DRAFT EPA/600/R-15/312
DO NOT CITE OR QUOTE External Review Draft
June 2016
www, epa. gov/research
Evaluating Urban Resilience to Climate Change:
A Multi-Sector Approach
NOTICE
THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not been formally
released by the U.S. Environmental Protection Agency and should not at this
stage be construed to represent Agency policy. It is being circulated for comment
on its technical accuracy and policy implications.
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC 20460
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DISCLAIMER
This document is distributed solely for the purpose of pre-dissemination peer review under
applicable information quality guidelines. It has not been formally disseminated by EPA. It does
not represent and should not be construed to represent any Agency determination or policy.
Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
This document is a draft for review purposes only and does not constitute Agency policy.
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CONTENTS
LIST OF TABLES v
LIST OF FIGURES vi
ACRONYMS AND ABBREVIATIONS viii
PREFACE x
AUTHORS, CONTRIBUTORS, AND REVIEWERS xi
EXECUTIVE SUMMARY xii
1. INTRODUCTION 1
1.1. MOTIVATION FOR A CLIMATE CHANGE AND URBAN RESILIENCE
ASSESSMENT FRAMEWORK 1
1.2. THE DOMESTIC AND INTERNATIONAL POLICY CONTEXT 5
1.3. OVERVIEW OF THE EPA CONCEPTUAL FRAMEWORK 5
2. BACKGROUND TO CONCEPTUAL FRAMEWORK AND TOOL
DEVELOPMENT 9
2.1. MULTICRITERIA ASSESSMENT AND MIXED METHODS 10
2.2. QUALITATIVE AND QUANTITATIVE INDICATOR DEVELOPMENT. 11
2.2.1. Qualitative Indicator (Question) Development 12
2.2.2. Quantitative Indi cator S el ecti on 17
2.3. EXAMPLES OF THRESHOLDS FROM PEER-REVIEWED
LITERATURE 18
2.4. EXAMPLE OF THRESHOLDS FROM GOVERNMENT
ORGANIZATIONS 19
2.5. EXAMPLES OF USING QUARTILES TO ASSIGN THRESHOLDS 20
2.6. DATA GATHERING APPROACH 21
3. DISCUSSION AND CONCLUSIONS 22
3 .1. VISUALIZING RESILIENCE 22
3.2. THE UTILITY OF QUALITATIVE ANALYSIS 24
3.3. INDICATORS OF RESILIENCE AND THEIR THRESHOLDS 25
3.4. SPECIFIC CHALLENGES IDENTIFIED THROUGH TOOL
APPLICATION 26
3.4.1. Discussions with Experts and Gathering City-Specific Knowledge....26
3.4.2. Lack of Data and Spatial/Temporal Data Variability 27
3.4.3. S ector Interconnect vity 30
3.4.4. Revisions to Questions and Indicators 30
3.4.5. Threshold Setting 31
3.4.6. Integrating Qualitative Information 31
3.5. FUTURE STEPS 32
APPENDIX A. TECHNICAL STEERING COMMITTEE MEMBERS 34
APPENDIX B. WASHINGTON, DC CASE STUDY 38
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CONTENTS (continued)
APPENDIX C. WORCESTER, MA CASE STUDY 82
APPENDIX D. COMPARISON OF RESULTS FOR WASHINGTON, DC AND
WORCESTER, MA 109
APPENDIX E. QUALITATIVE INDICATORS (QUESTIONS) 114
APPENDIX F. QUANTITATIVE INDICATORS 193
APPENDIX G. PARTICIPANTS 299
APPENDIX H. AGENDAS FOR WORKSHOPS IN WASHINGTON, DC 303
REFERENCES 307
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LIST OF TABLES
1. Example frameworks to assess community resilience to climate change 3
2. Potential climate changes of concern for urban areas 12
3. Water sector questions related to drought sensitivity, response, and learning 15
4. Example question and indicator from urban resilience tool 17
5. Macroinvertebrate index of biotic condition thresholds 18
6. Palmer drought severity index (PDSI) thresholds 19
7. Physical habitat index thresholds 20
8. Mobility management (yearly congestion costs saved by operational treatments
per capita) thresholds and scores 20
9. Percentage access to transportation stops thresholds and scores 21
10. Worcester, MA data availability 28
11. Quantitative Data Limitations 29
12. Major weather events and their impacts in the District of Columbia since 2003 ..41
13. Major weather and other events and their impacts in Worcester, MA 84
14. Washington, DC and Worcester, MA metrics at-a-glance 110
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LIST OF FIGURES
1. Urban climate resilience framework 6
2. Example quadrant plot 23
3. Washington, DC. Average question resilience and importance 58
4. Washington, DC. Average indicator resilience and importance 59
5. Washington, DC. Question quadrant mapping 61
6. Washington, DC. Indicator quadrant mapping 62
7. Washington, DC economy sector. Question and indicator quadrant mapping 63
8. Washington, DC energy sector. Question and indicator quadrant mapping 65
9. Washington, DC land use/land cover sector. Question and indicator quadrant
mapping 67
10. Washington, DC natural environment sector. Question and indicator quadrant
mapping 70
11. Washington, DC people sector. Question and indicator quadrant mapping 72
12. Washington, DC telecommunications sector. Question and indicator quadrant
mapping 75
13. Washington, DC transportation sector. Question and indicator quadrant
mapping 77
14. Washington, DC water sector. Question and indicator quadrant mapping 80
15. Worcester, MA. Average question resilience and importance 89
16: Worcester, MA. Average indicator resilience and importance 90
17. Worcester, MA. Question quadrant mapping 91
18. Worcester, MA. Indicator quadrant mapping 92
19. Worcester, MA economy sector. Question and indicator quadrant mapping 93
20. Worcester, MA energy sector. Question and indicator quadrant mapping 95
21. Worcester, MA land use/land cover sector. Question and indicator quadrant
mapping 97
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LIST OF FIGURES (continued)
22. Worcester, MA natural environment sector. Question and indicator quadrant
mapping 99
23. Worcester, MA people sector. Question and indicator quadrant mapping 100
24. Worcester, MA telecommunications sector. Question and indicator quadrant
mapping 102
25. Worcester, MA transportation sector. Question and indicator quadrant
mapping 104
26. Worcester, MA water sector. Question and indicator quadrant mapping 106
27. Washington, DC and Worcester, MA. Average indicator and question score
quadrant mapping Ill
28. Washington, DC and Worcester, MA. Average question resilience and
importance 112
29. Washington, DC and Worcester, MA. Average indicator resilience and
importance 113
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ACRONYMS AND ABBREVIATIONS
DCWASA
District of Columbia Water and Sewer Authority
DDOE
District Department of Environment
DDOT
District Department of Transportation
DHS
Department of Homeland Security
EMS
emergency medical service
FEMA
Federal Emergency Management Agency
GHG
greenhouse gas
GIS
geographic information system
DCHSEMA
District of Columbia Homeland Security and Emergency Management Agency
ICT
Information and Communications Technology
IPCC
Intergovernmental Panel on Climate Change
LEED
Leadership in Energy and Environmental Design
MBTA
Massachusetts Bay Transportation Authority
MCA
multicriteria assessment
MWCOG
Metropolitan Washington Council of Governments
NCPC
National Capital Planning Commission
OP
Office of Policy
ORD
Office of Research and Development
ORISE
Oak Ridge Institute for Science and Education
PEPCO
Potomac Electric Power Company
PHI
physical habitat index
TSC
Technical Steering Committee
UBPAD
Upper Blackstone Pollution Abatement District
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UFA
Urban Forestry Administration
UNFCCC
United Nations Framework Convention on Climate Change
U.S. DOT
U.S. Department of Transportation
USACE
U.S. Army Corps of Engineers
WARN
water/wastewater agency response network
WMATA
Washington Metropolitan Area Transit Authority
WWTP
wastewater treatment plant
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PREFACE
This report was prepared by the U.S. Environmental Protection Agency's (EPA) Air, Climate,
and Energy (ACE) research program, located within the Office of Research and Development,
with support from the Cadmus Group. The ACE research program provides scientific
information and tools to support EPA's strategic goal of taking action on climate change in a
sustainable manner. Such action includes both mitigation, which involves reductions in the
movement of heat-trapping greenhouse gases into the atmosphere, and adaptation, which
involves preparing for and adjusting to expected future climate. Both are important, but this
report focuses on adaptation to climate change. Climate change impacts are diverse, long-term
and not easily predictable. Adapting to climate change is difficult because it requires making
context specific and forward-looking decisions regarding a variety of climate change impacts and
vulnerabilities when the future is highly uncertain. Cities are on the front line for responding to
potential climate change impacts, but often do not know precisely the qualities or characteristics
that make them vulnerable or resilient to different impacts. This report supports the goal of
taking action on climate change in a sustainable manner by developing a conceptual framework
of urban resilience to climate change and using rigorously selected indicators to assess
community resilience to climate change. This framework is then successfully applied to two
quite different communities—Washington, DC and Worcester, MA—to evaluate their level of
resilience to climate change. Results support the utility of this indicator approach in helping
identify traits that enhance or inhibit each community's resilience to climate change in order to
focus adaptation planning on issues and areas that are least resilient to climate change impacts.
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
The Air, Climate and Energy (ACE) Research Program of EPA's Office of Research and
Development was responsible for producing this report. The report was prepared by The Cadmus
Group, Inc. in Waltham, MA, under EPA Contract No. EP-C-11-039; Task Order 10. Susan
Julius served as the Task Order Project Officer, providing overall direction and technical
assistance, and was a contributing author.
AUTHORS:
Julie Blue, The Cadmus Group, Inc.
Nupur Hiremath, The Cadmus Group, Inc.
Carolyn Gillette, The Cadmus Group, Inc.
Susan Julius, U.S. EPA, Office of Research and Development
INTERNAL REVIEWERS:
Britta Bierwagen, U.S. EPA, Office of Research and Development
Chris Clark, U.S. EPA, Office of Research and Development
Cathy Allen, U.S. EPA, Office of Policy
Kate Johnson, District Department of the Environment
Marissa Liang, Oak Ridge Institute for Science and Education Fellow
ACKNOWLEDGMENTS:
We would like to thank the members of the Technical Steering Committee (listed in Appendix
A), Washington, DC workshop participants, our collaborators at the District Department of the
Environment (DDOE), and those who spoke to us on behalf of the city of Worcester, MA for
their assistance during this project.
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EXECUTIVE SUMMARY
Global greenhouse gas emissions continue to rise and have been shown to lead to a range of
major and potentially adverse effects on the environment and public welfare. One of the
objectives of the Office of Research and Development (ORD) within the U.S. Environmental
Protection (EPA) is to provide the scientific basis for climate adaptation choices that support
sustainable, resilient solutions at individual, community, regional, and national scales. To
support this objective, ORD developed a tool that measures urban communities' resilience to
climate change. The tool incorporates both indicator data and input from local sector managers to
assess urban resilience for eight municipal management sectors: (1) water, (2) energy, (3)
transportation, (4) people (public health and emergency response), (5) economy, (6) land
use/land cover, (7) the natural environment, and (8) telecommunications. The tool is intended to
provide local-level managers with a way to prioritize threats to resilience using locally available
data across multiple sectors in order to inform adaptation planning. This report describes the tool
in detail and discusses results of applying it in two communities as case study examples
—Washington, DC and Worcester, MA. The applications are intended to help individual
communities as well as to identify important characteristics and activities that can be transferred
across communities to strengthen adaptive capacity at the national scale.
URBAN RESILIENCE DEFINITION, CONCEPTUAL FRAMEWORK, AND TOOL
A conceptual framework was developed based on our definition or urban climate resilience: the
ability of a city to reduce exposure and sensitivity to, and recover and learn from gradual
climatic changes or extreme climate events. This ability comes from a city's risk reduction and
response capacity, and includes retaining or improving physical, social, institutional,
environmental, and governance structures within a city. The components of urban climate
resilience reflected in the conceptual framework include three measures of vulnerability
(exposure, sensitivity, and response capacity), as well as the process of initiating responsive
action, learning from mistakes or ineffective responses, and building risk reduction capacity
(reducing exposure and sensitivity, and increasing response capacity). This cycle is supported or
affected by the presence of bridges to action (unforeseen, huge leaps made in response and
recovery capabilities), barriers to learning, and barriers to responding. These components guided
the selection of urban climate resilience indicators for the tool.
Because data were unavailable for some types of information identified by the conceptual
framework, a series of questions for local sector managers were developed to reflect factors
affecting resilience for which no indicators or appropriate datasets existed. A Technical Steering
Committee (TSC) guided the selection of questions for local sector managers and the selection of
indicators best suited to determine climate resilience for each climatic change/event of concern
that a city might have, and for each urban service potentially exposed. Questions were developed
primarily for assessing the abilities of the appropriate city sectors to respond to climate
changes/events, and to reduce future exposure/sensitivity, enhance response capacity, and learn
from past and future experiences.
The assessment approach the project team chose—using quantitative and qualitative (responses
to questions) data—makes use of detailed data sets when they are available, but recognizes that
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important elements of a city's resilience would be neglected if qualitative information provided
by city managers were to be excluded. For both the resilience indicators and the questions,
participants assigned an importance weight of 1 through 4. A weight of 1 indicates low
importance, and a weight of 4 indicates high importance. To score the questions, 4 possible
answers were developed for each question, with each question corresponding to a resilience
score of 1 through 4 (again with 1 representing low resilience and 4 representing high resilience).
To score the indicators, 4 quantitative ranges were applied to the data associated with each
indicator. These ranges also corresponded with a resilience score of 1 through 4. Participants
then selected the answers for the questions and reviewed the indicator ranges to determine the
resilience scores. Questions and indicators with high importance weights and high resilience
scores demonstrate where cities are most resilient overall. Questions and indicators with high
importance weights and low resilience scores demonstrate where cities are least resilient. Areas
of city performance with these combinations of rankings are the most critical areas of focus and
warrant attention as soon as possible.
Using published literature, threshold values were established for each indicator that defined the
upper and lower boundaries of the four resilience categories. These thresholds were designed to
represent resilience levels across all U.S. cities. When threshold values were not available in the
literature, panel data for U.S. cities were used. If data for an indicator were not available for a
sample of U.S. cities, case studies from one or several cities were analyzed to determine level of
resilience for those cases, and representativeness of that indicator for all U.S. cities.
DISCUSSION AND CONCLUSION
The tool was applied in both Worcester, MA and Washington, DC, cities representing a broad
spectrum of resources, planning, and risk. The use of these contrasting cities as case studies
allows for other cities on this spectrum to understand the applications of and potential outcomes
from use of the tool. It also allows us to test the strengths and weaknesses of the tool
methodology in a wide range of conditions and provides preliminary insight into the range (or
potential lack thereof) of risk exposures across cities with different geographic, economic,
population, and historical characteristics.
This project resulted in a comprehensive, transparent, and flexible tool for identifying the
greatest risks, successes, and priorities for decreasing urban vulnerability and increasing
resilience to climate change. The results can easily be analyzed with respect to the concepts of
exposure/sensitivity, response capacity, or learning, as the questions and indicators are
characterized accordingly. The visualizations developed to accompany the results of the
application of the tool in Washington, DC and Worcester, MA facilitates the interpretation of
case study results and are intended to further assist city managers in moving to the next step of
implementing climate change adaptation activities.
The data collected may be analyzed in the context of the framework, for the purposes of
identifying and prioritizing adaptation activities. This prioritization process may involve
categorization of critical vulnerabilities (i.e., sectors and issues within sectors for which
resilience is low but importance is high) into issues that can be addressed in a straightforward
manner with adaptation planning and implementation versus those over which there is less
control.
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The flexibility of the conceptual framework and tool were enhanced by the fact that use of the
tool does not necessarily require quantitative data. Indeed, the project team found that the
qualitative indicators (questions) were essential to the analysis, even when quantitative data were
readily available. The qualitative indicators (questions) can be mapped to specific events or types
of events, providing city managers and planners with a way to identify feedbacks and learn over
time. Additionally, application of the qualitative indicators fosters and requires interaction with
and between sector stakeholders, providing greater learning and coordination opportunities that
can be used to further refine the assessments of resilience and prioritization of activities in
response to the assessment findings.
Beyond the numeric values of resilience and importance collected across the sectors evaluated
(and the supporting data or responses that contributed to those scores), this effort collected
important information regarding the challenges that emerged for the knowledgeable
professionals in identifying and confirming appropriate and relevant sources of data to
effectively assess the proposed indicators. While the disparities in data available between the two
cities (both from background literature and through the data collection undertaken for the
purposes of this project) may complicate the data analysis effort in cities facing a relative lack of
data; the absence of such data in itself is a telling indicator of potential vulnerabilities to climate
change.
Major challenges encountered while developing and applying the tool included: gathering city-
specific knowledge (and gathering reasonably subjective knowledge); lack of data for some
sectors and temporal data variability; adequately identifying and capturing the interconnectivity
of sectors and the specific vulnerabilities that may exist as a result of such interconnectivities;
the adequacy or specificity of questions and indicators; and establishing reasonable thresholds
for all indicators.
Expansion and refinement of the application of the tool remains to be done. For example, much
of the work to be done on the interdependences among the sectors has not been undertaken by
this project. Future advancements in our understanding of these interdependences can be made
by examining linkages more closely, such as those between the Water and the Energy sectors.
However, interdependences have been addressed to some extent in that some questions and
indicators have been assigned to more than one sector, when appropriate.
Ultimately, EPA's urban resilience assessment tool offered valuable insight into the resilience of
Washington, DC and Worcester, and assessment results can be meaningfully incorporated into
ongoing planning. However, in many cases the information provided by the tool yielded as many
new questions as answers. With established new patterns of more extreme weather across the
globe, adaptation is essential for urban communities, and should be guided by an assessment of
sector-specific and overall resilience to climate change. Potential future expansions or
applications of this tool include: adapting it for on-line use; additional case studies that focus on
new users and expanded geographies and potentially examine the potential for pooling of
resources in the face of shared risk across multiple communities; and, sharing the lessons learned
and best practices that emerge out of the application of the tool by specific communities.
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1. INTRODUCTION
1.1. MOTIVATION FOR A CLIMATE CHANGE AND URBAN RESILIENCE
ASSESSMENT FRAMEWORK
Resilience has generally been defined as the ability to withstand or to recover from adverse
circumstances. This concept has been used in a number of fields including engineering and
environmental sciences (Anderies, 2014; Hopkins, 2010). Many different definitions exist,
although common themes that run through those definitions include the degree of disturbance
that can be tolerated before function is compromised, and the capacity to recover rapidly from a
physical disturbance (CARRI, 2013).
More recently in the sociological literature, resilience is treated as the ability of a socioecological
system to change and improve in response to stress rather than only bouncing back to a steady
state (Simmie and Martin, 2010). This is referred to as evolutionary resilience, with evolution in
this context meaning the ability to be flexible, diverse, and employ adaptive learning in the
context of changing circumstances. Evolutionary resilience frames recovery as a dynamic path of
an inter-related system progressing nonlinearly toward one of potentially multiple equilibria
rather than a direct path toward a single equilibrium (Kim and Lim, 2016).
When resilience entered the lexicon of climate change research, it was defined as the "amount of
change a system can undergo without changing state" (IPCC, 2001). Vulnerability was viewed as
the inverse of resilience, and was defined as "the degree to which a system is susceptible to, or
unable to cope with, adverse effects of climate change" (IPCC, 1997). However, since 2011, the
definition has been evolving. The first modifications of the definition by the Intergovernmental
Panel on Climate Change (IPCC) (2012) broadened it to include hazards and to bring in a focus
on short term disruptions as well as long term changes in averages. Then in 2014, the IPCC
definition was further expanded to include evolution in the ability to adapt, and learning and
transformation (IPCC, 2014), similar to the sociological definition.
These recent modifications to the definition of resilience allowed the climate change community
to better link the issue of climate change with sustainable development. As far back as the IPCC
2001 Assessment, it was recognized that adaptive capacity and sustainable development were
linked. Resilience, with its inclusion of future states (i.e., not just bouncing back, but bounding
forward), provides a more robust linkage and theoretical underpinning. Developing climate-
resilient pathways requires sustainable-development trajectories that also include adaptation and
mitigation to reduce climate change and its impacts.
This concept of climate resilience is key to preparing cities for the impacts of gradual and
extreme climate events. Increasing populations within urban ecosystems are putting heavier
demands on the supporting biophysical and socioeconomic systems (UN, 2014; Un-Habitat,
2011), and their activities are influencing natural systems, serving as forces for environmental
change at local, regional, national, and global scales (IPCC, 2014). Climate change represents yet
another source of vulnerability for both our natural and human systems.
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For urban ecosystems, the IPCC (2014) identifies one of the greatest threats of climate change to
be changes in the intensity and frequency of extreme weather events. Such events can severely
damage infrastructure and cause economic losses and injury or death to the population within an
urban ecosystem. Those urban areas that are along the coast may experience the combination of
sea level rise threatening water supplies and infrastructure damage from intense storms (Crosett
et al., 2004). The vulnerability of urban ecosystems is expected to be greater in coastal and
riverine areas and areas whose economies are closely linked with climate-sensitive resources
such as agricultural and forest products. Higher temperatures would have effects on urban air
quality, human health, energy and water requirements, and infrastructure. Urban ecosystems in
the Southwest, the Mountain West, the Southeast, and the Great Lakes may experience increased
strain on water resources due to pervasive drought conditions (USGCRP, 2014). Jenerette and
Larsen (2006) illustrate the susceptibility of many cities to climate change, particularly those in
more arid environments in which certain provisioning services, such as fresh water, may not be
feasibly obtained in sufficient quantity and at affordable rates. Finally, areas that experience
increases in annual precipitation and more intense precipitation events would have increases in
runoff volume and thus greater amounts of non-point source contamination in their water bodies.
The nonlinear, complex, and dynamic nature of climate change, and of urban socioeconomic and
environmental systems and their responses, poses significant challenges for existing methods and
frameworks. It is yet to be seen whether they are adequate to meet the challenges (Kim and Lim,
2016). Because of the convergence of population centers with exposures to climatic changes, in
particular to extreme events, developing approaches to analyze the degree of resilience to these
events to support planning efforts is needed and could significantly reduce the risks posed by
climate change.
There are a number of nascent efforts to develop robust indicator-based frameworks to measure
cities' resilience to the complex and dynamic risks posed by climate change in order to inform on
adaptation planning (Bahadur, 2015; Schipper and Langston, 2015). Table 1 below, adapted from
Schipper and Langston (2015), provides a sample of indicator framework efforts, their scope,
and the concepts of resilience they are designed to address. All of these frameworks go beyond
merely addressing the climate risk, natural hazard, and physical environment to incorporate
socioeconomic, learning and evolutionary aspects of resilience (Schipper and Langston, 2015).
The conceptual framework developed and applied by EPA for this project, and discussed in more
detail in Section 1.3, shifts the focus from domestic and international development to planning at
a city and sector level. Additionally, the evolutionary nature of resilience is acknowledged and
reflected within the conceptual framework and corresponding tool.
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Table 1. Example frameworks to assess community resilience to climate change
Framework
Rockefeller Foundation's
100 Resilient Cities
(ARUP's City Resilience
Framework)
http://www. lOOresilientcities
.ore/resilience#/- /
Scope
Health & Wellbeing; Economy &
Society; Infrastructure &
Enviromnent; Leadership & Strategy
Resilience Concept
"The capacity of individuals,
communities, institutions,
businesses, and systems within a
city to survive, adapt, and grow no
matter what kinds of chronic
stresses and acute shocks they
experience."
Indicator Approach
Qualitative indicators that are "driver"
statements representing actions that improve
cities' resilience (e.g.. Infrastructure: provide
reliable communication and mobility; Economy
& Society: ensure social stability, security, and
justice)
http://publications.arup.com/
publications/c/citv resilience
framework
Improving the quality of life
without compromising livelihood
options for others
Assessments of Impacts and
Adaptation of Climate
Change (AIACC) sustainable
livelihood approach
http://www.start.org/Proiects
/AIACC Project/working pa
pers/Working%20Papers/AI
ACC WP No017.pdf
Natural capital, financial capital,
physical capital, human capital, social
capital
Quantitative and qualitative indicators that
measure communities' ability to cope with and
recover from shocks and stresses, economic
efficiency and income stability, ecological
integrity, and social equity
UK Department for
International Development
Building Resilience and
Adaptation to Climate
Extremes and Disasters
framework (BRACED)
http ://www.braced. org/resour
ces/i/?id=cd95acf8-68dd-
4f48-9b41-24543f69f9fl
Adaptive capacity (assets and income,
strength and adaptability of
livelihoods, availability and use of
climate change information basic
services for vulnerable populations);
Anticipatory capacity (preparedness
and planning, capacity, coordination
and mobilization, risk information);
Absorptive capacity (savings and
safety nets, substitutable and diverse
assets and resources); Transformation
(leadership, empowerment and
decision-making processes, strategic
planning and policy, innovative
processes and technologies)
"Ability to anticipate, avoid, plan
for, cope with recover from and
adapt to (climate related) shocks
and stresses."
Quantitative and qualitative indicators that span
climate change impacts data, economic data,
livelihood data, ecological data, social and
institutional data, and data on planning and
decision making processes
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Framework
UNDP Community-Based
Resilience Analysis
(CoBRA) Framework
http://www.undp.org/content
/undp/en/home/librarypage/e
nvironment-
energy/sustainable land ma
nagement/CoBRA/cobra-
conceptual-framework. html
Scope
Natural capital, financial capital,
physical capital, human capital, social
capital
Resilience Concept
"Inherent as well as acquired
condition achieved by managing
risks over time at individual,
household, community and societal
levels in ways that minimize costs,
build capacity to manage and
sustain development momentum,
and maximize transformative
potential, [...] and manage change
by maintaining or transforming
living standards in the face of
shocks or stresses without
compromising their long-term
prospects."
Indicator Approach
"Composite set of context-specific multi-
sectoral quantitative and qualitative resilience
indicators." This process tool enables
communities to identify key building blocks of
resilience and assess attribution of various
interventions in attaining resilience
characteristics.
Characteristics of a Disaster
Resilient Community
http ://communitv. eldis. org/. 5
9e907ee/Characteristics2EDI
TION.pdf
Five thematic areas: governance, risk
assessment, knowledge and education,
risk management and vulnerability
reduction, and disaster preparedness
and response
"The capacity to (1) anticipate,
minimize and absorb potential
stresses or destructive forces
through adaptation or resistance; (2)
manage or maintain certain basic
functions and structures during
disastrous events; and (3) recover or
'bounce back' after an event."
Multiple dimensions for analysis, guided by the
five thematic areas and three sub-dimensions
(components of resilience, characteristics of a
disaster-resilient community, characteristics of
an enabling enviromnent). Specific resilience
indicators are at the level of activities, such as
hazards/risk data and assessment, public
awareness, knowledge and skills, financial
instruments, early warning systems, and so
forth.
US AID Measurement for
Community Resilience
(USAID) '
https://agrilinks.org/sites/def
ault/files/resource/files/FTF
%20Learning Agenda Com
munitv Resilience Qct%202
013.pdf
Food security, nutrition, health, social
capital (bonding social capital,
bridging social capital, linking social
capital), assets, ecosystem health,
poverty
"The general capacity of a
community to absorb change, seize
opportunity to improve living
standards, and to transform
livelihood systems while sustaining
the natural resource base. It is
determined by community capacity
for collective action as well as its
ability for problem solving and
consensus building to negotiate
coordinated response."
Combination of outcome measures and process
measures to establish a baseline food security /
nutrition index, health index, asset index, social
capital index, and economic / poverty index.
Baseline values are reanalyzed after
considering the nature of potential shocks and
stresses, community capacities to measure
resilience, and areas of collective action (e.g.,
disaster risk reduction, conflict management,
social protection, natural resource management,
management of public good and services).
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1.2. THE DOMESTIC AND INTERNATIONAL POLICY CONTEXT
Efforts to develop frameworks and tools to assess climate change resilience are supported and
driven by legislation and policy actions at the local, state, federal, and international levels. The
Intergovernmental Panel on Climate Change (IPCC) and the United Nations Framework
Convention on Climate Change (UNFCCC) have identified climate change adaptation planning
as a key element of the response to climate change on a global level (IPCC, 2007a; UNFCCC,
2010). Nationally, Executive Order 13693 requires consideration of climate change impacts on
operations and major facilities, in addition to national emissions reductions (Exec. Order 13693,
2015). Focusing more on vulnerability and adaptation, Executive Order 13690 (2015) requires
capital projects funded with taxpayer dollars to include consideration of increasing flood
severity. Currently, nearly 40 federal agencies have produced Climate Change Adaptation Plans,
Vulnerability Assessments, or metrics (Leggett, 2015), although many of these are high-level or
preliminary efforts.
Below the federal level, the majority of states have some climate planning statute (CES, 2014).
For example, New York's Community Risk Reduction and Resiliency Act (S6617B, 2014)
requires that all projects receiving state money consider the impacts of climate change during the
planning process. In 2012, Hurricane Sandy helped highlight infrastructure vulnerability to
natural disasters in New York State, encouraging the passage of S6617B that requires
consideration of sea level rise, storm surge and flooding in new developments, and infrastructure
regulations, permits and funding. At a local level, city governments must prepare for climate
change by protecting natural systems, the built environment, and the human population (Carmin
et al., 2012). Sixty-eight percent of cities worldwide have recognized the importance of
preparing for climate change and are in various stages of preparing or implementing adaptation
plans (Carmin et al., 2012). Currently in the United States, local governments or agencies in 21
states have developed a total of 66 adaptation plans (Georgetown Climate Center, 2014).
1.3. OVERVIEW OF THE EPA CONCEPTUAL FRAMEWORK
Consistent with the underlying principles in the literature and embodied in the frameworks in
Table 1, EPA developed a framework depicting the elements of resilience of an urban system
(see Figure 1). The framework builds on our definition of urban resilience to climate change (see
Box 1 for a list of working definitions), and employs a hybrid approach that uses both
quantitative and qualitative information to assess resilience. The framework includes not only the
concepts of vulnerability, exposure, and hazards that present risks to urban environments, but
also goes beyond a static view of the world and incorporates concepts of feedbacks, learning
through time, evolving in the ability to adapt and respond to challenges presented by gradual and
extreme climate change. The framework represents an ongoing process rather than just a
temporary state of response to external shocks (similar to Engle et al., 2013).
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Economy
Natural Environment
Response
(to address
risk)
raaacifai
Risk Reduction Capacity
Risk
Urban Climate Resilience
Learning
Climate Stressors
Other Exogenous
Factors
Response Capacity
Infrastructure
Exposure
Sensitivity
Ba= Bridges to action
Bl= Barriers to learning
Br= Barriers to responding
1 Figure 1. Urban climate resilience framework.
a These three elements—exposure, sensitivity, and response capacity—compose urban vulnerability.
(3 Learning outcomes are on three levels: reacting, refraining, and transforming (see Figure 1-3, IPCC, 2012).
Examples: reacting—increase a levee height; refraining—realizing the need to assess new storm duration frequency
distributions; transforming—assessing societal constructs and migrating to a more robust and comprehensive risk
management strategy.
*Risk reduction capacity is the ability to reduce exposure, reduce sensitivity, and/or increase the system's inherent
recovery potential in anticipation of harmful climatic changes/events.
2 In the framework itself, the left hand side focuses on anticipated future climate events and
3 system responses. Included in this side are the potential exposures to climate change, both
4 gradual and extreme, the potential sensitivity of sectors and systems to those exposures, and the
5 theoretical capability to respond to anticipated climate changes (response capacity, also referred
6 to as adaptive capacity in the climate change literature). The right side of the framework reflects
7 actual responses to real world experiences (whether by the community itself or through
8 observations of other communities and their experiences) of extreme weather events. Barriers to
9 action and bridges to better-than-anticipated responses are identified based on reflections after an
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event has occurred. The framework is meant to be applied iteratively through time to capture the
forward looking dynamic aspect of climate change and planning.
Box 1. Working definitions.
Urban Climate Resilience: The ability of a city or urban system, through its risk reduction and response
capacity capabilities, to reduce exposure and sensitivity to, and recover and learn from, gradual climatic
changes or extreme climate events, in order to retain or improve the integrity of its infrastructure and economic
systems, vital environmental services and resources, the health and welfare of its populations and communities,
and the flexibility and diversity of its institutional and governance structures (adapted from Leichenko, 2011).
Exposure: The presence of people; livelihoods; environmental services and resources; infrastructure; or
economic, social, or cultural assets in places that could be affected by climate change stressors (adapted from
IPCC, 2012).
Sensitivity: Predisposition of human beings, infrastructure, society, and ecosystems to be affected by exposure
to a climate stressor or an effect of that exposure (adapted from IPCC, 2012).
Response Capacity: Intrinsic capacity of a community to recover from alterations in its normal functioning due
to gradual changes in the climate or to extreme events that result in adverse human, material, economic, or
environmental effects.
Learning: Capability to recognize complex dynamics of socio-ecological systems in order to respond
appropriately to risk and make effective adaptation responses, identifying mistakes and shortcoming in those
responses following climate stressor events, and evolving as new information becomes available (drawn from
IPCC, 2012; Kasperson, 2012). (Learning outcomes are on three levels: reacting, reframing, and transforming
[see Figure 1-3, IPCC, 2012], Examples: reacting—increase levee height; reframing—realizing the need to
assess new storm duration frequency distributions; transforming—assessing societal constructs and migrating to
a more robust and comprehensive risk management strategy.)
Bridges to Action: Conditions under which unforeseen and huge leaps are made in a community's ability to
respond to and recover from alterations or disruptions in its normal functioning (e.g., due to social or technical
innovation).
Risk: A function of the exposure to and severity of the occurrence of a particular type of climate change
(gradual or extreme) and the way in which its consequences are likely to be mediated by the social vulnerability
of the human system. Risk can be assessed in terms of condition and predictive variables representing factors
such as economic well-being; health and education status; and preparedness and coping ability with respect to
particular climatic changes.
Risk Reduction Capacity: Ability to reduce risk by reducing exposure and sensitivity or increasing recovery
potential and adaptive capacity, to prepare for expected climatic changes or events.
Note: These definitions are considered operational definitions. Therefore, they might not be identical to the
definitions in the current literature, but they have been selected for their appropriateness to this application.
Increasing resilience of urban environments to climate impacts can happen on both sides of the
framework through reducing exposure or sensitivity of systems to potential impacts, expanding
the capability to respond, increasing learning, removing barriers that inhibit good responses, and
providing bridges to promote greater-than-anticipated responses.
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This framework serves as the basis for determining the type and breadth of indicators needed to
assess a city's resilience condition and evolution through time. Both quantitative and qualitative
indicators are employed to capture the various components and processes in the framework. This
hybrid approach provides more flexibility in the types and sources of information that can be
used, and reduces bias that can be present if there are limited quantitative datasets available for
specific places (Engle et al., 2013). Resilience research and indicators have often been based on
quantitative information alone, but interactions with stakeholders (city planners) can lead to
important qualitative information that considers local context, refines understanding of locally
specific vulnerability and resilience, and can calibrate and verify indicators (Engle et al., 2013).
The indicators selected are mapped to specific gradual and extreme climate events facing cities,
and to sectors within cities in order to be more informative to decision makers at the local level.
The framework provides thresholds established from the literature against which to measure
resilience, rather than relying on measures based on comparisons to other cities (relative
resilience). The value of understanding resilience (or lack of it), is in using that information to
take action to avoid or move farther away from and above thresholds in order to grow resilience.
Our approach provides for flexibility in the final selection of indicators to allow communities to
tailor the assessment of resilience to local situations. These innovative features combine
elements from other frameworks reviewed, but do not reside in any other single framework.
The remainder of this report provides a more in-depth description of the process of applying this
framework to the selection of qualitative and quantitative indicators (see Chapter 2), developing
the tool to assess urban resilience to climate change (see Chapter 2), and discussion of the results
and general insights from applying the tool to two case studies (see Chapter 3). Detailed results
of the two case studies—Washington, DC and Worcester, MA—are provided in Appendices B
and C, respectively, and comparison of results across the two case studies are provided in
Appendix D.
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2. BACKGROUND TO CONCEPTUAL FRAMEWORK AND TOOL DEVELOPMENT
The conceptual framework shown in Figure 1 captures the critical elements of resilience and
shows the boundaries (both spatial and conceptual) of our analysis (e.g., the region and beyond
are exogenous). This framework guided the selection of urban climate resilience indicators used
in our tool and tested at both case study sites. The project team tested and refined the tool in
Washington, DC and Worcester, MA to support work in those two communities and to create a
guide for city planners and others in other urban environments.
The framework is meant to be applied periodically to capture changes in resilience over time as
decision makers enact policies and take action to increase resilience to climate change. The
project team first outlined the full array of climate changes (means and extremes) that urban city
planners or managers might identify as being of greatest concern. The project team then
identified the city services that would be exposed to each climate change effect as well as the
city components (sectors/planning processes) that might be sensitive to those exposures. The
combination of these two factors provided us with the areas that need exposure and sensitivity
indicators.
For example, Section 2.2.1 focuses on drought as the climate stressor. The project team used
peer-reviewed scientific literature on drought resilience (as an example of resilience to a
particular effect of climate change) to identify qualitative indicators (also referred to throughout
the document as "questions") that help assess a city's capability to reduce exposure and
sensitivity to drought, respond to the risks drought poses, and promote learning from previous
experiences with drought. The combination of answers to the questions posed, and the
quantitative indicators of exposure and sensitivity, provide a measure of a community's overall
resilience to climate change. Answers and indicators are given greater or lesser weight depending
on the degree to which they contribute to resilience.
As discussed previously, the conceptual framework (see Figure 1) includes the three elements of
urban vulnerability (exposure, sensitivity, and response capacity) across any given sector, as well
as the process of initiating responsive action, learning, and building risk reduction capacity. This
cycle is supported or impacted by the presence of bridges to action, barriers to learning, and
barriers to responding.
EPA established a multisector Technical Steering Committee (TSC) to support the development
and implementation of an urban climate resilience tool, using the conceptual framework as a
foundation (see Appendix A for a list of TSC members). TSC members were selected from local,
state, and federal government agencies; academic institutions; nonprofit research institutions or
think tanks; and other venues. These individuals came from disciplines that represented different
aspects of planning and management relevant to an urban setting and to the eight municipal
management sectors within the tool: water, energy, transportation, people (public health and
emergency response), economy, land use/land cover, the natural environment, and
telecommunications. Each TSC member was assigned to one or more sector subcommittees
based on the relevance of his or her background to those sectors.
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The results of the work of the TSC to select qualitative (question) and quantitative (indicator)
metrics formed the basis of the urban climate resilience tool. This tool uses those indicators (see
Appendices E and F for qualitative and quantitative indicators, respectively), along with
threshold values for each quantitative indicator for eight city sectors mentioned above. Applying
the tool entails local government officials selecting indicators relevant to their community,
evaluating each indicator's importance for representing resilience, and answering indicator
questions and evaluating data results to score quantitative indicators of their community's
resilience to climate change. The process of developing this tool is described in more detail in
the sections that follow.
2.1. MULTICRITERIA ASSESSMENT AND MIXED METHODS
The project team, in consultation with the TSC, concluded that the analysis of a combination of
quantitative indicator data and more subjective responses to questions was the assessment
approach most likely to be of value to cities. Such an approach makes use of detailed data sets
when they are available, but recognizes that important elements of a city's resilience would be
neglected if more subjective information provided by city planners or managers were to be
excluded. Determining the results of these tool components using similar methods and scales was
critical to developing a set of unified, comparable outputs. To evaluate how best to integrate
these different types of data into a meaningful interpretation of resilience at the city scale, the
project team conducted a literature review on methodologies that can be used for combining
quantitative and qualitative information, with a focus on two areas: mixed methods and
multicriteria approaches.
Mixed methods research is positioned between the quantitative research and qualitative research
paradigms, as it attempts to synthesize viewpoints and methods from both. The main advantages
of mixed methods research include the following: it can provide stronger evidence for a
conclusion through the convergence of qualitative and quantitative findings; it can lead to the
formulation and answering of a broader range of research questions more so than a single
method; it can balance the strengths and weaknesses of differing methods; and it can be used to
increase the generalizability of the results of a study (Johnson and Onwuegbuzie, 2004).
Multicriteria analysis or multicriteria assessment (MCA) is a set of decision-support methods
that seeks to select one or a few preferred alternatives based on multiple criteria or objectives
(UNFCCC, 2005). MCA studies involving participant engagement (as our study does) solicit
input on preferences that is often converted to quantitative data on ordinal scales. In some
studies, the information gathered through interviews is exclusively qualitative (De Marchi et al.,
2000; Mendoza and Martins, 2006; Scolobig et al., 2008). In other studies, information from
participants is complemented by more definitively quantitative data (Scolobig et al., 2008) or
surveys (De Marchi et al., 2000; Scolobig et al., 2008). Because these types of MCA studies
combine quantitative and qualitative approaches, they are a subset of mixed methods research
that facilitates selection of alternatives or criteria by stakeholders or decision makers.
The project team developed the tool for the urban resilience case studies based on the approaches
taken by Hajkowicz (2008) and GEF (2010) (see Appendices B and C for Washington, DC and
Worcester, MA case study results and see Appendix D for comparison of results between case
studies). Hajkowicz (2008) used a multicriteria analysis method that included a priority matrix in
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which study participants ranked the issues presented according to the importance of the issue to
the participant. GEF (2010) is a more general mixed methods approach under which each
indicator used in the assessment was assigned an associated set of choices that provided a
quantitative rating (0 to 3) of that indicator. These studies offered the most practicable
approaches for working with indicators of resilience (when hard data were available), while also
addressing additional relevant issues via expert input from multiple individuals and providing
ways to handle both types of information similarly. The tool was designed for a single
respondent per sector, preferably the manager for that sector within city government. Consensus
among stakeholders was not a design requirement. This approach reduces time and costs and
targets the tool at those most in a position of power to implement and absorb tool findings.
The project team and sector subcommittees selected the quantitative and qualitative indicators
for the tool based on expert knowledge and the literature on climate change and urban resilience.
For each qualitative indicator (question), the project team developed four answers ranging from
least resilient to most resilient (see example question provided in Table 4). The project team
identified and gathered data for the quantitative indicators (see example indicator provided in
Table 4). Complete sets of the questions and indicators for the tool are presented by sector in
Appendices E and F.
For both the resilience indicators and the questions, the project team asked participants to assign
an importance weight of 1 through 4. A weight of 1 indicates low importance, and a weight of 4
indicates high importance. To score the questions, the project team developed four possible
answers to each question, with each question corresponding to a resilience score of 1 through 4
(again with 1 representing low resilience and 4 representing high resilience). To score the
indicators, the project team applied four quantitative ranges to the data associated with each
indicator (see Section 2.3 for additional information). These ranges also corresponded with a
resilience score of 1 through 4. Participants then selected the answers for the questions and
reviewed the indicator ranges to determine the resilience scores. Questions and indicators with
high importance weights and high resilience scores demonstrate where cities are most resilient
overall. Questions and indicators with high importance weights and low resilience scores
demonstrate where cities are least resilient. Areas of city performance with these combinations of
rankings are the most critical areas of focus of cities and should be addressed as soon as possible.
Quadrant plots are used to emphasize results that have this importance/resilience combination.
(See Figure 2 in Chapter 3, Section 3.1 for example quadrant plot and Figures 5 and 6 in
Appendix B for quadrant plots populated with data.)
2.2. QUALITATIVE AND QUANTITATIVE INDICATOR DEVELOPMENT
The project team met with each of the sector subcommittees twice to develop the sector-specific
qualitative and quantitative indicators and questions for the tool mentioned in the section above.
The qualitative indicators provided a way to obtain information for which no quantitative
indicator data were available.
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2.2.1. Qualitative Indicator (Question) Development
The TSC developed a four-step process to establish qualitative indicators (i.e., questions) best
suited to determine climate resilience. The final questions address all relevant climate stressors
and attempt to assess resilience in as comprehensive a manner as possible across all sectors (see
Appendix E for the full list of qualitative indicators). To demonstrate the process developed and
used by the TSC, the sections below lay out the process using drought as an example stressor and
water as an example sector. In practice, the process was repeated by the TSC for all relevant
climate stressors and across all sectors to develop the final list of questions.
2.2.1.1. Step 1: Identify Climatic Changes/Events of Concern.
Table 2 is an overview of all potential climate changes considered by the TSC for their potential
to affect urban areas. These correspond to the Climate Stressors referred to in Figure 1.
Stakeholders would select those that are of greatest concern for their urban area. Assessments of
resilience frameworks have suggested that it is critical to distinguish between short- and long-
term changes (i.e., extreme events vs. prolonged climate change). Furthermore, the most
effective methods of improving resilience are those that are targeted at long-term change, but
also address some immediate concern. (Engle et al., 2014)
Table 2. Potential climate changes of concern for urban areas
Wind
Temperature
Precipitation
Sea level rise
Gradual
± Mean maximum
± Average annual
± Average annual
+ Sea level
change
speed
± Seasonal average
± Season average
+ Coastal high
± Strong winds
± Daily min and
± Event magnitude/
water
max
duration
± Time between
events
Extreme
Heat wave (magnitude/duration)
events
+ Storm surge and
flooding
Droughts (intensity/duration)
Floods (magnitude/frequency)
Hurricanes (intensity/frequency)
Next, in Steps 2 through 4, the TSC evaluated and selected indicators for each component of the
framework to assess resilience. These steps were repeated for each climatic event or change of
concern.
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1 2.2.1.2. Step 2: Discuss Related Climate Stressors.
2 For the purposes of drought, the TSC evaluated the following:
3 • Change in the timing, form, or amount of precipitation that favor more frequent or
4 prolonged drought events
5 • Increased temperature (increased evapotranspiration)
6 • Increased wind (increased evapotranspiration)
7 2.2.1.3. Step 3: Discuss Urban Services Potentially Exposed to Drought and Urban Sectors
8 Potentially Responsible for Managing the Sensitivities of These Services.
9 Under this step, the TSC identified (a) urban services potentially exposed to drought that have
10 the potential to affect urban resilience and (b) the urban sectors responsible for managing
11 potential sensitivities of services to drought. This step corresponds to the "Exposure" and
12 "Sensitivity" elements in Figure 1 that help determine urban vulnerability.
13 Example Urban Services Potentially Exposed to Drought
14 • Water quality
15 • Groundwater supply
16 • Surface water supply
17 • Aquatic habitats, plants, and animals
18 • Terrestrial habitats, plants, and animals
19 • Recreational opportunities
20 • Look and feel of the landscape
21 • Local production/supply
22 2.2.1.4. Step 4: Evaluate the Ability to Reduce Exposure/Sensitivity, Enhance Response
23 Capacity, and Learn.
24 The final step of this exercise is similar to Step 3. The TSC discussed the urban services exposed
25 to drought (corresponding to the "Response" section of Figure 1) and supported development of
26 a series of questions to help determine a city's ability to (a) reduce exposure or sensitivity, (b)
27 increase response capacity, and (c) learn from past and future experiences with drought. Risk
28 reduction capacity encompasses (a), (b), and (c). In this project, these concepts also compose the
29 role of governance in urban climate resilience.
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Sample questions relevant to the water sector are shown for illustrative purposes in Table 3. The
questions are based on what Baker et al. (2009) has defined as the characteristics of an urban
area that determine resilience to drought: (a) current condition of the hydrologic environment
(both aquifers and water infrastructure), (b) the match between the scale of water governance and
the physical (hydrologic) scale in time and space, and (c) the capacity of the government to adapt
to hydrologic change (administrative and financial capacity to respond). (A similar approach can
be taken for all of the climate changes of concern and exposed services using available literature
and input from the steering committee.) Questions were selected to measure the capacity to
reduce risk, recover from drought, and learn in order to improve resilience in the future. As noted
previously, while the questions in Table 3 are relevant only to the water sector, questions were
developed for each sector to evaluate how that sector responded to drought. The final questions
address all areas of concern related to climate and resilience across all sectors.
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Table 3. Water sector questions related to drought sensitivity, response, and learning
Exposure/sensitivity (rs)
Increase response capacity (rc)
Learning related to drought (1)
Water quality
• Are there water bodies at
risk from water pollution
during drought?
Are there mechanisms in place to reduce
pollution to at-risk streams during drought?
Are there means of enhancing recovery of
water quality following drought, and are those
methods ready to implement?
Is there monitoring to assess
effectiveness of pollution reduction
and recovery strategies and means to
incorporate that information into
management planning?
Are there any barriers to responding
to or learning from drought events
past or future drought events?
Groundwater
supply
• Is the condition of
aquifers and water
infrastructure adequate to
address long-term
drought?
• Is the condition of
aquifers and water
infrastructure adequate to
address changes in
long-term drought risk
(duration, frequency,
severity)?
Are there options available to improve the
condition of aquifers and water infrastructure?
Do you have local control of your water
source(s) or are they managed by an outside
entity (private company, another state, etc.)?
Is resource control centralized or distributed?
Is there a joint institutional mechanism
through which water can be managed with
partners?
Does the joint institutional partnership provide
for flexibility to adjust management in the face
of extreme events?
Do water allocation laws (e.g., prior
appropriations doctrine) limit control of water
management?
Is water infrastructure and supply monitored
with respect to demand and distribution?
Does government allow for civic engagement
in resource management decision making?
Do mechanisms exist to generate funding for
actions that improve resource management?
Is there a mechanism in place to learn
from failures to execute drought
response plans for water supplies?
Do management entities regularly
evaluate management plans?
Have there ever been adjustments
made to management practices in
response to evaluations of past
drought responses?
Does the evaluation include the
assessment of potential future climate
change stressors?
Does the capacity exist to access and
assess monitoring data?
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Table 3. Water sector questions related to drought sensitivity, response, and learning (continued)
Exposure/sensitivity (rs) Increase response capacity (rc) Learning related to drought (1)
Surface water
•
Do you have local control of your water
• Do management entities regularly
supply
source(s) or are they managed by an outside
evaluate management plans?
entity (private company, another state, etc.)?
• Have there ever been adjustments
•
Is resource control centralized or distributed?
made to management practices in
•
Is there a joint institutional mechanism
response to evaluations of past
through which water can be managed with
drought responses?
partners?
• Does the evaluation include the
•
Does the joint institutional partnership provide
assessment of potential future climate
for flexibility to adjust management in the face
change stressors?
of extreme events?
• Does the capacity exist to access and
•
Do water allocation laws (e.g., prior
assess monitoring data?
appropriations doctrine) limit control of water
management?
•
Is water infrastructure and supply monitored
with respect to demand and distribution?
•
Does government allow for civic engagement
in resource management decision making?
•
Do mechanisms exist to generate funding for
actions that improve resource management?
Recreation • Do local water
•
Is open space used as an adaptation option for
management plans
protecting water resources during drought?
include provisions for
local parks and open
space?
• Will drought have
long-term impacts on
local parks and open
space?
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2.2.2. Quantitative Indicator Selection
To organize and obtain detailed data sets that were relevant to urban resilience, the project team
created a database of more than 1,400 indicators or metrics derived from the literature on climate
change and urban resilience. From this list, specific indicators were selected during meetings
with the subcommittees (see example indicator provided in Table 4 and Appendix F for the full
list of quantitative indicators).
Table 4. Example question and indicator from urban resilience tool
a. Example question
Sector ID# Question
Score = 4
Score = 3
Score = 2
Score = 1
(highest
(lowest
resilience)
resilience)
Economy 1 Is the economy of the
Largely
Somewhat
Somewhat
Largely
urban area largely
independent
independent
dependent
dependent
independent, or is it
largely dependent on
economic activity in
other urban areas?
b. Example indicator
Sector ID# Indicator Definition Value
Economy 1437 Percentage of city This indicator reflects the percentage of 11.0%
area in 500-year the metropolitan area that lies within the
floodplain 500-year floodplain.
