EPA/600/R-16/365F | January 2017 | www.epa.gov/research
United States
Environmental Protection
Agency
Evaluating Urban Resilience to Climate
Change: A Multi-Sector Approach
Office of Research and Development
Washington, D.C.
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EPA/600/R-16/365F
Final
January 2017
www, epa. gov/research
Evaluating Urban Resilience to Climate Change:
A Multisector Approach
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 has been reviewed in accordance with U.S. Environmental Protection Agency
policy and approved for publication. Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
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CONTENTS
LIST OF TABLES v
LIST OF FIGURES vi
ACRONYMS AND ABBREVIATIONS vii
PREFACE ix
AUTHORS, CONTRIBUTORS, AND REVIEWERS x
EXECUTIVE SUMMARY xi
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 12
2.2.1. Qualitative Indicator Development 12
2.2.2. Quantitative Indicator Selection 17
2.3. EXAMPLES OF THRESHOLDS FROM PEER-REVIEWED LITERATURE 18
2.4. EXAMPLES OF THRESHOLDS FROM GOVERNMENT
ORGANIZATIONS 19
2.5. EXAMPLES OF USING QUARTILES TO ASSIGN THRESHOLDS 20
2.6. DATA COLLECTION 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 Qualitative and Quantitative 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. PARTICIPANTS 38
APPENDIX C. AGENDAS FOR WORKSHOPS IN WASHINGTON, DC 42
APPENDIX D. WASHINGTON, DC CASE STUDY 46
APPENDIX E. WORCESTER, MA CASE STUDY 87
APPENDIX F. COMPARISON OF RESULTS FOR WASHINGTON, DC AND
WORCESTER, MA 115
APPENDIX G. QUALITATIVE INDICATORS: ORDERED 119
APPENDIX H. QUANTITATIVE INDICATORS: ORDERED 129
APPENDIX I. QUALITATIVE INDICATORS: TEMPLATE 139
in
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APPENDIX J. QUANTITATIVE INDICATORS: TEMPLATE 232
APPENDIX K. QUALITATIVE INDICATORS: WASHINGTON, DC 330
APPENDIX L. QUANTITATIVE INDICATORS: WASHINGTON, DC 419
APPENDIX M. QUALITATIVE INDICATORS: WORCESTER, MA 525
APPENDIX N. QUANTITATIVE INDICATORS: WORCESTER, MA 612
REFERENCES 665
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LIST OF TABLES
Table 1. Example frameworks to assess community resilience to climate change 3
Table 2. Potential climate changes of concern for urban areas 13
Table 3. Water sector questions related to drought sensitivity, response, and learning 15
Table 4. Example qualitative and quantitative indicator from urban resilience tool 17
Table 5. Macroinvertebrate Index of Biotic Condition thresholds 18
Table 6. Palmer Drought Severity Index (PDSI) thresholds 19
Table 7. Physical Habitat Index thresholds 20
Table 8. Mobility management (yearly congestion costs saved by operational treatments
per capita) thresholds and scores 20
Table 9. Percent access to transportation stops thresholds and scores 21
Table 10. Worcester, MA data availability 28
Table 11. Quantitative data limitations 29
Table 12. Major weather events and their impacts in the District of Columbia since 2003 49
Table 13. Major weather and other events and their impacts in Worcester, MA 89
Table 14. Washington, DC and Worcester, MA metrics at a glance 116
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LIST OF FIGURES
Figure 1. Urban climate resilience framework 6
Figure 2. Sample quadrant plot 23
Figure 3. Washington, DC: Average qualitative indicator resilience and importance 64
Figure 4. Washington, DC: Average quantitative indicator resilience and importance 64
Figure 5. Washington, DC: Qualitative indicator quadrant mapping 66
Figure 6. Washington, DC: Quantitative indicator quadrant mapping 67
Figure 7. Washington, DC economy sector: Qualitative and quantitative indicator
quadrant mapping 68
Figure 8. Washington, DC, energy sector: Qualitative and quantitative indicator quadrant
mapping 70
Figure 9. Washington, DC land use/land cover sector: Qualitative and quantitative
indicator quadrant mapping 72
Figure 10. Washington, DC natural environment sector: Qualitative and quantitative
indicator quadrant mapping 75
Figure 11. Washington, DC people sector: Qualitative and quantitative indicator
quadrant mapping 77
Figure 12. Washington, DC telecommunications sector: Qualitative and quantitative
indicator quadrant mapping 80
Figure 13. Washington, DC transportation sector: Qualitative and quantitative indicator
quadrant mapping 82
Figure 14. Washington, DC water sector: Qualitative and quantitative indicator quadrant
mapping 85
Figure 15. Worcester, MA: Average qualitative indicator resilience and importance 94
Figure 16. Worcester, MA: Average quantitative indicator resilience and importance 95
Figure 17. Worcester, MA: Qualitative indicator quadrant mapping 96
Figure 18. Worcester, MA: Quantitative indicator quadrant mapping 97
Figure 19. Worcester, MA economy sector: Qualitative and quantitative indicator
quadrant mapping 98
Figure 20. Worcester, MA energy sector: Qualitative and quantitative indicator quadrant
mapping 100
Figure 21. Worcester, MA land use/land cover sector: Qualitative and quantitative
indicator quadrant mapping 102
Figure 22. Worcester, MA natural environment sector: Qualitative and quantitative
indicator quadrant mapping 104
Figure 23. Worcester, MA people sector: Qualitative and quantitative indicator quadrant
mapping 106
Figure 24. Worcester, MA telecommunications sector: Qualitative and quantitative
indicator quadrant mapping 108
Figure 25. Worcester, MA transportation sector: Qualitative and quantitative indicator
quadrant mapping 110
Figure 26. Worcester, MA water sector: Qualitative and quantitative indicator quadrant
mapping 112
Figure 27. Washington, DC and Worcester, MA: Average quantitative indicator and
qualitative indicator score quadrant mapping 118
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ACRONYMS AND ABBREVIATIONS
CMRPC Central Massachusetts Regional Planning Commission
DCWASA District of Columbia Water and Sewer Authority
DDOE District Department of Environment
DDOT District Department of Transportation
EMS emergency medical service
FEMA Federal Emergency Management Agency
GEF Global Environment Facility
GIS geographic information system
DC HSEMA District of Columbia Homeland Security and Emergency Management
Agency
IPCC Intergovernmental Panel on Climate Change
LEED Leadership in Energy and Environmental Design
MCA multicriteria assessment
MWCOG Metropolitan Washington Council of Governments
NCPC National Capital Planning Commission
ORD Office of Research and Development
PEPCO Potomac Electric Power Company
PHI Physical Habitat Index
TSC Technical Steering Committee
UBPAD Upper Blackstone Pollution Abatement District
UFA Urban Forestry Administration
UNFCCC United Nations Framework Convention on Climate Change
U.S. DOT U.S. Department of Transportation
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US ACE 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's) 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 the expected future climate. Both are important, but this
report focuses on adaptation to climate change. Climate change impacts are diverse, long-term,
and not easy to predict. 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 describes
our research to support the goal of taking action on climate change in a sustainable manner
through development of a conceptual framework of urban resilience to climate change and
rigorous selection of indicators to assess community resilience to climate change. The report
includes the results of applying this framework successfully to two different communities
(Washington, DC and Worcester, MA) to evaluate their levels of resilience to climate change.
Results support the usefulness of this indicator-based approach in identifying traits that enhance
or inhibit each community's resilience 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, Eastern Research Group, Inc.
Nupur Hiremath, City of Sunnyvale, CA.
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
EXTERNAL REVIEWERS:
Vincent Lee, Associate Principal, ARUP
Eric R. Smith, Director of Planning and Development, Town of Athol
Fahim N. Tonmoy, National Climate Change Adaptation Research Facility, Griffith University
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 Agency (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 to inform adaptation planning. This report describes the tool in
detail and discusses the 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 of urban climate resilience: a
city's ability 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 data sets existed. A Technical
Steering Committee (TSC) guided the selection of questions for local sector managers and the
selection of quantitative 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 as qualitative indicators 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 data—
makes use of detailed data sets when they are available, but recognizes that important elements
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of a city's resilience would be neglected if qualitative information provided by city managers
were excluded. For both the quantitative and qualitative resilience indicators, 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 qualitative indicators, four possible answers
were developed for each indicator, with each indicator corresponding to a resilience score of 1
through 4 (with 1 representing low resilience and 4 representing high resilience). To score the
quantitative indicators, four 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 scores for the qualitative indicators and reviewed the quantitative indicator
ranges and corresponding resilience scores. Qualitative and quantitative indicators with high
importance weights and high resilience scores demonstrate where cities are most resilient
overall. Qualitative and quantitative 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 the level
of resilience for those cases and the representativeness of that indicator for all U.S. cities.
DISCUSSION AND CONCLUSION
The tool was applied in Worcester, MA and Washington, DC, cities representing different
endpoints of 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 and
potential outcomes of using 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 variety
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 qualitative and quantitative indicators
are characterized accordingly. The visualizations developed to accompany the results of the
application of the tool in Washington, DC and Worcester, MA 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.
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
categorizing 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 were essential to the analysis, even when quantitative data were readily
available. The qualitative indicators 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, the 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 resilience assessments and prioritize 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. The disparities in data available between the two
cities both complicate the data analysis effort and, for cities lacking data, is a telling indicator of
potential vulnerabilities to climate change.
Major challenges encountered while developing and applying the tool included: the need to
gather city-specific knowledge (and reasonable subjective knowledge); the lack of data for some
sectors and the fact of temporal data variability; the need to adequately identify and capture the
interconnectivity of sectors and the specific vulnerabilities that may exist as a result of
interconnectivities; the need to assess the adequacy or specificity of qualitative and quantitative
indicators; and the need to establish reasonable thresholds for all indicators.
Expansion and refinement of the tool remains to be done. For example, much of the remaining
work 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 qualitative and quantitative
indicators have been assigned to more than one sector, when appropriate.
Ultimately, this urban resilience assessment tool offers valuable insight into the resilience of
Washington, DC and Worcester, MA, 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 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 online use; conducting additional case studies
that focus on new users and expanded geographies and examine the potential for pooling
multiple communities' resources in the face of shared risk; and sharing the lessons learned and
best practices that emerge from the tool's application in 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 merely reverting to a steady state
(Simmie and Martin, 2010). This is referred to as evolutionary resilience, where evolution refers
to 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 interrelated
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). Since 2011, the definition
has been evolving. First, the Intergovernmental Panel on Climate Change (IPCC) (2012)
broadened the definition to include hazards and to introduce a focus on short-term disruptions as
well as long-term changes in averages. In 2014, the IPCC definition was further expanded to
include evolution in the ability to adapt, as well as learning and transformation (IPCC, 2014),
similar to the sociological definition.
These recent modifications to the definition of resilience has allowed the climate change
community to better link the issue of climate change with sustainable development. As far back
as 2001, the IPCC recognized that adaptive capacity and sustainable development were linked
(IPCC, 2001). 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 climate
change and associated 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 a 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 in areas whose economies are closely linked with climate-sensitive resources,
such as agricultural and forest products. Higher temperatures would affect 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 increased
runoff volume and thus, in addition to increased flooding, greater amounts of nonpoint source
contamination in their water bodies.
The nonlinear, complex, and dynamic nature of climate change; urban socioeconomic and
environmental systems; and their responses poses significant challenges for existing methods and
frameworks. It is yet to be seen whether these frameworks are adequate to meet the challenges
(Kim and Lim, 2016). Because of the convergence of population centers and exposures to
climatic changes, particularly 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
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 hazards, 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,
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 (Amp's
City Resilience Framework)
http://www. lOOresilientcities
.ore/resilience#/- /
http://publications.arup.com/
publications/c/citv resilience
framework
Scope
Health and wellbeing, economy and
society, infrastructure and
environment leadership and 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
and Society: ensure social stability, security,
and justice)
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
(BRACED) framework
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
United Nations Development
Programme's (UNDP's)
Community-Based
Natural capital, financial capital,
physical capital, human capital, social
capital
"Inherent as well as acquired
condition achieved by managing
risks over time at individual.
"Composite set of context-specific
multisectoral quantitative and qualitative
resilience indicators." This process tool enables
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Framework
Scope
Resilience Concept
Indicator Approach
Resilience Analysis
(CoBRA) framework
httt>://www.undaorg/content
/iuicId/c n/lio mc/1 ib ra rvoa ac/c
nvironment-
enerev/sustainable land ina
nasement/CoBRA/cobra-
co nccDtual-fra mcwo rk. html
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."
communities to identify key building blocks of
resilience and assess the attribution of various
interventions in attaining resilience
characteristics.
Characteristics of a Disaster
Resilient Community http://
discoverv.ucl.ac.uk/1346086
/l/1346086.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 subdimensions
(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.
United States Agency for
International Development
(USAID) Measurement for
Community Resilience
htft>s://aerilinks.org/sites/def
ault/files/resource/files/FTF
%20Learnine Asenda Com
munitv Resilience Oct%202
013.txJf
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
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, which 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 percentage 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 not only includes
the concepts of vulnerability, exposure, and hazards that present risks to urban environments, but
it also goes beyond a static view of the world and incorporates the concepts of feedbacks,
learning over time, and 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 a
temporary state of response to external shocks (similar to Engle et al., 2013).
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Climate Stressors
Other Exogenous
Factors
\Z.
Economy
Natural Environment
Infrastructure
Exposure
+
Sensitivity
+
Response Capacity
Urban Climate Resilience
Ba= Bridges to action
Bl= Barriers to learning
BR= Barriers to responding
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.
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In the framework above, the left-hand side focuses on anticipated future climate events and
system responses. Included here are the potential exposures to climate change, both gradual and
extreme, the potential sensitivity of sectors and systems to those exposures, and the theoretical
capability to respond to anticipated climate changes (response capacity, also referred to as
adaptive capacity in the climate change literature). The right side of the framework reflects
actual responses to real-world experiences of extreme weather events or gradual changes in
climate (whether by a community or through observations of other communities and their
experiences). Barriers to action and bridges to better-than-anticipated responses are identified
based on reflections after an 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, 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: Ability to recognize complex dynamics of socio-ecological systems in order to respond
appropriately to risk and make effective adaptation responses, identify mistakes and shortcomings in those
responses following climate stressor events, and evolve as new information becomes available (drawn
from IPCC, 2012; Kasperson, 2012).
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 are considered operational definitions. They have been selected for their appropriateness to
this application, even though they might not be identical to the definitions in the current literature.
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Increasing the 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 response capacity, increasing learning, removing barriers that inhibit good
responses, and providing bridges to promote greater-than-anticipated responses.
This framework serves as the basis for determining the type and breadth of indicators needed to
assess a city's resilience condition and evolution over 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 it reduces bias that can be present if there are limited quantitative data sets 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 specific
local 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 better inform 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 thereof) is in using that information to
take action to avoid or move farther from and above thresholds in order to grow resilience. Our
approach provides for flexibility in the final selection of indicators, which allows communities to
tailor the resilience assessment to local situations. These innovative features combine elements
from other frameworks but do not reside in any other single framework.
The remainder of this report describes in more depth the process of applying this framework to
the selection of qualitative and quantitative indicators (see Chapter 2) and developing the tool to
assess urban resilience to climate change (see Chapter 2). The report then discusses 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 D and
E, respectively, and a comparison of results across the two case studies is provided in Appendix
F.
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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 (see Table 2). 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 (i.e., questions for city
managers to collect information based on their experience) 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 (see Table 3). The combination of scores for
the qualitative indicators and the quantitative indicators of exposure and sensitivity provides a
measure of a community's overall resilience to climate events such as drought. In addition to the
resilience scores for the indicators, indicators are scored for importance, to reflect the degree to
which they contribute to resilience, acknowledging that some indicators reflect issues of higher
priority or more direct relevance to urban resilience than others.
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 TSC's work to select qualitative and quantitative metrics formed the basis of
the urban climate resilience tool. This tool uses those indicators (see Appendices G and H for
qualitative and quantitative indicators, respectively), along with threshold values for each
quantitative indicator for the eight city sectors mentioned above. To apply the tool, local
government officials select indicators relevant to their community, evaluate each indicator's
importance for representing resilience, score qualitative indicators, and evaluate 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 scores for qualitative indicators was the
assessment approach most likely to be valuable 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 excluded. Using similar methods and scales from these tool components was
critical to developing a set of unified, comparable outputs for analysis. 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 combine 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 synthesizes viewpoints and methods from both. The advantages of mixed-
methods research are that it can: provide stronger evidence for a conclusion through the
convergence of qualitative and quantitative findings; lead to formulating and answering a
broader range of research questions than a single method can; balance the strengths and
weaknesses of differing methods; and increase the generalizability of a study's results (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 stakeholders' or decision makers' selection of alternatives or criteria.
The project team developed the tool for the urban resilience case studies based on the approaches
taken by Hajkowicz (2008) and the Global Environment Facility (GEF; 2010) (see Appendices D
and E for Washington, DC and Worcester, MA case study results, and see Appendix F for
comparison of these results). Hajkowicz (2008) used a multicriteria analysis method that
included a priority matrix in which study participants ranked the issues presented according to
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the issue's importance to the participant. GEF (2010) is a more general mixed-methods
approach in which each indicator in the assessment was assigned a set of choices that provided a
quantitative rating (0 to 3) for that indicator. These studies offered the most practicable
approaches for working with indicators of resilience (when hard data were available), while
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 it
targets the tool at those with the greatest 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 scores (answers)
ranging from least resilient to most resilient (see example in Table 4). The project team
identified and gathered data for the quantitative indicators (see example in Table 4). Indicators
that are related are grouped together with a single indicator from that group designated as a
Primary Indicator and the remaining designated as Secondary Indicators. Groupings were
developed to assist cities in ensuring that they provide as comprehensive information as possible:
When data for primary indicators are not available, one or more secondary indicators from the
same group can be considered a reasonable replacement for the missing information; when data
for secondary indicators are not available, a primary indicator will certainly suffice. Complete
sets of the qualitative and quantitative indicators for the tool are presented by sector in
Appendices G and H.
For both the qualitative and quantitative indicators, 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. For the qualitative indicators, the project team developed four
possible ratings, with each indicator corresponding to a resilience score of 1 through 4 (again
with 1 representing low resilience and 4 representing high resilience). To score the quantitative
indicators, the project team applied four quantitative ranges to the data associated with each
indicator (see Section 2.3 for additional information). These ranges correspond with a resilience
score of 1 through 4. Participants then selected the scores for the qualitative indicators and
reviewed the quantitative indicator ranges to determine the resilience scores. Resilience scores
for indicators, sectors, or the city as a whole are best used for comparison over time within the
same city. Qualitative and quantitative indicators with high importance weights and high
resilience scores demonstrate where cities are most resilient overall. Qualitative and quantitative
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 for cities to address 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 sample quadrant plot and Figures 5 and 6 in Appendix F for quadrant plots
populated with data.)
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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 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.
2.2.1. Qualitative Indicator Development
The TSC developed a four-step process to establish qualitative indicators (i.e., questions) best
suited to determine climate resilience. The final qualitative indicators address all relevant
climate stressors and attempt to assess resilience as comprehensively as possible across all
sectors (see Appendix B 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 TSC repeated the process for
all relevant climate stressors across all sectors to develop the final list of questions as qualitative
indicators.
2.2.1.1. Step 1: Identify Climatic Changes/Events of Concern.
Table 2 is an overview of all potential climate changes that the TSC considered for their potential
to affect urban areas. These correspond to the climate stressors referred to in Figure 1.
Stakeholders would select stressors 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 target long-term change, but also address
some immediate concerns (Engle et al., 2014).
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Table 2. Potential climate changes of concern for urban areas
Wind
Temperature Precipitation Sea level rise
Gradual
change
Extreme
events
± Mean maximum
speed
± Strong winds
± Average annual ± Average annual + Sea level
± Seasonal average ± Season average + Coastal high
± Daily min and ± Event magnitude/ water
max duration
± Time between
events
Heat wave (magnitude/duration)
+ Storm surge and
flooding
Droughts (intensity/duration)
Floods (magnitude/frequency)
Hurricanes (intensity/frequency)
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.
2.2.1.2. Step 2: Discuss Related Climate Stressors.
For the purposes of drought, the TSC evaluated the following:
• Changes in the timing, form, or amount of precipitation that favor more frequent or
prolonged drought events
• Increased temperature (increased evapotranspiration)
• Increased wind (increased evapotranspiration)
2.2.1.3. Step 3: Discuss Urban Services Potentially Exposed to Drought and Urban Sectors
Potentially Responsible for Managing the Sensitivities of These Sendees.
Under this step, the TSC identified (a) urban services potentially exposed to drought that have
the potential to affect urban resilience and (b) the urban sectors responsible for managing
potential sensitivities of services to drought. This step corresponds to the "exposure" and
"sensitivity" elements in Figure 1 that help determine urban vulnerability. Example urban
services potentially exposed to drought include the following:
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• Water quality
• Groundwater supply
• Surface water supply
• Aquatic habitats, plants, and animals
• Terrestrial habitats, plants, and animals
• Recreational opportunities
• Look and feel of the landscape
• Energy supplied by hydropower, thermoelectric, or nuclear sources
2.2.1.4. Step 4: Evaluate the Ability to Reduce Exposure/Sensitivity, Enhance Response
Capacity, and Learn.
The final step of this exercise is similar to Step 3. The TSC discussed the urban services
exposed to drought (corresponding to the "response" section of Figure 1) and developed a series
of questions to help determine a city's ability to (a) reduce exposure or sensitivity, (b) increase
response capacity, and (c) learn from past and future experiences with drought. Risk reduction
capacity encompasses (a), (b), and (c). In this project, these concepts also compose the role of
governance in urban climate resilience.
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) define 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 government's capacity to adapt to
hydrologic change (administrative and financial capacity to respond). Questions were selected to
measure the capacity to reduce risk, recover from drought, and learn to improve future resilience.
While the questions in Table 3 are relevant only to the water sector, questions were developed as
qualitative indicators for each sector to evaluate how that sector responded to drought. The final
qualitative indicators address all areas of concern related to climate and resilience across all
sectors. A similar approach was taken for all of the climate changes of concern and exposed
services using available literature and input from the TSC Table 3. Water sector questions
related to drought sensitivity, response, and learning.
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Table 3. Water sector questions related to drought sensitivity, response, and learning.
Exposure/sensitivity
Increase response capacity
Learning related to drought
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 the
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 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 the 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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Exposure/sensitivity
Increase response capacity
Learning related to drought
Surface water
supply
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?
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?
Recreation • Do local water
management plans
include provisions for
local parks and open
space?
• Will drought have
long-term impacts on
local parks and open
space?
Is open space used as an adaptation option for
protecting water resources during drought?
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2.2.2. Quantitative Indicator Selection
To organize and obtain detailed data sets 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 C for the full list of
quantitative indicators).
Table 4. Example qualitative and quantitative indicator from urban
resilience tool
a. Example qualitative indicator
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 quantitative 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. The
thresholds 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
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indicator. If no analyses were available, an attempt was made to identify theoretical resilience
thresholds (presumably applicable to any site) based on modeling efforts.
Where such studies were not available, panel data for U.S. cities were examined to establish a
range of values for the indicator across the sampled cities, and published literature (academic
literature, news articles, etc.) was consulted to determine the indicator's resilience levels for
those cities. This step involved triangulating multiple qualitative assessments, which included
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 of all U.S. cities, in terms of resilience (Walker,
2006). Finally, if data or case studies were not available for cities, efforts were made to identify
state-level data or case studies from which resilience categories were established using the same
qualitative triangulation approach, and considering how resilience for the indicator might 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 (Palmer Drought Severity Index). Thresholds for
indicator #460 are adapted from Weigel et al. (2002). The original five thresholds and those
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 75 = 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 (Palmer Drought Severity Index) also uses thresholds adapted from a literature
source (Alley, 1984). The original 11 thresholds and those 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
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
-0.99 to -0.50 = Incipient drought
-1.99 to -1.00 = Mild drought
-2.99 to -2.00 = Moderate drought
-2.99 to -2.00 = Moderate drought
3
-3.99 to -3.00 = Severe drought
-3.99 to -3.00 = Severe drought
2
< -4.00 = Extreme drought
Less than or equal to
-4.00 = Extreme drought
1
2.4. EXAMPLES OF THRESHOLDS FROM GOVERNMENT ORGANIZATIONS
Thresholds for indicator #284 (Physical Habitat Index [PHI]) are drawn from a set of resource
briefs prepared by the U.S. National Park Service detailing research on the physical habitat
conditions of streams in the National Capital Region Network (Northrup, 2013). The original
four thresholds for PHI are listed in Table 7. No changes were needed for the thresholds to
correspond with resilience scores of 1 to 4.
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Table 7. Physical Habitat Index thresholds
Northrup (2013) thresholds
Thresholds for tool
Resilience score
81 to 100 = Minimally degraded
81 to 100 = Minimally degraded
4
66 to 80 = Partially degraded
66 to 80 = Partially degraded
3
51 to 65 = Degraded
51 to 65 = Degraded
2
0 to 50 = Severely degraded
0 to 50 = Severely degraded
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 (percent 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
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 or equal to $32 per person
4
$18 to less than $32 per person
3
$10 to less than $18 per person
2
$2 to less than $10 per person
1
Another example of using quartiles to define 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 cities in this report contained a value for "share (percentage) of working-age residents near a
transit stop." Thresholds were defined as the quartiles of values for the 100 cities in the report.
These thresholds are listed in Table 9.
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Table 9. Percent access to transportation stops thresholds and scores
Thresholds
Resilience score
76 to 100% of population near a transit stop
4
64 to 75% of population near a transit stop
3
48 to 63% of population near a transit stop
2
23 to 47% of population near a transit stop
1
2.6. DATA COLLECTION 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 qualitative and quantitative indicators) 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 qualitative and quantitative indicators) and to provide data for the qualitative
and quantitative indicators. This process was modified slightly to better reflect a workshop
approach, although ultimately one key District representative for each sector scored each
qualitative indicator. Additional details on the data collection approaches for Washington, DC
and Worcester are included in Appendices D and E, 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 the
conceptual framework presented in Figure 1 (see Chapter 1) as a foundation. The results can
easily be analyzed with respect to exposure/sensitivity, response capacity, or learning, as the
qualitative and quantitative 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 categorizing 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 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 D and E), and for comparisons across the two case studies (see Appendix F). The
quadrants are defined by the combination of resilience and importance scores (1 through 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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Sample Quadrant Plot
0)
O
O
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• Extent of telecommunications redundancy and availability of multiple communication
options, served by different infrastructure, for first responders and the public (qualitative
indicator)
By identifying areas where resilience is high, cities may apply lessons learned to other areas that
are also ranked as important, but perhaps significantly less resilient. Targeting planning efforts
at a sector's important and vulnerable points can also help cities prioritize limited resources for
areas of greatest concern.
However, changes in city characteristics or climate risks could potentially decrease resilience in
the future, and some level of monitoring and eventual reassessment is warranted. By contrast, a
city can identify and choose to limit resources invested in monitoring data points or issues
identified as highly resilient and of low importance (although 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 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 are issues to address first, especially in cases
where limited resources are available.
As noted previously, the tool is distinguished in part because it considers resilience across
multiple sectors. This allows for understanding the breadth of resilience across a city, relative
resilience among its sectors, and the 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 qualitative and quantitative indicators mean for a city's resilience, and for what steps a city
may take to improve its resilience.
The identification numbers assigned to each qualitative and quantitative indicator are included in
the visualizations to allow the reader to determine exactly what aspects of resilience are being
addressed within each quadrant. This is particularly easy in unique situations, for example when
a quadrant has few qualitative indicators populating it or few qualitative indicators from a
specific sector (even if many from other sectors). The ability to drill down into the data may also
be useful for testing hypotheses such as the interrelatedness of certain sectors and their aspects
(e.g., do qualitative indicators for sectors that are presumed to be interrelated often fall into the
same quadrants, at least for aspects that are presumably interrelated?).
More work is necessary to capture interdependencies among sectors. Future advancements in
our understanding of these interdependencies can be made by examining linkages more closely,
such as those between the water and the energy sectors. However, interdependencies have been
addressed to some extent, in that some qualitative and quantitative 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).
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 qualitative indicators and selecting the quantitative indicators, is that the
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qualitative indicators were found to be essential to the analysis, addressing data quality and
availability limitations at both the city and sector level in the two case study applications. While
Washington, DC and Worcester, MA contrast each other in data richness and institutional
support for climate change adaptation, 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 about the
resilience of this sector were it not for the qualitative indicators. 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 quantitative indicators alone. The qualitative
indicators, however, provided a fairly robust assessment of where Worcester's strengths and
weaknesses lay and what issues city managers considered important or more ancillary.
As noted previously, the tool and its conceptual framework 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 the fact that using tool does not necessarily
require data (i.e., if the user applies only the qualitative indicators). The qualitative indicators
can be mapped to specific events or types of events, allowing city managers and planners to
identify feedbacks and learn over time. As the framework is applied iteratively, qualitative
indicators can be reframed to identify specific factors that increase or decrease resilience relative
to the previous application of the framework. Additionally, applying the qualitative indicators
necessitates interaction with and between sector stakeholders. These interactions provide
additional learning and coordination opportunities that would not have been possible using
quantitative indicators alone, and these interactions can be used to further refine the resilience
assessments and prioritization of activities in response to the assessments' findings.
Furthermore, 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, contextual information, particularly for local or regional assessments.
Moving forward, it would be wise to evaluate a city's data availability before beginning the more
detailed assessment. If data availability is minimal, moving forward with only qualitative
indicators 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. Thresholds for the quantitative indicators were based on the
literature when possible, accounting for the full range of values the indicator takes on in cities
across the United States. Thresholds may need to be reevaluated if applying the tool
internationally.
Ideally, thresholds make the resilience assessment more informative because they bring a degree
of objectivity to the assessment that does not depend on comparisons with other cities (i.e., this is
not a relative resilience assessment). Yet thresholds make use of other cities' experiences in
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what values the indicators may have had when passing from resilient to less resilient, or vice
versa. Indicator thresholds can guide city decisions regarding adaptation (e.g., what to do, how
much to do), as the city attempts to avoid exceeding threshold levels that would indicate 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.
Although the qualitative indicators lack some of the objectivity provided by the indicator data,
they fill in gaps on issues that the quantitative indicators cannot address, often due to data
availability limitations, and sometimes because it is impossible to develop an indicator that
provides more objective information than the city managers' responses regarding a specific
question or issue. However, as the technology enables faster, more efficient, and less expensive
data gathering, and citizen-science efforts advance, issues in the tool that are currently addressed
only by qualitative indicators might eventually be addressed by quantitative indicators. As it
stands now, although, the disparities in available data from the literature and through data
collection between the two cities selected for case studies may not only indicate that analysis
efforts in cities facing similar issues with data may be difficult, but also the absence of such data
in itself indicates potential vulnerabilities to climate change.
The initial application of the tool in a given city, as detailed in the case studies in the appendices,
is merely a snapshot of a city's resilience at a given time. 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 facilitate
a city's learning and therefore increase a city's resilience. Resilience is dynamic and
evolutionary, and the framework used to develop this tool ensures that the dynamic nature would
be built in, allowing the tool to evolve through iterative application.
3.4. SPECIFIC CHALLENGES IDENTIFIED THROUGH TOOL APPLICATION
Beyond the numeric values of resilience and importance collected across the sectors 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 data sources to effectively assess the proposed indicators.
The following discussions identify and expand on previously mentioned challenges encountered
in developing and applying 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.
Different methods were attempted in the two case studies in this report: a workshop approach in
Washington, DC and one-on-one discussions in Worcester, MA. The results presented in
Appendix D (the Washington, DC case study) reflect the workshop approach, while the results
presented in Appendix E (the Worcester, MA case study) represent the responses of the local
government official selected as the most knowledgeable in the area addressed by each indicator.
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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 more participants may not mean more
viewpoints; groupthink may be an issue, especially if many different representatives of the same
agency give their opinion. It is possible that in a workshop setting, lower ranking members are
afraid to contradict their superiors. To complicate the issue further, a workshop approach is not
possible for many small cities 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,
findings based on a limited set of consultations may not be immediately actionable, but they may
be valuable in raising awareness of issues where more study is needed and the expense of
broadening data collection may be justified. Limited data collection may also be more useful in
an aggregate sense; while there may be little to learn about a specific item, there may be greater
confidence about the average state of the sector. Broadly, we may learn about the average of the
sectors overall; even if a sector is only represented by a single opinion, there can be overall
confidence that a city needs to approach its climate resilience planning more seriously in that
area. In addition, supplemental indicator data help balance out any subjectivity that could
influence responses or importance rankings for both qualitative and quantitative 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
Sector
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 national
databases on U.S. cities or states, where the District had the advantage of being treated as both a
state and a city.
However, data availability was still the most significant limitation in applying the urban
resilience tool to evaluate 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 conclusions 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 city.
Multiple data sets
Calculating the indicator value required more than one data set; in some
cases, combining data sets was challenging due to different spatial and
temporal resolutions.
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
Available data were out of date and inappropriate for measuring the
current resilience of the city.
Regional-scale data
Data were available only at a regional scale (e.g., county), not at the
municipal scale.
The 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). Aggregating data at a city level may hide
problems that are only severe in specific instances, or in the opposite case, make problems that
are limited to small areas appear much worse than they are. For example, localized flash
flooding may 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 its surrounding region
could inform resilience assessment and planning efforts.
Lastly, temporal variability may pose additional challenges. Many data are historical, yet
climate vulnerabilities are often the result of deviations from the historical pattern. For example,
under climate change scenarios, the new 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. In the District, rapid gentrification may have
rendered historical data sets less informative, even those collected as little as a decade ago. Data
sets may be of limited use because they do not reflect the future conditions that would inform
planning, or they must be modified (consuming time and expertise) to be useful.
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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, the continued provision of safe drinking water relies heavily on a
resilient energy sector, and 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 into eight 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 water recycling.
This tool also homogenizes some vulnerabilities that occur because of interconnectivity across
sectors. Low scores in one sector may be largely due to 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, the poor relative performance of a single sector that is
interconnected to others may lower scores in many sectors, masking the fact that a single sector
is responsible for low resilience across sectors.
3.4.4. Revisions to Qualitative and Quantitative Indicators
Several participants challenged the qualitative and quantitative indicators used. Generally, the
participants noted three concerns: (1) the proposed qualitative or quantitative indicator is not
assessing what really matters, (2) the proposed qualitative or quantitative indicator is poorly
defined, or (3) the proposed quantitative 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 create and apply meaningful qualitative and quantitative indicators. For example, with regard
to quantitative indicator #983 (average customer energy outage [hours] in recent major storm),
one participant stated that this indicator attempts to address a relevant metric, but it does not
speak to the most important variable: the indicator should account for when the power is out,
which is more important than the length of time the power is out. A power outage in the middle
of the night may have limited effects, and even frequent nighttime outages may not indicate 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. However, the issue of
when climate impacts occur is relevant to many of the indicators in the tool, and incorporating
timing, or similar details, into every indicator may be unnecessarily complicated for this tool.
