estimates, especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
158
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.4. Checklist for Percent of Population with Ratio of Income to Poverty Level Less
Than 0.5 Indicator
Percent of Population with Ratio of Income to Poverty Level
Less Than 0.5
Definition of the indicator
~
Definition
The percent of population whose ratio of household income to poverty level falls
below 0.5.
~
Interpretation
This indicator represents people with the least resources.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or
a more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number C17002. "Ratio of Income to Poverty Level in the Past 12 Months"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to
this email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (e.g., 2014-2018).
~
Variable/Topic
Select the topic "Poverty (Income Relative to Poverty Level)" under the Population
tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number C17002.
"Ratio of Income to Poverty Level in the Past 12 Months").
159
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Selection
Once the table of interest (Table Number C17002) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that
can be considered. However, caution must be used if the users want to select a
different table to make sure that the table selected captures the information needed.
Tabular data needs a field to link to the spatial data files. For example, if the data
are compiled at the BG level, there needs to be a field indicating the BG value that
can thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate percent of population whose ratio is less than 0.5
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-4. Divide the estimated population whose ratio is under
0.5 (AJY4E002 "Estimates: Under .50") by the total population (AJY4E001
"Estimates: Total").
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values.
~
Choosing
Symbology
Maps showing the distribution of this indicator will generally not be compared
across scenarios, time periods, or locations so a single symbology will not be
necessary. The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
160
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator provides a measure of extreme poverty status, which can help identify
the lowest-income population with the fewest access to resources. This indicator
helps assess the need for assistance and can inform prioritization during an
emergency response.
Key caveats/limitations
~
Poverty
Definition
The poverty level is determined by the Census definitions. The Census Bureau
uses the poverty line to measure economic well-being and to assess the need for
assistance. These data are included in federal allocation formulas for many
government programs (U.S. Census Bureau, 2019). Other cutoffs for identifying
those in need of assistance (e.g., those in the bottom decile of income levels) may
be considered depending on community needs.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS
estimates, especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year
Public Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
U.S. Census Bureau. (2018). American Community Survey General Handbook
2018, Chapter 3: Understanding and Using ACS Single-Year and Multiyear
Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aen
Resources
eral handbook 2018 ch03.pdf
U.S. Census Bureau. (2022). Poverty Thresholds.
https://www.census.aov/data/tables/time-series/demo/income-povertv/historical-
povertv-thresholds. html
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
161
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.5. Checklist for Percent of Population with Ratio of Income to Poverty Level
Between 0.5 and 1 Indicator
Percent of Population with Ratio of Income to Poverty Level between 0.5
and 1 Indicator
Definition of the indicator
~
Definition
The percent of population whose ratio of household income to poverty level falls
between 0.5 and 1.
~
Interpretation
This indicator represents people with very low resources, second only those whose
ratio of household income to poverty level is below 0.5.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number C17002. "Ratio of Income to Poverty Level in the Past 12 Months"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (e.g., 2014-2018).
~
Variable/Topic
Then select the topic "Poverty (Income Relative to Poverty Level) topic under the
Population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. You can either look through the tables to find the one of interest or you
can search the page for the specific table number.
162
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Selection
Once the table of interest (Table Number C17002) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
the users may find alternative tables (in addition to the table number listed here) that
can be considered. However, caution must be used if the users want to select a
different table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate percent between 0.5 and 1.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix S-5. Divide the population between 0.5 and 1 (AJY4E003 "Estimates:
.50 to .99") by the total population (AJY4E001 "Estimates: Total").
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
163
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator provides a measure of populations that are below the poverty line but
are not among the lowest income in the BG. This can help identify the second
lowest-income population with limited access to resources. This indicator helps
assess the need for assistance and can inform prioritization during an emergency
response.
Key caveats/limitations
~
Poverty
Definition
The poverty level is determined by the Census definitions. The Census Bureau uses
the poverty line measure to measure economic well-being and to assess the need
for assistance. These data are included in federal allocation formulas for many
government programs (U.S. Census Bureau, 2019). Other cutoffs for identifying
those in need of assistance (e.g., those in the second to lowest decile of income
levels) may be considered depending on community needs.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
U.S. Census Bureau. (2022). Poverty Thresholds.
https://www.census.aov/data/tables/time-series/demo/income-povertv/historical-
povertv-thresholds. html
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
164
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.6. Checklist for Percent of Households with Self-Employment Income Indicator
Percent of Households with Self-Employment Income Indicator
Definition of the indicator
~
Definition
The percentage of households whose income (in part or as a whole) is categorized
as "self-employment income."
~
Interpretation
This indicator represents households who may have less stable income sources and
less resources for recovery.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B19053. "Self-Employment Income in the Past 12 Months for
Households"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
From there select the Geographic Level of Block Group.
~
Temporal
Coverage
Next select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Then select the topic "Household and Family Income" in the Population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B19053.
"Self-Employment Income in the Past 12 Months for Households").
~
Selection
Once the table of interest (Table Number B19053) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
165
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
the users may find alternative tables (in addition to the table number listed here) that
can be considered. However, caution must be used if the users want to select a
different table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate households with self-employed income divided by total households.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-6. The total number of households with self-employment
income (AJZQE002 "Estimates: With self-employment income") was divided by the
total number of households (AJZQE001 "Estimates: Total").
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
166
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
Self-employed households may face more disruptions and may not have resources
to take preventive measures or recover quickly. They may also have low insurance
coverage and thus may face health issues. They may also have lower ability to
respond and recover from shocks, as they are not incorporated into the main
workforce and are thus less socially connected than other workers. This indicator will
help assess the need for assistance.
Key caveats/limitations
~
Size of Income
This indicator does not provide the magnitude of self-employment. Self-employed
individuals may earn large incomes and may not always be in need of assistance.
Depending on community needs, further screening is recommended to determine
priorities for response and planning.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
167
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.7. Checklist for Percent of Civilian Employed Population 16 Years and over Who
Work Outdoors Indicator
Percent of Civilian Employed Population 16 Years and over Who Work
Outdoors Indicator
Definition of the indicator
~
Definition
The total percentage of population of each Block Group (BG) that is over the age of
16 and who work outdoors
~
Interpretation
This indicator provides information on people who work in occupations that may
require them to work outdoors and may face high exposure risks if contaminant
releases occur during an extreme event.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number C24010. "Sex by Occupation for the Civilian Employed Population 16
Years and Over"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
BG
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~ET
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (e.g., 2014-2018).
~
Variable/Topic
Then select the topic "Sex" under the population tab.
168
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number C24010.
"Sex by Occupation for the Civilian Employed Population 16 Years and Over").
~
Selection
Once the table of interest (Table Number C24010) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear)
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
the users may find alternative tables (in addition to the table number listed here) that
can be considered. However, caution must be used if the users want to select a
different table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate sum of male and female in outdoor occupations.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-7. The sum of the following four variables were used as
the total outdoor occupations: 1. AJ1FE030 "Estimates: Male: Natural resources,
construction, and maintenance occupations"; 2. AJ1FE066 "Estimates: Female:
Natural resources, construction, and maintenance occupations"; 3. AJ1FE070
"Estimates: Female: Production, transportation, and material moving occupations";
and 4. AJ1FE034 "Estimates: Male: Production, transportation, and material moving
occupations". This sum was then divided by the total population above 16
(AJ1FE001 "Estimates: Total").
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
169
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator shows the number of workers that are most likely to be exposed to
hazardous waste and contaminants during a release event, which helps inform
response planning by providing information on where workers who may face high
risks of exposure may live.
Key caveats/limitations
~
Location of
Outdoor
Workers
Residence
This indicator provides information on where people who work in occupations that
may require them to work outdoors live. This does not always mean that all workers
in this category will always be outdoors. Further, it does not provide information on
the work location. Depending on community needs, further assessments may be
needed to determine outdoor exposures.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
170
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.8. Checklist for Percent of Households That Are Renters Indicator
Percent of Households That Are Renters Indicator
Definition of the indicator
~
Definition
The total percentage of households who are "renters".
~
Interpretation
This indicator provides information on the percentage of housing units that are
occupied by renters rather than owners.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B25003. "TENURE"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (e.g., 2014-2018).
~
Variable/Topic
Then select the topic "OccupancyA/acancy and Use" under the Housing tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B25003.
"TENURE").
~
Selection
Once the table of interest (Table Number B25003) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
171
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data needs a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate percent of renters of total households.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-8. The total number of renter occupied housing units
(AJ17E003 "Estimates: Renter occupied") was divided by the total number of
housing units (AJ17E001 "Estimates: Total").
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Emergency
Response/
Renters have fewer assets, less housing security, and less insurance than
homeowners. Rental housing units also often less maintained and protected.
172
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Adaptation
Planning
Renters may have less resources overall to take preventive measures, respond and
recover when an incident occurs. This indicator will help assess the need for
assistance.
Key caveats/limitations
~
Universe of
Renters
Not all renters are necessarily under-resourced.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
173
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.9. Checklist for Percent of Households Living in a Mobile Home/Boat/RVA/an
Indicator
Percent of Households Living in a Mobile Home/Boat/RVA/an Indicator
Definition of the indicator
~
Definition
Percent of households living in a mobile home/boat/RV/van
~
Interpretation
This indicator provides information on the percentage of housing units that are
mobile homes, boats, RVs, or vans.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B25024. "UNITS IN STRUCTURE"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Next select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Select the topic "OccupancyA/acancy and Use" under the Housing tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. You can either look through the tables to find the one of interest or you
can search the page for the specific table number.
~
Selection
Once the table of interest (Table Number B25024) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
174
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to add Mobile + Boat/RVA/an and then divide by housing units.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-9. Take the sum ofAJ2JE010 "Estimates: Mobile home"
and AJ2JE011 "Estimates: Boat, RV, van, etc." divided by the total AJ2JE001
"Estimates: Total".
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
175
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator provides a measure of housing security and risk of exposure by
showing the number of households living in temporary, outdoor, or mobile structures.
Households with mobile homes serving as their place of residence may have fewer
assets and resources, at higher risk of exposure to a release event, and less able to
respond and recover post-incident.
Key caveats/limitations
~
Include owned
and rented units
Mobile homes may be owned or rented. Households renting such units may have
fewer access to resources than those that own such units. Further, the condition of
the unit is not reflected in this indicator. Further information, such as age of the
structure and ownership status, would provide additional information.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httos://www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
176
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.10. Checklist for Percent of Households without Telephone Service Indicator
Percent of Households without Telephone Service Indicator
Definition of the indicator
~
Definition
Percent of households without telephone service
~
Interpretation
This indicator provides information on whether telephone service was available (and
working) in the housing unit that allows the respondent to make and receive calls.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B25043. "Tenure by Telephone Service Available by Age of
Householder"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (e.g., 2014-2018).
~
Topic
Then select the topic "OccupancyA/acancy and Use" under the Housing tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B25043.
"Tenure by Telephone Service Available by Age of Householder").
~
Selection
Once the table of interest (Table Number B25043) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear)
177
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the sum of renters and owners with no telephone service available
divided by the total number of households.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-10. Take the sum of (AJ2VE007 "Estimates: Owner
occupied: No telephone service available", and AJ2VE016 "Estimates: Renter
occupied: No telephone service available") divided by AJ2VE001 "Estimates: Total".
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
178
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator shows the percent of households that have limited communications
access, which prevents them from receiving timely warnings and ensuring efficient
evacuation. This leaves them socially isolated prior to, during, and after an incident.