For each of the quantitative indicators, threshold values were established defining the upper and
lower boundaries of the four resilience categories. Initial thresholds were established through a
review of published academic literature, panel data, case studies, and other reports, and were
later calibrated through discussions with expert stakeholders in each case study city
(Washington, DC and Worcester, MA). The initial thresholds were designed to represent
resilience levels across all U.S. cities. The literature review and threshold development for each
indicator followed a stepwise approach. An initial effort was made to identify published analyses
for U.S. cities describing categories of resilience with quantitative thresholds for the indicator. If
no such analyses were available, an effort was made to identify theoretical resilience thresholds
(presumably applicable to any site) based on modeling efforts.
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Where such studies were not available, panel data for U.S. cities were examined to establish the
range of values for the indicator across the sampled cities, and published literature (academic
literature, news articles, etc.) was consulted to determine levels of resilience for the indicator for
those cities. This step involved triangulating multiple qualitative assessments, including
interpreting discursive regimes, to establish levels of resilience (Olsen, 2004). If data for the
indicator were not available for a sample of U.S. cities, case studies from one or several cities
were analyzed to determine the level of resilience for those cases, and efforts were made to
determine how representative the case was for that indicator for all U.S. cities, in terms of
resilience (Walker, 2006). Finally, if data or case studies were not available for U.S. cities,
efforts were made to identify state level data or case studies, from which resilience categories
were established using the same qualitative triangulation approach, considering in addition the
ways in which resilience for the indicator may differ between the state and city level.
2.3. EXAMPLES OF THRESHOLDS FROM PEER-REVIEWED LITERATURE
Two examples of thresholds found in the literature are Indicator #460 (Macroinvertebrate Index
of Biotic Condition) and Indicator #1440 (Drought Severity Index). Thresholds for Indicator
#460 are adapted from Weigel et al. (2002). The original five thresholds and the thresholds
adapted to reflect a resilience score of 1 to 4 are listed in Table 5.
Table 5. Macroinvertebrate index of biotic condition thresholds
Weigel et al. (2002) thresholds
Adapted thresholds
Resilience score
75 to 80 = Very good biotic
condition
Greater than 15= very good
biotic condition
Resilience score = 4
60 to 70 = Good biotic condition
56 to 75 = Good biotic
condition
Resilience score = 3
50 to 55 = Fair biotic condition
46 to 55 = Fair biotic condition
Resilience score = 2
25 to 45 = Poor biotic condition
0 to 45 = Poor or very poor
biotic condition
Resilience score = 1
0 to 20 = Very poor biotic condition
Indicator #1440 (Drought Severity Index) also uses thresholds adapted from a literature source
(Alley, 1984). The original 11 thresholds and the thresholds adapted to reflect a resilience score
of 1 to 4 are listed in Table 6.
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Table 6. Palmer drought severity index (PDSI) thresholds
Alley, 1984 thresholds Adapted thresholds Resilience score
> 4.00—Extremely wet
Greater than or equal to -1.99
—Mild drought or no drought
Resilience score = 4
3.00 to 3.99—Very wet
2.00 to 2.99—Moderately wet
1.00 to 1.99—Slightly wet
0.50 to 0.99—Incipient wet spell
0.49 to -0.49—Near normal
-0.50 to -0.99—Incipient drought
-1.00 to -1.99—Mild drought
-2.00 to -2.99—Moderate drought
-2.00 to -2.99—Moderate drought
Resilience score = 3
-3.00 to -3.99—Severe drought
-3.00 to -3.99—Severe drought
Resilience score = 2
< -4.00—Extreme drought
Less than or equal to
-4.00—Extreme drought
Resilience score = 1
1 2.4. EXAMPLE OF THRESHOLDS FROM GOVERNMENT ORGANIZATIONS
2 Thresholds for Indicator #284 (Physical Habitat Index [PHI]) are drawn from a set of resource
3 briefs prepared by the U.S. National Park Service detailing research on the physical habitat
4 conditions of streams in the National Capital Region Network (Northrup, 2013). The original
5 four thresholds for PHI are listed in Table 7. No adaptations were needed to ensure that the
6 thresholds corresponded with the resilience scores of 1 to 4.
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Table 7. Physical habitat index thresholds
Northrup (2013) thresholds
Adapted thresholds
Resilience score
81 to 100—Minimally degraded
81 to 100—Minimally degraded
Resilience score = 4
66 to 80—Partially degraded
66 to 80—Partially degraded
Resilience score = 3
51 to 65—Degraded
51 to 65—Degraded
Resilience score = 2
0 to 50—Severely degraded
0 to 50—Severely degraded
Resilience score = 1
2.5. EXAMPLES OF USING QUARTILES TO ASSIGN THRESHOLDS
Thresholds could not be found in the literature for several indicators, so quartiles in the data sets
were used as the thresholds for these. Two examples are Indicator #1003 (Mobility Management)
and Indicator #1396 (Percentage Access to Transportation Stops). For Indicator #1003, the data
set was from the Urban Mobility Report produced by the Texas A&M Transportation Institute
(Schrank et al., 2012). This report contains operational cost savings for traffic congestion for a
list of 101 urban areas in the United States. Each urban area has a per capita operational cost
savings value. Thresholds for this indicator were defined as the quartiles of this per capita yearly
congestion cost savings data set. These thresholds are listed in Table 8.
Table 8. Mobility management (yearly congestion costs saved by operational
treatments per capita) thresholds and scores
Thresholds
Resilience score
Greater than $32 per person
Resilience score = 4
$18 to less than $32 per person
Resilience score = 3
$10 to less than $18 per person
Resilience score = 2
$2 to less than $10 per person
Resilience score = 1
Another example of using quartiles to define the thresholds among resilience categories is
Indicator #1396 (Percentage Access to Transportation Stops). Tomer et al. (2011) of the
Brookings Institution Metropolitan Policy Program detailed transit accessibility for 100 U.S.
cities. Each of the 100 cities in this report contained a value for "share (percentage) of working-
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age residents near a transit stop." Again, thresholds were defined as the quartiles of values for
the 100 cities listed in the report. These thresholds are listed in Table 9.
Table 9. Percentage access to transportation stops thresholds and scores
Thresholds
Resilience score
76 to 100% of population near a transit stop
Resilience score = 4
64 to 75% of population near a transit stop
Resilience score = 3
48 to 63% of population near a transit stop
Resilience score = 2
23 to 47% of population near a transit stop
Resilience score = 1
2.6. DATA GATHERING APPROACH
The project team designed the data collection approach for the two case studies based on
resources and data availability in Worcester, MA and Washington, DC. For Worcester, the tool
was used as designed: data were collected (via question and indicator) for each sector through a
series of discussions with the key city personnel responsible for the sector. For the District, the
project team convened two workshops to provide input on the tool (including input on individual
questions and indicators) and to provide data for the questions and indicators. This process was
modified slightly to reflect a more workshop-like approach, although ultimately one key District
representative for each sector scored each question. Additional details on the data collection
approaches for Washington, DC and Worcester are included in Appendices B and C,
respectively.
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3. DISCUSSION AND CONCLUSIONS
This project resulted in a comprehensive tool for identifying the greatest risks, successes, and
priorities related to urban vulnerability and resilience to climate change. This effort used as a
foundation the conceptual framework presented in Figure 1 (see Chapter 1). The results can
easily be analyzed with respect to the concepts of exposure/sensitivity, response capacity, or
learning, as the questions and indicators are characterized accordingly. In addition, the data
collected may be analyzed in the context of the framework, for the purposes of identifying and
prioritizing adaptation activities. This prioritization process may involve categorization of
critical vulnerabilities (i.e., sectors and issues within sectors for which resilience is low but
importance is high) into issues that can be addressed in a straightforward manner with adaptation
planning and implementation versus those over which we have limited or no control.
3.1. VISUALIZING RESILIENCE
Quadrant plots (see Figure 2) were used to visualize the data collected for each case study (see
Appendices B and C), and for comparisons across the two case studies (see Appendix D). The
quadrants are defined by the combination of resilience and importance scores (both ranked 1 to
4), and categorized based on priority into the following groups:
• Low priority = high resilience (3 or 4) and low importance (1 or 2)
• Small problems that can add up = resilience and importance both low (1 or 2)
• Monitor for changes = resilience and importance both high (3 or 4)
• Vulnerabilities to address = low resilience (1 or 2) and high importance (3 or 4)
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Example Quadrant Plot
Monitor for changes
Vulnerabilities to address
4
3
2
1
Low priority
Small problems that can add up
4 (high resilience) 3 2 1 (low resilience)
Resilience Score
Figure 2. Example quadrant plot.
These graphics facilitate the interpretation of case study results and are intended to further assist
city managers in moving to the next step of implementing climate change adaptation activities.
For example, if a qualitative or quantitative indicator ranked as highly important is also
identified as demonstrating high resilience, the city may be considered resilient with respect to
that data point or topic ("Monitor for Changes"), meaning the city is either inherently resilient or
has already taken steps to increase resilience. For example, Washington, DC received high
resilience and importance ratings for:
• thermal stress (quantitative indicator),
• extent to which green infrastructure was selected to provide the maximum ecological
benefits (qualitative indicator), and
• extent of information and communications technology redundancy, and availability of
multiple communication options, served by different infrastructure, for first responders
and the public (qualitative indicator).
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By identifying areas where resilience is high, cities may be able to apply lessons learned to other
areas that are also ranked as important, but may be significantly less resilient. Targeting planning
efforts at a sector's important and vulnerable points can also help cities prioritize what may be
limited resources to areas of greatest concern.
However, changes in city characteristics or climate risks could potentially decrease resilience
with respect to that issue in the future, and some level of monitoring and eventual reassessment
is warranted, should resilience decline. By contrast, a city can identify and choose to limit any
resources invested in monitoring data points or issues identified as highly resilient and of low
importance (though again, these ratings may change over time and the indicator cannot be
ignored entirely). The same can be said of low resilience and low importance qualitative and
quantitative indicators, which are considered as small problems that can add up over time. At the
most critical extreme are issues that are both important and have low resilience ("Vulnerabilities
to Address"). These issues should be addressed first, especially in cases where there are limited
resources available.
As noted previously, the tool is distinguished in part by its consideration of resilience across
multiple sectors. This allows for understanding the breadth of resilience across a city, relative
resilience among its sectors, and resilience of interdependent sectors such as water and energy.
Additionally, the visualizations can be used to assess progress over time as the tool is used
iteratively across sectors. The visualizations allow straightforward interpretations of what the
indicators and the questions mean for a city's resilience, and for what steps a city may want to
take to improve its resilience.
The identification numbers assigned to each qualitative and quantitative indicator are retained in
the visualizations to allow the reader to drill down into the information to determine exactly what
aspects of resilience are being addressed within each quadrant. This is particularly easy to do for
unique situations - such as when a quadrant has few questions populating it; when a quadrant has
few questions from a specific sector (even if many from other sectors); etc. This drill down
process may also be useful for testing hypotheses such as the interrelatedness of various aspects
of certain sectors (e.g., do sectors that are presumed to be interrelated often have questions
falling into the same quadrants, at least for those aspects of each sector that are presumed to be
interrelated?).
More work needs to be done to capture interdependences among the sectors. Future
advancements in our understanding of these interdependences can be made by examining
linkages more closely, such as those between the Water and the Energy sectors. However,
interdependences have been addressed to some extent in that some questions and indicators have
been assigned to more than one sector (e.g., Indicator 680: Ecological Connectivity - is used in
both the Land use/Land cover and the Natural Environment sectors), when appropriate.
3.2. THE UTILITY OF QUALITATIVE ANALYSIS
One of the most significant results of the case studies, and one that was anticipated by the TSC
when developing the questions (qualitative indicators) and selecting the quantitative indicators, is
that the qualitative questions were found to be essential to the analysis, addressing data quality
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and availability limitations at both the city and sector level in both case study applications. While
Washington, DC and Worcester, MA present a study in contrast of communities rich in data,
energy and enthusiasm, and institutional support for climate change adaptation and those that
lack sufficient data and resources related to climate, the project team encountered data
sufficiency and availability challenges with both. For example, data were available for only two
of the quantitative indicators relevant to the telecommunications sector in Washington, DC, and
no data were available for this sector in Worcester. Thus, little to nothing could be understood
through the tool about the resilience of this sector were it not for the questions. The resilience of
this sector, however, is critical for responding to extreme climate events. More broadly, the
insufficiency of available indicator data was a challenge across almost all sectors in Worcester.
Very little could be known about Worcester's resilience from the indicators alone. The questions,
however, provided a fairly robust assessment of where its strengths and weaknesses lay, and of
what issues city managers considered important or more ancillary.
As noted previously, the conceptual framework and therefore the tool reflect the inherently
evolving nature of resilience, and the tool's accessibility to the many small- and medium-sized
cities across the United States (and internationally) is enhanced by virtue of the fact that use of
the tool does not necessarily require data (i.e., if the user is applying only the qualitative
indicators). The qualitative indicators (questions) can be mapped to specific events or types of
events, providing city managers and planners with a way to identify feedbacks and learn over
time. As the framework is applied iteratively, questions can be reframed to identify specific
factors that increase or decrease resilience relative to the previous application of the framework.
Additionally, application of the qualitative indicators necessitates interaction with and between
sector stakeholders. Beyond the results derived from the indicators, these interactions provide
additional learning and coordination opportunities that would not have been possible through the
use of quantitative indicators alone, and that can be used to further refine the assessments of
resilience and prioritization of activities in response to the assessment findings. Furthermore,
many reviews of existing resilience frameworks (e.g., Schipper and Langston, 2014; Engle et al.,
2014) have suggested that quantitative indicators should not be used without context, as the
value of a single indicator can vary significantly in time and space. Individual quantitative
indicators may not appear relevant, or may be misleading, unless supplemented by qualitative
information on context, particularly for local or regional assessments.
Moving forward, it would be wise to evaluate data availability for a city before beginning to
undertake the more detailed assessment. If data availability is minimal, moving forward with
only the qualitative questions may be the most useful approach for evaluating the city's
resilience.
3.3. INDICATORS OF RESILIENCE AND THEIR THRESHOLDS
Quantitative indicators were incorporated into the tool to make the best possible use of data on
resilience when it was available. Thresholding of the indicators was based on the literature, when
possible, taking into account the full range of values the indicator took on in cities across the
United States. Thresholds may need to be re-evaluated when considering application of the tool
internationally.
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Ideally, the use of thresholds makes the resilience assessment more informative because the
thresholds bring a degree of objectivity to the assessment that does not depend on comparisons
with other cities (e.g., this is not a relative resilience assessment), and yet makes use of other
cities' experience in what values of the indicators may have reflected passing over from resilient
to less resilient, or vice versa. City decisions regarding adaptation (e.g., what to do, how much to
do) can be guided by indicator thresholds, as the city attempts to avoid moving beyond or above
indicator threshold levels that would reflect moving into a state of lowered resilience.
Understanding thresholds can increase management efficiency, can help cities prioritize
management goals more accurately, and, ultimately, may increase the likelihood of a city
achieving management targets in key areas.
As noted above, although the qualitative questions lack some of the objectivity provided by the
indicator data, they fill in gaps on issues that the quantitative indicators cannot address, often
because of data availability limitations, and sometimes because it is not possible to develop an
indicator that provides more objective information than the city managers' responses regarding a
specific question or issue. However, as the technology that enables faster, more efficient, and
less expensive data gathering, including citizen-science efforts, continues to advance, it may be
possible that issues in the tool that are currently addressed only by qualitative indicators can at a
later time be addressed by quantitative indicators. Additionally, while the disparities in data
available between the two cities selected for case studies (both from background literature and
through the data collection undertaken for the purposes of this project) may indicate that the
analysis effort in cities facing a relative lack of data may be difficult, the absence of such data in
itself is a telling indicator of potential vulnerabilities to climate change.
The initial application of the tool in a given city, as in the case studies detailed in the appendices,
is merely a snapshot in time of a city's resilience. The more important evaluation occurs over
time, when the tool is applied iteratively to a given city, and can thus better measure learning,
increases or decreases in overall resilience, increases or decreases in specific aspects of
resilience, and other changes in the urban system. Application of the tool over time can be used
to facilitate a city's learning and therefore increase the city's resilience. Resilience is dynamic
and evolutionary, and the framework used to develop this tool ensured that it would have that
dynamic nature built into it, allowing it to evolve over time through iterative application.
3.4. SPECIFIC CHALLENGES IDENTIFIED THROUGH TOOL APPLICATION
Beyond the numeric values of resilience and importance collected across the sectors evaluated
during the case studies (and the supporting data or responses that contributed to those scores),
this effort collected important information regarding the challenges that emerged in identifying
and confirming appropriate and relevant sources of data to effectively assess the proposed
indicators. The following discussions identify and expand on previously mentioned challenges
encountered in the development and application of the tool.
3.4.1. Discussions with Experts and Gathering City-Specific Knowledge
A major challenge encountered in applying the tool was gathering city-specific knowledge. Two
different methods were attempted in the two case studies presented in this report: a workshop
approach in Washington, DC and one-on-one discussions in Worcester, MA. The results
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presented in Appendix B (the Washington, DC case study) reflect the workshop approach, while
the results presented in Appendix C (the Worcester, MA case study) represent the responses of
the local government official selected as the most knowledgeable in the area addressed by each
question and indicator. The results are meaningful, and can provide city planners and managers
with a starting point for prioritizing climate change adaptation activities.
The opinion of one selected expert may not reflect the reality of the situation. There is also an
obvious tradeoff: more discussions equate to more time and expense. However, the workshop
approach may not necessarily be an improvement because adding more participants may not
mean more viewpoints, as groupthink may be an issue, especially if many different
representatives of the same agency give their opinion. It is possible that some lower ranking
members in a workshop setting are afraid to contradict their superiors. To complicate the issue
further, for many small cities, a workshop approach is not possible because there may be no
more than one or two people with sufficient local expertise to respond with meaningful data.
Ultimately, the value of the information depends on the context in which it is used. For example,
a finding based on a limited set of consultations may not be immediately actionable, but it may
be valuable in raising attention to issues where more study is needed and the greater expenses
associated with broader data collection may be justified. Limited data collection may also be
more useful in an aggregate sense: while there might not be much to learn about a specific item,
there may be greater confidence about the state of the sector on average. Broadly, we may learn
something about the average of the sectors overall. That is, even if a sector is only represented by
a single opinion, there can be confidence overall that a city needs to more seriously approach its
climate resilience planning in that area. In addition, supplemental indicator data help provide
balance to any subjectivity that may influence responses to questions or importance rankings for
both questions and indicators.
3.4.2. Lack of Data and Spatial/Temporal Data Variability
Lack of quantitative data was a significant limitation, particularly in the Worcester, MA case
study. Table 10 below highlights the lack of data for many sectors; no data were available for
two sectors and all desired data were available for only one sector. Based on the results of the
case studies, it is likely that this problem is systemic to many small- and medium-sized cities.
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Table 10. Worcester, MA data availability
Percentage of total indicators with
quantitative data
Economy
100%
Land use/land change
58%
People
50%
Water
50%
Transportation
42%
Natural environment
10%
Energy
0%
Telecommunications
0%
Data availability problems were not confined to Worcester; data availability in the District was
also limited in some cases. One advantage unique to the District was that data for several
indicators were available from sources maintained by District government entities and also from
national databases housing data on U.S. cities or states, with the advantage that the District was
treated as both a state and a city.
However, data availability was still the most significant limitation in the application of the urban
resilience tool in evaluating the District's resilience. Although some data were typically
available, many issues with data quality were observed. Factors characterizing data limitations
are listed in Table 11. Regardless of the city size, data limitations will continue to be an issue
when using this tool. However, this problem is not unique; nearly all real-world policy tools face
data gaps. Instead, it is important to remember that any analysis and findings must consider the
limitations specific to the city and sector and properly frame any conclusion or recommendations
in light of these limitations.
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Table 11. Quantitative Data Limitations
Data limitation
Description
Data not available
No data were available for the indicator.
Significant
postprocessing
Data were available but required significant processing to obtain the
value of the indicator for the District.
Multiple data sets
Calculating the indicator value required more than one data set, which in
some cases were challenging to combine due to different spatial and
temporal resolution.
Modeled data
Extensive modeling efforts were required to calculate the indicator
value.
Ongoing data
collection
Data collection efforts were proposed or ongoing and therefore
incomplete.
Outdated data
Data available were out of date and not appropriate for measuring the
current resilience of the city.
Regional-scale data
Data were available only at a regional scale (e.g., county) but not at the
District-scale.
Spatial variability of data can also be an issue. Within a city and within a sector, service quality
and vulnerability may vary, even from block to block depending on a host of factors (e.g.,
elevation, maintenance schedule, districting, etc.). Aggregating data at a city level may hide
problems that are severe only in specific instances, or in the opposite case, make problems
limited to small areas appear much worse than they are. For example, localized flash flooding
may well be more important than extreme large-scale events, especially in Worcester, given its
hilly topography. Given that the impacts of climate change stress are highly variable in space, a
geographic information system (GIS) approach (or other approaches in conjunction with GIS) to
mapping potential climate-related impacts across the urban area and the surrounding region of
which it is a part could help inform resilience assessment and planning efforts.
Lastly, temporal variability may pose additional challenges. Much data are historical, yet climate
vulnerabilities are often the result of changes from the historical pattern. For example, under
climate change scenarios, the 500-year flood area may be considerably larger than the
existing/historical 500-year flood area. Another way of looking at this is that the historical
500-year flood is likely to happen more frequently; flood maps must be revised in light of
climate change scenarios. In the District, rapid gentrification may have rendered historical
datasets less informative, even those collected as little as a decade ago. Datasets may be of
limited use because they do not reflect the future that needs to be planned for, or they must be
modified (consuming time and expertise to do so) in order to be of use.
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3.4.3. Sector Interconnectivity
This exercise also underscored the interconnectivity of sectors. For example, energy providers
are highly dependent on the transportation sector and emergency response during extreme
weather events. Likewise, continued provision of safe drinking water relies heavily on a resilient
energy sector, while disruptions to water service could have significant impacts on public health
and the economy, among other sectors. Cities may need to dig deeper into a specific sector to
understand the true nature of the vulnerability. The interconnected complexity associated with
measuring climate-related resilience requires considering each sector as a dynamic system
comprised of subsystems. For example, the water/wastewater sector could be divided up into
eight different subsystems: (1) water sources, (2) extraction of source water, (3) water treatment,
(4) water distribution to users, (5) wastewater discharge by water users, (6) wastewater (and
stormwater) collection, (7) wastewater treatment, and (8) effluent returns to receiving waters
and/or recycling for reuse.
This tool also homogenizes some vulnerabilities that occur because of interconnectivity across
sectors. Low scores in one sector may be largely because of another connected sector's poor
performance. For example, city water experts may rank the resilience of the water sector low
because they are aware that power for and transportation to water infrastructure may be difficult
or nonexistent in emergencies. In effect, these interconnections may drag down scores of many
sectors, driven by the poor relative performance of a single sector that is interconnected to many
others, and masking the fact that a single sector is responsible for low resilience across sectors.
3.4.4. Revisions to Questions and Indicators
Several participants challenged the questions and indicators used. Generally, the participants
noted three concerns: (1) the proposed question or indicator is not assessing what really matters,
(2) the proposed question or indicator is ill-defined, or (3) the proposed indicator would be very
difficult to measure and monitor.
Many of the issues that surfaced showed the deep technical and site-specific experience needed
to apply and create meaningful questions and indicators. For example, with regard to Indicator
#983 (average customer outage [hours] in recent major storm), one participant stated that
although this indicator attempts to address a relevant metric, it does not speak to the most
important variable at hand. Namely, the indicator should account for when the power is out,
which is more important than the length of time for which the power is out. That is, a power
outage in the middle of the night may have limited effects, and even frequent nighttime outages
may not be indicative of low resilience. However, a power outage in the middle of the day, or an
outage that occurs during a heat wave or extreme event leaves city residents much more
vulnerable.
These concerns show the need for continued refinement of the tool. At the same time, this also
highlights the utility of the framework used to develop this tool, which is iterative and
evolutionary by design. The questions and indicators selected for the tool are, in many cases,
akin to canaries in a coal mine. They provide a sense of what might happen in the future in a city
facing threats to its resilience due to climate change. The most important data may be that which
shows that a particular sector or subsector is close to reaching a resilience threshold, and that
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systems might fail if further stressed by future climate change. While iterative application of the
tool, and application of the tool across additional urban communities will help to refine some
questions and indicators further, we have attempted to select the most relevant resilience
questions and indicators in this first version of the tool - that is, those that best reflect important
aspects of resilience and show where a city is solidly resilient or, on the other hand, at risk from
future stressors.
3.4.5. Threshold Setting
Thresholds are not easily set for all indicators. Ideally, the thresholds used for determining when
the values of an indicator of resilience to climate change pass from a state of nonresilience to a
state of resilience would be determined objectively based on a full understanding of the indicator
and all its components. However, identifying objective thresholds for indicators is difficult as
they (a) are not widely applicable at different scales; (b) can be challenging to identify in social,
environmental, or economic settings unless they have been breached and a disturbance has been
observed, and (c) might vary spatially and temporally, making it difficult to apply them
uniformly across different cities (U.S. EPA, 2011). For most indicators used in this study,
objective thresholds were not available in the published literature. When available, thresholds
varied spatially across the United States, and were, in some cases, not applicable to Washington,
DC or Worcester, MA. For this tool, the challenge was resolved by asking the city planners or
managers to review draft thresholds and change them if needed based on their expert knowledge.
This is also an area where iterative application of the tool over time and in multiple locations can
help determine the most accurate thresholds to use; the framework used in this study has the
capacity to evolve as city planners or managers assess resilience and implement action plans.
3.4.6. Integrating Qualitative Information
The urban resilience tool aims to provide a quantitative measure of a city's resilience to climate
change. However, to reflect the many urban management areas and issues related to a city's
resilience to climate change, it is necessary to include information when measured data are not
available, or an issue does not lend itself to immediate quantitative measurement. Therefore,
information on specific metrics that are measured by city departments, government agencies at
other levels (e.g., state or federal), or other entities should be supplemented by more subjective
information based on managerial experience. Finding a methodology that integrates on the one
hand subjective information from experts on city sectors, and, on the other, metrics that indicate
resilience, on a single scale is key. In this tool, the project team used a mixed method approach
(discussed in Chapter 2, Section 2.1) to integrate information obtained by asking questions of
city planners or managers with information obtained through data searches to overcome this
challenge.
Integrating quantitative data from resilience indicators with qualitative information from
responses to questions was essential. Washington, DC is a city more likely than usual to have
indicator data available because of the District's unique relationship with the Federal government
and because in many ways it functions as its own state. But even there, subjective information
from city planners or managers was needed to address all of the desired facets of climate
resilience. Only with a mix of indicator data and questions was the tool able to fully capture
where cities were or were not climate resilient. In Worcester, which lacked detailed, up-to-date
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indicator data, the questions asked of city planners and managers were even more important. In
all likelihood, Worcester's data availability is a more representative data model for American
cities than the District's. Finding meaningful ways to incorporate expert judgment is essential for
any tool that wishes to find widespread use by urban areas across the United States.
Information from city planners and managers also supplemented known data shortcomings. Even
where indicator data were available, the information may not reflect the full spatial and temporal
diversity of the city. For example, the dataset may start or stop before capturing weather events
such as floods or droughts, which may occur only at long intervals, such as every 100 or 200
years. Additionally, poor sampling in existing studies may fail to include all subpopulations.
Information provided by city planners and managers based on their experience allows for these
data shortcomings to be partially accounted for by the tool.
The results of the case studies in Washington, DC and Worcester, MA suggest that there may be
more utility to focusing on qualitative information, especially in cities with limited data and
resources. Furthermore, by applying this tool iteratively to the same city over time, city planners
and managers can optimize the collection of qualitative information and potentially identify areas
where targeted quantitative data collection would be most beneficial. The efficient allocation of
limited resources is critical to increasing urban resilience, as cities are unable to address all
issues, and must prioritize their time, effort, and investments appropriately.
3.5. FUTURE STEPS
Urban centers in the United States (and globally) have a long way to go in attempting to adapt to
climate change. City planners and managers in both Washington, DC and Worcester, MA agreed
that the urban resilience assessment tool developed by EPA provided valuable insights into the
resilience of their respective cities and assessment results can be incorporated into ongoing
planning. However, in many cases the information provided by the tool yielded as many new
questions as answers. It is in this context that the evolutionary nature of the framework used to
develop this tool becomes particularly important. The greatest utility of the tool is in applying it
repeatedly over time to the same city to increase understanding of current and future resilience,
and critically evaluate the successes and failures of adaptation initiatives. With established new
patterns of more extreme weather across the globe, adaptation is essential. A first step for many
cities will be an assessment of their sector-specific and overall resilience to climate change.
Additional steps could include:
• Additional applications of the tool
With a sample size of two, it is difficult to draw conclusions about how the tool will
perform across a wider swath of American cities. With additional cities applying the tool
to perform assessments in different environments, the tool could be tested in new ways
and its applicability and value to a broad range of city governments could be further
refined. For example, it is not known how well the tool would perform for cities located
in western or southern parts of the country, facing different climate change risks, with
built environments that are more recent in origin than the early 1700-1800s. Likewise, it
is not known how well the tool would perform for resource-limited cities with similar risk
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profiles that choose to pool their resources to address climate vulnerability more
efficiently, as some cities and counties are starting to do now.
• Lesson sharing and best practices
Many regions have cities that share common histories and common climate risks.
Learning from similar cities will be essential if all cities are to address climate
vulnerabilities. How could the tool be used to develop successful adaptation strategies
and how could those adaptation strategies be shared with cities in a region that share the
same vulnerability profile (a similar set of values across vulnerability indicators)? Work
by Smit and Wandel (2006) on community-based adaptation analyses shows that
adaptation opportunities are multidimensional and are affected by exposures,
sensitivities, adaptive capacities, and other factors. Application of the tool by more cities
can help identify commonalities in these factors and help identify opportunities for
sharing best practices, policies, and other adaptive strategies more quickly and effectively
to meet the growing challenge of overcoming climate risks.
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2
Name
Agency
Expertise
Indicator
Sub-Committee
Baranowski, Curt
USEPA
Water utilities and security
•
Energy and Water
Cain, Alexis
USEPA Region 5
USEPA regional science
representative
•
Natural Environment
Carmin, JoAnn
Massachusetts
Institute of
Technology
Sociology and climate
adaptation
•
People
Chan, Steve
Harvard University
Information technology
•
Information and
Communications
Technology
Cutter, Susan
University of South
Carolina
Hazards and disasters
•
•
Energy and Water
Natural Environment
Farris, Laura
USEPA Region 8
Engineering and climate
change
•
•
•
Energy and Water
Transportation
Natural Environment
Fay, Kate
USEPA Region 8
USEPA regional science
representative
•
Natural Environment
Gonzalez, Larry
USEPA Region 7
USEPA regional science
representative
•
Natural Environment
Goold, Megan
USEPA Region 3
USEPA regional science
representative
•
Natural Environment
Greene, Cynthia
USEPA Region 1
USEPA regional science
representative
•
Natural Environment
Gross-Davis,
Carol Ann
USEPA Region 3
USEPA regional science
representative
•
•
Natural Environment
People
Hansen, Verle
USEPA
Land use planning
•
•
•
Energy and Water
Transportation
People
Hodgeson,
Kimberley
Cultivating
Sustainable
Communities
Public health and urban
planning
•
•
Energy and Water
People
Holway, Jim
Sonoran Institute
Land use and water
resources planning and
smart growth
•
•
•
Energy and Water
Transportation
Natural Environment
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APPENDIX A. TECHNICAL STEERING COMMITTEE MEMBERS
Technical Steering Committee Members
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Name
Agency
Expertise
Indicator
Sub-Committee
Hulting, Melissa
USEPA Region 5
USEPA regional science
representative
•
Natural Environment
Jackson, Laura
USEPA
Ecology
•
Natural Environment
Jencks, Rosey
San Francisco
Public Utilities
Commission
Stormwater engineering
•
Energy and Water
Jones, Bill
USEPA Region 3
USEPA regional science
representative
•
Natural Environment
Kafalenos, Robert
United States
Department of
Transportation
(USDOT)
Transportation
•
Transportation
Kasperson, Roger
Clark University
Risks and uncertainty
•
•
Energy and Water
People
Kreider, Andrew
USEPA Region 3
USEPA regional science
representative
•
Natural Environment
LaGro, James
University of
Wisconsin -
Madison
Urban planning
•
•
•
Energy and Water
Transportation
Natural Environment
Lawson, Linda
USDOT
Transportation
•
Transportation
Leichenko, Robin
Rutgers University
Economics and finance
•
Economy
Lupes, Rebecca
USDOT
Transportation
•
Transportation
Machol, Ben
USEPA Region 9
USEPA regional science
representative
•
Natural Environment
McCullough, Jody
USDOT
Transportation
•
Transportation
McGeehin,
Michael
Retired
Human health
•
People
Mitchell, Ken
USEPA Region 4
USEPA regional science
representative
•
Natural Environment
Narvaez,
Madonna
USEPA Region 10
USEPA regional science
representative
•
Natural Environment
Newman, Erin
USEPA Region 5
USEPA regional science
representative
•
Natural Environment
Olson, Kim
USEPA Region 7
USEPA regional science
representative
•
Natural Environment
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Name
Agency
Expertise
Indicator
Sub-Committee
Pincetl, Stephanie
University of
California Los
Angeles
Urban planning
• Energy and Water
• Transportation
• Natural Environment
Pyke, Chris
US Green Building
Council
Green building
• Energy and Water
Raven, Jeffrey
Architect
Architecture and
sustainability
• Energy and Water
• Transportation
• Natural Environment
• People
Quay, Ray
Decision Center for
a Desert City ASU
City planner
water and wastewater
• Land Use/Land Cover
• Natural Environment
Rimer, Linda
USEPA
Regional and local climate
adaptation
• Energy and Water
• People
Rosenberg, Julie
USEPA
Climate change, mitigation
and cities
• Energy and Water
• People
Ruth, Matthias
Northeastern
University
Governance
• Energy and Water
• Economy
Rypinski, Art
USDOT
Transportation
• Transportation
Santiago Fink,
Helen
US Agency for
International
Development
Planning and international
• Natural Environment
• People
Saracino, Ray
USEPA Region 9
USEPA regional science
representative
• N/A
Schary, Claire
USEPA Region 10
USEPA regional science
representative
• Natural Environment
Scheraga, Joel
USEPA
Economics and finance
• Economy
Shephard, Peggy
WE ACT for
Environmental
lustice
Environmental justice
• Energy and Water
• Economy
• People
Smith, Gavin
University of North
Carolina - Chapel
Hill
Disasters and hazards
• Energy and Water
• Natural Environment
Solecki, Bill
Hunter College of
the City University
of New York
Economics and finance
• Economy
Spector, Carl
City of Boston
Air quality
• Natural Environment
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Name
Agency
Expertise
Indicator
Sub-Committee
Stults, Missy
National Climate
Assessment
Urban sustainability
•
•
•
Energy and Water
Transportation
Natural Environment
Susman, Megan
USEPA
Urban planning and smart
growth
•
•
•
Energy and Water
Transportation
Natural Environment
Wilbanks, Tom
Department of
Energy
Energy systems
•
Energy and Water
Willard, Norman
USEPA Region 1
Climate change and state
and regional policy
•
•
Energy and Water
People
Wong, Shutsu
USEPA Region 1
USEPA regional science
representative
•
Natural Environment
Yarbrough, James
USEPA Region 6
USEPA regional science
representative
•
Natural Environment
Zinsmeister,
Emma
USEPA
Climate change, mitigation
and cities
•
•
Energy and Water
People
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APPENDIX B. WASHINGTON, DC CASE STUDY
This Appendix contains the Washington, DC case study. Section B.l provides background on the
known climate vulnerabilities faced by Washington, DC and background on the existing
planning, if any, the city has undertaken in order to address these vulnerabilities. Section B.2
reviews the results for Washington, DC. Results are by sector and accompanied by visual data
summaries.
B.l. WASHINGTON, DC BACKGROUND
Washington, DC (also referred to as DC or the District) is a major and growing east coast
population center that provides an opportunity to test the urban resilience tool in a city with
significant planning, financial, and data resources. The District has already begun climate change
resilience and adaptation efforts (see Section B. 1.1.2 in this appendix), allowing for testing of the
tool in an environment where the results can be used to augment existing or upcoming adaptation
planning efforts, including the Climate Change Adaptation Plan under development by the
District (DDOT, 2013). The outcome of this effort includes an unprecedented union of expert
judgment and quantitative data to assess the District's climate change resilience that is
complementary to DC's already extensive ongoing efforts. The combined outcome of these
initiatives provide the District with a more nuanced analysis of the areas in which resilience can
and should be strengthened, and supports many of the Sustainable DC1 initiative's existing
economic, environmental, public health, and quality of life goals (Sustainable DC, 2015;
discussed in more detail later in this appendix).
Washington, DC is located on the Atlantic Coastal Plain, at the confluence of the Anacostia and
Potomac Rivers, which flow into Chesapeake Bay. At its lowest point along the Potomac River,
the District is at sea level. The flat topography of the coastal plain puts the area at high risk from
sea level rise and storm surges from hurricanes and other storms.
The District's population (currently over 646,000) grew by an estimated 7.4% between 2010 and
2013 (U.S. Census Bureau, 2013a). The federal government and services provided to the federal
government constitute a large segment of the District's economy. Tourism is also a major
component of the local economy. These components of the economy are reflected in the sectors
that employ the greatest numbers of people: professional, scientific and technical services,
education, healthcare and social assistance, and public administration (U.S. Census Bureau,
2013b).
Despite a strong, stable economy that has produced a median income approximately 21% above
the national median, the District's poverty rates are higher than average (U.S. Census Bureau,
2013a). Therefore, the District's population ranges broadly from those with a great need for more
resilience to potential climate change impacts to those who are already highly resilient to
1 The Sustainable DC planning initiative began in 2011. Led by the District Department of the Environment and
Office of Planning, the initiative's goal is to "make DC the most sustainable city in the nation." (Sustainable DC,
2015).
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impacts. In recent years, the District has rapidly gentrified, with home prices in nearly one-fifth
of the city's census tracts moving from the bottom half to the top half of overall citywide
housing prices over the period 2000-2007. Nationally, this is the 5th highest rate of gentrification
(behind Boston, MA; Seattle, WA; New York City, NY; and San Francisco, CA) (Hartley,
2013). As a result, older data sets may not reflect current demographic realities.
B.l.l. Known Vulnerabilities
B. 1.1.1. Extreme Weather Events
The following types of extreme weather events have been identified by public agencies as posing
either a "high" or "medium-high" risk to counties in or near the DC metropolitan region, and
might be exacerbated by climate change: drought, extreme heat, flash/river flooding,
thunderstorms, tornadoes, winter weather (ice and snow), and tidal/coastal flooding (MWCOG,
2013c). Hurricanes, thunderstorms, lightning, hail, wind, and tornados are estimated to cost the
DC metropolitan region more than $14 million in damage annually (MWCOG, 2013a). Six
recent extreme weather events in the District have tested the resilience of the city's institutions
and material infrastructure as shown in Temperature
Not only have temperatures in the DC area risen over the past century, the pace of warming has
increased (MWCOG, 2008; Kaushal et al., 2010). The District Department of Transportation
(DDOT) has identified trees and vegetation as among the assets that are vulnerable to the effects
of rising temperatures (DDOT, 2013). In the coming century, surface air temperatures in the
region are projected to rise another 6.5°F (3.6°C) (IPCC, 2007b). The District is a documented
urban heat island, with downtown 10-15°F hotter than nearby rural regions on summer
afternoons. The number of days "dangerous" to health within city limits has increased from
8-10% of summer days in the 1950s and 1960s to 18% of summer days in the last decade
(Kalkstein et al., 2013). Potentially, some of these increases could be reversed. Modeling
suggests minor (10%) increases in reflectivity and vegetative cover would save approximately 20
lives per decade along with reductions in the number of heat-related hospital admissions
(Kalkstein et al., 2013).
Higher temperatures and the expected changes in rainfall patterns will change the ecological
profile (trees and vegetation) of the region. Over time, crop species and forest species currently
characteristic of the Mid-Atlantic region (e.g., apples and grapes; maple-beech-birch deciduous
forest) might no longer be viable. While overall forest productivity might increase, the increase
in temperatures is also likely to result in increased invasive species and reduced biodiversity, as
well as more frequent and more severe forest fires (MWCOG, 2008, 201 la, 2013a). The earlier
onset of spring resulting from this warming will affect individuals with pollen allergies, as well
as the local tourist industry (including the annual Cherry Blossom Festival). The peak bloom
date for cherry blossoms could be 5 to 13 days earlier in year 2050 than today (Chung et al.,
2011; Abu-Asab et al., 2001).
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Table 12.
B.l.1.2. Temperature
Not only have temperatures in the DC area risen over the past century, the pace of warming has
increased (MWCOG, 2008; Kaushal et al., 2010). The District Department of Transportation
(DDOT) has identified trees and vegetation as among the assets that are vulnerable to the effects
of rising temperatures (DDOT, 2013). In the coming century, surface air temperatures in the
region are projected to rise another 6.5°F (3.6°C) (IPCC, 2007b). The District is a documented
urban heat island, with downtown 10-15°F hotter than nearby rural regions on summer
afternoons. The number of days "dangerous" to health within city limits has increased from
8-10% of summer days in the 1950s and 1960s to 18% of summer days in the last decade
(Kalkstein et al., 2013). Potentially, some of these increases could be reversed. Modeling
suggests minor (10%) increases in reflectivity and vegetative cover would save approximately 20
lives per decade along with reductions in the number of heat-related hospital admissions
(Kalkstein et al., 2013).
Higher temperatures and the expected changes in rainfall patterns will change the ecological
profile (trees and vegetation) of the region. Over time, crop species and forest species currently
characteristic of the Mid-Atlantic region (e.g., apples and grapes; maple-beech-birch deciduous
forest) might no longer be viable. While overall forest productivity might increase, the increase
in temperatures is also likely to result in increased invasive species and reduced biodiversity, as
well as more frequent and more severe forest fires (MWCOG, 2008, 201 la, 2013a). The earlier
onset of spring resulting from this warming will affect individuals with pollen allergies, as well
as the local tourist industry (including the annual Cherry Blossom Festival). The peak bloom
date for cherry blossoms could be 5 to 13 days earlier in year 2050 than today (Chung et al.,
2011; Abu-Asab et al., 2001).
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Table 12. Major weather events and their impacts in the District of Columbia
since 2003
Weather event
Date
Impacts
Hurricane Isabel
September
2003
129,000 customers lost power primarily due to fallen trees
and strong winds (NOAA, 2008). The Anacostia River
surged over the seawall, causing severe damage to 12
National Park Service offices and the U.S. Park Police
Anacostia Operations Facility (NCPC, 2008).
Heavy
precipitation and
flash floods
June 2006
Heavy precipitation and subsequent flooding resulted in
major power failures that affected the federal triangle area
where several agency headquarters and national cultural
institutions are located (NCPC, 2008). Damage caused by the
6-hour downpour on June 26 (considered a 200-year storm
event) compromised building monitoring security and high-
speed communication systems, among other effects (Federal
Triangle Stormwater Study Working Group, 2011).
"Snowmaggedon"
February
2010
A major snowstorm on February 5-6, 2010 dropped
20 inches of snow on the capital and left over 100,000
Potomac Electric Power Company (PEPCO) customers
without power (Morrison et al., 2010). Called
"Snowmaggedon," it was preceded by "Snowpocalypse" on
December 19, 2009 (16-24 inches of snow) and followed
closely by "Snoverkill" on February 9-10, 2010 (Samenow,
2011). February 2010 snowfall in the District totaled
32.1 inches (Mussoline, 2013).
North American
derecho
June 2012
More than 107,000 PEPCO customers lost power as a result
of strong thunderstorms and straight-line wind; some
experienced blackouts for up to 8 days (DDOE, 2012). Some
DC residents were unable to reach 9-1-1 hotlines (FCC,
2013).
Heat wave
July 2012
A 1,000-foot section of Green Line track, 1 of the 6 subway
lines that service the District, had to be replaced due to heat-
caused warping (Kunkle and Evans, 2012) after multiple
days of temperatures in excess of 100°F.
Hurricane Sandy
October
2012
25% of cellular sites in affected areas (including the District
of Columbia) were disabled (Turetsky, 2013). More than
250,000 people in the Washington metro region lost power,
but power was restored to 90% of customers within 48 hours
(Preston et al., 2012).
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Threats to DC's infrastructure from higher temperatures include deterioration and buckling of
pavement; thermal expansion of joints on bridges; and premature deterioration of buildings,
other infrastructure, sealants, and paints. Maintenance requirements of roads, parking lots, and
airport runways might be affected (DDOT, 2013; MWCOG, 201 lc, 2013a). Buildings and
pavement currently cover more than 40% of the District, producing a pronounced urban heat
island effect (Chuang and Hoverter, 2012).
B.l.1.3. Risks to Human Health
From a human health perspective, a study aggregating data on risk factors (age, poverty,
linguistic isolation, educational attainment), land-cover characteristics, and observed temperature
patterns concluded that approximately 64% of the District's residents are at high risk of heat
stress (Aubrecht and Ozcelyn, 2013). Higher temperatures will increase the risks of vector-borne
diseases (those transmitted to people from insects) such as West Nile virus. Health risks from
urban heat also include heat stroke, dehydration, and respiratory diseases like asthma. The
elderly, children, the ill, and the homeless are particularly vulnerable to those health risks
(MWCOG, 2008). High rates of poverty and homelessness in DC make health risks a particular
concern. Approximately 17.8% of individuals and 14.5% of families in the District live in
poverty (compared to 13.2% of individuals and 9.6% of families nationally) (DDOT, 2010a).
Studies have documented that among DC children, for example, poverty is correlated with
asthma (Babin et al., 2007). DC's homelessness rate is higher than that of any state and higher
than that of all but four U.S. cities (Witte, 2012). Of the District's 4,300 homeless children,
nearly one-fifth have asthma (Bassuk et al., 2011).
The District's ability to cope with extreme events (and more generally with the challenge of
climate change) depends on resources available at the community and household levels. The
greater Washington region is the fourth largest economy in the United States. It is also home to
more Inc. 500 fastest-growing companies than any other city in the country (WDCEP, 2010). DC
is also the home of the federal government, which accounts for approximately 34% of the city's
employment and provides a measure of stability and access to resources. At the same time, the
District's growth as a "strong and resilient economy" in the past decade is credited to its
increased economic diversification, including the emergence of green businesses (Washington
DC Economic Partnership, 2010).
In contrast to the robust resources available at the District and regional level, a great deal of
vulnerability exists at the household level. As noted earlier, rates of poverty and homelessness in
DC are above the national average. The unemployment rate as of March 2014 was 7.5%, relative
to the national unemployment rate of 6.7% (BLS, 2015). Among residents 65 years and over,
18.2%) live below the poverty line, 43.1% have no vehicle available, and 1.6% lack home
telephone service (DDOT, 2010a). In 2010, one in five households in the District had a severe
household burden (defined as housing costs that equal or exceed 50% of household income). In
the very low-income bracket that ratio was 3 in 5 (Reed, 2012).
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B.l.1.4. Flooding and Impaired Waters
The District of Columbia lies within a region for which annual precipitation is projected to
increase by anywhere from 4 to 27% over the next century (IPCC, 2007b). The temporal and
geographic distribution of rainfall might change, and intense precipitation events might increase.
Washington, DC is highly susceptible to flooding due to (1) its location between the Potomac
and Anacostia Rivers near the entrance of the Potomac into the Chesapeake Bay, (2) the historic
filling or burial of three streams in the DC area, which served as a natural drainage system, and
(3) its low elevation and broad floodplains. Flood risks include overbank flooding of the
Potomac or Anacostia Rivers, urban drainage flooding from undersized and combined sewers,
and tidal/storm surge flooding (NCPC, 2008; Koster, 2011).
The National Mall Levee, part of the Potomac Park Levee System that protects downtown
Washington DC, was built in 1936 to protect the city from flooding of the Potomac and
Anacostia Rivers. The levee system currently has three open sections that must be closed during
a flood event. The National Mall Levee, one part of the system, received an "unacceptable"
rating from the U.S. Army Corps of Engineers (US ACE) in 2007, leading to a de-accreditation
by the Federal Emergency Management Agency (FEMA) and the release of new flood maps
showing most of downtown DC without flood protection. To correct the issue, the USACE
redesigned one closure (at 17th Street) and proposed making two closures (at 23rd Street and Fort
McNair) permanent for easier closure. These improvements reduced the District's flood risk to a
less than 1% chance of the levee being overtopped in any given year (NCPC, 2008). While
originally scheduled to be completed in 2011, work on the 17th Street levee was repeatedly
delayed but finally completed in 2014 (USACE, 2014).
Approximately one-third of the District is served by combined storm and sanitary sewers that
overflow into waterways if the flow exceeds the wastewater treatment plant's (WWTP's)
capacity. The District is under a consent decree to build storage upstream of the treatment plant
to hold excess storm/wastewater in flood events to prevent overflow to waterways (NCPC,
2008). In extreme high-flow events, if the sewer main capacity is exceeded, stormwater can back
up into the streets. Also, if the sewer outfall is inundated by high water level in the receiving
stream, the sewers can back up. The DC Water and Sewer Authority (DCWASA) has installed
gates at the outfall locations to help avoid these issues (NCPC, 2008).
Despite the projected increases in precipitation (which will replenish the Potomac River and
nearby aquifers), climate change could adversely affect the district's drinking water supply.
Higher temperatures might reduce the amount of precipitation that ultimately reaches the
district's water sources. In addition, less precipitation falling as snow in the watershed and more
falling as rain will lead to exaggerated seasonal runoff patterns (more streamflow in winter and
spring and longer low-flow periods in summer), contributing to seasonal problems in water
availability (Ahmed et al., 2013; MWCOG, 2008).
Climate change might also affect water quality and increase the burden placed on the water
treatment facilities that serve DC (Ahmed et al., 2013; MWCOG, 201 la) by decreasing the raw
water quality. For example, higher temperatures might contribute to increased algal blooms and
lower oxygen levels. More intense precipitation could also lead to increased nonpoint source
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1 pollution (suspended sediment, nutrients, and chemical contaminants in rivers and lakes).
2 Flooding could increase leaching from landfills, hazardous waste sites, and brownfield sites.
3 Threats to the District's landscape and built environment from more intense precipitation events
4 include the following: erosion; slope and roadway flooding and washout; roadway subsurface
5 deterioration; tunnel flooding; road embankment failures; scouring of bridge and culvert
6 abutments; culvert failures; drainage overloading and failure; tree and vegetation damage; power
7 and other utility failure; and increased occurrence of mold in buildings, as well as stream
8 degradation, loss of wetlands, effects on habitats and species, and changes in the water table that
9 could impact development, septic systems, and the water supply (DDOT, 2013; MWCOG,
10 2011a, b,c, 2013a).
11 Tropical storms such as hurricanes are expected to be fewer in number but characterized by
12 greater wind speeds and more intense precipitation (IPCC, 2007a).
13 B.l.1.5. Sea Level Rise
14 Sea level rise threatens military facilities, monuments and museums, federal agencies, roadways,
15 bridges, metro lines, railroads, educational institutions, and fire stations in the District. In DC,
16 sea level has risen 3.16 millimeters per year on average since 1924 (a total of 0.3 meters or 15
17 inches) (NOAA, 2013), and it is expected to rise further (Ayyub et al., 2012). Ayyub et al.
18 (2012) modeled impacts of a 0.1, 0.4, 1, 2.5, and 5 meter sea level rise, which indicated that a
19 further sea level rise between 0.1 and 2.5 meters would inundate between 103 and 302 properties
20 (residences, apartments, hotels, etc.) with combined property values between $2.1 billion and
21 $6.1 billion (in 2005 dollars). A sea level rise of 5.0 meters would affect 1,225 properties, with
22 an assessed value of $24.6 billion.