These concerns show the need for continued refinement of the tool. At the same time, this also
highlights the usefulness of the framework used to develop the tool, which is iterative and
evolutionary by design. The qualitative and quantitative indicators selected for the tool are, in
many cases, akin to canaries in a coal mine. They provide a sense of what might happen to a city
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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 the
system 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 refine some
qualitative and quantitative indicators, we have attempted to select the most relevant indicators in
this first version of the tool—those that best reflect important aspects of resilience and show
where a city is solidly resilient or at risk from future stressors.
3.4.5. Threshold-Setting
Thresholds are not easy to set for all indicators. Ideally, the thresholds that determine when the
values of an indicator pass from a state of nonresilience to a state of resilience would be
objectively determined based on a full understanding of the indicator and 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 in this study, objective thresholds were not
available in the published literature. When thresholds were available, they varied spatially
across the United States, and in some cases, were not applicable to Washington, DC nor
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.
Few thresholds were changed during the first two pilot applications of the tool, as threshold
determination is intended to take place primarily during the tool development phase. The intent
is that eventually, after additional applications of and refinements to the tool, the thresholds
would be unlikely to be changed. 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 can 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, 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 subjective information from
experts and metrics that indicate resilience on a single scale is key. In this tool, the project team
used a mixed-methods approach (discussed in Section 2.1) to integrate information obtained
from 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 that is more likely than usual to
have indicator data available because of its unique relationship with the federal government and
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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 quantitative data and qualitative questions was the tool able to fully capture
where cities were or were not climate-resilient. In Worcester, which lacked detailed, up-to-date
indicator data, the questions asked of city planners and managers were even more important. In
all likelihood, Worcester's data availability is more representative of American cities than the
District's. Finding meaningful ways to incorporate expert judgment is essential for any tool that
wishes to find widespread use in 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 data set may start or stop before capturing
weather events such as floods or droughts, which may occur only at long intervals (e.g., 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 adapting 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. In many cases the information provided by the tool yielded as many new questions as
answers; however, this aspect of the tool can assist city managers and utilities in identifying
further issues to pursue to improve their resilience. In this context, the evolutionary nature of the
framework used to develop this tool is particularly important. The tool's greatest utility is in
applying it repeatedly over time to the same city to better understand current and future
resilience and critically evaluate the successes and failures of adaptation initiatives. With new
patterns of more extreme weather across the globe, adaptation is essential. A first step for many
cities will be to assess 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 refined. For
example, it is not known how well the tool would perform for cities in western or
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southern parts of the country; these cities have built environments that are more recent
than the early 1700-1800s and face different climate change risks. Likewise, it is not
known how well the tool would perform for resource-limited cities with similar risk
profiles that 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 for all cities planning to address climate
vulnerabilities. How could the tool help develop successful adaptation strategies, and
how could those adaptation strategies be shared with cities that share the same
vulnerability profile (a similar set of values across indicators)? Smit and Wandel (2006)
showed that community-based adaptation opportunities are multidimensional and
affected by exposures, sensitivities, adaptive capacities, and other factors. Application of
the tool by more cities can help identify commonalities in these factors and opportunities
for sharing best practices, policies, and adaptive strategies more quickly and effectively
to meet the growing challenge of overcoming climate risks.
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APPENDIX A. TECHNICAL STEERING COMMITTEE MEMBERS
Technical Steering Committee Members
Name
Agency
Expertise
Indicator
Subcommittee
Baranowski, Curt
U.S. EPA
Water utilities and security
•
Energy and water
Cain, Alexis
U.S. EPA Region 5
U.S. EPA regional science
representative
•
Natural environment
Carmin, JoAnn
Massachusetts
Institute of
Technology
Sociology and climate
adaptation
•
People
Chan, Steve
Harvard University
Information technology
•
T elecommunications
Cutter, Susan
University of South
Carolina
Hazards and disasters
•
•
Energy and water
Natural environment
Farris, Laura
U.S. EPA Region 8
Engineering and climate
change
•
•
•
Energy and water
Transportation
Natural environment
Fay, Kate
U.S. EPA Region 8
U.S. EPA regional science
representative
•
Natural environment
Gonzalez, Larry
U.S. EPA Region 7
U.S. EPA regional science
representative
•
Natural environment
Goold, Megan
U.S. EPA Region 3
U.S. EPA regional science
representative
•
Natural environment
Greene, Cynthia
U.S. EPA Region 1
U.S. EPA regional science
representative
•
Natural environment
Gross-Davis,
Carol Ann
U.S. EPA Region 3
U.S. EPA regional science
representative
•
•
Natural environment
People
Hansen, Verle
U.S. EPA
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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Name
Agency
Expertise
Indicator
Subcommittee
Hulting, Melissa
U.S. EPA Region 5
U.S. EPA regional science
representative
• Natural environment
Jackson, Laura
U.S. EPA
Ecology
• Natural environment
Jencks, Rosey
San Francisco
Public Utilities
Commission
Stormwater engineering
• Energy and water
Jones, Bill
U.S. EPA Region 3
U.S. EPA regional science
representative
• Natural environment
Kafalenos, Robert
United States
Department of
Transportation
(U.S.DOT)
Transportation
• Transportation
Kasperson, Roger
Clark University
Risks and uncertainty
• Energy and water
• People
Kreider, Andrew
U.S. EPA Region 3
U.S. EPA regional science
representative
• Natural environment
LaGro, James
University of
Wisconsin-Madison
Urban planning
• Energy and water
• Transportation
• Natural environment
Lawson, Linda
U.S.DOT
Transportation
• Transportation
Leichenko, Robin
Rutgers University
Economics and finance
• Economy
Lupes, Rebecca
U.S.DOT
Transportation
• Transportation
Machol, Ben
U.S. EPA Region 9
U.S. EPA regional science
representative
• Natural environment
McCullough, Jody
U.S.DOT
Transportation
• Transportation
McGeehin,
Michael
Retired
Human health
• People
Mitchell, Ken
U.S. EPA Region 4
U.S. EPA regional science
representative
• Natural environment
Narvaez,
Madonna
U.S. EPA Region 10
U.S. EPA regional science
representative
• Natural environment
Newman, Erin
U.S. EPA Region 5
U.S. EPA regional science
representative
• Natural environment
Olson, Kim
U.S. EPA Region 7
U.S. EPA regional science
representative
• Natural environment
35
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Name
Agency
Expertise
Indicator
Subcommittee
Pincetl, Stephanie
University of
California Los
Angeles
Urban planning
• Energy and water
• Transportation
• Natural environment
Pyke, Chris
U.S. 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
Arizona State
University
City planning, water and
wastewater
• Land use/land cover
• Natural environment
Rimer, Linda
U.S. EPA
Regional and local climate
adaptation
• Energy and water
• People
Rosenberg, Julie
U.S. EPA
Climate change, mitigation,
and cities
• Energy and water
• People
Ruth, Matthias
Northeastern
University
Governance
• Energy and water
• Economy
Rypinski, Art
U.S.DOT
Transportation
• Transportation
Santiago Fink,
Helen
U.S. Agency for
International
Development
Planning and international
• Natural environment
• People
Saracino, Ray
U.S. EPA Region 9
U.S. EPA regional science
representative
• N/A
Schary, Claire
U.S. EPA Region 10
U.S. EPA regional science
representative
• Natural environment
Scheraga, Joel
U.S. EPA
Economics and finance
• Economy
Shephard, Peggy
WE ACT for
Environmental
Justice
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
36
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Name
Agency
Expertise
Indicator
Subcommittee
Spector, Carl
City of Boston
Air quality
•
Natural environment
Stults, Missy
National Climate
Assessment
Urban sustainability
•
•
•
Energy and water
Transportation
Natural environment
Susman, Megan
U.S. EPA
Urban planning and smart
growth
•
•
•
Energy and water
Transportation
Natural environment
Wilbanks, Tom
Department of
Energy
Energy systems
•
Energy and water
Willard, Norman
U.S. EPA Region 1
Climate change and state
and regional policy
•
•
Energy and water
People
Wong, Shutsu
U.S. EPA Region 1
U.S. EPA regional science
representative
•
Natural environment
Yarbrough, James
U.S. EPA Region 6
U.S. EPA regional science
representative
•
Natural environment
Zinsmeister,
Emma
U.S. EPA
Climate change, mitigation,
and cities
•
•
Energy and water
People
37
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APPENDIX B. PARTICIPANTS
Worcester, MA 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
City of Worcester
Karla Sangey
Director
Upper Blackstone Pollution
Abatement District
(UBPAD) Treatment Plant,
Millbury, MA
Mark Johnson
Deputy Director
UBPAD Treatment Plant,
Millbury, MA
38
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Washington, DC Workshop Participants
Name
Affiliation
Sector
First
Workshop
Second
Workshop
Brendan
Shane
Chief, Office of Policy and
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
Sent
Tanya Stern
Chief of Staff
Office of Planning
Economy
X
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 and
Business Development
Washington Gas
Energy
X
X
Shirley
Harmon
Pepco Holdings, Inc.
Energy
X
E9-1-1 Coordinator-COOP
Susan
Nelson
Coordinator
Office of Unified
Communications
Telecommunications
X
Christopher
Bennett
IT Program Manager
Office of the Chief Technology
Officer
Telecommunications
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
Tangirala
District Department of the
Environment
Natural environment
X
Dan
Guilbeault
Policy Analyst
District Department of the
Environment
Natural environment
X
39
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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
Holman
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.
40
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Workshop Observers*
Name
Affiliation
First
Second
Workshop
Workshop
Aaron Ray
Associate
Georgetown Climate Center
X
Amy Tarce
Urban planner
National Capital Planning Commission
X
X
Ann Kosmal
Convener, GSA Climate Adaptation and
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
West Harlem Environmental Action, Inc.
(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.
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APPENDIX C. AGENDAS FOR WORKSHOPS IN WASHINGTON, DC
Meeting on
Assessing Urban Resilience in Washington, DC
ll: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 and 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]
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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 and Discussion of Sectors' Contributions to DC's
Resilience
Debrief on qualitative and quantitative 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]
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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
9:00-9:40
9:40- 10:00
10:00- 10:20
10:20am - 10:30am
10:30am - 12:45pm
Project Background and Introduction to the Scoring Questions
Breakout Sessions
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 and Lunch: Scoring Questions
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
Led by Chi Ho Sham (Cadmus)
Laine Cidlowski
John Thomas
Land Use/Land Cover
Gregory Vernon
[Sarah Yardley]
[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 and Closing
Debrief on qualitative and quantitative indicators
Closing remarks — Susan Julius (EPA)
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APPENDIX D. WASHINGTON, DC CASE STUDY
This appendix contains the Washington, DC case study. Section D. 1 provides background on the
known climate vulnerabilities faced by Washington, DC and any existing planning the city has
undertaken to address these vulnerabilities. Section D.2 reviews the results for Washington, DC.
Results are by sector and accompanied by visual data summaries.
D.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 D.l. 1.2), which allows testing the tool in an
environment where the results can 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. It also supports many of the Sustainable DC1 initiative's existing economic,
environmental, public health, and quality of life goals (Sustainable DC, 2015).
Washington, DC is located on the Atlantic Coastal Plain at the confluence of the Anacostia and
Potomac Rivers, which flow into the 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 for it
constitute a large portion of the District's economy. Tourism is also a major component of the
local economy. These components 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 those who are already highly resilient to impacts. In recent years, the District
has rapidly gentrified; home prices in nearly one-fifth of the city's census tracts moved from the
bottom half to the top half of overall citywide housing prices over the period 2000-2007.
Nationally, this is the fifth highest rate of gentrification (behind Boston, MA; Seattle, WA; New
1 The Sustainable DC planning initiative began in 2011 and is led by the District Department of the Environment
and Office of Planning; its goal is to "make DC the most sustainable city in the nation." (Sustainable DC, 2015).
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York City, NY; and San Francisco, CA) (Hartley, 2013). As a result, older data sets may not
reflect current demographics.
D.l.l. KNOWN VULNERABILITIES
D.l.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.
Climate change may exacerbate these events, which include 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 damages 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 Table 12.
D.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 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 its 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).
Some of these increases could be potentially reversed through adaptation. Modeling suggests
minor (10%) increases in reflectivity and vegetative cover would save approximately 20 lives per
decade and also reduce 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).
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 for roads, parking lots, and
airport runways might be affected (DDOT, 2013; MWCOG, 201 lc, 2013a). Buildings and
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pavement currently cover more than 40%of the District, producing a pronounced urban heat
island effect (Chuang and Hoverter, 2012).
48
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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
Approximately 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 six-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 through 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 due to
strong thunderstorms and straight-line wind; some
experienced blackouts for up to eight 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, one of the six
subway lines servicing the District, had to be replaced due to
heat-induced warping (Kunkle and Evans, 2012) after
multiple days of temperatures exceeding 100°F.
Hurricane Sandy
October
2012
Twenty-five percent of cellular sites in affected areas
(including the District) 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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D.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 risk 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 these health risks
(MWCOG, 2008). High rates of poverty and homelessness in DC make these 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, poverty is correlated with asthma
(Babin et al., 2007). DC's homelessness rate is higher than that of any state and 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 climate change)
depends on resources available at community and household levels. The greater Washington
region is the fourth largest economy in the United States. It is also home to more Inc. 5000
fastest-growing companies than any other U.S. city (WDCEP, 2010). DC is also home to 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 three in five (Reed, 2012).
D.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 had been 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).
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The National Mall Levee, part of the Potomac Park Levee System in 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 (USACE) 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. These improvements reduced the District's chance of the levee being overtopped in
any given year to less than 1% (NCPC, 2008). Work on the 17th Street levee was originally
scheduled to be completed in 2011, but it was repeatedly delayed and 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 WWTP to hold
excess storm/wastewater in flood events and prevent overflow into waterways (NCPC, 2008). If
the sewer main capacity is exceeded in extreme high-flow events, stormwater can back up into
the streets. Also, if the sewer outfall is inundated by a 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 into the watershed and
more falling as rain will lead to exaggerated seasonal runoff patterns (more streamflow in
winter/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
pollution (suspended sediment, nutrients, and chemical contaminants in rivers and lakes).
Flooding could increase leaching from landfills, hazardous waste sites, and brownfield sites.
Threats to the District's landscape and built environment from more intense precipitation events
include erosion; slope and roadway flooding and washout; roadway subsurface deterioration;
tunnel flooding; road embankment failures; scouring of bridge and culvert abutments; culvert
failures; drainage overloading and failure; tree and vegetation damage; power and other utility
failure; increased occurrence of mold in buildings; stream degradation; effects on habitats and
species; and changes in the water table that could affect development, septic systems, and the
water supply (DDOT, 2013; MWCOG, 2011a, b, c, 2013a).
Tropical storms such as hurricanes are expected to be fewer in number but characterized by
greater wind speeds and more intense precipitation (IPCC, 2007a).
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D.l.1.5. SEA LEVEL RISE
Sea level rise threatens the District's military facilities, monuments, museums, federal agencies,
roadways, bridges, metro lines, railroads, educational institutions, and fire stations. In DC, sea
level has risen 3.16 millimeters per year on average since 1924 (a total of 0.3 meters or 15
inches; NOAA, 2013), and it is expected to rise further (Ayyub et al., 2012). Ayyub et al. (2012)
modeled impacts of a 0.1, 0.4, 1, 2.5, and 5-meter sea level rise, which indicated that further sea
level rise between 0.1 and 2.5 meters would inundate between 103 and 302 properties
(residences, apartments, hotels, etc.) with combined property values between $2.1 billion and
$6.1 billion (in 2005 dollars). A sea level rise of 5.0 meters would affect 1,225 properties with
an assessed value of $24.6 billion.
Threats to the District's landscape and infrastructure from sea level rise include the loss of
wetlands, erosion of roadway subsurface, bridge scouring, embankment failures, reduced vertical
clearance for bridges, flooding of roadways in low-lying areas, changes in floodplains, and
increased tunnel flooding (DDOT, 2013; MWCOG, 201 lb, c, 2013a). Sea level rise may also
increase the salinity of the coastal rivers that empty into the Chesapeake Bay. The salinity of the
rivers will also increase during droughts and seasonal low-flow periods brought on by warming
temperatures.
D.l.1.6. ENERGY DISRUPTIONS
As shown in 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, customers do not locally store electricity, 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 aboveground 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/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 disrupt other essential services (telecommunications, food distribution,
water and wastewater services, etc.). Although DC has one of the most robust public transit
systems in the country (MWCOG, 2008; Sustainable DC, 2013), the Mayor's Office has warned
that the city's transportation infrastructure is growing old and becoming less resilient to extreme
weather events (Sustainable DC, 2013).
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D.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-Virginia-Maryland-West Virginia 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; for example, Business Insider
ranked DC third in the nation for 2015 (Kiersz, 2015).
What makes planning and governance of the District unique among U.S. cities is the federal
government's oversight authority. 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 are critical factors in the District's planning and implementation of adaptation measures.
In addition, like many U.S. cities, the District's adaptation planning has been influenced by the
work of its regional council, which in this case is the Metropolitan Washington Council of
Governments (MWCOG). A 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.
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
inter-municipal agreements for projects benefitting 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 in
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
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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 its 2008 report and the
updated 2013-2016 Action Plan (MWCOG, 2013a). In planning 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 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 concerning the built
environment and infrastructure, regional greenhouse gas emissions, renewable energy,
transportation and land use, sustainability, and resiliency and outreach (MWCOG, 2013a).
Additionally, important work relevant to climate resilience was undertaken in the aftermath of
the extreme events listed in Table 12. For example, a Federal Triangle Stormwater Study
Working Group (2011) convened after the June 2006 downpour and flash flood noted how
facility managers and service providers developed strong working relationships in the wake of
the event, an experience noted also after Hurricane Irene in 2011. They continue to share short-
and long-term flood-proofing strategies (Federal Triangle Stormwater Study Working Group,
2011).
D.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 (DC HSEMA).2 To date, these entities have developed the following plans
and are implementing the recommended measures:
• Sustainable DC Plan (Sustainable DC, 2012; 2015)
• DDOT Climate Adaptation Plan (DDOT, 2013)
• DDOT Action Agenda—Progress Report 2010 (DDOT, 2010b)
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 dc.gov). Many other departments not mentioned in this report
also contribute data and personnel to the District's adaptation planning.
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• DDOE Climate of Opportunity: A Climate Action Plan for the District of Columbia
(DDOE, 2011)
• DCWAS A Long-Term Control Plan Modification for Green Infrastructure (DCWAS A,
2014)
• DC HSEMA District Response Plan (DC HSEMA, 2008)
In addition, DC HSEMA 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, telecommunications, and transportation.
Together, the plans and action measures, along with regional efforts (discussed later on), 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.
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.
All of the departments in the above list 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 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 adaptation.
D.1.4. CITY-WIDE ADAPTATION MEASURES
The sections below present the current goals and measures the District is carrying out 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.
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D.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 as part of all new DC construction projects
• Assess its energy infrastructure's vulnerability to climate change, given that past power
outages resulted from 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
D.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:
• Update its Green Building Act of 2006 and its Leadership in Energy and Environmental
Design (LEED®) certification standards for facilities that are 50,000 square feet or larger
• Provide incentives for LEED Gold standard certification to ensure that future buildings
will be resilient to climate change
• Require neighborhood-scale sustainability goals for all major redevelopment projects
(e.g., Walter Reed Army Medical Center)
• Adopt the 2012 International Green Construction Code or an equivalent for all new
construction and major renovations
D.l.4.3. ENERGY
By 2032, the District intends to reduce power outages to less than 100 minutes per year through
energy infrastructure improvements. DC officials will work with stakeholders to add local
renewable energy sources and decentralize its energy sources into a more effective power grid.
Starting in 2014, the District began a multiyear, $ 1-billion project to move high-voltage feeder
lines underground, spearheaded by the District of Columbia Power Line Undergrounding Task
Force (DCOCA, 2014) in order to reduce this vulnerability. DDOT and PEPCO jointly
implement this public-private project.
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D.l.4.4. FOOD
Because increased local food production can improve the District's resilience to climate change,
DC intends to boost its agricultural land use by 20 acres by 2032. Specific measures include the
following actions:
• Adopt the Sustainable Urban Agriculture Act and set up urban greenhouses and
agriculture projects, in particular beekeeping
• Evaluate the potential for rooftop gardens and use of public parks and recreation areas for
growing plots to streamline the process of finding land for community agriculture
• Retrofit at least 50% of DC public schools with gardens and integrate the planning,
planting, tending, and harvesting of those gardens into the curriculum
• Make temporary agricultural sites for gardens available wherever possible
The Plan recognizes that the role the food sector plays in the DC economy can be increased.
With that goal in mind, the District intends to produce or obtain 25% of its food within a
100-mile radius. Specific measures include the following:
• Initiate a comprehensive study on the sources of the District's food supply, ways in
which that supply can become more localized, and sales of food from community
gardens.
• Set up a nonprofit Food Policy Council to research the local food sector with the goal of
providing nutritious food through a self-sustaining system.
• Purchase locally grown food for the DC public schools and government events.
D.l.4.5. WATER—WETLANDS
The Sustainable DC Plan intends to help residents and businesses adapt to climate change. It
aims to protect the District against future flood risks by restoring wetlands and creating green
infrastructure for stormwater drainage. Expanded green areas will help mitigate rising
temperatures. Additional tree canopy will benefit the environment and District residents. The
following actions aim to preserve and enhance wetlands, and thus have a climate adaptation
dimension:
• Increase the wetlands along the Anacostia and Potomac Rivers by 140 acres or an
additional 50% by 2032.
• Coordinate open space guidelines with the National Park Service to control invasive
species.
• Develop an Urban Wetland Registry to be created by DDOE's wetlands conservation
planning team.
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• Restore habitat and biodiversity of the rivers through the Urban Wetland Registry.
• Require low-impact development planning for new waterfront development greater than
50,000 square feet, along with wetlands preservation activities.
D.l.4.6. WATER—STORMWATERAVASTEWATER
To reduce flooding and improve stormwater infrastructure by 2032, the District plans to:
• Use or capture 75% of its stormwater
• Install 2 million square feet of green roofs with the help of a rebate program
• Build an additional 2 million square feet of planted surfaces on public and private
buildings by 20183
• 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 or revised zoning requirements for housing developments to improve
stormwater 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 the
Department of Justice (DOJ) that required the District of Columbia Water and Sewer Authority
(DC Water) to design and construct underground storage tunnels to hold contaminated
wastewater during storms and wet weather, with the goal of reducing combined sewer overflow
(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
3 With more than 2.5 million square feet of green roofs, the District ranks highest among 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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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 by using inflatable dams, rehabilitating pump stations, and adding
separate municipal storm sewer systems. Throughout the 20-year project, the District will 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 amendment's approval 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
project to limit untreated sewage flow into area rivers through the construction of new tunnels.
Under the Green Infrastructure Plan, some proposed tunnels will not be built; instead, the
stormwater capacity intended for the tunnels will be mitigated through green infrastructure, such
as infiltration basins and green roofs. This will allow for rainfall to infiltrate soils before it
becomes stormwater runoff, alleviating the need for costly and disruptive tunnels. The new
approach allows for faster implementation and potentially boosts property values near restored
natural areas.
In 2013, the DDOE released new stormwater management regulations, which require new
development or substantial redevelopment to meet standards for onsite water retention (DDOE,
2013a). The goals of the regulations are to increase infiltration and decrease runoff 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 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 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 nineteenth-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 to
residents, rescuers, and repair crews. As a long-term solution, the DC Clean Rivers Project will
include building an estimated 600-million-dollar tunnel system 5 miles in length to provide
excess capacity (DCOCA, 2012).
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D.l.4.7. TRANSPORTATION
The transportation plans and measures adopted by the DDOT further strengthen 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. DDOT uses three planning documents as the bases for improving the
District's resilience to climate change: (1) the Climate Change Adaptation Plan (DDOT, 2013),
(2) the DDOT Action Agenda—Progress Report 2010 (DDOT, 2010b), and (3) the DDOT Urban
Forestry Administration (UFA) 's District of Columbia Assessment of Urban Forest Resources
and Strategy (DDOT, 2010c).
The 2013 Climate Change Adaptation Plan includes the District's vulnerability assessment for
transportation infrastructure and the corresponding adaptation planning and measures to promote
resilience to extremes in temperature, precipitation, sea level rise, and storms for its 4,000 miles
of roads, 240 bridges and tunnels, and watershed with associated trees and vegetation. In
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), listed impacts, and ranked vulnerabilities as
high, medium, or low for each indicator. Potential adaptation strategies that DDOT plans to use
include DDOT climate projection models through 2100; vulnerability assessments; staff training;
updating design standards and policies; updating 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 century and the next. To reduce
stormwater runoff, urban heat, and energy use, DDOT plans to retain all stormwater from
rainstorms of at least a 1.2-inches, use 15% less energy, provide electric car recharge stations,
use low-impact development, and provide public outreach on various adaptation measures.
DDOT already installed 1,200 solar-powered parking meters (saving energy) and interactive
electronic devices in bus shelters to provide real-time bus information to the public (as part of
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
Hurricane 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.
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D.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 tree cover of 30% 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. The
leaves 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
environment, as well as water and air quality. Recent weather events, such as Hurricane Sandy
and the 2012 derecho, caused significant tree loss and damage.
D.1.5. CITY-WIDE EMERGENCY RESPONSE
Although climate change planning and adaptation are not the express purpose of the DC HSEMA
Response Plan, the plan does cover extreme weather events. It covers traditional response
elements and involves multiple and redundant agencies, systems, and measures to increase
responsiveness and resilience. The communication, coordination, and control systems between
the Mayor's Office and local and federal agencies are thoroughly delineated in the plan.
Through DC HSEMA, the District addresses long-term disaster planning (as well as strategic
planning) that includes permanently replacing housing, dealing with environmental pollution,
and restoring infrastructure. The District Response Plan also addresses services for vulnerable
populations and the general public and builds in redundant public health and emergency response
systems. Because the plan includes so many different DC and federal agencies and because the
area has recently experienced a wide range of extreme events, the plan is used, tested, and
updated often. Staff, funding, and equipment are available within close proximity for almost any
emergency situation in the District.
Discussions with public health professionals in the DC metropolitan area determined that,
although no one agency is legally charged with coordination in an emergency, informal
relationships are well established among local and state health departments and other public
health partners, resulting in strong regional coordination (Stoto and Morse, 2008).
4 The 18 major U.S. cities 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; and Tacoma, WA.
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D.1.6. DATA COLLECTION APPROACH
In the case of Washington, DC, the project team convened participants from across the District
government for an initial and a follow-up workshop. Throughout this process, the project team
worked closely with the DDOE to identify participants, understand previous or planned
resilience and adaptation efforts in the District, and hold the workshops. Both workshops
included sessions in which participants provided data for the quantitative indicators and scored
the qualitative indicators. The project team's presentations at the beginning of each workshop
introduced participants to the tool's methodology and goals. The workshops also included
presentations by DDOE and the project team on existing resilience and adaptation work in DC.
A list of workshop attendees is provided in Appendix B. Full agendas for the workshops are
provided in Appendix C.
DDOE identified workshop participants who manage activities within some of the eight sectors
identified in the tool from agencies across the government. DDOE also identified workshop
participants who operate public services (e.g., public transportation). Most of these participants
had previously joined in DDOE-led sustainability or resilience efforts. Each sector had at least
one participant with in-depth knowledge of operations and status in that sector. Because the
project did not intend to achieve consensus or to quantify differences among participants, each
sector had one individual or a group of two to three individuals designated as the expert (or
experts) charged with tool implementation activities. Some sectors had more than one individual
in this role because they covered a broad range of topics (e.g., the water sector required experts
on 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 presented on the overall tool methodology,
including details on how to use the question component. At the time of the first DC workshop,
the thresholds were not yet developed. To provide the participants at this workshop with a
baseline resilience score, a climate change resilience expert at DDOE drafted a resilience score
for 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 tool, a note-taker to capture the discussions, and printed handouts of the
questions for the qualitative indicators. 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 on the tool's
indicator component. Breakout groups then reconvened to provide importance weights and
resilience scores for the indicators and suggest any relevant data sources that the team had not
previously identified. The workshop concluded with a debriefing that asked for participant
feedback on the tool and the process.
After the first workshop, the project team analyzed the results 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 qualitative and
quantitative indicators. Thresholds had also been developed for the tool for use at any site. This
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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 initial group. Thus, the follow-up workshop also began with a presentation 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 qualitative and quantitative indicators that the project team had
modified based on suggestions from the first workshop
• Selected the most appropriate data set for quantitative 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 debriefing session during which the project team asked
participants to consider which sectors might contribute most to climate resilience in the District.
The project team also asked participants to suggest ways of displaying results that would most
benefit continued resilience and adaptation work in the District. Appendix D includes the
graphical representations of the results from the two workshops.
D.2. WASHINGTON, DC Results
D.2.1. CITY-WIDE RESULTS
The average results on resilience and importance across all sectors in Washington, DC, based on
participants' responses to questions as qualitative indicators and the importance weight assigned
to each, 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 3 and 4, the "resilience score" represents an average score for all qualitative or
quantitative 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 vulnerable compared to another sector with a low
resilience score towards the left.
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Washington. D.C. Resilience: Qualitative Indicators
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For the qualitative indicators, 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. On average, there were no sectors that scored 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 to 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 quantitative indicators, there is much more variability in scores across sectors than in the
qualitative indicator data. The greater variability in the quantitative indicator data may be due to
limitations in the available data sets that focus on a particular subset or area of the sector that
may be performing better or worse than the sector overall. With the qualitative indicators, 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 quantitative indicators still add
value to the overall analysis.
Figures 3 and 4 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.
Additionally, there may be more localized risks within and across sectors. Therefore, 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 they highlight
potential "spikes" of high risk within sectors with overall lower averages. Both Figures 5 and 6
confirm the potential for type II error because many of the sectors show significant spread across
both the resilience and importance score axes.
Figures 5 and 6 also indicate the possible action pathways stemming from the results, and they
show that the District faces a significant number of moderate to highly critical vulnerabilities to
address 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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D.2.2.1. ECONOMY
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Figure 7. Washington, DC economy sector: Qualitative and quantitative
indicator quadrant mapping.
Overall, workshop participants and supporting data indicate that Washington, DC's economy
sector is independent, diverse, and robust. Washington, DC is an economic center and operates
independently of Maryland and Virginia. DC employment centers are also very diverse, which
underlies the District economy's resilience to climate change. The District has also taken steps
to understand the potential impact of climate-related events on the local economy (e.g., the
impact of major changes in energy policy).
The District leverages current resources to perform effective adaption planning and increase
resilience. According to workshop participants, adaption planning successfully considers costs
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and benefits, encourages pre- or post-event effectiveness evaluations, and frequently involves
analyses of past climate-related events. Furthermore, existing disaster response planning
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
below in relation to intracity disparities. While the District has successful adaptation planning
processes, the lack of flexibility in the planning process and few considerations of resilience-cost
tradeoffs can reduce the its effectiveness, thus decreasing the economy's resilience.
However, resilience scoring based on economic indicator data was mixed. 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 its 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 qualitative and quantitative indicators lie in the "monitor for
changes" quadrant (high resilience/high importance). In addition, most qualitative and
quantitative indicators {15%) are above the median for importance. These trends indicate that
the District has begun to recognize and 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 qualitative and quantitative indicators fall in the "vulnerabilities
to address" quadrant (low resilience/high importance).
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D.2.2.2. ENERGY
D.C. Energy Sector: Qualitative and Quantitative Indicators
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Figure 8, Washington, DC, energy sector: Qualitative and quantitative
indicator quadrant mapping.
Washington, DC is generally resilient with respect to energy supply. The District has a diverse
energy portfolio and redundant systems are in place for coping with extreme events at the
regional level, although coverage may be inadequate at the customer or building level. The total
energy source capacity per capita is 4.2 kilowatts, which indicates high resilience. In 2010,
electricity accounted for the majority of energy consumed in the District at 70.4%, followed by
natural gas (18.3%), petroleum (11.3%), and renewable sources (a low 2%). The District's main
electricity provider, PEPCO, runs a peak energy savings program that encourages customers to
track their energy use and incentivizes peak use reduction. Peaking plants in Maryland and
Virginia can help the system cope with higher peak demands at different times than currently
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experienced. PEPCO has developed plans to address potential increases in electricity for
cooling.
Although energy supply is generally resilient, 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 does not currently occur in
the District. According to workshop participants, the political and technical capacity could allow
generation from multiple sources. The District also reported high energy use per capita. In
2010, average electricity use per capita in the District was estimated as 15,034 kilowatt-hours,
above the national average of 12,954 kilowatt-hours and the third highest national per capita use
(World Bank, 2015). Another source (U.S. EIA, 2013) reported average total energy use at 208
million British thermal units per capita. The capacity of the District's source per service area is
also low at 13.28 million gallons per square mile. 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 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 to 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, 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 Interconnection (the regional
transmission operator for the District and surrounding area) uses a rigorous planning process that
assesses 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 extreme weather events negatively affect other District services,
particularly transportation. 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 qualitative and quantitative 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.2.2.3. LAND USE/LAND COVER
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Figure 9. Washington, DC land use/land cover sector: Qualitative and
quantitative indicator quadrant mapping.
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, vary across the District's neighborhoods.
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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 flooding and
impacts of sea level rise. The District also saw a 0.19% increase in impervious cover between
2001 and 2006.
While the percentage of the District's population living in the 500- and 100-year floodplains (2.5
and 1.6%, respectively) is relatively low, the monetary value of infrastructure in the 500-year
floodplain is high, and natural areas are highly vulnerable to flooding.
Only a small percentage of open/green space is required for new development, although the
requirement varies across the District. While residents place high importance on green space and
the District is requiring more public spaces to be green and/or pervious, increasing green space is
difficult in high-density areas. Developers are also reluctant to accommodate more green space,
as nearby National Park Services land is easily accessible to residents, and more than 90% of the
District is within a 10-minute walk of green space.
However, the District received general high resilience ratings in areas related to proactive
planning and sustainable development. The National Capital Planning Commission (NCPC)
works with the DC government on federal areas in the District and has a shared comprehensive
plan that includes sustainability policies.
The District is developing efforts to use urban forms to mitigate climate change impacts and
maximize the benefits of urban forms. However, the degree of implementation varies across the
District, and there is little focus on where in the District these initiatives are taking place.
Tree cover is considered very important from an economic perspective and for livability, and
there are mechanisms to support tree-shading programs in the District. Tree-planting efforts
have been fairly robust and successful, although the same cannot be said for tree preservation
efforts. Again, there is disparity in these efforts across neighborhoods.