Key caveats/limitations
~
Traditional
telephone
service
This indicator does not include information on the households who may have internet
access and VOIP-based communications means. However, households who rely
only on internet-based communications means are limited. Also, ACS added
instructions on including cell phones in 2008. This indicator also does not provide
information on whether multiple phone services (and therefore backups) are present.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httos://www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
179
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.11. Checklist for Percent of Households with No Internet Access Indicator
Percent of Households with No Internet Access Indicator
Definition of the indicator
~
Definition
Percent of households with no internet access
~
Interpretation
This indicator provides information on whether or not someone in the household
uses or can connect to the internet.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B28002- "PRESENCE AND TYPES OF INTERNET
SUBSCRIPTIONS IN HOUSEHOLD"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2. nhais. ora/main
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group
~
Temporal Scale
Select the Year filter for 5-Year Ranges (2014-2018)
~
Variable/Topic
Then select the topic "OccupancyA/acancy and Use" under the Housing tab
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B28002-
"PRESENCE AND TYPES OF INTERNET SUBSCRIPTIONS IN HOUSEHOLD").
~
Selection
Once the table of interest (Table Number B28002) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear)
180
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the number of households without internet access over the total
number of households.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-11. Take AJ37E013 "Estimates: No Internet access"
divided by AJ37E001 "Estimates: Total".
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
181
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator shows the percent of households that are disconnected from
information and communications lines, which prevents them from receiving timely
warnings and up-to-date information and ensuring efficient evacuation. This leaves
them socially isolated during and after an incident.
Key caveats/limitations
~
Type of Internet
Service
This indicator does not provide any information about the type of internet service or
the reliability of the service. This indicator also does not provide information on
whether multiple services (and therefore backups) are present. These questions are
not asked for the group quarters population, so do not include data about people
living in housing such as dorms, prisons, nursing homes, etc.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httos://www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
182
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.12. Checklist for Percent of Households Who Do Not Have a Vehicle Indicator
Percent of Households Who Do Not Have a Vehicle Indicator
Definition of the indicator
~
Definition
Percent of households who do not have a vehicle
~
Interpretation
This indicator provides information on vehicles available for the use of household
members. Motorcycles/other recreational vehicles, dismantled/ immobile vehicles
and vehicles used only for business purposes are excluded.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B25044. "Tenure by Vehicles Available"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2. nhais. ora/main
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (e.g., 2014-2018)
~
Variable/Topic
Then select the topic "OccupancyA/acancy and Use" under the Housing tab
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B25044.
"Tenure by Vehicles Available").
~
Selection
Once the table of interest (Table Number B25044) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear)
183
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the sum of renters and owners with no vehicle over the total number
of households.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-12. Take the sum of (AJ2WE003 "Estimates: Owner
occupied: No vehicle available", and AJ2WE010 "Estimates: Renter occupied: No
vehicle available") divided by AJ2WE001 "Estimates: Total".
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
184
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
Households without vehicles will be more isolated and less able to evacuate during
an extreme event. This indicator can help assess if household have sufficient access
to transportation in order to safely evacuate in the event of an emergency and can
inform design of evacuation and recovery plans. Households without vehicles will
also face challenges accessing medical and other basic necessities during and after
an event. They may also face isolation issues and thus experience higher impacts
than average.
Key caveats/limitations
~
Vehicle Capacity
This indicator does not provide information on the number of vehicles. If the
household size is large compared to the seating capacity, evacuation may still be
difficult.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
185
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.13. Checklist for Percent of Population with No High School Degree Indicator
Percent of Population over 25 with No High School Degree Indicator
Definition of the indicator
~
Definition
Percent of population over 25 with no high school degree
~
Interpretation
This indicator provides information on the population who do not have minimal
educational attainment levels.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B15003- "EDUCATIONAL ATTAINMENT FOR THE POPULATION 25
YEARS AND OVER"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2. nhais. ora/main
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Then select the topic "Educational Attainment" under the Population tab.
~
Tables
The tables that are available for the filters selected above will be populated below the
filters. Search the page for the specific table number (Table Number B15003.
"EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER").
~
Selection
Once the table of interest (Table Number B15003) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear)
186
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to select
the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate sum of (no schooling completed Nursery school Kindergarten 1st
grade to 12th grade, no diploma) and divide by total; Convert to percent.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-13. Take the sum of all education groups divided by the
total:
((AJYPE002, "Estimates: No Schooling Completed" +AJYPE003, "Estimates: Nursery
School," +AJYPE004, "Estimates: Kindergarten," +AJYPE005, "Estimates: 1st Grade,"
+AJYPE006, "Estimates: 2nd Grade," +AJYPE007, "Estimates: 3rd Grade,"
+AJYPE008, "Estimates: 4th Grade,"
+AJYPE009, "Estimates: 5th Grade," +AJYPE010, "Estimates: 6th Grade,"
+AJYPE011, "Estimates: 7th Grade," +AJYPE012, "Estimates: 8th Grade,"
+AJYPE013, "Estimates: 9th Grade," +AJYPE014, "Estimates: 10th Grade,"
+AJYPE015, "Estimates: 11th Grade,"
+AJYPE016, "Estimates: 12th Grade, No Diploma,")/,AJYPE001, "Estimates: Total)
*100
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary. The
symbology will be specific to the data for current area of interest.
187
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can either
be divided into equal intervals (recommended for percent values ranging from 0-100)
or quantiles (recommended for percent values not ranging from 0-100 or raw count
values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid using
a divergent color scheme (darker at both extremes and lighter in the middle), which
implies an inflection point such as 0 in a dataset containing positive and negative
values.
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator provides a measure of educational attainment within the community,
which in turn describes potential earnings opportunities and access to information.
Less educated residents may have difficulties understanding communication and
event preparedness materials, leaving them more isolated and vulnerable during and
after an event.
Key caveats/limitations
~
Limited
Information
Overall
Education
Levels
Populations who do have a high school degree may not necessarily have higher
educational qualifications and may still have limited earnings potential and
awareness.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aener
al handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
188
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.14. Checklist for Percent of Population with No Health Insurance Indicator
Percent of Population with No Health Insurance Indicator
Definition of the indicator
~
Definition
Percent of population with no health insurance
~
Interpretation
This indicator uses the Census definition of coverage which include plans and
programs that provide comprehensive health coverage.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B27010. "Types of Health Insurance Coverage by Age"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
From there select the Geographic Level of Block Group
~
Temporal
Coverage
Next select the Year filter for 5-Year Ranges (2014-2018)
~
Variable/Topic
Then select the topic Health Insurance under the population tab.
~
Tables
The tables that are available for the filters selected above will be populated below the
filters. Search the page for the specific table number (Table Number B27010. "Types
of Health Insurance Coverage by Age").
~
Selection
Once the table of interest (Table Number B27010) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear)
189
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to select
the option to filter your data your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the sum of "No health insurance coverage" across all age groups and
divide by Total.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-14. Take the sum of (AJ35E017 "Estimates: Under 19
years: No health insurance coverage", AJ35E033 "Estimates: 19 to 34 years: No
health insurance coverage", AJ35E050 "Estimates: 35 to 64 years: No health
insurance coverage", and AJ35E066 "Estimates: 65 years and over: No health
insurance coverage") divided by AJ35E001 "Estimates: Total".
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary. The
symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can either
be divided into equal intervals (recommended for percent values ranging from 0-100)
or quantiles (recommended for percent values not ranging from 0-100 or raw count
values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid using
a divergent color scheme (darker at both extremes and lighter in the middle), which
implies an inflection point such as 0 in a dataset containing positive and negative
values.
190
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
Households that lack health insurance will have difficulties accessing healthcare in
the event of an emergency. This indicator is useful for assessing the health
vulnerability of populations and their ability to recover after an incident. This can help
inform public health planning.
Key caveats/limitations
~
Census
Definition of
Health Insurance
Coverage
The definition is specific to the one used by the Census and does not include
insurance for specific conditions, dental, vision, life and disability insurance. This
indicator also does not capture those who have limited insurance, less access to
medical facilities and good quality of medical care.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aener
al handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
191
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.15. Checklist for Percent of Households with at Least 1 Person That Has a
Disability Indicator
Percent of Households with at Least 1 Person That Has a Disability
Indicator
Definition of the indicator
~
Definition
Percent of households with at least 1 person that has a disability
~
Interpretation
This indicator provides information on people who experience any of the six
difficulties (hearing, vision, cognitive, ambulatory, self-care, independent living).
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B22010. "Receipt of Food Stamps/SNAP in the Past 12 Months by
Disability Status for Households"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (e.g., 2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "disability" under the
population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B22010.
"Receipt of Food Stamps/SNAP in the Past 12 Months by Disability Status for
Households")
192
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Selection
Once the table of interest (Table Number B22010) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the sum of "Households with 1 or more persons with a disability"
across the 2 groups {received or did not receive food stamps} and divide by total.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-15. Take the sum of (AJ05E003 "Estimates: Household
received Food Stamps/SNAP in the past 12 months: Households with 1 or more
persons with a disability", and AJ05E006 "Estimates: Household did not receive
Food Stamps/SNAP in the past 12 months: Households with 1 or more persons with
a disability") divided by AJ05E001 "Estimates: Total".
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
193
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
People with disabilities are highly vulnerable to emergencies due to their
dependence on caregivers, reliance on medical equipment, and limited mobility,
which prevents them from adequately preparing for evacuation, shelter, and medical
needs. This indicator will help assess the overall health vulnerability of the
population, which will inform public health planning, emergency planning and
response.
Key caveats/limitations
~
Census
Definition of
Disability
The definition is specific to the one used by the Census and may not be comparable
with other definitions. Further, this variable does not provide information on the
severity of the disability.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
194
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.16. Checklist for Percent of Population under the Age of 18 Indicator
Percent of Population under the Age of 18 Indicator
Definition of the indicator
~
Definition
Percent of population under the age of 18
~
Interpretation
This indicator provides information on minor populations who may be predisposed to
more severe health impacts and are dependent on caregivers.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B01001. "SEX BY AGE"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2. nhais. ora/main
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Topic
Based on keywords in the indicator definition, select the topic "Age" under the
population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B01001.
"SEX BY AGE").
~
Selection
Once the table of interest (Table Number B01001) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
195
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the sum of {5-9 years, 10-14, and 15-17 years old} divided by the
total population.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-16. Take the sum of all age groups under 18 divided by
the total:
(((AJWBE003, "Estimates: Male: Under 5years," +AJWBE004, "Estimates: Male: 5
to 9 years," +AJWBE005, "Estimates: Male: 10 to 14 years," +AJWBE006,
"Estimates: Male: 15 to 17years," +AJWBE027, "Estimates: Female: Under 5 years,"
+AJWBE028, "Estimates: Female: 5 to 9 years," +AJWBE029, "Estimates: Female:
10 to 14 years,"
+AJWBE030, "Estimates: Female: 15 to 17 years")/AJWBE001, "Estimates: Total")
*100)
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
196
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
Children are more vulnerable to contaminant releases, as they are more susceptible
to health impacts. They are also more dependent on caregivers. This indicator
informs priorities for emergency response, adaptation, and public health planning by
identifying areas that have a high proportion of children.
Key caveats/limitations
~
Percent of
Population
This variable does not distinguish between infants who may be among the most
vulnerable and older children. It also does not provide information on how many
children are present within a household or the health status of children.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
197
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.17. Checklist for Percent of Population Who Are 65 or Over Indicator
Percent of Population Who Are 65 or Over Indicator
Definition of the indicator
~
Definition
Percent of population who are 65 or over
~
Interpretation
This indicator provides information on older populations who may be predisposed to
more severe health impacts, have mobility issues, and are dependent on caregivers.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B01001. "SEX BY AGE"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com):
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Based on the keywords in the indicator definition, select the topic "Age" under the
population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B01001.
"SEX BY AGE").