23 Threats to the District's landscape and infrastructure from sea level rise include: the loss of
24 wetlands; erosion of roadway subsurface; bridge scouring; embankment failures; reduced vertical
25 clearance for bridges; flooding of roadways in low lying areas; changes in floodplains; and
26 increased tunnel flooding (DDOT, 2013; MWCOG, 201 lb, c, 2013a). Sea level rise may also
27 increase the salinity of the coastal rivers that empty into the Chesapeake Bay. The salinity of the
28 rivers will also increase during droughts and seasonal low-flow periods brought on by warming
29 temperatures.
30 B.l.1.6. Energy Disruptions
31 As shown in Temperature
32 Not only have temperatures in the DC area risen over the past century, the pace of warming has
33 increased (MWCOG, 2008; Kaushal et al., 2010). The District Department of Transportation
34 (DDOT) has identified trees and vegetation as among the assets that are vulnerable to the effects
35 of rising temperatures (DDOT, 2013). In the coming century, surface air temperatures in the
36 region are projected to rise another 6.5°F (3.6°C) (IPCC, 2007b). The District is a documented
37 urban heat island, with downtown 10-15°F hotter than nearby rural regions on summer
38 afternoons. The number of days "dangerous" to health within city limits has increased from
39 8-10% of summer days in the 1950s and 1960s to 18% of summer days in the last decade
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(Kalkstein et al., 2013). Potentially, some of these increases could be reversed. Modeling
suggests minor (10%) increases in reflectivity and vegetative cover would save approximately 20
lives per decade along with reductions in the number of heat-related hospital admissions
(Kalkstein et al., 2013).
Higher temperatures and the expected changes in rainfall patterns will change the ecological
profile (trees and vegetation) of the region. Over time, crop species and forest species currently
characteristic of the Mid-Atlantic region (e.g., apples and grapes; maple-beech-birch deciduous
forest) might no longer be viable. While overall forest productivity might increase, the increase
in temperatures is also likely to result in increased invasive species and reduced biodiversity, as
well as more frequent and more severe forest fires (MWCOG, 2008, 201 la, 2013a). The earlier
onset of spring resulting from this warming will affect individuals with pollen allergies, as well
as the local tourist industry (including the annual Cherry Blossom Festival). The peak bloom
date for cherry blossoms could be 5 to 13 days earlier in year 2050 than today (Chung et al.,
2011; Abu-Asab et al., 2001).
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Table 12, losing electricity is a common result of extreme weather events. Electricity comprises
the majority of the District's energy infrastructure (70%), and it is more vulnerable to disruptions
than the infrastructure for natural gas and petroleum (DDOE, 2012). The District Department of
Environment (DDOE) cites two reasons for the greater vulnerability of electricity distribution.
First, there is no local storage of electricity by customers, so any disruption in electricity
distribution is immediately felt by the end-user. Second, distribution via overhead transmission
lines makes electricity vulnerable to storm damage. Some 40% (approximately 101,200) of
PEPCO's customers receive their electricity via above-ground power lines that are susceptible to
fallen trees, heavy winds, and other hazards (DDOE, 2012; PEPCO, 2010). Only a very small
fraction (0.1%) of energy consumed in the District is locally sourced (e.g., from solar), making
the city vulnerable to disruptions in its external supply (DDOE, 2012).
Threats to the District's landscape and built environment associated with intense precipitation
and flooding were noted earlier. Other storm-related or extreme event impacts (e.g., high winds)
can cause: damage to road surfaces, commuter and freight rail systems, bridges, and buildings;
stress on the urban tree canopy; and power failures (MWCOG, 201 la, b, c, d, 2013a; DDOT,
2013). In addition, storms might cause disruptions of other essential services (including
telecommunications, food distribution, water and wastewater services, etc.) Although DC has
one of the most robust public transit systems in the country (MWCOG, 2008; District of
Columbia, 2012), the Mayor's Office has warned that the city's transportation infrastructure is
growing old and becoming less resilient to extreme weather events (District of Columbia, 2012).
B.1.2. Region-Wide Adaptation and Mitigation Planning
A city's resilience to climate change depends in part on the resources at its disposal and its
economic strength. Measuring by GDP per Metropolitan Statistical Area, the Washington-
Arlington-Alexandria, DC-VA-MD-WV metropolitan statistical area was the sixth largest
economy in the United States in 2014, (BEA, 2015). When using wages, unemployment, growth
rates, housing costs, and other variables to determine relative economic strength rather than size,
DC usually remains highly-ranked, depending on the individual survey. For example, Business
Insider ranked DC third in the national for 2015 (Kiersz, 2015).
What makes planning and governance of the District unique among U.S. cities is the oversight
authority of the federal government. While DC is governed by its legislative body—the DC
Council—the U.S. Congress oversees the DC Council, reviews the Council's actions, and can
overturn some of the District's decisions and actions. Congressional oversight and the District's
close coordination with federal agencies such as FEMA, EPA, and Department of Homeland
Security (DHS) are critical factors in the District's planning and implementation of adaptation
measures.
In addition, like many cities across the country, the District's adaptation planning has been
influenced by the work of its regional council, in this case, the Metropolitan Washington Council
of Governments (MWCOG). A regional nonprofit organization, MWCOG is composed of 300
elected officials, representatives from 28 local jurisdictions in Virginia-Maryland-DC, along with
Maryland and Virginia county and state government officials. Four council members participate
on behalf of the District in MWCOG, and much of the District's current adaptation planning is
based on MWCOG's work.
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The Council is a resource-intensive organization with a significant role in coordinating data and
research to undertake regional projects that would be difficult for one entity or local jurisdiction
to accomplish alone, including those related to climate adaptation planning. MWCOG creates
intermunicipal agreements for projects that will benefit the region. It receives funding for studies
and projects through various agreements between the local-member jurisdictions and through
federal grants. MWCOG coordinates a cooperative purchasing effort (across member
municipalities), so the region benefits from economies and efficiencies of scale. Because it must
compete for federal funding for its studies and projects, MWCOG ensures its projects closely
match federal policies, objectives, and guidelines to keep regional efforts well-coordinated and
moving toward shared goals.
MWCOG prepares plans for regional (DC metro area) transportation, environment, housing,
health and human services, homeland security, and public safety operations. The Council exerts a
powerful regional influence. One of the best examples of a regional project planned and funded
through the MWCOG is the Blue Plains Advanced Wastewater Treatment Plant that currently
treats 43% of the metropolitan area's wastewater and should continue to do so for the next 40
years (MWCOG, 2013b).
MWCOG's Climate, Energy, and Environmental Policy Committee seeks to implement actions
to respond to or lessen climate change-related impacts, including emissions mitigation. In 2008,
the MWCOG Board adopted the National Capital Region Climate Change Report, which
identified strategies to mitigate the effects of climate change, such as meeting greenhouse gas
reduction targets (MWCOG, 2008). The report also included a range of adaptation strategies to
address the eventual impacts of climate change. Adaptation planning identifies strategies and
actions designed to decrease vulnerability to the immediate and long term effects of climate
change. The Committee manages and implements the measures in that 2008 report and its
updated version, the 2013-2016 Action Plan (MWCOG, 2013a). During the planning process for
these documents, MWCOG collaborated with stakeholders, EPA, and climate change experts. It
released summaries of potential climate change impacts, vulnerabilities, and adaptation strategies
for the region that will be used in a future guidebook (MWCOG, 2013c). MWCOG plans and
measures performance using data on climate-related drivers expected to affect the Mid-Atlantic
region, which it categorizes as urban island heat, variations in precipitation, severe storms, and
sea level rise over the next 50 years.
MWCOG forecasts that by 2030 the DC metro area will gain 1.6 million new residents and 1.2
million new jobs. MWCOG has estimated that greenhouse gas emissions will increase by 33%
by 2030 and 43% by 2050. Two MWCOG reports set forth regional plans for water and air,
healthy neighborhoods, resilient economies, and access to alternative housing and transportation.
Goals, targets, and measurements of progress appear in four broad categories: accessibility,
sustainability, prosperity, and livability (MWCOG, 2008, 2010). Similar to the Sustainable DC
Plan described above, the MWCOG 2013-2016 Action Plan sets goals through 2020 for the
District-Virginia-Maryland region to study, measure, and implement actions in the areas of the
built environment and infrastructure, regional greenhouse gas emissions, renewable energy,
transportation and land use, sustainability, and resiliency and outreach (MWCOG, 2013a).
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Additionally, important work relevant to climate resilience was undertaken in the aftermath of
the extreme events listed in Temperature
Not only have temperatures in the DC area risen over the past century, the pace of warming has
increased (MWCOG, 2008; Kaushal et al., 2010). The District Department of Transportation
(DDOT) has identified trees and vegetation as among the assets that are vulnerable to the effects
of rising temperatures (DDOT, 2013). In the coming century, surface air temperatures in the
region are projected to rise another 6.5°F (3.6°C) (IPCC, 2007b). The District is a documented
urban heat island, with downtown 10-15°F hotter than nearby rural regions on summer
afternoons. The number of days "dangerous" to health within city limits has increased from
8-10% of summer days in the 1950s and 1960s to 18% of summer days in the last decade
(Kalkstein et al., 2013). Potentially, some of these increases could be reversed. Modeling
suggests minor (10%) increases in reflectivity and vegetative cover would save approximately 20
lives per decade along with reductions in the number of heat-related hospital admissions
(Kalkstein et al., 2013).
Higher temperatures and the expected changes in rainfall patterns will change the ecological
profile (trees and vegetation) of the region. Over time, crop species and forest species currently
characteristic of the Mid-Atlantic region (e.g., apples and grapes; maple-beech-birch deciduous
forest) might no longer be viable. While overall forest productivity might increase, the increase
in temperatures is also likely to result in increased invasive species and reduced biodiversity, as
well as more frequent and more severe forest fires (MWCOG, 2008, 201 la, 2013a). The earlier
onset of spring resulting from this warming will affect individuals with pollen allergies, as well
as the local tourist industry (including the annual Cherry Blossom Festival). The peak bloom
date for cherry blossoms could be 5 to 13 days earlier in year 2050 than today (Chung et al.,
2011; Abu-Asab et al., 2001).
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Table 12. For example, a Federal Triangle Stormwater Study Working Group (2011) convened
after the June 2006 downpour and flash flood, noted that facility managers and service providers
developed strong working relationships in the wake of the event and subsequently in the wake of
Hurricane Irene in 2011. They continue to share short- and long-term floodproofing strategies
(Federal Triangle Stormwater Study Working Group, 2011).
B.1.3. City-Wide Planning
In the District, climate change adaptation planning occurs in several departments, including the
Mayor's Office, DDOT, DDOE, DCWASA, and the DC Homeland Security and Emergency
Management Agency (DCHSEMA).2 To date, these entities have developed the following plans
and are implementing recommended measures:
• Sustainable DC Plan (District of Columbia, 2012)
• DDOT Climate Adaptation Plan (DDOT, 2013)
• DDOT Action Agenda Progress Report (DDOT, 2010b)
• DDOE Climate of Opportunity: A Climate Action Plan for the District of Columbia
(DDOE, 2011)
• DCWASA: Long-Term Control Plan Modification for Green Infrastructure (DCWASA,
2014)
• DCHSEMA District Response Plan (DCHSEMA, 2008)
In addition, DCHSEMA is in the process of collecting feedback from the District Preparedness
System's (DPS) public and private partners as part of an effort to develop a comprehensive
District Hazard and Vulnerability Analysis. These plans cover the economy, energy, water, land
use/land cover, the natural environment, people, information and communications technology
(ICT), and transportation. Together, the plans and action measures, along with regional efforts to
be discussed later, form the basis of the District's current broad climate adaptation planning. It
should be noted that because of DC's unique role as the nation's capital, a certain amount of
redundancy between the responsible parties and actions has been purposely built into all of the
District's adaptation planning. Details of these plans are provided below.
The District Department of Health has also partnered with the RAND Corporation on Resilient
DC, a program to build community preparedness and resilience (RAND, 2013). The focus of the
effort is on building partnerships and collaborations among organizations in communities to
leverage existing expertise and capacity as well as reach out to underserviced and vulnerable
subpopulations.
2 More than half a million people live in Washington, DC, and the District's government includes more than
40 agencies or departments (2013, Mayor's office at dogov). Many other departments not mentioned in this report
also contribute data and personnel to the District's adaptation planning.
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All of the departments listed above followed a rigorous process in developing their plans. For
example, to prepare the 2013 Sustainable DC Plan, the Mayor's office and DDOE held public
meetings with two key advisory groups: the Green Ribbon Committee encompassing the public,
private and nonprofit sectors and the Green Cabinet composed of DC agency directors. One goal
of those meetings was to promote interagency coordination on the shared and individual agency
missions and actions as they relate to the overall plan. The public meetings and discussions
involved more than 4,700 people and allowed all involved departments to solicit feedback and
opinions from members of the general public. After the public meetings, nine working groups of
experts, DC government officials, and members of the public were created to address the
following components of sustainability: energy, food, climate, the built environment, nature,
transportation, water and waste, and the green economy. The resulting Sustainable DC Plan is a
citywide initiative to deal with a changing climate. Its overarching goals are to create jobs and
economic growth, improve health and well-being, increase equity and opportunity, and preserve
and protect the environment. The Plan covers both climate change mitigation and climate change
adaptation.
B.1.4. City-Wide Adaptation Measures
The descriptions below present the current goals and measures being carried out by the District
in eight broad areas: climate/environment, built environment, energy, food, water,
stormwater/wastewater, transportation, and nature/green space/trees. These measures are from
the Sustainable DC Plan, except where noted.
B.l.4.1. Climate/Environment
The District intends to advance physical adaptation and human preparedness to increase
resilience to future climate change through the Sustainable DC Plan's climate goals. By 2032,
DC will:
• Require climate change impact analyses be part of all new DC construction projects.
• Assess its energy infrastructure's vulnerability to climate change given that power
outages occurred in the past as a result of severe weather events.
• Have DC emergency services, utilities, and disaster preparedness agencies respond more
quickly and efficiently to climate-related weather emergencies.
• Require new housing developments to integrate climate adaptation solutions into
cost-effective building strategies so that buildings last for 50 years or more.
B.l.4.2. Built Environment
The Sustainable DC Plan tackles building codes and construction planning by setting a goal of
net zero energy use for all new construction projects by 2032. Specifically, the District will:
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1 • Update its Green Building Act of 2006 and its Leadership in Energy and Environmental
2 Design (LEED) certification standards for facilities that are 50,000 square feet or larger.
3 • Provide incentives for LEED Gold standard certification to ensure that future buildings
4 will be resilient to climate change.
5 • Require neighborhood-scale sustainability goals for all major redevelopment projects
6 (e.g., Walter Reed Army Medical Center).
7 • Adopt the 2012 International Green Construction Code, or equivalent, for all new
8 construction and major renovations.
9 B.l.4.3. Energy
10 By 2032, the District intends to reduce power outages to less than 100 minutes per year through
11 energy infrastructure improvements. DC officials will work with stakeholders to add local
12 renewable energy sources and decentralize its energy sources into a more effective power grid.
13 Starting in 2014, the District began a multiyear, $ 1-billion project to move high-voltage feeder
14 lines underground, spearheaded by the District of Columbia Power Line Undergrounding Task
15 Force (DCOCA, 2014) in order to reduce this vulnerability. The public-private project is jointly
16 implemented by DDOT and PEPCO.
17 B.l.4.4. Food
18 Because increased local food production can improve the District's resilience to climate change,
19 DC intends to boost its agricultural land use by 20 acres by 2032. Specific measures include the
20 following actions:
21 • Adopt the Sustainable Urban Agriculture Act and set up urban greenhouses and
22 agriculture projects, in particular beekeeping.
23 • Evaluate the potential for rooftop gardens and use of public parks and recreation areas for
24 growing plots to streamline the process of finding land for community agriculture.
25 • Retrofit at least 50% of DC public schools with gardens and integrate the planning,
26 planting, tending, and harvesting of those gardens into the curriculum.
27 • Make temporary agricultural sites for gardens available wherever possible.
28 The Plan recognizes that the role played by the food sector in the DC economy can be increased.
29 With that goal in mind, the District intends to produce or obtain 25% of its food within a
30 100-mile radius. Specific measures include the following:
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1 • Initiate a comprehensive study on the sources of the District's food supply, ways in
2 which that supply can become more localized, and on sales of food from community
3 gardens.
4 • Set up a Food Policy Council as a nonprofit to research the local food sector with a goal
5 of providing nutritious food through a self-sustaining system.
6 • Purchase locally grown food for the DC public schools and government events.
7 B.l.4.5. Water—Wetlands
8 The Sustainable DC Plan intends to help residents and businesses adapt to climate change. It
9 aims to protect the District against future flood risks by restoring wetlands and creating green
10 infrastructure for stormwater drainage. Expanded green areas will help to temper rising
11 temperatures. Additional tree canopy will provide benefits to the environment and District
12 residents. The following actions are aimed at preserving and enhancing wetlands, and thus have a
13 climate adaptation dimension:
14 • Increase the wetlands along the Anacostia and Potomac Rivers by 140 acres or an
15 additional 50% by 2032.
16 • Coordinate open space guidelines with the National Park Service to control invasive
17 species.
18 • Develop an Urban Wetland Registry to be created by DDOE's wetlands conservation
19 planning team.
20 • Restore habitat and biodiversity of the rivers through the Urban Wetland Registry.
21 • Require low-impact development planning for new waterfront development greater than
22 50,000 square feet along with wetlands preservation activities.
23 B. 1.4.6. Water—Stormwater/Wastewater
24 To reduce flooding and improve stormwater infrastructure by 2032, the District plans to:
25 • Use or capture 75% of its stormwater.
26 • Install 2 million square feet of green roofs with the help of a rebate program.
27 • Build an additional 2 million square feet of planted surfaces on public and private
28 buildings by 2018.3
3 With more than 2.5 million square feet of green roofs, the District ranks highest amongst North American cities
(GRHC, 2013). Green roofs and urban tree canopies contribute to community resilience by improving air and water
quality, moderating the urban heat island effect, reducing energy consumption, providing recreational opportunities,
mitigating flood impacts, and providing ecosystem services (Rodbell and Marshall, 2009; GRHC, 2013).
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• Add extensive green infrastructure elements for paved surfaces to capture pollutants and
reduce runoff.
• Double the number of homes in the DC RiverSmart Homes program for preventing
runoff by using green technologies.
• Replace gravel and impervious surfaces in alleys with permeable surfaces to create 25
miles of green alleys.
• Institute new/revised zoning requirements for housing developments to improve
storm water retention.
• Revise building codes to allow alternative water collection systems.
• Increase the use of green infrastructure in public right-of-ways.
• Provide financial incentives to promote efficient water use for landscaping and building.
• Promote water conservation through improved metering and monitoring for leaks, etc.
with alert systems.
Outside of the Sustainable DC Plan, the District has also made several other water-related
adaptation planning efforts, many in response to the 2005 Consent Decree from EPA and DOJ
that required DC Water to design and construct underground storage tunnels to hold
contaminated wastewater during storms and wet weather, with the goal to reduce CSO
discharges. The largest of these is the DCWASA's DC Clean River Project, a 20-year,
multibillion-dollar ongoing project consistent with EPA's policy directives for adaptive
management and in line with the requirements of the 2005 Consent Decree (DCWASA, 2012).
The Decree also required DC water to promote Green Infrastructure as another approach to CSO
control. The Clean River Project includes demonstration projects, public involvement, and green
infrastructure improvements in construction and land use, such as bioswales, green roofs,
permeable pavement, and other green technologies. Further, DC expects to reduce 96% of its
combined sewer overflows through the use of inflatable dams and pump station rehabilitation,
and by adding separate municipal storm sewer systems. Throughout the 20-year project, the
District will maintain its focus on meeting the requirements of the 2005 Consent Decree and
EPA water quality standards. The Potomac River, Rock Creek, and Anacostia Rivers are the
focus of the planned improvements, and the project requires coordination with DDOT for
easements such as the Blue Plains Tunnel and other infrastructure improvements. The District
hopes to benefit from the state of the art implementation, which should make the District's
wastewater system more resilient to extreme weather and precipitation events (DCWASA,
2012).
On May 19, 2015, the First Amendment to the 2005 Consent Decree was lodged and opened for
public comment. The Amendment requires DC Water to implement Green Infrastructure as part
of the existing DC Clean Rivers Project. In anticipation of the approval of the Amendment, and
in agreement with EPA, DOJ, and the District,, DC Water announced a Green Infrastructure Plan
in 2015 (DCWASA, 2015). The plan modifies the existing DC Clean Rivers Project, a $2.6
billion dollar project to limit untreated sewage flow into area rivers through the construction of
new tunnels. Under the Green Infrastructure Plan, some proposed tunnels will be not be built.
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Instead, the stormwater capacity intended to be carried by the unbuilt tunnels will be mitigated
through investment in green infrastructure such as infiltration basins and green roofs. This will
allow for infiltration of rainfall into soils before it becomes stormwater runoff, alleviating the
need for costly and disruptive tunnels. The new approach allows for faster implementation as
well as potentially boosting property values near restored natural areas.
In 2013, the District Department of the Environment released new stormwater management
regulations, which require new development, or substantial redevelopment, to meet standards for
on-site water retention (DDOE, 2013a). The main goals of the regulations are to increase
infiltration and decrease runoff in order to protect area waterways and comply with federal clean
water standards, as well as to create a more equitable distribution of stormwater throughout the
District. This contrasts to the former regulations, which focused on the timing and quality of
stormwater throughout the district, not reducing the overall quantity of stormwater generated. To
create a financial incentive for change, voluntary retrofits accumulate stormwater retention
credits, which can be bought by developers to offset required reductions at other sites. The
District further attempted to reduce stormwater creation by adopting new construction codes
based on the 2012 International Green Construction Code. These code changes support increased
onsite use of rainwater to reduce stormwater generation.
The District has also developed more focused adaptation planning. In 2012, the Mayor's Task
Force on the Prevention of Flooding in Bloomingdale and LeDroit Park, two DC
neighborhoods, issued final short, medium, and long-term recommendations to reduce the chance
of severe flooding. These neighborhoods are serviced by inadequate late 19th century combined
sewer and stormwater infrastructure, which has resulted in floods of mixed raw sewage and
stormwater during intense precipitation events, posing numerous health and safety risks for
residents, rescuers, and repair crews. As a long-term solution, part of the DC Clean Rivers
Project will include building an estimated $600-million tunnel system 5 miles in length to
provide excess capacity (DCOCA, 2012).
B.l.4.7. Transportation
Further strengthening the Sustainable DC Plan's goal of making the District's transportation
infrastructure capable of withstanding the upper limits of projected climate change impacts by
supporting DDOT in its use of climate change indicator data are the transportation plans and
measures adopted by the DDOT. DDOT uses three planning documents as the bases for
improving the District's resilience to climate change: the Climate Adaptation Plan (DDOT,
2013), DDOT Action Agenda Progress Report (DDOT, 2010b), and the DDOT Urban Forestry
Administration (UFA)'s Assessment of Urban Forest Resources and Strategy (DDOT, 2010c).
The 2013 Climate Adaptation Plan includes the District's vulnerability assessment for
transportation infrastructure and the corresponding adaptation planning and measures to promote
resilience to extremes of temperature, precipitation, sea level rise, and storms for its 4,000 miles
of roads, 240 bridges and tunnels, and its watershed with associated trees and vegetation. In its
planning and decision-making efforts, DDOT used the National Cooperative Highway Research
Program assessment tool to define the scope of its needs, access vulnerability, and integrate the
information collected. DDOT chose indicators such as sea level rise or temperature for each
category of community assets (e.g., bridges, trees, etc.), listed impacts, and ranked vulnerabilities
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as high, medium, or low for each indicator. Potential adaptation strategies that DDOT plans to
use include the following: DDOT climate projection models through 2100; vulnerability
assessments; training for staff; updating design standards and policies; updating the potential
strategies for adoption and use in all new projects; coordinating with other agencies; and seeking
funding for assets (DDOT, 2013).
The 2010 Action Agenda Progress Report highlights DDOT's low carbon footprint initiatives,
including establishing bike lanes throughout the District, and a bike-share program with 100
stations and 1,000 bicycles. DDOT is taking action to promote walking, bus-riding, and greater
use of the metro system to meet the challenges of this and the next century. To reduce
stormwater runoff, urban heat, and energy use, DDOT plans to retain all stormwater from at least
a 1.2-inch rain storm, use 15% less energy, provide electric car power recharge stations, use
low-impact development, and provide outreach to the public on various adaptation measures.
DDOT already installed 1,200 solar-powered parking meters (energy savings) and interactive
electronic devices in bus shelters to provide real-time bus information to the public (public
outreach). Bus, metro, bike-share, parking meters, and other pay-per-use transportation features
will operate on a "one card" system for all (DDOT, 2010b).
Additionally, the Washington Metropolitan Area Transit Authority received $20 million in
post-Sandy disaster recovery funds to invest in flood mitigation for MetroRail (U.S. DOT, 2014).
The majority of the money was spent upgrading venting structures to prevent flood water from
entering the system, while the remainder was spent on drainage improvements.
B.l.4.8. Nature/Green Space/Trees
A recent assessment found that tree canopy covers 35% of all land in the District (DDOT,
2010c), compared with an average of 30% tree cover measured across 18 major U.S. cities4
(Nowak and Greenfield, 2012). By 2032, the District intends to cover 40% of its land with tree
canopy (DDOE, 2013b) by planting 8,600 new trees per year through 2032 using heat-tolerant
species that will be more resilient to climate change. The District already has, according to the
plan, imposed a Green Area Ratio requirement for land use in all new development sites to
improve stormwater management, air quality, and urban heat island effects.
DDOT's UFA currently manages approximately 144,000 trees on streets, in parks, and in
recreation areas. DDOT believes that trees are one of the District's most important assets. Leaves
on trees help shade people and buildings during heat waves and the roots help trap water and soil
in place. Urban trees also prevent runoff, absorb pollutants and reduce urban heat island effects.
The UFA's 2010 Assessment of Urban Forest Resources and Strategy is a plan to increase the
urban canopy, protect and improve air and water quality, and build capacity in its community
forest program. Between 2006 and 2011, the District increased its tree canopy by 2.1 to 37.2%
(DDOT, 2011). The strategy includes actions that will promote resilience of the natural
4 The 18 major cities in the United States examined in this study were: Albuquerque, NM; Atlanta, GA; Baltimore,
MD; Boston, MA; Chicago, IL; Denver, CO; Houston, TX; Kansas City, MO; Los Angeles, CA; Miami, FL;
Minneapolis, MN; Nashville, TN; New York, NY; Pittsburgh, PA; Portland, OR; Spokane, WA; Tacoma, WA.
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1 environment, as well as water and air quality. Recent weather events such as Hurricane Sandy
2 and the 2012 derecho caused significant tree loss and damage.
3 B.1.5. City-Wide Emergency Response
4 Although climate change planning and adaptation are not the express purpose of the DCHSEMA
5 Response Plan, the plan does cover extreme weather events. It covers traditional response
6 elements and involves multiple and redundant agencies, systems, and measures to increase
7 responsiveness and resilience. The communication, coordination, and control systems between
8 the Mayor's Office and local and federal agencies are thoroughly delineated throughout the
9 document.
10 Through DCHSEMA, the District addresses long-term disaster planning (as well as strategic
11 planning) that includes permanent replacement of housing, dealing with environmental pollution,
12 and restoring infrastructure. The District Response Plan also addresses services for vulnerable
13 populations and the general public and building in redundant public health and emergency
14 response systems. Because the Plan includes so many different DC and federal agencies and
15 because the area has recently experienced a wide range of extreme events, the plan is used,
16 tested, and updated often. Staff, funding, and equipment are available within close proximity for
17 almost any emergency situation in the District.
18 Discussions with public health professionals in the DC metropolitan area determined that
19 although no one agency is legally charged with coordination in an emergency, informal
20 relationships are well established among local and state health departments and other public
21 health partners, resulting in strong regional coordination (Stoto and Morse, 2008).
22 B.1.6. Data Collection Approach
23 In the case of Washington, DC, the project team convened participants from across the District
24 government for an initial and a follow-up workshop. Throughout this process, the project teams
25 worked closely with the DDOE to identify participants, understand previous or planned
26 resilience and adaptation efforts in the District, and to hold the workshops. Both workshops
27 included sessions in which participants provided data and scoring for the indicators and
28 questions. Presentations by the project team at the beginning of each workshop introduced
29 participants to the tool's methodology and goals. The workshops also included presentations by
30 DDOE and the project team on existing resilience and adaptation work in DC. A list of workshop
31 attendees is provided in Appendix G. Full agendas for the workshops are provided in
32 Appendix H.
33 DDOE identified workshop participants who manage activities within some of the eight sectors
34 identified in the tool from agencies across the government. DDOE also identified workshop
35 participants who operate public services (e.g., public transportation). Most of these participants
36 had previously joined in DDOE-led sustainability or resilience efforts. Each sector had at least
37 one participant with in-depth knowledge of operations and status in that sector. Because the
38 project did not intend to achieve consensus or to quantify differences among participants, each
39 sector had one individual or a small group of two to three individuals designated as the expert(s)
40 charged with tool implementation activities in that sector. Some sectors had more than one
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individual in this role because they covered such a broad range of topics (e.g., implementing the
tool for the water sector required engagement of experts in drinking water quality, drinking water
supply, and wastewater).
The initial workshop began with a presentation by the project team introducing the project and a
presentation by DDOE that provided additional overviews of previous or ongoing climate
resilience work in the District. The project team gave a presentation on the overall tool
methodology, including details on how to use the question component of the tool. At the time of
the first DC workshop, the thresholds were not yet developed. To provide the participants at the
first workshop with a baseline resilience score, a climate change resilience expert at DDOE
provided a draft resilience score for those indicators of which he had knowledge. Participants
then divided into breakout groups, determined by the project team, for each sector. Each
breakout group was provided with a facilitator trained on the use of the tool, a note taker to
capture the discussions, and printed handouts containing the questions. With this support, the
breakout groups provided importance weights and resilience scores for the questions pertaining
to their sector. Following this session, the workshop continued with a presentation by the project
team and DDOE on climate adaptation work in the District and an overview by the project team
of the use of the indicators component of the tool. Breakout groups then reconvened to provide
importance weights and resilience scores for the indicators and suggest any data sources relevant
to the indicators that the team had not identified previously. The workshop concluded with a
debrief session that asked for participant feedback on the tool and the process.
After the first workshop, the project team analyzed the results from this workshop and
communicated with some participants individually to obtain clarification on results or suggested
data sources. The project team then convened a follow-up workshop to present additional data
identified during the first workshop, gather additional information, and provide clarification on
some questions and indicators. Thresholds were also available, having been developed for the
tool for use at any site. This methodology provided participants with guidance on resilience
scoring throughout the process. Due to individuals' availability, the group of participants at the
follow-up workshop was slightly different from the group at the initial workshop. Thus, the
follow-up workshop also began with a presentation by the project team to review the project and
the tool methodology. The first part of the workshop also included a presentation by DDOE on
the District's progress on developing a climate adaptation plan. The project team presented
preliminary results from the initial workshop. In the breakout sessions that followed,
participants:
• Reviewed scoring for questions and indicators that the project team had modified based
on suggestions from the first workshop.
• Selected the most appropriate data set for indicators for which participants at the initial
workshop had suggested alternate data sets.
• Provided any additional data or data suggestions.
The follow-up workshop ended with a debrief session during which the project team asked
participants to consider which sectors might contribute most to climate resilience in the District.
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The project team also asked participants to suggest ways of displaying results that would be most
beneficial to continued resilience and adaptation work in the District. Appendix B includes the
graphical representations of the results of the two workshops.
B.2. WASHINGTON, DC RESULTS
B.2.1. City-Wide Results
The average results on resilience and importance across all sectors in Washington, DC, based on
participants' responses to questions and the importance weight assigned to each question, are
summarized in Figure 3. The same information is supplied for indicators in Figure 4.
For both resilience and importance, scores ranged from 1 to 4, with one indicating lowest
resilience or lowest importance, and 4 indicating highest resilience or highest importance. In
Figures 2 and 3, the "Resilience" score represents an average score for all questions or indicators
in that sector. These sectors are ranked, from left to right, by the average importance score for
that sector. As such, a sector with a low resilience score towards the right of the plot may be
considered relatively more vulnerable compared to another sector with a low resilience score
towards the left.
Washington, D.C. Resilience: Questions
iNat. Env. aWa:er i Economy r Land Use ¦ Trans. bIT ¦ Energy ¦ People
Increasing importance score
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Figure 3. Washington, DC. Average question resilience and importance.
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Washington, D.C. Resilience: Indicators
¦ Land Use ¦ Economy BWaer "Nat. Env. ¦ Trans. "Energy ^People "IT
>
Increasing importance score
Sector
Figure 4. Washington, DC. Average indicator resilience and importance.
For the questions, no sectors received an average score of greater than 3 or less than 1. For the
importance scores, results were similarly clustered, although the overall scores were higher.
There were no sectors that scored, on average, in the top quartile of importance but the lowest
quartile for resilience—a situation that would suggest high vulnerability across the entire sector.
The people, transportation, and water sectors, had lower resilience scores but similar importance
scores as the other sectors, suggesting that these sectors in Washington DC may be more
vulnerable to the effects of climate change, and impacts to these sectors will create more
significant disruptions.
For the indicators, there is much more variability in scores across sectors than is observed in the
question data. The greater variability in the indicator data may be due to limitations in the data
sets available that focus attention onto a particular subset or area of the sector that may be
performing better or worse than the sector overall. With the questions, the project team had
fewer obstacles to achieving a comprehensive picture of resilience across all issues that might
affect the resilience of a sector. However, the indicators still add value to the overall analysis.
Figures 2 and 3 convey differing narratives for citywide preparedness. While Figure 3 suggests
that in Washington DC no one sector is more in need of urgent attention (high importance and
low resilience), Figure 4 highlights that, based on data available, the natural environment and
energy sectors both have lower resilience scores and similar importance scores compared to
other sectors, suggesting that these two sectors may need more attention. By contrast, the water
sector has high average resilience and relatively low average importance, so it may not be as
critical to focus on this sector compared to others.
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Additionally, there may be more localized risks within and across sectors. As such, note that
while the averages presented in in Figures 3 and 4 help identify an overall trend, they may also
mask important data points, increasing the risk of concluding that there is no evidence for action
when action is warranted (i.e., type II errors).
Figures 5 and 6 disaggregate the data summarized in Figures 3 and 4, and highlight potential
"spikes" of high risk within sectors with overall lower averages. Both Figures 5 and 6 confirm
the potential for type II error is real because many of the sectors show significant spread across
both the resilience and importance score axis.
Figures 5 and 6 also indicate the possible action pathways stemming from the results and show
that the District faces a significant number of moderate to highly critical vulnerabilities that
should be addressed across all eight sectors, along with a potential need for increased
monitoring. This is true for both the question (see Figure 5) and indicator (see Figure 6) data.
Comparatively, there are few low priority items and small problems. Overall, as indicated in
Figures 3 and 4, the water, transportation, and people sectors appear to pose the greatest concerns
in terms of resilience.
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Washington, D.C.: Questions
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Low priority
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• Water
• Trans.
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4 (high resilience) 3 2 1 (low resilience)
Resilience Score
Figure 5. Washington, DC. Question quadrant mapping.
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Washington, D.C.: Indicators
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4 (high resilience) 3 2 1 (low resilience)
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Figure 6. Washington, DC. Indicator quadrant mapping.
1 B.2.2. Sector-Specific Investigations
2 The sector-specific discussions below connect the results of the workshop exercises to potential
3 underlying drivers and roadblocks for each sector discussed in the existing literature. Workshop
4 participants also provided additional insight into each sector when providing additional
5 information regarding the assigned importance and resilience scores.
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1 B.2.2.1. Economy
D.C. Economy Sector: Questions and Indicators
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• Questions
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Resilience Score
Figure 7. Washington, DC economy sector. Question and indicator quadrant
mapping.
2 Overall, workshop participants and supporting data indicate that Washington, DC's economic
3 sector is independent, diverse, and robust. Washington, DC is an economic center and operates
4 independently of neighboring Maryland and Virginia. DC employment centers are also very
5 diverse, which underlies the District economy's resilience to climate change. The District has
6 also taken steps to understand the potential impact of climate-related events on the local
7 economy (e.g., the impact of major changes in energy policy).
8 The District leverages current resources to perform effective adaption planning and further
9 increase resilience. According to workshop participants, adaption planning successfully
10 considers costs and benefits, encourages pre- or post-event effectiveness evaluations, and
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frequently involves analyses of past climate-related events. Furthermore, existing disaster
response planning also increases resilience. The District Response Plan identifies which agency
is responsible for each function within disaster response needs. For example, during Hurricane
Sandy, the District government closed, but the agencies needed for communication or support
were activated and in emergency support centers. Workshop participants noted, however, that the
planning process is only somewhat flexible and that no mechanisms are in place to help
businesses return to normal operations after an extreme weather event. Additionally, adaption
plans account for few resilience-cost tradeoffs between the less resilient but lower cost strategy
of increasing protection from climatic changes and the more resilient but higher cost strategy of
moving residents from the most vulnerable portions of the urban area; this critical factor is
discussed further below in relation to the intracity disparities. While the District has successful
adaptive planning processes, the District may consider that the lack of flexibility in the planning
process and few considerations of resilience-cost tradeoffs can reduce the effectiveness of the
planning process, thus decreasing the District economy's resilience to climate change.
However, resilience scoring based on economic indicator data was mixed. Specifically, the
results indicate that while the District's economy may appear to be relatively resilient based on a
District-wide indicator, there may in fact be significant intracity disparities. For example, in
2012, the District's unemployment rate was relatively moderate (8.9%) and in 2011, 92.9% of
the noninstitutionalized population had health insurance, indicating high economic resilience.
However, these data mask a significant range in values across the District. In 2012, one ward had
a 2.8% unemployment rate, while another had a 22.4% unemployment rate.
In addition, approximately 18.2% of persons in the District live below the poverty line,
indicating low resilience. Again, however, this indicator does not reflect intracity disparity. High
poverty areas tend to be in low-lying areas, which are more vulnerable to sea level rise, storms,
and other extreme weather events resulting from climate change. However, workshop
participants did not rate this indicator as particularly important in the economy sector. A higher
importance score was given to the percentage of owned housing units that are affordable
(33.7%)). Workshop participants noted that DC has many vulnerable people with a high housing
burden.
Finally, indicator results may also mask disparities related to timing, rather than geography. For
example, the District experienced a 1.27% decrease in the homeless population from 2012 to
2013, indicating moderate resilience. However, workshop participants noted that DC might be
less resilient than the data suggest because DC has instituted an absolute right to shelter during
hypothermia season, so the point-in-time count of homeless persons in June is very different than
in January.
Figure 7 shows that 45% of the questions and indicators lie in the "Monitor for Changes"
quadrant (high resilience/high importance). In addition, most questions and indicators {15%) are
above the median for importance. These trends indicate that the District has begun to recognize
and work to address the need to have a resilient economy in the face of climate change. There is
room for improvement to ensure the District's economic resilience to climate change, as 30% of
the questions and indicators fall in the "Vulnerabilities to Address" quadrant (low resilience/high
importance).
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Although generally resilient in the area of energy supply, several factors make other areas of the
energy sector less resilient. Most of the energy supply originates outside of the District in
Maryland, Virginia, and West Virginia, and the diversified generation of energy is not currently
occurring in the District. According to workshop participants, the political and technical capacity
could allow for generation from multiple sources. The District also reported high energy use per
capita. In 2010, average per capita electricity use in the District was estimated at 15,034 kWh,
above the national average of 12,954 kWh and the third highest per capita use nationally (World
Bank, 2015). Another source (U.S. EIA, 2013) reported average total energy use at 208 million
Btu per capita. Capacity of the District's source per service area is also low at 13.28 MG/mi2
These values indicate overall low resilience to climate change, although the District does have
available smart grid opportunities to manage demand.
In terms of power outages, the resilience of the District to climate change is mixed. Based on
average power outages per year, the District has low resilience to climate change. However,
workshop participants disagreed with indicator thresholds, noting that the range of 1 to 24 hours
associated with a resilience score of 2 is too large, as residents can generally tolerate 1 to 2 hours
without power. A full 24 hours without power is a far more extreme situation, due to heat
buildup. The average response time to restore electrical power is approximately 2.5 hours, which
indicates moderately high resilience. However, during a June 29-July 7, 2012 derecho event,
over 100,000 customers in DC had power interrupted for a combined total of more than 3.6
million hours, equal to an average of 34.28 hours per customer. This high value is indicative of
low resilience to climate change.
Energy planning in the District indicates high resilience. PJM (the regional transmission operator
for the District and surrounding area) uses a rigorous planning process that includes assessing the
impacts of sea level rise on power generation facilities. Municipal managers in DC also draw on
data from past experiences with extreme weather events to assess the effects of these events on
oil and gas availability and pricing.
Energy services are at risk if other District services, particularly transportation, are negatively
affected by extreme weather events. In the event of a severe storm, PEPCO relies heavily on
DDOT and emergency response personnel to reopen roads so that they can repair any damage to
the electrical system.
As shown in Figure 8, the majority of the questions and indicator data plot in the "Monitor for
Changes" quadrant (high resilience/high importance). Ensuring a constant supply of electricity is
a critical need, and the District has developed emergency planning and procedures to restore
power as quickly as possible, accordingly.
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D.C. Land Use/Land Cover Sector: Questions and Indicators
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Figure 9. Washington, DC land use/land cover sector. Question and indicator
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Washington, DC demonstrates relatively high resilience related to land use or land cover. While
important and high-value infrastructure and natural areas are located in areas vulnerable to
flooding, the District has been proactive with land use/land cover planning, maximizing the
benefits of urban forms, reducing heat island effects and impervious surfaces, and implementing
green infrastructure and retrofits. As with most sectors, however, recognition of the importance
of resilience planning and adaptation in the context of land use and land cover, and the degree of
proactive response, do vary across the District's neighborhoods.
The District is influenced by tides, and areas of the District along the Potomac and Anacostia
Rivers and the Tidal Basin are at sea level. Therefore, the District is vulnerable to
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1 flooding/impacts of sea level rise. The District also saw a 0.19% increase in impervious cover
2 between 2001 and 2006,
3 While the percentage of the District's population living in the 500-year and 100-year floodplains
4 (2.5% and 1.6%, respectively) is relatively low, the monetary value of infrastructure in the
5 500-year floodplain is high, and natural areas are highly vulnerable to flooding.
6 Only a small percentage of open/green space is required for new development, though the
7 requirement varies across the District. While residents place high importance on green space and
8 the District is requiring more public spaces to be green and/or pervious, increasing green space is
9 difficult in high-density areas. Developers are also reluctant to accommodate more green space
10 as nearby National Park Services land is easily accessible to residents, and more than 90% of the
11 District is within a 10-minute walk of green space.
12 In general, however, the District received high resilience ratings in areas related to proactive
13 planning and sustainable development. The National Capital Planning Commission (NCPC)
14 works with the DC government on federal areas in the District and has a shared comprehensive
15 plan that includes sustainability policies.
16 The District is developing efforts to use urban forms to mitigate climate change impacts and
17 maximize the benefits of urban forms, although the degree of implementation varies across the
18 District, and there is little focus on where in the District these initiatives are taking place.
19 Tree cover is considered very important from an economic perspective and for livability, and
20 there are mechanisms to support tree shading programs in the District. Tree planting efforts have
21 been fairly robust and successful, although the same cannot be said for tree preservation efforts.
22 Again, there has been disparity in these efforts across neighborhoods.
23 The District and the National Park Service have inventoried land use/land cover types and these
24 data will be used in planning. There are also requirements in place for retrofits in development
25 on vulnerable land. Workshop participants noted that resilience in DC is mostly structural, as
26 opposed to being due to wetlands and buffers. For example, many federal buildings in the
27 floodplain have structural protections against flooding. Furthermore, there are codes to prevent
28 development in flood-prone areas, although existing requirements are not always followed.
29 Executive Order 11988 requires federal agencies to avoid building in floodplains to the extent
30 possible, but Congress ultimately decides where buildings are placed in DC. For example, the
31 site of the National Museum of African American History and Culture is in the bottom of the
32 watershed and will need extensive protection against flooding. Several new requirements have
33 also been proposed, but not passed, including restrictions on high-hazard users (such as dry
34 cleaners) or vulnerable populations (such as daycares) in floodplain areas.
35 In cases where flooding occurs, the District does encourage and provide resources for rebuilding
36 using more flood-resistant structures and methods, although regulations regarding rebuilding of
37 communities impacted by floods have not been enforced.
38 There are numerous existing incentives and requirements designed to reduce the amount of
39 impervious surface, prevent development in floodplains, and increase the use of green
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1 infrastructure for stormwater management. Incentives and requirements for the last item include
2 a green roof rebate (for new development with green roofs and adding green roofs to existing
3 structures), the RiverSmart Homes program, stormwater requirements, impervious surface
4 removal rebate (on water/sewer bills), impervious surface fees, and the green area ratio (which
5 considers green walls and other items in addition to green roofs). The green area ratio and
6 stormwater requirements take many factors into consideration, including habitat corridors and
7 use of native and/or low-water-use plant species.
8 Green infrastructure maintenance is covered to some extent by private parties (rebate recipients,
9 for example, are required to maintain their installations). However, not all green infrastructure
10 programs require follow-up to ensure the infrastructure (and its benefits) are being maintained.
11 The District also makes use of current and historical data, local academic research, and
12 stakeholders and resources (including coastal hazard maps with one-meter altitude contours) for
13 planning purposes and to better understand the impact of climate change on the area.
14 Figure 9 includes a majority of question and indicator data in the "Monitor for Changes"
15 quadrant (high resilience/high importance), indicating that the land use/land cover sector overall
16 has high resilience to climate change in the District in relation to the questions workshop
17 participants found to be important.
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D.C. Natural Environment Sector: Questions and Indicators
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Figure 10. Washington, DC natural environment sector. Question and
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The District's natural environment sector demonstrates low resilience with respect to the
condition and status of freshwater ecosystems, physical habitat, and undeveloped land. The
District has also conducted minimal planning with respect to open and green districts and
ventilation. Planning in other areas related to the natural environment has been relatively robust,
however, and plant species diversity and use of native plants in green infrastructure installations
indicate resilience. Workshop participants assigned limited importance to the issue of the
availability of environmental/ecosystem resources in situations where other District services are
affected by climatic events or changes.
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While no data were provided to quantify the extent to which freshwater ecosystems have been
altered, workshop participants noted that less than 10% of the area's original wetlands likely still
exist, and the Anacostia and Potomac Rivers have been channelized. While there are some
conservation efforts related to the Anacostia River, the efforts do not account for substantial land
coverage in the District, and no large-scale wetlands restoration projects are in place. The
District's flood capacity could be significantly compromised as a result.
While the calculated PHI score, a measure of degradation, was relatively high (62.31), workshop
participants think that it was likely too high, as none of the sites considered are urban. Streams
within the District likely do not have a PHI greater than 20. The District also received a
relatively low Benthic Index of Biotic Integrity score (1.56), indicating low resilience in terms of
water quality and biodiversity. Close to 19% of total species in the District are of "greatest
conservation need," and although no data were available, workshop participants indicated low
resilience relative to the ecological condition of undeveloped land and ecological connectivity of
natural ecosystems.
Plant species diversity is high, relative to the size of the urban area. Most of the introduced flora
are naturalized, but not disruptive. No data were available to determine the percentage change in
disruptive species; however, the workshop participants noted that this indicator is a vulnerability
to be addressed, as noted in Figure 10 (low resilience/high importance). The District also has
native species lists, and green infrastructure installations mostly use these species. While green
roofs cannot use only local or native plants, the guidance for rain gardens and infiltration
practices is to use local, native, or regional plants.
While the District does not have air quality districts or a thermal comfort index and has not
conducted an analysis of areas with good ventilation, DC does have regulatory and planning
tools for air and water quality and land use. In addition, air quality is more strongly determined
by local sources, not distant sources, and is therefore easier to control.
The District has conducted air quality analyses, implemented water protection plans, and is
currently working on invasive plant protection plans. The District also has plans for increasing
open and green space, although there may be no additional capacity for natural space in the
urban area, and no plans are in place to reclaim a developed area and turn it into green or open
space.
Much like the land use sector, this background helps explain the relatively widespread
distribution of scores in Figure 10, though a majority of data points lie in the "Vulnerabilities to
Address" sector. This underscores the District's relative low resilience with respect to questions
and indicators that the workshop participants found to be important.
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1 B.2.2.5. People
D.C. People Sector: Questions and Indicators
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Figure 11. Washington, DC people sector. Question and indicator quadrant
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2 The District is moderately resilient in terms of the impacts of climate change on its population.
3 Similar to other sectors, there is intracity variation in resilience, and vulnerable subpopulations
4 might be more negatively affected by climate change impacts. It is unclear to what extent
5 ongoing outreach has truly impacted these populations.
6 Interconnectivity issues are also a particular concern for this sector. Success of medical and fire
7 response depends on a functioning ICT sector, and availability of fuel and food supplies is
8 critical in a state of emergency. Water sector vulnerabilities can also have a significant (and
9 potentially devastating) impact on public health, while health care services are heavily reliant on
10 the energy sector. Transportation is critical for evacuations during a state of emergency.
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The District is less resilient in terms of the segments of the population that are particularly
vulnerable to the impacts of climate change, including the population affected by asthma (15.2%
of the adult population and 22.7% of residents under 18; these numbers are also on the rise), the
population vulnerable due to age (16.9% of the population is over the age of 65 or under the age
of 5), and the portion of the population living alone (4.3%). The latter tend to be elderly and
economically disadvantaged. However, the percentage of the population that is disabled is
relatively low (11.4%).
Population location is also an area of vulnerability. Two and a half percent of the population
lives within the 500-year floodplain, which is a small, high density area. In addition, only some
modes of transportation are accessible to vulnerable subpopulations (although the District scored
fairly well in terms of percentage of the population that has limited access to transportation due
to vulnerabilities, and for whom transportation failures might be life-threatening).
To date, there have been limited to no planning efforts related to identifying demographic
characteristics or locations of populations vulnerable to climate change. In addition, the District
has not evaluated its policies and programs to promote adaptive behavior in ways that take into
account vulnerable populations, although workshop participants recognized the importance of
such evaluations. While there are emergency services aimed at quickly responding to vulnerable
populations during power outages, these responses are slower than is optimal.
However, there are organizations across the District that actively promote adaptive behaviors at
the neighborhood or District level, and there are policies and outreach/education programs to
promote behavioral changes that facilitate climate change adaptation. These programs, which are
driven by both the government and private sector, are designed to reach the critical audiences in
the urban area. At the same time, workshop participants questioned whether these policies and
programs are designed and implemented in ways that promote health and well-being of
vulnerable populations.
The District is also resilient in terms of the number of emergency responders (the number of
police officers in the District is equivalent to 0.60% of the 2011 3-year American Community
Survey population) and average emergency response time for both fire and emergency medical
service services (EMS). Over 98% of fire response times are less than 6.5 minutes and the
average EMS response time (average between fire response times and medical emergency
response times) is 4.7 minutes. The robustness of emergency response capabilities is, however,
dependent on the resilience of the ICT sector. In addition, the District received a low resilience
rating for the number of M.D. and D.O. physicians per capita (0.0018 active patient care primary
physicians per capita).
The capacity of existing public health and emergency response systems is already limited and
would not be sufficient under more extreme conditions. Likewise, the current distribution of
public health workers and emergency response resources is not appropriate for the population
that would be affected during an extreme event. Planning and training for response to extreme
events have also been limited, both for emergency response staff and the general population (and
the most vulnerable populations in particular). The District might not have sufficient capacity to
provide public transportation for emergency evacuations, and to date, planning for this
possibility has been limited. Early warning systems are in place (including television and phone
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alerts) for meteorological extreme events, but these systems rely on the individual to take
responsibility to heed the warnings and instructions.