The District and the National Park Service have inventoried land use/land cover types and these
data will be used in planning. There are also requirements in place for retrofits in development
on vulnerable land. Workshop participants noted that resilience in DC is mostly structural, rather
than from wetlands and buffers. For example, many federal buildings in the floodplain have
structural protections against flooding. Furthermore, there are codes to prevent development in
flood-prone areas, although existing requirements are not always followed. Executive Order
11988 requires federal agencies to avoid building in floodplains to the extent possible, but
Congress ultimately decides where buildings are placed in DC. For example, the site of the
National Museum of African American History and Culture is in the bottom of the watershed
and will need extensive protection against flooding. Several new requirements have also been
proposed but not passed, including restrictions on high-hazard users (such as dry cleaners) or
vulnerable populations (such as daycares) in floodplain areas.
In cases where flooding occurs, the District encourages and provides resources for rebuilding
with more flood-resistant structures and methods, although regulations regarding rebuilding
communities impacted by floods have not been enforced.
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There are numerous existing incentives and requirements designed to reduce the amount of
impervious surface, prevent development in floodplains, and increase the use of green
infrastructure for stormwater management. Incentives and requirements for the last item include
a green roof rebate (for new development with green roofs and adding green roofs to existing
structures), the RiverSmart Homes program, stormwater requirements, impervious surface
removal rebate (on water/sewer bills), impervious surface fees, and the Green Area Ratio (which
considers green walls and other items in addition to green roofs). The Green Area Ratio and
stormwater requirements take many factors into consideration, including habitat corridors and
use of native and/or low-water-use plant species.
Green infrastructure maintenance is covered to some extent by private parties (for example,
rebate recipients are required to maintain their installations). However, not all green
infrastructure programs require follow-up to ensure the infrastructure (and its benefits) are being
maintained.
The District also uses current and historical data, local academic research, stakeholders, and
other resources (including coastal hazard maps with 1-meter altitude contours) for planning
purposes and to better understand the impact of climate change on the area.
Figure 9 includes the majority of question and indicator data in the "monitor for changes"
quadrant (high resilience/high importance), indicating that the land use/land cover sector overall
has high resilience to climate change in relation to the qualitative indicators workshop
participants found to be important.
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D.2.2.4. NA TUBAL ENVIRONMENT
Qualitative
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Figure 10. Washington, DC natural environment sector: Qualitative and
quantitative indicator quadrant mapping.
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 the use of native plants in green infrastructure
installations indicate resilience. Workshop participants assigned limited importance to the
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availability of environmental/ecosystem resources in situations where other District services are
affected by climatic events or changes.
While no data were provided to quantify the extent to which freshwater ecosystems have been
altered, workshop participants noted that less than 10 percent 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 Physical Habitat Index (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." 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, workshop participants noted that this indicator is a vulnerability to
address, as noted in Figure 10 (low resilience/high importance). The District also has native
species lists, and green infrastructure installations mostly use native species. While green roofs
cannot use only local or native plants, the guidelines for rain gardens and infiltration practices
are to use local, native, or regional plants.
While the District does not have air quality districts or a thermal comfort index and has not
analyzed areas with good ventilation, DC does have regulatory and planning tools for air quality,
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 plans to increase open
and green space, although there may be no additional capacity for natural space in the urban area.
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, although the majority of data points lie in the "vulnerabilities
to address" sector. This underscores the District's relative low resilience with respect to
qualitative and quantitative indicators that the workshop participants found to be important.
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D.2.2.5. PEOPLE
D.C. People Sector: Qualitative and Quantitative Indicators
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Figure 11. Washington, DC people sector: Qualitative and quantitative
indicator quadrant mapping.
The District is moderately resilient in terms of its population. Similar to other sectors, there is
intracity variation in resilience, and vulnerable subpopulations might be more negatively affected
by climate change impacts. It is unclear to what extent ongoing outreach has impacted these
populations.
Interconnectivity issues are a particular concern for this sector. The success of medical and fire
responses depends on a functioning telecommunications sector, and the availability of fuel and
food supplies is critical in a state of emergency. Water sector vulnerabilities can also have a
significant and potentially devastating impact on public health, while health care services are
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heavily reliant on the energy sector. Transportation is critical for evacuations during a state of
emergency.
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, with these numbers 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%) compared to the 2014 national average of 22.5% (CDC, 2016).
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 the percentage of the population with 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 for populations vulnerable to climate change. In addition, the District
has not evaluated its adaptation policies and programs to account for vulnerable populations,
although workshop participants recognized the importance of such evaluations. While some
emergency services are aimed at quickly responding to vulnerable populations during power
outages, these responses are slower than is optimal.
However, some organizations across the District 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, driven by
both the government and private sector, are designed to reach critical urban audiences. At the
same time, workshop participants questioned whether these policies and programs are designed
and implemented in ways that promote the 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 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. However, the robustness of emergency response capabilities is
dependent on the resilience of the telecommunications 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 (the
most vulnerable populations in particular). The District might not have sufficient capacity to
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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
alerts) for meteorological extreme events, but these systems rely on the individual 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 the changes in treatment necessitated by climate change and has
insufficient funding to do so. For example, the District currently does not have the appropriate
staff for West Nile virus surveillance.
Figure 11 shows a wide and relatively even distribution of responses across the resilience axis,
indicating that while the District has made strides to address the effect of climate change on the
District's population, work still needs to be done. In addition, workshop participants identified
all qualitative and quantitative indicators relating to the effect of climate change on the
population of the District as of high importance; no qualitative nor quantitative indicator was
ranked below the median for importance.
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D.2.2.6. TELECOMMUNICA TIONS
D.C. Telecommunications Sector:
Qualitative and Quantitative Indicators
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The greatest areas of vulnerability identified by workshop participants include the likelihood of
temporary loss of telecommunications infrastructure having a significant impact on local
economies, regional economies, and the population's access to FEMA emergency radio
broadcasts. In addition, the District's 9-1-1 service has no backup centers outside of the District,
only across different sections of the District. The District also 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 9-1-1 services is limited (there are more phone lines than staff members to
answer them). The District also has access to backup 9-1-1 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 qualitative (20 of 27) and quantitative indicators fall in the "monitor
for changes" segment (high resilience/high importance).
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D.2.2.7. TRANSPORTATION
D.C. Transportation Sector: Qualitative and Quantitative Indicators
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available to remedy this issue. Transportation infrastructure is particularly vulnerable to
flooding (both in terms of the impact on transportation availability and infrastructure, as well as
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 (via land, water, and air). Eighty-two percent of residents are near a transit
stop, and the District has high levels of transport diversity and intermodal passenger
connectivity. Most Metro stops provide access to bus connections, and the average distance of
non-work-related trips is fairly short (under 5 miles, although this might vary across District
neighborhoods). While the mean travel time to work for residents in the District is high, 29.6
minutes 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 the District has planned for green infrastructure
and requires its implementation. 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/bridge
infrastructure planning to consider extreme climate events.
The District also received a high resilience score for the 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, although whether these
actions truly alleviate congestion has not been determined.
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 flooding would significantly affect critical facilities. 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 places considerable stress on the District's
transportation infrastructure. Few materials currently used in the District's transportation
systems are compatible with anticipated temperature changes, 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, trains will derail. However, communication procedures are in place to prevent
risks associated with heat kinks.
Congestion is also 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), although workshop participants questioned the validity of the study. Another study
ranked the District third for congestion intensity and second for congestion costs (Litman, 2016).
The District is developing and implementing 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
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 District's current goals is to increase
modal redundancy (for non-climate-related reasons). The District is adding bike lanes and
streetcars, and it hopes to improve the Metro's redundancy and increase the bus system's
flexibility 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. Availability of transportation resources is generally at significant risk if
climatic events or changes affect other District services. Likewise, short- or long-term problems
in the transportation sector would significantly impact other sectors, particularly people and
economy. The District is also relatively dependent on the 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 District's population, risks remain high. In addition,
workshop participants identified all qualitative and quantitative indicators relating to the effect of
climate change on the District's transportation sector to be of high importance. Only one
indicator regarding travel time to work (which, as noted, is above the national average in the
District) ranked below the median for importance.
5 Yearly delay is defined as extra travel time during the year divided by the number of people who commute in
private vehicles in the urban area.
6 Areas with over 3 million population.
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D.2.2.8. WATER
D.C. Water Sector: Qualitative and Quantitative Indicators
4
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outside Washington, DC, limiting the District's control over water quality. Moreover, there is no
treatment to handle increases in nutrient loading.
Water infrastructure is at high risk during extreme events. More than 90% of stormwater and
wastewater pump stations are in the flood zone. Minimal backup power is available for drinking
water, stormwater, and wastewater services, and there is no redundant drinking water treatment
system. Likewise, there are no redundant wastewater or stormwater services.
The water sector is more resilient with respect to planning. The drinking water treatment plant
has redundant chemical suppliers, and there is a hierarchy of water use protocol during a
shortage or emergency. A water/wastewater agency response network (WARN) provides
technical resource support during emergencies, and storm sewers and drains to storm sewers
have been inventoried, although there is variability in the extent to which these inventories
inform planning (in part because one single agency does not own the stormwater infrastructure).
Drought and water availability are not a current or future concern for the District; the District
anticipates that its present ample water supply will only increase.
Figure 14 shows that the majority of qualitative and quantitative indicators are ranked as
important.
D.2.3. SUMMARY OF WASHINGTON, DC FINDINGS
The results of the DC case study show that the District's resilience to climate change is mixed,
with areas of both high and low resilience within each sector. Across most sectors, the District
demonstrated high resilience with respect to planning activities and a general awareness of the
need to prepare proactively for the potential impacts of extreme climatic events or the gradual
impacts of climate change. The District also benefits from an existing, robust transportation
system; network of parks and other green spaces; a relatively small size; and uniqueness in terms
of federal government presence and involvement. The role of the federal government in the
District perhaps grants it more expertise and resources for climate readiness and emergency
preparedness than other metropolises of a similar size might receive.
However, in some areas—especially transportation—the city's current infrastructure is less
resilient, particularly to the impacts of flooding or rising temperatures, and the resources to make
needed improvements are unavailable. In addition, the resilience scores across all sectors might
not accurately reflect significant intracity disparities in resilience. Workshop participants
frequently noted that disparities in economy, infrastructure, transportation access, and population
vulnerability could mean that climate change disproportionately affects some areas of the District
more than others. It is also unclear to what extent programs and messages regarding climate
change and adaptation and emergency response reach the most vulnerable subpopulations.
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APPENDIX E. WORCESTER, MA CASE STUDY
This appendix contains the Worcester, MA case study. Section E. 1 provides background on the
known climate vulnerabilities faced by Worcester and on the existing planning the city has
undertaken to address these vulnerabilities. Section E.2 reviews the results for Worcester, MA.
Results are by sector and accompanied by visual data summaries.
E.l. WORCESTER, MA BACKGROUND
Worcester, MA is a postindustrial city. Like many of its counterparts across New England, the
East Coast, and the Midwest, Worcester faces challenges in finding the resources to sustain
critical infrastructure, health services, and human services for current needs, let alone the
resources to prepare 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. Its current population is near 185,000. Like many cities in the North
and Midwest, its population peaked in the 1950s at just over 200,000 during the immediate
postwar years. After decades of decline (in accordance with national trends), population growth
became positive in the 1980s and is projected to remain so; total city residency increased by 5%
between 2000 and 2010 (WRRB, 2013).
Similar to other postindustrial cities, Worcester has faced the 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 city's employment
structure 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 cities in the Midwest and Northeast.
Continental humid climates are typified by large seasonal differences in temperatures with
precipitation 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. The city 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 a risk of landslides, which can
disrupt transportation, telecommunications, and other sectors.
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Like many 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 focuses largely on mitigation measures, such as
reducing greenhouse gas emissions. The plan currently does not include a focus on adaptive
measures.
Worcester is also part of the Central Massachusetts Regional Planning Commission (CMRPC), a
group focused on planning and responding to natural disasters. However, this group does not
concentrate on understanding changes in the intensity or frequency of these hazards, nor on ways
to adapt to 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.
E.1.1. 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 (CMRPC, 2012). Table 13 lists
several historic weather events that have impacted the city of Worcester.
Stormwater flooding, aggravated by urban runoff, is especially prevalent; Worcester accounts for
nearly half of historical claims in the region for damages related to stormwater flooding.
Riverine 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 to raise
awareness of flood hazards and maintain elevation certificates on new and improved buildings
(CMRPC, 2012).
Storms with high winds and winter storms can cause power outages that may 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 (CMRPC, 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 extreme heat or drought conditions.
Intense precipitation events, which are expect to increase in frequency, 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
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considered highly susceptible to contamination due to uncontrolled uses (i.e., activities on
privately owned 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 [sic]
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 vase-shaped spreading crowns and the 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 1950s and 1960s,
significantly reducing the city's tree canopy and its capacity
to take up 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 resulted in localized flooding and
power cuts (WBUR, 2011).
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Weather event
Date
Impacts
The Endless
Winter
2014-2015
Over the 2014-2015 winter, Worcester recorded 119.7
inches of snow, nearly double the average of 64.1 inches.
Worcester was the second snowiest city with a population
over 100,000 in America, less than 1 inch below the first
place city of Lowell, MA (Golden Snow Globe, 2015). The
late January "Blizzard of 2015" dumped more than 34 inches
on the city in 24 hours, breaking the 110-year record for the
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.
Asian
Long-Horned
Beetle; Emerald
Ash Borer
2000s-
Ongoing
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 temperature extremes. From 2007 to 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 (although the
city received a high resilience rating based on the percentage of roads and railroads within the
100-year floodplain). The current transportation designs and related infrastructure planning
regimes are not considering impacts of climate change or resilience. The system is designed to
state standards and under major budget constraints at present; if these standards were changed to
consider climate change scenarios, such considerations of climate risk would be incorporated.
The city recognizes the need for substantial local (and national) investment to simply repair
crumbling roads and bridges, let alone increase resilience.
E.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,
adapting, and responding to those effects. The project team reviewed information on 10 U.S.
cities similar to Worcester in size; although a majority had sustainability or hazard mitigation
plans addressing one or more specific areas (e.g., water supply, flood hazards), only one in 10
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had been comprehensively evaluated for resilience to climate change. Cities with more
developed climate change resilience evaluations and adaptation plans tended to be those that
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. A number of community resilience organizations support the program, including:
International Council for Local Environmental Initiatives (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 for evaluating resilience, studying climate threats,
and planning for them. They also lack the economies of scale that larger cities can benefit from.
Their smaller staff may be unable to participate as fully as those of larger cities in activities (e.g.,
conferences) that provide access to new information and opportunities for regional and national
partnerships. Further, smaller cities may be at greater risk of losing institutional knowledge as a
result of staff turnover. On the other hand, smaller cities with fewer stakeholders may find it
easier to develop consensus around policies and implement them. 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. 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, incorporating climate change considerations into planning, and infrastructure
replacement and design, is 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 are also cited as issues in some sectors. The city has
conducted some tabletop exercises for climate change adaptation planning, looking at past events
to assess the effectiveness of current measures. Funding for specifically adaptation-related
activities or measures is limited to nonexistent.
The city's Climate Action Plan (City of Worcester, 2006), focused on climate change mitigation
(i.e., reducing greenhouse gas emissions), also discusses several measures that have implications
for climate adaptation. 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 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 support adaptation efforts.
Detailed reporting of the city's progress toward the goals laid out in the 2006 Action Plan is not
available. However, Worcester received a "Green Community" designation in 2010; it qualified
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for this designation by developing and implementing 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 CMRPC, 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 climate change might exacerbate the
frequency or intensity, 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 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 including some
of Worcester and several neighboring communities. Mindful of the benefit of forests in
moderating climate (among other benefits), the assessment's authors recommend several actions
(e.g., providing incentives, producing educational materials, and assessing valuation studies) to
help protect forestland in the region from development.
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The city's ability to respond effectively to an extreme climatic event rests upon its existing
services, and discussions suggest that the city's emergency response capabilities are relatively
strong. Based on planning measures in place today, it is unclear whether the city will be
appropriately prepared in the long term to manage the impacts of gradual climate change across
all sectors. However, the findings suggest that the city, with appropriate resources and focus,
can incorporate adaptation and resilience considerations into existing plans, practices, and
programs.
E.1.3. DATA COLLECTION APPROACH
The primary data collection approach for the Worcester case study was discussions with key
individuals who have knowledge and experience in each of the eight sectors (see Appendix B).
Two Clark University faculty members oversaw a team to identify the most appropriate
individuals within the city of Worcester to provide feedback on the urban resilience tool's
qualitative and quantitative indicators. In addition, a literature search was conducted on the city
of Worcester. Relevant literature was reviewed for background information, as well as
information on key metrics for Worcester.
First, all participants discussed the relevance of each qualitative indicator. Then, they provided
an importance weight for each qualitative indicator. Finally, participants were asked to identify
the best score for each qualitative indicator from the question and options provided. The process
was repeated for the quantitative indicators, also requesting that participants discuss relevant
available data sets to determine the value of each indicator and review a threshold-based
resilience score (if provided).
The project team spoke with at least one primary individual with in-depth knowledge for each of
the eight sectors (see Appendix B). In the case of data analysis for the water and people sectors,
the project team recorded the response of the individual whom they deemed most qualified and
knowledgeable regarding the specific qualitative or quantitative indicator.
E.2. Worcester, MA Results
Figures 15 and 16 highlight overall trends in the Worcester, MA data. For both resilience and
importance, scores ranged from 1 to 4, with 1 indicating lowest resilience or lowest importance,
and 4 indicating highest resilience or highest importance. In Figures 15 and 16, the "resilience
score" represents an average score for all qualitative or quantitative 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 vulnerable compared to another sector with a low resilience score towards the left.
Note that there were no responses to questions for the people sector (i.e., no qualitative
indicators), and no data in the energy and telecommunications sectors for quantitative indicators.
In general, Figure 15 shows minimal spread in the qualitative indicator data for resilience.
Average importance scores for all sectors are also clustered. With the exception of the energy
sector's average resilience measurement, on average no other sector scores below the median for
resilience or importance. However, importance scores are almost always higher than resilience
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scores, suggesting potential across-the-board vulnerability. These data suggest that the most
sectors have similar levels of vulnerability, with the transportation, land use/land cover, and
energy sectors the least resilient on average.
For the quantitative indicator data in Figure 16, there is a wide range of scores. This is similar to
the wider range of quantitative indicator scores compared to qualitative indicator scores reported
for the District (see Figures 3 and 4). These data suggest that the natural environment and water
sectors are the most resilient sectors for which there are data. The other sectors (transportation,
people, economy, and land use/land cover) had similar importance scores and much lower
resilience scores, suggesting that these sectors may need more attention. All average resilience
scores for quantitative indicators (see Figure 16) are higher than the resilience scores for
qualitative indicators (see Figure 15).
Worcester. MA Resilience: Qualitative Indicators
¦ People "Water ¦Nat. Env. "Trans. "Land Use "Energy "Telecommunications "Economy
4
Increasing importance score
Figure 15. Worcester, MA: Average qualitative indicator resilience and
importance.
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Worcester MA Resilience: Quantitative indicators
¦ Nat. Env. ¦ Trans. ¦ People ¦ Water ¦ Economy ¦ Land Use ¦ Energy ¦ Telecommunications
Increasing importance score
Figure 16. Worcester, MA: Average quantitative indicator resilience and
importance.
Figure 17 disaggregates the data summarized in Figure 15 and highlights potential "spikes" of
high risk within sectors with overall lower averages. For example, qualitative or quantitative
indicators associated with the water sector fall in all four main quadrants of Figure 17, and all
seven sectors with data in Worcester have at least one qualitative indicator appearing in the
"vulnerabilities to address" domain.
Data collected in response to questions as qualitative indicators for Worcester cluster in the
"monitor for changes" quadrant (slightly more than 60% of the total). Of the seven sectors with
data, only economy is overwhelmingly restricted to the "monitor for changes" quadrant (9 out of
11 qualitative indicators). This suggests that most sectors in Worcester need to pursue a variety
of strategies to adequately prepare for climate change, as well as prioritize actions.
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Worcester, MA: Qualitative Indicators
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Figure 17. Worcester, MA: Qualitative indicator quadrant mapping.
Figure 18 offers the same presentation as Figure 17, but for quantitative indicator data across all
sectors. Note that no quantitative indicator data were available for the energy or
telecommunications sectors. Much like the qualitative indicator data in Figure 17, the majority
(four out of six) of the sectors with available data have at least one indicator in the
"vulnerabilities to address" quadrant, highlighting how averages can hide specific facets of
climate preparedness that cities need to address.
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Worcester, MA: Quantitative Indicators
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Figure 18. Worcester, MA: Quantitative indicator quadrant mapping.
E.2.1. SECTOR-SPECIFIC INVESTIGATIONS
The sections below connect the results above to potential underlying drivers and roadblocks for
each sector, discussed in the literature as well as from participant input. However, unlike
Washington, DC, little supplemental literature was available for Worcester. In addition, the
discussions were primarily limited to one representative (as the tool was designed), in contrast to
the participation of numerous representatives across sectors, as was the case at the DC
workshops.
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E.2.1.1. ECONOMY
Worcester Economy Sector: Qualitative and Quantitative Indicators
4
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Worcester received lower resilience scores for the economy due to vulnerable subpopulations,
including the growing homeless population (which increased 5.1% between January 2012 and
January 2013) and the percentage of the city's population living below the poverty line (19%,
based on 2007-2011 data). However, the 2006 passage of the Massachusetts health care reform
law required most state residents to obtain some level of health insurance coverage, and as a
result, 95% of the noninstitutionalized population has 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 depends on which sector is disrupted and on workers' skills and mobility
in each sector.
As Worcester continues to prepare 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's government, reducing
projects' efficiency and efficacy, and no funding is currently available for multipurpose adaptive
development projects (meeting both recreation and adaptive development needs), which may be
roadblocks to planning efforts.
Figure 19 shows that 69% of the qualitative and quantitative 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 economy sector relate to the city's vulnerable subpopulations and gaps in
adaptive planning funding. Figure 19 also demonstrates that the city may reconsider its approach
to adaption planning, possibly concentrating planning activities into one office, as this issue lies
in the "small problems that can add up" quadrant.
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E.2.1.2. ENERGY
Worcester Energy Sector: Qualitative and Quantitative Indicators
o
o
o
CO
o
o
n
ro
t
o
Q.
Monitor for changes
Vulnerabilities to address
0
©
O
O
Low priority
Small problems that can add up
• Qualitative
Indicators
4 (high resilience)
3 2
Resilience Score
1 (low resilience)
Figure 20. Worcester, MA energy sector: Qualitative and quantitative
indicator quadrant mapping.
The data on the energy sector came exclusively via qualitative indicators. No indicator data were
available for relevant quantitative indicators.
In terms of energy supply, the city's energy sector is relatively resilient, as energy supplies come
from outside the metropolitan area to only a moderate extent. The city has also made moderate
efforts to reduce energy demand. However, based on participant responses, the resilience of the
city's energy sector in terms of coping with or responding to stressors (extreme events, outages,
or higher peak demand/demand at different times) appears to be limited. The city's redundant
energy systems have only a small capacity in the event of a threat to the energy system; however,
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the participant rated this as a less important factor for the 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).
Beyond efforts to encourage energy consumption reductions, 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 would necessarily reduce
vulnerabilities to climate change. One participant made note of National Grid's smart grid pilot
project in Worcester; however, 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 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
city. Worcester offers a "Worcester Energy Program" to encourage energy savings.
Figure 20 shows that of the data available, 62.5% (or five of eight) qualitative indicators 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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E.2.1.3. LAND USE/LAND COVER
Worcester Land Use/Land Cover Sector:
Qualitative and Quantitative Indicators
Monitor for changes
Vulnerabilities to address
1439 ¦1436
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308 ¦ 254
• Qualitative
Indicatofs
¦ Quantitative
Indicators
©
Low priority
Small problems that can add up
4 (high resilience)
3 2
Resilience Score
1 (low resilience)
Figure 21. Worcester, MA land use/land cover sector: Qualitative and
quantitative indicator quadrant mapping.
While Worcester has many planning and zoning initiatives that may be relevant to climate
resilience, none of them have been justified by climate resilience or undertaken for the primary
purpose of resilience. The most significant discrepancy between importance score and resilience
score, indicating the greatest perceived vulnerability, concerns the location of valuable
infrastructure and continued development (without concern for retrofitting) in areas that are
vulnerable to extreme events, including flooding. The same discrepancy was identified
regarding the lack of financial incentives to prevent development in floodplains and reduce the
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amount of impervious surface, among other initiatives. These responses may speak to a greater
vulnerability in the economy sector than was indicated by the interview.
The city is not actively using resilient retrofits or urban forms to mitigate climate change impacts
or address urban heat island effects. 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;
providing funds to reduce the amount of impervious surfaces; and limiting further development
in vulnerable areas, as identified in the qualitative indicators.
Worcester is taking 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 from existing, established sources 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 qualitative and quantitative indicators with high resilience, all fall into the "monitor for
changes" quadrant, indicating that the participant ranked the actions the city has taken to
improve land use/land cover resilience as highly important. Of the qualitative and quantitative
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 qualitative and quantitative indicators,
80%) have the lowest resilience score of 1.
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E.2.1.4. NA TURAL ENVIRONMENT
Worcester Natural Environment Sector:
Qualitative and Quantitative Indicators
o
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o
o
CO
03
o
c
rc
o
Cl
Monitor for changes
Vulnerabilities to address
©
Q
O
O
o
0 ^
Low priority
Small problems that can add up
• Qualitative
Indicators
¦ Quantitative
Indicators
4 (high resilience)
3 2
Resilience Score
1 (low resilience)
Figure 22. Worcester, MA natural environment sector: Qualitative and
quantitative indicator quadrant mapping.
Worcester demonstrates relatively high resilience in this sector, based on existing regulatory and
planning tools/processes on water quality, air quality, and land use; coordination with other
entities on water quality issues; and green space initiatives. The city has also developed native
plant and animal species lists and uses these species in green infrastructure, as seen in Figure 22,
where most of the qualitative and quantitative indicators score at least a 3 for resilience.
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In addition, there are few wetland species at risk (rare, endangered, or threatened). While the
city has no plans for preserving areas with good ventilation, the participant assigned a lower
importance score to this qualitative indicator. However, the city demonstrates limited resilience
for the availability of environmental/ecosystem goods and services if other city goods and
services, such as power, water, and telecommunications, were affected by extreme climate events
or gradual changes. This may help explain some of the lower resilience-scoring qualitative and
quantitative indicators.
Figure 22 demonstrates that Worcester has high resilience in the natural environment sector, with
82% of the qualitative and quantitative indicators having a resilience score of at least 3.
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E.2.1.5. PEOPLE
Worcester People Sector: Qualitative and Quantitative Indicators
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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 as 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 deaths.
However, climate change-related programs for adaptive behavior at the community level have
been appropriately designed and promoted, although success has been limited, depending on the
issue and the type of change being sought. For example, the city uses cooling centers (typically
shopping malls) during extreme heat events. However, the elderly, especially those with asthma,
are vulnerable because they cannot easily move 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 risk if extreme climatic events or
gradual climate change affect other city goods and services, loss of water and sanitation services
can potentially create serious public health risks, especially for vulnerable subpopulations
(18.3% of the population is vulnerable due to age).
Figure 23 shows that 78% of the indicators have an importance score of at least 3, with resilience
scores distributed widely and relatively evenly across the resilience axis. Close to half of the
indicators fall into the "monitor for changes" quadrant (high resilience/high importance) and
33%) lie in the "vulnerabilities to address" quadrant.
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E.2.1.6. TELECOMMUNICATIONS
0)
i_
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IVorcester Telecommunications Sector:
Qualitative and Quantitative Indicators
Monitor for changes
Vulnerabilities to address
0
Q
0 ©
^ ^ ^
O
O
o
0
> Qualitative
Indicators
i Quantitative
Indicators
Low priority
Small problems that can add up
4 (high resilience)
3 2
Resilience Score
1 (low resilience)
Figure 24. Worcester, MA telecommunications sector: Qualitative and
quantitative indicator quadrant mapping.
The data gathered on the telecommunications sector came exclusively from qualitative
indicators. No quantitative indicator data were available for relevant indicators.
Worcester's telecommunications sector generally demonstrates high resilience regarding
emergency preparedness, robustness/vulnerability of the network and infrastructure, backup
power and redundancy, and past experience. The city has experienced extreme weather and
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other similar events in recent years, and telecommunications services were only impacted to a
limited extent.
Local authorities have established relationships 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 would be 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.
While disruption in the telecommunications sector may have impact the economy sector, there is
only some risk that disruptions to other city goods and services (power, water, etc.) would impact
the telecommunications sector.
However, the city appears to be less resilient in terms of a potential temporary loss of
telecommunications and its impact on the local and regional economies, as well as the location of
data centers, which are to some extent outside of the urban area (qualitative indicator #77).
However, for all other relevant qualitative indicators, 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 9-1-1 services, Worcester has an emergency notification system,
ALERT Worcester, which contacts residents and businesses. The city's website provides
information on 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 qualitative indicators (21 of 23) scored a 3 or above
for resilience and 74% of the qualitative indicators (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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E.2.1.7. TRANSPORTATION
Worcester Transportation Sector:
Qualitative and Quantitative Indicators
4
0
i_
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1
Monitor for changes
Vulnerabilities to address
mum
WlhlM
987 1
[l40s[
Low priority
Small problems that can add up
• Qualitative
Indicators
¦ Quantitative
Indicators
4 (high resilience) 3 2 1 (low resilience)
Resilience Score
Figure 25. Worcester, MA transportation sector: Qualitative and
quantitative indicator quadrant mapping.
Worcester's transportation is varied. The system includes a commuter rail line to and from
Boston (owned by the Massachusetts Bay Transportation Authority (MBTA), also known as the
"T"), Worcester Regional Transit Authority bus lines, and various local and state surface roads
and highways for vehicle transit.
Worcester's transportation sector demonstrates limited resilience, which could have significant
implications for the community's ability to respond to and recover from a major climatic event.
It is also unclear whether the city could maintain adequate transportation services in the face of
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gradual effects on the sector due to climate change. For example, the length of time that would
be required to restore major passenger rail transportation links in the urban area after a failure
could be more than a week; however, the participant rated this question as only moderately
important (score of 2).
The qualitative indicator scores did suggest that the city's transportation system is moderately
resilient despite the lack of resilience or adaptation planning; that its redundancy is generally
adequate; and that availability of transportation resources would not be heavily impacted if
climate change or extreme climatic events affect other city goods and services. However,
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. For example, 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 is at an early stage. In
addition, residents are resistant to changing their preferred modes of transit unless there is a
compelling reason or incentive. However, the Central Massachusetts Regional Planning
Commission, in coordination with the Montachusett Regional Planning Commission, completed
the first two phases of evacuation planning for Worcester in 2015. This included data gathering,
mapping, and completion of a day-long tabletop exercise. Future plans include training local
Emergency Management Directors on using the gathered data and identifying detours on major
highways in the region.
Figure 25 reflects some, but not all, of these data. Data points vary widely on the importance
axis, where approximately half of the qualitative and quantitative indicators have an importance
score above 3 and half below 3. Over 65% of qualitative and quantitative indicators received a
resilience score of 2 or lower, indicating that the transportation sector is relatively vulnerable.
However, 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 address highly
important factors related to transportation.
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E.2.1.8. WATER
Worcester Water Sector: Qualitative and Quantitative Indicators
4
1
Monitor for changes
Vulnerabilities to address
•
0
0 0
Low priority
Small problems that can add up
• Qualitative
Indicators
¦ Quantitative
Indicators
4 (high resilience) 3 2 1 (low resilience)
Resilience Score
Figure 26. Worcester, MA water sector: Qualitative and quantitative
indicator quadrant mapping.
The city's Department of Public Works and Parks oversees operation and maintenance of the
city's drinking water infrastmcture and supplies (10 surface water sources outside the city limits)
and sewer infrastructure. The city's drinking water is treated at a water treatment plant at a rate
of 50 million gallons per day, using a combination of ozone, coagulation, and filtration. The city
has had no Safe Drinking Water Act violations in the past five years, but it received the lowest
resilience rating for the ratio of water consumption to water availability.
Wastewater is treated at the Upper Blackstone WWTP before it is discharged into the Blackstone
River. Since its construction, the District has completed over $170 million in improvements to
the WWTP. In 2009, further improvements increased energy efficiency, provided solar power
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for the plant, and upgraded the solids management facilities. The improvements reduced the
plant's carbon footprint and increased its treatment capacity (UBWPAD, 2013).
Further improvements to the drinking water and wastewater infrastructure to increase climate
change resilience have been a challenge due to limited funding and environmental regulations set
forth by federal and state legislation. In Worcester, the Millbury WWTP remains vulnerable to
high-intensity storms in terms of both flooding (floodplain location proximate to the Blackstone
River) and the limited capacity to handle high stormwater throughput. These vulnerabilities
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 declines. 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 for the entire city.
Interconnectivity is a significant issue for this sector. The 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 due to the
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 about the limitations of backup power, the impact loss of power has on the
water sector, and the lack of hierarchy of water uses during a shortage or emergency, the city
otherwise demonstrates resilience with respect to emergency preparedness, emergency 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, a
WARN provides 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, incorporating past experiences into
planning approaches for water shortages or increases in the frequency of overflows. The city
also has programs related to long-term maintenance of water supplies, has inventoried storm
sewers and drains to storm sewers, and has used these inventories in planning efforts. However,
customer familiarity with conservations measures and the measures' implementation is
somewhat limited. In general, the city's properties are not equipped to harvest rainwater or
recharge groundwater, and residents practice 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 axes. Based on the available data, 71% of the qualitative and quantitative indicators
have resilience scores of 3 or above, demonstrating that 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
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of hierarchy of water uses during a shortage or emergency; the fact that properties in the city are
not equipped to harvest rainwater; and the fact that the availability of water resources is at
significant risk if other city services, particularly energy/power, are affected by climatic changes
or events.
E.2.2. SUMMARY OF WORCESTER, MA 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 (quantitative indicator data or
qualitative responses to questions) for the people, energy, and telecommunications sectors. The
City demonstrated relatively high resilience in respect to the economy, telecommunications,
water, and natural environment sectors. Resilience in the remaining sectors is largely mixed,
although more significant potential vulnerabilities were identified in the energy sector.
However, as no quantitative indicator data were available for the energy sector, a more complete
picture of the city's vulnerability 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, as well as
redundancy within both the water and telecommunications sectors.
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 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 implementing the framework and
addressing vulnerabilities that like-sized urban communities face.
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APPENDIX F. COMPARISON OF RESULTS FOR WASHINGTON, DC AND
WORCESTER, MA
This appendix presents cross-city comparison visualizations. While the value of this
visualization is 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 clearer as
additional data are gathered and confidence in those patterns increases.