~
Selection
Once the table of interest (Table Number B01001) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
198
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data needs a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the sum of {males and females ages 65-66,67-69,70-74,75-79,80-
84, and 85+} divided by the total population.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-17. Take the sum of all age groups above 65 divided by
the total population:
((AJWBE020, "Estimates: Male: 65 and 66 years," +AJWBE021, "Estimates: Male:
67 to 69 years,"+AJWBE022, "Male: 70 to 74 years,"+AJWBE023, "Estimates:
Male: 75 to 79years," +AJWBE024, "Estimates: Male: 80 to 84years," +AJWBE025,
"Estimates: Male: 85 years and over," +AJWBE044, "Estimates: Female: 65 and 66
years,"
+AJWBE045, "Estimates: Female: 67 to 69 years," +AJWBE046, "Estimates:
Female: 70 to 74 years," +AJWBE047, "Estimates: Female: 75 to 79 years,"
+AJWBE048, "Estimates: Female: 80 to 84years," +AJWBE049, "Estimates:
Female: 85 years and over)/AJWBE001, "Estimates: Total) *100
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
199
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
Elderly populations are at higher risk of negative health impacts from exposure to
release events. They may also have mobility issues and be dependent on
caregivers. This indicator informs priorities for emergency response, adaptation, and
public health planning by identifying areas that have a high proportion of elderly
individuals at risk.
Key caveats/limitations
~
Population over
65
This variable uses 65 as a threshold for identifying the most vulnerable. However, it
does not distinguish between people over 65 and people over 90, for example. It
also does not provide information on whether there are caregivers within the
household or their health status.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
200
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.18. Checklist for Percent of Households with Single Members Who Are 65 or
Over Indicator
Percent of Households with Single Members Who Are 65 or Over
Indicator
Definition of the indicator
~
Definition
Percent of households with single members who are 65 or over
~
Interpretation
This indicator provides information on older populations who may be more
vulnerable than the average population but do not have other household members
living with them.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B11007. "Households by Presence of People 65 Years and Over,
Household Size and Household Type"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "Age" under the
population tab.
201
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Tables
The tables that are available for the filters selected above will be populated below
the filters Search the page for the specific table number (Table Number B11007.
"Households by Presence of People 65 Years and Over, Household Size and
Household Type").
~
Selection
Once the table of interest (Table Number B11007) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate Households with one or more people 65 years and over: 1-person
household divided by the total population.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-18. Take (AJX8E003, "Estimates: Households with one or
more people 65years and over,"divided by AJX8E001, "Estimates: Total") *100
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
202
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
Elderly populations are at higher risk of negative health impacts from exposure to
release events. They may also have mobility issues and be dependent on
caregivers. Elderly people living alone do not have caregivers or support at home
and are more likely to be socially isolated and have more mobility issues than those
who have other household members., in addition to being more susceptible to health
consequences of exposure. This indicator informs priorities for emergency response,
adaptation, and public health planning by identifying areas that have a high
proportion of elderly individuals at risk and without immediate support.
Key caveats/limitations
~
Population over
65
This variable uses 65 as a threshold for identifying the most vulnerable. However, it
does not distinguish between people over 65 and people over 90, for example. It
also does not provide information on the health status.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
203
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.19. Checklist for Percent of Population with Female Household Heads Indicator
Percent of Population with Female Household Heads Indicator
Definition of the indicator
~
Definition
Percent of population with female household heads
~
Interpretation
This indicator provides information on the composition of the household and
indicates that the head of the household is female.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B09019. "Household Type (Including Living Alone) By Relationship"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "Households (Termed
"Families" before 1940)" under the population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B09019.
"Household Type (Including Living Alone) By Relationship").
~
Selection
Once the table of interest (Table Number B09019) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
204
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the sum of infamity and in nonfamily female householders divided
by the number of households.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-19. Take ((AJXHE006, "Estimates: In households: In
family households: Householder: Female," + AJXHE029, "Estimates: In households:
In nonfamily households: Householder: Female") divided by AJXHE001, "Estimates:
Total") *100.
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
205
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
Female-headed households may be more socially isolated and have less access to
resources, support systems, and family and community networks. This indicator
describes the amount of social connectivity and helps identify those who may be
impacted disproportionately more than average.
Key caveats/limitations
~
Census
Definition of
Household Head
Census designates one person in each household as the householder. Typically, this
is the person who is listed on line one of the survey questionnaire or one of the
people in whose name the home is owned, being bought, or rented and If there is no
such person in the household, any adult household member 15 years old and over
could be designated as the householder. If a household head is identified as a
female, it does not necessarily mean that this is a single parent or that there is no
partner living in the household.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
206
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.20. Checklist for Percent of Population That Is Black or African American Alone
Indicator
Percent of Population That Is Black or African American Alone Indicator
Definition of the indicator
~
Definition
Percent of population that is Black or African American alone
~
Interpretation
This indicator provides information on the racial demographics of the population.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B02001. "RACE"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2. nhais. ora/main
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "Race" under the
population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B02001.
"RACE").
~
Selection
Once the table of interest (Table Number B02001) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
207
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the number of Black or African American Alone over the total
population.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-20. Take AJWNE003 "Estimates: Black or African
American alone" divided by AJWNE001 "Estimates: Total"
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
208
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator provides demographic information for the population. Black
populations are often marginalized, so they may have less integration with social
networks, support, and communications in the event of an emergency. They may
also be less educated and/or under-resourced and historically received less financial
support/insurance coverage. They also often live close to contaminated sites/waste
facilities. Identifying such minority population groups may help determine priorities
and strategies for emergency response, adaptation, and public health planning.
Key caveats/limitations
~
One Racial
Group
This indicator represents one racial group and other minority groups need to be
considered as well.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httos://www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
209
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.21. Checklist for Percent of Population That Are Native Hawaiian or Other Pacific
Islander Alone Indicator
Percent of Population That Are Native Hawaiian or Other Pacific Islander
Alone Indicator
Definition of the indicator
~
Definition
Percent of population that are Native Hawaiian or Other Pacific Islander alone
~
Interpretation
This indicator provides information on the racial demographics of the population.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B02001. "RACE"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2. nhais. ora/main
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "Race" under the
population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B02001.
"RACE").
210
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Selection
Once the table of interest (Table Number B02001) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear)
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the number of Native Hawaiian or Other Pacific Islander Alone over
the total population.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-21. Take AJWNE006 "Estimates: Native Hawaiian and
Other Pacific Islander alone" divided by AJWNE001 "Estimates: Total"
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All Block Groups will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
211
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator provides demographic information for the population. Native/
indigenous populations are among minority groups, so they may have less
integration with social networks, support, and communications in the event of an
emergency. Identifying such minority population groups may help determine priorities
and strategies for emergency response, adaptation and public health planning.
Key caveats/limitations
~
One Racial
Group
This indicator represents one racial group and other minority groups need to be
considered as well.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
212
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.22. Checklist for Percent of Population That Are American Indian or Alaska Native
Alone Indicator
Percent of Population That Are American Indian or Alaska Native Alone
Indicator
Definition of the indicator
~
Definition
Percent of population that are American Indian or Alaska Native alone
~
Interpretation
This indicator provides information on the racial demographics of the population.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B02001. "RACE"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2. nhais. ora/main
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "Race" under the
population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B02001.
"RACE").
213
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Selection
Once the table of interest (Table Number B02001) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the number of American Indian or Alaska Native Alone over the total
population.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-22. Take AJWNE004 "Estimates: American Indian and
Alaska Native alone" divided by AJWNE001 "Estimates: Total"
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
214
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator provides demographic information for the population. American Indian
or Alaska Native populations are among minority groups, so they may have less
integration with social networks, support, and communications in the event of an
emergency. Identifying such minority population groups may help determine priorities
and strategies for emergency response, adaptation and public health planning.
Key caveats/limitations
~
One Racial
Group
This indicator represents one racial group and other minority groups need to be
considered as well.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
215
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.23. Checklist for Percent of Population That Are Asian Alone Indicator
Percent of Population That Are Asian Alone Indicator
Definition of the indicator
~
Definition
Percent of population that are Asian alone
~
Interpretation
This indicator provides information on the racial demographics of the population.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B02001. "RACE"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018)
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "Race" under the
population tab
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B02001.
"RACE").
~
Selection
Once the table of interest (Table Number B02001) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear)
216
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the number of Asian Alone over the total population.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-23. Take AJWNE005 "Estimates: Asian alone" divided by
AJWNE001 "Estimates: Total"
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
217
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator provides demographic information for the population. Asian
populations are among minority groups, so they may have less integration with social
networks, support, and communications in the event of an emergency. Identifying
such minority population groups may help determine priorities and strategies for
emergency response, adaptation and public health planning.
Key caveats/limitations
~
One Racial
Group
This indicator represents one racial group and other minority groups need to be
considered as well.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
218
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.24. Checklist for Percent of Population That Belongs to Other Non-White Races
Indicator
Percent of Population That Belongs to Other Non-White Races Indicator
Definition of the indicator
~
Definition
Percent of population that belongs to other non-White races
~
Interpretation
This indicator provides information on the racial demographics of the population.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B02001. "RACE"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com):
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "Race" under the
population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B02001.
"RACE").
~
Selection
Once the table of interest (Table Number B02001) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
219
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the sum of {some other race alone and two or more races} over the
total population.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-24. Take the sum of (AJWNE007 "Estimates: Some other
race alone", AJWNE009 "Estimates: Two or more races: Two races including Some
other race", and AJWNE010 "Estimates: Two or more races: Two races excluding
Some other race, and three or more races") divided by AJWNE001 "Estimates:
Total".
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
220
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator provides demographic information for the population. Other Non-White
Races populations are among minority groups, so they may have less integration
with social networks, support, and communications in the event of an emergency.
Identifying such minority population groups may help determine priorities and
strategies for emergency response, adaptation, and public health planning.
Key caveats/limitations
~
One Racial
Group
This indicator represents one racial group and other minority groups need to be
considered as well.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
221
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.25. Checklist for Percent of Population That Are Hispanic or Latino Indicator
Percent of Population That Are Hispanic or Latino Indicator
Definition of the indicator
~
Definition
Percent of population that are Hispanic or Latino
~
Interpretation
This indicator provides information on the ethnicity of the population.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B03003. "Hispanic or Latino Origin"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2. nhais. ora/main
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "Race" under the
population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B03003.
"Hispanic or Latino Origin").
~
Selection
Once the table of interest (Table Number B03003) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
222
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the number of Hispanics or Latino over the total population.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-25. Take AJWWE003, "Estimates: Hispanic or Latino"
divided by AJWWE001 "Estimates: Total"
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
223
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator provides ethnicity information for the population. Hispanic and Latino
populations are among minority groups, so they may have less integration with social
networks, support, and communications in the event of an emergency. In some
cases, they may be undocumented, or have limited English speaking ability.
Identifying such minority population groups may help determine priorities and
strategies for emergency response, adaptation, and public health planning.
Key caveats/limitations
~
Other
Characteristics
Need to Be
Considered
Being Hispanic does not necessarily mean that they are undocumented or have
limited English speaking ability. Other indicators that directly measure these
characteristics need to be considered as well.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
224
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.26. Checklist for Percent of Households That Have Limited English Speaking
Ability Indicator
Percent of Households That Have Limited English Speaking Ability
Indicator
Definition of the indicator
~
Definition
Percent of households that have limited English speaking ability
~
Interpretation
This indicator identifies households that may need English-language assistance.
This provides information on households in which no member 14 years old and over
(1) speaks only English at home or (2) speaks a language other than English at
home and speaks English "Very well."
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number C16002. "Household Language by Household Limited English
Speaking Status"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2. nhais. ora/main
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "Language" from the
Population tab.
225
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number C16002.
"Household Language by Household Limited English Speaking Status").
~
Selection
Once the table of interest (Table Number C16002) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the sum of the "Limited English speaking household" columns
across the different languages and divide by Total number of households.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-26. Take the sum of (AJY2E004 "Estimates: Spanish:
Limited English speaking household", AJY2E007 "Estimates: Other Indo-European
languages: Limited English speaking household", AJY2E010 "Estimates: Asian and
Pacific Island languages: Limited English speaking household", and AJY2E013
"Estimates: Other languages: Limited English speaking household") divided by
AJY2E001 "Estimates: Total".