Current rates of waterborne disease, heat-related deaths, and infectious disease are relatively low
(0.02% impacted, 0.0002% of deaths, and 1.34% of the population impacted, respectively).
Workshop participants noted that heat-related deaths are likely underreported and infectious
disease rates are skewed by sexually transmitted disease rates. In terms of avoiding or
responding to heat-related illness, the District is resilient. The District has multiple evacuation
and shelter-in-place options available to residents in the event of a heat wave, and already has
robust programs in place for providing public access to cooling centers, although broader efforts
to reduce heat island effects could still be implemented.
The District is likewise resilient for infectious disease response. Public health agencies have
identified infectious diseases and/or disease vectors that might become more prevalent in the
urban area under the expected climatic changes and have developed associated response plans to
reduce the associated morbidity/mortality. However, the healthcare community is not necessarily
prepared for changes in patients' treatment necessitated by climate change, and has insufficient
funding to do so. For example, the District currently does not have the staff for appropriate West
Nile virus surveillance.
Figure 11 shows a wide and relatively even distribution of responses across the resilience axis,
showing that, while the District has made strides to address the effect of climate change on the
population of the District, work still needs to be done. In addition, the workshop participants
identified all questions and indicators relating to the effect of climate change on the population
of the District to be of high importance; no questions or indicator were ranked below the median
for importance.
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1 B.2.2.6. Telecommunications
D.C. Telecommunications Sector: Questions and Indicators
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Figure 12. Washington, DC telecommunications sector. Question and
indicator quadrant mapping.
2 Washington, DC demonstrates fairly high resilience in the telecommunications sector. While loss
3 of telecommunications infrastructure could have a significant economic impact, the District's
4 emergency systems and preparedness and ability to maintain a communications network during
5 an extreme event are strong. The District demonstrated more limited resilience in terms of its
6 ability to transmit key messages and information to residents (indicating increased vulnerability
7 for the people sector).
8 The greatest areas of vulnerability identified by workshop participants include likelihood a
9 temporary loss of telecommunications infrastructure having a significant impact on the local and
10 regional economies and the population's access to FEMA emergency radio broadcasts. In
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addition, the District's 911 service has no backup centers outside of the District, only across
different sections of the District, and the District has key nodes in the telecommunications
system, the failure of which would severely affect the District's service.
However, the District's telecommunications infrastructure appears relatively resilient to the
gradual impacts of climate change or extreme climatic events. Few belowground infrastructure
components are vulnerable to expected rises in groundwater levels or salt water intrusion, and
few aboveground infrastructure components are vulnerable to expected winds. There is also a
backup tower network in the event that satellite-based communications are disrupted by wet
weather.
One data center was shut down and moved due to flooding concerns. During previous extreme
weather events and other natural disasters, the District's services were either unaffected or only
mildly affected. There is a great deal of redundancy built into the emergency communications
systems and the infrastructure has capacity for increased public demand in an emergency,
although staffing for 911 services is a limited factor (there are more phone lines than staff
members to answer phones). The District also has access to backup 911 networks that could
handle the majority of the load for the main emergency response networks, if necessary.
However, telecommunications systems do not have sufficient water and energy supply to handle
more than a small amount of the anticipated extra load in the case of sudden natural disasters.
The District does not have concerns regarding the vulnerability of the telecommunications
infrastructure to high temperatures or prolonged high temperatures, as long as there is power to
provide the necessary cooling (demonstrating interconnectivity between the energy and
telecommunications sectors).
Communications and links across infrastructure service providers and between local authorities
and the service providers are good, and stakeholders can quickly make and implement decisions
in emergency situations. District planners have also consulted with other city governments with
similar telecommunications systems to learn how those governments coped with natural disasters
and to plan for similar events accordingly.
There is some concern that the availability of telecommunications resources could be impacted if
other District services, particularly power, were impacted by climatic events or changes. Backup
power for these resources is provided, although the extent to which backup power is provided by
diesel generators is unclear. The District has 72-hours worth of diesel, so emergencies that
extend beyond that time frame pose a greater risk to this and other sectors.
This background supports the distribution pattern for telecommunications in Figure 12.
Seventy-four percent of the questions (20 of 27), and indicators fall in the "Monitor for Changes"
segment (high resilience/high importance).
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D.C. Transportation Sector: Questions and Indicators
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Figure 13. Washington, DC transportation sector. Question and indicator
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Washington DC demonstrates high resilience in terms of the level of accessibility and variety of
public transportation and travel-time scores, as well as the District's livability and walkability,
although some less dense areas of the District are not as walkable. Workshop participants noted
that if these indicators focused on range of livability or walkability across neighborhoods, the
District would have received lower resilience scores in these areas.
The District is also generally considering climate change adaptation and resilience for
transportation planning, and has implemented related measures to some extent. However, the
current transportation infrastructure, particularly the Metro, is not equipped to handle either the
gradual impacts of climate change or impacts of extreme climatic events, and limited or no
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funding is available to remedy this issue. Transportation infrastructure is particularly vulnerable
to flooding (both in terms of the impact on transportation availability and infrastructure, and
stormwater management) and heat, and recovery from a major climatic event could be complex
and lengthy.
The District's transportation system is highly flexible, and residents have access to seven modes
of transportation (including via water and air). Eighty-two percent of residents are near a transit
stop, and the District has both a high level of transport diversity and intermodal passenger
connectivity. Most Metro stops have access to bus connections, and the average distance of
nonwork related trips is fairly short (under 5 miles, though this might also vary across District
neighborhoods). While the mean travel time to work for residents in the District is high, at 29.6
minutes, as compared to the national average of 25.4 minutes, workshop participants assigned
this indicator a low importance ranking. In addition, the District ranks highly in terms of number
of telecommuters or potential telecommuters. Roadway connectivity is also high, but workshop
participants noted that a high number of intersections could also increase the likelihood of
accidents and the amount of road and traffic light maintenance required.
The District has taken proactive steps to develop and implement resilience-building approaches
and incorporate climate impact considerations into transportation projects, alongside reactive
disaster response plans. The District also has a severe weather plan. In terms of infrastructure,
the District has tested new or innovative materials that might be more capable of withstanding
the anticipated impacts of climate change and has planned for and requires implementation of
green infrastructure. However, workshop participants noted that while green infrastructure
planning has occurred, the plans have not necessarily been executed. District agencies have also
been working to upgrade bridges and update evacuation and road and bridge infrastructure
planning to consider extreme climate events.
The District also received a high resilience score for the amount of annual congestion costs saved
by operational treatment costs, calculated at $53 per capita. The District has high-occupancy
vehicle lanes and procedures for clearing traffic accidents from bridges. During traffic incidents,
DC and Virginia can quickly change grid patterns to keep traffic moving, though whether these
plans truly alleviate congestion has not been determined.
However, despite planning efforts, the District's transportation infrastructure is still highly
vulnerable and not equipped to handle the gradual impacts of climate change or the devastation
that a severe event could bring. Workshop participants assigned low resilience scores for
resistance of major transportation links and critical nonroad transportation facilities to the
anticipated impacts of climate change.
It is unclear whether critical facilities would be significantly impacted by flooding. Ten percent
of critical roadway and rail line miles are within the 500-year floodplain, and depending on the
data used, either 5.6 or 11% are within the 100-year floodplain. Workshop participants noted that
rain can hit the District quickly and heavily, causing vents and tunnels to flood. District culverts
are not sized to meet future (or even current) stormwater requirements, but upgrades will be
completed by 2030. In addition, 31 bridges (12.8% of District bridges) are structurally deficient.
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In addition to flooding, increased temperature also places considerable stress on the District's
transportation infrastructure. A few materials currently in use in the District's transportation
systems are compatible with anticipated changes in temperature, but many of the District's
transportation systems were built for the climate as it was at the time. When metro rails overheat,
they develop heat kinks, requiring the District to replace that part of the rail. If a kink goes
unnoticed, the trains will derail. However, communication procedures are in place to prevent
risks associated with heat kinks.
Congestion is also currently an issue for the District. One study ranked the District first in the
nation for yearly delay per auto commuter5 among the very large urban areas6 in 2014 (Schrank
et al., 2015), though workshop participants called the validity of the study into a question.
Another study ranked the District third for congestion intensity and second by congestion costs
(Litman, 2016).
The District is developing and has plans to replace aging infrastructure, but not all of these plans
account for the anticipated impacts of climate change. Funding for infrastructure repair and
replacement is also limited and very competitive. The District currently has no funding
mechanisms specifically for adapting transportation systems to climate change.
In terms of emergency response and recovery, residents are generally unaware of evacuation
procedures, and the length of time required to restore major high-traffic vehicle transportation
links in the urban area after a failure could be significant, but would vary depending on the
particular scenario. Even now, a short-duration problem on the Metro causes significant travel
delays, and the Metro system has very limited redundancy. One of the current goals for the
District is to increase modal redundancy (for nonclimate-related reasons). The District is adding
bike lanes and streetcars and hopes to improve the Metro's redundancy and increase the
flexibility of the bus system to reduce the impact of incidents on the Metro system.
Finally, the transportation sector is relatively reliant on other sectors, particularly energy, to
remain operational, and availability of transportation resources is generally at significant risk if
other District services are affected by climatic events or changes. Likewise, other sectors,
particularly people and economy, would be significantly impacted by short- or long-term
problems in the transportation sector. The District is also relatively dependent on long-range
transportation of goods and services.
Figure 13 indicates that the transportation sector in Washington, DC has significant
vulnerabilities to climate change impacts and shows a wide and relatively even distribution of
responses across the resilience axis. This indicates that while the District has made strides to
address the effect of climate change on the population of the District, risks remain high. In
addition, the workshop participants identified all questions and indicators relating to the effect of
climate change on the District's transportation sector to be of high importance. Only one
5 Extra travel time during the year divided by the number of people who commute in private vehicles in the urban
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1 indicator, regarding travel time to work (which, as noted previously, is above the national
2 average in the District), ranked below the median for importance.
3 B.2.2.S. Water
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Figure 14. Washington, DC water sector. Question and indicator quadrant
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4 The District's water sector exhibits low resilience to climate change, particularly with respect to
5 source and infrastructure (but exclusive of planning activities). Interconnectivity with other
6 sectors is important, as disruptions to water service could significantly impact public health and
7 the economy, land use/land cover, and the natural environment. The water sector is also heavily
8 dependent on the resilience of the energy and transportation sectors.
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1 There is only one source of drinking water for the District, the Potomac River, and there are few
2 interconnections with neighboring water systems. While water quality, in terms of numbers of
3 Safe Drinking Water Act violations, is sufficient, almost the entire Potomac watershed is outside
4 of Washington, DC, limiting the District's control over water quality. Moreover, treatment does
5 not exist to handle increases in nutrient loading.
6 Water infrastructure is at high risk during extreme events. More than 90% of stormwater and
7 wastewater pump stations are in the flood zone. Minimal backup power is available for drinking
8 water, stormwater, and wastewater services, and there is no redundant drinking water treatment
9 system. Likewise, there are no redundant wastewater or stormwater services.
10 The water sector is more resilient with respect to planning. The drinking water treatment plant
11 has redundant chemical suppliers, and there is a hierarchy of water uses protocol during a
12 shortage or emergency. A water/wastewater agency response network (WARN) provides
13 technical resources support during emergencies, and storm sewers and drains to storm sewers
14 have been inventoried, although there is variability in the extent to which these inventories are
15 used in planning (in part, because the stormwater infrastructure is not owned by one single
16 agency).
17 Drought and water availability are not a concern for the District now or in the future. Rather, the
18 District anticipates that the currently ample water supply will only increase.
19 Figure 14 shows the majority of questions and indicators ranked as important.
20 B.2.3. Summary of Washington, DC Findings
21 The results of the DC case study show that the District's resilience to climate change is mixed,
22 with some areas of both high and low resilience within each sector. Across most sectors, the
23 District demonstrated high resilience with respect to planning activities and a general awareness
24 of the need to prepare proactively for the potential impact of extreme climatic events or the
25 gradual impacts of climate change. The District also benefits from an existing, robust
26 transportation system, network of parks and other green spaces, relatively small size, and
27 uniqueness in terms of federal government presence and involvement. The latter attribute brings
28 to bear more expertise and resources on climate readiness and emergency preparedness than
29 other similarly sized metropolises perhaps enjoy.
30 However, in some areas—particularly transportation—the city's current infrastructure is less
31 resilient, particularly to the impacts of flooding or rising temperatures, and the resources to make
32 any needed improvements are not available. In addition, the resilience scores across all sectors
33 might not accurately reflect significant intracity disparities in resilience. Workshop participants
34 noted frequently that disparities in economy, infrastructure, transportation access, and
35 vulnerability of the population could mean that the impacts of climate change disproportionately
36 impact some areas of the District more than others. It is also unclear to what extent programs and
37 messages regarding climate change and adaptation and emergency response are reaching the
38 most vulnerable subpopulations.
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APPENDIX C. WORCESTER, MA CASE STUDY
This appendix contains the Worcester, MA case study. Section C.l provides background on the
known climate vulnerabilities faced by Worcester and background on the existing planning, if
any, the city has undertaken in order to address these vulnerabilities. Section C.2 reviews the
results for Worcester, MA. Results are by sector and accompanied by visual data summaries.
C.l. WORCESTER, MA BACKGROUND
Worcester, MA is a postindustrial city, which—like many of its counterparts across New
England, the East Coast, and the Midwest—faces challenges finding the resources needed to
sustain critical infrastructure and health and human services for current needs, let alone the
resources needed to prepare for and incorporate responses to the threats posed by climate change.
Worcester is the second largest city in New England. The city is located in central Massachusetts
approximately 45 miles (72 kilometers) west of Boston, the state capital and largest New
England city. The current population is near 185,000. Like many cities in the North and
Midwest, population peaked in the 1950s when Worcester's population was just over 200,000
during the immediate postwar years. Following national trends, after decades of decline,
population growth reversed and became positive in the 1980s and is projected to remain so for
the future; total city residency increased by 5% between 2000 and 2010 (WRRB, 2013).
Similar to other postindustrial cities, Worcester has faced the significant challenge of reinventing
and revitalizing its economy. While the city grew and prospered from the mid-1800s through
World War II, driven by thriving textile, metalworking, and machine tool industries, it faced
economic decline through the second half of the twentieth century. However, the city has seen
some economic recovery in recent years from growth in the biomedical/life sciences, health
services, and higher education sectors (City of Worcester, 2004; WMRB, 2008), similar to other
large "rust belt" cities such as Buffalo and Cleveland (populations approximately 258,000 and
390,000, respectively) (U.S. Census Bureau, 2015a; U.S. Census Bureau, 2015b). Recovery has
not been constant: from 2001 to 2007, Worcester lost more than 2,200 jobs, or 2% of its total
employment base (Boyle, 2011). The biotechnology cluster in particular has become an
increasingly important anchor in the regional economy and a key component in the state's
economic development initiatives (O'Sullivan, 2006). As a result, the employment structure of
the city has shifted: the leading employers are currently hospitals and associated medical service
organizations (WRRB, 2015). Median household income remains below the national average
(U.S. Census Bureau, 2013c).
Worcester has a continental humid climate, similar to many other cities in the Midwest and
Northeast. Continental humid climates are typified by large seasonal differences in temperatures
with precipitation occurring throughout the year (Kottek et al., 2006). Worcester is vulnerable to
a range of climate extremes, from damaging ice, blizzards, and cold air events to heat waves, and
must therefore plan for a broad range of contingencies. The hilly topography surrounding the city
can magnify disaster impacts and complicate recovery efforts (CMRPC, 2012). For example,
there is a greater risk of water being funneled into valleys and rivers and of landslides, which can
disrupt transportation, telecommunications, and other sectors.
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Like many other small- to medium-sized cities across both New England and the United States,
Worcester has undertaken relatively little climate change-related adaptation planning. The city
has an existing Climate Action Plan that is focused largely on mitigation measures such as
reducing greenhouse gas (GHG) emissions. The plan currently does not include a focus on
adaptive measures.
Worcester is also part of the Central Massachusetts Regional Planning Commission, a group
focused on planning and responding to natural disasters. However, this group does not focus on
understanding changes in the intensity or frequency of these hazards nor on ways to adapt to any
such changes. Partially due to the lack of planning, data are also limited. Worcester is therefore a
good test for the tool in a data- and planning-limited environment. Additionally, Worcester is
more typical of many American cities in that it does not receive high levels of support and
coordination for many city functions from the federal government, unlike the District.
C.l.l. Known Vulnerabilities
Worcester is located in a continental humid climate, with year-round precipitation and large
seasonal temperature fluctuations, making the city vulnerable to climate extremes on both ends
of the spectrum. The city is vulnerable to flooding and severe storms (including hurricanes,
Nor'easters, and winter storms, with associated flooding and high winds), as well as extreme
cold, ice-damming of rivers, extreme heat, and urban fires (CMCRP, 2012). Table 13 lists
several historic weather events that have impacted the city of Worcester.
Stormwater flooding, aggravated by urban runoff, is especially prevalent; the city of Worcester
accounts for nearly half of historical claims in the region for damages related to this risk.
Riverine flooding and dam flooding are also concerns. Worcester contains six dams considered
"high hazard" and four deemed "critical" by the Office of Dam Safety. The 100-year floodplain
in Worcester contains several critical facilities, including a fire department and three medical
clinics. On the other hand, Worcester has received higher marks than any other community in the
region from the National Flood Insurance Program for its aggressive program of raising
awareness of flood hazards and maintaining elevation certificates on new and improved
buildings (CMCRP, 2012).
Storms with high winds and winter storms are capable of causing power outages that can
threaten vulnerable populations. Extreme cold and extreme heat are also public health and safety
concerns, especially with regard to the city's homeless population. Although the Central
Massachusetts Region-wide Pre-Disaster Mitigation Plan (CMCRP, 2012) does not explicitly
address climate change, it is likely that Worcester's susceptibility to urban fires (there were 815
fires between 2004 and 2009) could be exacerbated by conditions of extreme heat or drought.
Intense precipitation events, the frequency of which is expected to increase, can place a strain on
sewer and wastewater infrastructure. The city has both separate and combined sewage and
stormwater systems. The oldest part of the system, a combined sewer system that covers 4 square
miles, includes pipes constructed of brick in the mid to late 1800s (City of Worcester, 2013a).
Changing precipitation patterns and higher temperatures could affect water quality as well.
Worcester's water supplies meet all federal and state drinking water standards but are considered
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1 highly susceptible to contamination due to uncontrolled uses (i.e., activities on privately owned
2 lands) in the 40-square-mile watershed (City of Worcester Water Operations, 2014).
Table 13. Major weather and other events and their impacts in Worcester, MA
Weather event
Date
Impacts
The Great New
England
Hurricane
September
1938
Structural damage and flooding were heavy. According to the
Worcester Gazette, "Buildings were partially collapsed...
roofs ripped off, church steeples toppled, store fronts blown
out.. .chimneys leveled, signs torn down and the streets
littered with glass..." (Herwitz, 2012). Severe flash flooding
also affected the city and surrounding areas, with 10-17
inches of rainfall reported for both the hurricane and storms
in the preceding days (Foskett, 2013). Tree damage was so
severe that a temporary sawmill set up in Hawden Park
processed lumber for over 2 months (Foskett, 2013),
including nearly 4,000 street trees downed (Herwitz and
Nash, 2001).
Worcester
Tornado
June 9,
1953
Ninety-four people were killed, more than 1,000 people were
injured, and more than 10,000 people were left homeless in
Worcester and the surrounding areas from an F4 tornado. The
storm remains the deadliest New England tornado on record
(Fortier, 2013; Herwick, 2014).
Dutch Elm
Disease
1950s
Due to their vase-shaped spreading crowns and ability to
grow in compacted soils, elms were widely used as street and
landscaping trees. An introduced fungal blight killed virtually
all of Worcester's elms during the 50s and 60s, significantly
reducing the tree canopy of the city and its capacity to uptake
stormwater, filter air, and provide shade during hot summers
(Herwitz and Nash, 2001).
Hurricane
Gloria
September
1985
Heavy winds and rain damaged trees and power lines.
President Reagan declared much of New England, including
Worcester, a federal disaster area (FEMA, 1985).
April Fool's
Day Blizzard
April 1997
Thirty-three inches of heavy snow fell, setting the record for
the snowiest April and causing extensive damage to trees and
power infrastructure across Massachusetts (Rosen, 2015).
The late arrival of the blizzard meant that many plows and
snow-removal equipment were already in storage, making
restoration of transportation networks difficult (Marcus,
1997).
Hurricane Irene
August
2011
Heavy winds and rains accompanied by flooding resulted in
localized flooding and power cuts (WBUR, 2011).
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Weather event Date Impacts
The Endless 2014-2015 Over the 2014-2015 winter, Worcester recorded 119.7
Winter inches of snow, nearly double the average of 64.1 inches.
Worcester was the 2nd snowiest city with population over
100,000 in America, less than 1 inch below lst-place Lowell,
MA (Golden Snow Globe, 2015). The late January "Blizzard
of 2015" dumped more than 34 inches onto the city in 24
hours, breaking the 110-year-old record for snowiest day in
the city's history (Eliasen, 2015). The winter taxed
transportation infrastructure, made commutes dangerous as
snowbanks obscured sightlines for pedestrians and drivers,
and resulted in structural damage from rooftop ice dams.
These introduced insects kill maple, ash, beech, and other
common New England trees, damaging the urban forest and
reducing its capacity to provide water retention, air filtration,
shade, and scenic values. At present, over 25,000 trees in the
Worcester area have been destroyed in order to prevent
further spread of the pests (Freeman, 2009), and quarantine
measures have been put in place to prevent movement of
infected firewood (City of Worcester, 2015).
Residents with few resources (e.g., the poor and the homeless) may be particularly vulnerable to
extreme weather events and extremes of temperature. During the period of 2007-2011,
approximately 19% of the city population was living below the poverty level, compared to
10.7% for the state overall (U.S. Census Bureau, 2013). In January of 2013, approximately 1,202
Worcester residents were homeless, with 22 unsheltered (living on the street) (CMHA, 2013).
More than 4% of the city's roads and railroads are within the 500-year floodplain (though the
city received a high resilience rating based on the percentage of roads and railroads within the
100-year floodplain), but the current transportation designs and transportation and related
infrastructure planning regimes are not considering anticipated impacts of climate change or
resilience. The system is designed to state standards and is under major budget constraints at
present; only if these standards were to be changed to consider climate change scenarios would
such considerations of climate risk be incorporated. The city recognizes the need for substantial
investment locally (and nationwide) to simply repair crumbling roads and bridges, let alone
increase resilience to climate shock.
C.1.2. Existing Adaptation and Mitigation Planning
Worcester, like many smaller U.S. cities, is subject to the effects of climate change but has fewer
resources than major metropolitan centers, such as Washington, DC, for addressing, planning
for, adapting to, and responding to those effects. The project team reviewed information on ten
U.S. cities similar in size to Worcester and found that although a majority had sustainability or
hazard mitigation plans addressing one or more specific areas (e.g., water supply, flood hazards),
Asian 2000s-
Long-Horned Ongoing
Beetle; Emerald
Ash Borer
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only one in ten had been evaluated for resilience to climate change in a comprehensive manner.
Cities with more developed climate change resilience evaluations and adaptation plans tended to
be those which took advantage of external resources such as the Resilient Communities for
America program (RC4A, 2013). This national campaign encourages communities to formally
pledge to develop more resilient cities, and provides critical resources for community leaders to
assist in this process. The program is supported by a number of community resilience
organizations including: ICLEI - Local Governments for Sustainability, the National League of
Cities, the U.S. Green Building Council, and the World Wildlife Fund.
Smaller cities generally have fewer resources with which to evaluate resilience and to study and
plan for climate threats. They also lack the economies of scale that larger cities can take
advantage of. Their smaller staff may be unable to participate as fully as those of larger cities in
activities (e.g., conferences) that provide staff with access to new information and opportunities
for regional and national partnerships. Further, they may be at greater risk of losing institutional
knowledge as a result of staff turnover. On the other hand, with fewer stakeholders, smaller cities
may find it easier to develop consensus around and implement policies. In addition, smaller cities
are generally less vulnerable to the urban heat island effect (ICLEI, 2010).
Worcester's climate planning is typical of a smaller city. This section summarizes literature on
Worcester's present efforts and future plans to adapt to climate change. To date, climate
adaptation has not received sustained attention as a matter of city policy. Across all sectors,
dedicated planning and initiatives geared towards increasing resilience, through emergency
preparedness, improved redundancy, incorporation of climate change considerations into
planning, and infrastructure replacement and design, is still somewhat scattered and limited. In
many cases, these limitations are the result of lack of funding, although limited awareness and
interest among residents and coordination across city government were also cited as issues in
some sectors. The city has conducted some table-top exercises of climate change adaptation
planning, looking at past events to assess effectiveness of current measures. Funding specifically
for adaptation-related activities or measures is limited to nonexistent.
The city's Climate Action Plan (City of Worcester, 2006), though focused on climate change
mitigation (i.e., reducing greenhouse gas emissions), discusses several measures that have
implications for climate adaptation as well. For example, diversifying Worcester's energy
portfolio to include more local and alternative energy sources will improve the city's ability to
cope with extreme events. Specific projects noted in the plan include a 100 kW hydropower
turbine at the water filtration plant, a 250 kW wind turbine at new North High School, and a
biodiesel (B-20) pilot program at Hope Cemetery. Similarly, improved energy efficiency will
reduce vulnerability to climate-related disruptions and free up resources to cope with
environmental challenges. Energy efficiency measures discussed in the plan include investing in
fuel-efficient vehicles, implementing anti-idling technologies and policies for city vehicles, and
promoting the adoption of energy-efficient appliances. Other actions discussed in the plan,
including developing a municipal green building policy (e.g., promotion of cool roofs), planting
community gardens, and protecting open and green spaces, will help mitigate the urban heat
island effect. The plan also calls for improved collection of energy and climate data, which can
also support adaptation efforts.
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Detailed reporting of the city's progress in meeting the goals laid out in the 2006 Action Plan is
not available. However, in 2010 Worcester received a "Green Community" designation, which it
qualified for by developing and implementing certain specific plans and policies (e.g., policies
that make it easier to site and permit renewable/alternative energy projects, and plans and
policies to reduce energy consumption by the municipal government or the city as a whole).
Worcester participates in the Central Massachusetts Regional Planning Commission, which has
developed a predisaster mitigation plan that addresses multiple hazards (CMRPC, 2012).
Although the plan does not specifically discuss climate change, it addresses a number of hazards
where the frequency or intensity might be exacerbated by climate change, including flooding,
severe winter storms, Nor'easters, hurricanes, tropical storms, drought, extreme heat, and
extreme cold. The plan discusses actions, potential funding sources, priority listings, and
proposed schedules to address these hazards. Proposed actions include:
• Identifying and prioritizing structural mitigation projects such as stormwater drainage and
dam repair.
• Adding catch basins at low elevation points and upsizing some existing basins.
• Performing a hydraulic analysis at strategic locations.
• Constructing a relief surface sewer.
• Evaluating and repairing dams in the city.
• Cleaning/managing stormwater structures and basins.
• Increasing communication/coordination between all government, municipal, private, and
nonprofit agencies regarding predisaster mitigation.
• Helping residents build working relationships with the utility company to improve
communications during events.
• Implementing/improving hazard warning systems and notifications to vulnerable
populations.
• Developing educational and outreach tools to reach typically marginalized populations.
• Integrating disaster mitigation concerns into projects for various sectors, including
transportation and land use.
• Planning for capital needs.
• Collaborating with other interested parties to identify predisaster mitigation activities.
The Commonwealth of Massachusetts's Executive Office of Environmental Affairs (2004) has
developed a Lower Worcester Plateau Ecoregion Assessment that covers an area that includes
some of Worcester and several neighboring communities. Mindful of the benefit of forests in
moderating climate (among other benefits), the assessment's authors recommend taking several
actions (including providing incentives, producing education material, and assessing valuation
studies) to help preserve forestland in the region from development.
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1 The city's ability to respond effectively to an extreme climatic event rests upon its existing
2 services (and discussions suggest that the city's emergency response capabilities are relatively
3 strong) and it is not clear that in the long term, based on planning measures in place today, the
4 city will be appropriately prepared to manage the impacts of gradual climate change across all
5 sectors. However, the findings do suggest that the city, with appropriate resources and focus, has
6 the ability to incorporate adaptation and resilience considerations into existing plans, practices,
7 and programs.
8 C.1.3. Data Collection Approach
9 The primary data collection approach for the Worcester case study was discussions with key
10 individuals with knowledge and experience in each of the eight sectors (see Appendix G). Two
11 Clark University faculty members oversaw a team to identify the most appropriate individuals
12 within the city of Worcester to provide feedback on the urban resilience tool questions and
13 indicators. In addition, to obtain background information on the City of Worcester, a literature
14 search was conducted. Relevant literature was reviewed for background information as well as
15 information on key metrics for Worcester.
16 First, all participants discussed the relevance of each question. Then, they provided an
17 importance weight for each question. Finally, participants were asked to identify the best answer
18 to each question from the options provided. The process was repeated for the indicators, also
19 requesting that participants discuss relevant available data sets to determine the value of each
20 indicator and review a threshold-based resilience score (if provided).
21 The project team spoke with at least one primary individual with in-depth knowledge of each of
22 the eight sectors (see Appendix G). In the case of the water and people sectors, for data analysis
23 purposes, the project team recorded the response of the individual whom they deemed to be most
24 qualified and knowledgeable regarding the specific question or indicator.
25 C.2. CITY-WIDE RESULTS
26 Figures 14 and 15 highlight overall trends in the Worcester, MA data. For both resilience and
27 importance, scores ranged from 1 to 4, with 1 indicating lowest resilience or lowest importance,
28 and 4 indicating highest resilience or highest importance. In Figures 2 and 3, the "Resilience"
29 score represents an average score for all questions or indicators in that sector. These sectors are
30 ranked, from left to right, by the average importance score for that sector. As such, a sector with
31 a low resilience score towards the right of the plot may be considered relatively more vulnerable
32 compared to another sector with a low resilience score towards the left.
33 Note that there were no responses to questions for the People sector, and no data in the energy
34 and information technology (IT) sectors for indicators.
35 In general, Figure 15 shows minimal spread in the question data for resilience. Average
36 importance scores for all sectors are also clustered. With the exception of the energy sector's
37 average resilience measurement, no other sector scores below the median on average for either
38 resilience or importance. However, importance scores are almost always higher than resilience
39 scores, suggesting potential across-the-board vulnerability. These data suggest that the most
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1 sectors have similar levels of vulnerability, with the transportation, land use, and energy sectors
2 the least resilience on average.
3 From the indicator data in Figure 16, there is a wide range of scores. This is similar to the wider
4 range of indicator scores compared to question scores reported for the District (see Figures 2 and
5 3). These data suggest that the natural environment and water sectors are the most resilient
6 sectors for which there is data. The other sectors (transportation, people, economy, and land use)
7 had similar importance scores and much lower resilience scores, suggesting that these sectors
8 may need more attention. All average resilience scores for indicators (Figure 16) are higher than
9 the resilience scores for questions (Figure 15).
Worcester. MA Resilience: Questions
.¦F-e-cf-s "Water ¦ Nat. Eriv. ¦ Trans. ¦ Land Use ¦ Energy "IT ¦Economy
>
Increasing importance score
4
Sector
Figure 15. Worcester, MA. Average question resilience and importance.
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Worcester. MA Resilience: indicators
¦ Nat. Env. ¦ Trans, e People ¦ Water ¦ Economy ¦ Land Use BEre
y
Increasing importance score
Sector
Figure 16: Worcester, MA. Average indicator resilience and importance.
1 Figure 17 disaggregates the data summarized in Figure 15 and highlights potential "spikes" of
2 high risk within sectors with overall lower averages. For example, questions or indicators
3 associ ated with the water sector fall in all four main quadrants of Figure 17, and all seven sectors
4 with data in Worcester have at least one question appearing in the "Vulnerabilities to Address"
5 domain.
6 Data collected in response to questions for Worcester cluster in the "Monitor for Changes"
7 quadrant (slightly more than 60% of the total). Of the seven sectors with data, only economy is
8 overwhelmingly restricted to the "Monitor for Changes" quadrant (9 out of 11 questions). This
9 suggests most sectors in Worcester need to pursue a variety of strategies to adequately prepare
10 for climate change, as well as prioritize actions carefully.
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Worcester, MA: Questions
4
CD
i_
o
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CO
Q>
o
£
TO
t
O
a
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1
Monitor for changes
Vulnerabilities to address
© ©
© © ©
© 0 © ©
© © ^ © ©
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© © ©
©
© © ©
© 0 0
Q ©
© © © © <25
© © © © ©
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Low priority
Small problems that can add up
• Water
• Trans.
• People
• Nat. Env.
• Land Use
• IT
• Energy
• Economy
4 (high resilience) 3 2 1 (low resilience)
Resilience Score
Figure 17. Worcester, MA. Question quadrant mapping.
1 Figure 18 offers the same presentation as Figure 17, but for indicator data across all sectors. Note
2 that no indicator data were available for the Energy or Telecommunications sectors. Much like
3 the question data in Figure 17, the majority (four out of six) of the sectors with available data
4 have at least one entry in the "Vulnerabilities to Address" quadrant, highlighting how averages
5 can hide specific facets of climate preparedness that need to be addressed within cities.
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Worcester, MA: Indicators
4
1
Monitor for changes
Vulnerabilities to address
1376
Low priority
Small problems that can add up
¦ Water
¦ Trans.
¦ People
¦ Nat. Env.
¦ Land Use
¦ Energy
¦ Economy
4 (high resilience) 3 2 1 (low resilience)
Resilience Score
Figure 18. Worcester, MA. Indicator quadrant mapping.
1 C.2.1. Sector-Specific Investigations
2 The sections below connect the results observed above to potential underlying drivers and
3 roadblocks for each sector discussed in the literature as well as from input from participants.
4 However, unlike Washington, DC, little supplemental literature was available for Worcester. In
5 addition, the discussions were primarily limited to one representative (as the tool was designed),
6 in contrast to the participation of numerous representatives across sectors, as was the case at the
7 DC workshops.
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1 C.2.1.1. Economy
Worcester Economy Sector: Questions and Indicators
1
Monitor for changes
Vulnerabilities to address
© © ©
o o o
O
EB O O
Hl375^H 722 1
Low priority
Small problems that can add up
• Questions
¦ Indicators
4 (high resilience) 3 2 1 (low resilience)
Resilience Score
Figure 19. Worcester, MA economy sector. Question and indicator quadrant
mapping.
2 Overall, diversity of economic options and the generally improving economic outlook for the
3 city potentially led to the moderate to high resilience scores. While manufacturing still plays an
4 important role, the education (Worcester holds over 13 colleges and universities) and healthcare
5 sectors now account for nearly half of total city employment and make up the largest share of the
6 city's economy. The biotechnology industry in the city is also growing, leveraging the educated
7 workforce and healthcare sectors. The city is not heavily dependent on climate-sensitive sectors
8 such as agriculture.
9 Worcester received lower resilience scores for the economy due to vulnerable subpopulations,
10 including the growing homeless population (increase of 5.1% between January 2012 and January
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2013) and the percentage of the population in the city living below the poverty line (19%, based
on 2007-2011 data). However as a result of the 2006 passage of the Massachusetts insurance
healthcare reform law requiring most state residents to obtain some level of health insurance
coverage, 95% of the noninstitutionalized population does have access to health insurance.
In the event of climate shock, it is unclear whether jobs lost in one sector could be replaced by
expanding the economy and job opportunities in another sector. The participant noted that
resilience in this regard is dependent on which sector is disrupted and on workers' skills and
mobility in each particular sector.
As Worcester continues to prepare its economy and improve its economic resilience to climate
change, the city may consider exploring additional funding opportunities and modifying its
management approaches to climate change adaptive planning. The participant noted that
adaption planning responsibilities are spread out over multiple offices within Worcester
government, reducing the efficiency and efficacy of projects, and no funding is currently
available for multipurpose adaptive development projects (meeting both recreation and adaptive
development needs), which may serve as road blocks to planning efforts.
Figure 19 shows that 69% of the questions and indicators lie in the "Monitor for Changes"
quadrant (high resilience/high importance), indicating that Worcester's economy has high
resilience to climate change. Worcester's "Vulnerabilities to Address" (low resilience/high
importance) in its economic sector relate to the city's vulnerable subpopulations and its gaps in
adaptive planning funding. Figure 19 also demonstrates that the city may also consider its
approach to adaption planning, possibly concentrating the activities into one office, as this issue
lies in the "Small Problems that Can Add Up" quadrant.
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1 C.2.1.2. Energy
Worcester Energy Sector: Questions and Indicators
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Figure 20. Worcester, MA energy sector. Question and indicator quadrant
mapping.
2 The data gathered on the Energy sector came exclusively via responses to questions on this
3 sector. No indicator data were available for relevant indicators.
4 In terms of energy supply, the city's energy sector is relatively resilient, as energy supplies come
5 from outside the metropolitan area to only a moderate extent. The city has also made moderate
6 efforts to reduce energy demand. However, based on participant responses, the resilience of the
7 city's energy sector in terms of coping with or responding to stressors (extreme events, outages,
8 or higher peak demand/demand at different times) appears to be limited. The city's redundant
9 energy systems only have a small amount of capacity in the event of an occurrence that would
10 threaten the energy system; however, the participant rated this as a less important factor for the
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city. Additionally, the response time to restore electrical power after a major event may take
more than a day, and the electrical generation capacity cannot handle higher peak demands or
peaks at different times than it currently experiences; the participant ranked these issues with
high and moderately high importance, respectively.
Diverse and local sources of power and heat contribute to a city's resilience. Worcester relies on
a combination of electricity, natural gas, and oil. Several renewable energy sources contribute to
the local power grid, including a wind turbine located at a city high school (McCauley and
Stephens, 2012).
In addition, beyond efforts to encourage reduction in energy consumption, the city is not actively
pursuing alternative approaches to better manage demand or reduce risk through distributed
generation or smart grid technologies. At the same time, the city is aware of an increased
frequency of extreme events that threaten its electricity systems, and the potential benefits of
moving towards decentralized systems, including more distributed renewable generation.
However, the participant did not believe that increased decentralization will necessarily help
reduce vulnerabilities to climate change. A participant made note of National Grid's smart grid
pilot project in Worcester, but based on the current status of this pilot program, the opportunities
it presents for managing demand in Worcester are not particularly significant. Therefore, these
areas of vulnerability may remain for the city in the longer term.
Problems were also noted in acquiring relevant energy usage data for Worcester especially
because energy consumption data are recorded by distribution circuit, which does not match
community or city limits. In addition, load zones in central Massachusetts are not defined by the
city. Worcester offers a "Worcester Energy Program" to encourage energy savings.
Figure 20 shows that of the data available, 62.5% (or 5 of 8) questions lie in the "Vulnerabilities
to Address" quadrant, indicating that there are significant steps Worcester can take to increase its
energy sector's resilience to climate change.
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1 C.2.1.3. Land Use/Land Cover
Worcester Land Use/Land Cover Sector: Questions and Indicators
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Figure 21. Worcester, MA land use/land cover sector. Question and indicator
quadrant mapping.
2 While Worcester has many planning and zoning initiatives that may be relevant to climate
3 resilience, none of them have been justified by or undertaken for the primary purpose of climate
4 resilience. The most significant discrepancy between importance score and resilience score,
5 indicating the greatest perceived vulnerability, concerns the location of valuable infrastructure
6 and continued development (without concern for retrofitting) in areas that are vulnerable to
7 extreme events, including flooding. The same discrepancy was identified regarding the lack of
8 financial incentives to prevent development in floodplains and reduce the amount of impervious
9 surface, among other initiatives. These responses may speak to a greater vulnerability in the
10 economy sector than was indicated by the interview.
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The city is not actively pursuing the use of resilient retrofits or urban forms to mitigate climate
change impacts or address urban heat island effects. In addition, although the participant placed
limited importance on the latter two initiatives, the city demonstrates low resilience related to the
percentage of city land that is urban (100%) and percentage of impervious cover. This
underscores the need for using retrofits or urban forms to mitigate climate change impacts or
address heat island effects and for funding to reduce the amount of impervious surfaces and for
limiting further development in vulnerable areas, as identified in the response to the questions.
Worcester is taking some steps to improve the city's land use/land cover resilience to climate
change. The participant noted that there are mechanisms to support tree shading programs in
urban areas; however, additional funding is needed through existing sources established within
the city. Additionally, incentives exist to integrate green stormwater infrastructure into
infrastructure planning to support flood mitigation. When green infrastructure was used, the
participant noted that the infrastructure was selected with minimal attention to the ecological
benefits provided.
However, the city has taken advantage of existing resources, including local academic research
and other stakeholders, and has taken into account historical land use and land cover changes to
better understand and account for climate stresses and resilience in land use planning.
Of the questions and indicators with high resilience, all fall into the "Monitor for Change"
quadrant, indicating that the participant ranked the actions the city has taken to improve land
use/land cover resilience as highly important. Of the questions and indicators that fall in the low
resilience quadrants, 60% fall into the "Vulnerabilities to Address" quadrant (low resilience/high
importance) and 40% fall into the "Small Problems that Can Add Up" quadrant (low
resilience/low importance); of these questions and indicators, 80% have the lowest resilience
score of 1.
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1 C.2.1.4. Natural Environment
Worcester Natural Environment Sector: Questions and Indicators
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Figure 22. Worcester, MA natural environment sector. Question and
indicator quadrant mapping.
2 Worcester demonstrates relatively high resilience in this sector, based on existing regulatory and
3 planning tools and processes on water and air quality and land use, coordination with other
4 entities on water quality issues, and green space initiatives. The city has also developed native
5 plant or animal species lists and uses these species in green infrastructure, as can be seen in
6 Figure 22, where most of the questions and indicators score at least a 3 for resilience.
7 In addition, there are few wetlands species at risk (rare, endangered, or threatened). While the
8 city has no plans in place for preserving areas with good ventilation, the participant assigned a
9 lower importance score to this question. However, the city demonstrates limited resilience with
10 regard to the availability of environmental/ecosystem goods and services if other city goods and
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1 services such as power, water, and telecommunications were impacted by extreme climate events
2 or gradual climatic changes. This may help explain some of the lower-resilience scoring
3 questions and indicators.
4 As indicated above, Figure 22 demonstrates that Worcester has high resilience in the natural
5 environment sector, with 82% of the questions and indicators having a resilience score of at least
6 3.
7 C.2.1.5. People
Worcester People Sector: Questions and Indicators
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784
675 1387
Low priority
Small problems that can add up
Questions
Indicators
4 (high resilience) 3 2 1 (low resilience)
Resilience Score
Figure 23. Worcester, MA people sector. Question and indicator quadrant
mapping.
8 As noted previously, data for the People sector are limited to indicator data. No responses were
9 provided to the relevant questions associated with this sector.
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Planning resources for response to extreme events (handled by the Emergency Management
Division of the Department of Public Health) are limited in terms of availability and
comprehensiveness. Likewise, city-level public health programs are not forward-thinking and do
not address climate change-related health issues, although state-level programs do. In addition,
the capacity of emergency response systems and transportation resources and the capacity and
distribution of public health works and emergency response resources in the event of an extreme
event are somewhat to very limited. The number of police officers per capita is also low
(0.0024). Transportation is a particular issue for vulnerable subpopulations. Response time is
highly dependent on location, day of the week, and time of day. Maps showing data in space and
at different days/times would be informative to gauge resilience. Fire response teams are
routinely faster than medical emergency management services response teams because fire
stations are spread out. Average fire response time was estimated at about 3.0 minutes. The
importance of response times depends on the situation and the nature of the emergency. For
example, in the aftermath of a major storm with a limited number of ambulances, response times
for large numbers of injured people would be critical compared to situations with large numbers
of dead.
However, there are appropriately designed and promoted climate change-related programs for
adaptive behavior at the community level, although success has been limited, depending on the
issue at hand and the type of change being sought. For example, the city has cooling centers
(typically shopping malls) that are used during extreme heat events. However, the elderly,
especially those with asthma, are vulnerable because they cannot easily be moved to public
cooling centers.
Urban planning and infectious disease response planning activities do account for the potential
impacts of climate change and recognize potentially vulnerable subpopulations.
While availability of public health goods and services is only at some risk if other city goods and
services are impacted by extreme climatic events or gradual climate change, loss of water and
sanitation services has the potential to create serious public health risks, especially for vulnerable
subpopulations (and, importantly, 18.3% of the population is vulnerable due to age).
Figure 23 shows that 78% of the indicators have at least an importance score of 3, with resilience
scores distributed widely and relatively evenly across the resilience axis. Close to half of the
indicators fall into the "Monitoring for Change" quadrant (high resilience/high importance) and
33%) lie in the "Vulnerabilities to Address" quadrant.
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1 C.2.1.6. Telecommunications
Worcester Telecommunications Sector: Questions and Indicators
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© ©
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Low priority
Small problems that can add up
• Questions
Indicators
4 (high resilience) 3 2 1 (low resilience)
Resilience Score
Figure 24. Worcester, MA telecommunications sector. Question and
indicator quadrant mapping.
2 The data gathered on the Telecommunications sector came exclusively via responses to questions
3 on this sector. No indicator data were available for relevant indicators.
4 Worcester's telecommunications sector generally demonstrates high resilience regarding
5 emergency preparedness, robustness/vulnerability of the network and infrastructure, backup
6 power and redundancy, and past experience. The city has experienced extreme weather and other
7 similar events in recent years, and telecommunications services were only impacted to a limited
8 extent.
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Local authorities have established relations with telecommunications service providers, and the
systems and agreements in place ensure swift decision making and prioritization during
emergencies. The city also has adequate communication systems in place to broadcast
emergency information to the public.
Telecommunications infrastructure is generally not located where it is vulnerable to high winds
or flooding, and the infrastructure has the capacity for increased demand in emergency
circumstances. It also unlikely that the capacity of first responder communication systems would
be exceeded during a disaster or emergency, and the city has adequate backup
telecommunications systems and power for those systems, as well as a great deal of
telecommunications redundancy.
In addition, while disruption in the telecommunications sector may have an impact on the
economic sector, there is only some risk that disruptions to other city goods and services (e.g.,
power, water, etc.) would impact the telecommunications sector.
However, the city appears to be less resilient in terms of the potential impact of a temporary loss
of telecommunications and its impact on the local and regional economies, as well as location of
data centers, which are to some extent outside of the urban area (Question #77). However, for all
other relevant questions, the participant indicated that the city's telecommunications sector is
generally resilient.
Telecommunications resilience includes lines of communication between government and
citizenry. In addition to 911 services, Worcester has an emergency notification system, ALERT
Worcester, which contacts residents and businesses in emergencies. The city's website provides
information for preventing heat-related illness, the location and status of cooling centers during
heat waves, and a citizen's guide to emergency preparedness. The website includes a voluntary
emergency preparedness registry so that individuals with disabilities can provide information on
their location and needs during emergencies (City of Worcester, 2013a).
As shown in Figure 24, the majority of the questions (21 of 23) scored a 3 or above for resilience
and 74% of the questions (17 of 23) scored in the "Monitor for Changes" quadrant (high
resilience/high importance), demonstrating that Worcester's telecommunications sector is
resilient to climate change.
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1 C.2.1.7. Transportation
Worcester Transportation Sector: Questions and Indicators
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4 (high resilience) 3 2 1 (low resilience)
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Figure 25. Worcester, MA transportation sector. Question and indicator
quadrant mapping.
2 Worcester's transportation is varied. The system includes a commuter rail line to/from Boston
3 (owned by the Massachusetts Bay Transportation Authority, otherwise known as the MBTA or
4 the "T"), Worcester Regional Transit Authority bus lines, as well as various local and state
5 surface roads and highways for vehicle transit.
6 Worcester's transportation sector demonstrates limited resilience, which could have significant
7 implications to the community's ability to respond appropriately to and recover from a major
8 climatic event. It is also unclear whether the city would be able to maintain adequate
9 transportation services in the face of gradual impacts to the sector from climate change. For
10 example, the length of time that would be required to restore major passenger rail transportation
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links in the urban area after a failure could be more than 1 week; however, the participant rated
this question as only moderately important (score of 2).
While the responses to the questions did suggest that the city's transportation system, despite the
lack of resilience or adaptation planning, is moderately resilient, that redundancy is generally
adequate, and that availability of transportation resources will not be heavily impacted if climate
change or extreme climatic events impact other city goods and services, restoration of services in
the event of a failure would be fairly slow. The length of time to restore major freight rail
services in the event of a climate-related disruption depends on the nature and volume flow of
the freight (food, medical goods, raw materials, etc.) and the nature of the disruption. The time to
restore major high-traffic assets would depend on the nature of the specific asset and its
disruption (e.g., a November 2013 multiple-vehicle accident due to icy conditions on a section of
interstate passing through downtown took a full day to clear). The participant noted that risk and
recovery mapping of the transit and transportation system would be desirable for resilience
planning.
The city also demonstrates low resilience related to community knowledge of evacuation
procedures. While residents are slightly familiar with evacuation procedures within their own
communities, coordination among neighboring communities and towns does not exist. In
addition, residents are resistant to changing their preferred modes of transit unless there is a
compelling reason or incentive.
Figure 25 reflects some, but not all, of these data. Data points vary widely on the importance
axis, where approximately half of the questions and indicators have an importance score above 3
and half below 3. Over 65% of questions and indicators received a resilience score of 2 or below,
indicating that the transportation sector is relatively vulnerable to climate change, though no
scores fall in the lowest resilience (score of 1) and medium-to-high importance (scores of 3 and
4) quadrants, indicating that Worcester has taken some steps to begin to address highly important
factors related to transportation.
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1 C.2.1.8. Water
Worcester Water Sector: Questions and Indicators
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Figure 26. Worcester, MA water sector. Question and indicator quadrant
mapping.
2 The city's Department of Public Works and Parks oversees operation and maintenance of the
3 city's drinking water infrastructure and supplies (10 surface water sources outside of the city
4 limits) and sewer infrastructure. The city's drinking water is treated at a 50-million-gallon-per-
5 day water treatment plant, using a combination of ozone, coagulation, and filtration. The city has
6 had no Safe Drinking Water Act violations in the past 5 years, but received the lowest resilience
7 rating for ratio of water availability to water consumption.
8 Wastewater is treated at the Upper Blackstone WWTP before it is discharged into the Blackstone
9 River. Since its construction, the District has completed over $170 million in improvements to
10 the WWTP. In 2009, more improvements were made to increase energy efficiency, provide solar
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power for the plant, and upgrade the solids management facilities. The improvements reduced
the carbon footprint of the plant and increased treatment capacity (UBWPAD, 2013).
Making further improvements to the drinking water and wastewater infrastructure to increase
climate change resilience has been a challenge due to limited funding and federal and state
environmental regulations set forth by federal and state legislation. In Worcester, the Millbury
WWTP remains vulnerable to high-intensity storms both in terms of flooding (floodplain
location proximate to the Blackstone River), and the limited capacity to handle high stormwater
throughput. These vulnerabilities also contributed to low resilience ratings.
In 2012, monitoring conducted by several federal agencies determined that most of central
Massachusetts was in a moderate drought. Water consumption in the city peaked in 1988 around
27 million gallons per day and currently averages 22 million gallons per day. The Department of
Public Works and Parks depends solely on user rates for revenue, requiring rate increases as
consumption declined. The need for infrastructure improvements was highlighted by a
November 2012 water main break that flooded parts of the Worcester State University campus,
requiring water services to be shut off to the whole city.
Interconnectivity is a significant issue for this sector. Availability of water resources is at
significant risk if other city services, particularly energy/power, are affected by climatic changes
or events. Wastewater treatment typically has a high dependence on electrical power because of
energy-intensive unit processes of the primary, secondary, and tertiary treatment stages. There is
full backup power on the collection side but only some on the treatment side.