F.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
climate-resilient futures. Table 14 highlights some of the key features of both cities. Choosing
these contrasting cities allows cities within a broad spectrum in terms of resources, planning, and
risk to understand the applications and potential outcomes of using the tool. It also allows us to
test the strengths and weaknesses of the tool's methodology in a wide range of conditions and
provides preliminary insight into the range (or potential lack) 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, heat waves, severe
storms
(MWCOG, 2013a)
Drought, heat waves,
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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F.2. Results—quadrant map comparisons
Figure 27 maps the qualitative and quantitative indicator sector averages from both Washington,
DC and Worcester, MA on a quadrant graph. This approach does not show intrasector areas of
higher or lower vulnerability, but it facilitates comparison between the two cities and between
qualitative and quantitative indicator-based results. Overall, the results for both cities for all
sectors cluster moderately tightly, with the center of the cluster falling in 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 qualitative indicators, the spread of the data is slightly less than one point in both
resilience and importance scores. The spread for the quantitative indicator data is greater, but it
is difficult to determine whether this spread is meaningful because much less quantitative
indicator data were available—particularly for Worcester. 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.
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Comparison of Washington, D.C. and Worcester, MA Results
Monitor for changes
Vulnerabilities to address
~ o
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¦ Water
¦Trans.
¦ People
¦ Nat. Env.
¦ Lar>d Use
i T elecom municati oris
¦ Energy
¦ Economy
ri
Low priority
Small problems that can add up
Quant. Qual.
Washington, D.C.
Worcester, MA
4 (high resilience) 3 2 1 {low resilience)
Resilience Score
Figure 27. Washington, DC and Worcester, MA: Average quantitative
indicator and qualitative indicator score quadrant mapping.
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APPENDIX G. QUALITATIVE INDICATORS: ORDERED
Qualitative
Indicator
ID#
Sector
Name/Question
1
Economy
Is the economy of the urban area largely independent, or is it largely
dependent on economic activity in other urban areas?
2
Economy
Does the urban area have mechanisms to help businesses quickly return
to normal operations?
3
Economy
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?
4
Economy
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?
5
Economy
Has the urban area's resilience to major changes in energy policy/prices
been assessed?
6
Economy
Is funding available for adaptive development projects that could also
serve as recreation areas (e.g., retention areas along waterways that
could also serve as parks)? Are such multipurpose projects required or
are there incentives for these projects?
7
Economy
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?
8
Economy
How many people are in place to respond to emergencies, and what is
the level of communication connectivity of emergency response teams
and offices?
9
Economy
Is comprehensive adaptation planning possible with the urban area's
current resources? If so, is adaptation planning already occurring?
10
Economy
Is planning for climate change adaptation in the urban area incorporated
into one office within the local government, or is planning spread out
across several offices within the government?
11
Economy
How flexible are planning processes for short-term and long-term
responses? For example, is there flexibility in changing planning
priorities if necessary?
12
Economy
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?
13
Economy
Does adaptation planning for the urban area consider the costs and
benefits of possible decisions, and does it encourage both pre- and post-
event evaluations of the effectiveness of adaptation measures?
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14
Economy
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 is in coastal cities, where some areas can be protected by a
seawall, or households and institutions in vulnerable areas can be
moved inland. Do current adaptation plans account for the resilience-
cost tradeoffs in this decision?)
15
Energy
Do you have a diverse energy portfolio?
16
Energy
Are there redundant systems in place for coping with extreme events?
17
Energy
To what extent do energy supplies come from outside the metropolitan
area?
18
Energy
Is the availability of energy goods and services at risk if other city
goods and services (e.g., water, transportation, telecommunications) are
affected by extreme climatic events or gradual climatic changes?
19
Energy
How many minutes per year or hours per year do you have power
outages?
20
Energy
What is the response time to restore electrical power after an outage?
21
Energy
Does capacity exist to handle a higher peak demand or peaks at
different times?
22
Energy
To what extent have efforts been made to reduce energy demand?
23
Energy
What are the opportunities for distributed generation sources (e.g.,
different capacity for energy generation from different sources
including renewable)?
24
Energy
Are there smart grid opportunities to manage demand?
25
Land Use/Land
Cover
Can resilience planning/adaptation be incorporated into existing
programs that communities engage in regularly (e.g., zoning, hazard
mitigation plans)?
26
Land Use/Land
Cover
Has the city made efforts to use urban forms to mitigate climate change
impacts and to maximize benefits (e.g., urban tree canopy cover)?
27
Land Use/Land
Cover
Are urban forms used that address (lessen) urban heat island effects
(e.g., through increasing evapotranspiration or increasing urban
ventilation)?
28
Land Use/Land
Cover
Does zoning encourage green roofs or other practices that reduce urban
heat?
29
Land Use/Land
Cover
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?
30
Land Use/Land
Cover
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?
31
Land Use/Land
Cover
What percentage of open/green space is required for new development
(to encourage increases in such space)?
120
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32
Land Use/Land
Cover
Are there mechanisms for the local government to purchase land that is
unfavorable for redevelopment due to the results of extreme events
(e.g., flooding from a hurricane)? If so, what are those mechanisms?
33
Land Use/Land
Cover
Are there policies or zoning practices in place that will allow transfer of
ownership of undeveloped land subject to flooding or excessive erosion
to the city (or allow nonpermanent structures only)? Are these policies
or zoning practices enforced?
34
Land Use/Land
Cover
Where developed land is located in areas vulnerable to extreme events,
are resilient retrofits being implemented or planned?
35
Land Use/Land
Cover
Are there codes to prevent development in flood-prone areas?
36
Land Use/Land
Cover
Are regulations in place regarding whether communities that are
affected by floods will be rebuilt in the same location?
37
Land Use/Land
Cover
Have the regulations regarding rebuilding of communities affected by
floods been enforced to date?
38
Land Use/Land
Cover
Do incentives exist to integrate green stormwater infrastructure into
infrastructure planning to mitigate flooding?
39
Land Use/Land
Cover
Are there incentives to reduce the amount of impervious surface, to
prevent development in floodplains, to use urban forestry to reduce
impacts, to use green infrastructure for stormwater management, etc.?
40
Land Use/Land
Cover
To what extent was green infrastructure selected to provide the
maximum ecological benefits?
41
Land Use/Land
Cover
Has green infrastructure maintenance been built into the budget?
42
Natural
Environment
Is the availability of environmental/ecosystem goods and services at
risk if other city goods and services (e.g., power, water,
telecommunications) are affected by extreme climatic events or gradual
climatic changes?
43
Natural
Environment
What regulatory and planning tools related to air quality, water quality,
and land use are already available locally? For example, does the urban
area have invasive plant ordinances or tree planting requirements?
44
Natural
Environment
Do plans exist for increasing open and green space?
45
Natural
Environment
Has the continuity of open or green spaces been assessed and addressed
in planning efforts?
46
Natural
Environment
Do native plant or animal species lists exist for the urban area, and are
these species (rather than nonnative species) used in green
infrastructure?
47
Natural
Environment
Does the urban area coordinate with other nearby entities on water
quality?
48
Natural
Environment
To what degree do local versus distant sources influence air quality?
49
Natural
Environment
Does the urban area have air quality districts?
121
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50
Natural
Environment
Has an air quality analysis been completed at multiple
scales/resolutions?
51
Natural
Environment
Does the urban area have health warnings or alerts for days when air
quality may be hazardous?
52
Natural
Environment
Has an analysis of areas with good ventilation (e.g., aligned with
prevailing breezes, good tree canopy cover) been completed?
53
Natural
Environment
Do plans exist for preserving areas with good ventilation (e.g., those
aligned with prevailing breezes)?
54
Natural
Environment
Does the urban area have a district-scale (i.e., higher resolution than
city scale) thermal comfort index?
55
People
How available and how comprehensive are your planning resources for
responding to extreme events?
56
People
Are government-led, community-based, or other organizations actively
promoting adaptive behaviors at the neighborhood or city level?
57
People
Do policies and outreach/education programs promote behavioral
changes that facilitate climate change adaptation?
58
People
Are emergency response staff well trained to respond to large-scale
extreme weather events?
59
People
Is the distribution of public health workers and emergency response
resources appropriate for the population that would be affected during
an extreme event?
60
People
Is there sufficient capacity in public health and emergency response
systems for responding to extreme events?
61
People
Does the city have the capacity to provide public transportation for
emergency evacuations?
62
People
What evacuation and shelter-in-place options are available to residents
in the event of a heat wave?
63
People
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, using fire hydrants for cooling), given predicted
climatic changes?
64
People
Is the health care community, including primary care physicians,
prepared for changes in patients" treatments necessitated by climate
change (e.g., emerging infectious diseases)?
65
People
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?
66
People
Do public health programs incorporate longer time frames (e.g., 10 or
more years), and do they address climate change-related health issues
(e.g., movement of deer ticks to more northerly locations)?
67
People
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?
122
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68
People
Have public health agencies developed plans for responding to
increased disease and vector exposure in ways that may reduce the
associated morbidity/mortality?
69
People
Do planners in the urban area know the demographic characteristics of
populations vulnerable to climate change?
70
People
Do planners in the urban area know the locations of populations most
vulnerable to climate change effects?
71
People
Are there services and emergency responses aimed at quickly reaching
vulnerable populations during power outages?
72
People
Are policies and programs to promote adaptive behavior designed with
frames/messaging that reach the critical audiences in the urban area?
73
People
Are policies and programs to promote adaptive behavior designed and
implemented in ways that promote the health and well-being of
vulnerable populations?
74
People
Are policies and programs to promote adaptive behavior evaluated in
ways that take into account vulnerable populations?
75
T elecommunications
What natural disasters has the area experienced in the past, and what
services were retained or largely unaffected despite these disasters?
76
T elecommunications
How would a temporary loss of telecommunication infrastructure affect
the local and regional economies?
77
T elecommunications
Are data centers located within or outside of the urban area?
78
T elecommunications
For each telecommunication service, are there key nodes whose failure
would severely affect the service?
79
T elecommunications
How robust is the telecommunication network in terms of resilience to
damage to or failure of key nodes?
80
T elecommunications
Are there parts of the telecommunication infrastructure that are
particularly vulnerable to high temperatures or prolonged high
temperatures?
81
T elecommunications
Are there satellite-based communications on frequency bands (e.g., the
Ka band) that are vulnerable to wet-weather disruption?
82
T elecommunications
Are your telecommunication infrastructure components located wisely
with respect to your anticipated climate stressors (i.e., aboveground,
underground, or serviced by satellite)?
83
T elecommunications
Are aboveground infrastructure components vulnerable to wind (e.g.,
cell towers)?
84
T elecommunications
Are belowground infrastructure components vulnerable to rising water
or salt water intrusion?
85
T elecommunications
If the area has satellite-based communications that are vulnerable to
wet-weather disruption, does the area have a backup tower network?
123
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86
T elecommunications
Does your community have sufficient access to backup
telecommunication systems? What is the capacity of the
telecommunication infrastructure?
87
T elecommunications
Is backup power for telecommunication systems provided? If so, is it
provided by diesel generators?
88
T elecommunications
What is the extent of telecommunication redundancy? Do first
responders and the public have multiple communication options, served
by different infrastructures?
89
T elecommunications
What percentage of telecommunication system capacity is required for
the baseline level of use?
90
T elecommunications
Does telecommunication infrastructure have the capacity for increased
public demand in an emergency?
91
T elecommunications
Do local authorities have established relations with telecommunication
infrastructure service providers? Are emergency protocols and plans in
place?
92
T elecommunications
Do local private-sector telecommunication infrastructure service
providers have the authority and resources to make quick decisions and
implement them in and after an emergency?
93
T elecommunications
Can local authorities and telecommunication providers give
first-responder and decision-maker communications priority during an
expected surge in traffic in emergency situations?
94
T elecommunications
Are public-address systems (e.g., loudspeakers, text messages, radio
broadcasts, emergency television broadcasts) in place to provide
instructions to the public in case of an emergency?
95
T elecommunications
What modes do authorities in the urban area use to communicate
emergency information and alerts? Are these modes low or high
bandwidth?
96
T elecommunications
What is the likelihood that the capacity of local first responder
communication systems would be exceeded during a disaster?
97
T elecommunications
Does the area have access to backup emergency call/response (911)
networks if the primary networks fail or are overloaded?
98
T elecommunications
Is the availability of telecommunication 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?
99
T elecommunications
Do telecommunication systems have enough energy and water supply
to handle an extra load in the case of sudden natural disasters?
100
Transportation
Is the availability of transportation goods and services at risk if other
city goods and services (e.g., power, water, telecommunications) are
affected by extreme climatic events or gradual climatic changes?
101
Transportation
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?
124
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102
Transportation
How resistant to potential impacts of climate change are critical
transportation facilities (e.g., high-traffic vehicle or rail bridges,
tunnels)?
103
Transportation
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?
104
Transportation
What length of time would be required to restore major high-traffic
vehicle transportation facilities in the urban area if they experience a
failure?
105
Transportation
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?
106
Transportation
To what extent is the area dependent on long-range transportation of
goods and services versus locally available goods and services (food,
energy, etc.)?
107
Transportation
What flexibility has been built into the transportation system (different
modes)?
108
People
How accessible are different modes of transportation (e.g., to what
proportion of the population, what subpopulations [vulnerable
people])?
109
People
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 (e.g., due to reduced access to specialized medical care
or equipment)?
110
Transportation
How familiar is the community with evacuation procedures?
111
Transportation
What length of time would be required to restore major passenger rail
transportation facilities in the urban area if they experience a failure?
112
Transportation
What length of time would be required to restore major freight rail
transportation facilities in the urban area if they experience a failure?
113
Transportation
What length of time would be required to restore major bicycle and
pedestrian transportation links in the urban area if they experience a
failure?
114
Transportation
Are urban areas set up to provide accessibility (e.g., to jobs) if mobility
is interrupted or impeded?
115
Transportation
Do current planning regimes include proactive resilience building, or is
only reactive disaster response being addressed?
116
Transportation
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?
117
Transportation
Do plans exist to replace aging infrastructure? If so, do these plans
account for the anticipated impacts of climate change on this
infrastructure?
118
Transportation
Are the materials currently in use in transportation systems, such as the
common asphalt formulations and rail types, compatible with
anticipated changes in temperature?
125
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119
Transportation
Have new or innovative materials been tested that may be more capable
of withstanding the anticipated impacts of climate change (e.g., higher
temperatures)?
120
Transportation
To what extent is green infrastructure implemented or planned to
reduce climate change impacts on transportation systems?
121
Water
Does the water supply draw from a diversity of sources?
122
Water
To what extent do water supplies come from outside the metropolitan
area?
123
Water
Is there a recharge plan in place for groundwater supplies?
124
Water
Do programs for long-term maintenance of water supplies (e.g., erosion
control methods, reforestation of the watershed) exist?
125
Water
Is there a hierarchy of water uses to be implemented during a shortage
or emergency?
126
Water
Does the water system have emergency interconnections with adjacent
water systems or other emergency sources of supply?
127
Water
Are water and wastewater treatment plants located in a flood zone?
128
Water
Are groundwater supplies susceptible to salt water intrusion and sea
level rise?
129
Water
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?
130
Water
Does treatment capacity exist to accommodate nutrient loading?
131
Water
Does the drinking water treatment plant have redundant treatment
chemical suppliers?
132
Water
Are there redundant drinking water systems in place for coping with
extreme events, including supply, treatment, and distribution systems?
133
Water
Is backup power for water supply, treatment, and distribution systems
provided?
134
Water
How diverse are individual properties (i.e., are they equipped to harvest
rainwater or recharge groundwater so they can create or augment local
water supplies)?
135
Water
Are there redundant wastewater and stormwater systems in place for
coping with extreme events, including collection systems and
wastewater treatment systems?
136
Water
Does a water/wastewater agency response network provide technical
resources/support to the urban area's water system during emergencies?
137
Water
Have storm sewers and drains to storm sewers been inventoried, and are
these inventories used in planning?
126
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138
Water
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?
139
Water
Has the water utility conducted a water audit to identify current losses
(e.g., leaks, billing errors, inaccurate meters, unauthorized usage)?
140
Water
To what extent have efforts been made to reduce water demand?
141
Water
Are customers familiar with water conservation measures, and are they
willing to implement these measures?
142
Land Use/Land
Cover
Are coastal hazard maps with 1-meter altitude contours available, and
are these maps used in planning?
143
People
Are early warning systems for meteorological extreme events
available?
147
Energy
Do municipal managers draw on past data/experiences of extreme
weather events to assess the effects of these events on oil and gas
availability and pricing? (DOE, 2013)
148
Energy
Has the city consulted with local power companies to develop plans for
potential increases in electricity demand for summer cooling? (DOE,
2013)
149
Energy
Has the city coordinated with local water suppliers and power
generation facilities to discuss potential climate-induced water
shortages and their impacts on cooling the power generation facilities?
(DOE, 2013)
150
Energy
Do municipal managers in coastal areas consider the impacts of sea
level rise on power generation facilities?
151
Land Use/Land
Cover
Have institutional land practices (i.e., zoning, land use planning)
potentially been hindered by other government agencies seeking to shift
financial resources when it comes to climate change planning?
152
Land Use/Land
Cover
Does knowledge of historical land use/land cover changes contribute to
planners" understanding of climate stresses?
153
Land Use/Land
Cover
Have specific historical land use/land cover changes been recognized as
increasing or decreasing vulnerability to climate stresses?
154
Land Use/Land
Cover
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?
158
People
Do municipal managers consider local stakeholder knowledge and local
resources (e.g., libraries, archives) in climate change resilience
planning?
160
T elecommunications
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?
162
Transportation
Have municipalities considered new methods of designing
roads/bridges to prepare for heavily traveled routes during an extreme
climate event (e.g., coastal evacuation routes)?
163
Water
Have water utility companies incorporated past experience or
experience from other locations/utilities into developing plans for water
shortages related to climate-induced stresses?
127
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164
Water
Does the water department or utility for the city consider past
experience in addressing anticipated increases in the frequency of sewer
overflows?
165
Economy
What financial capacity is indicated by the city's bond ratings?
166
Water
Is backup power for wastewater collection and treatment provided?
167
Land Use/Land
Cover
In general, what is the monetary value of infrastructure located within
the 500-year floodplain in the city?
168
Transportation
How resistant to potential impacts of climate change are critical
nonroad transportation facilities (e.g., high-traffic rail bridges, tunnels)?
169
Transportation
Do plans exist to replace aging infrastructure? If so, do these plans
account for the anticipated impacts of climate change on this
infrastructure?
128
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APPENDIX H. QUANTITATIVE INDICATORS: ORDERED
Quantitative
Indicator
ID#
Sector
Name
Definition
17
Natural
Environment
Altered wetlands
(percentage of
wetlands lost)
This indicator reflects the percentage of wetland areas
that have been excavated, impounded, diked, partially
drained, or farmed.
51
Land Use/Land
Cover
Coastal
Vulnerability Index
rank
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).
66
Natural
Environment
Percentage change
in disruptive
species
This indicator reflects the percentage 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.
194
Land Use/Land
Cover
Percentage of
natural area that is
in small natural
patches
This indicator measures the percentage of the total
natural area in a city that is in patches of less than 10
acres. 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.
209
People
Percentage of
population living
within the 500-year
floodplain
This indicator reflects age of population living within
the 500-year floodplain.
254
Land Use/Land
Cover
Ratio of perimeter
to area of natural
patches
This indicator is calculated as the average ratio of the
perimeter to area.
273
Natural
Environment
Percentage of total
wildlife species of
greatest
conservation need
This indicator reflects the percentage of total wildlife
species that are listed as having the "greatest
conservation need."
129
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284
Natural
Environment
Physical Habitat
Index (PHI)
PHI includes eight characteristics (riffle quality,
stream bank stability, quantity of woody debris, in-
stream habitat for fish, suitability of streambed surface
materials for macroinvertebrates, shading, distance to
nearest road, and embeddedness of substrates). Scores
range from 0-100 (81-100 = minimally degraded, 66-
80 = partially degraded, 51-65 = degraded, 0-
50 = severely degraded).
308
Land Use/Land
Cover
Percentage of city
land that is urban
and suburban
This indicator presents the extent/acreage of urban and
suburban areas (i.e., not rural) as a percentage of the
total land area
322
People
Percentage of
population affected
by waterborne
diseases
This indicator reports the percentage of population
affected by waterborne diseases.
326
Natural
Environment
Wetland species at
risk (number of
species)
Number of wetland and freshwater species at risk
(rare, threatened, or endangered).
393
People
Percentage of
vulnerable
population that is
homeless
This indicator reflects the percentage of population 65
and older and under 5 years that is homeless.
437
Water
Percentage change
in streamflow
divided by
percentage change
in precipitation
This indicator reflects percentage change in
streamflow (O) divided by percentage change in
precipitation (/') for 1,291 gauged watersheds across
the continental U.S. from 1931 to 1988.
460
Natural
Environment
Macroinvertebrate
Index of Biotic
Condition
The Benthic Index of Biotic Integrity (BIBI) 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 Percentage, Long-Lived Taxa Richness,
Percentage Tolerant, Percentage Predator, and
Percentage Dominance.
465
Natural
Environment
Change in plant
species diversity
from pre-European
settlement
Change in the plant species diversity from
pre-European settlement (baseline) to present, within a
given city/area.
675
People
Asthma prevalence
(percentage of
population affected
by asthma)
This indicator presents asthma prevalence for U.S.
children (age 0-17) and adults (age 18 and older). It
is calculated as the percentage of population reporting
asthma. Asthma attack prevalence is based on the
number of adults/children who reported an asthma
episode or attack in the past 12 months.
130
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676
People
Percentage of
population affected
by notifiable
diseases
This indicator reflects percentage 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
chlamydia, coccidioidomycosis, cryptosporidiosis,
Dengue virus, Escherichia coli, ehrlichiosis,
giardiasis, gonorrhea, Haemophilus influenzae,
hepatitus A, hepatitis B, hepatitis 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.
680
Natural
Environment
Ecological
connectivity
(percentage of area
classified as hub or
corridor)
This indicator reflects the percentage 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).
681
Natural
Environment
Relative ecological
condition of
undeveloped land
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.
682
Natural
Environment
Percentage change
in bird population
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.
690
People
Emergency medical
service response
times
This indicator measures average annual response
times (in minutes) for emergency medical service
calls.
709
Economy
Percentage of
owned housing
units that are
affordable
This indicator measures (1) the percentage of owned
housing units where selected monthly ownership costs
(rent, mortgages, real estate taxes, insurance, utilities,
fuel, fees) as a percentage of household income
(SMOCAPI) exceeds 35% or (2) the percentage of
rented housing units where gross rent as a percentage
of household income (GRAPI) exceeds 35%.
131
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711
Economy
Overall
unemployment rate
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 the percentage of sectors in a city's
economy that employ < 40% of the city's population.
Sectors that employ 1% or less of the city's population
are not considered, as they provide very minimal
employment opportunities.
111
Economy
Percentage access
to health insurance
of
noninstitutionalized
population
This indicator measures the percentage of
noninstitutionalized residents with health insurance.
722
Economy
Percentage change
in homeless
population
This indicator measures the percentage change in the
homeless population.
725
People
Number of
physicians per
capita
This indicator reflects the total number of M.D. and
D.O. physicians per capita.
728
People
Adult care (homes
per capita)
The number of adult day care homes and assisted
living homes per capita of population over 65 years.
757
People
Average police
response time
This indicator reflects the average response time for
police to respond to emergency situations.
784
People
Number of sworn
police officers per
capita
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.
798
People
Percentage of fire
response times less
than 6.5 minutes
This indicator reflects the percentage of fire response
times less than 6.5 minutes (from city stations to city
locations).
825
Land Use/Land
Cover
Percent change in
impervious cover
This indicator reflects the change in the percentage of
the metropolitan area that is impervious surface
(roads, buildings, sidewalks, parking lots, etc.).
132
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898
Energy
Annual energy
consumption per
capita by main use
category
(commercial use)
The indicator measures the annual energy
consumption (2010) per capita within the commercial
use sector.
924
Energy
Energy intensity by
use
This indicator measures energy intensity in
manufacturing, transportation, agriculture,
commercial and public services, and the residential
sector.
949
Energy
Percentage energy
consumed for
electricity
The indicator measures electricity consumption per
year in kWh as a percentage of total energy
consumption.
950
Energy
Percentage of
electricity
generation from
noncarbon sources
This indicator measures the percentage of total
electricity generation from noncarbon energy sources
in a city.
951
Energy
Percentage of total
energy use from
renewable sources
This indicator measures the percentage of total energy
use from renewable sources.
967
Energy
Total energy source
capacity per capita
This indicator measures the total capacity of all
energy sources (MW) per capita.
970
Energy
Average capacity
of a decentralized
energy source
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.
971
Energy
Energy source
capacity per unit
area
This indicator measures the total capacity of energy
sources per unit area served (MW/sq mi).
983
Energy
Average customer
energy outage
(hours) in recent
major storm
This indicator measures the average customer energy
outage hours divided by number of electricity
customers for a storm event in June 2012.
985
Transportation
Transport system
user satisfaction
This indicator reflects the overall user satisfaction
with the transport system. It is defined as the average
user satisfaction with bus service, rail service, and the
accuracy of passenger information displays.
987
Transportation
Employment
accessibility (mean
travel time to work
relative to national
average)
This indicator is defined as the mean travel time to
work in a city relative to the U.S. average.
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988
Transportation
Walkability score
This indicator reflects the walkability score of the
community (points out of 100).
991
Transportation
Percentage
transport diversity
Highest public expenditure for a single mode of
transportation as a percentage of the total expenditures
for all transportation modes.
1003
Transportation
Mobility
management
(yearly congestion
costs saved by
operational
treatments per
capita)
This indicator reports on the yearly congestion costs
saved by operational treatments (in billions of 2011
dollars). Operational treatments include freeway
incident management, freeway ramp metering, arterial
street signal coordination, arterial street access
management, and high-occupancy vehicle lanes.
1010
Transportation
Community
Livability Index
The Community Livability Index is the equally
weighted average of the Community Service
Indicator, the Crime Indicator, the Retail Opportunity
Indicator, the Educational Indicator, the
Environmental Quality Indicator, the Housing
Affordability Indicator, and the Transit Livability
Indicator. Details of the calculation are provided in
Ripplinger et al. (2012;
http: //www .ugpti. org/pubs/pdf/DP262 .pdf).
1157
People
Percentage of
housing units with
air conditioning
This indicator reflects the percentage of housing units
with air conditioning.
1170
People
Percentage of
population
experiencing heat-
related deaths
This indicator reflects the percentage of the population
experiencing heat-related deaths.
1171
People
Percentage of
population affected
by food poisoning
This indicator reflects the percentage of population
affected by food poisoning (i.e., Salmonella spp.,
unsafe drinking water).
1346
Water
Percentage of
infiltration and
inflow (I/I) in
wastewater
Water that enters the wastewater system through
infiltration and inflow (I/I) as a percentage of total
wastewater from all wastewater 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; cross-connections with storm drains and
downspouts; and through holes in manhole covers.
1347
Water
Wet weather flow
bypass volume
relative to the 5-
year average
Volume of wastewater that bypassed treatment in an
average year for all wastewater treatment plants
divided by the 5-year average.
1369
Water
Annual coefficient
of variation (CV)
of unregulated
streamflow
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
134
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the unregulated mean annual streamflow (QS)'. (Hurd
et al., 1999).
1375
Economy
Percentage of
population living
below the poverty
line
This indicator measures the percentage of the
population living below the poverty line.
1376
People
Percentage of
population that is
disabled
This indicator reflects the percentage of the
noninstitutionalized 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).
1387
People
Percentage of
population
vulnerable due to
age
This indicator reflects percentage of population above
65 or under 5 years old.
1390
People
Percentage of
population that is
living alone
This indicator reflects the percentage of population
that is 65 years or older and living alone.
1396
Transportation
Percent access to
transportation stops
This indicator reflects the percentage of the population
that is near a transit stop.
1399
Transportation
Percentage of roads
and railroads within
the city that are
located within 10
feet of water
This indicator measures the percentage of roadway
miles and rail line miles that are within 10 feet of a
body of water.
1400
Transportation
Percentage of roads
and railroads within
the city in the 500-
year floodplain
This indicator measures the percentage of roadway
miles and rail line miles that are within the 500-year
floodplain.
1401
Transportation
Percentage of roads
and railroads within
the city in the 100-
year floodplain
This indicator measures the percentage of roadway
miles and rail line miles that are within the 100-year
floodplain.
1402
Transportation
Total annual hours
of rail line closure
due to heat and
maintenance
problems
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.
135
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1403
Transportation
Percentage of city
culverts that are
sized to meet
current stormwater
capacity
requirements
This indicator measures the percentage of current
culverts that cross transportation facilities in the
metropolitan region that are sized to meet current
stormwater capacity requirements.
1404
Transportation
Percentage of city
culverts that are
sized to meet future
stormwater
capacity
requirements
This indicator measures the percentage of current
culverts that cross transportation facilities in the
metropolitan region that are sized to meet projected
stormwater capacity requirements for 2030.
1406
Transportation
Percentage decline
in repeat
maintenance events
This indicator measures the percentage 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.
1408
Transportation
Percentage of
bridges that are
structurally
deficient
This indicator measures the percentage of bridges that
are structurally deficient. Bridges are considered
structurally deficient if significant load-carrying
elements are found to be in poor or worse condition
due to deterioration 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.
1410
Transportation
Average Hours of
passenger transit
delay per capita
due to heat related
issues
This indicator measures the average hours of
passenger transit delay per capita due to heat related
issues.
1411
Transportation
Roadway
connectivity
(number of
intersections per
square mile)
This indicator measures the number of intersections
per square mile.
1412
Transportation
Miles of pedestrian
facilities per street
mile
This indicator measures the miles of pedestrian
facilities (sidewalks) per street mile.
1413
Transportation
Percentage of short
walkable sidewalks
in urban areas
This indicator measures the percentage of sidewalks
within the urban area that are less than 330 feet.
1417
Transportation
Percentage funding
spent on
pedestrian/bicycle
projects connected
to community
activity centers
Percentage of program funds spent on pedestrian or
bicycle projects that include at least one connection to
activity centers (e.g., schools; universities; downtown
and employment districts; senior facilities;
hospital/medical clinics; parks, recreation, and
sporting; grocery stores; museums and tourist
attractions).
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1419
Transportation
Intermodal freight
connectivity (ratio
of intermodal
connections used
per year to
individual modes)
This indicator measures the 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.
1420
Transportation
Intermodal
passenger
connectivity
(percentage of
terminals with at
least one
intermodal
connection for the
most common
mode)
This indicator measures the percentage of active
passenger terminals for the most common mode (e.g.,
rail, air) with at least one intermodal passenger
connection. Intermodal connections allow passengers
to use a combination of modes and give travelers
additional transportation alternatives that
unconnected, parallel systems do not offer.
1422
Transportation
Average distance of
all nonwork trips
This indicator measures the average distance from a
given home to the nearest grocery store, high school,
and health care facility (i.e., nonwork trips).
1424
Transportation
Roundabouts
N/A
1426
Transportation
City congestion
rank
This indicator measures the congestion rank of the
metropolitan area relative to all U.S. metropolitan
areas.
1428
Water
Total number of
Safe Drinking
Water Act (SDWA)
violations
This indicator measures the total number of SDWA
violations over the last 5 years.
1429
Transportation
Tele work rank
This indicator measures the telework rank of the
metropolitan area relative to all other extra-large
metropolitan areas in the U.S. The rank is based on
the percentage of jobs within the metropolitan region
that could be accomplished by telecommuting if
employer policies were to permit it.
1433
T elecommunica-
tions
Percentage of
system capacity
needed to carry
baseline level of
traffic
N/A
1434
T elecommunica-
tions
Baseline
percentage of water
supply for
telecommunication
systems that comes
from outside the
metropolitan area
N/A
137
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1435
T elecommunica-
tions
Baseline
percentage of
energy supply for
telecommunication
systems that comes
from outside the
metropolitan area
N/A
1436
Land Use/Land
Cover
Percentage of city
area in 100-year
floodplain
This indicator reflects the percentage of the
metropolitan area that lies within the 100-year
floodplain.
1437
Land Use/Land
Cover
Percentage of city
area in 500-year
floodplain
This indicator reflects the percentage of the
metropolitan area that lies within the 500-year
floodplain.
1438
Land Use/Land
Cover
Percentage of city
population in 100-
year floodplain
This indicator reflects the percentage of the city
population living within the 100-year floodplain.
1439
Land Use/Land
Cover
Percentage of city
population in 500-
year floodplain
This indicator reflects the percentage of the city
population living within the 500-year floodplain.
1440
Land Use/Land
Cover
Palmer Drought
Severity Index
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 that had the
greatest total precipitation deficit.
1441
T elecommunica-
tions
Percentage of
community with
access to FEMA
emergency radio
broadcasts
Percentage of community with access to FEMA
emergency radio broadcasts.
1442
Water
Ratio of water
consumption to
water availability
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.
1443
People
Deaths from
extreme weather
events
This indicator measures the number of deaths in the
last 5 years due to extreme events (cold, flood, heat,
lightning, tornado, tropical cyclone, wind, and winter
storms).
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APPENDIX I. QUALITATIVE INDICATORS: TEMPLATE
A complete set of the qualitative indicators by sector developed for the tool.
1.1. 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
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#1: Is the economy of the urban area largely independent, or is it largely dependent on
economic activity in other urban 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
Largely dependent
1 (lowest resilience)
Somewhat dependent
2
Somewhat independent
3
Largely independent
4 (highest resilience)
#2: Does the urban area have mechanisms to help businesses quickly return to normal
operations?
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)
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#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?
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)
#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)
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#5: Has the urban area's resilience to major changes in energy policy/prices been assessed?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
No
Yes
Importance Weights
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#6: Is funding available for adaptive development projects that could also serve as
recreation areas (e.g., retention areas along waterways that could also serve as parks)? Are
such multipurpose 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)
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)
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#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?
Relevance Importance Weight
Yes 3
Answer Resilience Score
Yes 1 (lowest resilience)
No 3 (highest resilience)
#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
Importance Weights
1 (not very important)
2
3
4 (very important)
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.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
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#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)
4 (highest resilience)
#10: Is planning for climate change adaptation in the urban area incorporated into one
office within the local government or is planning spread out across several offices within
the government?
Relevance
Importance Weight
Answer
Adaptation planning responsibilities are not incorporated
into any offices within the local government.
Adaptation planning responsibilities are spread out over
multiple offices within the local government.
Adaptation planning is shared between two or three offices
within the local government.
Adaptation planning is incorporated into one office within
the local government.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
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#11: How flexible are planning processes for short-term and long-term responses? For
example, is there flexibility in changing planning priorities if necessary?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Planning processes are fairly inflexible.
Planning processes are somewhat flexible.
Planning processes are moderately flexible.
Planning processes are very flexible.
Resilience Score
1 (lowest resilience)
2
3
4 (highest resilience)
#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?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight
1 (not very important
2
3
4 (very important)
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)
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#13: Does adaptation planning for the urban area consider the costs and benefits of
possible decisions, and does it encourage both pre-event and postevent evaluations of the
effectiveness of adaptation measures?