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
226
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
Households with limited English will be linguistically isolated due to limited ability to
receive communications, public information, and communicate with responders, thus
making them more vulnerable in emergencies. This indicator is useful to assess the
capacity of the population to access and understand communications materials and
information in an emergency.
Key caveats/limitations
~
Measure Based
on Perceptions
This indicator reflects the perceptions of the person filling in the questionnaire rather
than own perceptions of each household member.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
227
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.27. Checklist for Percent of the Population Who Are over 18 and Non-U.S.
Citizens Indicator
Percent of the Population Who Are over 18 and Non-U.S. Citizens
Indicator
Definition of the indicator
~
Definition
Percent of the population who are over 18 and non-U. S. citizens
~
Interpretation
This indicator provides information on respondents who indicated that they were not
U.S. citizens at the time of the survey.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B29002. "Citizen, Voting-Age Population by Educational Attainment
and Table Number B01001. "SEX BY AGE"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2.nhais.ora/main.
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "Language" under the
population tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table numbers (Table Number B29002-
"Citizen, Voting-Age Population by Educational Attainment and Table Number
B01001- "SEX BY AGE").
228
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Selection
Once the table of interest (Table Number B29002 and Table Number B01001) has
been found, select that table and hit "continue" on the top right of the page (a
dialogue box will appear).
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the {total population over 18 minus the total Citizen population
divided} by the total population over 18.
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-27. First calculate the total population over 18 using table
B01001:
Take the total population AJWBE001 "Estimates: Total" minus the sum of male and
female age groups below 18 (AJWBE003, "Estimates: Male: Under 5 years,"
+AJWBE004, "Estimates: Male: 5 to 9 years," +AJWBE005, "Estimates: Male: 10 to
14 years," +AJWBE006, "Estimates: Male: 15 to 17years," +AJWBE027, "Estimates:
Female: Under 5years,"
+AJWBE028, "Estimates: Female: 5 to 9 years," +AJWBE029, "Estimates: Female:
10 to 14 years," +AJWBE030, "Estimates: Female: 15 to 17years)
Next, pull the total variable for B29002 (AJ4QE001 "Estimates: Total") and calculate
the percent of population who are over 18 and non-U.S. citizens with the following
equation:
((Over 18 Population - AJ4QE001 "Estimates: Total")-Over 18 Population)
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
229
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator shows the proportion of immigrants in a community, who typically have
less integration with social networks, support, and communications in the event of an
emergency. They may also face cultural and language barriers. Identifying such
minority population groups may help determine priorities and strategies for
emergency response, adaptation and public health planning.
Key caveats/limitations
~
Representative
of Survey Year
Citizenship status may change over years if an individual is going through
naturalization.
~
Immigration
Status
This indicator does not reflect immigration status.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
https://www.census.aov/content/dam/Census/librarv/publications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
230
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.28. Checklist for Percent of Households That Moved within the Last 3 Years
Indicator
Percent of Households That Moved within the Last 3 Years Indicator
Definition of the indicator
~
Definition
Percent of households that moved within the last 3 years
~
Interpretation
This indicator provides information on those who moved into their current residence
within the last 3 years.
Data source
~
Data Source
American Community Survey (ACS) five-year data for historical periods (e.g., 2014-
2018). The five-year sample provides increased statistical reliability of the data
compared with that of single-year estimates, particularly for small geographic areas
and small population subgroups (U.S. Census, 2018). The Census Decadal data or a
more recent ACS year can also be considered when available,
~
Table Number/
Name
Table Number B25038. "Tenure by Year Householder Moved into Unit"
~
Temporal
Resolution
Annual value representing the time period chosen
~
Spatial
Resolution
Block Group (BG)
~
Data Format
Excel spreadsheet or shapefile/geodatabase
Data Retrieval
~
Webpage
The data can be accessed through a portal within IPUMS.com:
httos://data2. nhais. ora/main
~
Account
An IPUMS account needs to be created with the user email. The data are sent to this
email. This could take up to an hour depending on how much data you have
requested.
~
Spatial Scale
After logging in, select the Geographic Level of Block Group.
~
Temporal
Coverage
Select the Year filter for 5-Year Ranges (2014-2018).
~
Variable/Topic
Based on keywords in the indicator definition, select the topic "OccupancyA/acancy
and Use" under the Housing tab.
~
Tables
The tables that are available for the filters selected above will be populated below
the filters. Search the page for the specific table number (Table Number B25038.
"Tenure by Year Householder Moved into Unit').
231
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Selection
Once the table of interest (Table Number B25038) has been found, select that table
and hit "continue" on the top right of the page (a dialogue box will appear).
~
Review and
Submit
This will then take you to the "Review and Submit" page where you will want to
select the option to filter your data for your selected area. Click Submit.
Decisions needed for calculation
~
Download Source
The data can be accessed through IPUMS or directly from the Census website. The
IPUMS portal provides an easier way to download the needed tables and is
recommended.
~
Data Table
Selection
Multiple tables may be displayed when the search for keywords is conducted, and
users may find alternative tables (in addition to the table number listed here) that can
be considered. However, caution must be used if the users want to select a different
table to make sure that the table selected captures the information needed.
Tabular data need a field to link to the spatial data files. For example, if the data are
compiled at the BG level, there needs to be a field indicating the BG value that can
thereby be linked to the same value in the BG spatial data.
~
Calculations
Use to calculate the sum of 2017 or later and 2015-2016 across owners and renters
and divide by Total Number of Households (check that "total" is all households).
Calculation steps and assumptions
~
Calculation on
Data
See Appendix Equation S-28. Take the sum of (AJ2QE003 "Estimates: Owner
occupied: Moved in 2017 or later", AJ2QE004 "Estimates: Owner occupied: Moved
in 2015 to 2016", AJ2QE010 "Estimates: Renter occupied: Moved in 2017 or later",
AJ2QE011 "Estimates: Renter occupied: Moved in 2015 to 2016") divided by
AJ2QE001 "Estimates: Total"
Decisions needed for mapping and interpretation
~
Mapping Limited
to Block Groups
Containing Data
NA - All BGs will have demographic values
~
Choosing
Symbology
Maps showing the distribution of percent will generally not be compared across
scenarios, time periods, or locations so a single symbology will not be necessary.
The symbology will be specific to the data for current area of interest.
~
Binning the Data
by Block Group
Percent values will be continuous from minimum to maximum values. Data can
either be divided into equal intervals (recommended for percent values ranging from
0-100) or quantiles (recommended for percent values not ranging from 0-100 or raw
count values). Using quantiles is also recommended when the data are not evenly
distributed across the entire range to discern variations within concentrated ranges.
Use a maximum of 5-7 categories so that the map reader can readily distinguish
between color categories and visually match them to the legend. Deciles or other
percentiles can also be used depending on user needs.
232
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
~
Choosing Colors
Use a color gradation that becomes darker as the vulnerability increases. Avoid
using a divergent color scheme (darker at both extremes and lighter in the middle),
which implies an inflection point such as 0 in a dataset containing positive and
negative values.
Examples of how the indicator can be useful
~
Emergency
Response/
Adaptation
Planning
This indicator shows the proportion of recent migrants in a community, who typically
have less integration with social networks, support, and communications in the event
of an emergency. They may also face cultural and language barriers. Identifying
such population groups may help determine priorities and strategies for emergency
response, adaptation and public health planning.
Key caveats/limitations
~
No Information
on Where They
Moved From
This indicator does not include information on where people moved from. If they lived
in neighborhoods close by, they may not necessarily be socially isolated. On the
other hand, if they moved from a different region of the country or from abroad, they
may be more isolated.
~
Uncertainties for
Small BGs
Margins of error need to be considered for assessing uncertainties in ACS estimates,
especially for small BGs.
Citations
Dataset/Tool
U.S. Census Bureau. (2019). 2014-2018 American Community Survey 5-year Public
Use Microdata Samples, Block Groups & Larger Areas [CSV Data file].
httosJ/www. nhais. ora/
Additional
Resources
U.S. Census Bureau. (2018). American Community Survey General Handbook 2018,
Chapter 3: Understanding and Using ACS Single-Year and Multiyear Estimates.
httos://www.census.aov/content/dam/Census/librarv/Dublications/2018/acs/acs aene
rat handbook 2018 ch03.pdf
ACS: American Community Survey, BG: Block Group, IPUMS: Integrated Public Use Microdata Series
233
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
References
Janetos, A. C., & Kenney, M. A. (2016). Developing better indicators to track climate impacts. Front Ecol
Environ, 13, 403-403. doi:10.1890/1540-9295-13.8.403
Maxwell, K. (2018). A coupled human-natural systems framework of community resilience. Journal of
Natural Resources Policy Research, 8(1-2), 110-130. doi: 10.5325/naturesopolirese.8.1-2.0110
U.S. Census Bureau. (2019). American Community Survey and Puerto Rico Community Survey 2018
Subject Definitions, https://www2.census.gov/programs-
surveys/acs/tech docs/subject definitions/2018 ACSSubjectDefinitions.pdf
U.S. Census Bureau. (2022). Glossary, https://www.census.gov/programs-
surveys/geographv/about/glossary.html. Accessed February 22, 2022.
USDA. (n.d.). Wind rose resources.
https://www.nrcs.usda.gov/wps/portal/wcc/home/climateSupport/windRoseResources/
U.S. EPA. (2012a). Hazardous Waste Listings, https://www.epa.gov/sites/default/files/2016-
01/documents/hw listref sep2012.pdf. Accessed February 18, 2022.
U.S. EPA. (2012b). Adaptation of Superfund Remediation to Climate Change, Table 1.
U.S. Environmental Protection Agency (U.S. EPA). (2022). Defining hazardous waste: Listed,
characteristic and mixed radiological wastes, https://www.epa.gov/hw/defining-hazardous-
waste-listed-characteristic-and-mixed-radiological-wastes. Accessed February 18, 2022.
234
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Appendix A. Indicator Descriptions, Potential Contaminants and
Brownfield Sites and Vulnerability of Superfund Remediation
Technology
235
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Table A.1. Description of Options for Indicators.