Despite concerns with the limitations of backup power and impact of loss of power on the water
sector and lack of hierarchy of water uses during a shortage or emergency, the city otherwise
demonstrates resilience with respect to emergency preparedness and response and redundancy.
The water system has emergency connections with adjacent water systems or emergency sources
of supply, as well as redundant treatment and distribution systems. The drinking water treatment
plant also has redundant treatment chemical supplies. In addition, there is a WARN that can
provide technical resources and support during emergencies. Worcester is also part of a regional
stormwater initiative with neighboring towns.
The city has also undertaken water-related planning efforts, including incorporating past
experiences into planning approaches for water shortages or increases in frequency of overflows.
The city also has programs related to long-term maintenance of water supplies and has
inventoried storm sewers and drains to storm sewers, and has used these inventories in planning
efforts. However, customer familiarity with and implementation of conservation measures is
somewhat limited. In general, properties in the city are not equipped to harvest rainwater or
recharge groundwater, and residents are practicing rainwater harvesting on a very limited scale.
Lawn watering habits are the primary concern with regard to water conservation.
Figure 26 shows that the data from the water sector spreads fully across both the importance and
resilience axis. Based on the available data, 71% of the questions and indicators have resilience
scores of 3 or above, demonstrating Worcester's water sector is relatively resilient. The three
topics located in the "Vulnerabilities to Address" quadrant (low resilience/high importance),
which may be of greatest concern to the city, include the city's lack of hierarchy of water uses
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during a shortage or emergency; the fact that properties in the city are not equipped to harvest
rainwater; and the fact that availability of water resources is at significant risk if other city
services, particularly energy/power, are affected by climatic changes or events.
C.2.2. Summary of Worcester Findings
As with Washington, DC the findings for Worcester indicate mixed resilience. In addition, the
comprehensiveness of the results is limited by lack of data (indicator data or responses to
questions) for the People, Energy, and IT sectors. The City demonstrated relatively high
resilience in respect to the Economy, Telecommunications, Water, and Natural Environment
sectors. Resilience in the remaining sectors was largely mixed, although more significant
potential vulnerabilities were identified in the Energy sector. However, as no indicator data were
available for the Energy sector, a more complete picture of the City's vulnerability in that sector
was not available.
Positive trends in economic diversification point to the potential for additional resources for and
interest in future adaptation activities (as well as the need for such activities). However, data
collected for the Land Use/Land Cover sector indicate potential vulnerabilities in the Economy
sector due to the location of key infrastructure and continued development (without concern for
retrofitting) in areas that are vulnerable to extreme events. Disruption to water services could
have significant impacts on other sectors, and vice versa. However, the city otherwise
demonstrates resilience with respect to emergency preparedness and response and redundancy
within both the Water and Telecommunications sector.
While the framework was tested and implemented through a workshop process in Washington,
DC, the interviews in Worcester were conducted primarily with one sector representative, as the
framework design ultimately intended. Therefore, the limitations observed in Worcester related
to data availability, previous or ongoing efforts related to climate change adaptation and
resilience, and resources may be more indicative of the challenges to implementation of the
framework and addressing vulnerabilities that like-sized urban communities may face.
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APPENDIX D. COMPARISON OF RESULTS FOR WASHINGTON, DC AND
WORCESTER, MA
This Appendix presents cross-city comparison visualizations. While the value of this
visualization is currently limited by the small sample size of two cities, the value will increase as
the tool is used for evaluating the resilience of additional cities because patterns will become
more emergent as additional data is gathered and confidence in those patterns increases.
D.l. CITY COMPARISON
Washington, DC and Worcester, MA provide contrasting examples of the risks faced by urban
areas and the resources that mid- to large-sized communities may have to plan for more
climate-resilient futures. Table 14 highlights some of the key features of both cities. Choosing
these contrasting cities allows for cities within a broad spectrum in terms of resources, planning,
and risk, to understand the applications of and potential outcomes from use of the tool. It also
allows us to test the strengths and weaknesses of the tool methodology in a wide range of
conditions and also provides preliminary insight into the range (or potential lack thereof) of risk
exposures across cities with different geographic, economic, population, and historical
characteristics.
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Table 14. Washington, DC and Worcester, MA metrics at-a-glance
Washington, DC
Worcester, MA
USA average
Population
658,893
(U.S. Census Bureau,
2013a)
183,016
(U.S. Census Bureau, 2013c)
Population growth,
2010-2013
+7.9%
(U.S. Census Bureau,
2013a)
+1.1%
(U.S. Census Bureau, 2013c)
+2.5%
(U.S. Census
Bureau, 2013 a)
Median household
income
$65,830
(U.S. Census Bureau,
2013a)
$45,932
(U.S. Census Bureau, 2013c)
$53,046
(U.S. Census
Bureau, 2013 a)
Percentage below
poverty level
18.6%
(U.S. Census Bureau,
2013a)
21.4%
(U.S. Census Bureau, 2013c)
15.4%
(U.S. Census
Bureau, 2013 a)
Total number of
firms
55,887
(U.S. Census Bureau,
2013a)
11,799
(U.S. Census Bureau, 2013c)
Chief industries
Federal Services, Tourism
(U.S. Census Bureau,
2013b)
Education, Medical, Biotech
(City of Worcester, 2004;
Research Bureau, 2008)
Topography
Coastal Plain
Hilly
Region
Southeastern Seaboard
New England
Hazards
Sea Level Rise, Hurricanes,
Drought, Heatwaves,
Severe Storms
(MWCOG, 2013a)
Drought, Heatwaves,
Tornados, Severe Storms,
Blizzards
(CMRPC, 2012)
Climate Adaptation
planning
High
Low
Capacity for climate
adaptation
High
Low
Similar cities
New York City, Boston,
Atlanta, Miami
Cleveland, Pittsburgh,
Detroit, Buffalo, St Louis,
Providence
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D.2. RESULTS - QUADRANT MAP COMPARISONS
Error! Reference source not found, maps the question and indicator sector averages from both
Washington, DC and Worcester, MA on a on a quadrant graph. This approach does not show
intrasector areas of higher or lower vulnerability, but facilitates comparison between the two
cities and between question- and indicator-based results. Overall, the results for both cities for all
sectors cluster moderately tightly, with the center of the cluster falling into the "Vulnerabilities
to Address" quadrant. There does not appear to be a close match of data points representing the
same sectors between cities. Given the low sample size and the lack of spread in the data, there is
an insufficient basis to conclude that any sector is more or less vulnerable than another overall.
For the questions, the spread of the data is slightly less than one point in both resilience and
importance scores. Spread for the indicator data is greater, although because much less indicator
data was available—particularly for Worcester—it is difficult to determine whether this spread is
meaningful. The narrow range of variability in the results for the two cities is striking, given the
differences in indicator data quality and availability between the two locations.
Comparison of Washington, D.C. and Worcester, MA Results
4
1
Monitor for changes
Vulnerabilities to address
•
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The Figure 28 bar chart facilitates comparisons of resilience and importance scores between the
two case study cities. Neither city has systematically higher scores across all sectors for either
importance or resilience, although, overall, Worcester's resilience scores are slightly lower than
Washington DCs. Looking at the questions by sector, both cities ranked natural environment as
lower average importance than other sectors, and ranked transportation as having lower
resilience than other sectors. Deferred maintenance, infrastructure at the end of its usable life,
and lack of secured capital for future improvements are likely reasons for the perceived low
resilience of the transportation sector in these two cities, both of which are older eastern cities
that have inherited sufficient amounts of infrastructure designed for the needs of a different era.
Water and energy sector resilience rankings tend to be lower for questions as well, perhaps
because these sectors rely on raw resources over which people sometimes have less control (i.e.,
more limited climate change adaptation options). These rankings are in contrast to
telecommunications, which received some of the highest resilience and importance scores across
both cities. While the scores may accurately reflect high resilience for this sector, it is unclear
whether they reflect an overconfidence based on a more tenuous connection of this sector to
natural resources.
For the indicator data in Figure 29, there is much more of a spread between the two cities and
among sectors. However, given how few indicator data sets were available, especially for
Worcester, it is difficult to draw many conclusions from this pattern.
Question Resilience and Importance
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Figure 28. Washington, DC and Worcester, MA. Average question resilience
and importance.
This document is a draft for review purposes onlv and does not constitute Agency policy,
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Resilience and Importance
¦ Resilience, DC ¦ Resilience, MA v Importance, DC f* Importance, MA
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Figure 29. Washington, DC and Worcester, MA. Average indicator resilience
and importance.
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APPENDIX E. QUALITATIVE INDICATORS (QUESTIONS)
A complete set of the questions by sector developed for the tool.
Economy
The questions below have been developed for the Economy sector. Each question is flagged with one or
more of the following gradual change climate stressor and/or extreme event climate stressor (from the
urban resilience framework developed for this project):
In addition, each question has up to four possible Answers. Each answer has been assigned a Resilience
Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each question, please:
1. Discuss the Relevance of the question to the Economy sector. (If unsure, please select the Not sure -
remind me later option). Questions may be selected as Yes (relevant) on the basis of the stressors
previously selected as being most relevant to Washington, DC, or based on any other criteria.
2. For questions marked as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
3. For questions marked as Yes (relevant), identify the best Answer to the question from the options
provided.
Stressors
Gradual Changes
Wind Speed
Extreme Events
Magnitude/ duration of heat
waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
Temperature
Precipitation
Sea Level Rise
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #1: Is the economy of the urban area largely independent, or is it largely dependent on economic
2 activity in other urban areas?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Resilience Score
Largely dependent
1 (lowest resilience)
Somewhat dependent
2
Somewhat independent
3
Largely independent
4 (highest resilience)
3
4
5 #2: Does the urban area have mechanisms to help businesses quickly return to normal operations?
6
7
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#3: If jobs are lost in one sector of the urban area, does the capacity exist to expand the economy
and job opportunities in another sector?
10
11
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#4: Has the vulnerability of critical infrastructure been assessed? Are there plans to relocate or
protect vulnerable infrastructure in ways that promote resilience and protect other infrastructure
and properties?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Vulnerability has not been assessed and there are no plans to
protect infrastructure in ways that promote resilience.
Vulnerability may or may not have been assessed, but
infrastructure is insufficiently protected.
Yes, vulnerability has been assessed and infrastructure is
somewhat protected in ways that promote resilience.
Yes, vulnerability has been assessed and infrastructure is
protected in ways that promote resilience.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#5: Has the urban area's resilience to major changes in energy policy/prices been assessed?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #6: Is funding available for adaptive development projects that could also serve as recreation areas
2 (e.g., retention areas along waterways that could also serve as parks)? Are such multi-purpose
3 projects required or are there incentives for these projects?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
4
5
6
7
Answer
No funding is available for these adaptive development projects
and requirements or incentives do not exist for these projects.
Funding is available for these adaptive development projects and
requirements or incentives exist for these projects.
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#7: Is a significant portion of the population of the urban area either seasonal residents or transient
populations that may have a lesser degree of understanding of changes occurring within that area?
Action Needed:
-Suggest reviewing/modifying the importance weight to 2 since scoring was not considered optimal at
September 10 meeting.
Relevance: Importance Weight:
Yes 3
Answer
Yes
No
Resilience Score: 1
1 (lowest resilience)
3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#8: How many people are in place to respond to emergencies, and what is the level of
communication connectivity of emergency response teams and offices?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Answer
Many fewer people than necessary are in place for emergency
response relative to urban area population, and communication
connectivity teams and offices is poor.
Too few people than necessary are in place for emergency
response relative to urban area population, and communication
connectivity teams and offices is fair.
Enough people are in place for emergency response relative to
urban area population, and communication connectivity teams
and offices is good.
A large number of people are in place for emergency response
relative to urban area population, and communication
connectivity teams and offices is excellent.
Importance Weights
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#9: Is comprehensive adaptation planning possible with the urban area's current resources? If so, is
adaptation planning already occurring?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Resources do not allow for comprehensive adaptation planning.
Resources would allow for adaptation planning, but no
adaptation planning is occurring.
Some adaptation planning is occurring.
A great deal of adaptation planning is occurring.
Resilience Score
1 (lowest resilience)
2
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #10: Is planning for climate change adaptation in the urban area incorporated into one office
2 within the local government or is planning spread out across several offices within the government?
Action Needed:
-Suggest reviewing/modifying the importance weight to 1 since scoring was not considered optimal at
September 10 meeting.
Relevance: Importance Weight:
Yes 4
Answer Resilience Score: 2
Adaptation planning responsibilities are not incorporated into 1 (lowest resilience)
any offices within the local government.
Adaptation planning responsibilities are spread out over multiple 2
offices within the local government.
Adaptation planning is shared between two or three offices 3
within the local government.
Adaptation planning is incorporated into one office within the 4 (highest resilience)
local government.
3
4
5 #11: How flexible are planning processes for short-term and long-term responses? For example, is
6 there flexibility in changing planning priorities if necessary?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
Planning processes are fairly inflexible. 1 (lowest resilience)
Planning processes are somewhat flexible. 2
Planning processes are moderately flexible. 3
Planning processes are very flexible. 4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#12: Does adaptation planning for the urban area include retrospective analyses of past events
(including analyses of past climate events in other cities if helpful) to help determine whether
decisions on adaptation measures would be effective?
Action Needed:
-Please review the amended question and answers and provide the best answer.
Relevance: Importance Weight:
Yes 4
Answer
Adaptation planning does not involve analyses of past climate-
related events OR adaptation planning is not occurring.
Adaptation planning occasionally involves analyses of past
climate-related events.
Adaptation planning sometimes involves analyses of past
climate-related events.
Adaptation planning frequently involves analyses of past
climate-related events.
Resilience Score:
1 (lowest resilience)
4 (highest resilience)
#13: Does adaptation planning for the urban area consider the costs and benefits of possible
decisions, and does it encourage both pre-event and post-event evaluations of the effectiveness of
adaptation measures?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Adaptation planning does not consider costs and benefits and
does not encourage pre-event or post-event effectiveness
evaluations.
Adaptation planning does consider costs and benefits but does
not encourage pre-event or post-event effectiveness evaluations.
Adaptation planning does consider costs and benefits and
encourages pre-event or post-event effectiveness evaluations.
Adaptation planning does consider costs and benefits and
requires pre-event or post-event effectiveness evaluations.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#14: Do adaptation plans account for tradeoffs between the less resilient but lower-cost strategy of
increasing protection from climatic changes and the more resilient but higher-cost strategy of
moving residents from the most vulnerable portions of the urban area? (One example of such a
tradeoff might be: if parts of the city were on a coastline and could either be protected by a seawall
or moved inland, would adaptation plans account for the resilience-cost tradeoffs in this decision?)
Action Needed:
-Please review the amended question and answers and provide the best answer.
Relevance: Importance Weight:
Yes (relevant) 1 (not very important)
No (not relevant) 2
3
4 (very important)
Answer
Resilience Score:
1 (lowest resilience)
Adaptation plans do not explicitly consider resilience-cost
tradeoffs or no adaptation plans exist.
Adaptation plans consider one or two resilience-cost tradeoffs.
2
3
4 (highest resilience)
Adaptation plans consider some resilience-cost tradeoffs.
Adaptation plans consider many resilience-cost tradeoffs.
#165: What financial capacity or credit risk is indicated by the city's bond rating(s)?
Action Needed:
-Please provide a relevance, importance weight, and answer for this question.
Relevance:
Yes (relevant)
No (not relevant)
Importance Weight:
1 (not very important)
2
3
4 (very important)
Answer
The bond rating(s) indicate(s) high vulnerability or very high
credit risk/default.
The bond rating(s) indicate(s) some vulnerability or substantial
to high credit risk.
The bond rating(s) indicate(s) adequate financial capacity or
some credit risk.
The bond rating(s) indicate(s) strong financial capacity/minimal
to low credit risk.
Resilience Score:
1 (lowest resilience)
2
4 (highest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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Energy
The questions below have been developed for the Energy sector. Each question is flagged with one or
more of the following gradual change climate stressor and/or extreme event climate stressor (from the
urban resilience framework developed for this project):
Stressors
Gradual Changes
Wind Speed
Temperature
Precipitation
Sea Level Rise
Extreme Events
Magnitude/ duration of heat
waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
In addition, each question has up to four possible Answers. Each answer has been assigned a Resilience
Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each question, please:
1. Discuss the Relevance of the question to the Energy sector. (If unsure, please select the
Not sure - remind me later option). Questions may be selected as Yes (relevant) on the
basis of the stressors previously selected as being most relevant to Washington, DC, or
based on any other criteria.
2. For questions marked as Yes (relevant), discuss an Importance Weight, where 1 = not
very important and 4 = very important.
3. For questions marked as Yes (relevant), identify the best Answer to the question from the
options provided.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #15: Do you have a diverse energy portfolio?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
2
3
4 #16: Are there redundant systems in place for coping with extreme events?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No, redundant energy systems are not in place. 1 (lowest resilience)
Yes, but these redundant energy systems have only a small 2
amount of the capacity necessary.
Yes, and these redundant energy systems have some of the 3
capacity necessary.
Yes, and these redundant energy systems have all the capacity 4 (highest resilience)
necessary.
5
6
7 #17: To what extent do energy supplies come from outside the metropolitan area?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
They come exclusively from outside the area. 1 (lowest resilience)
To a great extent 2
To a moderate extent 3
Only to a small extent 4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#18: Is the availability of energy goods and services at risk if other city goods and services (e.g.,
water, transportation, information and communications technology) are affected by extreme
climatic events or gradual climatic changes?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
Availability of energy resources is at significant risk if other city 1 (lowest resilience)
services are affected by climatic events or changes.
Availability of energy resources is at moderate risk if other city 2
services are affected by climatic events or changes.
Availability of energy resources is at some risk if other city 3
services are affected by climatic events or changes.
Availability of energy resources is at minimal risk if other city 4 (highest resilience)
services are affected by climatic events or changes.
#19: How many minutes per year or hours per year do you have power outages?
Action Needed:
-Answers originally given for Maryland; please give answers for DC if data for DC was reviewed.
Relevance: Importance Weight:
Yes 4
Answer
More than 1 day per year for all outage events
More than 1 hour to 1 day per year for all outage events
More than 30 minutes to 1 hour per year for all outage events
Less than 30 minutes per year for all outage events
Resilience Score: 2
1 (lowest resilience)
2
3
4 (highest resilience)
#20: What is the response time to restore electrical power after an outage?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
More than 1 day after a major event 1 (lowest resilience)
More than 3 hours to 1 day after a major event 2
More than 1 hour to 4 hours after a major event 3
Less than 1 hour after a major event 4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #21: Does capacity exist to handle a higher peak demand or peaks at different times?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
Electricity generation capacity cannot handle higher peak
1 (lowest resilience)
demands or peaks at different times than currently experienced.
Electricity generation capacity can handle higher peak demands
3 (highest resilience)
or peaks at different times than currently experienced.
#22: To what extent have efforts been made to reduce energy demand?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
Few to no efforts have been made to reduce energy demand.
1 (lowest resilience)
Fair efforts have been made to reduce energy demand.
2
Moderate efforts have been made to reduce energy demand.
3
Significant efforts have been made to reduce energy demand.
4 (highest resilience)
5
This document is a draft for review purposes only and does not constitute Agency policy.
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#23: What are the opportunities for distributed generation sources i.e., different capacity for
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energy generation from different sources (including renewable)?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Political and technical capacity do not allow for generation
from multiple sources.
Political and technical capacity could allow for generation
from multiple sources, but such diversified generation is
not currently occurring.
Political and technical capacity currently provide for
generation from multiple sources, not including
renewables.
Political and technical capacity currently provide for
generation from multiple sources, including renewables.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#24: Are there smart grid opportunities to manage demand?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No
Yes
Resilience Score
1 (lowest resilience)
3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #147: Do municipal managers draw on past data/experiences of extreme weather events to assess
2 the effects of these events on oil and gas availability and pricing? (DOE, 2013).
Action Needed:
-Please provide a relevance, importance weight, and answer for this question.
Relevance: Importance Weight:
Yes (relevant) 1 (not very important)
No (not relevant) 2
3
4 (very important)
Answer Resilience Score:
No 1 (lowest resilience)
Yes 3 (highest resilience)
3
4
5 #148: Has the city consulted with local power companies to develop plans for potential increases in
6 electricity demand for summer cooling? (DOE, 2013).
Action Needed:
-Please provide a relevance, importance weight, and answer for this question.
Relevance: Importance Weight:
Yes (relevant) 1 (not very important)
No (not relevant) 2
3
4 (very important)
Answer Resilience Score:
The city has not consulted with local power companies and is not 1 (lowest resilience)
developing plans for potential increase in electricity for cooling.
The city has consulted with local power companies regarding 2
potential increase in electricity for cooling, but is not yet
developing related plans. OR the city has developed such plans,
but did not consult with local power companies.
The city has consulted with local power companies and is 3
developing plans for potential increase in electricity for cooling.
The city has consulted with local power companies and 4 (highest resilience)
developed plans for potential increase in electricity for cooling.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #149: Has the city coordinated with local water suppliers and power generation facilities to discuss
2 potential climate-induced water shortages and their impacts on cooling the power generation
3 facilities?(DOE, 2013).
Action Needed:
-Please provide a relevance, importance weight, and answer for this question.
Relevance: Importance Weight:
Yes (relevant) 1 (not very important)
No (not relevant) 2
3
4 (very important)
Answer Resilience Score:
No 1 (lowest resilience)
Yes 3 (highest resilience)
4
5
6 #150: Do municipal managers in coastal areas consider the impacts of sea level rise on power
7 generation facilities?
Action Needed:
-Please provide a relevance, importance weight, and answer for this question.
Relevance: Importance Weight:
Yes (relevant) 1 (not very important)
No (not relevant) 2
3
4 (very important)
Answer Resilience Score:
No 1 (lowest resilience)
Yes, but these considerations are not incorporated into planning 2
for these facilities.
Yes, and these considerations are being incorporated into 3
planning for these facilities.
Yes, and these considerations are incorporated into planning for 4 (highest resilience)
these facilities.
This document is a draft for review purposes only and does not constitute Agency policy.
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Information and Communication Technology
The questions below have been developed for the Information and Communications Technology sector.
Each question is flagged with one or more of the following gradual change climate stressor and/or
extreme event climate stressor (from the urban resilience framework developed for this project):
In addition, each question has up to four possible Answers. Each answer has been assigned a Resilience
Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each question, please:
1. Discuss the Relevance of the question to the Information and Communications Technology sector. (If
unsure, please select the Not sure - remind me later option). Questions may be selected as Yes
(relevant) on the basis of the stressors previously selected as being most relevant to Washington, DC,
or based on any other criteria.
2. For questions marked as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
3. For questions marked as Yes (relevant), identify the best Answer to the question from the options
provided.
Stressors
Gradual Changes
Wind Speed
Extreme Events
Magnitude/ duration of heat
Temperature
Precipitation
Sea Level Rise
waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #75: What natural disasters has the area experienced in the past, and what services were retained
2 or largely unaffected despite these disasters?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
Area has either not experienced many natural disasters in recent
1 (lowest resilience)
history, or services were significantly impaired during recent
natural disasters.
Area has experienced some extreme weather or other natural
2
disasters, but some services were significantly affected.
Area has experienced some extreme weather or other natural
3
disasters, and most services were unaffected or affected in minor
ways.
Area has experienced major extreme weather events or other
4 (highest resilience)
natural disasters, and majority of services were retained or were
largely unaffected.
#76: How would a temporary loss of information and communications technology infrastructure
affect the local and regional economies?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
Major effect
1 (lowest resilience)
Moderate effect
2
Small effect
3
Little to no effect
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#77: Are data centers located within or outside of the urban area?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
2
3
4
5
Answer
Within
Mostly within the urban area, but somewhat outside the urban
area.
Mostly outside the urban area, but somewhat within the urban
area.
Outside
Resilience Score
1 (lowest resilience)
2
4 (highest resilience)
#78: For each telecommunications service, are there key nodes whose failure would severely affect
the service?
Action Needed:
-Due to answers being made a gradient, please review/answer the amended question.
Relevance: Importance Weight:
Yes 4
Answer
Resilience Score: 2
There are many key nodes whose failure would severely affect 1 (lowest resilience)
service.
There are some key nodes whose failure would severely affect 2
service.
There are a few key nodes whose failure would severely affect 3
service.
No, there are no nodes whose failure would severely affect 4 (highest resilience)
service.
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
4
5
6
7
8
9
10
11
#79: How robust is the information and communications technology network in terms of resilience
to damage to or failure of key nodes?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
The information and communications technology network is not
resilient to damage or failure of key nodes.
The information and communications technology network is
slightly resilient to damage or failure of key nodes.
The information and communications technology network is
somewhat resilient to damage or failure of key nodes.
The information and communications technology network is
very resilient to damage or failure of key nodes.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#80: Are there parts of the information and communications technology infrastructure that are
particularly vulnerable to high temperatures or prolonged high temperatures?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#81: Are there satellite-based communications on frequency bands (e.g., the Ka band) that are
vulnerable to wet-weather disruption?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#82: Are your information and communications technology infrastructure components located
wisely with respect to your anticipated climate stressors (i.e., aboveground, underground, or
serviced by satellite)?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#83: Are above-ground infrastructure components vulnerable to
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
wind (e.g., cell towers)?
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
All above-ground infrastructure components are vulnerable to
expected winds.
Some above-ground infrastructure components are vulnerable to
expected winds.
Few above-ground infrastructure components are vulnerable to
expected winds.
No above-ground infrastructure components are vulnerable to
expected winds.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
13 3 DRAFT—DO NOT CITE OR QUOTE
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#84: Are below-ground infrastructure components vulnerable to rising water, salt water intrusion?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
2
3
4
5
Answer
All below-ground infrastructure components are vulnerable to
expected rises in groundwater levels or from salt water intrusion.
Some below-ground infrastructure components are vulnerable to
expected rises in groundwater levels or from salt water intrusion.
Few below-ground infrastructure components are vulnerable to
expected rises in groundwater levels or from salt water intrusion.
No below-ground infrastructure components are vulnerable to
expected rises in groundwater levels or from salt water intrusion.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#85: If the area has satellite-based communications that are vulnerable to wet-weather disruption,
does the area have a backup tower network?
Action Needed:
-Due to answers being made a gradient, please review/answer the amended question.
Relevance: Importance Weight:
Yes 4
Answer
The area does not have a tower network that could provide
backup.
The area has a tower network that could provide a small amount
ofbackup.
The area has a tower network that could provide some backup.
The area has a tower network that could provide full backup to
satellite-based communications.
Resilience Score:
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#86: Does your community have sufficient access to backup information and communications
technology systems? What is the capacity of the information and communications technology
infrastructure?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
There are no backup systems. Capacity of the information and
communications technology infrastructure is low.
There are some minimal backup systems, but information and
communications technology infrastructure capacity is likely to
be a problem during an emergency.
There are some backup systems in place. Capacity of the systems
is moderate.
Backup systems are in place. Capacity of the information and
communications technology systems is high.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#87: Is backup power for information and communications technology systems provided? If so, is it
provided by diesel generators?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Backup power is not provided.
Backup power is provided, but it is provided by diesel
generators.
Backup power is provided and is only partially provided by
diesel generators.
Backup power is provided and is not provided by diesel
generators.
Resilience Score
1 (lowest resilience)
2
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
13 5 DRAFT—DO NOT CITE OR QUOTE
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1 #88: What is the extent of information and communications technology redundancy? Do first
2 responders and the public have multiple communication options, served by different
3 infrastructure?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
There is little to no redundancy. 1 (lowest resilience)
There is a small amount of redundancy. 2
There is a moderate amount of redundancy. There are more than 3
one communications options, served by different infrastructure.
There is a great deal of redundancy. There are multiple 4 (highest resilience)
communications options, served by different infrastructure.
4
5
6 #89: What percentage of telecommunications system capacity is required for the baseline level of
7 use?
Action Needed:
-Please provide the importance weight.
Relevance: Yes Importance Weight:
1 (not very important)
2
3
4 (very important)
Answer Resilience Score: 4
Greater than 85% 1 (lowest resilience)
Greater than 70 to 85% 2
60 to 70% 3
Less than 60% 4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
13 6 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
4
5
6
7
8
9
10
11
12
#90: Does information and communications technology infrastructure have the capacity for
increased public demand in an emergency?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#91: Do local authorities have established relations with information and communications
technology infrastructure service providers? Are emergency protocols and plans in place?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#92: Do local private-sector information and communications technology infrastructure service
providers have the authority and resources to make quick decisions and implement them in and
after an emergency?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
13 7 DRAFT—DO NOT CITE OR QUOTE
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#93: Can local authorities and information and communications technology providers give first-
responder and decision-maker communications priority during expected surge in traffic in
emergency situations?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#94: Are public-address systems, etc., in place to provide instructions to the public in case of
emergency?
Action Needed:
-Due to answers being made a gradient, please review/answer the amended question.
Relevance: Not Sure Importance Weight: 4
Yes (relevant)
No (not relevant)
Answer
There are no public-address systems in place.
There are insufficient public-address systems in place.
Some public-address systems are in place, but there could be
more.
Sufficient public-address systems are in place.
Resilience Score: 2
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
13 8 DRAFT—DO NOT CITE OR QUOTE
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#95: What modes do authorities in the urban area use to communicate emergency information and
alerts? Are these modes low- or high-bandwidth?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
Authorities do not use multiple modes (e.g., text messaging, 1 (lowest resilience)
email, phone calls), or none of the modes used is low bandwidth.
Authorities use one to two modes (e.g., text messaging, email, 2
phone calls) and one or two of these modes is low bandwidth.
Authorities use multiple modes (e.g., text messaging, email, 3
phone calls) and one or two of these modes are low bandwidth.
Authorities use multiple modes (e.g., text messaging, email, 4 (highest resilience)
phone calls) and some of these modes are low bandwidth.
#96: What is the likelihood that the capacity of local first responder communications systems would
be exceeded during a disaster?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
It is very likely that the capacity of local first responder
communications would be exceeded during a disaster.
It is somewhat likely that the capacity of local first responder
communications would be exceeded during a disaster.
It is somewhat unlikely that the capacity of local first responder
communications would be exceeded during a disaster.
It is very unlikely that the capacity of local first responder
communications would be exceeded during a disaster.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
13 9 DRAFT—DO NOT CITE OR QUOTE
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#97: Does the area have access to backup emergency call/response (911) networks if the primary
networks fail or are overloaded?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No, or the backup network could handle only a minimal amount
of the load for the main emergency response network.
Yes, but the backup network could handle only some of the load
for the main emergency response network.
Yes, and the backup network could handle the most of the load
for the main emergency response network.
Yes, and the backup network could handle the entire load for the
main emergency response network.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#98: Is the availability of information and communications technology goods and services at risk if
other city goods and services (e.g., power, water, transportation) are affected by extreme climatic
events or gradual climatic changes?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Availability of information and communications technology
resources is at significant risk if other city services are affected
by climatic events or changes.
Availability of information and communications technology
resources is at moderate risk if other city services are affected by
climatic events or changes.
Availability of information and communications technology
resources is at some risk if other city services are affected by
climatic events or changes.
Availability of information and communications technology
resources is at minimal risk if other city services are affected by
climatic events or changes.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#99: Do telecommunications systems have enough energy and water supply to handle extra load in
the case of sudden natural disasters?
Action Needed:
-Due to answers being made a gradient, please review/answer the amended question.
-Please provide importance weight.
Relevance: Importance Weight:
Yes 1 (not very important)
2
3
4 (very important)
Resilience Score: 2
1 (lowest resilience)
2
3
4 (highest resilience)
Answer
Systems do not have enough to handle any of the anticipated
extra load.
Systems have enough to handle a small amount of the
anticipated extra load.
Systems have enough to handle some of the anticipated extra
load.
Systems have enough to handle all of the anticipated extra load.
#160: Have city planners consulted with other city governments with similar telecommunication
systems to learn from their experience with natural disasters and prepare for similar events?
Action Needed:
-Please provide a relevance, importance weight, and answer for this question.
Relevance: Importance Weight:
Yes (relevant) 1 (not very important)
No (not relevant) 2
3
4 (very important)
Answer Resilience Score:
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Land Use/Land Cover
The questions below have been developed for the Land Use/Land Cover sector. Each question is flagged
with one or more of the following gradual change climate stressor and/or extreme event climate stressor
(from the urban resilience framework developed for this project):
In addition, each question has up to four possible Answers. Each answer has been assigned a Resilience
Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each question, please:
1. Discuss the Relevance of the question to the Land Use/Land Cover sector. (If unsure, please select
the Not sure - remind me later option). Questions may be selected as Yes (relevant) on the basis of
the stressors previously selected as being most relevant to Washington, DC, or based on any other
criteria.
2. For questions marked as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
3. For questions marked as Yes (relevant), identify the best Answer to the question from the options
provided.
Stressors
Gradual Changes
Wind Speed
Extreme Events
Magnitude/ duration of heat
Temperature
Precipitation
Sea Level Rise
waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
This document is a draft for review purposes only and does not constitute Agency policy.
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#25: Can resilience planning/adaptation be incorporated into existing programs in which
communities engage in regularly (e.g., zoning, hazard mitigation plans, etc.)?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Resilience planning/adaptation would be difficult to incorporate
in regular planning programs.
Resilience planning/adaptation could be incorporated in regular
planning programs, but this may be difficult.
Resilience planning/adaptation could be incorporated in regular
planning programs with some effort.
Resilience planning/adaptation is incorporated in regular
planning programs.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#26: Has the city made effort to use urban form to mitigate climate change impacts and to
maximize benefits (e.g., urban tree canopy cover)?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
The city is not considering and has not developed efforts to use
urban form to mitigate climate change impacts and maximize the
benefits of urban forms.
The city is considering development of efforts to use urban form
to mitigate climate change impacts and maximize the benefits of
urban forms.
The city is developing efforts to use urban form to mitigate
climate change impacts and maximize the benefits of urban
forms.
The city has developed and implemented efforts to use urban
form to mitigate climate change impacts and maximize the
benefits of urban forms.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#27: Are urban forms used that address (lessen) urban heat island effects (e.g., through increasing
evapotranspiration or increasing urban ventilation)?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
These forms are not used in new development and
retrofits/renovations of old development.
These forms are infrequently used in new development and
retrofits/renovations of old development.
These forms are sometimes used in new development and
retrofits/renovations of old development.
These forms are often used in new development and
retrofits/renovations of old development.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#28: Does zoning encourages green roofs, other practices that reduce urban heat?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Zoning does not allow green roofs and other practices that
reduce the urban heat island effect.
Zoning discourages green roofs and other practices that reduce
the urban heat island effect.
Zoning allows green roofs and other practices that reduce the
urban heat island effect.
Zoning encourages green roofs and other practices that reduce
the urban heat island effect.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#29: Are there mechanisms to support tree shading programs in urban areas (to reduce urban heat
and improve air quality)? Are there innovative ways to fund such programs?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No, such mechanisms do not exist.
Yes, there are such mechanisms; additional funding is needed,
likely through new or innovative sources.
Yes, there are such mechanisms; additional funding is needed
but could be provided through existing sources.
Yes, there are such mechanisms, and they are well funded.
Resilience Score
1 (lowest resilience)
2
4 (highest resilience)
#30: Have land use/land cover types, such as soil and vegetation types and areas of tree canopy
cover, been inventoried and are these inventories used in planning?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Land use/land cover types are not inventoried and are not
planned to be inventoried.
Plans exist to inventory land use/land cover types OR inventories
exist but existing inventories are not used in planning.
Land use/land cover types are being inventoried and these
inventories are used or will be used in planning.
Land use/land cover types have been inventoried and these
inventories are used in planning.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#31: What percentage of open/green space is required for new development (to encourage increases
in such space)?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No open/green space is required for new development.
A small percentage of open/green space is required for new
development.
A moderate percentage of open/green space is required for new
development.
A high percentage of open/green space is required for new
development.
Resilience Score
1 (lowest resilience)
2
4 (highest resilience)
#32: Are there mechanisms for the local government to purchase land that is unfavorable for
redevelopment due to results of extreme events (e.g., flooding from a hurricane)? If so, what are
those mechanisms?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
No, such mechanisms do not exist.
1 (lowest resilience)
Yes, there are such mechanisms, but they are only preliminary
2
and are slightly helpful.
Yes, there are such mechanisms and they are somewhat helpful.
3
Yes, there are such mechanisms and they are helpful.
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
146 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
4
5
6
7
8
9
10
11
#33: Are there policies or zoning practices in place that allow transfer of ownership of
undevelopable land to the city (or allow non-permanent structures only) and are they enforced?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
Policies do not allow ownership transfer. 1 (lowest resilience)
Policies allow ownership transfer, but these policies are enforced 2
only rarely.
Policies allow ownership transfer, but these policies are only 3
enforced some of the time.
Policies allow ownership transfers, and these policies are 4 (highest resilience)
enforced.
#34: Where developed land is located in areas vulnerable to extreme events, are resilient retrofits
being planned/implemented?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#35: Are there codes to prevent development in flood-prone areas?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#36: Are there regulations in place regarding whether communities that are affected by floods will
be rebuilt in the same location?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No, regulations do not exist regarding the location of rebuilding
efforts for communities affected by floods.
Regulations regarding location of rebuilding efforts for
communities affected by floods.
Yes, regulations exist and encourage communities strongly
affected by floods to rebuild using more flood-resistant
structures and methods.
Yes, regulations exist and encourage communities strongly
affected by floods to be rebuilt in locations less prone to
flooding.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#37: Have the regulations regarding rebuilding of communities affected by floods been enforced to
date?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
148 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
4
5
6
7
8
9
10
#38: Do incentives exist to integrate green stormwater infrastructure into infrastructure planning
to mitigate flooding?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#39: Are there incentives to reduce amount of impervious surface, to prevent development in flood
plains, to use urban forestry to reduce impacts, to use green infrastructure for stormwater
management, etc.?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No, such incentives do not exist.
Yes, incentives exist to promote green infrastructure-oriented
solutions to stormwater management.
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#40: To what extent was green infrastructure selected to provide the maximum ecological benefits?
Relevance Importance Weights
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
1 (not very important)
2
3
4 (very important)
Answer
Green infrastructure does not exist or green infrastructure does
not provide ecological benefits.
Green infrastructure was selected with minimal attention to the
ecological benefits provided.
Green infrastructure was selected to provide some ecological
benefits.
Green infrastructure was selected to provide the maximum
ecological benefits.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#41: Has green infrastructure maintenance been built into the budget?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#142: Are coastal hazard maps with 1 meter altitude contours available, and are these maps used in
planning?
Action Needed:
-Please provide the importance weight.
Relevance: Importance Weight:
Yes 1 (not very important)
2
3
4 (very important)
Resilience Score: 3
1 (lowest resilience)
2
3
4 (highest resilience)
Answer
Such maps have not been developed and are not planned to be
developed.
Plans exist to develop such maps OR such maps exist but are not
used in planning.
Such maps are being developed and these maps are used or will
be used in planning.
Such maps exist and these maps are used in planning.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #151: Have institutional land practices (i.e. zoning, land use planning) potentially been hindered by
2 other government agencies seeking to shift financial resources when it conies to climate change
3 planning?
Action Needed:
-Please provide a relevance, importance weight, and answer for this question.
Relevance:
Importance Weight:
Yes (relevant)
1 (not very important)
No (not relevant)
2
7.
D
4 (very important)
Answer
Resilience Score:
Yes
1 (lowest resilience)
No
3 (highest resilience)
#152: Does knowledge of historical land-use / land-
¦cover changes contribute to planners'
understanding of climate stresses?
Action Needed:
-Please provide the importance weight.
Relevance:
Importance Weight:
Yes
1 (not very important)
2
3
4 (very important)
Answer
Resilience Score: 3
No
1 (lowest resilience)
Yes
3 (highest resilience)
8
This document is a draft for review purposes only and does not constitute Agency policy.
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#153: Have specific historical land-use / land-cover changes been recognized as increasing or
decreasing vulnerability to climate stresses?
Action Needed:
-Please provide the importance weight.
Relevance: Importance Weight:
Yes 1 (not very important)
2
3
4 (very important)
Answer Resilience Score: 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#154: Does the city consider the knowledge of local academic research and other stakeholders (e.g.,
farmers, forest managers, land use managers) in land use planning related to climate resilience?
Action Needed:
-Please provide the importance weight.
Relevance: Importance Weight:
Yes 1 (not very important)
2
3
4 (very important)
Answer Resilience Score: 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#167: In general, what is the monetary value of infrastructure located within the 500-year
floodplain in the city?
Action Needed:
-Please provide the importance weight.
Relevance:
Yes
Importance Weight:
1 (not very important)
2
3
4 (very important)
Answer
The monetary value of infrastructure in the 500-year floodplain
is high.
The monetary value of infrastructure in the 500-year floodplain
is moderate.
The monetary value of infrastructure in the 500-year floodplain
is low.
The monetary value of infrastructure in the 500-year floodplain
is very low.
Resilience Score: 1
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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15
Natural Environment
The questions below have been developed for the Natural Environment sector. Each question is flagged
with one or more of the following gradual change climate stressor and/or extreme event climate stressor
(from the urban resilience framework developed for this project):
In addition, each question has up to four possible Answers. Each answer has been assigned a Resilience
Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each question, please:
1. Discuss the Relevance of the question to the Natural Environment sector. (If unsure, please select
the Not sure - remind me later option). Questions may be selected as Yes (relevant) on the basis
of the stressors previously selected as being most relevant to Washington, DC, or based on any
other criteria.
2. For questions marked as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
3. For questions marked as Yes (relevant), identify the best Answer to the question from the options
provided.
Stressors
Gradual Changes
Wind Speed
Extreme Events
Magnitude/ duration of heat
Temperature
Precipitation
Sea Level Rise
waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
This document is a draft for review purposes only and does not constitute Agency policy.
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2
3
#42: Is the availability of environmental/ecosystem goods and services at risk if other city goods and
services (e.g., power, water, information and communications technology) are affected by extreme
climatic events or gradual climatic changes?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
4
5
6
7
Answer
Availability of environmental/ecosystem resources is at
significant risk if other city services are affected by climatic
events or changes.
Availability of environmental/ecosystem resources is at
moderate risk if other city services are affected by climatic
events or changes.
Availability of environmental/ecosystem resources is at some
risk if other city services are affected by climatic events or
changes.
Availability of environmental/ecosystem resources is at minimal
risk if other city services are affected by climatic events or
changes.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#43: What regulatory and planning tools are already available locally related to air quality, water
quality and land use? For example, does the urban area have invasive plant ordinances or tree
planting requirements?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
The urban area does not have regulatory and planning tools for
air and water quality and land use.
The urban area has few regulatory and planning tools for air and
water quality and land use.
The urban area has several regulatory and planning tools for air
and water quality and land use.
The urban area has many regulatory and planning tools for air
and water quality and land use.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#44: Do plans exist for increasing open and green space?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No
Yes
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#45: Has the continuity of open or green spaces been assessed and addressed in planning efforts?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Continuity of open or green spaces has not been assessed and is
not planned to be assessed.
Plans exist to assess the continuity of open or green spaces OR
an assessment has been completed but is not addressed in
planning efforts.
Continuity of open or green spaces is being assessed and is or
will be addressed in planning efforts.
Continuity of open or green spaces has been assessed and is
addressed in planning efforts.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#46: Do native plant or animal species lists exist for the urban area, and are these species (rather
than nonnative species) used in green infrastructure?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Native species lists do not exist and are not being developed.
Native species lists exist, but green infrastructure uses mostly
nonnative species OR native species lists are under development.
Native species lists exist and green infrastructure uses mostly
these species.
Native species lists exist and green infrastructure uses only these
species.
Resilience Score
1 (lowest resilience)
2
4 (highest resilience)
#47: Does the urban area coordinate with other nearby entities on water quality?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#48: To what degree do local versus distant sources influence air quality?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Air quality is much more strongly determined by distant sources
than local sources and is therefore harder for the urban area to
control.
Air quality is somewhat more strongly determined by distant
sources than local sources and is therefore harder for the urban
area to control.
Air quality is somewhat more strongly determined by local
sources than distant sources and is therefore easier for the urban
area to control.
Air quality is much more strongly determined by local sources
than distant sources and is therefore easier for the urban area to
control.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#49: Does the urban area have air quality districts?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No
Yes
Resilience Score
1 (lowest resilience)
3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#50: Has an air quality analysis been completed at multiple scales/resolutions?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
An air quality analysis has not been completed.
An air quality analysis has been completed at a one
scale/resolution.
Air quality analysis has been completed at a few
scales/resolutions.
Air quality analysis has been completed at many
scales/resolutions.
Resilience Score
1 (lowest resilience)
2
4 (highest resilience)
#51: Does the urban area have health warnings or alerts for days when air quality may be
hazardous?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#52: Has an analysis of areas with good ventilation (e.g., aligned with prevailing breezes, good tree
canopy cover) been completed?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
An analysis of areas with good ventilation has not been planned
or completed.
An analysis of areas with good ventilation is planned.
An analysis of areas with good ventilation is in progress.
An analysis of areas with good ventilation has been completed.
Resilience Score
1 (lowest resilience)
2
3
4 (highest resilience)
#53: Do plans exist for preserving areas with good ventilation (e.g., those aligned with prevailing
breezes)?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#54: Does the urban area have a district-scale (i.e., higher resolution than city-scale) thermal
comfort index?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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People
The questions below have been developed for the People sector. Each question is flagged with one or
more of the following gradual change climate stressor and/or extreme event climate stressor (from the
urban resilience framework developed for this project):
In addition, each question has up to four possible Answers. Each answer has been assigned a Resilience
Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each question, please:
1. Discuss the Relevance of the question to the People sector. (If unsure, please select the Not sure -
remind me later option). Questions may be selected as Yes (relevant) on the basis of the stressors
previously selected as being most relevant to Washington, DC, or based on any other criteria.
2. For questions marked as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
3. For questions marked as Yes (relevant), identify the best Answer to the question from the options
provided.
Stressors
Gradual Changes
Wind Speed
Extreme Events
Magnitude/ duration of heat
Temperature
Precipitation
Sea Level Rise
waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
This document is a draft for review purposes only and does not constitute Agency policy.
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#55: How available and how comprehensive are your planning resources for responding to extreme
events?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Comprehensive planning resources for responding to extreme
events do not exist or are difficult to access for some of the
population.
Comprehensive planning resources for responding to extreme
events are difficult to access for some of the population.
Comprehensive planning resources for responding to extreme
events are readily available to some of the population.
Comprehensive planning resources for responding to extreme
events are readily available to most or all of the population.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#56: Are government-led, community-based, or other organizations actively promoting adaptive
behaviors at the neighborhood or city level?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#57: Do policies and outreach/education programs promote behavioral changes that facilitate
climate change adaptation?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#58: Are emergency response staff trained well to respond to large-scale extreme weather events?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Training does not include instruction in triage and other
procedures, such as coordination, during emergencies that affect
large numbers of people.
Training includes minimal instruction in triage and other
procedures, such as coordination, during emergencies that affect
large numbers of people.
Yes, training includes some instruction in triage and other
procedures, such as coordination, during emergencies that affect
large numbers of people.
Yes, training includes triage and other procedures, such as
coordination, during emergencies that affect large numbers of
people.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#59: Is the distribution of public health workers and emergency response resources appropriate for
the population that would be affected during an extreme event?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
The distribution of such services could use improvement.
Yes, such services are well-distributed.
Resilience Score
1 (lowest resilience)
3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #60: Is there sufficient capacity in public health and emergency response systems for responding to
2 extreme events?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
No
1 (lowest resilience)
Yes
3 (highest resilience)
#61: Does the city have the capacity to provide public transportation for emergency evacuations?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
Insufficient capacity
1 (lowest resilience)
Fair capacity
2
Moderate capacity
3
Extensive capacity
4 (highest resilience)
#62: What evacuation and shelter-in-place options are available to residents in the event of a heat
wave?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
No evacuation or shelter-in-place options are available to
1 (lowest resilience)
residents in the event of a heat wave.
One to two evacuation and shelter-in-place options are available
2
to residents in the event of a heat wave.
Several evacuation and shelter-in-place options are available to
3
residents in the event of a heat wave.
Many evacuation and shelter-in-place options are available to
4 (highest resilience)
residents in the event of a heat wave.
This document is a draft for review purposes only and does not constitute Agency policy.
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#63: Do plans exist to provide public access to cooling centers or for other heat adaptation
strategies (e.g., opening public swimming pools earlier or later than normal; use of fire hydrants for
cooling), given predicted climatic changes?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Plans do not exist to provide heat adaptation strategies.
Plans exist to provide one or a few heat adaptation strategies.
Plans exist to provide some heat adaptation strategies.
Plans exist to provide many heat adaptation strategies.
Resilience Score
1 (lowest resilience)
2
3
4 (highest resilience)
#64: Is the healthcare community, including primary care physicians, prepared for changes in
patients' treatments necessitated by climate change (e.g., emerging infectious diseases)?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
The healthcare community is poorly prepared.
The healthcare community's level of preparation is fair.
Yes, the healthcare community is moderately prepared.
Yes, the healthcare community is we 11-prepared.
Resilience Score
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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3
#65: Is the availability of public health goods and services at risk if other city goods and services
(e.g., power, water, public transportation) are affected by extreme climatic events or gradual
climatic changes?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
4
5
6
7
Answer
Availability of public health resources is at significant risk if
other city services are affected by climatic events or changes.
Availability of public health resources is at moderate risk if other
city services are affected by climatic events or changes.
Availability of public health resources is at some risk if other
city services are affected by climatic events or changes.
Availability of public health resources is at minimal risk if other
city services are affected by climatic events or changes.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#66: Do public health programs incorporate longer time frames (e.g., 10+ years) and do they
address climate change-related health issues (e.g., movement of deer ticks to more northerly
locations)?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Public health programs are not designed to address climate-
related health issues.
Public health programs incorporate long-term timeframes and
are address climate-related health issues.
Resilience Score
1 (lowest resilience)
3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#67: Have public health agencies identified infectious diseases and/or disease vectors that may
become more prevalent in the urban area under the expected climatic changes?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#68: Have public health agencies developed plans for responding to increased disease and vector
exposure in ways that may reduce the associated morbidity/mortality?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#69: Do planners in the urban area know the demographic characteristics of populations
vulnerable to climate change?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#70: Do planners in the urban area know the locations of populations most vulnerable to climate
change effects?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#71: Are there services and emergency responses aimed at quickly reaching vulnerable populations
during power outages?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Services and emergency responses are not made especially
available to vulnerable populations during power outages.
Yes, but these services and responses are provided slower than
they are needed.
Yes, and these services and responses are provided somewhat
rapidly.
Yes, and these services and responses are provided rapidly.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#72: Are policies and programs to promote adaptive behavior designed with frames/messaging that
reach the critical audiences in the urban area?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#73: Are policies and programs to promote adaptive behavior designed and implemented in ways
that promote health and well-being of vulnerable populations?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No
Yes
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#74: Are policies and programs to promote adaptive behavior evaluated in ways that take into
account vulnerable populations?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#108: How accessible are different modes of transportation (e.g., to what proportion of the
population, and what subpopulations, e.g., vulnerable people)?
Action Needed:
-Please review/answer the question since it has been moved from another sector.
Relevance:
Yes
Importance Weight:
4
Answer
Few to no modes of transportation are accessible to vulnerable
subpopulations.
Some modes of transportation are accessible to vulnerable
subpopulations.
Many modes of transportation are accessible to vulnerable
subpopulations.
All modes of transportation are accessible to vulnerable
subpopulations.
Resilience Score: 2
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #109: What proportion of the population has limited access to transportation options due to
2 compromised health or lower income levels? For what proportion of this population might
3 transportation failures be life-threatening (i.e., due to reduced access to specialized medical care or
4 equipment)?
Action Needed:
-Please review/answer the question since it has been moved from another sector.