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
Adaptation planning does not consider costs and benefits
1 (lowest resilience)
and does not encourage pre-event or postevent
effectiveness evaluations.
Adaptation planning does consider costs and benefits but
2
does not encourage pre-event or postevent effectiveness
evaluations.
Adaptation planning does consider costs and benefits and
3
encourages pre-event or postevent effectiveness
evaluations.
Adaptation planning does consider costs and benefits and
4 (highest resilience)
requires pre-event or postevent effectiveness evaluations.
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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 is: in coastal cities, some areas can be protected by a
seawall, or households and institutions in vulnerable areas can be moved inland. Do
current adaptation plans account for the resilience-cost tradeoffs in this decision?)
Relevance
Importance Weight
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score
Adaptation plans do not explicitly consider resilience-cost
1 (lowest resilience)
tradeoffs or no adaptation plans exist.
Adaptation plans consider one or two resilience-cost
2
tradeoffs.
Adaptation plans consider some resilience-cost tradeoffs.
3
Adaptation plans consider many resilience-cost tradeoffs.
4 (highest resilience)
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#165: What financial capacity or credit risk is indicated by the city's bond rating(s)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
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)
4 (highest resilience)
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1.2. 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.
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#15: Do you have a diverse energy portfolio?
Relevance
Imvortance 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
2
O
J
4 (highest resilience)
#16: Are there redundant systems in place for coping with extreme events?
Relevance
Imvortance 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
4 (highest resilience)
capacity necessary.
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#17: To what extent do energy supplies come from outside the metropolitan area?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
They come exclusively from outside the area.
To a great extent
To a moderate extent
Only to a small extent
Resilience Score
1 (lowest resilience)
2
3
4 (highest resilience)
#18: Is the availability of energy goods and services at risk if other city goods and services
(e.g., water, transportation, telecommunications) 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 energy resources is at significant risk if
other city services are affected by climatic events or
changes.
Availability of energy resources is at moderate risk if other
city services are affected by climatic events or changes.
Availability of energy resources is at some risk if other city
services are affected by climatic events or changes.
Availability of energy resources is at minimal risk if other
city services are affected by climatic events or changes.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
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#19: How many minutes per year or hours per year do you have power outages?
Relevance
Importance Weight
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 per year for all outage events
1 (lowest resilience)
More than 1 hour to 1 day per year for all outage events
2
More than 30 minutes to 1 hour per year for all outage
3
events
Less than 30 minutes per year for all outage events
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)
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#21: Does capacity exist to handle a higher peak demand or peaks at different times?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Electricity generation capacity cannot handle higher peak
demands or peaks at different times than currently
experienced.
Electricity generation capacity can handle higher peak
demands or peaks at different times than currently
experienced.
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#22: To what extent have efforts been made to reduce energy demand?
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 efforts have been made to reduce energy
demand.
Fair efforts have been made to reduce energy demand.
Moderate efforts have been made to reduce energy demand.
Significant efforts have been made to reduce energy
demand.
Resilience Score
1 (lowest resilience)
2
3
4 (highest resilience)
153
-------
#23: What are the opportunities for distributed generation sources (i.e., different capacity
for 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 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)
154
-------
#147: Do municipal managers draw on past data/experiences of extreme weather events to
assess the effects of these events on oil and gas availability and pricing? (DOE, 2013)
Relevance Importance Weight
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)
#148: Has the city consulted with local power companies to develop plans for potential
increases in electricity demand for summer cooling? (DOE, 2013)
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight
1 (not very important)
2
3
4 (very important)
Answer
The city has not consulted with local power companies and
is not developing plans for potential increase in electricity
for cooling.
The city has consulted with local power companies
regarding 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
developing plans for potential increase in electricity for
cooling.
The city has consulted with local power companies and
developed plans for potential increase in electricity for
cooling.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
155
-------
#149: Has the city coordinated with local water suppliers and power generation facilities to
discuss potential climate-induced water shortages and their impacts on cooling the power
generation facilities?(DOE, 2013)
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
No
Yes
Importance Weight
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#150: Do municipal managers in coastal areas consider the impacts of sea level rise on
power generation facilities?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
No
Yes, but these considerations are not incorporated into
planning for these facilities.
Yes, and these considerations are being incorporated into
planning for these facilities.
Yes, and these considerations are incorporated into
planning for these facilities.
Importance Weight
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
2
4 (highest resilience)
156
-------
1.3. 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
157
-------
#25: Can resilience planning/adaptation be incorporated into existing programs that
communities engage in regularly (e.g., zoning, hazard mitigation plans)?
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 efforts to use urban forms 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)
158
-------
#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 or 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)
159
-------
#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)
160
-------
#31: What percentage of open/green space is required for new development (to encourage
increases in such space)?
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 open/green space is required for new development. 1 (lowest resilience)
A small percentage of open/green space is required for new 2
development.
A moderate percentage of open/green space is required for 3
new development.
A high percentage of open/green space is required for new 4 (highest resilience)
development.
#32: Are there mechanisms for the local government to purchase land that is unfavorable
for redevelopment due to the 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
2
preliminary and are slightly helpful.
Yes, there are such mechanisms and they are somewhat
3
helpful.
Yes, there are such mechanisms and they are helpful.
4 (highest resilience)
161
-------
#33: Are there policies or zoning practices in place that allow transfer of ownership of
undevelopable land subject to flooding or excessive erosion to the city (or allow
nonpermanent structures only)? Are these policies or zoning practices enforced?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Policies do not allow ownership transfer.
Policies allow ownership transfer, but these policies are
enforced only rarely.
Policies allow ownership transfer, but these policies are
only enforced some of the time.
Policies allow ownership transfers, and these policies are
enforced.
Resilience Score
1 (lowest resilience)
2
4 (highest resilience)
#34: Where developed land is located in areas vulnerable to extreme events, are resilient
retrofits being implemented or planned?
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)
162
-------
#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)
#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)
163
-------
#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)
#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)
164
-------
#39: Are there incentives to reduce the amount of impervious surface, to prevent
development in floodplains, 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
Yes (relevant)
No (not relevant)
Not sure—remind me later
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.
Importance Weights
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
4 (highest resilience)
165
-------
#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?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight
1 (not very important)
2
3
4 (very important)
Answer
Resilience Score
Such maps have not been developed and are not planned to be 1 (lowest resilience)
developed.
Plans exist to develop such maps OR such maps exist but are not 2
used in planning.
Such maps are being developed and these maps are used or will 3
be used in planning.
Such maps exist and these maps are used in planning. 4 (highest resilience)
166
-------
#151: Have institutional land practices (i.e., zoning, land use planning) potentially been
hindered by other government agencies seeking to shift financial resources when it conies
to climate change planning?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
Yes
No
Importance Weight
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#152: Does knowledge of historical land use/land cover changes contribute to planners'
understanding of climate stresses?
Relevance Importance Weight
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)
167
-------
#153: Have specific historical land use/land cover changes been recognized as increasing or
decreasing vulnerability to climate stresses?
Relevance Importance Weight
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)
#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?
Relevance Importance Weight
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)
168
-------
#167: In general, what is the monetary value of infrastructure located within the 500-year
floodplain in the city?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
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 (lowest resilience)
4 (highest resilience)
169
-------
1.4. 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
170
-------
#42: Is the availability of environmental/ecosystem goods and services at risk if other city
goods and services (e.g., power, water, telecommunications) 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 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)
171
-------
#43: What regulatory and planning tools related to air quality, water quality, and land use
are already available locally? For example, does the urban area have invasive plant
ordinances or tree planting requirements?
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
The urban area does not have regulatory and planning tools 1 (lowest resilience)
for air and water quality and land use.
The urban area has few regulatory and planning tools for 2
air and water quality and land use.
The urban area has several regulatory and planning tools 3
for air and water quality and land use.
The urban area has many regulatory and planning tools for 4 (highest resilience)
air and water quality and land use.
#44: Do plans exist for increasing open and green space?
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)
172
-------
#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)
#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
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score
Native species lists do not exist and are not being
1 (lowest resilience)
developed.
Native species lists exist, but green infrastructure uses
2
mostly nonnative species OR native species lists are under
development.
Native species lists exist and green infrastructure uses
3
mostly these species.
Native species lists exist and green infrastructure uses only
4 (highest resilience)
these species.
173
-------
#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)
#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)
174
-------
#49: Does the urban area have air quality districts?
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)
#50: Has an air quality analysis been completed at multiple scales/resolutions?
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
An air quality analysis has not been completed.
1 (lowest resilience)
An air quality analysis has been completed at a one
2
scale/resolution.
Air quality analysis has been completed at a few
3
scales/resolutions.
Air quality analysis has been completed at many
4 (highest resilience)
scales/resolutions.
175
-------
#51: Does the urban area have health warnings or alerts for days when air quality may be
hazardous?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
No
Yes
Importance Weights
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#52: Has an analysis of areas with good ventilation (e.g., aligned with prevailing breezes,
good tree canopy cover) been completed?
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
An analysis of areas with good ventilation has not been
1 (lowest resilience)
planned or completed.
An analysis of areas with good ventilation is planned.
2
An analysis of areas with good ventilation is in progress.
3
An analysis of areas with good ventilation has been
4 (highest resilience)
completed.
176
-------
#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)
177
-------
1.5. 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
178
-------
#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
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)
179
-------
#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)
#58: Are emergency response staff well trained 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)
180
-------
#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)
#60: Is there sufficient capacity in public health and emergency response systems for
responding to 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)
181
-------
#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
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No evacuation or shelter-in-place options are available to
residents in the event of a heat wave.
One to two evacuation and shelter-in-place options are
available to residents in the event of a heat wave.
Several evacuation and shelter-in-place options are
available to residents in the event of a heat wave.
Many evacuation and shelter-in-place options are available
to residents in the event of a heat wave.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
182
-------
#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, using 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
4 (highest resilience)
#64: Is the healthcare community, including primary care
in patients' treatments necessitated by climate change (e.g.
physicians, prepared for changes
,, 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 well-prepared.
Resilience Score
1 (lowest resilience)
2
3
4 (highest resilience)
183
-------
#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)
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 or more years),
and do they address climate change-related health issues (e.g., movement of deer ticks to
more northerly locations)?
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
Public health programs are not designed to address climate- 1 (lowest resilience)
related health issues.
Public health programs incorporate long-term timeframes 3 (highest resilience)
and are address climate-related health issues.
184
-------
#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)
185
-------
#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)
#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)
186
-------
#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)
187
-------
#73: Are policies and programs to promote adaptive behavior designed and implemented in
ways that promote the health and well-being of 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)
#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)
188
-------
#108: How accessible are different modes of transportation (e.g., to what proportion of the
population, what subpopulations [vulnerable people])?
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 modes of transportation are accessible to
1 (lowest resilience)
vulnerable subpopulations.
Some modes of transportation are accessible to vulnerable
2
subpopulations.
Many modes of transportation are accessible to vulnerable
3
subpopulations.
All modes of transportation are accessible to vulnerable
4 (highest resilience)
subpopulations.
189
-------
#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 (e.g., due to reduced access to specialized
medical care or equipment)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight
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)
190
-------
#143: Are early warning systems for meteorological extreme events available?
Relevance Importance Weight
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)
#158: Do municipal managers consider local stakeholder knowledge and local resources
(e.g., libraries, archives) in climate change resilience planning?
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)
191
-------
1.6. Telecommunications
The questions below have been developed for the telecommunication 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 telecommunication 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.
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
192
-------
#75: What natural disasters has the area experienced in the past, and what services were
retained 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
1 (lowest resilience)
recent history, or services were significantly impaired
during recent natural disasters.
Area has experienced some extreme weather or other
2
natural disasters, but some services were significantly
affected.
Area has experienced some extreme weather or other
3
natural disasters, and most services were unaffected or
affected in minor ways.
Area has experienced major extreme weather events or
4 (highest resilience)
other natural disasters, and majority of services were
retained or were largely unaffected.
#76: How would a temporary loss of telecommunication 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)
193
-------
#77: Are data centers located within or outside of 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
Within
1 (lowest resilience)
Mostly within the urban area, but somewhat outside the
2
urban area.
Mostly outside the urban area, but somewhat within the
3
urban area.
Outside
4 (highest resilience)
#78: For each telecommunication service, are there key nodes whose failure would severely
affect the service?
Relevance
Importance Weight
Yes (relevant)
1 (not very important
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score
There are many key nodes whose failure would severely
1 (lowest resilience)
affect service.
There are some key nodes whose failure would severely
2
affect service.
There are a few key nodes whose failure would severely
3
affect service.
No, there are no nodes whose failure would severely affect
4 (highest resilience)
service.
194
-------
#79: How robust is the telecommunication network in terms of resilience to damage to or
failure of key nodes?
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
The telecommunication network is not resilient to damage
1 (lowest resilience)
or failure of key nodes.
The telecommunication network is slightly resilient to
2
damage or failure of key nodes.
The telecommunication network is somewhat resilient to
3
damage or failure of key nodes.
The telecommunication network is very resilient to damage
4 (highest resilience)
or failure of key nodes.
#80: Are there parts of the telecommunication 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)
195
-------
#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)
#82: Are your telecommunication 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)
196
-------
#83: Are aboveground infrastructure components vulnerable to wind (e.g., cell towers)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
All aboveground infrastructure components are vulnerable
to expected winds.
Some aboveground infrastructure components are
vulnerable to expected winds.
Few aboveground infrastructure components are vulnerable
to expected winds.
No aboveground infrastructure components are vulnerable
to expected winds.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#84: Are belowground infrastructure components vulnerable to rising water or 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)
Answer
All belowground infrastructure components are vulnerable
to expected rises in groundwater levels or from salt water
intrusion.
Some belowground infrastructure components are
vulnerable to expected rises in groundwater levels or from
salt water intrusion.
Few belowground infrastructure components are vulnerable
to expected rises in groundwater levels or from salt water
intrusion.
No belowground infrastructure components are vulnerable
to expected rises in groundwater levels or from salt water
intrusion.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
197
-------
#85: If the area has satellite-based communications that are vulnerable to wet-weather
disruption, does the area have a backup tower network?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight
1 (not very important)
2
3
4 (very important)
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 of backup.
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)
#86: Does your community have sufficient access to backup telecommunication systems?
What is the capacity of the telecommunication 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
telecommunication infrastructure is low.
There are some minimal backup systems, but
telecommunication 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
telecommunication systems is high.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
198
-------
#87: Is backup power for telecommunication 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
Resilience Score
1 (lowest resilience)
2
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 3
diesel generators.
Backup power is provided and is not provided by diesel 4 (highest resilience)
generators.
#88: What is the extent of telecommunication redundancy? Do first responders and the
public have multiple communication options, served by different 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 3
more than one communications options, served by different
infrastructure.
There is a great deal of redundancy. There are multiple 4 (highest resilience)
communication options, served by different infrastructure.
199
-------
#89: What percentage of telecommunication system capacity is required for the baseline
level of use?
Relevance
Importance Weight
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score
Greater than 85%
1 (lowest resilience)
70 to 85%
2
60 to 70%
3
Less than 60%
4 (highest resilience)
#90: Does telecommunication infrastructure have the capacity for increased public deman
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)
200
-------
#91: Do local authorities have established relations with telecommunication 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 telecommunication 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)
201
-------
#93: Can local authorities and telecommunication providers give first responder and
decision-maker communications priority during an expected surge in traffic in emergency
situations?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
No
Yes
Importance Weights
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#94: Are public-address systems (e.g., loud speakers, text messages, radio broadcasts,
emergency television broadcasts) in place to provide instructions to the public in case of an
emergency?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight
1 (not very important)
2
3
4 (very important)
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
1 (lowest resilience)
2
3
4 (highest resilience)
202
-------
#95: What modes do authorities in the urban area use to communicate emergency
information and alerts? Are these modes low or high bandwidth?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Authorities do not use multiple modes (e.g., text
messaging, email, phone calls), or none of the modes used
is low bandwidth.
Authorities use one to two modes (e.g., text messaging,
email, phone calls) and one or two of these modes is low
bandwidth.
Authorities use multiple modes (e.g., text messaging,
email, phone calls) and one or two of these modes are low
bandwidth.
Authorities use multiple modes (e.g., text messaging,
email, phone calls) and some of these modes are low
bandwidth.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
203
-------
#96: What is the likelihood that the capacity of local first responder communication
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)
#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)
204
-------
#98: Is the availability of telecommunication 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 telecommunication resources is at
significant risk if other city services are affected by
climatic events or changes.
Availability of telecommunication resources is at moderate
risk if other city services are affected by climatic events or
changes.
Availability of telecommunication resources is at some risk
if other city services are affected by climatic events or
changes.
Availability of telecommunication resources is at minimal
risk if other city services are affected by climatic events or
changes.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
205
-------
#99: Do telecommunication systems have enough energy and water supply to handle an
extra load in the case of sudden natural disasters?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight
1 (not very important)
2
3
4 (very important)
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.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#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?
Relevance Importance Weight
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)
206
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1.7. 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
207
-------
#100: Is the availability of transportation goods and services at risk if other city goods and
services (e.g., power, water, telecommunications) 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 transportation resources is at significant risk
if other city services are affected by climatic events or
changes.
Availability of transportation resources is at moderate risk
if other city services are affected by climatic events or
changes.
Availability of transportation resources is at some risk if
other city services are affected by climatic events or
changes.
Availability of transportation resources is at minimal risk if
other city services are affected by climatic events or
changes.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#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)
208
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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)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
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)
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)
209
-------
#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 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 week 1 (lowest resilience)
Approximately 1 week 2
4 to 6 days 3
1 to 3 days 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)
210
-------
#106: To what extent is the area dependent on long-range transportation of goods and
services versus locally available goods and services (food, energy, etc.)?
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
90-100% dependent on long-range transportation of goods
and services
1 (lowest resilience)
50-90% dependent on long-range transportation of goods
and services
2
10-50%) dependent on long-range transportation of goods
and services
3
0-10%o dependent on long-range transportation of goods
and services
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)
211
-------
#108: How accessible are different modes (e.g., to what proportion of the population, what
subpopulations [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)
212
-------
#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 (e.g., due to reduced access to specialized
medical care or equipment)?
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)
#110: How familiar is the community with evacuation procedures?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Unfamiliar
Only slightly familiar (or only some subpopulations are
familiar)
Somewhat familiar
Very familiar
Resilience Score
1 (lowest resilience)
2
4 (highest resilience)
213
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#111: What length of time would be required to restore major passenger rail
transportation facilities 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 1 week 1 (lowest resilience)
Approximately 1 week 2
4 to 6 days 3
1 to 3 days 4 (highest resilience)
#112: What length of time would be required to restore major freight rail transportation
facilities 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 1 week
Approximately 1 week
4 to 6 days
1 to 3 days
Resilience Score
1 (lowest resilience)
2
3
4 (highest resilience)
214
-------
#113: What length of time would be required to restore major bicycle and pedestrian
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 1 week 1 (lowest resilience)
4 to 6 days 2
1 to 3 days 3
Less than 1 day 4 (highest resilience)
#114: Are urban areas set up to provide accessibility (e.g., to jobs) if mobility is interrupted
or 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)
215
-------
#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 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 funding mechanisms exist to adapt transportation 1 (lowest resilience)
systems to climatic changes, and none could be established.
No funding mechanisms exist to adapt transportation 2
systems to climatic changes, but mechanisms could be
established.
Funding mechanisms are being developed to adapt 3
transportation systems to climatic changes.
Funding mechanisms exist to adapt transportation systems 4 (highest resilience)
to climatic changes.
216
-------
#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 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 currently used materials are compatible with anticipated 1 (lowest resilience)
changes in temperature.
A few currently used materials are compatible with 2
anticipated changes in temperature.
Some currently used materials are compatible with 3
anticipated changes in temperature.
All currently used materials are compatible with anticipated 4 (highest resilience)
changes in temperature.
217
-------
#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
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
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
3
planned
Widespread implementation with additional projects
4 (highest resilience)
planned
218
-------
#162: Have municipalities considered new methods of designing roads/bridges to prepare
for heavily traveled routes during an extreme climate event (e.g., coastal evacuation
routes)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
No
Yes
Importance Weight
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#168: How resistant to potential impacts of climate change are critical non-road
transportation facilities (e.g., high-traffic rail bridges, tunnels)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Critical non-road transportation facilities are not at all
resistant or have non redundancy.
Critical non-road transportation facilities are not very
resistant or have low levels of redundancy.
Critical non-road transportation facilities are moderately
resistant or have moderate levels of redundancy.
Critical non-road transportation facilities are very resistant
or have high levels of redundancy.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
219
-------
#169: 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 exit to replace aging infrastructure.
Resilience Score
1 (lowest resilience)
Plans are being developed or already exist to replace aging 2
infrastructure, but they do not account for anticipated
impacts of climate change.
Plans are being developed or already exist to replace aging 3
infrastructure, but only some of these plans account for
anticipated impacts of climate change.
Plans exist to replace aging infrastructure and these plans 4 (highest resilience)
account for anticipated impacts of climate change.
220
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1.8. 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
221
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#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)
#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)
#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)
222
-------
#124: Do programs for long-term maintenance of water supplies (e.g., erosion control
methods, reforestation 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)
223
-------
#127: Are water and wastewater treatment plants located in a flood zone?
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
At least 50% of water and wastewater treatment plant
1 (lowest resilience)
capacity is located in a flood zone.
30% to 49%) of water and wastewater treatment plant
2
capacity is located in a flood zone.
10%o to 29% of water and wastewater treatment plant
3
capacity is located in a flood zone.
Less than 10%> of water and wastewater treatment plant
4 (highest resilience)
capacity is located in a flood zone.
#128: Are groundwater supplies susceptible to salt water intrusion and sea level rise?
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
Groundwater supplies are very susceptible to salt water
1 (lowest resilience)
intrusion given anticipated sea level rise.
Groundwater supplies are moderately susceptible to salt
2
water intrusion given anticipated sea level rise.
Groundwater supplies are slightly susceptible to salt water
3
intrusion given anticipated sea level rise.
No, groundwater supplies are not susceptible to salt water
4 (highest resilience)
intrusion and sea level rise.
224
-------
#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 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)
#130: Does treatment capacity exist to accommodate nutrient loading?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
Drinking water treatment capacity cannot accommodate
nutrient loading in source water.
Drinking water treatment capacity can accommodate
expected levels of nutrient loading in source water.
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#131: Does the drinking water treatment plant have redundant treatment chemical
suppliers?
Relevance Importance Weight
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)
225
-------
#132: Are there redundant drinking water systems in place for coping with extreme events,
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
2
a small amount of the capacity necessary.
Yes, and these redundant drinking water systems have
3
some of the capacity necessary.
Yes, and these redundant drinking water systems have all
4 (highest resilience)
the 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)
226
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#134: How diverse are individual properties (i.e., are they equipped to harvest rainwater or
recharge groundwater so they can create or augment local water supplies)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No individual properties are equipped to either harvest
rainwater or recharge groundwater.
Few individual properties are equipped to either harvest
rainwater or recharge groundwater.
Some individual properties are equipped to either harvest
rainwater or recharge groundwater.
Most individual properties are equipped to either harvest
rainwater or recharge groundwater.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
#135: Are there redundant wastewater and stormwater systems in place for coping with
extreme events, including collection systems and wastewater treatment systems?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights
1 (not very important)
2
3
4 (very important)
Answer
No, redundant wastewater and stormwater systems are not
in place.
Yes, but these redundant wastewater and stormwater
systems have only a small amount of the capacity
necessary.
Yes, and these redundant wastewater and stormwater
systems have some of the capacity necessary.
Yes, and these redundant wastewater and stormwater
systems have all the capacity necessary.
Resilience Score
1 (lowest resilience)
4 (highest resilience)
227
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#136: Does a water/wastewater agency response network provide technical
resources/support to 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)
#137: Have storm sewers and drains to storm sewers been inventoried, and are these
inventories 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
1 (lowest resilience)
inventoried and are not planned to be inventoried.
Plans exist to inventory storm sewers and drains to storm
2
sewers OR these inventories exist but are not used in
planning.
Storm sewers and drains to storm sewers are being
3
inventoried and these inventories are used or will be used
in planning.
Storm sewers and drains to storm sewers have been
4 (highest resilience)
inventoried and these inventories are used in planning.
228
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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 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 water resources is at significant risk if other 1 (lowest resilience)
city services are affected by climatic events or changes.
Availability of water resources is at moderate risk if other 2
city services are affected by climatic events or changes.
Availability of water resources is at some risk if other city 3
services are affected by climatic events or changes.
Availability of water resources is at minimal risk if other 4 (highest resilience)
city services are affected by climatic events or changes.
#139: Has the water utility conducted a water audit to identify current losses (e.g., leaks,
billing errors, inaccurate meters, unauthorized usage)?
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)
229
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#140: To what extent have efforts been made to reduce water 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 water demand. 1 (lowest resilience)
Fair efforts have been made to reduce water demand. 2
Moderate efforts have been made to reduce water demand. 3
Significant efforts have been made to reduce water 4 (highest resilience)
demand.
#141: Are customers familiar with water conservation measures, and are they willing to
implement these measures?
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
Customers are not familiar with OR are not willing to
1 (lowest resilience)
implement water conservation measures.
Customers are marginally familiar with and somewhat or
2
marginally willing to implement water conservation
measures.
Customers are somewhat familiar with and willing to
3
implement water conservation measures.
Customers are familiar with and willing to implement
4 (highest resilience)
water conservation measures.
230
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#163: Have water utility companies incorporated past experience or experience from other
locations/utilities in developing plans for water shortages related to climate induced
stresses?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
No
Yes
Importance Weights
1 (not very important)
2
3
4 (very important)
Resilience Score
1 (lowest resilience)
3 (highest resilience)
#164: Does the water department or utility for the city consider past experience in
addressing anticipated increases in the frequency of sewer overflows?
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)
#166: Is backup power for wastewater collection and treatment 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)
231
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APPENDIX J. QUANTITATIVE INDICATORS: TEMPLATE
A complete set of the quantitative indicators by sector developed for the tool.
J.l. 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
nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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.
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
232
-------
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,
discuss your score and indicate the reason for your disagreement.
233
-------
#709: Percentage of owned housing units that are affordable
Definition: This indicator measures (1) the percentage of owned housing units where selected
monthly ownership costs (rent, mortgages, real estate taxes, insurance, utilities, fuel, fees) as
a percentage of household income (SMOCAPI) exceeds 35% or (2) the percentage of rented
housing units where gross rent as a percentage of household income (GRAPI) exceeds 35%.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
0 to 30%
Threshold-Based Score:
1 (lowest resilience)
Your Score:
1 (lowest
resilience)
30 to 45%
45 to 60%
Greater than 60%
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
234
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#717: Percentage access to health insurance of noninstitutionalized population
Definition: This indicator measures the percentage of noninstitutionalized residents with
health insurance.
Grouped with Indicators: #725
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Less than 85%
1 (lowest resilience)
1 (lowest
resilience)
85 to 90%
2
2
90 to 95%
3
3
Greater than 95%
4 (highest resilience)
4 (highest
resilience)
235
-------
#711: Overall 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 the percentage of sectors in a city's economy that employ < 40% of the
city's population. Sectors that employ 1% or less of the city's population are not considered,
as they provide very minimal employment opportunities.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
0 to less than 83%
1 (lowest resilience)
1 (lowest
resilience)
83 to less than 91%
2
2
91 to less than 100%
3
3
100%
4 (highest resilience)
4 (highest
resilience)
236
-------
#722: Percentage change in homeless population
Definition: This indicator measures the percentage change in the homeless population.
Grouped with Indicators: N/A
Data Setfs):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 10%
1 (lowest resilience)
1 (lowest
resilience)
1 to 10%
2
2
negative 10 to 0%
3
3
Less than negative 10%
4 (highest resilience)
4 (highest
resilience)
237
-------
#1375: Percentage of population living below the poverty line
Definition: This indicator measures the percentage of the population living below the poverty
line.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
16 to 20%
2
2
12 to 16%
3
3
Less than 12%
4 (highest resilience)
4 (highest
resilience)
238
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J.2. 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
nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
239
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#949: Percentage energy consumed for electricity
Definition: The indicator measures electricity consumption per year in kWh as a percentage
of total energy consumption.
Grouped with Indicators: #950, #951
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
N/A
Threshold-Based Score: N/A
1 (lowest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
240
-------
#971: Energy source capacity per unit area
Definition: This indicator measures the total capacity of energy sources per unit area served
(MW/sq mi).
Grouped with Indicators: #970
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Less than 10 megawatts per
square mile
10 to 50 megawatts per square
mile
50 to 100 megawatts per square
mile
Greater than 100 megawatts per
square mile
Threshold-Based Score:
1 (lowest resilience)
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
4 (highest
resilience)
241
-------
#983: Average customer energy outage (hours) in recent major storm
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
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Alternate Data Setfs):
Notes on Alternate Data Setfs):
Alternate Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
1 (not very important)
2
3
4 (very important)
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 40 hours
1 (lowest resilience)
1 (lowest
resilience)
20 to 40 hours
2
2
10 to 20 hours
3
3
Less than 10 hours
4 (highest resilience)
4 (highest
resilience)
242
-------
#898: Annual energy consumption per capita by main use category (commercial use)
Definition: The indicator measures the annual energy consumption (2010) per capita within
the commercial use sector.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Alternate Data Set(s):
Notes on Alternate Data Set(s):
Alternate Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
1 (not very important)
2
3
4 (very important)
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 4.0 tons of oil
1 (lowest resilience)
1 (lowest
equivalent
resilience)
3.0 to 4.0 tons of oil equivalent
2
2
2.0 to 3.0 tons of oil equivalent
3
3
Less than or equal to 2.0 tons of
4 (highest resilience)
4 (highest
oil equivalent
resilience)
243
-------
#967: Total energy source capacity per capita
Definition: This indicator measures the total capacity of all energy sources (MW) per capita.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Less than 1.0 megawatt per
1 (lowest resilience)
1 (lowest
capita
resilience)
1.0 to 2.0 megawatts per capita
2
2
2.0 to 5.0 megawatts per capita
3
3
Greater than 5.0 megawatts per
4 (highest resilience)
4 (highest
capita
resilience)
244
-------
SECONDARY INDICATORS
#950: Percentage of electricity generation from noncarbon sources
Definition: This indicator measures the percentage of total electricity generation from
noncarbon energy sources in a city.
Grouped with Indicators: #949, #951
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Less than 25%
1 (lowest resilience)
1 (lowest
resilience)
25 to 50%
2
2
50 to 75%
3
3
Greater than 75%
4 (highest resilience)
4 (highest
resilience)
245
-------
#951: Percentage of total energy use from renewable sources
Definition: This indicator measures the percentage of total energy use from renewable
sources.
Grouped with Indicators: #949, #950
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Alternate Data Set(s):
Notes on Alternate Data Set(s):
Alternate Indicator Value:
Relevance:
Importance Weight:
3
1 (not very important)
2
3
4 (very important)
Proposed
Resilience Score:
Thresholds:
Less than 20%
20 to 40%
40 to 60%
Greater than 60%
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
246
-------
#970: Average capacity of a decentralized energy source
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
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Not Sure
Yes (relevant)
No (not relevant)
Importance Weight:
1 (not very important)
2
3
4 (very important)
Proposed
Resilience Score:
Thresholds:
Less than 5,000 megawatts per
square mile
5,000 to 10,000 megawatts per
square mile
10,000 to 15,000 megawatts per
square mile
Greater than 15,000 megawatts
per square mile
Threshold-Based Score: N/A
1 (lowest resilience)
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
4 (highest
resilience)
247
-------
#924: Energy intensity by use
Definition: This indicator measures energy intensity in manufacturing, transportation,
agriculture, commercial and public services, and the residential sector.
Grouped with Indicators:
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 3,000 Btu per
1 (lowest resilience)
1 (lowest
dollar
resilience)
2,000 to 3,000 Btu per dollar
2
2
1,500 to 2,000 Btu per dollar
3
3
Less than 1,500 Btu per dollar
4 (highest resilience)
4 (highest
resilience)
248
-------
J.3. 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 nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
249
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#437: Percentage change in streamflow divided by percentage 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 U.S. from 1931 to 1988.
Grouped with Indicators: #1369
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
Greater than 3.0 (unitless ratio)
Threshold-Based Score:
1 (lowest resilience)
Your Score:
1 (lowest
resilience)
2.0 to 3.0 (unitless ratio)
1.0 to 2.0 (unitless ratio)
Less than 1.0 (unitless ratio)
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
250
-------
#825: Percentage change in impervious cover
Definition: This indicator reflects the change in the percentage of the metropolitan area that is
impervious surface (roads, buildings, sidewalks, parking lots, etc.).
Grouped with Indicators: #303, #308
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Alternate Data Setfs):
Notes on Alternate Data Set(s):
Alternate Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
1 (not very important)
2
3
4 (very important)
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 1%
1 (lowest resilience)
1 (lowest
resilience)
0 to 1%
2
2
Negative 1 to 0%
3
3
Less than negative 1%
4 (highest resilience)
4 (highest
resilience)
251
-------
#1436: Percentage of city area in 100-year floodplain
Definition: This indicator reflects the percentage of the metropolitan area that lies within the
100-year floodplain.
Grouped with Indicators: #1437, #1438, #1439
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
5 to 20%
2
2
1 to 5%
3
3
Less than 1%
4 (highest resilience)
4 (highest
resilience)
252
-------
#51: Coastal Vulnerability Index rank
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), and f = mean wave
height (m).
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
5 (very high vulnerability)
1 (lowest resilience)
1 (lowest
resilience)
4 (high vulnerability)
2
2
3 (moderate vulnerability)
3
3
Less than or equal to 2 (low or
4 (highest resilience)
4 (highest
no vulnerability)
resilience)
253
-------
#194: Percentage of natural area that is in small natural patches
Definition: This indicator measures the percentage of the total natural area in a city that is in
patches of less than 10 acres. 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
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight:
1 (not very important)
2
3
4 (very important)
Proposed
Resilience Score:
Thresholds:
Greater than 80%
60 to 80%
40 to 60%
Less than 40%
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
254
-------
#254: Ratio of perimeter to area of natural patches
Definition: This indicator is calculated as the average ratio of the perimeter to area.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Yes (relevant)
No (not relevant)
Not sure—remind me later
1 (not very important)
2
3
4 (very important)
Thresholds:
Greater than 0.025 (unitless
ratio)
0.015 to 0.025 (unitless ratio)
0.005 to 0.015 (unitless ratio)
Less than 0.005 (unitless ratio)
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
255
-------
#1440: Palmer Drought Severity Index
Definition:
(1) Calculate potential evapotranspiration (PET) for selected time periods using temperature
data and the Thornthwaite equation.
(2) Find the precipitation deficit (precipitation minus PET) for the selected time period,
where more negative values indicate greatest precipitation deficit.