ID*
Indicator Definition**
Description of Options for Indicators
Extreme Event: Heat
1.1.1
Extreme Heat: Maximum Summer
Temperature for [for selected time
period, scenario]
¦ Maximum Summer Temperature, mean over years in historical period
¦ Maximum Summer Temperature, mean over years in time period 2040-2059 (scenario
RCP 4.5)
¦ Maximum Summer Temperature, mean over years in time period 2040-2059 (scenario
RCP 8.5)
¦ Difference between mean Maximum Summer Temperature in time period 2040-2059
(scenario RCP 4.5) and historical mean
¦ Difference between mean Maximum Summer Temperature in time period 2040-2059
(scenario RCP 8.5) and historical mean
¦ Percent difference between mean Maximum Summer Temperature in time period
2040-2059 (scenario RCP 4.5) and historical mean
¦ Percent difference between mean Maximum Summer Temperature in time period
2040-2059 (scenario RCP 8.5) and historical mean
1.1.2
Threshold-based Extreme Heat: Annual
maximum temperature for "extreme
heat days" for [for selected time period,
scenario]
¦ Annual maximum temperature for "extreme heat days" (defined as Maximum daily
temperature > 99th percentile of maximum daily temperatures over 1986-2005), mean
over years in historical period
¦ Annual maximum temperature for "extreme heat days" (defined as Maximum daily
temperature > 99th percentile of maximum daily temperatures over 1986-2005), mean
over years in time period 2040-2059 (scenario RCP 4.5)
¦ Annual maximum temperature for "extreme heat days" (defined as Maximum daily
temperature > 99th percentile of maximum daily temperatures over 1986-2005), mean
over years in time period 2040-2059 (scenario RCP 8.5)
1.1.3
Threshold-based Extreme Heat:
Change in the annual count of "extreme
heat days" between [selected time
period, scenario] and historical
¦ Difference between count of "extreme heat days" in time period 2040-2059 (scenario
RCP 4.5) and historical mean, mean overyears in time period 2040-2059
¦ Difference between count of "extreme heat days" in time period 2040-2059 (scenario
RCP 8.5) and historical mean, mean overyears in time period 2040-2059
236
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
ID*
Indicator Definition**
Description of Options for Indicators
Extreme Event: Wildfire
1.1.4
Wildfire: Fraction of Block Group Area
Burned for [selected time period,
scenario]
¦ Fraction of Block Group Burned, mean over years in historical period
¦ Fraction of Block Group Burned, mean over years in time period 2040-2059 (scenario
RCP 4.5)
¦ Fraction of Block Group Burned, mean over years in time period 2040-2059 (scenario
RCP 8.5)
¦ Difference between mean Fraction of Block Group Burned in time period 2040-2059
(scenario RCP 4.5) and historical mean
¦ Difference between mean Fraction of Block Group Burned in time period 2040-2059
(scenario RCP 8.5) and historical mean
¦ Percent difference between mean Fraction of Block Group Burned in time period
2040-2059 (scenario RCP 4.5) and historical mean
¦ Percent difference between mean Fraction of Block Group Burned in time period
2040-2059 (scenario RCP 8.5) and historical mean
Extreme Event: Flood
1.1.5
Flood: Percent of Block Group within a
[selected degree of flood] floodplain
¦ Percent of Block Group Within 100-year Floodplain
¦ Percent of Block Group Within 500-year Floodplain
1.1.6
Precipitation-based Flood: Annual % of
precipitation depth falling during "heavy
events" for [selected time period,
scenario]
¦ Annual percent of precipitation depth falling during "heavy events" (defined as Daily
depth > 99th percentile for 1986-2005), mean overyears in historical period
¦ Annual percent of precipitation depth falling during "heavy events" (defined as Daily
depth > 99th percentile for 1986-2005), mean overyears in time period 2040-2059
¦ Annual percent of precipitation depth falling during "heavy events" (defined as Daily
depth > 99th percentile for 1986-2005), mean overyears in time period 2040-2059
1.1.7
Threshold-based Flood: Change in the
average annual percent of precipitation
depth falling during "heavy events"
between [selected time period,
scenario] and historical
¦ Change in the average annual percent of precipitation depth falling during "heavy
events" (defined as Daily depth > 99th percentile for 1986-2005) between time period
2040-2059 (scenario RCP 4.5) and historical mean
¦ Change in the average annual percent of precipitation depth falling during "heavy
events" (defined as Daily depth > 99th percentile for 1986-2005) between time period
2040-2059 (scenario RCP 8.5) and historical mean
1.1.8
Physically based Flood: Mean Height
Above the Nearest Drainage
¦ Mean Height Above the Nearest Drainage
237
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
ID*
Indicator Definition**
Description of Options for Indicators
Extreme Event: Drought
1.1.9
Drought: Count of drought (defined by
SPEI-6) months for [selected time
period, scenario]
¦ Count of drought months (as defined by SPEI-6 < -0.8), total over years in time period
1986-2005
¦ Count of drought months (as defined by SPEI-6 < -0.8), total over years in time period
1986-2006
¦ Count of drought months (as defined by SPEI-6 < -0.8), total over years in time period
1986-2007
1.1.10
Threshold-based Drought: Change in
the count of drought (defined by SPEI-
6) months between [selected time
period, scenario] and historical
¦ Change in the total count of drought months (as defined by SPEI-6 < -0.8), between
time period 2040-2059 (scenario RCP 4.5) and historical mean
¦ Change in the total count of drought months (as defined by SPEI-6 < -0.8), between
time period 2040-2059 (scenario RCP 8.5) and historical mean
1.1.11
Drought: Count of drought (defined by
SPEI-12) months for [selected time
period, scenario]
¦ Count of drought months (as defined by SPEI-12 < -0.8), total overyears in time
period 1986-2005
¦ Count of drought months (as defined by SPEI-12 < -0.8), total over years in time
period 1986-2006
¦ Count of drought months (as defined by SPEI-12 < -0.8), total overyears in time
period 1986-2007
1.1.12
Threshold-based Drought: Change in
the count of drought (defined by SPEI-
12) months between [selected time
period, scenario] and historical
¦ Change in the total count of drought months (as defined by SPEI-12 < -0.8), between
time period 2040-2059 (scenario RCP 4.5) and historical mean
¦ Change in the total count of drought months (as defined by SPEI-12 < -0.8), between
time period 2040-2059 (scenario RCP 8.5) and historical mean
Facilities: Counts
1.2.1
Total count of sites/waste facilities
¦ Total number of sites/waste facilities
1.2.2
Count of sites/waste facilities per
square km
¦ Density of sites/waste facilities
1.2.3
Sites/waste facilities count by type
¦ Total number of each of the 15 types of sites/waste facilities (e.g., Superfund)
Facilities: Hazardous Waste
1.2.4
Tons of hazardous waste
¦ Quantity of managed waste stream in LQGs that are included in the BRS; Includes
Environmental attributes (E)
1.2.5
Sites/waste facilities count (by hazard
type)
¦ Number of sites/waste facilities producing each of the 6 types of hazardous wastes;
Includes Environmental attributes (E)
238
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
ID*
Indicator Definition**
Description of Options for Indicators
1.2.6
Waste tonnage (by hazard type)
¦ Quantity of each of the 6 types of managed hazardous waste streams; Includes
Environmental attributes (E)
Facilities: Brownfields and Superfund
1.2.7
Brownfield count with contaminant;
cleanup unknown (by contaminant)
¦ 20 contaminants; Includes Environmental attributes and Regulatory attributes
1.2.8
Superfund count with vulnerable
remedy technology (by extreme event)
¦ Number of Superfund Sites with a remedy present that is vulnerable to a specific
extreme event.
Facilities: Tanks
1.2.9
Count of specific type of tank
(UST/AST)
¦ open/temp closed for USTs (ASTs and USTs separately)
¦ open/temp closed for USTs (ASTs and USTs separately)
1.2.10
Total tank capacity (UST/AST)
¦ Includes physical attributes
Transport and Fate: Surface Water
1.3.1
Count of sites/waste facilities in a
floodplain [100-year and 500-year]
¦ Count of sites/waste facilities Wthin 100-year Floodplain
¦ Count of sites/waste facilities Wthin 500-year Floodplain
1.3.2
Count of sites/waste facilities within a
specific hydrologic distance of a flowline
¦ Count of sites/waste facilities Wthin 500m of an NHD Flowline
¦ Count of sites/waste facilities Wthin 1 km of an NHD Flowline
¦ Count of sites/waste facilities Wthin 2 km of an NHD Flowline
¦ Count of sites/waste facilities Wthin 5 km of an NHD Flowline
1.3.3
Shortest Hydrologic Distance (m)
Upstream to a Site/ Waste Facility
¦ Shortest Hydrologic Distance (m) Upstream to a Site/ Waste Facility
1.3.4
Count of Upstream Facilities within a
specific hydrologic distance of a
community
¦ Count of sites/waste facilities Wthin 500 m of a Community
¦ Count of sites/waste facilities Wthin 1 km of a Community
¦ Count of sites/waste facilities Wthin 3km of a Community
¦ Count of sites/waste facilities Wthin 5 km of a Community
Transport and Fate: Air
1.3.5
Shortest Distance to a site/waste facility
upwind [season]
¦ Spring Season: Shortest distance to a facility
¦ Summer Season: Shortest distance to a facility
¦ Fall Season: Shortest distance to a facility
¦ Wnter Season: Shortest distance to a facility
239
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
ID*
Indicator Definition**
Description of Options for Indicators
1.3.6
Count of sites/waste facilities "upwind"
within a specific season and distance of
a community
¦ Spring Season: Count of sites/waste facilities Within 40 km
¦ Summer Season: Count of sites/waste facilities Within 40 km
¦ Fall Season: Count of sites/waste facilities Wthin 40 km
¦ Wnter Season: Count of sites/waste facilities Wthin 40 km
¦ Spring Season: Count of sites/waste facilities Wthin 25 km
¦ Summer Season: Count of sites/waste facilities Wthin 25 km
¦ Fall Season: Count of sites/waste facilities Wthin 25 km
¦ Wnter Season: Count of sites/waste facilities Wthin 25 km
¦ Spring Season: Count of sites/waste facilities Wthin 15 km
¦ Summer Season: Count of sites/waste facilities Wthin 15 km
¦ Fall Season: Count of sites/waste facilities Wthin 15 km
¦ Wnter Season: Count of sites/waste facilities Wthin 15 km
¦ Spring Season: Count of sites/waste facilities Wthin 5 km
¦ Summer Season: Count of sites/waste facilities Wthin 5 km
¦ Fall Season: Count of sites/waste facilities Wthin 5 km
¦ Wnter Season: Count of sites/waste facilities Wthin 5 km
1.3.7
Minimum response time, [by season]
¦ Spring Season: Minimum Response Time
¦ Summer Season: Minimum Response Time
¦ Fall Season: Minimum Response Time
¦ Wnter Season: Minimum Response Time
1.3.8
Count of sites/waste facilities that are
within specific response time ranges,
[by season]
¦ Spring Season: Count of sites/waste facilities Wthin 20 Minute Response Time
¦ Summer Season: Count of sites/waste facilities Wthin 20 Minute Response Time
¦ Fall Season: Count of sites/waste facilities Wthin 20 Minute Response Time
¦ Wnter Season: Count of sites/waste facilities Wthin 20 Minute Response Time
¦ Spring Season: Count of sites/waste facilities Wthin 15 Minute Response Time
¦ Summer Season: Count of sites/waste facilities Wthin 15 Minute Response Time
¦ Fall Season: Count of sites/waste facilities Wthin 15 Minute Response Time
¦ Wnter Season: Count of sites/waste facilities Wthin 15 Minute Response Time
¦ Spring Season: Count of sites/waste facilities Wthin 10 Minute Response Time
¦ Summer Season: Count of sites/waste facilities Wthin 10 Minute Response Time
¦ Fall Season: Count of sites/waste facilities Wthin 10 Minute Response Time
¦ Wnter Season: Count of sites/waste facilities Wthin 10 Minute Response Time
¦ Spring Season: Count of sites/waste facilities Wthin 5 Minute Response Time
¦ Summer Season: Count of sites/waste facilities Wthin 5 Minute Response Time
¦ Fall Season: Count of sites/waste facilities Wthin 5 Minute Response Time
¦ Wnter Season: Count of sites/waste facilities Wthin 5 Minute Response Time
¦ Spring Season: Count of sites/waste facilities Wthin 2 Minute Response Time
¦ Summer Season: Count of sites/waste facilities Wthin 2 Minute Response Time
¦ Fall Season: Count of sites/waste facilities Wthin 2 Minute Response Time
¦ Wnter Season: Count of sites/waste facilities Wthin 2 Minute Response Time
240
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
ID*
Indicator Definition**
Description of Options for Indicators
Household/Receptor Characteristics
2.1.1
Total population
2.1.2
Count of households/occupied housing units
2.1.3
Median household Income
2.1.4
Percent of population with ratio of income to poverty level less than 0.5
2.1.5
Percent of population with ratio of income to poverty level between 0.5 and 1
2.1.6
Percent of households with self-employment income
2.1.7
Percent of civilian employed population 16 years and over who work outdoors
2.1.8
Percent of households that are renters
2.1.9
Percent of households living in a mobile home/boat/RV/van
2.1.10
Percent of households without telephone service
2.1.11
Percent of households with no Internet access
2.1.12
Percent of households who do not have a vehicle
2.1.13
Percent of population over 25 with no high school degree
2.1.14
Percent of population with no health insurance
2.1.15
Percent of households with at least 1 person that has a disability
2.1.16
Percent of population under age of 18
2.1.17
Percent of population who are 65 or over
2.1.18
Percent of households with single members who are 65 or over
2.1.19
Percent of population with female household heads
2.1.20
Percent of population that is Black or African American alone
2.1.21
Percent of population that are Native Hawaiian or Other Pacific Islander alone
241
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
ID*
Indicator Definition**
Description of Options for Indicators
2.1.22
Percent of population that are American Indian or Alaska Native alone
2.1.23
Percent of population that are Asian alone
2.1.24
Percent of population that belongs to other non-White races
2.1.25
Percent of population that are Hispanic or Latino
2.1.26
Percent of households that have limited English speaking ability
2.1.27
Percent of the population who are over 18 and non-U.S. citizens
2.1.28
Percent of households that moved within the last 3 years
* ID num
Dering X.Y.Z: X denotes exposure/sensitivity (exposure: 1; sensitivity: 2); Y denotes 3 sources of exposure (extreme events:!, sites/
waste facilities: 2, fate/transport: 3, and 1 source of sensitivity (population characteristics: 1), and Z denotes the indicator (numbered
sequentially)
** All indicators are by Block Group
242
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Table A.2. List of Possible Contaminants Found in Brownfield Sites (from ACRES-CIMC)
Contaminant
Controlled substances
Cadmium
Petroleum
Chromium
Asbestos
Copper
Lead
Mercury
PAHs
Nickel
PCBs
Pesticides
VOCs
SVOCs
Selenium
Other Metals
Iron
Other
Arsenic
Unknown
243
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Table A.3. Vulnerability of Superfund Remediation Technologies to Flooding, Drought,
Fire and Extreme Heat
This table, which builds upon U.S. EPA (2012), lists commonly applied contaminated site cleanup remediation
technologies that may be present at a cleanup site and indicates whether they are vulnerable to damage by the
extreme events addressed in this document (flooding, drought, fire, and extreme heat). This handbook applies
this table to develop indicators that identify which of these remediation technologies are present at Superfund
sites and what events these sites may be vulnerable to. Reasons for this simple yes/no ranking are included,
including basic assumptions about the site's operational status and condition. Site conditions should be checked
to ensure that these assumptions are appropriate. For example, if the technologies in place do not allow contact
with contaminants during wildfires, the site may not be vulnerable to fire.