Relevance: Not Sure
Importance Weight:
Yes (relevant)
1 (not very important)
No (not relevant)
2
-2
4 (very important)
Answer
Resilience Score:
10% or more of the population has limited access to
1 (lowest resilience)
transportation due to vulnerabilities, and transportation failures
may be life-threatening for 25% or more of this population.
10% or more of the population has limited access to
2
transportation due to vulnerabilities, and transportation failures
may be life-threatening for less than 25% of this population.
Less than 10% of the population has limited access to
3
transportation due to vulnerabilities, and transportation failures
may be life-threatening for 25% or more of this population.
Less than 10% of the population has limited access to
4 (highest resilience)
transportation due to vulnerabilities, and transportation failures
may be life-threatening for less than 25% of this population.
#143: Are early warning systems for meteorological extreme events available?
Action Needed:
-Please provide a relevance, importance weight, and answer for this question.
Relevance:
Importance Weight:
Yes (relevant)
1 (not very important)
No (not relevant)
2
T,
4 (very important)
Answer
Resilience Score:
No
1 (lowest resilience)
Yes
3 (highest resilience)
8
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #158: Do municipal managers consider local stakeholder knowledge and local resources (libraries,
2 archives) in climate change resilience planning?
Action Needed:
-Please provide a relevance, importance weight, and answer for this question.
Relevance:
Importance Weight:
Yes (relevant)
1 (not very important)
No (not relevant)
2
q
J
4 (very important)
Answer
Resilience Score:
No
1 (lowest resilience)
Yes
3 (highest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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Transportation
The questions below have been developed for the Transportation sector. Each question is flagged with
one or more of the following gradual change climate stressor and/or extreme event climate stressor (from
the urban resilience framework developed for this project):
In addition, each question has up to four possible Answers. Each answer has been assigned a Resilience
Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each question, please:
1. Discuss the Relevance of the question to the Transportation sector. (If unsure, please select the Not
sure - remind me later option). Questions may be selected as Yes (relevant) on the basis of the
stressors previously selected as being most relevant to Washington, DC, or based on any other
criteria.
2. For questions marked as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
3. For questions marked as Yes (relevant), identify the best Answer to the question from the options
provided.
Stressors
Gradual Changes
Wind Speed
Extreme Events
Magnitude/ duration of heat
Temperature
Precipitation
Sea Level Rise
waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
This document is a draft for review purposes only and does not constitute Agency policy.
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#100: Is the availability of transportation goods and services at risk if other city goods and services
(e.g., power, water, information and communications technology) are affected by extreme climatic
events or gradual climatic changes?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
Availability of transportation resources is at significant risk if
1 (lowest resilience)
other city services are affected by climatic events or changes.
Availability of transportation resources is at moderate risk if
2
other city services are affected by climatic events or changes.
Availability of transportation resources is at some risk if other
3
city services are affected by climatic events or changes.
Availability of transportation resources is at minimal risk if other
4 (highest resilience)
city services are affected by climatic events or changes.
#101: How much risk is assumed in the design of transportation systems (bridges, culverts) and
does it span the anticipated changes in precipitation, temperature, and storm intensities under
climate change?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
None
1 (lowest resilience)
Low
2
Medium
3
High
4 (highest resilience)
9
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#102: How resistant to potential impacts of climate change are critical transportation facilities (e.g.,
high-traffic vehicle or rail bridges, tunnels)?
Action Needed:
-Please review and answer the amended question.
Relevance:
Yes
Importance Weight:
4
Answer
Critical transportation facilities are not at all resistant or have no
redundancy.
Critical transportation facilities are not very resistant or have low
levels of redundancy.
Critical transportation facilities are moderately resistant or have
moderate levels of redundancy.
Critical transportation facilities are very resistant or have high
levels of redundancy.
Resilience Score:
1 (lowest resilience)
2
4 (highest resilience)
#103: What degree of redundancy exists for major transportation links? Are there single points of
failure? What are the implications of losing a particular link, and how rapidly can you recover?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Little to no redundancy exists for most links, so there is a single
point of failure in transportation systems and recovery would be
slow.
Some redundancy exists for most links, so few systems have
single points of failure, but recovery would be slow.
Some redundancy exists for most links, so few systems have
single points of failure and recovery would be rapid.
Significant redundancy exists for most links, so few to no
systems have single points of failure, and recovery would be
rapid.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#104: What length of time would be required to restore major high-traffic vehicle transportation
links in the urban area if they experience a failure?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
More than one week
Approximately one week
Four to six days
One to three days
Resilience Score
1 (lowest resilience)
2
3
4 (highest resilience)
#105: Are any portions of the transportation system less important if the duration of the
disturbance is a few days? What if the duration of the disturbance is more on the order of weeks?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No; all components of the transportation system are critical to
the functioning of transportation in the area.
A few portions of the transportation system are less important if
the disturbance is a few days, but not if the disturbance is a few
weeks.
Several portions of the transportation system are less important if
the disturbance is a few days, but not if the disturbance is a few
weeks.
Some portions of the transportation system are less important
whether the disturbance is a few days or a few weeks.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#106: To what extent is the area dependent on long range transportation of goods and services
versus locally available goods and services (e.g., food, energy, etc.)?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
90-100% dependent on long-range transportation of goods and
services
50-90% dependent on long-range transportation of goods and
services
10-50% dependent on long-range transportation of goods and
services
0-10% dependent on long-range transportation of goods and
services
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#107: What flexibility has been built into the transportation system (different modes)?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
1-2 modes available 1 (lowest resilience)
3-4 modes available 2
5-6 modes available 3
7 or more modes available 4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#108: How accessible are different modes (e.g., to what proportion of the population, and what
subpopulations, e.g., vulnerable people)?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Few to no modes of transportation are accessible to vulnerable
subpopulations.
Some modes of transportation are accessible to vulnerable
subpopulations.
Many modes of transportation are accessible to vulnerable
subpopulations.
All modes of transportation are accessible to vulnerable
subpopulations.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#109: What proportion of the population has limited access to transportation options due to
compromised health or lower income levels? For what proportion of this population might
transportation failures be life-threatening (i.e., due to reduced access to speci
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
10% or more of the population has limited access to
transportation due to vulnerabilities, and transportation failures
may be life-threatening for 25% or more of this population.
10% or more of the population has limited access to
transportation due to vulnerabilities, and transportation failures
may be life-threatening for less than 25% of this population.
Less than 10% of the population has limited access to
transportation due to vulnerabilities, and transportation failures
may be life-threatening for 25% or more of this population.
Less than 10% of the population has limited access to
transportation due to vulnerabilities, and transportation failures
may be life-threatening for less than 25% of this population.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #110: How familiar is the community with evacuation procedures?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
Unfamiliar 1 (lowest resilience)
Only slightly familiar (Or only some subpopulations are 2
familiar)
Somewhat familiar 3
Very familiar 4 (highest resilience)
2
3
4 #111: What length of time would be required to restore major passenger rail transportation links in
5 the urban area if they experience a failure?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
More than one week 1 (lowest resilience)
Approximately one week 2
Four to six days 3
One to three days 4 (highest resilience)
6
7
#112: What length of time would be required to restore major freight rail transportation links in
the urban area if they experience a failure?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
More than one week 1 (lowest resilience)
Approximately one week 2
Four to six days 3
One to three days 4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #113: What length of time would be required to restore major bicycle and pedestrian
2 transportation links in the urban area if they experience a failure?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
Approximately one week 1 (lowest resilience)
Four to six days 2
One to three days 3
Less than a day 4 (highest resilience)
3
4
5 #114: Are urban areas set up to provide accessibility, e.g., to jobs, if mobility is interrupted or
6 impeded?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#115: Do current planning regimes include proactive resilience-building, or is only reactive disaster
response being addressed?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Current planning regime only addresses reactive disaster
response.
Current planning regime only addresses reactive disaster
response, but proactive resilience-building approaches are being
developed.
Proactive resilience-building approaches have been developed
and are being implemented alongside reactive disaster response
plans.
Proactive resilience-building approaches are implemented
alongside reactive disaster response plans.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#116: Are there funding mechanisms that exist or could be put into place to complete the necessary
work on the transportation system to adapt to anticipated climatic changes and increased risks?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No funding mechanisms exist to adapt transportation systems to
climatic changes and none could be established.
No funding mechanisms exist to adapt transportation systems to
climatic changes, but mechanisms could be established.
Funding mechanisms are being developed to adapt transportation
systems to climatic changes.
Funding mechanisms exist to adapt transportation systems to
climatic changes.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#117: Do plans exist to replace aging infrastructure? If so, do these plans account for the
anticipated impacts of climate change on this infrastructure?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No plans exist to replace aging infrastructure.
Plans are being developed or already exist to replace aging
infrastructure, but they do not account for anticipated impacts of
climate change.
Plans are being developed or already exist to replace aging
infrastructure, but only some of these plans account for
anticipated impacts of climate change.
Plans exist to replace aging infrastructure and these plans
account for anticipated impacts of climate change.
Resilience Score
1 (lowest resilience)
2
4 (highest resilience)
#118: Are the materials currently in use in transportation systems, such as the common asphalt
formulations and rail types, compatible with anticipated changes in temperature?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Answer
No currently used materials are compatible with anticipated
changes in temperature.
A few currently used materials are compatible with anticipated
changes in temperature.
Some currently used materials are compatible with anticipated
changes in temperature.
All currently used materials are compatible with anticipated
changes in temperature.
Importance Weights
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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2
3
4
5
6
7
8
9
10
11
#119: Have new or innovative materials been tested that may be more capable of withstanding the
anticipated impacts of climate change (e.g., higher temperatures)?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#120: To what extent is green infrastructure implemented or planned to reduce climate change
impacts on transportation systems?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Resilience Score
Not implemented or planned 1 (lowest resilience)
Planned, but not yet implemented 2
Some implementation, with further green infrastructure planned 3
Widespread implementation, with additional projects planned 4 (highest resilience)
#162: Have municipalities considered new methods of designing roads/bridges to prepare for
heavily traveled routes during an extreme climate event (i.e. coastal evacuation routes)?
Action Needed:
-Please provide a relevance, importance weight, and answer for this question.
Relevance: Importance Weight:
Yes (relevant) 1 (not very important)
No (not relevant) 2
3
4 (very important)
Answer Resilience Score:
No 1 (lowest resilience)
Yes 3 (highest resilience)
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
Water
The questions below have been developed for the Water sector. Each question is flagged with one or more
of the following gradual change climate stressor and/or extreme event climate stressor (from the urban
resilience framework developed for this project):
In addition, each question has up to four possible Answers. Each answer has been assigned a Resilience
Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each question, please:
1. Discuss the Relevance of the question to the Water sector. (If unsure, please select the Not sure -
remind me later option). Questions may be selected as Yes (relevant) on the basis of the stressors
previously selected as being most relevant to Washington, DC, or based on any other criteria.
2. For questions marked as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
3. For questions marked as Yes (relevant), identify the best Answer to the question from the options
provided.
Stressors
Gradual Changes
Wind Speed
Extreme Events
Magnitude/ duration of heat
Temperature
Precipitation
Sea Level Rise
waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #121: Does the water supply draw from a diversity of sources?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
2
3
4 #122: To what extent do water supplies come from outside the metropolitan area?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
They come exclusively from outside the area. 1 (lowest resilience)
To a great extent 2
To a moderate extent 3
Only to a small extent 4 (highest resilience)
5
6
7 #123: Is there a recharge plan in place for groundwater supplies?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
184 DRAFT—DO NOT CITE OR QUOTE
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1
2
3
4
5
6
7
8
9
10
#124: Do programs for long-term maintenance of water supplies (e.g., erosion control methods, re-
forestation of the watershed) exist?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#125: Is there a hierarchy of water uses to be implemented during a shortage or emergency?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
#126: Does the water system have emergency interconnections with adjacent water systems or other
emergency sources of supply?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#127: Are water and wastewater treatment plants located in a flood zone?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
At least 50% of water and wastewater treatment plant capacity is
located in a flood zone.
30% to 49% of water and wastewater treatment plant capacity is
located in a flood zone.
10% to 29% of water and wastewater treatment plant capacity is
located in a flood zone.
Less than 10% of water and wastewater treatment plant capacity
is located in a flood zone.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#128: Are groundwater supplies susceptible to salt water intrusion and sea level rise?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Answer
Groundwater supplies are very susceptible to salt water intrusion
given anticipated sea level rise.
Groundwater supplies are moderately susceptible to salt water
intrusion given anticipated sea level rise.
Groundwater supplies are slightly susceptible to salt water
intrusion given anticipated sea level rise.
No, groundwater supplies are not susceptible to salt water
intrusion and sea level rise.
Importance Weights
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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2
3
4
5
6
7
8
9
#129: If groundwater supplies are susceptible to salt water intrusion and sea level rise, is the water
treatment plant equipped to deal with higher levels of salinity?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No
Yes
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#130: Does treatment capacity exist to accommodate nutrient loading?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
Drinking water treatment capacity cannot accommodate nutrient 1 (lowest resilience)
loading in source water.
Drinking water treatment capacity can accommodate expected 3 (highest resilience)
levels of nutrient loading in source water.
#131: Does the drinking water treatment plant have redundant treatment chemical suppliers?
Action Needed:
-Please review and answer the amended question.
Relevance: Importance Weight:
Yes 3
Answer Resilience Score: 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #132: Are there redundant drinking water systems in place for coping with extreme events,
2 including supply, treatment, and distribution systems?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
No, redundant drinking water systems are not in place.
1 (lowest resilience)
Yes, but these redundant drinking water systems have only a
2
small amount of the capacity necessary.
Yes, and these redundant drinking water systems have some of
3
the capacity necessary.
Yes, and these redundant drinking water systems have all the
4 (highest resilience)
capacity necessary.
#133: Is backup power for water supply, treatment, and distribution systems provided?
Relevance
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not Sure - Remind Me Later
3
4 (very important)
Answer
Resilience Score
No backup power is provided.
1 (lowest resilience)
Minimal backup power is provided.
2
Some backup power is provided.
3
Full backup power is provided.
4 (highest resilience)
6
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #134: How diverse are individual properties i.e., are they equipped to harvest rainwater or
2 recharge groundwater so they can create or augment local water supplies?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No individual properties are equipped to either harvest rainwater 1 (lowest resilience)
or recharge groundwater.
Few individual properties are equipped to either harvest 2
rainwater or recharge groundwater.
Some individual properties are equipped to either harvest 3
rainwater or recharge groundwater.
Most individual properties are equipped to either harvest 4 (highest resilience)
rainwater or recharge groundwater.
3
4
5 #135: Are there redundant wastewater and stormwater systems in place for coping with extreme
6 events, including collection systems and wastewater treatment systems?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No, redundant wastewater and stormwater systems are not in 1 (lowest resilience)
place.
Yes, but these redundant wastewater and stormwater systems 2
have only a small amount of the capacity necessary.
Yes, and these redundant wastewater and stormwater systems 3
have some of the capacity necessary.
Yes, and these redundant wastewater and stormwater systems 4 (highest resilience)
have all the capacity necessary.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #136: Does a Water/Wastewater Agency Response Network provide technical resources/support to
2 the urban area's water system during emergencies?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
3
4
5 #137: Have storm sewers and drains to storm sewers been inventoried and are these inventories
6 used in planning?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
Storm sewers and drains to storm sewers are not inventoried and 1 (lowest resilience)
are not planned to be inventoried.
Plans exist to inventory storm sewers and drains to storm sewers 2
OR these inventories exist but are not used in planning.
Storm sewers and drains to storm sewers are being inventoried 3
and these inventories are used or will be used in planning.
Storm sewers and drains to storm sewers have been inventoried 4 (highest resilience)
and these inventories are used in planning.
This document is a draft for review purposes only and does not constitute Agency policy.
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#138: Is the availability of water goods and services at risk if other city goods and services (e.g.,
power, transportation, public health) are affected by extreme climatic events or gradual climatic
changes?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Availability
services are
Availability
services are
Availability
services are
Availability
services are
of water resources is at significant risk if other city
affected by climatic events or changes,
of water resources is at moderate risk if other city
affected by climatic events or changes,
of water resources is at some risk if other city
affected by climatic events or changes,
of water resources is at minimal risk if other city
affected by climatic events or changes.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#139: Has the water utility conducted a water audit to identify current losses (e.g. leaks, billing
errors, inaccurate meters, unauthorized usage)?
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Answer Resilience Score
No 1 (lowest resilience)
Yes 3 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1
#140: To what extent have efforts been made to reduce water demand?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
2
3
4
5
Answer
Few to no efforts have been made to reduce water demand.
Fair efforts have been made to reduce water demand.
Moderate efforts have been made to reduce water demand.
Significant efforts have been made to reduce water demand.
Resilience Score
1 (lowest resilience)
2
3
4 (highest resilience)
#141: Are customers familiar with water conservation measures and are they willing to implement
these measures?
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Customers are not familiar with OR are not willing to implement
water conservation measures.
Customers are marginally familiar with and somewhat or
marginally willing to implement water conservation measures.
Customers are somewhat familiar with and willing to implement
water conservation measures.
Customers are familiar with and willing to implement water
conservation measures.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
APPENDIX F. QUANTITATIVE INDICATORS
A complete set of the quantitative indicators by sector developed for the tool.
Economy
The indicators below have been developed for the Economy sector. Indicators that are related are grouped
together such that a single indicator from that group was considered a Primary Indicator and the
remaining were considered Secondary Indicators. Primary Indicators and Non-Grouped Indicators are
presented in the first half of this handout, followed by the Secondary Indicators.
Each indicator has a Definition. Each question is flagged with one or more of the following gradual
change climate stressor and/or extreme event climate stressor (from the urban resilience framework
developed for this project):
Where it was possible to identify a data set that would provide data for the indicator for Washington, DC,
Data Set(s) and associated Notes on Available Data are included. Indicators are assigned a Proposed
Resilience Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each indicator, please:
1. Discuss the Relevance of the indicator to the Economy sector. (If unsure, please select the Not
Sure - Remind Me Later option). Indicators may be selected as Yes (relevant) on the basis of the
stressors previously selected as being most relevant to Washington, DC, or based on any other
criteria. Secondary Indicators may be considered, if the Primary Indicator is not adequately
defined or does not have available dataset(s).
2. When possible, Dataset(s) for Washington, D.C. are provided where data were available. In some
cases, no dataset(s) were identified. Please suggest dataset(s) that may be better than the
dataset(s) identified or where data gaps exist.
3. For indicators selected as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
4. Review the Proposed Resilience Score (if provided), which is on a scale of 1 = lowest resilience
to 4 = highest resilience, for the indicator. If you disagree with this score, please discuss Your
Score and indicate the reason for your disagreement.
Stressors
Gradual Changes
Extreme Events
Magnitude/ duration of heat waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
Wind Speed
Temperature
Precipitation
Sea Level Rise
This document is a draft for review purposes only and does not constitute Agency policy.
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#709: Percentage of owned housing units that are affordable
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
Definition: This indicator measures the percent of owned housing units whose selected monthly
ownership costs (rent, mortgages, real estate taxes, insurance, utilities, fuel, fees) as a percentage of
household income (SMOCAPI) exceeds 35% or percentage of rented housing units whose gross rent
as a percentage of household income (GRAPI) exeeds 35%.
Grouped with indicators: N/A
Dataset(s):
District of Columbia - Selected Housing Characteristics from 2011 American Community Survey
(ACS) 5-year estimates
(http://occ.dc.gov/DC/Planning/DC+Data+and+Maps/DC+Data/2011+ACS+5+Year+Estimates)
Notes on Dataset(s):
The American Community Survey (ACS) is an ongoing survey administered by the US Census
Bureau that provides data every year ~ giving communities the current information they need to plan
investments and services. Information from the survey generates data that help determine how more
than $400 billion in federal and state funds are distributed each year. The DC Office of Planning has
prepared tables from the 2011 ACS 5-year estimates on their website. The data for this indicator is
found in the "DC Housing Characteristics" document: 28.7% of the 85,992 mortgaged housing units
for which data are available have SMOCAPI >35%; 11.1% of the 24,319 unmortgaged housing units
for which data are available have SMOCAPI > 35; 40.7% of rented housing units for which data are
available have gross rent > 35%.
Indicator Value:
33.7% of housing units
Relevance: Yes Importance Weight: 3 Proposed Resilience Score: 3
Thresholds: Threshold-based Score: 2 Your Score: Score not yet assigned
Oto 30% 1 (lowest resilience) 1 (lowest resilience)
Greater than 30 to 45%
Greater than 45 to 60%
Greater than 60%
2
3
4 (highest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1
PRIMARY INDICATORS & NON-GROUPED INDICATORS
2 #717: Percent access to health insurance of non-institutionalized population
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator measures the percent of non-institutionalized residents with health
insurance
Grouped with indicators: #125
Dataset(s):
District of Columbia - Selected Economic Characteristics from 2011 American Community Survey
(ACS) 3-year estimates
(http://occ.dc.gov/DC/Planning/DC+Data+and+Maps/DC+Data/2011++ACS+3+Year+Estimates/Eco
nomic+Characteristics)
Notes on Dataset(s):
Of the 594,576 civilian noninstitutionalized population, 92.9% have health insurance.
Indicator Value:
92.90%
Relevance: Yes Importance Weight: 3 Proposed Resilience Score: 4
Thresholds: Threshold-based Score: 3 Your Score: 4
Less than 85%
85 to 90%
Greater than 90 to 95%
Greater than 95%
2
3
4 (highest resilience)
1 (lowest resilience)
2
3
4 (highest resilience)
1 (lowest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #711: Diversity of Employment Opportunities (unemployment rate)
Definition: Employment is a measure of economic viability and self-sufficiency. Employment
opportunities spread across different industries create a more stable employment base. A
diversification of industries also offers opportunities to a diverse labor market. This indicator
measures overall unemployment rate.
Grouped with indicators: N/A
Relevance Importance Weights
Dataset(s):
US Bureau of Labor Statistics - Unemployment Rates for States Average for Year: 2012
(http: //www .bis .gov/lau/lastrk 12 .htm)
Notes on Dataset(s):
The US Bureau of Labor Statistics tracks a variety of measures of employment nationwide. This
measure of unemployment is an annual average for 2012, which is 8.9% for DC.
Indicator Value:
8.9% unemployment
Proposed Resilience Score Your Score
4 1 (lowest resilience)
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
2
3
4 (very important)
1 (not very important)
2
3
4 (highest resilience)
2
This document is a draft for review purposes only and does not constitute Agency policy.
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#722: Percent change in homeless population
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
Definition: This indicator measures the percent change in the homeless population.
Grouped with indicators: N/A
Dataset(s):
Metropolitan Washington Council of Governments - Homelessness in Metropolitan Washington:
Results and Analysis from the 2013 Point-in-Time Count of Homeless Persons in the Metropolitan
Washington Region (http://www.mwcog.org/uploads/pub-documents/qF5cXlw20130508134424.pdf)
Notes on Dataset(s):
This report details trends in homelessness for counties and municipalities in metropolitan Washington
(including counties in Maryland, Virginia, and the District of Columbia). Table 2 on page 5 lists the
number of literally homeless persons by county jurisdiction for each year from 2009 through 2013.
There were 6,954 literally homeless in 2012 and 6,865 literally homeless in 2013, a 1.27% decrease
year over year.
Indicator Value:
-1.27% change in homeless population
Relevance: Yes Importance Weight: 3 Proposed Resilience Score: 2
Thresholds:
Greater than 10%
Greater than 0 to 10%
Greater than negative 10 to 0%
Less than negative 10%
Threshold-based Score: 3
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #1375: Percent of population living below the poverty line
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
Definition: This indicator measures the percent of the population living below the poverty line.
Grouped with indicators: N/A
Dataset(s):
District of Columbia - Selected Housing Characteristics from 2011 American Community Survey
(ACS) 5-year estimates
(http://occ.dc.gov/DC/Planning/DC+Data+and+Maps/DC+Data/2011+ACS+5+Year+Estimates)
Notes on Dataset(s):
The American Community Survey (ACS) is an ongoing survey administered by the US Census
Bureau that provides data every year ~ giving communities the current information they need to plan
investments and services. Information from the survey generates data that help determine how more
than $400 billion in federal and state funds are distributed each year. The DC Office of Planning has
prepared tables from the 2011 ACS 5-year estimates on their website. The data for this indicator is
found in the "DC EconomicCharacteristics" document: 18.2% of people had an income in the last 12
months below the poverty level.
Indicator Value:
18.20% of people
Relevance: Yes Importance Weight: 2 Proposed Resilience Score: 1
Thresholds:
Threshold-based Score: 2
Your Score: Score not vet assigned
Greater than 20%
1 (lowest resilience)
1 (lowest resilience)
Greater than 16 to 20%
2
2
12 to 16%
3
3
Less than 12%
4 (highest resilience)
4 (highest resilience)
2
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Energy
The indicators below have been developed for the Energy sector. Indicators that are related are grouped
together such that a single indicator from that group was considered a Primary Indicator and the
remaining were considered Secondary Indicators. Primary Indicators and Non-Grouped Indicators are
presented in the first half of this handout, followed by the Secondary Indicators.
Each indicator has a Definition. Each question is flagged with one or more of the following gradual
change climate stressor and/or extreme event climate stressor (from the urban resilience framework
developed for this project):
Where it was possible to identify a data set that would provide data for the indicator for Washington, DC,
Data Set(s) and associated Notes on Available Data are included. Indicators are assigned a Proposed
Resilience Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each indicator, please:
1. Discuss the Relevance of the indicator to the Energy sector. (If unsure, please select the Not Sure
- Remind Me Later option). Indicators may be selected as Yes (relevant) on the basis of the
stressors previously selected as being most relevant to Washington, DC, or based on any other
criteria. Secondary Indicators may be considered, if the Primary Indicator is not adequately
defined or does not have available dataset(s).
2. When possible, Dataset(s) for Washington, D.C. are provided where data were available. In some
cases, no dataset(s) were identified. Please suggest dataset(s) that may be better than the
dataset(s) identified or where data gaps exist.
3. For indicators selected as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
4. Review the Proposed Resilience Score (if provided), which is on a scale of 1 = lowest resilience
to 4 = highest resilience, for the indicator. If you disagree with this score, please discuss Your
Score and indicate the reason for your disagreement.
Stressors
Gradual Changes
Extreme Events
Magnitude/ duration of heat waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
Wind Speed
Temperature
Precipitation
Sea Level Rise
This document is a draft for review purposes only and does not constitute Agency policy.
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PRIMARY INDICATORS & NON-GROUPED INDICATORS
#949: Percent energy consumed for electricity
Action Needed:
-No thresholds were identified for this indicator. Please decide whether this data gap might
demonstrate that the indicator is not relevant.
Definition: The indicator measures electricity consumption per year in kwh as a percent of total
energy consumption
Grouped with indicators: #950, #951
Dataset(s):
District of Columbia - Energy Assurance Plan 2012
(http://ddoe.dc.gov/sites/default/files/dc/sites/ddoe/publication/attachments/Energy%20Assurance%20
Plan.pdf)
Notes on Dataset(s):
Figure 1 on page 15 breaks down energy sources in DC for 2010: Electricity: 70.4%; Natural Gas:
18.3%; Petroleum: 11.3%; All Others: 0.1%.
Indicator Value:
70.40%
Relevance: Yes Importance Weight: 3 Proposed Resilience Score: 3
Yes (relevant)
No (not relevant)
Thresholds
N/A
Threshold-based Score: N/A Your Score : 3
1 (lowest resilience) 1 (lowest resilience)
2 2
3 3
4 (highest resilience) 4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#971: Energy source capacity per unit area
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
Definition: This indicator measures the total capacity of energy sources per unit area served (MW/sq
mi).
Grouped with indicators: #970
Dataset(s):
1) Pepco - Service Area Map (http://www.pepco.com/business/services/new/map/)
2) PJM - Load Forecast Report January 2013
(https: //www .pj m. com/~/media/ documents/reports/2013 -load-forecast-report. ashx)
Notes on Dataset(s):
1) Pepco service area 640 square miles.
2) Table B-l. Pepco peak demand 6,800 MW in 2012. Assume 20% reserve capacity, therefore,
PEPCO peak capacity = 8,500 MW. Capacity of source = 8,500 MW/640 sq mi = 13.28 MW/sq mi
Indicator Value:
13.28 MW/sq mi
Relevance: Yes
Importance Weight: 3 Proposed Resilience Score: 2
Thresholds:
Less than 10 megawatt per square
mile
10 to 50 megawatt per square mile
Greater than 50 to 100 megawatt
per square mile
Greater than 100 megawatt per
square mile
Threshold-based Score: 2 Your Score: Score not yet assigned
1 (lowest resilience)
2
3
1 (lowest resilience)
2
3
4 (highest resilience) 4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #983: Average customer energy outage (hours) in recent major storm
Action Needed:
-Please decide if original or alternate data set is more appropriate.
-Please decide if you agree with threshold-based score and provide explanation if threshold-based score
is not chosen.
-Please review/modify importance weight if appropriate.
Definition: This indicator measures the average customer energy outage hours divided by number of
electricity customers for a storm event in June 2012.
Grouped with indicators: #862
Dataset(s):
Potomac Electric Power Company (PEPCO) - Major Service Outage Report June 29-July 7, 2012
DERECHO (http://www.dcpsc.org/edocket/docketsheets_pdf_FS.asp?caseno=S003-2012-
E&docketno= 1 &flag=D&show_result=Y)
Notes on Dataset(s):
This document is the service outage report for the storm event in June 2012. Page 54 details outage
information for the storm: 107,321 customers in DC had power interrupted for a combined 3,679,479
hours, equal to 34.28 hours per customer.
Indicator Value:
34.28 hours per customer
Alternate Dataset(s):
1) Potomac Electric Power Company Comprehensive Reliability Plan for District of Columbia
(http://www.pepco.com/_res/documents/dccomprehensivereliabilityplan.pdf)
SAIFI - System Average Interruption Frequency Index
SAIDI - System Average Interruption Duration Index
Notes on Alternate Dataset(s):
See page 40 for SAIFI graph, page 41 for CAIDI graph, and page 42 for SAIDI graph.
Values in 2009:
SAIFI ~ 1.05 minutes
CAIDI ~ 135 minutes (2.25 hours)
SAIDI ~ 140 minutes (2.33 hours)
Report SAIDI as this matches most closely with the indicator definition
Alternate Indicator Value:
2.33 hours
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 4
1 (not very important)
2
3
4 (very important)
Thresholds:
Threshold-based Score: 2
Your Score : 3
Greater than 40 hours
1 (lowest resilience)
1 (lowest resilience)
Greater than 20 to 40 hours
2
2
10 to 20 hours
3
3
Less than 10 hours
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #898: Annual energy consumption per capita by main use category (commercial use)
Action Needed:
-Please decide if original or alternate data set is more appropriate.
-Please decide if you agree with threshold-based score and provide explanation if threshold-based
score is not chosen.
-Please review/modify importance weight if appropriate.
Definition: The indicator measures the annual energy consumption (2010) per capita within the
commercial use sector.
Grouped with indicators: N/A
Dataset(s):
District of Columbia - Energy Assurance Plan 2012
(http://ddoe.dc.gov/sites/default/files/dc/sites/ddoe/publication/attachments/Energy%20Assurance%20
Plan.pdf)
Notes on Dataset(s):
Figure 3 on page 16 breaks down electricity consumption in DC by sector for 2010 in million kilowatt
hours (equal to gigawatt hours): residential: 2,123; commercial: 9,209; industrial: 230; transportation:
315; total: 11,877. The 9,209 million kWh for the commercial sector is the largest sector. On a per
capita basis (using a 2010 population of 601,723), this is equal to 15,304 kWh per capita.
Indicator Value:
15,304 kWh per capita or 1.316 tons of oil equivalent
Alternate Dataset(s):
1) U.S. Energy Information Administration, Table CT5. Commercial Sector Energy Consumption
Estimates, Selected Years, 1960-2011, District of Columbia
(http://www.eia.gov/state/seds/data.cfm?incfile=/state/seds/sep_use/com/use_com_DC.html&sid=DC
)
2) 2010 Census population
Notes on Alternate Dataset(s):
124.9 trillion Btu in 2010 in District of Columbia
2010 Census population: 601,723
0.000208 trillion Btu per capita
OR
0.208 billion Btu per capita
OR
208 million Btu per capita
(Threshold units are unspecified; I chose the units that put it within 1 order of magnitude of the
thresholds)
Alternate Indicator Value:
0.208 billion Btu per capita
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 2
1 (not very important)
2
3
4 (very important)
This document is a draft for review purposes only and does not constitute Agency policy.
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Thresholds:
Greater than 4.0 tons of oil
equivalent
Greater than 3.0 to 4.0 tons of oil
equivalent
Greater than 2.0 to 3.0 tons of oil
equivalent
Less than or equal to 2.0 tons of oil
equivalent
1
2
3 #967: Total energy source capacity per capita
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
Definition: This indicator measures the total capacity of all energy sources (MW) per capita.
Grouped with indicators: N/A
Dataset(s):
1) Pepco - Service Area Map (http://www.pepco.com/business/services/new/map/)
2) PJM - Load Forecast Report January 2013
(https: //www .pj m. com/~/media/ documents/reports/2013 -load-forecast-report. ashx)
Threshold-based Score: 4
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 2
1 (lowest resilience)
2
3
4 (highest resilience)
Notes on Dataset(s):
1) Pepco population served = 2,022,000
2) Table B-l. Pepco peak demand 6,800 MW in 2012. Assume 20% reserve capacity, therefore,
PEPCO peak capacity = 8,500 MW. Capacity of source per capita = 8,500 MW/640 sq mi = 0.0042
MW per capita or 4.2 kW per capita
Indicator Value:
4.2 kW per capita
Relevance: Yes
Importance Weight: 3
Proposed Resilience Score: 3
Thresholds:
Less than 1.0 megawatt per capita
1.0 to 2.0 megawatt per capita
Greater than 2.0 to 5.0 megawatt
per capita
Greater than 5.0 megawatt per
capita
Threshold-based Score: 3 Your Score: Score not yet
assigned
1 (lowest resilience) 1 (lowest resilience)
2 2
3 3
4 (highest resilience) 4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 SECONDARY INDICATORS
2 #950: Percent of electricity generation from non-carbon sources
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator measures the percent of total electricity generation from non-carbon energy
sources in a city.
Grouped with indicators: #949, #951
Dataset(s):
US Environmental Protection Agency - Green Power Community Challenge Rankings
(http://www.epa.gOv/greenpower/communities/gpcrankings.htm#content)
Notes on Dataset(s):
As tracked by EPA's Green Power Partnership program, 1.045 terawatt hours of green power was
consumed in DC over a yearlong period from 2012-2013. This is 11.4% of total electricity use.
Indicator Value:
11.40%
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 3
Thresholds: Threshold-based Score: 1 Your Score: 3
Less than 25%
25 to 50%
Greater than 50 to 75%
Greater than 75%
2
3
4 (highest resilience)
1 (lowest resilience)
2
3
4 (highest resilience)
1 (lowest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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#951: Percent of total energy use from renewable sources
Action Needed:
-Please decide if original or alternate data set is more appropriate.
-Please decide if you agree with threshold-based score and provide explanation if threshold-based
score is not chosen.
-Please review/modify importance weight if appropriate.
Definition: This indicator measures the percent of total energy use from renewable sources.
Grouped with indicators: #949, #950
Dataset(s):
Department of Energy - District of Columbia Energy Consumption
(http://apps 1 .eere .energy. gov/states/consumption.cfm/state=DC?)
Notes on Dataset(s):
DOE cites no renewable energy generation from renewable energy sources from 2002 - 2010.
However, this does not include the 1.045 terawatts of renewable electricity consumption described
in indicator 950. This represents 1.98% of the 52.75 terawatts of total energy consumption.
Indicator Value:
1.98%
Alternate Dataset(s):
1) U.S. Energy Information Administration, Table CT2. Primary Energy Consumption Estimates,
Selected Years, 1960-2011, District of Columbia
(http://www.eia.gov/state/seds/data.cfm?incfile=/state/seds/sep_use/total/use_tot_DCcb.html&sid=
DC)
Notes on Alternate Dataset(s):
1) Source about energy consumption (use).
Consumption: 0.7 trillion Btu renewable/186.7 trillion Btu total = 0.37%
Alternate Indicator Value:
0.37%
Relevance: Yes
Importance Weight: 3
1 (not very important)
2
3
4 (very important)
Proposed Resilience Score: 3
Thresholds:
Threshold-based Score: 1
Your Score: 2
Less than 20%
1 (lowest resilience)
1 (lowest resilience)
20 to 40%
2
2
Greater than 40 to 60%
3
3
Greater than 60%
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#970: Average capacity of a decentralized energy source
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicator measures the average capacity of a decentralized energy source (m3/acre).
Decentralized energy sources are those that can be used as a supplementary source to the existing
centralized energy system. They are typically located closer to the site of actual energy consumption
than centralized sources.
Grouped with indicators: #971
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A
Indicator Value:
N/A
Relevance: Not Sure
Yes (relevant)
No (not relevant)
Importance Weight: N/A
1 (not very important)
2
3
4 (very important)
Proposed Resilience Score: 2
Thresholds:
Less than 5,000 megawatt per
square mile
5,000 to 10,000 megawatt per
square mile
Greater than 10,000 to 15,000
megawatt per square mile
Greater than 15,000 megawatt per
square mile
Threshold-based Score: N/A Your Score: Score not yet
assigned
1 (lowest resilience)
2
3
4 (highest resilience)
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#924: Energy intensity by use
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
Definition: This indicator measures energy intensity in manufacturing, transportation, agriculture,
commercial, and public services, and the residential sector.
Grouped with indicators:
Dataset(s):
Department of Energy - District of Columbia Energy Consumption
(http://apps 1 .eere .energy. gov/states/consumption.cfm/state=DC?)
Notes on Dataset(s):
This DOE webpage estimates the energy intensity of gross state product in 2010 at 1,800 Btu per
dollar.
Indicator Value:
1800 Btu/dollar
Relevance: Yes Importance Weight: 3 Proposed Resilience Score: 2
Thresholds:
Threshold-based Score: 3
Your Score: Score not vet
assigned
Greater than 3,000 Btu per dollar
1 (lowest resilience)
1 (lowest resilience)
Greater than 2,000 to 3,000Btu per
2
2
dollar
Greater than 1,500 to 2,000 Btu per
3
3
dollar
Less than 1,500 Btu per dollar
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Information and Communication Technology
The indicators below have been developed for the Information and Communications Technology sector.
Indicators that are related are grouped together such that a single indicator from that group was
considered a Primary Indicator and the remaining were considered Secondary Indicators. Primary
Indicators and Non-Grouped Indicators are presented in the first half of this handout, followed by the
Secondary Indicators.
Each indicator has a Definition. Each question is flagged with one or more of the following gradual
change climate stressor and/or extreme event climate stressor (from the urban resilience framework
developed for this project:
Where it was possible to identify a data set that would provide data for the indicator for Washington, DC,
Data Set(s) and associated Notes on Available Data are included. Indicators are assigned a Proposed
Resilience Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each indicator, please:
1. Discuss the Relevance of the indicator to the Information and Communications Technology
sector. (If unsure, please select the Not Sure - Remind Me Later option). Indicators may be
selected as Yes (relevant) on the basis of the stressors previously selected as being most relevant
to Washington, DC, or based on any other criteria. Secondary Indicators may be considered, if the
Primary Indicator is not adequately defined or does not have available dataset(s).
2. When possible, Dataset(s) for Washington, D.C. are provided where data were available. In some
cases, no dataset(s) were identified. Please suggest dataset(s) that may be better than the
dataset(s) identified or where data gaps exist.
3. For indicators selected as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
4. Review the Proposed Resilience Score (if provided), which is on a scale of 1 = lowest resilience
to 4 = highest resilience, for the indicator. If you disagree with this score, please discuss Your
Score and indicate the reason for your disagreement.
Stressors
Gradual Changes
Extreme Events
Magnitude/ duration of heat waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
Wind Speed
Temperature
Precipitation
Sea Level Rise
This document is a draft for review purposes only and does not constitute Agency policy.
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PRIMARY INDICATORS & NON-GROUPED INDICATORS
#1433: Percentage of system capacity needed to carry baseline level of traffic
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: N/A
Grouped with indicators: N/A
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
N/A
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: N/A
Yes (relevant)
No (not relevant)
Thresholds:
Greater than 70%
Greater than 50 to 70%
30 to 50%
Less than 30%
Threshold-based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #1434: Baseline percentage of water supply for telecomm. systems that conies from outside the
2 metropolitan area
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: N/A
Grouped with indicators: N/A
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
N/A
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: N/A
Yes (relevant)
No (not relevant)
Thresholds:
Greater than 50%
Greater than 20 to 50%
5 to 20%
Less than 5%
Threshold-based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1435: Baseline percentage of energy supply for telecomm. systems that conies from outside the
metropolitan area
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: N/A
Grouped with indicators: N/A
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
N/A
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 4
Yes (relevant)
No (not relevant)
Thresholds:
Greater than 60%
Greater than 30 to 60%
10 to 30%
Less than 10%
Threshold-based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1441: Percent of community with access to FEMA emergency radio broadcasts
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: Percent of community with access to FEMA emergency radio broadcasts.
Grouped with indicators: N/A
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
N/A
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 3
Yes (relevant)
No (not relevant)
Thresholds:
Less than 80%
80 to 88%
Greater than 88 to 96%
Greater than 96%
Threshold-based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Land Use/Land Cover
The indicators below have been developed for the Land Use/Land Cover sector. Indicators that are related
are grouped together such that a single indicator from that group was considered a Primary Indicator
and the remaining were considered Secondary Indicators. Primary Indicators and Non-Grouped
Indicators are presented in the first half of this handout, followed by the Secondary Indicators.
Each indicator has a Definition. Each question is flagged with one or more of the following gradual
change climate stressor and/or extreme event climate stressor (from the urban resilience framework
developed for this project):
Where it was possible to identify a data set that would provide data for the indicator for Washington, DC,
Data Set(s) and associated Notes on Available Data are included. Indicators are assigned a Proposed
Resilience Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each indicator, please:
1. Discuss the Relevance of the indicator to the Land Use/Land Cover sector. (If unsure, please
select the Not Sure - Remind Me Later option). Indicators may be selected as Yes (relevant) on
the basis of the stressors previously selected as being most relevant to Washington, DC, or based
on any other criteria. Secondary Indicators may be considered, if the Primary Indicator is not
adequately defined or does not have available dataset(s).
2. When possible, Dataset(s) for Washington, D.C. are provided where data were available. In some
cases, no dataset(s) were identified. Please suggest dataset(s) that may be better than the
dataset(s) identified or where data gaps exist.
3. For indicators selected as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
4. Review the Proposed Resilience Score (if provided), which is on a scale of 1 = lowest resilience
to 4 = highest resilience, for the indicator. If you disagree with this score, please discuss Your
Score and indicate the reason for your disagreement.
Stressors
Gradual Changes
Extreme Events
Magnitude/ duration of heat waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
Wind Speed
Temperature
Precipitation
Sea Level Rise
This document is a draft for review purposes only and does not constitute Agency policy.
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PRIMARY INDICATORS & NON-GROUPED INDICATORS
#437: % change in streamflow divided by % change in precipitation
Definition: The proportional change in streamflow (Q) divided by the proportional change in
precipitation (P) for 1,291 gauged watersheds across the continental US.
Grouped with indicators: #1369
Dataset(s):
1) USGS Hydro-Climatic Data Network (HCDN) dataset for 1931-1988, POTOMAC River
(http://pubs.usgs.gov/wri/wri934076/stations/01646502.html)
2) National Climatic Data Center (NCDC) - Washington DC precipitation archive (1871-2013).
(http ://www. erh .noaa.gov/lwx/climate/dca/ dcaprecip .txt)
Notes on Dataset(s):
1) Includes data on mean annual streamflow (cfs) from 1931 to 1988. Calculate percent change in
streamflow from 1931 to 1988. Calculate percent change in precipitation from 1931-1988. Divide
percent change in streamflow by percent change in precipitation.
2) Includes total precipitation (in) from 1871-2013.
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Indicator Value:
-14.36%
Proposed Resilience Score
Your Score
1 (lowest resilience)
N/A
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #825: Percent change in impervious cover
Action Needed:
-Please decide if original or alternate data set is more appropriate.
-Please decide if you agree with threshold-based score and provide explanation if threshold-based
score is not chosen.
-Please review/modify importance weight if appropriate.
Definition: This indicator reflects the change in the percent of the metropolitan area that is impervious
surface (roads, buildings, sidewalks, parking lots, etc.).
Grouped with indicators: #303, #308
Dataset(s):
1) NLCD 2001/2006 Percent developed imperviousness change dataset:
http: //www .mrlc .gov/nlcd06_data.php
Notes on Dataset(s):
Calculate the average percent change in imperviousness across DC for the time period of 2001-2006.
Clip the raster file to the town boundary, then calculate the product of the Count and Red (the percent
change in imperviousness) fields. Sum this product and divide by the sum of the Count field.
Percent change in impervious surface cover = 0.19% increase
Indicator Value:
0.19% increase
Alternate Dataset(s):
1) NLCD 2001/2006 Percent developed imperviousness change dataset:
http: //www .mrlc .gov/nlcd06_data.php
Notes on Alternate Dataset(s):
Calculate the average percent change in imperviousness across DC for the time period of 2001-2006.
Clip the raster file to the town boundary, then calculate the product of the Count and Red (the percent
change in imperviousness) fields. Sum this product and divide by the sum of the Count field.
Percent change in impervious surface cover = 0.19% increase
Alternate Indicator Value:
0.19% increase
Relevance: Yes
Importance Weight: 4
1 (not very important)
2
3
4 (very important)
Proposed Resilience Score: 1
Thresholds:
Threshold-based Score: 2
Your Score: 2
Greater than 1%
1 (lowest resilience)
1 (lowest resilience)
Greater than 0 to 1%
2
2
Negative 1 to 0%
3
3
Less than negative 1%
4 (highest resilience)
4 (highest resilience)
2
This document is a draft for review purposes only and does not constitute Agency policy.
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#1436: Percent of city area in 100-year floodplain
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator reflects the percent of the metropolitan area that lies within the 100-year
floodplain.
Grouped with indicators: #1437, #1438, #1439
Dataset(s):
1) DC.gov - 2010 Floodplains (http://data.dc.gov/Metadata.aspx?id=48)
Notes on Dataset(s):
1) This GIS dataset describes the areas of 100- and 500-year floodplains as determined by the Federal
Emergency Management Agency.
Indicator Value:
8.50%
Relevance: Yes Importance Weight: 1 Proposed Resilience Score: 1
Thresholds:
Threshold-based Score: 2
Your Score: 1
Greater than 20%
1 (lowest resilience)
1 (lowest resilience)
Greater than 5 to 20%
2
2
1 to 5%
3
3
Less than 1%
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#51: Coastal Vulnerability Index Rank
Action Needed:
-On further review, it was found that the USGS and NASA do not include DC in the areas for which
they calculate CVI. Thus this indicator does not have a value for Washington, DC and has been
marked as not relevant.
Definition: This indicator reflects the Coastal Vulnerability Index Rank. The ranks are as follows: 1 =
none, 2 = low, 3 = moderate, 4 = high, 5 = very high. The index allows six physical variables to be
related in a quantifiable manner that expresses the relative vulnerability of the coast to physical
changes due to sea-level rise. The six variables are: a = Geomorphology; b = Coastal Slope (%); c =
Relative sea-level change (mm/year); d = Shoreline erosion/accretion (m/year); e = Mean tide average
(m); e = Mean wave height (m).
Grouped with indicators: N/A
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A
Indicator Value:
N/A
Relevance: No
Importance Weight: 4
Proposed Resilience Score: 2
Thresholds:
5 (very high vulnerability)
4 (high vulnerability)
3 (moderate vulnerability)
Less than or equal to 2 (low or no
vulnerability)
Threshold-based Score: N/A Your Score: Not relevant
1 (lowest resilience)
2
3
4 (highest resilience)
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#194: Percent of natural area that is in small natural patches
Action Needed:
-Please decide if original or alternate data set is more appropriate.
-Please decide if you agree with threshold-based score and provide explanation if threshold-based
score is not chosen.
-Please assign an importance weight.
Definition: This indicator measures the percent of the total natural area in a city that is in patches of
less 10 acres in area. Smaller patches of natural habitat generally provide lower-quality habitat for
plants and animals and provide less solitude and fewer recreational opportunities for people. About
half of all natural lands in urban and suburban areas are in patches smaller than 10 acres.
Grouped with indicators: N/A
Dataset(s):
DC.gov GIS Data Catalog: http://data.dc.gov/Main_DataCatalog.aspx
1) National Parks
2) Recreation Parks
3) Wetlands - only non-riverine wetlands (palustrine, lacustrine).
4) Community Gardens
5) Wooded Areas
6) Existing land use - Parks and Open Spaces only
7) Existing land use - Water only
Notes on Dataset(s):
Union layers 1,2,3,4,5,6. Erase layer 7 from the resulting union. The area of the polygons resulting is
the total natural area.
Select all polygons from result with Shape_Area < 1 acre. Divide the sum of these polygons' areas by
the total natural area calculated above.
Total natural area = 10210.3 acres (41319581.2 mA2)
Total area of all natural patches less than 10 acres = 1600.3 acres (6476087.4 mA2)
Percent of natural area that is small natural patches = 15.7%
Indicator Value:
15.7%
Relevance: Not Sure Importance Weight: Proposed Resilience Score: 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
3
4 (very important)
Thresholds:
Threshold-based Score: 4
Your Score: Score not vet assigned
Greater than 80%
1 (lowest resilience)
1 (lowest resilience)
Greater than 60 to 80%
2
2
40 to 60%
3
3
Less than 40%
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#254: Ratio of perimeter to area of natural patches
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicator is calculated as the average ratio of the perimeter-to-area.
Grouped with indicators: N/A
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
N/A
Relevance: Not Sure Importance Weight: Proposed Resilience Score: 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
3
4 (very important)
Thresholds:
Threshold-based Score: N/A
Your Score : 3
Greater than 0.025 (unitless ratio)
1 (lowest resilience)
1 (lowest resilience)
Greater than 0.015 to 0.025
2
2
(unitless ratio)
0.005 to 0.015 (unitless ratio)
3
3
Less than 0.005 (unitless ratio)
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1440: Palmer Drought Severity Index
Definition: • Calculate potential evapotranspiration (PET) for selected time periods using temperature
data and the Thornthwaite equation.
• Find the precipitation deficit (precipitation minus PET) for the selected time period, where more
negative values indicate greatest precipitation deficit.
• Using a moving window sum, find, the 1,3,6, or 12 month period which had the greatest total
precipitation deficit.
Grouped with indicators: N/A
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Dataset(s):
1) National Weather Service - Palmer Drought Severity and Crop Moisture Indices
(http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/cdus/palmer_drought/)
Notes on Dataset(s):
1) Raw value based on an average of data for Climate Division 4 in MD (Upper Southern -
Montgomery County) and Climate Division 2 in VA (Northern - Arlington). There is no data for DC
specifically.
Indicator Value:
-0.13
Proposed Resilience Score Your Score
N/A 1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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SECONDARY INDICATORS
#308: % of land that is urban/suburban
Definition: This indicator presents the extent/acreage of urban and suburban areas as a percentage of
the total U.S. land area, for the most recent 50-year period and compared to pre-settlement estimates.
It also reports on a key component of freshwater ecosystems (freshwater wetlands) and will report on
the area of brackish water, a key component of coastal and ocean ecosystems when data become
available.
Grouped with indicators: #303, #825
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Dataset(s):
1) US Census Bureau - Percent Urban Land Use by State
(http ://www2 .census .gov/geo/ua/PctUrbanRural_State .xls)
2) DC.gov - Data Catalog (http://data.dc.gov/Main_DataCatalog.aspx)
Notes on Dataset(s):
1) Urban versus Rural land use. Used this source for raw value.