(3) Using a moving window sum, find the 1-, 3-, 6-, or 12-month period that had the greatest
total precipitation deficit.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Less than or equal to negative
4.0 (extreme drought)
Negative 3.99 to negative 3.0
(severe drought)
Negative 2.99 to negative 2.0
(moderate drought)
Greater than or equalt to
negative 1.99 (mild or no
drought)
Threshold-Based Score:
1 (lowest resilience)
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
4 (highest
resilience)
256
-------
SECONDARY INDICATORS
#308: Percentage 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
presettlement 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: Importance Weights:
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Data Setfs):
Notes on Data Set(s):
Indicator Value:
Proposed Resilience Score: Your Score:
1 (lowest resilience)
2
3
4 (highest resilience)
257
-------
#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
etal., 1999).
Grouped with Indicators: #437
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 0.60 (unitless ratio)
1 (lowest resilience)
1 (lowest
resilience)
0.40 to 0.60 (unitless ratio)
2
2
0.20 to 0.40 (unitless ratio)
3
3
Less than 0.20 (unitless ratio)
4 (highest resilience)
4 (highest
resilience)
258
-------
#1437: Percentage of city area in 500-year floodplain
Definition: This indicator reflects the percentage of the metropolitan area that lies within the
500-year floodplain.
Grouped with Indicators: #1436, #1438, #1439
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 30%
1 (lowest resilience)
1 (lowest
resilience)
10 to 30%
2
2
2 to 10%
3
3
Less than 2%
4 (highest resilience)
4 (highest
resilience)
259
-------
#1438: Percentage of city population in 100-year floodplain
Definition: This indicator reflects the percentage of the city population living within the 100-
year floodplain.
Grouped with Indicators: #1436, #1437, #1439
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
5 to 20%
2
2
1 to 5%
3
3
Less than 1%
4 (highest resilience)
4 (highest
resilience)
260
-------
#1439: Percentage of city population in 500-year floodplain
Definition: This indicator reflects the percentage of the city population living within the 500-
year floodplain.
Grouped with Indicators: #1436, #1437, #1438
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 30%
1 (lowest resilience)
1 (lowest
resilience)
10 to 30%
2
2
2 to 10%
3
3
Less than 2%
4 (highest resilience)
4 (highest
resilience)
261
-------
J.4. 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 nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
262
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#682: Percentage change in bird population
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
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
Less than negative 66%
Threshold-Based Score:
1 (lowest resilience)
Your Score:
1 (lowest
resilience)
Negative 66 to 0%
0 to 66%
Greater than 66%
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
263
-------
#17: Altered wetlands (percentage of wetlands lost)
Definition: This indicator reflects the percentage of wetland areas that have been excavated,
impounded, diked, partially drained, or farmed.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 60%
1 (lowest resilience)
1 (lowest
resilience)
40 to 60%
2
2
20 to 40%
3
3
Less than 20%
4 (highest resilience)
4 (highest
resilience)
264
-------
#66: Percentage change in disruptive species
Definition: This indicator reflects the percentage 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
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Yes (relevant)
No (not relevant)
Not sure—remind me later
Thresholds:
Greater than 100%
50 to 100%
10 to 50%
Less than 10%
Threshold-Based Score: Your Score:
1 (lowest resilience) 1 (lowest
resilience)
2 2
3 3
4 (highest resilience) 4 (highest
resilience)
265
-------
#273: Percentage of total wildlife species of greatest conservation need
Definition: This indicator reflects the percentage of total wildlife species that are listed as
having the "greatest conservation need."
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
5 to 20%
2
2
1 to 5%
3
3
Less than 1%
4 (highest resilience)
4 (highest
resilience)
266
-------
#284: Physical Habitat Index (PHI)
Definition: PHI includes eight characteristics (riffle quality, stream bank stability, quantity of
woody debris, instream habitat for fish, suitability of streambed surface materials for
macroinvertebrates, shading, distance to nearest road, and embeddedness 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
Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
0 to 50 (severely degraded)
1 (lowest resilience)
1 (lowest
resilience)
51 to 65 (degraded)
2
2
66 to 80 (partially degraded)
3
3
81 to 100 (minimally degraded)
4 (highest resilience)
4 (highest
resilience)
267
-------
#326: Wetland species at risk (number of species)
Definition: Number of wetland and freshwater species at risk (rare, threatened, or
endangered).
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
Greater than 160 species at risk
Threshold-Based Score:
1 (lowest resilience)
Your Score:
1 (lowest
resilience)
100 to 160 species at risk 2
50 to less than 100 species at 3
risk
Less than 50 species at risk 4 (highest resilience)
2
3
4 (highest
resilience)
268
-------
#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 Percentage, Long-Lived Taxa Richness, Percentage Tolerant, Percentage Predator, and
Percentage Dominance.
Grouped with Indicators: N/A
Data Set(s):
Notes on data sets(s):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
0 to 45 (poor or very poor biotic
condition)
46 to 55 (fair biotic condition)
56 to 75 (good biotic condition)
Greater than 75 (very good
biotic condition)
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
269
-------
#465: Change in plant species diversity from pre-European settlement
Definition: Change in the plant species diversity from pre-European settlement (baseline) to
present, within a given city/area.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
1 (lowest
resilience)
2
Less than 0.2 Shannon Diversity 1 (lowest resilience)
Index
0.2 to 0.4 Shannon Diversity 2
Index
0.4 to 0.6 Shannon Diversity 3
Index
Greater than 0.60 Shannon 4 (highest resilience)
Diversity Index
4 (highest
resilience)
3
270
-------
SECONDARY INDICATORS
#680: Ecological connectivity (percentage of area classified as hub or corridor)
Definition: This indicator reflects the percentage 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
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
Less than 10%
Threshold-Based Score: N/A
1 (lowest resilience)
Your Score: 2
1 (lowest
resilience)
10 to 25%
25 to 50%
Greater than 50%
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
271
-------
#681: Relative ecological condition of undeveloped land
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
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
Less than 120 White and
Maurice Index score
120 to 180 White and Maurice
Index score
180 to 230 White and Maurice
Index score
Greater than 230 White and
Maurice Index score
Threshold-Based Score: N/A
1 (lowest resilience)
4 (highest resilience)
2
3
Your Score:
1 (lowest
resilience)
2
4 (highest
resilience)
3
272
-------
J.5. 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
nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
273
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#675: Asthma Prevalence (Percentage of population affected by asthma)
Definition: This indicator presents asthma prevalence for U.S. children (age 0-17) and adults
(age 18 and older). It is calculated as the percentage of population reporting asthma. Asthma
attack prevalence is based on the number of adults/children who reported an asthma episode
or attack in the past 12 months.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 12%
1 (lowest resilience)
1 (lowest
resilience)
9 to 12%
2
2
6 to 9%
3
3
Less than 6%
4 (highest resilience)
4 (highest
resilience)
274
-------
#676: Percentage of population affected by notifiable diseases
Definition: This indicator reflects percentage 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 chlamydia, coccidioidomycosis,
cryptosporidiosis, Dengue virus, Escherichia coli, ehrlichiosis, Giardiasis, gonorrhea,
Haemophilus influenzae, hepatitus A, hepatitus B, hepatitus 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
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Greater than 3 to 4%
2 to 3%
1 to 2%
Less than 1%
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
275
-------
#690: Emergency medical service response times
Definition: This indicator measures average annual response times (in minutes) for
emergency medical service calls.
Grouped with Indicators: #757, #784, #798
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 12 minutes
1 (lowest resilience)
1 (lowest
resilience)
10 to 12 minutes
2
2
8 to 10 minutes
3
3
Less than 8 minutes
4 (highest resilience)
4 (highest
resilience)
276
-------
#1387: Percentage of population vulnerable due to age
Definition: This indicator reflects percentage of population above 65 or under 5 years old.
Grouped with Indicators: #393. #728. #1157, #1170
Data Setfs):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
15 to 20%
2
2
10 to 15%
3
3
Less than 10%
4 (highest resilience)
4 (highest
resilience)
277
-------
#209: Percentage of population living within the 500-year floodplain
Definition: This indicator reflects percentage of population living within the 500-year
floodplain.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 30%
1 (lowest resilience)
1 (lowest
resilience)
10 to 30%
2
2
2 to 10%
3
3
Less than 2%
4 (highest resilience)
4 (highest
resilience)
278
-------
#725: Number of physicians per capita
Definition: This indicator reflects the total number of M.D. and D.O. physicians per capita.
Grouped with Indicators: #717
Data Setfs):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Less than 0.02 physicians per
1 (lowest resilience)
1 (lowest
capita
resilience)
0.02 to 0.03 physicians per
2
2
capita
0.03 to 0.04 physicians per
3
3
capita
Greater than 0.04 physicians per
4 (highest resilience)
4 (highest
capita
resilience)
279
-------
#1376: Percentage of population that is disabled
Definition: This indicator reflects the percentage of the noninstitutionalized 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
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
15 to 20%
2
2
10 to 15%
3
3
Less than 10%
4 (highest resilience)
4 (highest
resilience)
280
-------
#1390: Percentage of population that is living alone
Definition: This indicator reflects the percentage of population that is 65 years or older and
living alone.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 30%
1 (lowest resilience)
1 (lowest
resilience)
20 to 30%
2
2
10 to 20%
3
3
Less than 10%
4 (highest resilience)
4 (highest
resilience)
281
-------
#1443: Deaths from extreme weather events
Definition: This indicator measures the number of deaths 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
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Greater than 150 deaths
100 to 150 deaths
50 to 100 deaths
Less than 50 deaths
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
282
-------
SECONDARY INDICATORS
#322: Percentage of population affected by waterborne diseases
Definition: This indicator reports the percentage of population affected by waterborne
diseases.
Grouped with Indicators: #676, #1171
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 2%
1 (lowest resilience)
1 (lowest
resilience)
1 to 2%
2
2
0 to 1%
3
3
0%
4 (highest resilience)
4 (highest
resilience)
283
-------
#393: Percentage of vulnerable population that is homeless
Definition: This indicator reflects the percentage of population 65 and older and under 5
years that is homeless.
Grouped with Indicators: #728, #1157, #1170, #1387
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 30%
1 (lowest resilience)
1 (lowest
resilience)
20 to 30%
2
2
10 to 20%
3
3
Less than 10%
4 (highest resilience)
4 (highest
resilience)
284
-------
#728: Adult care (homes per capita)
Definition: The number of adult day care homes and assisted living homes per capita of
population over 65 years.
Grouped with Indicators: #393, #1157, #1170,
#1387
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Alternate Data Setfs):
Notes on Alternate Data Setfs):
Alternate Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
1 (not very important)
2
3
4 (very important)
Thresholds:
Threshold-Based Score:
1 (lowest resilience)
Your Score:
1 (lowest
resilience)
2
Less than 0.00010 adult homes
per capita of elderly population
0.00010 to 0.00020 adult homes
per capita of elderly population
2
0.00020 to 0.00040 adult
3
3
homes per capita of elderly
population
Greater than 0.00040 adult
homes per capita of elderly
population
4 (highest resilience)
4 (highest
resilience)
285
-------
#757: Average police response time
Definition: This indicator reflects the average response time for police to respond to
emergency situations.
Grouped with Indicators: #690, #784, #798
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
1 (not very important)
2
3
4 (very important)
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 12 minutes
1 (lowest resilience)
1 (lowest
resilience)
10 to 12 minutes
2
2
8 to 10 minutes
3
3
Less than 8 minutes
4 (highest resilience)
4 (highest
resilience)
286
-------
#784: Number of sworn police officers per capita
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
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Less than 0.10 police officers
per capita
0.10 to 0.20 police officers per
capita
0.20 to 0.50 police officers per
capita
Greater than 0.50 police officers
per capita
Threshold-Based Score:
1 (lowest resilience)
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
4 (highest
resilience)
287
-------
#798: Percentage of fire response times less than 6.5 minutes
Definition: This indicator reflects the percentage of fire response times less than 6.5 minutes
(from city stations to city locations).
Grouped with Indicators: #690, #757, #784
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Less than 85%
1 (lowest resilience)
1 (lowest
resilience)
85 to 90%
2
2
90 to 95%
3
3
Greater than 95%
4 (highest resilience)
4 (highest
resilience)
288
-------
#1157: Percentage of housing units with air conditioning
Definition: This indicator reflects the percentage of housing units with air conditioning.
Grouped with Indicators: #393, #728, #1170,
#1387
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Less than 70%
1 (lowest resilience)
1 (lowest
resilience)
70 to 88%
2
2
88 to 94%
3
3
Greater than 94%
4 (highest resilience)
4 (highest
resilience)
289
-------
#1170: Percentage of population experiencing heat-related deaths
Definition: This indicator reflects the percentage of the population experiencing heat-related
deaths.
Grouped with Indicators: #393, #728, #1157,
#1387
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 2.0%
1 (lowest resilience)
1 (lowest
resilience)
1.0 to 2.0%
2
2
0.5 to 1.0%
3
3
Less than 0.5%
4 (highest resilience)
4 (highest
resilience)
290
-------
#1171: Percentage of population affected by food poisoning
Definition: This indicator reflects the percentage of population affected by food poisoning
(i.e., Salmonella spp., unsafe drinking water).
Grouped with Indicators: #322, #676
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
15 to 20%
2
2
10 to 15%
3
3
Less than 10%
4 (highest resilience)
4 (highest
resilience)
291
-------
J.6. Telecommunications
The indicators below have been developed for the telecommunication 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 nongrouped 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 sets 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 telecommunication 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
292
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#1433: Percentage of system capacity needed to carry baseline level of traffic
Definition: Percentage of system capacity needed to carry baseline level of traffic.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance: Importance Weight: Proposed Resilience
Score:
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
293
Thresholds:
Greater than 70%
50 to 70%
30 to 50%
Less than 30%
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
-------
#1434: Baseline percentage of water supply for telecommunication systems that comes
from outside the metropolitan area
Definition:
Grouped with Indicators:
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed Resilience
Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 50%
1 (lowest resilience)
1 (lowest
resilience)
20 to 50%
2
2
5 to 20%
3
3
Less than 5%
4 (highest resilience)
4 (highest
resilience)
294
-------
#1435: Baseline percentage of energy supply for telecommunication systems that comes
from outside the metropolitan area
Definition:
Grouped with Indicators:
Data Setfs):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 60%
1 (lowest resilience)
1 (lowest
resilience)
30 to 60%
2
2
10 to 30%
3
3
Less than 10%
4 (highest resilience)
4 (highest
resilience)
295
-------
#1441: Percentage of community with access to FEMA emergency radio broadcasts
Definition: Percentage of community with access to FEMA emergency radio broadcasts.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Less than 80%
1 (lowest resilience)
1 (lowest
resilience)
80 to 88%
2
2
88 to 96%
3
3
Greater than 96%
4 (highest resilience)
4 (highest
resilience)
296
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3.1. 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 nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
297
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PRIMARY INDICATORS AND NONGROUPED INDICATORS
#988: Walkability score
Definition: This indicator reflects the walkability score of the community (points out of 100).
Grouped with Indicators: #987, #1396, #1417
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
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)
298
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#1402: Total annual hours of rail line closure due to heat and maintenance problems
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
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Greater than 6 hours
3 to 6 hours
1 to 3 hours
Less than 1 hour
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
299
-------
#1404: Percentage of city culverts that are sized to meet future stormwater capacity
requirements
Definition: This indicator measures the percentage 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
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed Resilience
Score:
Thresholds:
Threshold-Based Score:
Your Score:
Less than 70%
1 (lowest resilience)
1 (lowest
resilience)
70 to 85%
2
2
85 to 95%
3
3
Greater than 95%
4 (highest resilience)
4 (highest
resilience)
300
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#1412: Miles of pedestrian facilities per street mile
Definition: This indicator measures the miles of pedestrian facilities (sidewalks) per street
mile.
Grouped with Indicators: #1413
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Less than 0.5 miles of sidewalk
1 (lowest resilience)
1 (lowest
to street miles
resilience)
0.5 to 1.0 miles of sidewalk to
2
2
street miles
1.0 to 2.0 miles of sidewalk to
3
3
street miles
Greater than 2.0 miles of
4 (highest resilience)
4 (highest
sidewalk to street miles
resilience)
301
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#1420: Intermodal passenger connectivity (percentage of terminals with at least one
intermodal connection for the most common mode)
Definition: This indicator measures the percentage of active passenger terminals for the most
common mode (e.g., rail, air, etc.) with at least one intermodal passenger connection.
Intermodal connections allow passengers to use a combination of modes and give travelers
additional transportation alternatives that unconnected, parallel systems do not offer.
Grouped with Indicators: #1419
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Less than 55%
55 to 70%
70 to 85%
Greater than 85%
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
302
-------
#985: Transport system user satisfaction
Definition: This indicator reflects the overall user satisfaction with the transport system. It is
defined as the average user satisfaction with bus service, rail service, and the accuracy of
passenger information displays.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds: Threshold-Based Score: Your Score:
0 to 20 (very or totally 1 (lowest resilience) 1 (lowest
dissatisfied) resilience)
21 to 60 (somewhat 2 2
dissastisfied)
61 to 80 (somewhat satisfied) 3 3
81 to 100 (very or totally 4 (highest resilience) 4 (highest
satisfied) resilience)
303
-------
#991: Percentage transport diversity
Definition: Highest public expenditure for a single mode of transportation as a percentage of
the total expenditures for all transportation modes.
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)
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Proposed Resilience Score: Your Score:
1 (lowest resilience)
2
3
4 (highest resilience)
304
-------
#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 dollars). 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
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
$2 to less than $10 per person
Threshold-Based Score:
1 (lowest resilience)
Your Score:
1 (lowest
resilience)
$10 to less than $18 per person
$18 to less than $32 per person
Greater than or equal to $32 per
person
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
305
-------
#1010: Community Livability Index
Definition: The Community Livability Index is the equally weighted average of the
Community Service Indicator, the Crime Indicator, the Retail Opportunity Indicator, the
Educational Indicator, the Environmental Quality Indicator, the Housing Affordability
Indicator, and the Transit Livability Indicator. Details of the calculation are provided in
Ripplinger et al. (2012; http://www.ugpti.org/pubs/pdf/DP262.pdf).
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
Less than 60 (most aspects of
living are substantially
constrained or severely
restricted)
61 to 70 (negative factors have
an impact on day-to-day living)
71 to 80 (day-to-day living is
fine, in genera, but some aspects
of life may entail problems)
81 to 100 (there are few, if any
challenges to living standards)
Threshold-Based Score:
1 (lowest resilience)
4 (highest resilience)
2
3
Your Score:
1 (lowest
resilience)
4 (highest
resilience)
2
3
306
-------
#1399: Percentage of roads and railroads within the city that are located within 10 feet of
water
Definition: This indicator measures the percentage of roadway miles and rail line miles that
are within 10 feet of a body of water.
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)
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Proposed Resilience Score: Your Score:
1 (lowest resilience)
2
3
4 (highest resilience)
307
-------
#1400: Percentage of roads and railroads within the city in the 500-year floodplain
Definition: This indicator measures the percentage of roadway miles and rail line miles that
are within the 500-year floodplain.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 5%
1 (lowest resilience)
1 (lowest
resilience)
2 to 5%
2
2
1 to 2%
3
3
Less than 1%
4 (highest resilience)
4 (highest
resilience)
308
-------
#1401: Percentage of roads and railroads within the city in the 100-year floodplain
Definition: This indicator measures the percentage of roadway miles and rail line miles that
are within the 100-year floodplain.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
10 to 20%
2
2
5 to 10%
3
3
Less than 5%
4 (highest resilience)
4 (highest
resilience)
309
-------
#1406: Percentage decline in repeat maintenance events
Definition: This indicator measures the percentage 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
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance: Importance Weight: Proposed Resilience
Score:
1 (not very important)
2
3
4 (very important)
Thresholds:
Less than 10%
Threshold-Based Score:
1 (lowest resilience)
Your Score:
1 (lowest
resilience)
10 to 25%
25 to 50%
Greater than 50%
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
310
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#1408: Percentage of bridges that are structurally deficient
Definition: This indicator measures the percentage of bridges that are structurally deficient.
Bridges are considered structurally deficient if significant load-carrying elements are found to
be in poor or worse condition due to deterioration 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.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Greater than 10%
5 to 10%
2 to 5%
Less than 2%
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
311
-------
#1411: Roadway connectivity (number of intersections per square mile)
Definition: This indicator measures the number of intersections per square mile.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Less than 80 intersections per
1 (lowest resilience)
1 (lowest
square mile
resilience)
80 to 250 intersections per
2
2
square mile
250 to 290 intersections per
3
3
square mile
Greater than 290 intersections
4 (highest resilience)
4 (highest
per square mile
resilience)
312
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#1422: Average distance of all nonwork trips
Definition: This indicator measures the average distance from a given home to the nearest
grocery store, high school, and health care facility (i.e., nonwork trips).
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Less than 5 miles
5 to 10 miles
10 to 30 miles
Greater than 30 miles
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
313
-------
#1426: City congestion rank
Definition: This indicator measures the congestion rank of the metropolitan area relative to all
U.S. metropolitan areas.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
1 to 25 (unitless rank)
1 (lowest resilience)
1 (lowest
resilience)
26 to 50 (unitless rank)
2
2
51 to 75 (unitless rank)
3
3
76 to 100 (unitless rank)
4 (highest resilience)
4 (highest
resilience)
314
-------
#1429: Telework rank
Definition: This indicator measures the telework rank of the mtropolitan area relative to all
other extralarge metropolitan areas in the U.S. The rank is based on the percentage of jobs
within the metropolitan region that could be accomplished by telecommuting if employer
policies were to permit it.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
13 to 16 (unitless rank)
1 (lowest resilience)
1 (lowest
resilience)
9 to 12 (unitless rank)
2
2
5 to 8 (unitless rank)
3
3
1 to 4 (unitless rank)
4 (highest resilience)
4 (highest
resilience)
315
-------
SECONDARY INDICATORS
#987: Employment accessibility (mean travel time to work relative to national average)
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
Data Set(s):
Notes on Data Setfs):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
1 (lowest
resilience)
Greater than 1.18 (unitless ratio) 1 (lowest resilience)
0.98 to 1.18 (unitless ratio) 2
0.79 to less than 0.98 (unitless 3
ratio)
Less than 0.79 (unitless ratio) 4 (highest resilience)
2
3
4 (highest
resilience)
316
-------
#1396: Percentage access to transportation stops
Definition: This indicator reflects the percentage of the population that is near a transit stop.
Grouped with Indicators: #987. 988, #1417
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
23 to 47%
1 (lowest resilience)
1 (lowest
resilience)
48 to 63%
2
2
64 to 75%
3
3
76 to 100%
4 (highest resilience)
4 (highest
resilience)
317
-------
#1403: Percentage of city culverts that are sized to meet current stormwater capacity
requirements
Definition: This indicator measures the percentage of current culverts that cross
transportation facilities in the metropolitan region that are sized to meet current stormwater
capacity requirements.
Grouped with Indicators: #1404
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed Resilience
Score:
Yes (relevant)
1 (not very important)
No (not relevant)
2
3
4 (very important)
Thresholds:
Threshold-Based Score:
Your Score:
Less than 75%
1 (lowest resilience)
1 (lowest
resilience)
75 to 90%
2
2
90 to 95%
3
3
Greater than 95%
4 (highest resilience)
4 (highest
resilience)
318
-------
#1410: Hours of passenger delay due to heat related issues
Definition: N/A
Grouped with Indicators: #1402
Relevance: Importance Weights:
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Data Setfs):
Notes on Data Set(s):
Indicator Value:
Proposed Resilience Score: Your Score:
1 (lowest resilience)
2
3
4 (highest resilience)
319
-------
#1413: Percentage of short walkable sidewalks in urban areas
Definition: This indicator measures the percentage of sidewalks within the urban area that are
less than 330 feet.
Grouped with Indicators: #1412
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Your Score:
Less than 60%
1 (lowest resilience)
1 (lowest
resilience)
60 to 75%
2
2
75 to 90%
3
3
Greater than 90%
4 (highest resilience)
4 (highest
resilience)
320
-------
#1417: Percentage 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 and
employment districts; senior facilities; hospital/medical clinics; parks, recreation, and
sporting; grocery stores; museums and tourist attractions).
Grouped with Indicators: #987, #988, #1396
Relevance:
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights:
1 (not very important)
2
3
4 (very important)
Data Setfs):
Notes on Data Set(s):
Indicator Value:
Proposed Resilience Score: Your Score:
1 (lowest resilience)
2
3
4 (highest resilience)
321
-------
#1419: Intermodal freight connectivity (ratio of intermodal connections used per year to
individual modes)
Definition: This indicator measures the 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
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Yes (relevant)
No (not relevant)
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Less than 0.5 ratio of
intermodal containers to
individual carloads
0.5 to 1.0 ratio of intermodal
containers to individual carloads
1 to 2 ratio of intermodal
containers to individual carloads
Greater than 2 ratio of
intermodal containers to
individual carloads
Threshold-Based Score:
1 (lowest resilience)
4 (highest resilience)
Your Score:
1 (lowest
resilience)
4 (highest
resilience)
322
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J.8. 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
nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
323
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PRIMARY INDICATORS AND NONGROUPED INDICATORS
#1346: Percentage of infiltration and inflow (I/I) in wastewater
Definition: Water that enters the wastewater system through infiltration and inflow (I/I) as a
percentage of total wastewater from all wastewater 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, cross-connections with storm drains, downspouts, and through holes in
manhole covers.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
1 (not very important)
2
3
4 (very important)
Proposed
Resilience Score:
Thresholds:
Greater than 50%
35 to 50%
20 to 35%
Less than 20%
Threshold-Based Score:
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
324
-------
#1347: Wet weather flow bypass volume relative to the 5-year average
Definition: Volume of wastewater that bypassed treatment in an average year for all
wastewater treatment plants divided by the 5-year average.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed Resilience
Score:
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 2 (unitless ratio)
1 (lowest resilience)
1 (lowest
resilience)
1 to 2 (unitless ratio)
2
2
1 (unitless ratio)
3
3
Less than 1 (unitless ratio)
4 (highest resilience)
4 (highest
resilience)
325
-------
#1428: Total number of Safe Drinking Water Act (SDWA) violations
Definition: This indicator measures the total number of SDWA violations over the last 5
years.
Grouped with Indicators: N/A
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance: Importance Weight: Proposed
Resilience Score:
1 (not very important)
2
3
4 (very important)
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 4 violations
1 (lowest resilience)
1 (lowest
resilience)
3 to 4 violations
2
2
1 to 2 violations
3
3
0 violations
4 (highest resilience)
4 (highest
resilience)
326
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#1442: Ratio of water consumption to water availability
Definition: This indicator measures the fraction of available water that is currently consumed.
It is calculated by dividing total water consumption by the total available water from surface
water and groundwater sources.
Grouped with Indicators: N/A
Data Set(s):
1442
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed
Resilience Score:
Thresholds:
Threshold-Based Score:
Greater than 0.20 (unitless ratio) 1 (lowest resilience)
0.13 to 0.20 (unitless ratio)
0.06 to 0.13 (unitless ratio)
Less than 0.06 (unitless ratio)
2
3
4 (highest resilience)
Your Score:
1 (lowest
resilience)
2
3
4 (highest
resilience)
327
-------
#437: Percentage change in streamflow divided by percentage change in precipitation
Definition: This indicator reflects percentage change in streamflow (Q) divided by percentage
change in precipitation (P) for 1,291 gauged watersheds across the continental U.S. from
1931 to 1988.
Grouped with Indicators: #1369
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance:
Importance Weight:
Proposed Resilience
Score:
Yes (relevant)
1 (not very important)
No (not relevant)
2
3
4 (very important)
Thresholds:
Threshold-Based Score:
Your Score:
Greater than 3.0 (unitless ratio)
1 (lowest resilience)
1 (lowest
resilience)
2.0 to 3.0 (unitless ratio)
2
2
1.0 to 2.0 (unitless ratio)
3
3
Less than 1.0 (unitless ratio)
4 (highest resilience)
4 (highest
resilience)
328
-------
#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
etal., 1999).
Grouped with Indicators: #437
Data Set(s):
Notes on Data Set(s):
Indicator Value:
Relevance: Importance Weight: Proposed Resilience
Score:
Yes (relevant)
No (not relevant)
1 (not very important)
2
3
4 (very important)
Thresholds:
Greater than 0.60 (unitless ratio)
Threshold-Based Score:
1 (lowest resilience)
Your Score:
1 (lowest
resilience)
0.40 to 0.60 (unitless ratio)
0.20 to 0.40 (unitless ratio)
Less than 0.20 (unitless ratio)
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
329
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APPENDIX K. QUALITATIVE INDICATORS: WASHINGTON, DC
A complete set of the qualitative indicators by sector developed for the tool.
K.l. 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
330
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#1: Is the economy of the urban area largely independent, or is it largely dependent on
economic activity in other urban areas?
Relevance
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 4
Largely dependent
1 (lowest resilience)
Somewhat dependent
2
Somewhat independent
3
Largely independent
4 (highest resilience)
#2: Does the urban area have mechanisms to help businesses quickly return to normal
operations?
Relevance
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 1
No
1 (lowest resilience)
Yes
3 (highest resilience)
331
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#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?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
No
Yes
Resilience Score 3
1 (lowest resilience)
3 (highest resilience)
#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 4
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 2
1 (lowest resilience)
4 (highest resilience)
332
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#5: Has the urban area's resilience to major changes in energy policy/prices been assessed?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
No
Yes
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Resilience Score 3
1 (lowest resilience)
3 (highest resilience)
#6: Is funding available for adaptive development projects that could also serve as
recreation areas (e.g., retention areas along waterways that could also serve as parks)? Are
such multipurpose projects required or are there incentives for these projects?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
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 3
1 (lowest resilience)
3 (highest resilience)
333
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#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?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
Yes
No
Importance Weight
1 (not very important)
2
3
4 (very important)
Resilience Score 1
1 (lowest resilience)
3 (highest resilience)
#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
Importance Weights 3
1 (not very important)
2
3
4 (very important)
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.
Resilience Score 4
1 (lowest resilience)
4 (highest resilience)
334
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#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 4
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 4
1 (lowest resilience)
4 (highest resilience)
#10: Is planning for climate change adaptation in the urban area incorporated into one
office within the local government or is planning spread out across several offices within
the government?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
Adaptation planning responsibilities are not incorporated
into any offices within the local government.
Adaptation planning responsibilities are spread out
over multiple offices within the local government.
Adaptation planning is shared between two or three offices
within the local government.
Adaptation planning is incorporated into one office within
the local government.
Importance Weight
1 (not very important)
2
3
4 (very important)
Resilience Score 2
1 (lowest resilience)
4 (highest resilience)
335
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#11: How flexible are planning processes for short-term and long-term responses? For
example, is there flexibility in changing planning priorities if necessary?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
Planning processes are fairly inflexible.
Planning processes are somewhat flexible.
Planning processes are moderately flexible.
Planning processes are very flexible.
Resilience Score 2
1 (lowest resilience)
2
3
4 (highest resilience)
#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?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 4
1 (not very important)
2
3
4 (very important)
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 4
1 (lowest resilience)
4 (highest resilience)
336
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#13: Does adaptation planning for the urban area consider the costs and benefits of
possible decisions, and does it encourage both pre-event and postevent evaluations of the
effectiveness of adaptation measures?
Relevance
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
Adaptation planning does not consider costs and benefits
1 (lowest resilience)
and does not encourage pre-event or postevent
effectiveness evaluations.
Adaptation planning does consider costs and benefits but
2
does not encourage pre-event or postevent effectiveness
evaluations.
Adaptation planning does consider costs and benefits
3
and encourages pre-event or postevent effectiveness
evaluations.
Adaptation planning does consider costs and benefits and
4 (highest resilience)
requires pre-event or postevent effectiveness evaluations.
337
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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 is: in coastal cities, some areas can be protected by a
seawall, or households and institutions in vulnerable areas can be moved inland. Do
current 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 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
Adaptation plans do not explicitly consider resilience-cost
1 (lowest resilience)
tradeoffs or no adaptation plans exist.
Adaptation plans consider one or two resilience-cost
2
tradeoffs.
Adaptation plans consider some resilience-cost tradeoffs.
3
Adaptation plans consider many resilience-cost tradeoffs.
4 (highest resilience)
338
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#165: What financial capacity or credit risk is indicated by the city's bond rating(s)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 1
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 4
1 (lowest resilience)
4 (highest resilience)
339
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K.2. 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.
340
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#15: Do you have a diverse energy portfolio?
Relevance
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
No
1 (lowest resilience)
Yes
3 (highest resilience)
#16: Are there redundant systems in place for coping with extreme events?
Relevance
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
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
3
the capacity necessary.
Yes, and these redundant energy systems have all the
4 (highest resilience)
capacity necessary.
341
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#17: To what extent do energy supplies come from outside the metropolitan area?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
They come exclusively from outside the area.
To a great extent
To a moderate extent
Only to a small extent
Resilience Score 2
1 (lowest resilience)
2
3
4 (highest resilience)
#18: Is the availability of energy goods and services at risk if other city goods and services
(e.g., water, transportation, telecommunications) are affected by extreme climatic events or
gradual climatic changes?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
Availability of energy resources is at significant risk if
other city services are affected by climatic events or
changes.
Availability of energy resources is at moderate risk if other
city services are affected by climatic events or changes.
Availability of energy resources is at some risk if other
city services are affected by climatic events or changes.
Availability of energy resources is at minimal risk if other
city services are affected by climatic events or changes.
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
342
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#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 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
More than 1 day per year for all outage events 1 (lowest resilience)
More than 1 hour to 1 day per year for all outage events 2
More than 30 minutes to 1 hour per year for all outage 3
events
Less than 30 minutes per year for all outage events 4 (highest resilience)
#20: What is the response time to restore electrical power after an outage?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
More than 1 day after a major event
More than 3 hours to 1 day after a major event
More than 1 hour to 4 hours after a major event
Less than 1 hour after a major event
Resilience Score 3
1 (lowest resilience)
2
4 (highest resilience)
343
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#21: Does capacity exist to handle a higher peak demand or peaks at different times?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
Electricity generation capacity cannot handle higher peak
demands or peaks at different times than currently
experienced.
Electricity generation capacity can handle higher peak
demands or peaks at different times than currently
experienced.
Resilience Score 3
1 (lowest resilience)
3 (highest resilience)
#22: To what extent have efforts been made to reduce energy demand?
Relevance
Importance Weights 3
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
Few to no efforts have been made to reduce energy
1 (lowest resilience)
demand.
Fair efforts have been made to reduce energy demand.
2
Moderate efforts have been made to reduce energy
3
demand.
Significant efforts have been made to reduce energy
4 (highest resilience)
demand.
344
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#23: What are the opportunities for distributed generation sources (i.e., different capacity
for energy generation from different sources including renewable)?
Relevance
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
Political and technical capacity do not allow for generation
1 (lowest resilience)
from multiple sources.