Remedy
Media
Drought
Flooding
Fire
Extreme Heat
Assumptions
In-situ
solidification/
stabilization
soil/source
Y
Y
N
N
assumes injection is not
ongoing
In-situ thermal
treatment
soil/source
N
Y
N
N
assumes above-ground
components removed before
event
In-situ
bioremediation
soil/source
Y
Y
N
N
assumes injection is not
ongoing, and contaminants
are well below ground surface
Onsite
containment (cap)
soil/source
Y
Y
Y
Y
assumes flood damages
earthworks, drought damages
vegetative cover; contact with
contaminants may occur
through cap for fire and
extreme heat
Soil vapor
extraction
soil/source
Y
Y
Y
Y
onsite external equipment can
be damaged by fire or heat
Vapor intrusion
mitigation
soil/source
N
Y
Y
Y
flood can raise water table,
cutting off mitigation flow;
volatized contaminants may
be in contact with fire or
extreme heat
Multiphase
extraction
soil/source
Y
Y
Y
Y
onsite external equipment can
be damaged by fire or heat
Excavation and
physical
separation
soil/source
N
N
N
N
assumes onsite operations are
complete or paused
Excavation and
recycling
soil/source
N
N
N
N
assumes onsite operations are
complete or paused
Excavation and
offsite disposal
soil/source
N
N
N
N
assumes onsite operations are
complete or paused
Excavation and
offsite treatment
soil/source
N
N
N
N
assumes onsite operations are
complete or paused
244
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Remedy
Media
Drought
Flooding
Fire
Extreme Heat
Assumptions
Excavation and
onsite
bioremediation
soil/source
N
Y
Y
Y
aboveground equipment can
be damaged by flood, fire;
process disturbed by fire, heat
Surface water
treatment
surface water
Y
Y
Y
Y
onsite external equipment can
be damaged by fire or heat
Sediment
containment
sediment
Y
Y
N
N
drought can expose
containment structures; floods
can wash them away
Sediment
excavation and
treatment
sediment
N
Y
N
N
floods can interrupt excavation
operations, or spread
unexcavated sediments
Sediment in-situ
treatment
sediment
Y
Y
N
N
drought can expose treatment
areas; floods can wash away
treatment materials
In-situ
bioremediation
groundwater
Y
Y
N
N
injection is not ongoing, and
contaminants are well below
ground surface
In-situ chemical
treatment
groundwater
Y
Y
N
N
injection is not ongoing, and
contaminants are well below
ground surface
Permeable
reactive barrier
groundwater
Y
Y
N
N
extreme event occurs post-
installation
Pump and treat
groundwater
Y
Y
Y
Y
onsite external equipment can
be damaged by fire or heat
Air sparging
groundwater
Y
Y
Y
Y
onsite external equipment can
be damaged by fire or heat
Vertical
Engineered Barrier
groundwater
Y
Y
N
N
extreme event occurs post-
installation
Onsite
containment
(NOS)
groundwater
Y
Y
N
N
extreme event occurs post-
installation
Onsite
containment
(hydraulic)
groundwater
Y
Y
N
N
extreme event occurs post-
installation
Monitored natural
attenuation (MNA)
groundwater
Y
Y
N
N
no subsurface remediation
components
Vapor intrusion
mitigation
groundwater
N
Y
N
N
flood can raise water table,
cutting off mitigation flow,
extreme event occurs post-
installation
Adapted from U.S. EPA, Adaptation of Superfund Remediation to Climate Change, February 2012, Table 1.
245
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Table A.4. Data Sources for Each Type of Site/Waste Facility
Source Category/Facility
Type
Programs Collecting
Data
Publicly Available Databases
Data Queries
Hazardous Waste Operators
RCRA (Resource
Conservation and Recovery
Act) Subtitle C Hazardous
Waste Generators: large,
small, conditionally exempt
small quantities generated
EPA RCRA (Office of
Resource Conservation
and Recovery [ORCR])
RCRAInfo Envirofacts
RCRAInfo FTP
RCRAInfo API
Biennial Reporting Service (BRS)
Facility Registry Service (FRS)
Sites/Waste Facilities
downloaded using the
RCRAInfo API that were
labeled as "Large Quantity
Generators"
RCRA Subtitle C
Hazardous Waste
Treatment, Storage, and
Disposal Facilities (TSDFs):
landfills, surface
impoundments, land
application units, waste
piles, tanks, etc.
EPA RCRA (ORCR)
RCRAInfo Envirofacts
RCRAInfo FTP
RCRAInfo API
Biennial Reporting Service (BRS)
Facility Registry Service (FRS)
Sites/Waste Facilities
downloaded using the
RCRAInfo API that were
labeled as "TSDF" or'TSD"
RCRA Subtitle C
Hazardous Waste
Transporters
EPA RCRA (ORCR)
RCRAInfo Envirofacts
RCRAInfo FTP
RCRAInfo API
Biennial Reporting Service (BRS)
Facility Registry Service (FRS)
Sites/Waste Facilities
downloaded using the
RCRAInfo API that were
labeled as "Transporter"
RCRA Subtitle C
Hazardous Waste Transfer
Facilities
EPA RCRA (ORCR)
RCRAInfo Envirofacts
RCRAInfo FTP
RCRAInfo API
Biennial Reporting Service (BRS)
Facility Registry Service (FRS)
Sites/Waste Facilities
downloaded using the
RCRAInfo API that were
labeled as "Transfer Facility"
RCRA Subtitle C Other
Hazardous Waste
Operators
EPA RCRA (ORCR)
RCRAInfo Envirofacts
RCRAInfo FTP
RCRAInfo API
Biennial Reporting Service (BRS)
Facility Registry Service (FRS)
Sites/Waste Facilities
downloaded using the
RCRAInfo API that were
labeled as "Transfer Facility"
246
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Source Category/Facility
Type
Programs Collecting
Data
Publicly Available Databases
Data Queries
Waste Cleanup Sites
RCRA Subtitle C Corrective
Action Sites
EPA RCRA (ORCR)
RCRAInfo Envirofacts
Sites downloaded using the
RCRAInfo API that were
labeled as "Subject to
Corrective Action" or
"Corrective Action Workload"
RCRAInfo FTP
RCRAInfo API
Biennial Reporting Service (BRS)
FRS
Brownfields Sites
EPA Brownfields
Cleanups in My Community (CIMC)
Sites downloaded from
ACRES-CIMC
Assessment, Cleanup and
Redevelopment Exchange System
(ACRES)
FRS
Superfund Sites
EPA Superfund/BRAC
and NIH
Superfund Enterprise Management
System (SEMS) Envirofacts
Sites downloaded from
SEMS and labeled as being
on the National Priorities List
(NPL)
FRS
Toxmap (National Priorities List
Sites)
Non-NPL Sites
EPA Superfund/BRAC,
State Superfund
SEMS Envirofacts, State Databases
(e.g., AZ)
Sites downloaded from
SEMS and labeled as being
not on the National Priorities
List (NPL)
FRS
Removal/Emergency
Response Sites
EPA Emergency
Response/Removals
(Office of Emergency
Management, OEM)
FRS
Sites included in OSRR
EPRB data provided by EPA
Region
SEMS Envirofacts
Other Facilities/Sites
Fuel terminals and other
sites subject to SPCC and
FRP regulations to prevent
and respond to oil spills
EPA Emergency
Response/Removals
(OEM)
EPA OSC website
Sites included in FRS data
and labeled as "OIL" or
included in OSRR EPRB
data and labeled as "Oil
Spill..."
OIL
FRS
Incident Waste Facilities
EPA ORDand OEM
l-Waste
Sites/Waste Facilities
included in l-Waste data
provided by EPA Region
Solid Waste Landfills
(Nonhazardous)
State (e.g., ADEQ)
AZURITE (AZ Only)
Sites/Waste Facilities
included in GHGRP data,
included in FRS data and
labeled as "LMOP," or
included in state and local
landfill datasets.
Landfill Methane Outreach
Program (LMOP)
FRS LMOP
EPA Greenhouse Gas
Reporting Program
(GHGRP)
EPA GHGRP Database
247
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Source Category/Facility
Type
Programs Collecting
Data
Publicly Available Databases
Data Queries
Petroleum Storage Tanks
(USTs & ASTs)
State Databases (e.g.,
ADEQ)
AZ UST Database (AZ Only)
USTs include sites/waste
facilities included in state
and local UST datasets and
labeled as "open" or
"temporarily closed." USTs
also include sites/waste
facilities included in FRS
data and labeled as "LUST-
ARRA." ASTs included sites/
waste facilities in state and
local AST datasets provide
by EPA Region.
Leaking Underground
Storage Tank - American
Recovery and
Reinvestment Act (LUST-
ARRA)
FRS LUST-ARRA
Other Sites/Waste Facilities
identified by local
decision-makers as
needed
Other Sites/Waste Facilities
Any site/waste facility that
the community identifies as
a site of interest and does
not fit into any of the
categories listed above.
FRS: Facility Registry Service, RCRAInfo or RCRA: Resource Conservation and Recovery Act Info, API: Application Programming Interface,
BRS: Biennial Reporting System, ACRES: Assessment Cleanup and Redevelopment Exchange System, CIMC: Cleanups in My Community,
SEMS: Superfund Enterprise Management System, l-Waste: Incident Waste Assessment and Tonnage Estimator, GHGRP: Greenhouse Gas
Reporting Program, OSRR EPRB: Office of Site Remediation and Restoration Emergency Planning and Response Branch.