2) Contains GIS data, including overlays of density bins. Includes 19 land use types (alleys,
commercial, federal public, high density residential, industrial, institutional, local public, low-medium
density residential, low density residential, medium density residential, mixed use, parking, parks and
open spaces, "public, quasi-public, institutional", roads, "transport, communications, utilities",
transportation right of way, water)
Indicator Value:
100% Urban
Proposed Resilience Score
N/A
Importance Weights
1 (not very important)
2
3
4 (very important)
Your Score
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1369: Annual CV of unregulated streamflow
Definition: The coefficient of variation (CV) of unregulated streamflow is an indicator of annual
streamflow variability. It is computed as the ratio of the standard deviation of unregulated annual
streamflow (oQs) to the unregulated mean annual streamflow (QS)'. (Hurd et al., 1999). Measure of
variability in region's hydrology; standard deviation of regional annual internal water flow divided by
the mean annual internal water flow in each region (Lane et al., 1999). (Cumulative)
Grouped with indicators: #437
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Dataset(s):
1) USGS Hydro-Climatic Data Network (HCDN) dataset for 1931-1988, POTOMAC River
(http://pubs.usgs.gov/wri/wri934076/stations/01646502.html)
Notes on Dataset(s):
1) USGS HCDN has 1 site in DC, site "POTOMAC R (ADJUSTED NR WASH, DC" (number
01646502). Downloaded raw streamflow data from HCDN. Calculated as the average of the annual
CV of streamflow for all 58 years of data (note that a 'year' is from Oct 1-Sept 30). See file
ID437_HCDN_Streamflowdata_DC.xlsx
Indicator Value:
1.221
Proposed Resilience Score Your Score
N/A 1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1437: Percent of city area in 500-year floodplain
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator reflects the percent of the metropolitan area that lies within the 500-year
floodplain.
Grouped with indicators: #1436, #1438, #1439
Dataset(s):
1) District of Columbia - 2010 Floodplains (http://data.dc.gov/Metadata.aspx?id=48)
Notes on Dataset(s):
1) This GIS dataset describes the areas of 100- and 500-year floodplains as determined by the Federal
Emergency Management Agency.
Indicator Value:
11.00%
Relevance: Yes Importance Weight: 1 Proposed Resilience Score: 2
Thresholds: Threshold-based Score: 2 Your Score: 1
Greater than 30% 1 (lowest resilience) 1 (lowest resilience)
Greater than 10 to 30%
2 to 10%
Less than 2%
2
3
4 (highest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1438: Percent of city population in 100-year floodplain
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator reflects the percent of the city population living within the 100-year
floodplain.
Grouped with indicators: #1436, #1437, #1439
Dataset(s):
1) District of Columbia - 2010 Floodplains (http://data.dc.gov/Metadata.aspx?id=48)
2) US Census Bureau - District of Columbia 2010 Census blocks with population
(http://www2.census.gov/geo/tiger/TIGER2010BLKPOPHU/tabblock2010_ll_pophu.zip)
Notes on Dataset(s):
1) This GIS dataset describes the areas of 100- and 500-year floodplains as determined by the Federal
Emergency Management Agency.
2) This GIS dataset describes the population by Census block across the District of Columbia. By
cross-tabulating this layer with the 100-year floodplains, 1.60% of the total population was found to
be in the floodplain.
Indicator Value:
1.60%
Relevance: Yes Importance Weight: 2 Proposed Resilience Score: 1
Thresholds:
Threshold-based Score: 3
Your Score: 4
Greater than 20%
1 (lowest resilience)
1 (lowest resilience)
Greater than 5 to 20%
2
2
1 to 5%
3
3
Less than 1%
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1439: Percent of city population in 500-year floodplain
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator reflects the percent of the city population living within the 500-year
floodplain.
Grouped with indicators: #1436, #1437, #1438
Dataset(s):
1) District of Columbia - 2010 Floodplains (http://data.dc.gov/Metadata.aspx?id=48)
2) US Census Bureau - District of Columbia 2010 Census blocks with population
(http://www2.census.gov/geo/tiger/TIGER2010BLKPOPHU/tabblock2010_ll_pophu.zip)
Notes on Dataset(s):
1) This GIS dataset describes the areas of 100- and 500-year floodplains as determined by the Federal
Emergency Management Agency.
2) This GIS dataset describes the population by Census block across the District of Columbia. By
cross-tabulating this layer with the 500-year floodplains, 2.50% of the total population was found to
be in the floodplain.
Indicator Value:
2.50%
Relevance: Yes Importance Weight: 2 Proposed Resilience Score: 2
Thresholds:
Threshold-based Score: 3
Your Score: 4
Greater than 30%
1 (lowest resilience)
1 (lowest resilience)
Greater than 10 to 30%
2
2
2 to 10%
3
3
Less than 2%
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Natural Environment
The indicators below have been developed for the Natural Environment sector. Indicators that are related
are grouped together such that a single indicator from that group was considered a Primary Indicator
and the remaining were considered Secondary Indicators. Primary Indicators and Non-Grouped
Indicators are presented in the first half of this handout, followed by the Secondary Indicators.
Each indicator has a Definition. Each question is flagged with one or more of the following gradual
change climate stressor and/or extreme event climate stressor (from the urban resilience framework
developed for this project:
Where it was possible to identify a data set that would provide data for the indicator for Washington, DC,
Data Set(s) and associated Notes on Available Data are included. Indicators are assigned a Proposed
Resilience Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each indicator, please:
1. Discuss the Relevance of the indicator to the Natural Environment sector. (If unsure, please
select the Not Sure - Remind Me Later option). Indicators may be selected as Yes (relevant) on
the basis of the stressors previously selected as being most relevant to Washington, DC, or based
on any other criteria. Secondary Indicators may be considered, if the Primary Indicator is not
adequately defined or does not have available dataset(s).
2. When possible, Dataset(s) for Washington, D.C. are provided where data were available. In some
cases, no dataset(s) were identified. Please suggest dataset(s) that may be better than the
dataset(s) identified or where data gaps exist.
3. For indicators selected as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
4. Review the Proposed Resilience Score (if provided), which is on a scale of 1 = lowest resilience
to 4 = highest resilience, for the indicator. If you disagree with this score, please discuss Your
Score and indicate the reason for your disagreement.
Stressors
Gradual Changes
Extreme Events
Magnitude/ duration of heat waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
Wind Speed
Temperature
Precipitation
Sea Level Rise
This document is a draft for review purposes only and does not constitute Agency policy.
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1 PRIMARY INDICATORS & NON-GROUPED INDICATORS
2 #682: Percent change in bird population
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator reflects the number of species with "substantial" increases or decreases in
the number of observations (not a change in the number of species) divided by the total number of
bird species.
Grouped with indicators: #680, #681
Dataset(s):
1) DDOE - Wildlife Action Plan: (http://ddoe.dc.gov/publication/wildlife-action-plan)
Notes on Dataset(s):
1) Chapter 3, Table 4, page 45 indicates that of 249 known bird species in DC, 35 are on the District's
list of species of greatest conservation need.
35 bird species of greatest conservation need
249 total bird species
35/249= 14.1%
Values in columns N and O are appended with "decrease", as these 35 species are assumed to be rare
or declining.
Indicator Value:
-14.1%
(decrease)
Relevance: Yes
Importance Weight: 2
Proposed Resilience Score: 4
Thresholds:
Less than negative 66%
Negative 66 to 0%
Greater than 0 to 66%
Greater than 66%
Threshold-based Score: 2
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 3
1 (lowest resilience)
2
3
4 (highest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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#17: Altered Freshwater Ecosystems (percent miles changed)
Definition: This indicator of alteration reports the percentage of:
• Stream and river miles that have been leveed, channelized, or impounded behind a dam;
• Ponds and lake shoreline-miles that have agricultural or urban/suburban land cover within about 100
feet of the water's edge (reservoirs and constructed lakes are excluded);
• Riparian zone miles (the habitat at the edge of streams and rivers) that have agricultural or
urban/suburban land cover within about 100 feet of the water's edge; and
• Wetland acres that have been excavated, impounded, diked, partially drained, or farmed.
Grouped with indicators: N/A
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A
Indicator Value:
No data available.
Proposed Resilience Score
2
Your Score
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#66: Percent change in disruptive species
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicator reflects the percent change in disruptive species found in metropolitan
areas. Disruptive species are those that have negative effects on natural areas and native species or
cause damage to people and property.
Grouped with indicators: N/A
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A
Indicator Value:
N/A
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 1
Yes (relevant)
No (not relevant)
Thresholds:
Greater than 100%
Greater than 50 to 100%
10 to 50%
Less than 10%
Threshold-based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 1
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#273: Percent of total wildlife species of greatest conservation need
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator reflects the percent of total wildlife species that are listed as having the
"greatest conservation need."
Grouped with indicators: N/A
Dataset(s):
1) DDOE Wildlife Action Plan (http://ddoe.dc.gov/publication/wildlife-action-plan)
Notes on Dataset(s):
1) Lists 782 total species in DC, of which 148 are of "greatest conservation need" (148/782=18.9%)
Indicator Value:
18.90%
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 1
Thresholds:
Threshold-based Score: 2
Your Score: 3
Greater than 20%
1 (lowest resilience)
1 (lowest resilience)
Greater than 5 to 20%
2
2
1 to 5%
3
3
Less than 1%
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #284: Physical Habitat Index (PHI)
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: PHI includes 8 characteristics (riffle quality, stream bank stability, quantity of woody
debris, instream habitat for fish, suitability of stream bed surface materials for macroinvertebrates,
shading, distance to nearest road, and embededdness of substrates). Scores range from 0-100 (81-100
= minimally degraded, 66-80 = partially degraded, 51-65 = degraded, 0-50 = severely degraded).
Grouped with indicators: N/A
Dataset(s):
1) National Park Service - Biological Stream Survey Monitoring -
(http ://science .nature .nps.gov/im/units/ncrn/monitor/stream_survey/index.cfm)
Notes on Dataset(s):
1) Select PDF files for "Stream Physical Habitat" from The National Capital Region Network
(NCRN) Stream Physical Habitat Reports
PHI includes 8 characteristics (riffle quality, stream bank stability, quantity of woody debris, instream
habitat for fish, suitability of stream bed surface materials for macroinvertebrates, shading, distance to
nearest road, and embededdness of substrates). Raw value based on averaging the scores for available
sites (Catoctin - 61.67, GW Parkway - 67, and Prince William Park - 58.25) to develop one value for
DC.
Indicator Value:
PHI = 62.31
Relevance: Yes Importance Weight: 3 Proposed Resilience Score: 3
Thresholds:
Threshold-based Score: 2
Your Score: 1
0 to 50 (severely degraded)
1 (lowest resilience)
1 (lowest resilience)
61 to 65 (degraded)
2
2
66 to 80 (partially degraded)
3
3
81 to 100 (minimally degraded)
4 (highest resilience)
4 (highest resilience)
2
This document is a draft for review purposes only and does not constitute Agency policy.
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#326: Wetland species at risk (number of species)
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: Number of wetland and freshwater species at risk, either rare, threatened, or endangered.
Grouped with indicators: N/A
Dataset(s):
Government of the District of Columbia, Department of the Environment, Fisheries and Wildlife
Division, District of Columbia 2006 Wildlife Action Plan (http://green.dc.gov/publication/wildlife-
action-plan): PDF:
http://green.dc.gov/sites/default/files/dc/sites/ddoe/publication/attachments/Wildlife%20Action%20Pl
an%20Ch%204-5.pdf
Notes on Dataset(s):
Total number of unique species identified as being of "greatest conservation need" in the habitat
sections of emergent non-tidal wetlands (page 95), forested wetlands (page 103), emergent tidal
wetlands (page 109), tidal mudflats (page 115), and vernal pools (page 125).
Indicator Value:
62 wetland species of greatest conservation need
Relevance: Yes Importance Weight: 3 Proposed Resilience Score: 1
Thresholds:
Threshold-based Score: 3
Your Score: 1
Greater than 160 species at risk
1 (lowest resilience)
1 (lowest resilience)
100 to 160 species at risk
2
2
50 to less than 100 species at risk
3
3
Less than 50 species at risk
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#460: Macroinvertebrate Index of Biotic Condition
Definition: The Benthic Index of Biotic Integrity (BIB I) score is the average of the score of 10
individual metrics, including Total Taxa Richness, Ephemeroptera Taxa Richness, Plecoptera Taxa
Richness, Trichoptera Taxa Richness, Intolerant Taxa Richness, Clinger Taxa Richness and Percent,
Long-Lived Taxa Richness, Percent Tolerant, Percent Predator, and Percent Dominance.
Grouped with indicators: N/A
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Dataset(s):
1) National Park Service - Resource Brief on Macroinvertebrates at Rock Creek Park in 2012
(https://irma.nps.gov/App/Reference/DownloadDigitalFile?code=453985&file=ROCR_Macro_RB.pdf)
Notes on Dataset(s):
1) Raw value based on average of scores for all six locations
(Broad Branch: 1.33
Fenwick Branch: 1.33
Hazen Creek: 1.67
Luzon Branch: 1.67
Pinehurst Branch: 1.33
Soapstone Valley Stream: 2)
Indicator Value:
1.56
Proposed Resilience Score
3
Your Score
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#465: Change in plant species diversity from pre-European settlement
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: Change in the plant species diversity from pre-European settlement (baseline) to present,
within a given city/area.
Grouped with indicators: N/A
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A
Indicator Value:
N/A
Relevance: Yes
Importance Weight: 3
Proposed Resilience Score: 3
Thresholds:
Less than 0.2 Shannon Diversity
Index
0.2 to 0.4 Shannon Diversity Index
Greater than 0.4 to 0.6 Shannon
Diversity Index
Greater than 0.60 Shannon
Diversity Index
Threshold-based Score: N/A Your Score: 3
1 (lowest resilience)
2
3
4 (highest resilience)
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 SECONDARY INDICATORS
2 #680: Ecological Connectivity (Percent of area classified as hub or corridor)
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicator reflects the percent of the metropolitan area identified as a "hub" or
"corridor." Hubs are large areas of important natural ecosystems such as the Okefenokee National
Wildlife Refuge in Georgia and the Osceola National Forest in Florida. Corridors (i.e., "connections"
are links to support the functionality of the hubs (e.g., the Pinhook Swamp which connects the
Okefenokee and Osceola hubs).
Grouped with indicators: #681, #682
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
N/A
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 3
Thresholds: Threshold-based Score: N/A Your Score: 2
Less than 10%
10 to 25%
Greater than 25 to 50%
Greater than 50%
2
3
4 (highest resilience)
1 (lowest resilience)
2
3
4 (highest resilience)
1 (lowest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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#681: Relative Ecological Condition of Undeveloped Land
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicator characterizes the ecological condition of undeveloped land based on three
indices derived from criteria representing diversity, self-sustainability, the rarity of certain types of
land cover, species, and higher taxa (White and Maurice, 2004). In this context, "undeveloped land"
refers to all land use not classified as urban, industrial, residential, or agricultural.
Grouped with indicators: #680, #682
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
N/A
Relevance: Yes
Importance Weight: 4
Proposed Resilience Score: 4
Thresholds:
Less than 120 White and Maurice
Index score
120 to 180 White and Maurice
Index score
Greater than 180 to 230 White and
Maurice Index score
Greater than 230 White and
Maurice Index score
Threshold-based Score: N/A Your Score: 2
1 (lowest resilience)
1 (lowest resilience)
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
People
The indicators below have been developed for the People sector. Indicators that are related are grouped
together such that a single indicator from that group was considered a Primary Indicator and the
remaining were considered Secondary Indicators. Primary Indicators and Non-Grouped Indicators are
presented in the first half of this handout, followed by the Secondary Indicators.
Each indicator has a Definition. Each question is flagged with one or more of the following gradual
change climate stressor and/or extreme event climate stressor (from the urban resilience framework
developed for this project:
Where it was possible to identify a data set that would provide data for the indicator for Washington, DC,
Data Set(s) and associated Notes on Available Data are included. Indicators are assigned a Proposed
Resilience Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each indicator, please:
1. Discuss the Relevance of the indicator to the People sector. (If unsure, please select the Not Sure
- Remind Me Later option). Indicators may be selected as Yes (relevant) on the basis of the
stressors previously selected as being most relevant to Washington, DC, or based on any other
criteria. Secondary Indicators may be considered, if the Primary Indicator is not adequately
defined or does not have available dataset(s).
2. When possible, Dataset(s) for Washington, D.C. are provided where data were available. In some
cases, no dataset(s) were identified. Please suggest dataset(s) that may be better than the
dataset(s) identified or where data gaps exist.
3. For indicators selected as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
4. Review the Proposed Resilience Score (if provided), which is on a scale of 1 = lowest resilience
to 4 = highest resilience, for the indicator. If you disagree with this score, please discuss Your
Score and indicate the reason for your disagreement.
Stressors
Gradual Changes
Extreme Events
Magnitude/ duration of heat waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
Wind Speed
Temperature
Precipitation
Sea Level Rise
This document is a draft for review purposes only and does not constitute Agency policy.
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1
PRIMARY INDICATORS & NON-GROUPED INDICATORS
2 #676: Percent of population affected by notifiable diseases
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
-Please review indicator given that the name and definition have been amended.
Definition: This indicator reflects percent occurrence of notifiable diseases as reported by health
departments to the National Notifiable Diseases Surveillance System (NNDSS). A notifiable disease
is one for which regular, frequent, and timely information regarding individual cases is considered
necessary for the prevention and control of the disease (CDC, 2005b). The "notifiable diseases"
included are as follows: Chlamydia, Coccidioidomycosis, Cryptosporidiosis, Dengue Virus, E Coli,
Ehrlichiosis, Giardiasis, Gonorrhea, Haemophilus influenzae, Hep A, Hep B, Hep C, Legionellosis,
Lyme Disease, Malaria, Meningococcal Disease, Mumps, Pertussis (whooping cough), Rabies,
Salmonellosis, Shigellosis, Spotted Fever Rickettsiosis/Rocky Mountain Spotted Fever, Streptococcus
pneumoniae, Syphilis, Tuberculosis, Varicella (Chicken pox), and West Nile/Meningitis/Encephalitis.
Grouped with indicators: #322, #1171
Dataset(s):
1) Centers for Disease Control and Prevention - Morbidity and Mortality Weekly Report
(http://wonder.cdc .gov/mmwr/mmwrmorb .asp)
2) US Census Bureau - District of Columbia QuickFacts
(http ://quickfacts .census .gov/qfd/states/11000 .html)
Notes on Dataset(s):
An average of 8,085 cases of notifiable diseases were reported for each year from 2010 through 2012
in DC by the CDC. This is equivalent to 1.34% of the population of DC.
Indicator Value:
1.34%
Relevance: Yes
Importance Weight: 4
Proposed Resilience Score: 2
Thresholds:
Threshold-based Score: 3
Your Score: Score not vet assigned
Greater than 3 to 4%
1 (lowest resilience)
1 (lowest resilience)
Greater than 2 to 3%
2
2
1 to 2%
3
3
Less than 1%
4 (highest resilience)
4 (highest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #690: Emergency Medical Service Response Times
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
Definition: This indicator measures average annual response times (in minutes) for Emergency
Medical Service calls.
Grouped with indicators: #757, #784, #798
Dataset(s):
District of Columbia - Fire and Emergency Medical Services Department FY2013 Peformance Plan
(http://oca.dc.gov/sites/default/files/dc/sites/oca/publication/attachments/FEMS13.pdf)
Notes on Dataset(s):
The Key Performance Indicators table on page 4 lists the average response times for fire calls as 1
minute 52 seconds, and the average response times for medical emergencies as 4 minutes 42 seconds.
Indicator Value:
4.7 minutes
Relevance: Yes
Importance Weight: 4
Proposed Resilience Score: 4
Thresholds:
Threshold-based Score: 4
Your Score: 4
Greater than 12 minutes
1 (lowest resilience)
1 (lowest resilience)
Greater than 10 to 12 minutes
2
2
8 to 10 minutes
3
3
Less than 8 minutes
4 (highest resilience)
4 (highest resilience)
2
This document is a draft for review purposes only and does not constitute Agency policy.
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#1387: Percent of population vulnerable due to age
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator reflects percent of population above 65 or under 5 years old.
Grouped with indicators: #393, #728
Dataset(s):
1) US Census Bureau - Census 2010 population
Notes on Dataset(s):
32,613 underage 5; 68,809 age 65 and over. Total population = 601,723. Percent vulnerable = 16.9%
Indicator Value:
16.90%
Relevance: Yes
Importance Weight: 4
Proposed Resilience Score: 1
Thresholds:
Threshold-based Score: 2
Your Score: 1
Greater than 20%
1 (lowest resilience)
1 (lowest resilience)
Greater than 15 to 20%
2
2
10 to 15%
3
3
Less than 10%
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#209: Percent of population living within the 500-year floodplain
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen. Note that the percent population in the 100-year floodplain is
covered in another sector.
Definition: This indicator reflects percent of population living within the 500-year floodplain.
Grouped with indicators: N/A
Dataset(s):
1) District of Columbia - 2010 Floodplains (http://data.dc.gov/Metadata.aspx?id=48)
2) US Census Bureau - District of Columbia 2010 Census blocks with population
(http://www2.census.gov/geo/tiger/TIGER2010BLKPOPHU/tabblock2010_ll_pophu.zip)
Notes on Dataset(s):
1) This GIS dataset describes the areas of 100- and 500-year floodplains as determined by the Federal
Emergency Management Agency.
2) This GIS dataset describes the population by Census block across the District of Columbia. By
cross-tabulating this layer with the 500-year floodplains, the population in the floodplain was 15,147,
or 2.50% of the total population.
Indicator Value:
2.50%
Relevance: Yes
Importance Weisht:4
Proposed Resilience Score: 1
Thresholds:
Greater than 30%
Greater than 10 to 30%
2 to 10%
Less than 2%
Threshold-based Score: 3
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Score not yet
assigned
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#725: Number of physicians per capita
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
Definition: This indicator reflects the total number of MD Physicians and DO Physicians per capita.
Grouped with indicators: #717
Dataset(s):
Association of American Medical Colleges - 2011 State Physician Workforce Data Book
(https://www.aamc.org/download/263512/data)
Notes on Dataset(s):
This report from the Association of American Medicla Colleges details the number of physicians by
state as reported in the American Medical Association Physician Masterfile. Table 4 on page 19 lists
the number of active patient care (i.e. not medical research) primary care (i.e. not specialist) M.D. and
O.D. physicians for each state. DC has 1,110 M.D. and O.D. physicians for a population of 610,589.
This averages to 0.0018179 active patient care primary physicians per capita.
Indicator Value:
0.0018179 physicians per capita
Relevance: Yes Importance Weight: 3 Proposed Resilience Score: 3
Thresholds: Threshold-based Score: 1 Your Score: Score not yet assigned
Less than 0.02 physicians per 1 (lowest resilience) 1 (lowest resilience)
capita
0.02 to 0.03 physicians per capita
Greater than 0.03 to 0.04
physicians per capita
Greater than 0.04 physicians per
capita
2
3
4 (highest resilience) 4 (highest resilience)
2
3
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #1376: Percent of population that is disabled
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
-Please review amended importance weight.
Definition: This indicator reflects the percent of the non-institutionalized population that is disabled.
Disabled individuals are those who have one or more of the following:
Hearing difficulty (deaf or having serious difficulty hearing); Vision difficulty (blind or having
serious difficulty seeing, even when wearing glasses); Cognitive difficulty (having difficulty
remembering, concentrating, or making decisions because of a physical, mental, or emotional
problem); Ambulatory difficulty (serious difficulty walking or climbing stairs); Self-care difficulty
(difficulty bathing or dressing); and Independent living difficulty (difficulty doing errands because of
a physical, mental, or emotional problem).
Grouped with indicators: N/A
Dataset(s):
1) Selected Social Characteristics in the District of Columbia - 2009-2011 American Community
Survey 3-Year Estimates
(http://occ.dc.gov/DC/Planning/DC+Data+and+Maps/DC+Data/2011++ACS+3+Year+Estimates/Soci
al+Characteristics)
Notes on Dataset(s):
1) Percent of total civilian noninstitutionalized population with a disability
Indicator Value:
11.40%
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 2
1 (not very important)
2
3
4 (very important)
Thresholds:
Greater than 20%
Greater than 15 to 20%
10 to 15%
Less than 10%
Threshold-based Score: 3
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 1
1 (lowest resilience)
2
3
4 (highest resilience)
2
This document is a draft for review purposes only and does not constitute Agency policy.
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#1390: Percent of population that is living alone
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator reflects the percent of population that is 65 years or older and living alone.
Grouped with indicators: N/A
Dataset(s):
1) US Census Bureau - Fact Finder
(http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_ll_lYR_Bl
1007&prodType=table)
2) US Census Bureau - Fact Finder
(http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_ll_lYR_S0
201 &prodType=table)
3) US Census Bureau - Fact Finder
(http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=DEC_10_113_113
DP 1 &prodType=table)
Notes on Dataset(s):
1) Data based on 2011 American Community Survey. Provides population of people living alone (1-
person household) that are over 65 and under 65.
2) Data from 2011 American Community Survey. Look under "Households by Type" for percentage
of males and females living alone.
3) Data from 2010 Census - 8808 male + 17105 female age 65 and over living alone (total population
= 601,723, so 4.3%)
Indicator Value:
4.30%
Relevance: Yes
Importance Weight: 4
Proposed Resilience Score: 2
Thresholds:
Threshold-based Score: 4
Your Score: 2
Greater than 30%
1 (lowest resilience)
1 (lowest resilience)
Greater than 20 to 30%
2
2
10 to 20%
3
3
Less than 10%
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1443: Deaths per capita from extreme weather events
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator measures the number of deaths per capita in the last 5 years due to extreme
events (cold, flood, heat, lightning, tornado, tropical cyclone, wind, and winter storms).
Grouped with indicators: N/A
Dataset(s):
1) CDC - Deaths Associated with Hurricane Sandy — October-November 2012
(http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6220al.htm)
2) CNN - Officials: At least 43 killed as a result of Hurricane Irene
(http://www.cnn.com/201 l/US/08/30/irene.fatalities/index.html)
3) NOAA - Weather Fatalities (
http: //www .nws .noaa.gov/om/hazstats. shtml)
4) 2010 Census population
Notes on Dataset(s):
1) According to CDC website, there were no deaths associated with Hurricane Sandy in DC.
2) According to CNN, there were no deaths associated with Hurricane Irene in DC.
3) NOAA's web site gives deaths due to cold, flood, heat, lightning, tornado, tropical cyclone, wind,
and winter storm for each year. Based on this source, summing 2008-2012 (five year period) - only
one person, divided by 2010 census population (601,723)
Indicator Value:
0.0002%
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 2
Thresholds: Threshold-based Score: 4 Your Score: 3
Greater than 150 deaths per capita 1 (lowest resilience) 1 (lowest resilience)
Greater than 100 to 150 deaths per 2 2
capita
50 to 100 deaths per capita
Less than 50 deaths per capita
3
4 (highest resilience)
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 SECONDARY INDICATORS
2 #322: Percent of population affected by waterborne diseases
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator reports the percent of population affected by waterborne diseases.
Grouped with indicators: #676, #1171
Dataset(s):
1) Centers for Disease Control and Prevention - Morbidity and Mortality Weekly Report
(http://wonder.cdc .gov/mmwr/mmwrmorb .asp)
2) US Census Bureau - District of Columbia QuickFacts
(http ://quickfacts .census .gov/qfd/states/11000 .html)
Notes on Dataset(s):
An average of 115 cases of waterborne diseases were reported for each year from 2010 through 2012
in DC by the CDC. This is equivalent to 0.02% of the population of DC.
Indicator Value:
0.02%
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 1
Thresholds: Threshold-based Score: 3 Your Score: 1
Greater than 2%
Greater than 1 to 2%
Greater than 0 to 1%
0%
2
3
4 (highest resilience)
1 (lowest resilience)
2
3
4 (highest resilience)
1 (lowest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #393: Percent of vulnerable population that is homeless
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicator reflects the percent of population 65 and older and under 5 years that is
homeless.
Grouped with indicators: #728, #1157, #1170, #1387
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A
Indicator Value:
N/A
Relevance: Not Sure Importance Weight: Proposed Resilience Score: 1
Yes (relevant)
No (not relevant)
Thresholds:
Greater than 30%
Greater than 20 to 30%
10 to 20%
Less than 10%
1 (not very important)
2
3
4 (very important)
Threshold-based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#725: Medical Community
Definition: The number of MD Physicians and DO Physicians
Grouped with indicators: #717
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Dataset(s):
Association of American Medical Colleges - 2011 State Physician Workforce Data Book
(http s: //www. aamc. org/download/263512/data)
Notes on Dataset(s):
This report from the Association of American Medicla Colleges details the number of physicians by
state as reported in the American Medical Association Physician Masterfile. Table 4 on page 19 lists
the number of active patient care (i.e. not medical research) primary care (i.e. not specialist) M.D. and
O.D. physicians for each state. DC has 1,110 M.D. and O.D. physicians for a population of 610,589.
This averages to 0.0018179 active patient care primary physicians per capita.
Indicator Value:
0.0018179 physicians per capita
Proposed Resilience Score Your Score
3 1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #728: Adult Care (Homes per capita)
Action Needed:
-Please decide if original or alternate data set is more appropriate.
-Please decide if you agree with threshold-based score and provide explanation if threshold-based
score is not chosen.
Definition: The number of Adult Day Care Homes and Assisted Living Homes per capita of
population >65 years.
Grouped with indicators: #393. #1157, #1170, #1387
Dataset(s):
1) Alternatives for Seniors - Adult Day Care search (https://www.alternativesforseniors.com/adult-
day-care/dc/washington?radius=10)
2) Alternatives for Seniors - Assisted Living search (https://www.alternativesforseniors.com/assisted-
living/dc/washington?page=1 &radius=10)
3) District of Columbia - Census 2010 Age Groups By Ward
(http://occ.dc.gov/DC/Planning/DC+Data+and+Maps/DC+Data/Tables/Data+by+Geography/Census+
Tracts/Census+2010+Age+Groups+By+Ward)
Notes on Dataset(s):
1) There are 9 adult day care facilities within 10 miles of DC.
2) There are 42 assisted living facilities within 10 miles of DC.
3) The population age 65 and older in DC is 68,809.
Indicator Value:
0.0007 homes per capita
Alternate Dataset(s):
(1) The District of Columbia Office on Aging (DCOA) - SENIOR NEEDS ASSESSMENT INITIAL
DATA COLLECTION 9/5/2012 -
(http://dcoa.dc.gov/sites/default/files/dc/sites/dcoa/publication/attachments/DCOA%2520Senior%252
0Needs%2520Assessment%252010-12.pdf)
(2) District of Columbia - Census 2010 Age Groups By Ward
(http://occ.dc.gov/DC/Planning/DC+Data+and+Maps/DC+Data/Tables/Data+by+Geography/Census+
Tracts/Census+2010+Age+Groups+By+Ward)
Notes on Alternate Dataset(s):
(1) DCOA reports 50 assisted living apartment developments totaling over 7,000 units.
(2) The population age 65 and older in DC is 68,809.
Normalized - 50/68,809 = 0.00073
Alternate Indicator Value:
0.00073 homes per capita senior population
Relevance: Yes Importance Weight: Proposed Resilience Score: 4
1 (not very important)
2
3
4 (very important)
This document is a draft for review purposes only and does not constitute Agency policy.
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Thresholds:
Less than 0.00010 adult
homes per capita of elderly
population
0.00010 to 0.00020 adult
homes per capita of elderly
population
Greater than 0.00020 to
0.00040 adult homes per
capita of elderly population
Greater than 0.00040 adult
homes per capita of elderly
population
Threshold-based Score: 4
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
#757: Average police response time
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
Definition: This indicator reflects the average response time for police to respond to emergency
situations.
Grouped with indicators: #690, #784, #798
Dataset(s):
1) District of Columbia - Metropolitan Police Department FY 2013 Performance Plan
(http://oca.dc.gov/sites/default/files/dc/sites/oca/publication/attachments/MPD13.pdf)
Notes on Dataset(s):
The Key Performance Indicators table on page 4 lists the average response times for calls as 5.7
minutes.
Indicator Value:
5.7 minutes
Relevance: Yes Importance Weight: Proposed Resilience Score: 4
1 (not very important)
2
3
4 (very important)
Thresholds:
Threshold-based Score: 4
Your Score: Score not vet
assigned
Greater than 12 minutes
1 (lowest resilience)
1 (lowest resilience)
Greater than 10 to 12 minutes
2
2
8 to 10 minutes
3
3
Less than 8 minutes
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#784: Number of sworn police officers per capita
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator is calculated by dividing the number of sworn police officers by the total
population. We multiply the result by 1,000. According to the FBI, sworn officers meet the following
criteria: "they work in an official capacity, they have full arrest powers, they wear a badge
(ordinarily), they carry a firearm (ordinarily), and they are paid from governmental funds set aside
specifically for payment of sworn law enforcement representatives." In counties with relatively few
people, a small change in the number of officers may have a significant effect on rates from year to
year.
Grouped with indicators: #690, #757, #798
Dataset(s):
1) District of Columbia - Metropolitan Police Department Annual Report 2012
(http://mpdc.dc.gov/sites/default/files/dc/sites/mpdc/publication/attachments/2012_AR_l.pdf)
2) US Census Bureau - American Community Survey 2011 3-year estimates
(http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_ll_3YR_D
P02&prodType=table)
Notes on Dataset(s):
The Total Personnel table on page 34 of the DC MPD Annual Report lists 3,814 sworn personnel.
This is 0.60% of the 2011 3-year ACS population of 605,045.
Indicator Value:
0.60%
Relevance: Yes
Importance Weight: 3 Proposed Resilience Score: 3
Thresholds:
Less than 0.10 police officers per
capita
0.10 to 0.20 police officers per
capita
Greater than 0.20 to 0.50 police
officers per capita
Greater than 0.50 police officers
per capita
Threshold-based Score: 4 Your Score: 3
1 (lowest resilience) 1 (lowest resilience)
2
4 (highest resilience)
3
2
4 (highest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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#798: Fire Response Times
Definition: Percentage of fire response times less than 6 minutes (from city stations to city
locations).
Grouped with indicators: #690, #757, #784
Dataset(s):
District of Columbia - Fire and Emergency Medical Services Department FY2013 Peformance Plan
(http://oca.dc.gov/sites/default/files/dc/sites/oca/publication/attachments/FEMS13.pdf)
Notes on Dataset(s):
The Key Performance Indicators table on page 4 lists EMTs arriving to 86.51% of medical calls
within 6.5 minutes and fire trucks responding to 98.19% of fire calls within 6.5 minutes.
Indicator Value:
98.19%
Proposed Resilience Score Your Score
4 1 (lowest resilience)
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1157: Percent of housing units with air conditioning
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
Definition: This indicator reflects the percent of housing units with air conditioning.
Grouped with indicators: #393, #782, #1170, #1387
Dataset(s):
1) US Census Bureau - American Housing Survey for the Washington Metropolitan Area: 2007
(http://www.census.gov/housing/ahs/files/washington07.pdf)
Notes on Dataset(s):
Table 1-4 on page 5 says that 1,881,300 housing units have central AC, while 212,700 housing units
have 1 or more room AC units. Combined, this is 98.15% of 2,133,500 total housing units.
Indicator Value:
98.15%
Relevance: Yes
Importance Weight:
1 (not very important)
2
3
4 (very important)
Proposed Resilience Score: 3
Thresholds:
Threshold-based Score: 4
Your Score: Score not vet assigned
Less than 70%
1 (lowest resilience)
1 (lowest resilience)
70 to 88%
2
2
Greater than 88 to 94%
3
3
Greater than 94%
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #1170: Percent of population experiencing heat-related deaths
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator reflects the percent of the population experiencing heat-related deaths.
Grouped with indicators: #393, #782, #1157, #1387
Dataset(s):
1) NOAA - Weather Fatatlities (http://www.nws.noaa.gov/om/hazstats.shtml)
2) 2010 Census population
Notes on Dataset(s):
1) 22 heat-related deaths between 1995 and 2012
2) 601,723 population of DC from 2010 Census
Annual heat-related deaths = 22/17 years =1.3 heat-related deaths/year. % heat-related deaths per
capita = 1.3/601723 = 0.0002% annually
Indicator Value:
0.0002%
Relevance: Yes
Importance Weight: 4
Proposed Resilience Score: 2
Thresholds:
Threshold-based Score: 4
Your Score: 2
Greater than 2.0%
1 (lowest resilience)
1 (lowest resilience)
Greater than 1.0 to 2.0%
2
2
0.5 to 1.0%
3
3
Less than 0.5%
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1171: Percent of population affected by food poisoning
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicator reflects the percent of population affected by food poisoning (i.e.,
Salmonella spp, unsafe drinking water).
Grouped with indicators: #322, #676
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A
Indicator Value:
N/A
Relevance: Yes Importance Weight: Proposed Resilience Score: 2
1 (not very important)
2
3
4 (very important)
Thresholds: Threshold-based Score: N/A
Greater than 20% 1 (lowest resilience)
Greater than 15 to 20% 2
10 to 15% 3
Less than 10% 4 (highest resilience)
Your Score: 1
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Transportation
The indicators below have been developed for the Transportation sector. Indicators that are related are
grouped together such that a single indicator from that group was considered a Primary Indicator and
the remaining were considered Secondary Indicators. Primary Indicators and Non-Grouped Indicators
are presented in the first half of this handout, followed by the Secondary Indicators.
Each indicator has a Definition. Each question is flagged with one or more of the following gradual
change climate stressor and/or extreme event climate stressor (from the urban resilience framework
developed for this project:
Where it was possible to identify a data set that would provide data for the indicator for Washington, DC,
Data Set(s) and associated Notes on Available Data are included. Indicators are assigned a Proposed
Resilience Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each indicator, please:
1. Discuss the Relevance of the indicator to the Transportation sector. (If unsure, please select the
Not Sure - Remind Me Later option). Indicators may be selected as Yes (relevant) on the basis of
the stressors previously selected as being most relevant to Washington, DC, or based on any other
criteria. Secondary Indicators may be considered, if the Primary Indicator is not adequately
defined or does not have available dataset(s).
2. When possible, Dataset(s) for Washington, D.C. are provided where data were available. In some
cases, no dataset(s) were identified. Please suggest dataset(s) that may be better than the
dataset(s) identified or where data gaps exist.
3. For indicators selected as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
4. Review the Proposed Resilience Score (if provided), which is on a scale of 1 = lowest resilience
to 4 = highest resilience, for the indicator. If you disagree with this score, please discuss Your
Score and indicate the reason for your disagreement.
Stressors
Gradual Changes
Extreme Events
Magnitude/ duration of heat waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
Wind Speed
Temperature
Precipitation
Sea Level Rise
This document is a draft for review purposes only and does not constitute Agency policy.
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1
PRIMARY INDICATORS & NON-GROUPED INDICATORS
2 #988: Walkability Score
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator reflects the walkability score of the community (points out of 100).
Grouped with indicators: #987, #1396, #1417
Dataset(s):
1) WalkScore - Washington DC Score (http://www.walkscore.eom/DCAVashington_D.C.)
Notes on Dataset(s):
1) website on walkability. DC score of 73 meaning that it is very walkable and most errands can be
accomplished on foot. As a comparison, NYC has a walk score of 98, Phoenix is 45, Chicago is 74,
Boston is 79, and Dallas is 47.
Indicator Value:
73 score "very walkable"
Relevance: Yes
Importance Weight: 3
Proposed Resilience Score: 4
Thresholds:
Threshold-based Score: 3
Your Score : 4
0 to 49 "car dependent"
1 (lowest resilience)
1 (lowest resilience)
50 to 69 "somewhat walkable"
2
2
70 to 89 "very walkable"
3
3
90 to 100 "walker's paradise"
4 (highest resilience)
4 (highest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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#1402: Total annual hours of rail line closure due to heat and maintenance problems
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicator measures: (1) Total annual hours that rail lines within the metropolitan
transit system are closed due to heat kinks and (2) Total annual hours that transit vehicles are unable
to operate due to maintenance problems associated with extreme heat stress.
Grouped with indicators: #1410
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A
Indicator Value:
N/A
Relevance: Yes
Yes (relevant)
No (not relevant)
Thresholds:
Greater than 6 hours
3 to 6 hours
1 to 3 hours
Less than 1 hours
Importance Weight: 4
Threshold-based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Proposed Resilience Score: 1
Your Score: 2
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1404: Percent of city culverts that are sized to meet future stormwater capacity requirements
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicator measures the percent of current culverts that cross transportation facilities in
the metropolitan region that are sized to meet projected stormwater capacity requirements for 2030.
Grouped with indicators: #1403
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
N/A
Relevance: Not Sure Importance Weight: Proposed Resilience Score: N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
3
4 (very important)
Thresholds: Threshold-based Score: N/A
Less than 70% 1 (lowest resilience)
70 to 85% 2
Greater than 85 to 95% 3
Greater than 95% 4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1412: Connectivity of pedestrian facilities
Definition: Miles of pedestrian facilities per capita.
Grouped with indicators: #1413
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Dataset(s):
1) DC.gov - Data Catalog (data.dc.gov)
2) Transportation for America - Dangerous By Design: Metro Area Pedestrian Safety Rankings by
State (http://t4america.Org/resources/dangerousbydesign2009/metroranking/#dc)
3) WalkScore - Website on Walkability (http://www.walkscore.eom/DC/Washington_D.C.)
4) Washington DC GIS database: http://dcatlas.dcgis.dc.gov/catalog/results.asp
Notes on Dataset(s):
1) Requires researcher to have appropriate software to open files. Enter key words into search at
bottom of page under "Browse Catalog" and get shape file for sidewalks or pedestrian walkways.
2) Study on pedestrian safety by state.
3) Website on walkability, bikability... in DC.
4) Raw Value determined based on 743991.81 meters of sidewalk in DC (2326.41 miles). There are
632,323 people in DC, which equals 0.004 miles of sidewalk per person.
Divide SHAPE_LEN field by 2, which gives a reliable estimate of the of length of each sidewalk
Indicator Value:
0.004 miles of sidewalk per person.
Proposed Resilience Score Your Score
3 1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1420: Intermodal passenger connectivity (percent of terminals with at least one intermodal
connection for the most common mode)
Definition: Intermodal connections, which allow passengers to use a combination of modes, give
travelers additional transportation alternatives that unconnected, parallel systems do not offer.
Intermodal passenger terminals not only facilitate connectivity for travelers, but also enhance the
livability of communities by offering multiple transportation options to residents. This indicator tracks
the percentage of active passenger terminals for the most common mode (e.g., rail, air, etc.) with at
least one intermodal passenger connection.
Grouped with indicators: #1419
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
No data available.
Proposed Resilience Score Your Score
4 1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#985: Transport system user satisfaction
Definition: Overall transport system user satisfaction ratings.
Grouped with indicators: N/A
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Dataset(s):
1) US Census Bureau - Fact Finder
(http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_ll_lYR_S080
2&prodType=table)
2) US Census Bureau - Fact Finder
(http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_ll_lYR_S080
1 &prodType=table)
3) Federal Highway Administration - PARTNERS IN MOTION AND CUSTOMER SATISFACTION
IN THE WASHINGTON, D C METROPOLITAN AREA
(http ://ntl .bts .gov/lib/j podocs/rept_mis/9909 .pdf)
4) WMATA - Vital Signs Report
(http://www.wmata.com/about_metro/docs/Vital_Signs_July_2010.pdf)
5) Washington Metropolitan Area Transit Authority - Customer Satisfaction Survey Results
(http://www.wmata.com/about_metro/board_of_directors/board_docs/120612_4CCustomerSurvey.pdf)
Notes on Dataset(s):
1) Census data indicates 30.1 min travel time to work, includes travel time by type of transportation (not
satisfaction)
2) Census data with commuting characteristics
3) 1999 study on transportation satisfaction in DC.
4) 2010 report on Metro performance - includes on-time stats
5) 2012 customer satisfaction survey results. The satisfaction scores are as follows:
Bus service: 84%
Rail service: 80%
Accuracy of Passenger Information Displays: 74%
Average of these is 79.3%
Indicator Value:
79.3% satisfaction
Proposed Resilience Score Your Score
3 1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #991: Transport diversity
Definition: Variety and quality of transport options available in a community.
Grouped with indicators: N/A
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A
Indicator Value:
No data available.
Proposed Resilience Score Your Score
4 1 (lowest resilience)
2
3
4 (highest resilience)
2
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #1003: Mobility management (yearly congestion costs saved by operational treatments per capita)
Definition: Implementation of mobility management programs can address problems and increase
transport system efficiency. This indicator reports on the yearly congestion costs saved by operational
treatments (in billions of 2011$). Operational treatments include: Freeway incident management,
freeway ramp metering, arterial street signal coordination, arterial street access management, and high
occupancy
vehicle lanes.
Grouped with indicators: N/A
Relevance Importance Weights
Dataset(s):
1) Texas A&M - Urban Mobility Report
(http://d2dtl5nnlpfr0r.cloudfront.net/tti.tamu.edu/documents/mobility-report-2012.pdf)
Notes on Dataset(s):
1) Texas A&M Urban Mobility report. DC ranked worst for congestion - p 24. See indicator 1426
for more information about rebuttal of this study.
Table 8 page 50 - $298.3 million in 2011 operational treatment savings - relative to $356.3 for very
large (> 3 million population) areas.
Indicator Value:
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
2
3
4 (very important)
1 (not very important)
$495/person
Proposed Resilience Score
Your Score
1 (lowest resilience)
3
2
3
4 (highest resilience)
2
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #1010: Community livability
Definition: Degree to which transport activities support community livability objectives (local
environmental quality).
Grouped with indicators: N/A
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A
Indicator Value:
No data available.
Proposed Resilience Score Your Score
3 1 (lowest resilience)
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
2
3
4 (highest resilience)
2
This document is a draft for review purposes only and does not constitute Agency policy.
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#1399: Number of roadway/rail miles, other transportation facilities within x feet from coast
Definition: Miles of unarmored or unreinforced roadway or miles of rail lines that are with 10 vertical
feet of the Mean High Water elevation.
Grouped with indicators: N/A
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Dataset(s):
1) ICF International - The Potential Impacts of Global
Sea Level Rise on Transportation
Infrastructure (http://www.bv.transports .gouv.qc.ca/mono/0965210 .pdf)
2) DC.gov - DC GIS Data Clearinghouse/Catalog (http://dcatlas.dcgis.dc.gov/catalog/results.asp)+L68
Notes on Dataset(s):
1) Study on potential impact of rising sea level and storm surge on transportation. Example map is for
DC on page 15.
2) GIS data. Shapefiles include Roads, railroads, water bodies, and 2010 FEMA floodplain dataGIS
data (roads and railroads layer). Types of roads selected from roads layer = "alley", "hidden road",
"paved drive", and "road". Each road polygon was divided by 2 to account for conversion from
perimeter to length.
Number of miles of rail within 10 feet of a coast line (in this case a water body).
Rail = 9 miles intersect with a 10 foot buffer of water bodies
Road =104 miles intersect with a 10 foot buffer of water bodies
Indicator Value:
Rail - 9 miles within 10 feet of water
Road - 104 miles within 10 feet of water
Proposed Resilience Score
N/A
Your Score
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1400: Number of roadway/rail miles, other transportation facilities within the 500 year floodplain
Definition: Total miles of all roadway or total miles of all rail lines that lie within the 500 year
floodplain.
Grouped with indicators: N/A
Dataset(s):
1) FEMA - Maps
(https://msc.fema.gov/webapp/wcs/stores/servlet/mapstore/homepage/MapSearch.html)
2) DC.gov - AtlasPlus (http://atlasplus.dcgis.dc.gov/)
3) ID 1400 ID1401 MapFloodRailStreet DC
Notes on Dataset(s):
1) FEMA maps - Site takes you to home page, need to enter location
2) DC mapping tool. Allows overlay of flood plain and rail and roads
3) Word file with print screen of map from above site.
Number of miles of rail or road within the 500 year floodplain.
Rail = 12 miles that intersect with the 500 year flood plain
Road = 194 miles that intersect with the 500 year flood plain
Indicator Value:
Rail - 12 miles
Road -194 miles
Proposed Resilience Score Your Score
2 1 (lowest resilience)
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1401: Miles of roadway/rail miles subject to inundation in the 100 year flood
Definition: N/A
Grouped with indicators: N/A
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Dataset(s):
1) FEMA - Maps
(https://msc.fema.gov/webapp/wcs/stores/servlet/mapstore/homepage/MapSearch.html)
2) DC.gov - AtlasPlus (http://atlasplus.dcgis.dc.gov/)
3) ID 1400 ID1401 MapFloodRailStreet DC
Notes on Dataset(s):
1) FEMA maps - Site takes you to home page, need to enter location
2) DC mapping tool. Allows overlay of flood plain and rail and roads
3) Word file with print screen of map from above site.
Number of miles of rail or road within the 500 year floodplain.
Rail = 12 miles that intersect with the 500 year flood plain
Road =194 miles that intersect with the 500 year flood plain
Indicator Value:
Rail - 12 miles
Road - 194 miles
Proposed Resilience Score Your Score
2 1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1406: Percent decline in repeat maintenance events
Action Needed:
-Please assign an importance weight.
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
Definition: This indicator measures the percent decline in repeat maintenance events, thereby
representing a stable transportation system. The most recent transportation bill states that roadways and
bridges subject to repeat maintenance must be studied so as to avoid repeated use of emergency funds for
infrastructure that keeps getting damaged.
Grouped with indicators: N/A
Dataset(s):
Used expenditures on maintenance from 2006-2012 as a proxy for this indicator.
1) District Department of Transportation, Operating Appendices (http://cfo.dc.gov/node/289642)
2) Washington Metropolitan Area Transit Authority, Approved 20XX Annual Budget
(http://www.wmata.com/about_metro/public_rr.cfm)
3) U.S. Department of Labor Bureau of Labor Statistics, Consumer Price Index
(ftp ://ftp .bls.gov/pub/special .requests/cpi/cpiai .txt)
Notes on Dataset(s):
Summed the "preventive & routine roadway maintenance" and "street and bridge maintenance" (if
available) from the DDOT appendices for each year, and combined with the "preventive maintenance"
(for MetroBus, MetroRail, and MetroAccess) from the WMATA annual budgets. Inflated each year's
dollars to 2012 dollars, and then calculated the percent change from year to year in total dollars spent on
maintenance by the DDOT and WMATA before averaging this percentage change.
All values inflated to 2012 dollars.
2006: $0,784 M (DDOT) + $23.57 M (WMATA)
2007: $1.57 M (DDOT) + $22.92 M (WMATA) (+1%)
2008: $93.45 M (DDOT) + $22.07 M (WMATA) (+372%)
2009: $49.18 M (DDOT) + $22.15 M (WMATA) (-38%)
2010: $37.17 M (DDOT) + $32.32 M (WMATA) (-3%)
2011: $5.24 M (DDOT) + $61.96 M (WMATA) (-3%)
2012: $4.49 M (DDOT) + $30.70 M (WMATA) (-48%)
Average percent change: +47%
Indicator Value:
47%
Relevance: Yes Importance Weight: Proposed Resilience Score: N/A
1 (not very important)
2
3
4 (very important)
Thresholds:
Less than 10%
10 to 25%
Greater than 25 to 50%
Greater than 50%
Threshold-based Score: 3
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
#1408: Number of structurally deficient bridges (Source: National Bridge Inventory)
This document is a draft for review purposes only and does not constitute Agency policy.
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Definition: Bridges are considered structurally deficient if significant load-carrying elements are
found to be in poor or worse condition due to deterioration and/or damage, or the adequacy of the
waterway opening provided by the bridge is determined to be extremely insufficient to the point of
causing intolerable traffic interruptions. Structural assessments together with condition ratings
determine whether a bridge should be classified as structurally deficient. Condition ratings range from
0 (failed) through 9 (excellent). Condition ratings of 4 and below indicate poor or worse conditions
and result in structural deficiencies.