Political and technical capacity could allow for
2
generation from multiple sources, but such diversified
generation is not currently occurring.
Political and technical capacity currently provide for
3
generation from multiple sources, not including
renewables.
Political and technical capacity currently provide for
4 (highest resilience)
generation from multiple sources, including renewables.
#24: Are there smart grid opportunities to manage demand?
Relevance
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
No
1 (lowest resilience)
Yes
3 (highest resilience)
345
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#147: Do municipal managers draw on past data/experiences of extreme weather events to
assess 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 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#148: Has the city consulted with local power companies to develop plans for potential
increases in electricity demand for summer cooling? (DOE, 2013)
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 4
1 (not very important)
2
3
4 (very important)
Answer
The city has not consulted with local power companies and
is not developing plans for potential increase in electricity
for cooling.
The city has consulted with local power companies
regarding 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
developing plans for potential increase in electricity for
cooling.
The city has consulted with local power companies and
developed plans for potential increase in electricity for
cooling.
Resilience Score 4
1 (lowest resilience)
4 (highest resilience)
346
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#149: Has the city coordinated with local water suppliers and power generation facilities to
discuss potential climate-induced water shortages and their impacts on cooling the power
generation facilities?(DOE, 2013)
Action Needed:
Please provide a relevance, importance weight, and answer for this question.
Relevance Importance Weight N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#150: Do municipal managers in coastal areas consider the impacts of sea level rise on
power generation facilities?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight N/A
1 (not very important)
2
3
4 (very important)
Answer
No
Yes, but these considerations are not incorporated into
planning for these facilities.
Yes, and these considerations are being incorporated into
planning for these facilities.
Yes, and these considerations are incorporated into
planning for these facilities.
Resilience Score N/A
1 (lowest resilience)
2
4 (highest resilience)
347
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K.3. 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
348
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#25: Can resilience planning/adaptation be incorporated into existing programs that
communities engage in regularly (e.g., zoning, hazard mitigation plans)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 3
1 (lowest resilience)
4 (highest resilience)
#26: Has the city made efforts to use urban forms 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 4
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 3
1 (lowest resilience)
4 (highest resilience)
349
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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 4
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 4
1 (lowest resilience)
4 (highest resilience)
#28: Does zoning encourages green roofs or other practices that reduce urban heat?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 4
1 (lowest resilience)
4 (highest resilience)
350
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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 4
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 3
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 3
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 3
1 (lowest resilience)
4 (highest resilience)
351
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#31: What percentage of open/green space is required for new development (to encourage
increases in such space)?
Relevance
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
No open/green space is required for new development.
1 (lowest resilience)
A small percentage of open/green space is required for
2
new development.
A moderate percentage of open/green space is required for
3
new development.
A high percentage of open/green space is required for new
4 (highest resilience)
development.
#32: Are there mechanisms for the local government to purchase land that is unfavorable
for redevelopment due to the results of extreme events (e.g., flooding from a hurricane)? If
so, what are those mechanisms?
Relevance
Importance Weights 2
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
No, such mechanisms do not exist.
1 (lowest resilience)
Yes, there are such mechanisms, but they are only
2
preliminary and are slightly helpful.
Yes, there are such mechanisms and they are somewhat
3
helpful.
Yes, there are such mechanisms and they are helpful.
4 (highest resilience)
352
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#33: Are there policies or zoning practices in place that allow transfer of ownership of
undevelopable land subject to flooding or excessive erosion to the city (or allow
nonpermanent structures only)? Are these policies or zoning practices enforced?
Relevance Importance Weights 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
Policies do not allow ownership transfer. 1 (lowest resilience)
Policies allow ownership transfer, but these policies are 2
enforced only rarely.
Policies allow ownership transfer, but these policies are 3
only 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 implemented or planned?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
353
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#35: Are there codes to prevent development in flood-prone areas?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#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 4
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 3
1 (lowest resilience)
4 (highest resilience)
354
-------
#37: Have the regulations regarding rebuilding of communities affected by floods been
enforced to date?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
#38: Do incentives exist to integrate green stormwater infrastructure into infrastructure
planning to mitigate flooding?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
355
-------
#39: Are there incentives to reduce the amount of impervious surface, to prevent
development in floodplains, 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 4
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 3
1 (lowest resilience)
3 (highest resilience)
#40: To what extent was green infrastructure selected to provide the maximum ecological
benefits?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 4
1 (lowest resilience)
4 (highest resilience)
356
-------
#41: Has green infrastructure maintenance been built into the budget?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
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?
Relevance
Importance Weight 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
Such maps have not been developed and are not planned to
1 (lowest resilience)
be developed.
Plans exist to develop such maps OR such maps exist but
2
are not used in planning.
Such maps are being developed and these maps are
3
used or will be used in planning.
Such maps exist and these maps are used in planning.
4 (highest resilience)
357
-------
#151: Have institutional land practices (i.e., zoning, land use planning) potentially been
hindered by other government agencies seeking to shift financial resources when it conies
to climate change planning?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
Yes
No
Importance Weight 3
1 (not very important)
2
3
4 (very important)
Resilience Score 3
1 (lowest resilience)
3 (highest resilience)
#152: Does knowledge of historical land use/land cover changes contribute to planners'
understanding of climate stresses?
Relevance Importance Weight 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
358
-------
#153: Have specific historical land use/land cover changes been recognized as increasing or
decreasing vulnerability to climate stresses?
Relevance Importance Weight 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 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 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
359
-------
#167: In general, what is the monetary value of infrastructure located within the 500-year
floodplain in the city?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 4
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)
360
-------
K.4. 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
361
-------
#42: Is the availability of environmental/ecosystem goods and services at risk if other city
goods and services (e.g., power, water, telecommunications) are affected by extreme
climatic events or gradual climatic changes?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 2
1 (not very important)
2
3
4 (very important)
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 2
1 (lowest resilience)
4 (highest resilience)
362
-------
#43: What regulatory and planning tools related to air quality, water quality, and land use
are already available locally? For example, does the urban area have invasive plant
ordinances or tree planting requirements?
Relevance Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
The urban area does not have regulatory and planning tools 1 (lowest resilience)
for air and water quality and land use.
The urban area has few regulatory and planning tools for 2
air and water quality and land use.
The urban area has several regulatory and planning 3
tools for air and water quality and land use.
The urban area has many regulatory and planning tools for 4 (highest resilience)
air and water quality and land use.
#44: Do plans exist for increasing open and green space?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
363
-------
#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 3
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 2
1 (lowest resilience)
4 (highest resilience)
#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
Importance Weights 3
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
Native species lists do not exist and are not being
1 (lowest resilience)
developed.
Native species lists exist, but green infrastructure uses
2
mostly nonnative species OR native species lists are under
development.
Native species lists exist and green infrastructure uses
3
mostly these species.
Native species lists exist and green infrastructure uses only
4 (highest resilience)
these species.
364
-------
#47: Does the urban area coordinate with other nearby entities on water quality?
Relevance Importance Weights 1
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#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 3
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 3
1 (lowest resilience)
4 (highest resilience)
365
-------
#49: Does the urban area have air quality districts?
Relevance
Importance Weights 3
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 1
No
1 (lowest resilience)
Yes
3 (highest resilience)
#50: Has an air quality analysis been completed at multiple scales/resolutions?
Relevance
Importance Weights 3
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
An air quality analysis has not been completed.
1 (lowest resilience)
An air quality analysis has been completed at a one
2
scale/resolution.
Air quality analysis has been completed at a few
3
scales/resolutions.
Air quality analysis has been completed at many
4 (highest resilience)
scales/resolutions.
366
-------
#51: Does the urban area have health warnings or alerts for days when air quality may be
hazardous?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
No
Yes
Importance Weights 1
1 (not very important)
2
3
4 (very important)
Resilience Score 3
1 (lowest resilience)
3 (highest resilience)
#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 3
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
1 (lowest resilience)
2
3
4 (highest resilience)
367
-------
#53: Do plans exist for preserving areas with good ventilation (e.g., those aligned with
prevailing breezes)?
Relevance Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
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 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
368
-------
K.5. 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
369
-------
#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 3
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 2
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
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
No
Yes
Resilience Score 3
1 (lowest resilience)
3 (highest resilience)
370
-------
#57: Do policies and outreach/education programs promote behavioral changes that
facilitate climate change adaptation?
Relevance Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#58: Are emergency response staff well trained to respond to large-scale extreme weather
events?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 2
1 (lowest resilience)
4 (highest resilience)
371
-------
#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 4
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
1 (lowest resilience)
3 (highest resilience)
#60: Is there sufficient capacity in public health and emergency response systems for
responding to extreme events?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
372
-------
#61: Does the city have the capacity to provide public transportation for emergency
evacuations?
Relevance Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
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
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
No evacuation or shelter-in-place options are available to
residents in the event of a heat wave.
One to two evacuation and shelter-in-place options are
available to residents in the event of a heat wave.
Several evacuation and shelter-in-place options are
available to residents in the event of a heat wave.
Many evacuation and shelter-in-place options are available
to residents in the event of a heat wave.
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
373
-------
#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, using fire
hydrants for cooling), given predicted climatic changes?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 3
1 (lowest resilience)
2
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 Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
The healthcare community is poorly prepared. 1 (lowest resilience)
The healthcare community's level of preparation is fair. 2
Yes, the healthcare community is moderately prepared. 3
Yes, the healthcare community is well-prepared. 4 (highest resilience)
374
-------
#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 4
1 (not very important)
2
3
4 (very important)
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 3
1 (lowest resilience)
4 (highest resilience)
#66: Do public health programs incorporate longer time frames (e.g., 10 or more years),
and do they address climate change-related health issues (e.g., movement of deer ticks to
more northerly locations)?
Relevance Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
Public health programs are not designed to address climate- 1 (lowest resilience)
related health issues.
Public health programs incorporate long-term 3 (highest resilience)
timeframes and are address climate-related health
issues.
375
-------
#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 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
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 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
376
-------
#69: Do planners in the urban area know the demographic characteristics of populations
vulnerable to climate change?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
#70: Do planners in the urban area know the locations of populations most vulnerable to
climate change effects?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
377
-------
#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 4
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 2
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 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
378
-------
#73: Are policies and programs to promote adaptive behavior designed and implemented in
ways that promote the health and well-being of vulnerable populations?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#74: Are policies and programs to promote adaptive behavior evaluated in ways that take
into account vulnerable populations?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
379
-------
#108: How accessible are different modes of transportation (e.g., to what proportion of the
population, what subpopulations [vulnerable people])?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 4
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 2
1 (lowest resilience)
4 (highest resilience)
380
-------
#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 (e.g., due to reduced access to specialized
medical care or equipment)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 4
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 3
1 (lowest resilience)
4 (highest resilience)
#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 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
381
-------
#158: Do municipal managers consider local stakeholder knowledge and local resources
(e.g., libraries, archives) in climate change resilience planning?
Action Needed:
Please provide a relevance, importance weight, and answer for this question.
Relevance
Importance Weight 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
-i
J
4 (very important)
Answer
Resilience Score 1
No
1 (lowest resilience)
Yes
3 (highest resilience)
382
-------
K.6. Telecommunications
The questions below have been developed for the telecommunication 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 telecommunication 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
383
-------
#75: What natural disasters has the area experienced in the past, and what services were
retained or largely unaffected despite these disasters?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
Area has either not experienced many natural disasters in
recent history, or services were significantly impaired
during recent natural disasters.
Area has experienced some extreme weather or other
natural disasters, but some services were significantly
affected.
Area has experienced some extreme weather or other
natural disasters, and most services were unaffected or
affected in minor ways.
Area has experienced major extreme weather events or
other natural disasters, and majority of services were
retained or were largely unaffected.
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
#76: How would a temporary loss of telecommunication infrastructure affect the local and
regional economies?
Relevance
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 1
Major effect
1 (lowest resilience)
Moderate effect
2
Small effect
3
Little to no effect
4 (highest resilience)
384
-------
#77: Are data centers located within or outside of the urban area?
Relevance Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
Within 1 (lowest resilience)
Mostly within the urban area but somewhat outside the 2
urban area.
Mostly outside the urban area but somewhat within the 3
urban area.
Outside 4 (highest resilience)
#78: For each telecommunication 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 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
There are many key nodes whose failure would severely
1 (lowest resilience)
affect service.
There are some key nodes whose failure would severely
2
affect service.
There are a few key nodes whose failure would severely
3
affect service.
No, there are no nodes whose failure would severely affect
4 (highest resilience)
service.
385
-------
#79: How robust is the telecommunication 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 4
1 (not very important)
2
3
4 (very important)
Answer
The telecommunication network is not resilient to damage
or failure of key nodes.
The telecommunication network is slightly resilient to
damage or failure of key nodes.
The telecommunication network is somewhat resilient to
damage or failure of key nodes.
The telecommunication network is very resilient to
damage or failure of key nodes.
Resilience Score 4
1 (lowest resilience)
4 (highest resilience)
#80: Are there parts of the telecommunication infrastructure that are particularly
vulnerable to high temperatures or prolonged high temperatures?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
386
-------
#81: Are there satellite-based communications on frequency bands (e.g., the Ka band) that
are vulnerable to wet-weather disruption?
Relevance Importance Weights 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
#82: Are your telecommunication infrastructure components located wisely with respect to
your anticipated climate stressors (i.e., aboveground, underground, or serviced by
satellite)?
Relevance Importance Weights 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
387
-------
#83: Are aboveground infrastructure components vulnerable to wind (e.g., cell towers)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
All aboveground infrastructure components are vulnerable
to expected winds.
Some aboveground infrastructure components are
vulnerable to expected winds.
Few aboveground infrastructure components are
vulnerable to expected winds.
No aboveground infrastructure components are vulnerable
to expected winds.
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
#84: Are belowground infrastructure components vulnerable to rising water or salt water
intrusion?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
All belowground infrastructure components are vulnerable
to expected rises in groundwater levels or from salt water
intrusion.
Some belowground infrastructure components are
vulnerable to expected rises in groundwater levels or from
salt water intrusion.
Few belowground infrastructure components are
vulnerable to expected rises in groundwater levels or
from salt water intrusion.
No belowground infrastructure components are vulnerable
to expected rises in groundwater levels or from salt water
intrusion.
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
388
-------
#85: If the area has satellite-based communications that are vulnerable to wet-weather
disruption, does the area have a backup tower network?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
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 of backup.
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.
Importance Weight 4
1 (not very important)
2
3
4 (very important)
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
#86: Does your community have sufficient access to backup telecommunication systems?
What is the capacity of the telecommunication infrastructure?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
There are no backup systems. Capacity of the
telecommunication infrastructure is low.
There are some minimal backup systems, but
telecommunication 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
telecommunication systems is high.
Resilience Score 4
1 (lowest resilience)
4 (highest resilience)
389
-------
#87: Is backup power for telecommunication systems provided? If so, is it provided by
diesel generators?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 4
1 (lowest resilience)
2
4 (highest resilience)
#88: What is the extent of telecommunication redundancy? Do first responders and the
public have multiple communication options, served by different infrastructure?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
There is little to no redundancy.
There is a small amount of redundancy.
There is a moderate amount of redundancy. There are
more than one communication options, served by different
infrastructure.
There is a great deal of redundancy. There are multiple
communication options, served by different
infrastructure.
Resilience Score 4
1 (lowest resilience)
2
3
4 (highest resilience)
390
-------
#89: What percentage of telecommunication system capacity is required for the baseline
level of use?
Relevance Importance Weight 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 4
Greater than 85% 1 (lowest resilience)
70 to 85% 2
60 to 70% 3
Less than 60% 4 (highest resilience)
#90: Does telecommunication infrastructure have the capacity for increased public demand
in an emergency?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#91: Do local authorities have established relations with telecommunication infrastructure
service providers? Are emergency protocols and plans in place?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
391
-------
#92: Do local private-sector telecommunication infrastructure service providers have the
authority and resources to make quick decisions and implement them in and after an
emergency?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#93: Can local authorities and telecommunication providers give first responder and
decision-maker communications priority during an expected surge in traffic in emergency
situations?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
392
-------
#94: Are public-address systems (e.g., loud speakers, text messages, radio broadcasts,
emergency television broadcasts) in place to provide instructions to the public in case of an
emergency?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 4
1 (not very important)
2
3
4 (very important)
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 3
1 (lowest resilience)
2
4 (highest resilience)
#95: What modes do authorities in the urban area use to communicate emergency
information and alerts? Are these modes low or high bandwidth?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer
Authorities do not use multiple modes (e.g., text
messaging, email, phone calls), or none of the modes used
is low bandwidth.
Authorities use one to two modes (e.g., text messaging,
email, phone calls) and one or two of these modes is low
bandwidth.
Authorities use multiple modes (e.g., text messaging,
email, phone calls) and one or two of these modes are low
bandwidth.
Authorities use multiple modes (e.g., text messaging,
email, phone calls) and some of these modes are low
bandwidth.
Resilience Score N/A
1 (lowest resilience)
4 (highest resilience)
393
-------
#96: What is the likelihood that the capacity of local first responder communication
systems would be exceeded during a disaster?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 3
1 (lowest resilience)
4 (highest resilience)
#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 4
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 3
1 (lowest resilience)
4 (highest resilience)
394
-------
#98: Is the availability of telecommunication 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 4
1 (not very important)
2
3
4 (very important)
Answer
Availability of telecommunications resources is at
significant risk if other city services are affected by
climatic events or changes.
Availability of telecommunications resources is at
moderate risk if other city services are affected by climatic
events or changes.
Availability of telecommunications resources is at some
risk if other city services are affected by climatic events
or changes.
Availability of telecommunications resources is at minimal
risk if other city services are affected by climatic events or
changes.
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
395
-------
#99: Do telecommunication systems have enough energy and water supply to handle an
extra load in the case of sudden natural disasters?
Action Needed:
(1) Due to answers being made a gradient, please review/answer the amended question.
(2) Please provide importance weight.
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 3
1 (not very important)
2
3
4 (very important)
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.
Resilience Score 2
1 (lowest resilience)
4 (highest resilience)
#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?
Relevance Importance Weight 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
396
-------
K.7. 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
397
-------
#100: Is the availability of transportation goods and services at risk if other city goods and
services (e.g., power, water, telecommunications) are affected by extreme climatic events or
gradual climatic changes?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
Availability of transportation resources is at significant
risk if other city services are affected by climatic events
or changes.
Availability of transportation resources is at moderate risk
if other city services are affected by climatic events or
changes.
Availability of transportation resources is at some risk if
other city services are affected by climatic events or
changes.
Availability of transportation resources is at minimal risk if
other city services are affected by climatic events or
changes.
Resilience Score 1
1 (lowest resilience)
4 (highest resilience)
#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 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
None
1 (lowest resilience)
Low
2
Medium
3
High
4 (highest resilience)
398
-------
#102: How resistant to potential impacts of climate change are critical transportation
facilities (e.g., high-traffic vehicle or rail bridges, tunnels)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 4
1 (not very important)
2
3
4 (very important)
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 2
1 (lowest resilience)
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 4
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 2
1 (lowest resilience)
4 (highest resilience)
399
-------
#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 Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
More than 1 week 1 (lowest resilience)
Approximately 1 week 2
4 to 6 days 3
1 to 3 days 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 N/A
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)
400
-------
#106: To what extent is the area dependent on long-range transportation of goods and
services versus locally available goods and services (food, energy, etc.)?
Relevance N/A
Importance Weights
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score
90-100% dependent on long-range transportation of goods
1 (lowest resilience)
and services
50-90% dependent on long-range transportation of goods
2
and services
10-50%) dependent on long-range transportation of goods
3
and services
0-10%o dependent on long-range transportation of goods
4 (highest resilience)
and services
#107: What flexibility has been built into the transportation system (different modes)?
Relevance
Importance Weights 3
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
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)
401
-------
#108: How accessible are different modes (e.g., to what proportion of the population, what
subpopulations [vulnerable people])?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 2
1 (lowest resilience)
4 (highest resilience)
402
-------
#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 (e.g., due to reduced access to specialized
medical care or equipment)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 3
1 (lowest resilience)
4 (highest resilience)
#110: How familiar is the community with evacuation procedures?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer
Unfamiliar
Only slightly familiar (or only some subpopulations are
familiar)
Somewhat familiar
Very familiar
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Resilience Score 1
1 (lowest resilience)
2
4 (highest resilience)
403
-------
#111: What length of time would be required to restore major passenger rail
transportation facilities in the urban area if they experience a failure?
Relevance Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score N/A
More than 1 week 1 (lowest resilience)
Approximately 1 week 2
4 to 6 days 3
1 to 3 days 4 (highest resilience)
#112: What length of time would be required to restore major freight rail transportation
facilities in the urban area if they experience a failure?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer
More than 1 week
Approximately 1 week
4 to 6 days
1 to 3 days
Resilience Score N/A
1 (lowest resilience)
2
3
4 (highest resilience)
404
-------
#114: Are urban areas set up to provide accessibility (e.g., to jobs) if mobility is interrupted
or impeded?
Relevance Importance Weights 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#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 3
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 3
1 (lowest resilience)
4 (highest resilience)
405
-------
#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 Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
No funding mechanisms exist to adapt transportation 1 (lowest resilience)
systems to climatic changes, and none could be established.
No funding mechanisms exist to adapt transportation 2
systems to climatic changes, but mechanisms could be
established.
Funding mechanisms are being developed to adapt 3
transportation systems to climatic changes.
Funding mechanisms exist to adapt transportation systems 4 (highest resilience)
to climatic changes.
#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 Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
No currently used materials are compatible with anticipated 1 (lowest resilience)
changes in temperature.
A few currently used materials are compatible with 2
anticipated changes in temperature.
Some currently used materials are compatible with 3
anticipated changes in temperature.
All currently used materials are compatible with anticipated 4 (highest resilience)
changes in temperature.
406
-------
#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 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
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 Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
Not implemented or planned 1 (lowest resilience)
Planned but not yet implemented 2
Some implementation with further green infrastructure 3
planned
Widespread implementation with additional projects 4 (highest resilience)
planned
407
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#162: Have municipalities considered new methods of designing roads/bridges to prepare
for heavily traveled routes during an extreme climate event (e.g., coastal evacuation
routes)?
Relevance Importance Weight 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
408
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K.8. 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:
4. 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.
5. For questions marked as .yes (relevant), discuss an importance weight, where 1 = not very
important and 4 = very important.
6. 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
409
-------
#121: Does the water supply draw from a diversity of sources?
Relevance Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
#122: To what extent do water supplies come from outside the metropolitan area?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
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)
#123: Is there a recharge plan in place for groundwater supplies?
Relevance Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
410
-------
#124: Do programs for long-term maintenance of water supplies (e.g., erosion control
methods, reforestation of the watershed) exist?
Relevance Importance Weights 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
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 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
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 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
411
-------
#127: Are water and wastewater treatment plants located in a flood zone?
Relevance
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 1
At least 50% of water and wastewater treatment plant
1 (lowest resilience)
capacity is located in a flood zone.
30% to 49% of water and wastewater treatment plant
2
capacity is located in a flood zone.
10%) to 29% of water and wastewater treatment plant
3
capacity is located in a flood zone.
Less than 10%> of water and wastewater treatment plant
4 (highest resilience)
capacity is located in a flood zone.
#128: Are groundwater supplies susceptible to salt water intrusion and sea level rise?
Relevance
Importance Weights N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score N/A
Groundwater supplies are very susceptible to salt water
1 (lowest resilience)
intrusion given anticipated sea level rise.
Groundwater supplies are moderately susceptible to salt
2
water intrusion given anticipated sea level rise.
Groundwater supplies are slightly susceptible to salt water
3
intrusion given anticipated sea level rise.
No, groundwater supplies are not susceptible to salt water
4 (highest resilience)
intrusion and sea level rise.
412
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#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 Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#130: Does treatment capacity exist to accommodate nutrient loading?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
Drinking water treatment capacity cannot
accommodate nutrient loading in source water.
Drinking water treatment capacity can accommodate
expected levels of nutrient loading in source water.
Resilience Score 1
1 (lowest resilience)
3 (highest resilience)
#131: Does the drinking water treatment plant have redundant treatment chemical
suppliers?
Relevance Importance Weight 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
413
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#132: Are there redundant drinking water systems in place for coping with extreme events,
including supply, treatment, and distribution systems?
Relevance
Importance Weights 3
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 1
No, redundant drinking water systems are not in place.
1 (lowest resilience)
Yes, but these redundant drinking water systems have only
2
a small amount of the capacity necessary.
Yes, and these redundant drinking water systems have
3
some of the capacity necessary.
Yes, and these redundant drinking water systems have all
4 (highest resilience)
the capacity necessary.
#133: Is backup power for water supply, treatment, and distribution systems provided?
Relevance
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 1
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)
414
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#134: How diverse are individual properties (i.e., are they equipped to harvest rainwater or
recharge groundwater so they can create or augment local water supplies)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer
No individual properties are equipped to either harvest
rainwater or recharge groundwater.
Few individual properties are equipped to either harvest
rainwater or recharge groundwater.
Some individual properties are equipped to either harvest
rainwater or recharge groundwater.
Most individual properties are equipped to either harvest
rainwater or recharge groundwater.
Resilience Score N/A
1 (lowest resilience)
4 (highest resilience)
#135: Are there redundant wastewater and stormwater systems in place for coping with
extreme events, including collection systems and wastewater treatment systems?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
No, redundant wastewater and stormwater systems are
not in place.
Yes, but these redundant wastewater and stormwater
systems have only a small amount of the capacity
necessary.
Yes, and these redundant wastewater and stormwater
systems have some of the capacity necessary.
Yes, and these redundant wastewater and stormwater
systems have all the capacity necessary.
Resilience Score 1
1 (lowest resilience)
4 (highest resilience)
415
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#136: Does a water/wastewater agency response network provide technical
resources/support to the urban area's water system during emergencies?
Relevance Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#137: Have storm sewers and drains to storm sewers been inventoried, and are these
inventories used in planning?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
Storm sewers and drains to storm sewers are not
inventoried and are not planned to be inventoried.
Plans exist to inventory storm sewers and drains to storm
sewers OR these inventories exist but are not used in
planning.
Storm sewers and drains to storm sewers are being
inventoried and these inventories are used or will be
used in planning.
Storm sewers and drains to storm sewers have been
inventoried and these inventories are used in planning.
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
416
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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 4
1 (not very important)
2
3
4 (very important)
Answer
Availability of water resources is at significant risk if
other city services are affected by climatic events or
changes.
Availability of water resources is at moderate risk if other
city services are affected by climatic events or changes.
Availability of water resources is at some risk if other city
services are affected by climatic events or changes.
Availability of water resources is at minimal risk if other
city services are affected by climatic events or changes.
Resilience Score 1
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 N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
417
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#140: To what extent have efforts been made to reduce water demand?
Relevance
Imvortance Weights 2
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
Few to no efforts have been made to reduce water demand.
1 (lowest resilience)
Fair efforts have been made to reduce water demand.
2
Moderate efforts have been made to reduce water demand.
3
Significant efforts have been made to reduce water
4 (highest resilience)
demand.
#141: Are customers familiar with water conservation measures, and are they willing to
implement these measures?
Relevance
Imvortance Weights 2
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
Customers are not familiar with OR are not willing to
1 (lowest resilience)
implement water conservation measures.
Customers are marginally familiar with and somewhat or
2
marginally willing to implement water conservation
measures.
Customers are somewhat familiar with and willing to
3
implement water conservation measures.
Customers are familiar with and willing to implement
4 (highest resilience)
water conservation measures.
418
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APPENDIX L. QUANTITATIVE INDICATORS: WASHINGTON, DC
A complete set of the quantitative indicators by sector developed for the tool.
L.l. 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
nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available.
In some cases, no data sets were identified. Please suggest data sets that may be better
than the data sets 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.
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
419
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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.
420
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PRIMARY INDICATORS AND NONGROUPED INDICATORS
#709: Percentage of owned housing units that are affordable
Action Needed:
Please review the indicator and decide if you agree with the threshold-based score. Provide
an explanation if a threshold-based score is not chosen.
Definition: This indicator measures (1) the percentage of owned housing units where selected
monthly ownership costs (rent, mortgages, real estate taxes, insurance, utilities, fuel, fees) as
a percentage of household income (SMOCAPI) exceeds 35% or (2) the percentage of rented
housing units where gross rent as a percentage of household income (GRAPI) exeeds 35%.
Grouped with Indicators: N/A
Data Set(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+Estimat
es)
Notes on Data Set(s):
The American Community Survey (ACS) is an ongoing survey administered by the U.S.
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°/
6; 40.7%) of the rented housing units for which data are
available have gross rent > 35%.
Indicator Value:
33.7%) of housing units
Relevance:
Importance Weight:
Proposed
Yes
3
Resilience Score: 3
Thresholds:
Threshold-Based Score: 2
Your Score: 2
0 to 30%
1 (lowest resilience)
1 (lowest
resilience)
Greater than 30 to 45%
2
2
45 to 60%
3
3
Greater than 60%
4 (highest resilience)
4 (highest
resilience)
421
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#711: Overall 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 the percentage of sectors in a city's economy that employ < 40% of the
city's population. Sectors that employ 1% or less of the city's population are not considered,
as they provide very minimal employment opportunities.
Grouped with Indicators: N/A
Data Set(s):
1) NAICS (American FactFinder - search Washington City, DC go to the Economic Census) -
EC0700A1: All sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007 -
http://factfinder2.census.gov/faces/nav/jsf/pages/community_facts.xhtml
Notes on Data Set(s):
12 NAICS code (some aggregate codes). One NAICS code employs less than 1% of the
employed population (as represented in the NAICS table). The remaining 11 NAICS codes
employ between 1 and 40% of the employed population. 100% of the sectors.
Indicator Value:
100%
Relevance: Importance Weight: Proposed
Yes 2 Resilience Score:
4
Thresholds:
Threshold-Based Score: 4
Your Score: 4
0 to less than 83%
1 (lowest resilience)
1 (lowest
resilience)
83 to less than 91%
2
2
91 to less than 100%
3
3
100%
4 (highest resilience)
4 (highest
resilience)
422
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#717: Percentage access to health insurance of noninstitutionalized population
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator measures the percentage of noninstitutionalized residents with
health insurance.
Grouped with Indicators: #725
Data Set(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+Estim
ates/Economic+Characteri sties)
Notes on Data Set(s):
Of the 594,576 civilian noninstitutionalized population, 92.9% have health insurance.
Indicator Value:
92.90%
Relevance: Importance Weight: Proposed
Yes 3 Resilience Score: 4
Thresholds: Threshold-Based Score: 3 Your Score: 3
Less than 85% 1 (lowest resilience) 1 (lowest
resilience)
85 to 90%
90 to 95%
Greater than 95%
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
423
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#722: Percentage change in homeless population
Action Needed:
Please review the indicator and decide if you agree with the threshold-based score. Provide
an explanation if a threshold-based score is not chosen.
Definition: This indicator measures the percentage change in the homeless population.
Grouped with Indicators: N/A
Data Set(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/qF5 cX 1 w20130508134424. pdf)
Notes on Data Set(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 homeless persons by county jurisdiction for each year from
2009 through 2013. There were 6,954 homeless in 2012 and 6,865 homeless in 2013, a
1.27% decrease year over year.
Indicator Value:
-1.27% change in homeless population
Relevance: Importance Weight: Proposed
Yes 3 Resilience Score: 2
Thresholds:
Threshold-Based Score: 3
Your Score: 2
Greater than 10%
1 (lowest resilience)
1 (lowest
resilience)
Greater than 0 to 10%
2
2
negative 10 to 0%
3
3
Less than negative 10%
4 (highest resilience)
4 (highest
resilience)
424
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#1375: Percentage of population living below the poverty line
Action Needed:
Please review the indicator and decide if you agree with the threshold-based score. Provide
an explanation if a threshold-based score is not chosen.
Definition: This indicator measures the percentage of the population living below the poverty
line.
Grouped with Indicators: N/A
Data Set(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+Estimat
es)
Notes on Data Set(s):
The American Community Survey (ACS) is an ongoing survey administered by the U.S.
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 Economic Characteristics"
document: 18.2% of people had an income in the last 12 months below the poverty level.
Indicator Value:
18.2% of people
Relevance: Importance Weight: Proposed Resilience Score: 1
Yes 2
Thresholds:
Threshold-Based Score: 2 Your Score:
Greater than 20%
1 (lowest resilience) 1 (lowest resilience)
16 to 20%
2 2
12 to 16%
3 3
Less than 12%
4 (highest resilience) 4 (highest resilience)
425
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L.2. 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
nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
426
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PRIMARY INDICATORS AND NONGROUPED INDICATORS
#898: Annual energy consumption per capita by main use category (commercial use)
Action Needed:
(1) Please decide if the original or alternate data set is more appropriate.
(2) Please decide if you agree with the threshold-based score and provide an explanation if a
threshold-based score is not chosen.
(3) 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
Data Set(s):
District of Columbia—Energy Assurance Plan 2012
(http://ddoe.dc.gov/sites/default/files/dc/sites/ddoe/publication/attachments/Energy%20Assur
ance%20Plan.pdf)
Notes on Data Set(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 Data Set(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 Data Set(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
427
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Relevance:
Importance Weight:
Proposed
Yes
4
Resilience Score: 2
Thresholds:
Threshold-Based Score: 4
Your Score: 1
Greater than 4.0 tons of oil
1 (lowest resilience)
1 (lowest
equivalent
resilience)
3.0 to 4.0 tons of oil equivalent
2
2
2.0 to 3.0 tons of oil equivalent
3
3
Less than or equal to 2.0 tons of
4 (highest resilience)
4 (highest
oil equivalent
resilience)
428
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#949: Percentage energy consumed for electricity
Action Needed:
No thresholds have been 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 percentage
of total energy consumption.
Grouped with Indicators: #950, #951
Data Set(s):
District of Columbia—Energy Assurance Plan 2012
(http://ddoe.dc.gov/sites/default/files/dc/sites/ddoe/publication/attachments/Energy%20Assur
ance%20Plan.pdf)
Notes on Data Set(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:
70.4%
Relevance: Importance Weight: Proposed
Yes 3 Resilience Score: 3
Thresholds: Threshold-Based Score: N/A Your Score: 3
N/A 1 (lowest resilience) 1 (lowest
resilience)
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
429
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#967: Total energy source capacity per capita
Action Needed:
Please review the indicator and decide if you agree with the threshold-based score. Provide
an explanation if a 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
Data Set(s):
(1) Pepco—Service Area Map (http://www.pepco.com/business/services/new/map/)
(2) PJM—Load Forecast Report January 2013
(https://www.pjm.eom/~/media/documents/reports/2013-load-forecast-report.ashx)
Notes on Data Setfs):
(1) Pepco population served = 2,022,000
(2) Table B-l. Pepco peak demand was 6,800 MW in 2012. Assume 20% reserve capacity;
therefore, PEPCO peak capacity = 8,500 MW. Capacity of source per capita = 8,500
MW ^ 2,022,000 people in service ar= 0.0042 MW per capita or 4.2 kW per capita
Indicator Value:
4.2 kW per capita
Relevance:
Importance Weight:
Yes
Proposed
Resilience Score: 3
Thresholds:
Less than 1.0 megawatt per
capita
1.0 to 2.0 megawatts per capita
2.0 to 5.0 megawatts per
capita
Greater than 5.0 megawatts per
capita
Threshold-Based Score: 3
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 3
1 (lowest
resilience)
2
3
4 (highest
resilience)
430
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#971: Energy source capacity per unit area
Action Needed:
Please review the indicator and decide if you agree with the threshold-based score. Provide
an explanation if a 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
Data Set(s):
(1) Pepco—Service Area Map (http://www.pepco.com/business/services/new/map/)
(2) PJM—Load Forecast Report January 2013
(https://www.pjm.eom/~/media/documents/reports/2013-load-forecast-report.ashx)
Notes on Data Set(s):
(1) Pepco service area 640 square miles.