248
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Appendix B. Equations
B.1. Vulnerability Source 1.1. Exposure: Extreme Events
Indicator 1.1.1. Equations for Extreme Heat Indicator
MaxSumT/ust/sce?I = (±) ! ( Max{ MaxDa ilyTe m p (y )
EH-la
Where:
MaxSumThist/scen = Average maximum summer temperature for the historic/future time period
MaxDailyTempiy = Maximum daily temperature for day i in year y
Summer = All days in the months of June-August (defined as the summer season)
N = Number of years in the historical/future time period
MaxSumTsce„ — MaxSumTh(St
Perce ntMaxSumT^p,, =
MaxSumTh(St
EH-lb
Where:
MaxSumTscen = Average maximum summer temperature for the future scenario
MaxSumThist = Average maximum summer temperature for the historic time period
PercentMaxSumTscen = The percent change in maximum summer temperature from historic
conditions to the future scenario
Indicators 1.1.2 & 1.1.3. Equations for Threshold-Based Extreme Heat Indicator
EHDiy =1 if MaxTempiy > MaxTemp99
=0 otherwise
TBEH-1
Where:
EHDiy = Extreme heat day Boolean indicator for day i in year y. Computed for all days in all years
of the assessment period.
MaxTempiy = Maximum daily temperature for day i in year y
MaxTempgg = 99th percentile for daily maximum temperatures for extreme heat days over the
historic period
Wy
TotEHDy = EHDiy
i = 1
TBEH-2
Where:
TotEHDy =Total number of extreme heat days for year y
EHDiy = Extreme heat day daily time series of Boolean indicators
Wy = Total number of days in year y
249
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
N
TotEHD = ^ TotEHDy
y=l
TBEH-3
Where:
TotEHD = Total number of extreme heat days for assessment period
TotEHDy = Total number of extreme heat days for year y
N = Number of years in the assessment period that contain extreme heat days
, .. ES,(MaxTemply«EHD,,,)
AvgMaxTempy =
TBEH-4
Where:
AvgMaxTempy = Average maximum daily temperature for extreme heat days in year y
MaxTempiy = Maximum daily temperature for day i in year y
EHDiy = Extreme heat day Boolean indicator for day i in year y
TotEHDy = Total number of extreme heat days for year y
Wy = Total number of days in year y
/Ey=i AvgMaxTempy \
AvgMaxTemp = ( — 1/ 20
TBEH-5
Where:
AvgMaxTemp = Average annual average maximum daily temperature for extreme heat days
AvgMaxTempy = Average maximum daily temperature for extreme heat days in year y
N = Number of years that contain extreme heat days
T^;ffr-,TTT^ TotEHDsce„a?.(0 TotEHDh(Sto?.(C
uillhriiJ scenari0 20
TBEH-6
Where:
DiffEHDscenario = Change in annual total number of extreme heat days from the historic period to
the future scenario period
TotEHDscenario = Total number of extreme heat days for future scenario period
TotEHDhistoric = Total number of extreme heat days for the historic period
Indicators 1.1.4. Equations for Wildfire Indicator
MaxBurned;
AvgMaxBurnedh(St/sce„ =
WF-1
Where:
AvgMaxBurnedh(St/sce„ = Fraction of Block Group burned for historical/ future period.
MaxBurned; = Maximum fraction of the grid cell burned value for historical/ future period.
N = Number of wildfires in a given time period.
250
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
DiffBurnecLce?, = AvgMaxBur„edsce„-AvgMaxBur„ed;iistoJ,c
AvgMaxBurned,listoric
WF-2
Where:
DiffBurnedSCen = Percent change in the Fraction of Block Group burned from the historic period to
the future scenario
AvgMaxBurnedh(St = Fraction of Block Group burned for historical period.
AvgMaxBurnedsce„ = Fraction of Block Group burned for future period.
Indicator 1.1.5. Equation for Floodplain-Based Flood Indicator
PercentBGFP = BGAreaFp / BGAreaTotai * 100
FBF-1
Where:
PercentBGFP = Percent of the Block Group area within the floodplain
BGAreaFp = Block Group area within the floodplain (determined through geospatial analysis)
BGAreaiotai = Total Block Group area
Indicator 1.1.6. Equations for Precipitation-Based Flood Indicator
Wy
R99y = ^ Piy where Piy > P99
i = 1
PBF-1
Where:
Piy = Daily precipitation depth for day i in year y
P99 = 99th percentile for daily precipitation depth over the historic period
R99y = Total precipitation depth due to heavy events in year y
Wy = Total number of days in year y with precipitation
Note:
For illustration we use the 99th percentile to define heavy events as the top 1% of precipitation
events. Alternate percentiles can be used depending on user needs and local conditions.
•b
ii
PBF-2
Where:
Py = Total precipitation depth in year y
Piy = Daily precipitation depth for day i in year y
Wy = Total number of days in year y with precipitation
251
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
R99y
PR"y = —jr~
ry
PBF-3
Where:
PR99y = Percentage of precipitation depth falling as heavy events in year y
R99y = Total precipitation depth due to heavy events in year y
Py = Total precipitation depth in year y
Note:
For illustration we use the 99th percentile to define heavy events as the top 1% of precipitation
events. Alternate percentiles can be used depending on user needs and local conditions.
I,Yy=1PR99y
PR99 = y
PBF-4
Where:
PR99 = Annual average percent of precipitation falling as heavy events across all years
PR99y = Percentage of precipitation depth falling as heavy events in year y
Y = 20 (years)
Note:
For illustration we use the 99th percentile to define heavy events as the top 1% of precipitation
events. Alternate percentiles can be used depending on user needs and local conditions.
Indicator 1.1.7. Equation for Threshold-Based Flood Indicator
T,y=1 ^99y.scen ~ £y = 1 R^y,Hist
AK99sce„ — j,
Ly = 1 KWy,Hist
TBF-1
Where:
R99y,scen = Total precipitation depth due to heavy events in year y of the future scenario period
R99y,Hist = Total precipitation depth due to heavy events in year y of the historic time period
Y = 20 (years)
AR99scen = The percent change in heavy event precipitation depth from historic conditions to the
future scenario
Note:
For illustration we use the 99th percentile to define heavy events as the top 1% of precipitation
events. Alternate percentiles can be used depending on user needs and local conditions.
Indicator 1.1.8. Equation for Physically Based Flood Indicator
YJn-1HAND
HANDmean =
PhBF-1
Where:
HANDmean= Mean HAND value
HAND = Height above nearest drainage value for each raster cell falling within a BG (determined
through geospatial analysis)
N = total number of raster cells falling within a BG
252
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicators 1.1.9 & 1.1.11. Equation for Drought Indicator
Dt= Pi- PET,
DRT-1
Where:
D, = Values aggregated at selected time scale then transformed to a normal distribution, with
final SPEI values presented as standard deviations
i = month
Pi = Precipitation for month i
PET, = Potential evapotranspiration for month i
Indicators 1.1.10 & 1.1.12. Equation for Threshold-Based Drought Indicator
A Drought = DroughtFuture - DroughtHistoric
TBD-1
Where:
ADrought = Change in the count of drought months between the historic and future period
Droughtputure = Count of all months with SPEI values less than or equal to -0.8 (i.e., drought
conditions) over the 20-year period future period
DroughtHistoric = Count of all months with SPEI values less than or equal to -0.8 (i.e., drought
conditions) over the 20-year period historic period
B.2. Vulnerability Source 1.2. Exposure: Sites/Waste Facilities
Indicator 1.2.1. Equation for Total Count of Sites/Waste Facilities Indicator
Fb
SWF-1
S<\I
II
Where:
Tb = Total count of facilities in Block Group b
Fb = The maximum number of facilities within Block Group b
f = an individual facility
Indicator 1.2.2. Equation for Count of Sites/Waste Facilities per Square Kilometer Indicator
TK - Tb
b SqKb
SWF-2
Where:
TKb = Total count of facilities per square kilometer in Block Group b
Tb = Total count of facilities in Block Group b
SqKb = Total square kilometers in Block Group b
253
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.2.3. Equation for Sites/Waste Facilities Count by Type Indicator
Fbx
Tbx = ^ fx
i = 1
SWF-3
Where:
Tbx = Total count of facilities of type x in Block Group b
Fbx = The maximum number of facilities of type x within Block Group b
fx = Facility of type x
Indicator 1.2.4. Equation for Tons of Hazardous Waste Indicator
II
s
SWF-4
Where:
Tnb = The total tons of hazardous waste present in Block Group b
HFb = The total number of facilities with hazardous waste within Block Group b
W, = The amount of waste present at hazardous waste facility i.
Indicator 1.2.5. Equation for Sites/Waste Facilities Count (by Hazard Type) Indicator
IKI"
II
-e
SWF-5
Where:
Tbh = Total count of facilities storing hazardous waste of hazard type h in Block Group b
Fbh = The maximum number of facilities storing hazardous waste of hazard type h within Block
Group b
fh = Facility storing hazardous waste of hazard type h
Indicator 1.2.6. Equation for Waste Tonnage (by Hazard Type) Indicator
Fbh
Tnbh = ^ Wih
i = 1
SWF-6
Where:
Tnbh = The total tons of hazardous waste of hazard type h present in Block Group b
Fbh = The total number of facilities storing hazardous waste of hazard type h within Block Group b
Wih = The amount of waste of hazard type h present at facility i.
254
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 1.2.7. Equation for Brownfield Count with Contaminant; Cleanup Unknown (by
Contaminant) Indicator
&bx
bfix
i = 1
SWF-7
Where:
BNbx = Total count of brownfields sites with contaminant type x found and no information on
cleanup in Block Group b
Bbx = The maximum number of brownfields sites with contaminant type x in Block Group b
bfix = Brownfield site with contaminant x found and no information on cleanup in Block Group b
Indicator 1.2.8. Equation for Superfund Count with Vulnerable Remedy Technology (by
Extreme Event)
sb
$bx Six
i = 1
SWF-8
Where:
Sbx = Total count of superfund sites with remedy vulnerable to extreme event type x in Block
Group b
Sb = The maximum number of Superfund sites in Block Group b
Six = Superfund site with remedy vulnerable to extreme event type x in Block Group b
Indicator 1.2.9. Equation for Count of Specific Type of Tank (UST/AST)
TNKb
TNKbx = ^ tnkbx
b = 1
SWF-9
Where:
TNKbx = Total count of tanks of type x in Block Group b
TNKb = The maximum number of tanks in Block Group b
Tnkbx = Tank of type x in Block Group b
Indicator 1.2.10. Equation for Total Tank Capacity (UST/AST)
TNKb
TNKCbx = ^ tnkcbx
b = 1
SWF-10
Where:
TNKCbx = Sum of capacity of tanks of type x in Block Group b
TNKb = The maximum number of tanks in Block Group b
Tnkcbx = Capacity of an individual tank of type x in Block Group b
255
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
B.3. Vulnerability Source 1.3. Exposure: Transport and Fate
No equations were provided since programming rather than equations were used.