Grouped with indicators: N/A
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Dataset(s):
1) Transportation for America - The Fix We're In For: The State of Our Bridges
(http://t4america.org/resources/bridges/#?latlng=38.90723089999999,-
77.03646409999999&bridge_id= )
2) Transportation for America - The Fix We're In For: The State of Our Bridges
(http://t4america.org/resources/bridges/states/?state=dc)
Notes on Dataset(s):
1) Bridges located on map. Structurally deficient in red. DC ranked 16th with 31 deficient bridges
2) 31 bridges deemed structurally deficient (12.8%) in DC as of 2012. This ranks DC as 16th out of
51 US states. The highest ranked state is PA (24.5 % deficiency rate) while Florida is the lowest
ranked state (2.2%).
Indicator Value:
12.8% deficient bridges
Proposed Resilience Score Your Score
1 1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1411: Roadway connectivity (number of intersections per square mile)
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator measures the number of intersections per square mile.
Grouped with indicators: N/A
Dataset(s):
1) DC.gov - DC GIS Data Clearinghouse/Catalog (http://dcatlas.dcgis.dc.gov/catalog/results.asp)
Notes on Dataset(s):
1) Roads GIS layer indicates there are 7385 intersections in DC. The size of DC is 68.3 sq. mi. The
number of intersections per sq. mi. is 7385/68.3 = 108 intersections per sq. mi.
Indicator Value:
108 intersections per sq mi
Relevance: Yes
Importance Weight: 3
Proposed Resilience Score: 3
Thresholds:
Threshold-based Score: 2
Your Score: 3
Less than 80 intersections per
1 (lowest resilience)
1 (lowest resilience)
square mile
80 to 250 intersections per square
2
2
mile
Greater than 250 to 290
3
3
intersections per square mile
Greater than 290 intersections per
4 (highest resilience)
4 (highest resilience)
square mile
This document is a draft for review purposes only and does not constitute Agency policy.
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#1422: Average distance of all non-work trip distances
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicator measures the average of the distances from a given home to the nearest
grocery store, high school, and healthcare facility (i.e., non-work trips).
Grouped with indicators: N/A
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
N/A
Relevance: Yes Importance Weight: 3 Proposed Resilience Score:
Yes (relevant)
No (not relevant)
Thresholds:
Less than 5 miles
5 to 10 miles
Greater than 10 to 30 miles
Greater than 30 miles
Threshold-based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #1424: Roundabouts
Definition: N/A
Grouped with indicators: N/A
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Dataset(s):
1) Wikipedia - List of Circles in Washington DC
(http://en.wikipedia.org/wiki/List_of_circles_in_Washington,_D.C.)
2) Georgia Instistute of Technology - AN ANALYTICAL REVIEW OF STATEWIDE
ROUNDABOUT PROGRAMS AND POLICIES
(https://smartech.gatech.edu/bitstream/handle/1853/37285/pochowski_alek_l_201012_mast.pdf7sequenc
3) DC.gov - DC GIS Data Clearinghouse/Catalog (http://dcatlas.dcgis.dc.gov/catalog/results.asp)
4) DC.gov - District Department of Transportation Traffic Volume Map 2010
(http://dc.gov/portal/site/DC/menuitem.08af0b 147702eefl 85a5351092509ca0/?vgnextoid=e 126c8142a9
8a31 OVgnVCM 1000002905c90aRCRD&vgnextchannel=6fc 126da972c5210VgnVCM2000007f6f0201
RCRD)
Notes on Dataset(s):
1) Wikipedia list of circles in DC
2) Masters thesis on value of roundabouts and summary of state programs. See pages 28, 42, 48, 54 and
60 and appendix A for state-specific information. Data set on page 42 used for Raw Value.
4) Data on DC traffic volume, containing values for some roundabouts.
Indicator Value:
e=l)
3e-005
Proposed Resilience Score
Your Score
1 (lowest resilience)
3
2
3
4 (highest resilience)
2
This document is a draft for review purposes only and does not constitute Agency policy.
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#1426: System reliability (congestion) freight and people
Definition: Variation in travel time for the same trip from day to day ("same trip" implies the same
purpose, from the same origin, to the same destination, at the same time of the day, using the same
mode, and by the same route).
Grouped with indicators: N/A
Dataset(s):
1) INRIX - Traffic Scorecard (http://scorecard.inrix.com/scorecard/default.asp)
2) INRIX Scorecard Word File (ID1426_INRIXscorecard_DC.docx)
3) Texas A&M -= Urban Mobility Report
(http://d2dtl5nnlpfr0r.cloudfront.net/tti.tamu.edu/documents/mobility-report-2012.pdf)
4) Victoria Transport Policy Institute - Congestion Costing Critique
Critical Evaluation of the "Urban Mobility Report"
29 August 2013 (http://www.vtpi.org/UMR_critique.pdf)
5) CEOs for Cities - Driven Apart - (http://www.ceosforcities.org/research/driven-apart/)
Notes on Dataset(s):
1) INRIX scorecard - ranks DC 13th most congested metro in US.
2) Word file from INRIX website (above) with DC-specific data
3) Texas A&M Transportation Institute "Urban Mobility Report". DC ranked worst for congestion.
See p 24
4) Rebuttal of Texas Transportation Institute's study above. See pages 9 and 20 for DC-specific info.
5) Also a rebut of Texas Transportation Institute's methodology by "Driven Apart" summarizing... "It
reveals how sprawl is lengthening our commutes and why misleading mobility measures are making
things worse by suggesting more highways are the solution."
Indicator Value:
13th most congested Metro in US
Proposed Resilience Score Your Score
4 1 (lowest resilience)
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1429: Number of telecommuters or the potential number of telecommuters (companies that allow
it and the total number of employees in these companies)
Definition: Percent of jobs within the metropolitan region that could be accomplished by telecommuting
if employer policies were to permit it.
Grouped with indicators: N/A
Dataset(s):
1) Sperlings Best Places - Washington, D.C. Ranked Best City for Teleworking
(http://dc.about.com/gi/o.htm?zi=l/XJ&zTi=l&sdn=dc&cdn=citiestowns&tm=35&gps=360_404_1194
_815&f=00&su=p284.13.342.ip_p554.23.342.ip_&tt=2&bt=9&bts=9&zu=http%3A//www.bestplaces.n
et/docs/studies/telework06.aspx)
2) Telecommute News - Summer Storms Reveal Need for Federal Telework Programs
(http://www.telecommutenews.com/current_telecommuting_news/summer-storms-reveal-need-for-
federal-telework-programs/)
Notes on Dataset(s):
1) Sperlings Best Places ranks DC as the number one teleworking extra large metro area in the US
(72.1% of office workers). For comparison, NYC is ranked 6 (65.8%), Phoenix is 15 (62.9%), Chicago
is 4 (63.9%), Boston is 2 (69.7%), and Dallas is 8 (65.4%). For Sperling's data sources, see this site
(http: //www .bestplace s .net/docs/datasource. aspx)
Raw value based on ranking
2) Site mentions Washington Post article about value of telecommute particularly during emergencies.
Also notes Telework Act of 2010
Indicator Value:
1st for extra-large metro areas
Proposed Resilience Score Your Score
2 1 (lowest resilience)
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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SECONDARY INDICATORS
#987: Employment Accessibility (mean travel time to work relative to national)
Definition: This indicator is defined as the mean travel time to work in a city relative to the U.S.
average.
Grouped with indicators: #988, #1396, #1417
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Dataset(s):
1) US Census Bureau - State and County Quick Facts
(http ://quickfacts .census .gov/qfd/states/11000 .html)
2) The Brookings Institute - Washington-Arlington-Alexandria, DC-VA-MD-WV Metro
(http://www.brookings.edU/~/media/Series/jobs%20and%20transitAVashingtonDC.PDF)
Notes on Dataset(s):
1) Census - Mean travel time to work (minutes), workers age 16+, 2007-2011: 29.6 min (25.4 for US);
so mean travel time as a ratio with nat'l. average = 29.6/25.4 = 1.16
2) contains information the share of all jobs reachable via transit in 90 minutes
Indicator Value:
1.16 ratio to national average
Proposed Resilience Score
4
Importance Weights
1 (not very important)
2
3
4 (very important)
Your Score
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1396: Access to transportation
Definition: % of the population that has necessary access to Single Occupant Vehicle, transit, or
paratransit services, or who can bike or walk, to meet their daily needs to access food, work,
education,
healthcare, businesses and services as well as other needs.
Grouped with indicators: #988, #1417
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Dataset(s):
1) US Census Bureau - State and County Quick Facts
(http ://quickfacts .census .gov/qfd/states/11000 .html)
2) The Brookings Institute - Missed Opportunity: Transit and Jobs in Metropolitan America
(http ://www.brookings .edu/research/reports/2011/05/12-j obs-and-transit)
3) The Brookings Institute - Washington-Arlington-Alexandria, DC-VA-MD-WV Metro
(http://www.brookings.edU/~/media/Series/jobs%20and%20transit/WashingtonDC.PDF)
4) DC.gov - Data Catalog (data.dc.gov)
5) Metropolitan Washington Council of Governments - Customer Satisfaction, Demand
for rollDC Continues to Grow
(http://connectedcommunities.us/showthread.php?p=50087)
6) WMATA (Metro) Website (http://www.wmata.com/about_metro/?forcedesktop=l)
7) WMATA (Metro) website (http://www.wmata.com/rail/?forcedesktop=l)
8) WMATA (Metro) Website (http://www.wmata.com/bus/?forcedesktop=l)
Notes on Dataset(s):
1) Mean travel time to work (minutes), workers age 16+, 2007-2011: 29.6 min (25.4 for US)
2)Brookings study states that 82% of the working age population in DC are near a transit stop. This
can be compared to the top 100 US metro average of 69%
3)Brookings study - DC-specific info
4) Requires researcher to have appropriate software to open files. Enter key words into search at
bottom of page under "Browse Catalog" and get shape file appropriate transportation method.
5) RollDC - related to wheelchair access
6) DC Metro website
7) DC Metro Rail website
8) DC Metro Bus website
Indicator Value:
82% near transit stop
Proposed Resilience Score
4
Importance Weights
1 (not very important)
2
3
4 (very important)
Your Score
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1403: Percent of city culverts that are sized to meet current stormwater capacity requirements
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicator measures the percent of current culverts that cross transportation facilities in
the metropolitan region that are sized to meet current stormwater capacity requirements.
Grouped with indicators: #1404
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
N/A
Relevance: Yes Importance Weight: Proposed Resilience Score: N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
3
4 (very important)
Thresholds: Threshold-based Score: N/A
Less than 75% 1 (lowest resilience)
75 to 90% 2
Greater than 90 to 95% 3
Greater than 95% 4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #1410: Hours of passenger delay due to heat related issues
Definition: N/A
Grouped with indicators: #1402
Relevance
Yes (relevant)
No (not relevant)
Not Sure - Remind Me Later
Importance Weights
1 (not very important)
2
3
4 (very important)
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A
Indicator Value:
No data available.
Proposed Resilience Score Your Score
2 1 (lowest resilience)
2
3
4 (highest resilience)
2
This document is a draft for review purposes only and does not constitute Agency policy.
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#1413: Percent of short walkable sidewalks in urban areas
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator measures the percent of sidewalks within the urban area that are less than
330 feet.
Grouped with indicators: #1412
Dataset(s):
1) DC.gov - Data Catalog (data.dc.gov)
2) Transportation for America - Dangerous By Design: Metro Area Pedestrian Safety Rankings by
State (http://t4america.Org/resources/dangerousbydesign2009/metroranking/#dc)
3) WalkScore - Website on Walkability (http://www.walkscore.eom/DC/Washington_D.C.)
Notes on Dataset(s):
1) Raw value obtained by calculating the number of sidewalks less than 330 feet (11,369) and
dividing this number by total sidewalks (39,142) for value of 29%. Requires researcher to have
appropriate software to open files. Enter key words into search at bottom of page under "Browse
Catalog" and get shape file for sidewalks or pedestrian walkways.
2) Study on pedestrian safety by state
3) website on walkability, bikability... in DC
Indicator Value:
29% of sidewalks
Relevance: Yes Importance Weight: 3 Proposed Resilience Score: 4
Thresholds: Threshold-based Score: 1 Your Score: 3
Less than 60% 1 (lowest resilience) 1 (lowest resilience)
60 to 75%
Greater than 75 to 90%
Greater than 90%
2
3
4 (highest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1417: Percent funding spent on pedestrian/bicycle projects connected to community activity
centers
Definition: Percentage of program funds spent on pedestrian or bicycle projects that include at least
one connection to activity centers (e.g., schools; universities; downtown & employment districts;
senior facilities; hospital/medical clinics; parks, recreation, and sporting; grocery stores; museums and
tourist attractions).
Grouped with indicators: #987, #988, #1396
Relevance Importance Weights
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not Sure - Remind Me Later 3
4 (very important)
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
No data available.
Proposed Resilience Score Your Score
3 1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #1419: Intermodal freight connectivity (ratio of intermodal connections used per year to individual
2 modes)
Action Needed:
-No data have been found for this indicator. Please decide whether this data gap might demonstrate
that the indicator is not relevant to DC.
Definition: This indicators measures number of intermodal connections per year relative to distinct
modes. Intermodal connections allow freight to use a combination of modes and give shippers
additional transportation alternatives that unconnected, parallel systems do not offer.
Grouped with indicators: #1420
Dataset(s):
No dataset(s) identified. Please suggest dataset(s) that might be appropriate.
Notes on Dataset(s):
N/A.
Indicator Value:
N/A
Relevance: Yes Importance Weight: 4 Proposed Resilience Score: 4
Yes (relevant)
No (not relevant)
Thresholds:
Threshold-based Score: N/A Your Score: Score not yet
assigned
1 (lowest resilience) 1 (lowest resilience)
Less than 0.5 ratio of intermodal
containers to individual carloads
0.5 to 1.0 ratio of intermodal
2
2
containers to individual carloads
Greater than 1 to 2 ratio of
3
3
intermodal containers to individual
carloads
Greater than 2 ratio of intermodal
containers to individual carloads
4 (highest resilience)
4 (highest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Water
The indicators below have been developed for the Water sector. Indicators that are related are grouped
together such that a single indicator from that group was considered a Primary Indicator and the
remaining were considered Secondary Indicators. Primary Indicators and Non-Grouped Indicators are
presented in the first half of this handout, followed by the Secondary Indicators.
Each indicator has a Definition. Each question is flagged with one or more of the following gradual
change climate stressor and/or extreme event climate stressor (from the urban resilience framework
developed for this project:
Where it was possible to identify a data set that would provide data for the indicator for Washington, DC,
Data Set(s) and associated Notes on Available Data are included. Indicators are assigned a Proposed
Resilience Score on a scale of 1 = lowest resilience to 4 = highest resilience.
For each indicator, please:
1. Discuss the Relevance of the indicator to the Water sector. (If unsure, please select the Not Sure
- Remind Me Later option). Indicators may be selected as Yes (relevant) on the basis of the
stressors previously selected as being most relevant to Washington, DC, or based on any other
criteria. Secondary Indicators may be considered, if the Primary Indicator is not adequately
defined or does not have available dataset(s).
2. When possible, Dataset(s) for Washington, D.C. are provided where data were available. In some
cases, no dataset(s) were identified. Please suggest dataset(s) that may be better than the
dataset(s) identified or where data gaps exist.
3. For indicators selected as Yes (relevant), discuss an Importance Weight, where 1 = not very
important and 4 = very important.
4. Review the Proposed Resilience Score (if provided), which is on a scale of 1 = lowest resilience
to 4 = highest resilience, for the indicator. If you disagree with this score, please discuss Your
Score and indicate the reason for your disagreement.
Stressors
Gradual Changes
Extreme Events
Magnitude/ duration of heat waves
Drought intensity/ duration
Flood magnitude/frequency
Hurricane intensity/ frequency
Storm surge/ flooding
Wind Speed
Temperature
Precipitation
Sea Level Rise
This document is a draft for review purposes only and does not constitute Agency policy.
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1
PRIMARY INDICATORS & NON-GROUPED INDICATORS
2 #1346: Percent of Infiltration and Inflow (I/I) in wastewater
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
-Please review importance weight given that definition has been amended.
Definition: Water that enters the wastewater system through infiltration and inflow (I/I) as a percent
of total wastewater from all watewater treatment plants in the city. Infiltration is the seepage of
groundwater into sewer pipes through cracks, holes, joint failures, or faulty connections. Inflow is
surface water that enters the wastewater system from yard, roof and footing drains, from cross-
connections with storm drains, downspouts, and through holes in manhole covers.
Grouped with indicators: N/A
Dataset(s):
DC Water - Wastewater Treatment (http://www.dcwater.com/wastewater/default.cfm)
Notes on Dataset(s):
This webpage says "On an average day, more than 330 million gallons of raw sewage flow into the
Blue Plains Advanced Wastewater Treatment Plant from area jurisdictions." The design flow is "370
million gallons a day," as cited on the same page. 370 MGD / 330 MGD = 1.1212.
Indicator Value:
1.1212
Relevance: Yes Importance Weight: 3 Proposed Resilience Score: 3
1 (not very important)
2
3
4 (very important)
Thresholds:
Threshold-based Score: 4
Your Score: 3
Greater than 50%
1 (lowest resilience)
1 (lowest resilience)
Greater than 35 to 50%
2
2
20 to 35%
3
3
Less than 20%
4 (highest resilience)
4 (highest resilience)
3
This document is a draft for review purposes only and does not constitute Agency policy.
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#1347: Wet weather flow bypass volume relative to the 5-year average
Action Needed:
Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
-Please review indicator given that definition has been amended.
Definition: Volume of wastewater that bypassed treatment in an average year for all watewater
treatment plants divided by the 5-year average.
Grouped with indicators: N/A
Dataset(s):
District of Columbia Water and Sewer Authority - Biannual Report April 2013 Clean Rivers Project
News (http://www.dcwater.com/news/publications/CSO_Apr_2013_web.pdf)
Notes on Dataset(s):
This document states that DCWater estimates that 2.402 billion gallons of water are discharged to the
Anacostia River, Potomac River, and Rock Creek during years with average rainfall.
Indicator Value:
2.402 billion gallons
Relevance: Yes
Importance Weight: 2 Proposed Resilience Score: N/A
Thresholds:
Greater than 2 (unitless ratio)
Greater than 1 to 2 (unitless ratio)
1 (unitless ratio)
Less than 1 (unitless ratio)
Threshold-based Score: 1
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Score not yet assigned
1 (lowest resilience)
2
3
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 #1428: Total number of Safe Drinking Water Act (SDWA) violations
Action Needed:
-Please review indicator and decide if you agree with threshold-based score and provide explanation if
threshold-based score is not chosen.
-Please review indicator given that SDWA violations do not make the indicator irrelevant but does
make resiliency very high.
Definition: This indicator measures the total number of SDWA violations over the last 5 years
Grouped with indicators: N/A
Dataset(s):
US Environmental Protection Agency - EnviroFacts: D.C. Water and Sewer Authority
(http ://oaspub .epa.gov/enviro/sdw_report_v2 ,first_table ?pws_id=DC0000002&state=DC&source=Pur
ch_surface_water&population=617996&sys_num=0)
Notes on Dataset(s):
This EPA SDWIS violation report shows that no SDWA regulatory violations have occurred in the
past 5 years.
Indicator Value:
0 violations
Relevance: Yes Importance Weight: N/A Proposed Resilience Score: 3
1 (not very important)
2
3
4 (very important)
Thresholds:
Greater than 4 violations
3 to 4 violations
1 to 2 violations
0 violations
Threshold-based Score: 4
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: Not relevant
1 (lowest resilience)
2
3
4 (highest resilience)
2
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#1442: Ratio of water availability to water consumption
Action Needed:
-Please decide if threshold-based score or score from previous meeting is more appropriate and
provide explanation if threshold-based score is not chosen.
Definition: This indicator measures the fraction of available water that is currently consumed. It is
calculated by dividing the total available water from surface water and groundwater sources by total
water consumption.
Grouped with indicators: N/A
Dataset(s):
1) Metropolitan Washington Council of Governments - History and Background: Drought Monitoring
in the Metropolitan Washington Region (http://www.mwcog.org/uploads/committee-
documents/k 1 lbW 19d20130409105 942 .pdf)
2) US Army Corps of Engineers - Washington Aqueduct Annual Financial Report FY2012
(http://www.nab.usace.army.mil/Portals/63/docs/Washington_Aqueduct/FY_2012_Washington_Aque
duct_Annual_Financial_Report.pdf)
Notes on Dataset(s):
1) Slide 6 of this presentation lists a safe yield of Potomac River at 380 MGD.
2) Page 2 of this report lists the water consumption at 50,951.31 MG annually for DC, Arlington
County, Falls Church; equivalent to 139.59 MGD.
380 MGD / 139.59 MGD = 2.722
Indicator Value:
2.722
Relevance: Yes
Importance Weight: 1
Proposed Resilience Score: 4
Thresholds:
Threshold-based Score: 1
Your Score: 4
Greater than 0.20 (unitless ratio)
1 (lowest resilience)
1 (lowest resilience)
Greater than 0.13 to 0.20 (unitless
2
2
ratio)
0.06 to 0.13 (unitless ratio)
3
3
Less than 0.06 (unitless ratio)
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#437: Percent change in streamflow divided by percent change in precipitation
Action Needed:
-Stormwater experts need to review the indicator and provide an importance weight and resilience
score.
Definition: This indicator reflects percent change in streamflow (Q) divided by percent change in
precipitation (P) for 1,291 gauged watersheds across the continental US from the period of 1931-
1988.
Grouped with indicators: #1369
Dataset(s):
1) USGS Hydro-Climatic Data Network (HCDN) dataset for 1931-1988, POTOMAC River
(http://pubs.usgs.gOv/wri/wri934076/stations/01646502.html)
2) National Climatic Data Center (NCDC) - Washington DC precipitation archive (1871-2013).
(http ://www. erh .noaa.gov/lwx/ climate/dca/ dcaprecip .txt)
Notes on Dataset(s):
1) Includes data on mean annual streamflow (cfs) from 1931 to 1988. Calculate percent change in
streamflow from 1931 to 1988. Calculate percent change in precipitation from 1931-1988. Divide
percent change in streamflow by percent change in precipitation.
2) Includes total precipitation (in) from 1871-2013.
Indicator Value:
-14.36
Relevance: Not Sure
Importance Weight:
Proposed Resilience Score: N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
q
J
4 (very important)
Thresholds:
Threshold-based Score: 1
Your Score: Score not vet assigned
Greater than 3.0 (unitless ratio)
1 (lowest resilience)
1 (lowest resilience)
Greater than 2.0 to 3.0 (unitless
2
2
ratio)
1.0 to 2.0 (unitless ratio)
3
3
Less than 1.0 (unitless ratio)
4 (highest resilience)
4 (highest resilience)
This document is a draft for review purposes only and does not constitute Agency policy.
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#1369: Annual CV of unregulated streamflow
Action Needed:
-Stormwater experts need to review the indicator and provide an importance weight and resilience
score.
Definition: The coefficient of variation (CV) of unregulated streamflow is an indicator of annual
streamflow variability. It is computed as the ratio of the standard deviation of unregulated annual
streamflow (oQs) to the unregulated mean annual streamflow (QS)' (Hurd et al., 1999).
Grouped with indicators: #437
Dataset(s):
1) USGS Hydro-Climatic Data Network (HCDN) dataset for 1931-1988, POTOMAC River
(http://pubs.usgs.gOv/wri/wri934076/stations/01646502.html)
Notes on Dataset(s):
1) USGS HCDN has 1 site in DC, site "POTOMAC R (ADJUSTED NR WASH, DC" (number
01646502). Downloaded raw streamflow data from HCDN. Calculated as the average of the annual
CV of streamflow for all 58 years of data (note that a 'year' is from Oct 1-Sept 30). See file
ID43 7_HCDN_Streamflowdata_DC .xlsx
Indicator Value:
1.221
Relevance: Not Sure
Importance Weight:
Proposed Resilience Score: N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
q
J
4 (very important)
Thresholds:
Threshold-based Score: 1
Your Score: Score not vet assigned
Greater than 0.60 (unitless ratio)
1 (lowest resilience)
1 (lowest resilience)
Greater than 0.40 to 0.60 (unitless
2
2
ratio)
0.20 to 0.40 (unitless ratio)
3
3
Less than 0.20 (unitless ratio)
4 (highest resilience)
4 (highest resilience)
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1 Thresholds
Thresholds
Score 1
Score 4
Indicator
(lowest
(highest
ID#
Indicator Name
resilience)
Score 2 Score 3
resilience)
i. Economy
709
Percentage of owned
housing units that are
affordable
0 to 30%
Greater than
30 to 45%
Greater than
45 to 60%
Greater than
60%
711
Overall unemployment
rate
0 to Less than
83%
83 to Less
than 91%
91 to Less
than 100%
100%
717
Percent access to health
insurance of non-
institutionalized
population
Less than 85%
85 to 90%
Greater than
90 to 95%
Greater than
95%
722
Percent change in
homeless population
Greater than
10%
Greater than
Oto 10%
Greater than
negative 10 to
0%
Less than
negative 10%
1375
Percent of population
living below the
poverty line
Greater than
20%
Greater than
16 to 20%
12 to 16%
Less than 12%
ii.
Energy
898
Annual energy
consumption per capita
by main use category
(commercial use)
Greater than
4.0 tons of oil
equivalent
Greater than
3.0 to 4.0
tons of oil
equivalent
Greater than
2.0 to 3.0 tons
of oil
equivalent
Less than or
equal to 2.0
tons of oil
equivalent
924
Energy intensity by use
Greater than
3,000 Btu per
dollar
Greater than
2,000 to
3,000Btu per
dollar
Greater than
1,500 to 2,000
Btu per dollar
Less than
1,500 Btu per
dollar
949
Percent energy
consumed for
electricity
N/A
N/A
N/A
N/A
950
Percent of electricity
generation from non-
carbon sources
Less than 25%
25 to 50%
Greater than
50 to 75%
Greater than
75%
951
Percent of total energy
use from renewable
sources
Less than 20%
20 to 40%
Greater than
40 to 60%
Greater than
60%
967
Total energy source
capacity per capita
Less than 1.0
megawatt per
capita
1.0 to 2.0
megawatt per
capita
Greater than
2.0 to 5.0
megawatt per
capita
Greater than
5.0 megawatt
per capita
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Thresholds
Score 1 Score 4
Indicator (lowest (highest
ID# Indicator Name resilience) Score 2 Score 3 resilience)
970
Average capacity of a
decentralized energy
source
Less than
5,000
megawatt per
square mile
5,000 to
10,000
megawatt per
square mile
Greater than
10,000 to
15,000
megawatt per
square mile
Greater than
15,000
megawatt per
square mile
971
Energy source capacity
per unit area
Less than 10
megawatt per
square mile
10 to 50
megawatt per
square mile
Greater than
50 to 100
megawatt per
square mile
Greater than
100 megawatt
per square mile
983
Average customer
energy outage (hours)
in recent major storm
Greater than
40 hours
Greater than
20 to 40
hours
10 to 20 hours
Less than 10
hours
iii.
Information and Communications Technology
1433
Percentage of system
capacity needed to
carry baseline level of
traffic
Greater than
70%
Greater than
50 to 70%
30 to 50%
Less than 30%
1434
Baseline percentage of
water supply for
telecomm. systems that
comes from outside the
metropolitan area
Greater than
50%
Greater than
20 to 50%
5 to 20%
Less than 5%
1435
Baseline percentage of
energy supply for
telecomm. systems that
comes from outside the
metropolitan area
Greater than
60%
Greater than
30 to 60%
10 to 30%
Less than 10%
1441
Percent of community
with access to FEMA
emergency radio
broadcasts
Less than 80%
80 to 88%
Greater than
88 to 96%
Greater than
96%
iv.
Land Use/Land Cover
51
Coastal Vulnerability
Index Rank
5 (very high
vulnerability)
4 (high
vulnerability)
3 (moderate
vulnerability)
Less than or
equal to 2 (low
or no
vulnerability)
194
Percent of natural area
that is in small natural
patches
Greater than
80%
Greater than
60 to 80%
40 to 60%
Less than 40%
254
Ratio of perimeter to
area of natural patches
Greater than
0.025 (unitless
ratio)
Greater than
0.015 to
0.025
(unitless
ratio)
0.005 to 0.015
(unitless
ratio)
Less than
0.005 (unitless
ratio)
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Thresholds
Score 1 Score 4
Indicator (lowest (highest
ID# Indicator Name resilience) Score 2 Score 3 resilience)
825
Percent change in
Greater than
Greater than
Negative 1 to
Less than
impervious cover
1%
0 to 1%
0%
negative 1%
1436
Percent of city area in
Greater than
Greater than
1 to 5%
Less than 1%
100-year floodplain
20%
5 to 20%
1437
Percent of city area in
Greater than
Greater than
2 to 10%
Less than 2%
500-year floodplain
30%
10 to 30%
1438
Percent of city
Greater than
Greater than
1 to 5%
Less than 1%
population in 100-year
20%
5 to 20%
floodplain
1439
Percent of city
Greater than
Greater than
2 to 10%
Less than 2%
population in 500-year
30%
10 to 30%
floodplain
1440
Drought Severity Index
Less than
Negative 3.99
Negative 2.99
Greater than
negative 4.0
to negative
to negative
negative 1.99
(extreme
3.0 (severe
2.0 (moderate
(mild or no
drought)
drought)
drought)
drought)
v.
Natural Environment
17
Altered wetlands
Greater than
Greater than
20 to 40%
Less than 20%
(percent of wetlands
60%
40 to 60%
lost)
66
Percent change in
Greater than
Greater than
10 to 50%
Less than 10%
disruptive species
100%
50 to 100%
273
Percent of total wildlife
Greater than
Greater than
1 to 5%
Less than 1%
species of greatest
20%
5 to 20%
conservation need
284
Physical Habitat Index
0 to 50
61 to 65
66 to 80
81 to 100
(PHI)
(severely
(degraded)
(partially
(minimally
degraded)
degraded)
degraded)
326
Wetland species at risk
Greater than
100 to 160
50 to less than
Less than 50
(number of species)
160 species at
species at risk
100 species at
species at risk
risk
risk
460
Macroinvertebrate
0 to 45 (poor
46 to 55 (fair
56 to 75
Greater than 75
Index of Biotic
or very poor
biotic
(good biotic
(very good
Condition
biotic
condition)
condition)
biotic
condition)
condition)
465
Change in plant species
Less than 02
0.2 to 0.4
Greater than
Greater than
diversity from pre-
Shannon
Shannon
0.4 to 0.6
0.60 Shannon
European settlement
Diversity
Diversity
Shannon
Diversity Index
Index
Index
Diversity
Index
680
Ecological
Less than 10%
10 to 25%
Greater than
Greater than
Connectivity (Percent
25 to 50%
50%
of area classified as
hub or corridor)
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Thresholds
Score 1 Score 4
Indicator (lowest (highest
ID# Indicator Name resilience) Score 2 Score 3 resilience)
681
Relative Ecological
Less than 120
120 to 180
Greater than
Greater than
Condition of
White and
White and
180 to 230
230 White and
Undeveloped Land
Maurice Index
Maurice
White and
Maurice Index
score
Index score
Maurice
score
Index score
682
Percent change in bird
Less than
negative 66
Greater than 0
Greater than
population
negative 66%
to 0%
to 66%
66%
vi.
People
209
Percent of population
Greater than
Greater than
2 to 10%
Less than 2%
living within the 500-
30%
10 to 30%
year floodplain
322
Percent of population
Greater than
Greater than
Greater than 0
0%
affected by waterborne
2%
1 to 2%
to 1%
diseases
393
Percent of vulnerable
Greater than
Greater than
10 to 20%
Less than 10%
population that is
30%
20 to 30%
homeless
675
Asthma Prevalence
Greater than
Greater than
6 to 9%
Less than 6%
(Percent of population
12%
9 to 12%
affected by asthma)
676
Percent of population
Greater than 3
Greater than
1 to 2%
Less than 1%
affected by notifiable
to 4%
2 to 3%
diseases
690
Emergency Medical
Greater than
Greater than
8 to 10
Less than 8
Service Response
12 minutes
10 to 12
minutes
minutes
Times
minutes
725
Number of physicians
Less than 0.02
0.02 to 0.03
Greater than
Greater than
per capita
physicians per
physicians
0.03 to 0.04
0.04 physicians
capita
per capita
physicians per
per capita
capita
728
Adult Care (Homes per
Less than
0.00010 to
Greater than
Greater than
capita)
0.00010 adult
0.00020 adult
0.00020 to
0.00040 adult
homes per
homes per
0.00040 adult
homes per
capita of
capita of
homes per
capita of
elderly
elderly
capita of
elderly
population
population
elderly
population
population
757
Average police
Greater than
Greater than
8 to 10
Less than 8
response time
12 minutes
10 to 12
minutes
minutes
minutes
784
Number of sworn
Less than 0.10
0.10 to 0.20
Greater than
Greater than
police officers per
police officers
police
0.20 to 0.50
0.50 police
capita
per capita
officers per
police officers
officers per
capita
per capita
capita
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Thresholds
Indicator
ID#
Indicator Name
Score 1
(lowest
resilience)
Score 2
Score 3
Score 4
(highest
resilience)
798
Percent of fire response
times less than 6.5
minutes
Less than 85%
85 to 90%
Greater than
90 to 95%
Greater than
95%
1157
Percent of housing
units with air
conditioning
Less than 70%
70 to 88%
Greater than
88 to 94%
Greater than
94%
1170
Percent of population
experiencing heat-
related deaths
Greater than
2.0%
Greater than
1.0 to 2.0%
0.5 to 1.0%
Less than 0.5%
1171
Percent of population
affected by food
poisoning
Greater than
20%
Greater than
15 to 20%
10 to 15%
Less than 10%
1376
Percent of population
that is disabled
Greater than
20%
Greater than
15 to 20%
10 to 15%
Less than 10%
1387
Percent of population
vulnerable due to age
Greater than
20%
Greater than
15 to 20%
10 to 15%
Less than 10%
1390
Percent of population
that is living alone
Greater than
30%
Greater than
20 to 30%
10 to 20%
Less than 10%
vii. Transportation
985
Transport system user
satisfaction
0 to 20: very
or totally
dissatisfied
21 to 60:
somewhat
dissatisfied
61 to 80:
somewhat
satisfied
81 to 100: very
or totally
satisfied
987
Employment
Accessibility (mean
travel time to work
relative to national
average)
Greater than
1.18 (unitless
ratio)
0.98 to 1.18
(unitless
ratio)
0.79 to less
than 0.98
(unitless
ratio)
Less than 0.79
(unitless ratio)
988
Walkability Score
0 to 49 "car
dependent"
50 to 69
"somewhat
walkable"
70 to 89 "very
walkable"
90 to 100
"walker's
paradise"
991
Percent transport
diversity
N/A
N/A
N/A
N/A
1003
Mobility management
(yearly congestion
costs saved by
operational treatments
per capita)
$2 to less than
$10 per person
$10 to less
than $ 18 per
person
$18 to less
than $32 per
person
Greater than
$32 per person
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Thresholds
Score 1
Score 4
Indicator
(lowest
(highest
ID#
Indicator Name
resilience)
Score 2
Score 3
resilience)
1010
Community livability
Less than 60
61 to 70
71 to 80
81 to 100
index
(Most aspects
(Negative
(Day-to-day
(There are few,
of living are
factors have
living is fine,
if any,
substantially
an impact on
in general, but
challenges to
constrained or
day-to-day
some aspects
living
severely
living)
of life may
standards)
restricted)
entail
problems)
1396
Percent access to
23 to 47%
48 to 63%
64 to 75%
76 to 100%
transportation stops
1399
Percent of roads and
N/A
N/A
N/A
N/A
railroads within the city
that are located within
10 feet of water
1400
Percent of roads and
Greater than
Greater than
1 to 2%
Less than 1%
railroads within the city
5%
2 to 5%
in the 500-year
floodplain
1401
Percent of roads and
Greater than
Greater than
5 to 10%
Less than 5%
railroads within the city
20%
10 to 20%
in the 100-year
floodplain
1402
Total annual hours of
Greater than 6
3 to 6 hours
1 to 3 hours
Less than 1
rail line closure due to
hours
hours
heat and maintenance
problems
1403
Percent of city culverts
Less than 75%
75 to 90%
Greater than
Greater than
that are sized to meet
90 to 95%
95%
current stormwater
capacity requirements
1404
Percent of city culverts
Less than 70%
70 to 85%
Greater than
Greater than
that are sized to meet
85 to 95%
95%
future stormwater
capacity requirements
1406
Percent decline in
Less than 10%
10 to 25%
Greater than
Greater than
repeat maintenance
25 to 50%
50%
events
1408
Percent of bridges that
Greater than
Greater than
2 to 5%
Less than 2%
are structurally
10%
5 to 10%
deficient
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Thresholds
Score 1 Score 4
Indicator (lowest (highest
ID# Indicator Name resilience) Score 2 Score 3 resilience)
1411
Roadway connectivity
Less than 80
80 to 250
Greater than
Greater than
(number of
intersections
intersections
250 to 290
290
intersections per square
per square
per square
intersections
intersections
mile)
mile
mile
per square
per square mile
mile
1412
Miles of pedestrian
Less than 0.5
0.5 to 1.0
Greater than
Greater than
facilities per street mile
miles of
miles of
1.0 to 2.0
2.0 miles of
sidewalk to
sidewalk to
miles of
sidewalk to
street miles
street miles
sidewalk to
street miles
street miles
1413
Percent of short
Less than 60%
60 to 75%
Greater than
Greater than
walkable sidewalks in
75 to 90%
90%
urban areas
1419
Intermodal freight
Less than 0.5
0.5 to 1.0
Greater than 1
Greater than 2
connectivity (ratio of
ratio of
ratio of
to 2 ratio of
ratio of
intermodal connections
intermodal
intermodal
intermodal
intermodal
used per year to
containers to
containers to
containers to
containers to
individual modes)
individual
individual
individual
individual
carloads
carloads
carloads
carloads
1420
Intermodal passenger
Less than 55%
55 to 70%
Greater than
Greater than
connectivity (percent
70 to 85%
85%
of terminals with at
least one intermodal
connection for the most
common mode)
1422
Average distance of all
Greater than
Greater than
5 to 10 miles
Less than 5
non-work trip distances
30 miles
10 to 30
miles
miles
1426
City congestion rank
1 to 25
26 to 50
51 to 75
76 to 100
(unitless rank)
(unitless
(unitless rank)
(unitless rank)
rank)
1429
Tele-work rank
13 to 16
9 to 12
5 to 8
1 to 4 (unitless
(unitless rank)
(unitless
(unitless rank)
rank)
rank)
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Thresholds
Score 1 Score 4
Indicator (lowest (highest
ID# Indicator Name resilience) Score 2 Score 3 resilience)
viii.
Water
437
Percent change in
Greater than
Greater than
1.0 to 2.0
Less than 1.0
streamflow divided by
percent change in
3.0 (unitless
ratio)
2.0 to 3.0
(unitless
(unitless
ratio)
(unitless ratio)
precipitation
ratio)
1346
Percent of Infiltration
and Inflow (I/I) in
wastewater
Greater than
50%
Greater than
35 to 50%
20 to 35%
Less than 20%
1347
Wet weather flow
Greater than 2
Greater than
1 (unitless
Less than 1
bypass volume relative
(unitless ratio)
1 to 2
ratio)
(unitless ratio)
to the 5-year average
(unitless
ratio)
1369
Annual CV of
Greater than
Greater than
0.20 to 0.40
Less than 0.20
unregulated streamflow
0.60 (unitless
ratio)
0.40 to 0.60
(unitless
ratio)
(unitless
ratio)
(unitless ratio)
1428
Total number of Safe
Greater than 4
3 to 4
1 to 2
0 violations
Drinking Water Act
violations
violations
violations
(SDWA) violations
1442
Ratio of water
Less than 0.06
0.06 to 0.13
Greater than
Greater than
availability to water
(unitless ratio)
(unitless
0.13 to 0.20
0.20 (unitless
consumption
ratio)
(unitless
ratio)
ratio)
This document is a draft for review purposes only and does not constitute Agency policy.
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1 APPENDIX G. PARTICIPANTS
Worcester: Participants
Sector
Participant
Name
Participant Title
Organization
Economy
Timothy Murray
President and CEO
Worcester Regional
Chamber of Commerce
Energy
John Odell
Worcester Energy
Manager
City of Worcester
Land Use/Land
Cover
Luba Zhaurova
Acting City Planner
City of Worcester
Natural
Environment
Rob Antonelli, Jr.
Assistant Commissioner of
Parks and Recreation
City of Worcester
People
Derek Brindisi
Director, Worcester
Department of Public
Health
City of Worcester
Kerry Clark
Seth Peters
Colleen Turpin
Worcester Department of
Public Health
City of Worcester
Telecommunications
David Clemons
Director of Emergency
Communications and
Management
City of Worcester
Transportation
Bill Moisuk
Principal Planner
(transportation)
Central MA Regional
Planning Commission
Water
Konstantin Eliadi
Director, Water and Sewer
Operations
City of Worcester
Phil Guerin
Director of Environmental
Systems,
Worcester Dept. Public
Works (WDPW)
City of Worcester
Karla Sangey
Director
Upper Blackstone Pollution
Abatement District
(UBPAD) Treatment Plant,
Millbury, MA
Mark Johnson
Deputy Director
Upper Blackstone Pollution
Abatement District
(UBPAD) Treatment Plant,
Millbury, MA
This document is a draft for review purposes only and does not constitute Agency policy.
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1 Washington, DC Workshop Participants:
Name
Affiliation
Sector
First
Workshop
Second
Workshop
Brendan
Shane
Chief, Office of Policy &
Sustainability
District Department of the
Environment
Cross-cutting*
X
X
Wendy Hado
District Department of the
Environment
Cross-cutting*
X
Maribeth
DeLorenzo
Sr. Policy Specialist
Department of Housing and
Community Development
Economy
X
Tanya Stern
Chief of Staff
DC Office of Planning
Economy
X
Sent
responses
ahead of time
Andrea
Limauro
Office of Planning
Economy
X
Emil King
Policy Analyst
District Department of the
Environment
Energy
X
X
Jessica
Daniels
District Department of the
Environment
Energy
X
X
Wesley
McNealy
Director, Corporate
Environmental Services
Pepco Holdings, Inc.
Energy
X
X
Sean Skulley
Sr. Specialist, Sustainability &
Business Development
Washington Gas
Energy
X
X
Shirley
Harmon
Pepco Holdings, Inc.
Energy
X
E9-1-1 Coordinator-COOP
Coordinator
Office of Unified
Communications
Information and
Susan Nelson
Communications
Technology
X
Christopher
Bennett
IT Program Manager
Office of the Chief Technology
Officer
Information and
Communications
Technology
X
X
Laine
Cidlowski
Urban Sustainability Planner
Office of Planning
Land Use/Land
Cover
X
X
Damien Ossi
Wildlife Biologist
District Department of the
Environment
Natural
Environment
X
Rama
District Department of the
Natural
X
Tangirala
Environment
Environment
Dan
Guilbeault
Policy Analyst
District Department of the
Environment
Natural
Environment
X
This document is a draft for review purposes only and does not constitute Agency policy.
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Name
Affiliation
Sector
First
Workshop
Second
Workshop
John Davies-
Cole
State Epidemiologist
District Department of Health
People
X
X
LaVerne
Hawkins
Jones
Asthma Control Program
Manager
Department of Health
People
X
John Thomas
State Forester
District Department of
Transportation
Transportation
X
Rachel Healy
Sustainability Project Manager
Washington Metropolitan Area
Transit Authority
Transportation
X
Gregory
Vernon
Washington Metropolitan Area
Transit Authority
Transportation
X
Phetmano
Phannavong
Environmental Engineer
District Department of the
Environment
Water
X
Shabir
Choudhary
Section Chief
Washington Aqueduct
Water
X
Maureen
Holm an
Sustainability Manager
DC Water
Water
X
X
Jonathan
Reeves
Emergency Response and
Planning Coordinator
DC Water
Water
X
* Attendees with cross-cutting expertise were asked to select the Sector group to which they believed
they could contribute the best input.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 Workshop Observers*:
Name
Affiliation
First
Workshop
Second
Workshop
Aaron Ray
Associate
Georgetown Climate Center
X
Amy Tarce
Urban planner
National Capital Planning Commission
X
X
Convener, GSA Climate Adaptation and
Ann Kosmal
Resiliency Team
Office of Federal High-Performance Green
Buildings
General Services Administration
X
X
Emily Seyller
U.S. Global Change Research Program
X
Gerald (Jerry)
Filbin
Office of Policy Coordinator for Climate Change
Adaptation
Environmental Protection Agency
X
Jalonne White-
Newsome
Federal Policy Analyst
WE ACT for Environmental Justice
X
Robin Snyder
General Services Administration
X
Sara Hoverter
Green Committee
Georgetown Law
X
Shana Udvardy
Climate Adaptation Policy Advisor
Center for Clean Air Policy
X
* Observers were asked to select the Sector group to which they believed they could contribute the best
input.
2
This document is a draft for review purposes only and does not constitute Agency policy.
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APPENDIX H. AGENDAS FOR WORKSHOPS IN WASHINGTON, DC
Meeting on
Assessing Urban Resilience in Washington, DC
11:30am - 4:00pm
November 18, 2013
District Department of the Environment Offices,
5th Floor, 1200 First Street NE, Washington DC 20002
Organized by
United States Environmental Protection Agency (EPA) Office of Research and Development,
District Department of the Environment (DDOE), and The Cadmus Group, Inc.
11:30am - 12:15pm
11:30- 11:45
11:45 - 12:15
12:15pm - 12:30pm
12:30pm - 2:30pm
Project Background and Introduction
Welcome; DDOE's climate urban resilience workshops and
climate adaptation plan — Brendan Shane (DDOE)
Indicator thresholds & preliminary tool results for DC — Julie
Blue (Cadmus)
Break
Working Lunch and Breakout Sessions: Scoring Questions and
Indicators
Water
Led by Laura Dufresne (Cadmus)
Shabir Choudhary
Jonathan Reeves
Steve Saari
[Holly Wootten]
Energy
Led by Vanessa Leiby (Cadmus)
Jessica Daniels
Shirley Harmon
Emil King
Wesley McNealy
Sean Skulley
[Angie Murdukhayeva]
Natural Environment
Led by Nathan Smith (Cadmus)
Cecily Beall
Rama Tangirala
[Jenna Tipaldi]
Economy
Led by Patricia Hertzler (Cadmus)
Maribeth DeLorenzo
Andrea Limauro
Tanya Stern
[Tara Fortier]
This document is a draft for review purposes only and does not constitute Agency policy.
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2:30pm - 4:00pm
2:30-3:30
3:30-4:00
Transportation
Led by Damon Fordham (Cadmus)
Daniel Lee
[Sarah Yardley]
Land Use/Land Cover
Led by Chi Ho Sham (Cadmus)
Laine Cidlowski
[Anna Weber]
People
Led by Victoria Kiechel (Cadmus)
Victoria Alabi
John Davies-Cole
Russell Gardner
LaVerne Hawkins Jones
Jamal Jones
Wes McDermott
[Kristin Taddei]
The following attendees may join any sector, or move among
sectors:
Amanda Campbell, Ann Kosmal, Brendan Shane, Robin Snyder,
Amy Tarce, and Jalonne White-Newsome.
Debrief & Discussion of Sectors' Contributions to DC's
Resilience
Debrief on questions and indicators; discussion of sectors'
contributions to DC's resilience — Julie Blue (Cadmus)
Closing remarks — Susan Julius (EPA)
Telecommunications
Led by Ken Klewicki (Cadmus)
Christopher Bennett
Donte Lucas
[Ken Klewicki]
This document is a draft for review purposes only and does not constitute Agency policy.
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Meeting on
Assessing Urban Resilience in Washington, DC
9:00am - 4:45pm
September 10, 2013
District Department of the Environment Offices,
5th Floor, 1200 First Street NE, Washington DC 20002
Organized by
United States Environmental Protection Agency (EPA) Office of Research and Development,
District Department of the Environment (DDOE), and The Cadmus Group, Inc.
9:00am - 10:20am Project Background and Introduction to the Scoring Questions
Breakout Sessions
9:00 - 9:40 Welcome and attendee introductions; urban resilience project
framework; complementary projects and health work — Susan Julius (EPA) and John Heermans
(DDOE)
Background on DC case study — Nathan Smith (Cadmus)
Methodology for urban resilience tool — Julie Blue (Cadmus)
Break
Breakout Sessions & Lunch: Scoring Questions
9:40- 10:00
10:00- 10:20
10:20am - 10:30am
10:30am - 12:45pm
Water
Led by Tracy Mehan (Cadmus)
Shabir Choudhary
Maureen Holman
[Ken Klewicki]
Energy
Led by Vanessa Leiby (Cadmus)
Wesley McNealy
Sean Skulley
[Angie Murdukhayeva]
Natural Environment
Led by Nathan Smith (Cadmus)
Jessica Daniels
Emil King
Damien Ossi
Phetmano Phannavong
Rama Tangirala
[Jenna Tipaldi]
Economy
Led by Patricia Hertzler (Cadmus)
Lee Goldstein
Tanya Stern
[Tara Fortier]
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Transportation
Led by Damon Fordham (Cadmus)
Rachel Healy
Land Use/Land Cover
Led by Chi Ho Sham (Cadmus)
Laine Cidlowski
Gregory Vernon
[Sarah Yardley]
John Thomas
[Anna Weber]
Telecommunications
Led by Holly Wootten (Cadmus)
Tegene Baharu
Chris Bennett
Donte Lucas
People
Led by Victoria Kiechel (Cadmus)
Russell Gardner
Peggy Keller
[Victoria Kiechel]
12:45pm - 1:25 pm
12:45 - 1:05
1:05 - 1:25
1:25pm - 3:15pm
3:15pm - 4:45pm
3:15-4:15
4:15-4:45
Susan Nelson
[Holly Wootten]
The following attendees may join any sector, or move between
sectors:
Gerald (Jerry) Filbin, Sara Hoverter, Ann Kosmal, Aaron Ray,
Emily Seyller, Brendan Shane, Amy Tarce, and Shana Udvardy.
Adaptation Planning and Introduction to Indicator Breakout
Session
Adaptation in DC and upcoming adaptation plan — Clare
Stankwitz (Cadmus) and John Heermans (DDOE)
Background on indicators and data sources — Julie Blue (Cadmus)
Breakout Session: Indicators
(Same as morning breakout groups)
Debrief & Closing
Debrief on questions and indicators
Closing remarks — Susan Julius (EPA)
This document is a draft for review purposes only and does not constitute Agency policy.
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3 Iittp://b.3cdn.tiet/m.eli/9892745b6de8a.5ef59 q2m.6vc53b.pdf.
4 WMRB (Worcester Municipal Research Bureau). (2008). Benchmarking economic development in Worcester:
5 2008. Worcester, MA: The Worcester Municipal Research Bureau, Inc. Available online at
6 http://www.wrrb.org/reDorts/Droiect-coHaboratioiis/berichnicirkirig-ecopomic-developmerit/.
7 World Bank. (2015). Electric power consumption (kWh per capita). The World Bank Group. Available online at
8 http://data.worldbank.org/indicator/EG.USE.ELEC.KH.PC.
9 WRRB (Worcester Regional Research Bureau). (2015). Worcester almanac: 2015. Report 15-01. Worcester MA:
10 Worcester Regional Research Bureau. Inc. Available online at http://www.wrrb.org/wp-
1 1 coritent/uploads/2015/02AV orcester-Almanac-2015.pdf
This document is a draft for review purposes only and does not constitute Agency policy.
317 DRAFT—DO NOT CITE OR QUOTE
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