B.4. Vulnerability Source 2.1. Sensitivity: Household/Receptor Characteristics
Indicator 2.1.1. Equation for Total Population
TP
Eq S-l
Where:
TP = The total population (AJWME001)
Indicator 2.1.2. Equation for Count of Households/Occupied Housing Units
OHc
Eq S-2
Where:
OHc= The total number of occupied housing units within the county (AJ1UE001)
Indicator 2.1.3. Equation for Median Household Income
MIC
Eq #S-3
Where:
MIC= The median income of the county (AJZAE001)
Indicator 2.1.4. Equation for Percent of Population with Ratio of Income to Poverty Level Less
Than 0.5
PRr
Pr = —~~x 100
C Tc
Eq #S-4
Where:
Pc= The percent of population with ratio of household income to poverty level less than 0.5
PRC = The estimated number of individuals under 0.5 (AJY4E002)
Tc = The total population (AJY4E001) for whom poverty level was assessed
Indicator 2.1.5. Equation for Percent of Population with Ratio of Income to Poverty Level
Between 0.5 and 1
PR2r
P2C = ———-x 100
'C
Eq #S-5
Where:
Pc= The percent of population with ratio of household income to poverty level between 0.5 and
1
PR2C = The estimated number of individuals between 0.5 and 1 (AJY4E003)
Tc = The total population (AJY4E001) for whom poverty level was assessed
256
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.6. Equation for Percent of Households with Self-Employment Income
SEr
SEIr = —XlOO
c H
Eq #S-6
Where:
SEIC= The percent of households with self-employment income
PEC = The estimated number of households with self-employment income (AJZQE002)
H = The total number of households (AJZQE001)
Indicator 2.1.7. Equation for Percent of Civilian Employed Population 16 Years and over Who
Work Outdoors
(MN + FN + MP + FP)
0WC = - — x 100
c T 16c
Eq #S-7
Where:
OWc= Percent of civilian employed population 16 years and over who work outdoors
MN = Estimates: Male: Natural resources, construction, and maintenance occupations
(AJ1FE030)
FN= Estimates: Female: Natural resources, construction, and maintenance occupations
(AJ1FE066)
MP = Estimates: Female: Production, transportation, and material moving occupations
(AJ1FE070)
FP = Estimates: Male: Production, transportation, and material moving occupations (AJ1FE034)
T16c = The total population above the age of 16 (AJ1FE001)
Indicator 2.1.8. Equation for Percent of Households That Are Renters
Rc
REC = —XlOO
Hc
Eq #S-8
Where:
REC= Percent of households that are renters
Rc = The total number of renter occupied housing units (AJ17E003)
Hc = the total number of housing units (AJ17E001)
Indicator 2.1.9. Equation for Percent of Households Living in a Mobile Home/Boat/RVA/an
Mr + Br
MHr =— -x 100
Hc
Eq #S-9
Where:
MHC= Percent of households living in Mobile Homes, Boats, RVs, or Vans
Mc= total number of mobile home units (AJ2JE010)
Bc= total number of households boat, RV, van, etc units (AJ2JE011)
Hc = the total number of housing units (AJ17E001)
257
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.10. Equation for Percent of Households without Telephone Service
0 + R
T = ——x 100
Eq #S-10
Where:
T= Percent of households without telephone service
0 = The total number of owner occupied: No telephone service available (AJ2VE007)
R = the total number of renter occupied: No telephone service available (AJ2VE001)
Hc = the total number of housing units (AJ2WE001)
Indicator 2.1.11. Equation for Percent of Households with No Internet Access
N
I =— xlOO
Eq #S-11
Where:
/= Percent of households without internet access
N= The total number of estimates: No internet access (AJ37E013)
Hc = The total number of households (AJ37E001)
Indicator 2.1.12. Equation for Percent of Households Who Do Not Have a Vehicle
0 + R
V = —— xlOO
HC
Eq #S-12
Where:
V= Percent of households with no vehicle available
0 = The total number of owner occupied: No vehicle available (AJ2WE003)
R = the total number of renter occupied: No vehicle available (AJ2WE003)
Hc = the total number of housing units (AJ2WE001)
258
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.13. Equation for Percent of Population with No High School Degree
Zi-2 Ei
E = l~J x 100
Hc
Eq #S-13
Where:
£= Percent of population without a high school degree
E2= percent of population wi1
th no schooling completed (AJYPE002)
E3= percent of population wi1
th highest level of school completed: nursery school (AJYPE003)
E4= percent of population wi1
th highest level of school completed: kindergarten (AJYPE004)
E5= percent of population wi1
th highest level of school completed: 1st grade (AJYPE005)
E6= percent of population wi1
th highest level of school completed: 2nd grade (AJYPE006)
E7= percent of population wi1
th highest level of school completed: 3rd grade (AJYPE007)
E8= percent of population wi1
th highest level of school completed: 4th grade (AJYPE008)
E9= percent of population wi1
th highest level of school completed: 5th grade (AJYPE009)
£¦10= percent of population \a
rith highest level of school completed: 6th grade (AJYPE010)
£¦11= percent of population \a
rith highest level of school completed: 7th grade (AJYPE011)
£12= percent of population \a
rith highest level of school completed: 8th grade (AJYPE012)
£13= percent of population \a
rith highest level of school completed: 9th grade (AJYPE013)
£14= percent of population \a
rith highest level of school completed: 10th grade (AJYPE014)
£15= percent of population \a
rith highest level of school completed: 11th grade (AJYPE015)
£16= percent of population \a
(AJYPE016)
rith highest level of school completed: 12th grade, no diploma
Hc = the total population (AJYPE001)
Indicator 2.1.14. Equation for Percent of Population with No Health Insurance
H1 + H2 + H3 + HA
H = - x 100
HC
Eq #S-14
Where:
H= Percent of population with no health insurance Indicator
H1= Percent of population under 19 years: No health insurance coverage (AJ35E017)
H2= Percent of population 19 to 34 years: No health insurance coverage (AJ35E033)
H3= Percent of population 35 to 64 years: No health insurance coverage (AJ35E050)
H4= Percent of population 65 years and over: No health insurance coverage (AJ35E066)
Hc = the total population
259
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.15. Equation for Percent of Households with at Least 1 Person That Has a
Disability
D1 + D2
D = x 100
HC
Eq #S-15
Where:
D= Percent of households with at least 1 person that has a disability Indicator
D1 = Household received Food Stamps/SNAP in the past 12 months: Households with 1 or more
persons with a disability (AJ05E003)
D2 = Household did not receive Food Stamps/SNAP in the past 12 months: Households with 1 or
more persons with a disability (AJ05E006)
Hc = the total number of households
Indicator 2.1.16. Equation for Percent of Population under Age of 18
P18
TP 18 = x 100
T
lC
Eq #S-16
Where:
TP18= Percent of population under 18
P18 = Population under 18- The sum of the following variables:
AJWBE003 "Estimates: Male: Under 5 years"
AJWBE004 "Estimates: Male: 5 to 9 years"
AJWBE005 "Estimates: Male: 10 to 14 years"
AJWBE006 "Estimates: Male: 15 to 17 years"
AJWBE027 "Estimates: Female: Under 5 years"
AJWBE028 "Estimates: Female: 5 to 9 years"
AJWBE029 "Estimates: Female: 10 to 14 years"
AJWBE030 "Estimates: Female: 15 to 17 years"
Tc = Total population
260
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.17. Equation for Percent of Population Who Are 65 or Over
P65
TP 65 = — x 100
T
LC
Eq #S-17
Where:
TP65= Percent of population above 65
P65 = Population above 65 - The sum of the following variables:
AJWBE020
"Estimates: Male: 65 and 66 years"
AJWBE021
"Estimates: Male: 67 to 69 years"
AJWBE022
"Estimates: Male: 70 to 74 years"
AJWBE023
"Estimates: Male: 75 to 79 years"
AJWBE024
"Estimates: Male: 80 to 84 years"
AJWBE025
"Estimates: Male: 85 years and over"
AJWBE044
"Estimates: Female: 65 and 66 years"
AJWBE045
"Estimates: Female: 67 to 69 years"
AJWBE046
"Estimates: Female: 70 to 74 years"
AJWBE047
"Estimates: Female: 75 to 79 years"
AJWBE048
"Estimates: Female: 80 to 84 years"
AJWBE049
"Estimates: Female: 85 years and over"
Tc = Total population
Indicator 2.1.18. Equation for Percent of Households with Single Members Who Are 65 or Over
PS65
TS 65 = x 100
T
1 C
Eq #S-18
Where:
TS65= Percent of households with single members who are 65 or over
P65 = Households with one or more people 65 years and over: 1-person household (AJX8E003)
Tc = Total population (AJX8E001)
Indicator 2.1.19. Equation for Percent of Population with Female Household Heads
Fhl + Fhl
TF = x 100
T
1 C
Eq #S-19
Where:
TF= % of population with female household heads
Fhl= In family households: Householder: Female (AJXHE006)
Fh2= In households: In nonfamily households: Householder: Female (AJXHE029)
Tc = Total population (AJXHE001)
261
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.20. Equation for Percent of Population That Is Black or African American Alone
AA
TB =—x 100
T
1 C
Eq #S-20
Where:
TB= Percent of population that is Black or African American alone
AA= Black or African American alone (AJWNE003)
Tc = Total population (AJWNE001)
Indicator 2.1.21. Equation for Percent of Population That Are Native Hawaiian or Other Pacific
Islander Alone
PI
NH = —xlOO
T
1 C
Eq #S-21
Where:
NH= % of population that are Native Hawaiian or Other Pacific Islander
PI= Native Hawaiian and Other Pacific Islander alone (AJWNE006)
Tc = Total population (AJWNE001)
Indicator 2.1.22. Equation for Percent of Population That Are American Indian or Alaska Native
Alone
A
AI =— xlOO
T
1 C
Eq #S-22
Where:
AI = % population that are American Indian or Alaska Native
A= American Indian and Alaska Native alone (AJWNE004)
Tc = Total population (AJWNE001)
Indicator 2.1.23. Equation for Percent of Population That Are Asian Alone
A
AI =— xlOO
T
1 C
Eq #S-23
Where:
AI = % of population that are Asian
A= Asian alone (AJWNE005)
Tc = Total population (AJWNE001)
262
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.24. Equation for Percent of Population That Belongs to Other Non-White Races
SO + S02 + R
OR = x 100
T
1 C
Eq #S-24
Where:
AI = Percent of population that belongs to other non-white races
SO = Some other race alone (AJWNE007)
SO2 =Two or more races: Two races including Some other race (AJWNE009)
R= Two or more races: Two races excluding Some other race, and three or more races
(AJWNE010)
Tc = Total population (AJWNE001)
Indicator 2.1.25. Equation for Percent of Population That Are Hispanic or Latino
tc
II
o
o
Eq #S-25
Where:
TH = % of population that are that are Hispanic or Latino
H = Hispanic or Latino (AJWWE003)
Tc = Total population (AJWNE001)
Indicator 2.1.26. Equation for Percent of Households That Have Limited English Speaking
Ability
SI + S2 + S3 + S4
TH = x 100
T
1 C
Eq #S-26
Where:
TH = % of population that have limited English speaking capability
SI = Spanish: Limited English speaking household (AJY2E004)
S2 = Other Indo-European languages: Limited English speaking household (AJY2E007)
S3 = Asian and Pacific Island languages: Limited English speaking household (AJY2E010)
S4 = Other languages: Limited English speaking household (AJY2E013)
Tc = Total population (AJY2E001)
263
-------
Handbook on Indicators of Community Vulnerability to Extreme Events
Indicator 2.1.27. Equation for Percent of the Population Who Are over 18 and Non-U.S.
Citizens
018 — TC
US = x 100
018
Eq #S-27
Where:
US = % of population who are over 18 and non-U.S. citizens
018 = Population over 18 (AJWBE003+AJWBE004+AJWBE005+AJWBE006+AJWBE027
+AJ WBE028+AJ WBE029+AJ WBE030)
TC = Over 18 population who are US citizens (AJ4QE001)
Indicator 2.1.28. Equation for Percent of Households That Moved within the Last 3 Years
M1+M2 + M3 + M4
M = x 100
H
Eq #S-28
Where:
M = Percent of households that moved within the last 3 years
Ml = Owner occupied: Moved in 2017 or later (AJ2QE003)
M2 = Owner occupied: Moved in 2015 to 2016 (AJ20E004)
M3 = Renter occupied: Moved in 2017 or later (AJ20E010)
M4 = Renter occupied: Moved in 2015 to 2016 (AJ20E011)
H = Total number of households (AJ2QE001)
264
-------
PRESORTED STANDARD
POSTAGE & FEES PAID
EPA
PERMIT NO. G-35
United States
Environmental Protection
Agency
Office of Research and Development (8101R)
Washington, DC 20460
Official Business
Penalty for Private Use
$300
Recycled/Recyclable Printed on paper that contains a minimum of
50% postconsumer fiber content processed chlorine free
------- |