(2) Table B-l. Pepco peak demand was 6,800 MW in 2012. Assume 20% reserve capacity;
therefore, PEPCO peak capacity = 8,500 MW. Capacity of source = 8,500 MW -H540 sq
mi = 13.28 MW/sq mi
Indicator Value:
13.28 MW/sq mi
Relevance:
Importance Weight:
Yes
Proposed
Resilience Score: 2
Thresholds:
Less than 10 megawatts per
square mile
10 to 50 megawatts per square
mile
50 to 100 megawatts per square
mile
Greater than 100 megawatts per
square mile
Threshold-Based Score: 2
1 (lowest resilience)
4 (highest resilience)
Your Score: 2
1 (lowest
resilience)
2
4 (highest
resilience)
431
-------
#983: Average customer energy outage (hours) in recent major storm
Action Needed:
(1) Please decide if the original or alternate data set is more appropriate.
(2) Please decide if you agree with the threshold-based score and provide an explanation if a
threshold-based score is not chosen.
(3) 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
Data Set(s):
Potomac Electric Power Company (PEPCO)—Major Service Outage Report June 29-July 7,
2012 DERECHO
Notes on Data Set(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 Data Set(s):
1) Potomac Electric Power Company Comprehensive Reliability Plan for District of
Columbia
(http://www.pepco.com/uploadedFiles/wwwpepcocom/DCComprehensiveReliabilityPlan(l).
pdf)
SAIFI—System Average Interruption Frequency Index
SAIDI—System Average Interruption Duration Index
CAIDI—Customer Average Interruption Duration Index
Notes on Alternate Data Set(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 because this is the closest to the indicator definition
Alternate Indicator Value:
2.33 hours
Relevance: Importance Weight: Proposed Resilience Score:
Yes 4 4
432
-------
Thresholds:
Threshold-Based Score: 2
Your Score: 2
Greater than 40 hours
1 (lowest resilience)
1 (lowest resilience)
20 to 40 hours
2
2
10 to 20 hours
3
3
Less than 10 hours
4 (highest resilience)
4 (highest resilience)
433
-------
SECONDARY INDICATORS
#924: Energy intensity by use
Action Needed:
Please review the indicator and decide if you agree with the threshold-based score. Provide
an explanation if a 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:
Data Set(s):
Department of Energy—District of Columbia Energy Consumption
Notes on Data Set(s):
This DOE webpage estimates the energy intensity of gross state product in 2010 at 1,800 Btu
per dollar.
Indicator Value:
1,800 Btu per dollar
Relevance: Importance Weight: Proposed
No N/A Resilience Score: 3
Thresholds:
Threshold-Based Score: 3
Your Score
Greater than 3,000 Btu per
1 (lowest resilience)
1 (lowest
dollar
resilience)
2,000 to 3,000 Btu per dollar
2
2
1,500 to 2,000 Btu per dollar
3
3
Less than 1,500 Btu per dollar
4 (highest resilience)
4 (highest
resilience)
434
-------
#950: Percentage of electricity generation from noncarbon sources
Action Needed:
Please decide if the threshold-based score or a score from previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator measures the percentage of total electricity generation from
noncarbon energy sources in a city.
Grouped with Indicators: #949, #951
Data Set(s):
US Environmental Protection Agency—Green Power Community Challenge Rankings
(http://www.epa.gOv/greenpower/communities/gpcrankings.htm#content)
Notes on Data Set(s):
As tracked by the EPA's Green Power Partnership program, 1.045 terawatt hours of green
power was consumed in DC over a yearlong period from 2012-2013.
electricity use.
This is 11.4% of total
Indicator Value:
11.4%
Relevance:
Yes
Importance Weight:
4
Proposed
Resilience Score: 3
Thresholds:
Threshold-Based Score: 1
Your Score: 1
Less than 25%
1 (lowest resilience)
1 (lowest
resilience)
25 to 50%
2
2
50 to 75%
3
3
Greater than 75%
4 (highest resilience)
4 (highest
resilience)
435
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#951: Percentage of total energy use from renewable sources
Action Needed:
(1) Please decide if the original or alternate data set is more appropriate.
(2) Please decide if you agree with the threshold-based score and provide an explanation if a
threshold-based score is not chosen.
(3) Please review/modify importance weight if appropriate.
Definition: This indicator measures the percentage of total energy use from renewable
sources.
Grouped with Indicators: #949, #950
Data Set(s):
Department of Energy—District of Columbia Energy Consumption
Notes on Data Set(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 Data Set(s):
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.htm
l&sid=DC)
Notes on Alternate Data Set(s):
Source describes 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: 4
Proposed
Resilience Score: 3
Thresholds:
Threshold-Based Score: 1
Your Score: 1
Less than 20%
1 (lowest resilience)
1 (lowest
resilience)
20 to 40%
2
2
40 to 60%
3
3
Greater than 60%
4 (highest resilience)
4 (highest
resilience)
436
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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
(mw/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
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance:
Yes
Importance Weight: 3
Proposed
Resilience Score: 2
Thresholds:
Less than 5,000 megawatts per
square mile
5,000 to 10,000 megawatts per
square mile
10,000 to 15,000 megawatts per
square mile
Greater than 15,000 megawatts
per square mile
Threshold-Based Score: N/A
1 (lowest resilience)
Your Score: Score
not yet assigned
1 (lowest
resilience)
2
4 (highest resilience)
4 (highest
resilience)
437
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L.3. 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 nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
438
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PRIMARY INDICATORS AND NONGROUPED INDICATORS
#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), and f = mean wave
height (m).
Grouped with Indicators: N/A
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance: Importance Weight: Proposed
No N/A Resilience Score:
N/A
Thresholds:
5 (very high vulnerability)
Threshold-Based Score: N/A
1 (lowest resilience)
Your Score: N/A
1 (lowest
4 (high vulnerability)
3 (moderate vulnerability)
Less than or equal to 2 (low or
no vulnerability)
2
3
4 (highest resilience)
resilience)
2
3
4 (highest
resilience)
439
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#194: Percentage of natural area that is in small natural patches
Action Needed:
(1) Please decide if the original or alternate data set is more appropriate.
(2) Please decide if you agree with the threshold-based score and provide an explanation if a
threshold-based score is not chosen.
(3) Please assign an importance weight.
Definition: This indicator measures the percentage of the total natural area in a city that is in
patches of less than 10 acres. 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
Data Set(s):
DC.gov GIS Data Catalog:
(1) National Parks
(2) Recreation Parks
(3) Wetlands—only nonriverine 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 Data Set(s):
Union layers 1, 2, 3, 4, 5, and 6. Erase layer 7 from the resulting union. The area of the
polygons resulting is the total natural area. Select all polygons from the result with
Shape Area < 1 acre. Divide the sum of these polygon areas by the total natural area
calculated above.
Total natural area = 10,210.3 acres (41,319,581.2 m2)
Total area of all natural patches less than 10 acres = 1,600.3 acres (6,476,087.4 m2)
Percentage of natural area that is small natural patches = 15.7%
Indicator Value:
15.7%
Relevance: Importance Weight: N/A Proposed
Not sure—remind me later Resilience Score: 3
Thresholds:
Threshold-Based Score: 4
Your Score: Score
not yet assigned
Greater than 80%
1 (lowest resilience)
1 (lowest
resilience)
60 to 80%
2
2
40 to 60%
3
3
Less than 40%
4 (highest resilience)
4 (highest
resilience)
440
-------
#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
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Setfs):
N/A
Indicator Value:
N/A
Relevance:
Importance Weight: N/A
Proposed
Not sure—remind me later
Resilience Score: 2
Thresholds:
Threshold-Based Score: N/A
Your Score: N/A
Greater than 0.025 (unitless
1 (lowest resilience)
1 (lowest
ratio)
resilience)
0.015 to 0.025 (unitless ratio)
2
2
0.005 to 0.015 (unitless ratio)
3
3
Less than 0.005 (unitless ratio)
4 (highest resilience)
4 (highest
resilience)
441
-------
#437: Percentage change in streamflow divided by percentage 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 U.S. from 1931 to 1988.
Grouped with Indicators: #1369
Data Set(s):
(1) USGS Hydro-Climatic Data Network (HCDN) data set 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).
Notes on Data Set(s):
(1) Includes data on mean annual streamflow (cfs) from 1931 to 1988. Calculate percentage
change in streamflow and precipitation. Divide percentage change in streamflow by
percentage change in precipitation.
(2) Includes total precipitation (in) from 1871 to 2013.
Indicator Value:
-14.36%
Relevance: Importance Weight: Proposed
Not sure—remind me later N/A Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 1
Your Score: N/A
Greater than 3.0 (unitless ratio) 1 (lowest resilience)
1 (lowest
2.0 to 3.0 (unitless ratio)
1.0 to 2.0 (unitless ratio)
Less than 1.0 (unitless ratio)
2
3
4 (highest resilience)
resilience)
2
3
4 (highest
resilience)
442
-------
#825: Percentage change in impervious cover
Action Needed:
(1) Please decide if the original or alternate data set is more appropriate.
(2) Please decide if you agree with the threshold-based score and provide an explanation if a
threshold-based score is not chosen.
(3) Please review/modify importance weight if appropriate.
Definition: This indicator reflects the change in the percentage of the metropolitan area that is
impervious surface (roads, buildings, sidewalks, parking lots, etc.).
Grouped with Indicators: #303, #308
Data Set(s):
NLCD 2001/2006 Percentage developed imperviousness change data set:
http://www.mrlc.gov/nlcd06_data.php
Notes on Data Set(s):
Calculate the average percentage change in imperviousness across DC for the time period
2001-2006.
Clip the raster file to the town boundary, then calculate the product of the Count and Red (the
percentage change in imperviousness) fields. Sum this product and divide by the sum of the
Count field.
Percentage change in impervious surface cover = 0.19% increase.
Indicator Value:
0.19% increase
Relevance: Importance Weight: 4 Proposed Resilience Score:
Yes 1
Thresholds:
Threshold-Based Score: 2
Your Score: 2
Greater than 1%
1 (lowest resilience)
1 (lowest resilience)
0 to 1%
2
2
Negative 1 to 0%
3
3
Less than negative 1%
4 (highest resilience)
4 (highest resilience)
443
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#1436: Percentage of city area in 100-year floodplain
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator reflects the percentage of the metropolitan area that lies within the
100-year floodplain.
Grouped with Indicators: #1437. #1438, #1439
Data Set(s):
DC.gov—2010 Floodplains
Notes on Data Set(s):
This GIS data set describes the areas of 100- and 500-year floodplains as determined by the
Federal Emergency Management Agency.
Indicator Value:
8.50%
Relevance: Importance Weight: Proposed
Yes 1 Resilience Score: 1
Thresholds:
Threshold-Based Score: 2
Your Score: 2
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
5 to 20%
2
2
1 to 5%
3
3
Less than 1%
4 (highest resilience)
4 (highest
resilience)
444
-------
#1440: Palmer Drought Severity Index
Definition:
Measurement of dryness based on recent precipitation and temperature, calculated using a
supply-and-demand model of soil moisture.
Grouped with Indicators: N/A
Data Set(s):
National Weather Service—Palmer Drought Severity and Crop Moisture Indices
(http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/cdus/palmer_drought/)
Notes on Data Set(s):
(1) Calculate potential evapotranspiration (PET) for selected time periods using temperature
data and the Thornthwaite equation.
(2) Find the precipitation deficit (precipitation minus PET) for the selected time period,
where more negative values indicate greatest precipitation deficit.
(3) Using a moving window sum, find the 1-, 3-, 6-, or 12-month period that had the greatest
total precipitation deficit.
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
Relevance:
Importance Weight:
Yes
Proposed
Resilience Score: 4
Thresholds:
Less than or equalt to negative
4.0 (extreme drought)
Negative 3.99 to negative 3.0
(severe drought)
Negative 2.99 to negative 2.0
(moderate drought)
Greater than or equal to
negative 1.99 (mild or no
drought)
Threshold-Based Score: 4
1 (lowest resilience)
4 (highest resilience)
Your Score: 4
1 (lowest
resilience)
2
4 (highest
resilience)
445
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SECONDARY INDICATORS
#308: Percentage 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
presettlement 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
Data Set(s):
Notes on Data Set(s):
(1) Urban versus Rural land use. Use this source for the 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
Relevance: Importance Weights:
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Proposed Resilience Score: Your Score:
N/A 1 (lowest resilience)
2
3
4 (highest resilience)
446
-------
#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).
Grouped with Indicators: #437
Data Set(s):
USGS Hydro-Climatic Data Network (HCDN) data set for 1931-1988, POTOMAC River
(http://pubs.usgs.gov/wri/wri934076/stations/01646502.html)
Notes on Data Set(s):
USGS HCDN has one 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
October 1 to September 30). See file ID437_HCDN_Streamflowdata_DC.xlsx.
Indicator Value:
1.221
Relevance: Importance Weight: Proposed
Not sure—remind me later N/A Resilience Score: 1
Thresholds:
Threshold-Based Score: 1
Your Score: Score
not yet assigned
Greater than 0.60 (unitless ratio)
1 (lowest resilience)
1 (lowest
resilience)
0.40 to 0.60 (unitless ratio)
2
2
0.20 to 0.40 (unitless ratio)
3
3
Less than 0.20 (unitless ratio)
4 (highest resilience)
4 (highest
resilience)
447
-------
#1437: Percentage of city area in 500-year floodplain
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator reflects the percentage of the metropolitan area that lies within the
500-year floodplain.
Grouped with Indicators: #1436. #1438, #1439
Data Set(s):
District of Columbia—2010 Floodplains
Notes on Data Set(s):
This GIS data set describes the areas of 100- and 500-year floodplains as determined by the
Federal Emergency Management Agency.
Indicator Value:
11.00%
Relevance: Importance Weight: Proposed
Yes 1 Resilience Score: 2
Thresholds:
Threshold-Based Score: 2
Your Score: 2
Greater than 30%
1 (lowest resilience)
1 (lowest
resilience)
10 to 30%
2
2
2 to 10%
3
3
Less than 2%
4 (highest resilience)
4 (highest
resilience)
448
-------
#1438: Percentage of city population in 100-year floodplain
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator reflects the percentage of the city population living within the 100-
year floodplain.
Grouped with Indicators: #1436. #1437, #1439
Data Set(s):
(1) District of Columbia—2010 Floodplains
(2) U.S. Census Bureau—District of Columbia 2010 census blocks with population
(http://www2.census.gOv/geo/tiger/TIGER2010BLKPOPHU/tabblock2010_ll_pophu.zip)
Notes on Data Set(s):
(1) This GIS data set describes the areas of 100- and 500-year floodplains as determined by
the Federal Emergency Management Agency.
(2) This GIS data set 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: Importance Weight: Proposed
Yes 2 Resilience Score: 1
Thresholds:
Threshold-Based Score: 3
Your Score: 4
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
5 to 20%
2
2
1 to 5%
3
3
Less than 1%
4 (highest resilience)
4 (highest
resilience)
449
-------
#1439: Percentage of city population in 500-year floodplain
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator reflects the percentage of the city population living within the 500-
year floodplain.
Grouped with Indicators: #1436. #1437, #1438
Data Set(s):
(1) District of Columbia—2010 Floodplains
(2) U.S. Census Bureau—District of Columbia 2010 census blocks with population
(http://www2.census.gOv/geo/tiger/TIGER2010BLKPOPHU/tabblock2010_ll_pophu.zip)
Notes on Data Set(s):
(1) This GIS data set describes the areas of 100- and 500-year floodplains as determined by
the Federal Emergency Management Agency.
(2) This GIS data set 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: Importance Weight: Proposed
Yes 2 Resilience Score: 2
Thresholds:
Threshold-Based Score: 4
Your Score: 4
Greater than 30%
1 (lowest resilience)
1 (lowest
resilience)
10 to 30%
2
2
2 to 10%
3
3
Less than 2%
4 (highest resilience)
4 (highest
resilience)
450
-------
L.4. 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 nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than
the data sets 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
451
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#17: Altered wetlands (percentage of wetlands lost)
Definition: This indicator reflects the percentage of wetland areas that have been excavated,
impounded, diked, partially drained, or farmed.
Grouped with Indicators: N/A
Data Set(s):
United States Geological Survey, National Water Summary on Wetland Resources, Water
Supply Paper 2425, District of Columbia State Summary
(http://water.usgs.gov/nwsumAVSP2425/state_highlights_summary.html)
Notes on Data Set(s):
Value represents fraction of wetlands "drained or filled since the District was established in
the 1790s."
Indicator Value:
87%
Relevance: Importance Weight: Proposed
Yes 3 Resilience Score: 2
Thresholds: Threshold-Based Score: 1 Your Score: 1
Greater than 60% 1 (lowest resilience) 1 (lowest
resilience)
40 to 60%
20 to 40%
Less than 20%
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
452
-------
#66: Percentage 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 percentage 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
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance:
Yes
Importance Weight:
Proposed
Resilience Score: 1
Thresholds:
Greater than 100%
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)
453
-------
#273: Percentage of total wildlife species of greatest conservation need
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator reflects the percentage of total wildlife species that are listed as
having the "greatest conservation need."
Grouped with Indicators: N/A
Data Set(s):
DDOE Wildlife Action Plan (http://ddoe.dc.gov/publication/wildlife-action-plan)
Notes on Data Set(s):
Lists 782 total species in DC, of which 148 are of "greatest conservation need"
(148 h- 78 = 8.9%).
Indicator Value:
18.90%
Relevance:
Importance Weight:
4
Proposed
Resilience Score: 1
Yes
Thresholds:
Threshold-Based Score: 2
1 (lowest resilience)
Your Score: 2
1 (lowest
resilience)
Greater than 20%
5 to 20%
1 to 5%
Less than 1%
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
454
-------
#284: Physical Habitat Index (PHI)
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: PHI includes eight characteristics (riffle quality, stream bank stability, quantity of
woody debris, instream habitat for fish, suitability of streambed surface materials for
macroinvertebrates, shading, distance to nearest road, and embeddedness 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
Data Set(s):
National Park Service—Biological Stream Survey Monitoring -
(http://science.nature.nps.gov/im/units/ncrn/monitor/stream_survey/index.cfm)
Notes on Data Set(s):
Select PDF files for "Stream Physical Habitat" from The National Capital Region Network
(NCRN) Stream Physical Habitat Reports
Raw value is 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: Importance Weight: Proposed
Yes 3 Resilience Score: 3
Thresholds:
Threshold-Based Score: 2
Your Score: 1
0 to 50 (severely degraded)
1 (lowest resilience)
1 (lowest
resilience)
51 to 65 (degraded)
2
2
66 to 80 (partially degraded)
3
3
81 to 100 (minimally degraded)
4 (highest resilience)
4 (highest
resilience)
455
-------
#326: Wetland species at risk (number of species)
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: Number of wetland and freshwater species at risk (rare, threatened, or
endangered).
Grouped with Indicators: N/A
Data Set(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%20Acti
on%20Plan%20Ch%204-5 .pdf
Notes on Data Set(s):
Total number of unique species identified as being of "greatest conservation need" in the
habitat sections of emergent nontidal 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: Importance Weight: Proposed
Yes 3 Resilience Score: 1
Thresholds:
Threshold-Based Score: 3
Your Score: Score
yet to be assigned
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
3
3
risk
Less than 50 species at risk
4 (highest resilience)
4 (highest
resilience)
456
-------
#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 Percentage, Long-Lived Taxa Richness, Percentage Tolerant, Percentage Predator, and
Percentage Dominance.
Grouped with Indicators: N/A
Data Set(s):
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 data sets(s):
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
Relevance:
Yes
Importance Weight:
Proposed
Resilience Score: 3
Thresholds:
0 to 45 (poor or very poor
biotic condition)
46 to 55 (fair biotic condition)
56 to 75 (good biotic condition)
Greater than 75 (very good
biotic condition)
Threshold-Based Score: 1
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 1
1 (lowest
resilience)
2
3
4 (highest
resilience)
457
-------
#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
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance:
Yes
Importance Weight:
Proposed
Resilience Score: 3
Thresholds:
Less than 0.2 Shannon Diversity
Index
0.2 to 0.4 Shannon Diversity
Index
0.4 to 0.6 Shannon Diversity
Index
Greater than 0.60 Shannon
Diversity Index
Threshold-Based Score: N/A
1 (lowest resilience)
Your Score: 3
1 (lowest
resilience)
2
4 (highest resilience)
4 (highest
resilience)
458
-------
682: Percentage change in bird population
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a 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
Data Set(s):
DDOE—Wildlife Action Plan: (http://ddoe.dc.gov/publication/wildlife-action-plan)
Notes on Data Set(s):
Chapter 3, Table 4, page 45 indicates that of the 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:
Threshold-Based Score: 2
Your Score: 3
Less than negative 66%
1 (lowest resilience)
1 (lowest
resilience)
Negative 66 to 0%
2
2
0 to 66%
3
3
Greater than 66%
4 (highest resilience)
4 (highest
resilience)
459
-------
SECONDARY INDICATORS
#680: Ecological connectivity (percentage 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 percentage 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
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance:
Yes
Importance Weight:
Proposed
Resilience Score: 3
Thresholds:
Less than 10%
10 to 25%
25 to 50%
Greater than 50%
Threshold-Based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 2
1 (lowest
resilience)
2
3
4 (highest
resilience)
460
-------
#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
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance:
Importance Weight:
Yes
Proposed
Resilience Score: 4
Thresholds:
Less than 120 White and
Maurice Index score
120 to 180 White and Maurice
Index score
180 to 230 White and Maurice
Index score
Greater than 230 White and
Maurice Index score
Threshold-Based Score: N/A
1 (lowest resilience)
Your Score: 2
1 (lowest
resilience)
2
4 (highest resilience)
4 (highest
resilience)
461
-------
L.5. 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
nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
462
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#209: Percentage of population living within the 500-year floodplain
Action Needed:
Please review the indicator and decide if you agree with the threshold-based score. Provide
an explanation if a threshold-based score is not chosen. Note that the percentage of
population in the 100-year floodplain is covered in another sector.
Definition: This indicator reflects percentage of population living within the 500-year
floodplain.
Grouped with Indicators: N/A
Data Set(s):
(1) District of Columbia—2010 Floodplains
(2) U.S. Census Bureau—District of Columbia 2010 census blocks with population
(http://www2.census.gOv/geo/tiger/TIGER2010BLKPOPHU/tabblock2010_ll_pophu.zip)
Notes on Data Set(s):
(1) This GIS data set describes the areas of 100- and 500-year floodplains as determined by
the Federal Emergency Management Agency.
(2) This GIS data set 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 Weight: 4
Proposed
Resilience Score: 1
Thresholds:
Threshold-Based Score: 3
Your Score: 2
Greater than 30%
1 (lowest resilience)
1 (lowest
resilience)
10 to 30%
2
2
2 to 10%
3
3
Less than 2%
4 (highest resilience)
4 (highest
resilience)
463
-------
#676: Percentage of population affected by notifiable diseases
Action Needed:
(1) Please review the indicator and decide if you agree with the threshold-based score.
Provide an explanation if a threshold-based score is not chosen.
(2) Please review the indicator given that the name and definition have been amended.
Definition: This indicator reflects percentage 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 chlamydia, coccidioidomycosis,
cryptosporidiosis, Dengue virus, Escherichia coli, ehrlichiosis, giardiasis, gonorrhea,
Haemophilus influenzae, hepatitus A, hepatitus B, hepatitus 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
Data Set(s):
(1) Centers for Disease Control and Prevention—Morbidity and Mortality Weekly Report
(http://wonder.cdc.gov/mmwr/mmwrmorb.asp)
(2) U.S. Census Bureau—District of Columbia QuickFacts
Notes on Data Set(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: 3
Greater than 3 to 4%
1 (lowest resilience)
1 (lowest
resilience)
2 to 3%
2
2
1 to 2%
3
3
Less than 1%
4 (highest resilience)
4 (highest
resilience)
464
-------
#690: Emergency medical service response times
Action Needed:
Please review the indicator and decide if you agree with the threshold-based score. Provide
an explanation if a 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
Data Set(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 Data Set(s):
The Key Performance Indicators table on page 4 list 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)
10 to 12 minutes
2
2
8 to 10 minutes
3
3
Less than 8 minutes
4 (highest resilience)
4 (highest
resilience)
465
-------
#725: Number of physicians per capita
Action Needed:
Please review the indicator and decide if you agree with the threshold-based score. Provide
an explanation if a threshold-based score is not chosen.
Definition: This indicator reflects the total number of M.D. and D.O. physicians per capita.
Grouped with Indicators: #717
Data Set(s):
Association of American Medical Colleges—2011 State Physician Workforce Data Book
(https://www.aamc.org/download/263512/data)
Notes on Data Set(s):
This report from the Association of American Medical 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:
Less than 0.02 physicians per
capita
0.02 to 0.03 physicians per
capita
0.03 to 0.04 physicians per
capita
Greater than 0.04 physicians per
capita
Threshold-Based Score: 1
1 (lowest resilience)
Your Score: 1
1 (lowest
resilience)
2
4 (highest resilience)
4 (highest
resilience)
466
-------
#1376: Percentage of population that is disabled
Action Needed:
(1) Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
(2) Please review the amended importance weight.
Definition: This indicator reflects the percentage of the noninstitutionalized 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
Data Set(s):
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+Estim
ates/Social+Characteri sties)
Notes on Data Set(s):
Percentage of total civilian noninstitutionalized population with a disability
Indicator Value:
11.40%
Relevance: Yes
Thresholds:
Greater than 20%
15 to 20%
10 to 15%
Less than 10%
Importance Weight: 4
Threshold-Based Score: 3
1 (lowest resilience)
2
3
4 (highest resilience)
Proposed
Resilience Score: 2
Your Score: 3
1 (lowest
resilience)
2
3
4 (highest
resilience)
467
-------
#1387: Percentage of population vulnerable due to age
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator reflects percentage of population above 65 or under 5 years old.,
Grouped with Indicators: #393, #728, #1157, #1170
Data Set(s):
U.S. Census Bureau—census 2010 population
Notes on Data Set(s):
32,613 under age 5; 68,809 age 65 and over. Total population = 601,723. Percentage
vulnerable = 16.9%
Indicator Value:
16.9%
Relevance: Yes
Importance Weight: 4
Proposed
Resilience Score: 1
Thresholds:
Greater than 20%
15 to 20%
10 to 15%
Less than 10%
Threshold-Based Score: 2
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 2
1 (lowest
resilience)
2
3
4 (highest
resilience)
468
-------
#1390: Percentage of population that is living alone
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator reflects the percentage of population that is 65 years or older and
living alone.
Grouped with Indicators: N/A
Data Set(s):
(1) U.S. Census Bureau—Fact Finder
(http://factfinder2.census.gOv/faces/tableservices/j sf/pages/productview.xhtml?pid=ACS_ll
1YRB11007&prodType=table)
(2) U.S. Census Bureau—Fact Finder
(http://factfinder2.census.gOv/faces/tableservices/j sf/pages/productview.xhtml?pid=ACS_ll
lYR_S0201&prodType=table)
(3) U.S. Census Bureau—Fact Finder
(http://factfinder2.census.gOv/faces/tableservices/j sf/pages/productview.xhtml?pid=DEC_10
113113DPl&prodType=table)
Notes on Data Set(s):
(1) Data based on 2011 American Community Survey provides the 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
the percentage of males and females living alone.
(3) Data from the 2010 census—8,808 males + 17,105 females age 65 and over living alone
(total population = 601,723, so 4.3%).
Indicator Value:
4.3%
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)
20 to 30%
2
2
10 to 20%
3
3
Less than 10%
4 (highest resilience)
4 (highest
resilience)
469
-------
#1443: Deaths from extreme weather events
Definition: This indicator measures the number of deaths 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
Data Set(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/2011/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 Data Set(s):
(1) According to the 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 website gives deaths due to cold, flood, heat, lightning, tornado, tropical
cyclone, wind, and winter storm for each year. Based on this source, only one person in the
5-year period from 2008 to 2012 died.
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
1 (lowest resilience)
1 (lowest
resilience)
100 to 150 deaths
2
2
50 to 100 deaths
3
3
Less than 50 deaths
4 (highest resilience)
4 (highest
resilience)
470
-------
SECONDARY INDICATORS
#322: Percentage of population affected by waterborne diseases
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator reports the percentage of population affected by waterborne
diseases.
Grouped with Indicators: #676, #1171
Data Set(s):
(1) Centers for Disease Control and Prevention—Morbidity and Mortality Weekly Report
(http://wonder.cdc.gov/mmwr/mmwrmorb.asp)
(2) U.S. Census Bureau—District of Columbia QuickFacts
Notes on Data Set(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: 3
Greater than 2%
1 (lowest resilience)
1 (lowest
resilience)
1 to 2%
2
2
0 to 1%
3
3
0%
4 (highest resilience)
4 (highest
resilience)
471
-------
#393: Percentage 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 percentage of population 65 and older and under 5
years that is homeless.
Grouped with Indicators: #728. #1157, #1170, #1387
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance:
Yes
Importance Weight: N/A
Proposed
Resilience Score: 1
Thresholds:
Threshold-Based Score: N/A
Your Score: Score
not yet assigned
Greater than 30%
1 (lowest resilience)
1 (lowest
resilience)
20 to 30%
2
2
10 to 20%
3
3
Less than 10%
4 (highest resilience)
4 (highest
resilience)
472
-------
#728: Adult care (homes per capita)
Action Needed:
(1) Please decide if the original or alternate data set is more appropriate.
(2) Please decide if you agree with the threshold-based score and provide an explanation if a
threshold-based score is not chosen.
Definition: The number of adult day care homes and assisted living homes per capita of
population over 65 years.
Grouped with Indicators: #393. #1157, #1170,
#1387
Data Set(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=l&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 Data Set(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 Data Set(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/flles/dc/sites/dcoa/publication/attachments/DCOA%2520Sen
ior%2520Needs%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 Data Set(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: Importance Weight: N/A Proposed
Yes Resilience Score: 4
473
-------
Thresholds:
Threshold-Based Score: 4
Your Score: 4
Less than 0.00010 adult homes
per capita of elderly population
0.00010 to 0.00020 adult homes
per capita of elderly population
0.00020 to 0.00040 adult
homes per capita of elderly
population
Greater than 0.00040 adult
homes per capita of elderly
population
1 (lowest resilience)
4 (highest resilience)
1 (lowest
resilience)
2
4 (highest
resilience)
#757: Average police response time
Action Needed:
Please review the indicator and decide if you agree with the threshold-based score. Provide
an explanation if a 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
Data Set(s):
District of Columbia—Metropolitan Police Department FY2013 Performance Plan
(http://oca.dc.gov/sites/default/files/dc/sites/oca/publication/attachments/MPD13.pdf)
Notes on Data Set(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: N/A
Proposed
Resilience Score: 4
Thresholds:
Threshold-Based Score: 4
Your Score: 4
Greater than 12 minutes
1 (lowest resilience)
1 (lowest
resilience)
10 to 12 minutes
2
2
8 to 10 minutes
3
3
Less than 8 minutes
4 (highest resilience)
4 (highest
resilience)
474
-------
#784: Number of sworn police officers per capita
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a 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
Data Set(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) U.S. Census Bureau—American Community Survey 2011 3-year estimates
(http://factfinder2.census.gOv/faces/tableservices/j sf/pages/productview.xhtml?pid=ACS_ll_
3YR_DP02&prodType=table)
Notes on Data Set(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
0.20 to 0.50 police officers per
capita
Greater than 0.50 police
officers per capita
Threshold-Based Score: 4
1 (lowest resilience)
Your Score: 4
1 (lowest
resilience)
2
4 (highest resilience)
4 (highest
resilience)
475
-------
#798: Percentage of fire response times less than 6.5 minutes
Definition: This indicator reflects the percentage of fire response times less than 6.5 minutes
(from city stations to city locations).
Grouped with Indicators: #690, #757, #784
Data Set(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 Data Set(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%
Relevance:
Yes
Importance Weight:
3
Proposed
Resilience Score: 4
Thresholds:
Threshold-Based Score: 4
Your Score: 4
Less than 85%
1 (lowest resilience)
1 (lowest
resilience)
85 to 90%
2
2
90 to 95%
3
3
Greater than 95%
4 (highest resilience)
4 (highest
resilience)
476
-------
#1157: Percentage of housing units with air conditioning
Action Needed:
Please review the indicator and decide if you agree with the threshold-based score. Provide
an explanation if a threshold-based score is not chosen.
Definition: This indicator reflects the percentage of housing units with air conditioning.
Grouped with Indicators: #393. #728, #1170, #1387
Data Set(s):
U.S. Census Bureau—American Housing Survey for the Washington Metropolitan Area:
2007 (http://www.census.gov/housing/ahs/files/washington07.pdf)
Notes on Data Set(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: N/A
Proposed
Resilience Score: 3
Thresholds:
Threshold-Based Score: 4
Your Score: 4
Less than 70%
1 (lowest resilience)
1 (lowest
resilience)
70 to 88%
2
2
88 to 94%
3
3
Greater than 94%
4 (highest resilience)
4 (highest
resilience)
All
-------
#1170: Percentage of population experiencing heat-related deaths
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator reflects the percentage of the population experiencing heat-related
deaths.
Grouped with Indicators: #393. #728, #1157, #1387
Data Set(s):
(1) NOAA—Weather Fatatlities (http://www.nws.noaa.gov/om/hazstats.shtml)
(2) 2010 census population
Notes on Data Set(s):
(1) 22 heat-related deaths between 1995 and 2012.
(2) 601,723 population of DC from the 2010 census.
Annual heat-related deaths = 22 17 years =1.3 heat-related deaths/year. The percentage of
heat-related deaths per capita = 1.3 601,723 = 0.0002% annually.
Indicator Value:
0.0002%
Relevance: Yes
Importance Weight: 4
Proposed
Resilience Score: 2
Thresholds:
Threshold-Based Score: 4
Your Score: 4
Greater than 2.0%
1 (lowest resilience)
1 (lowest
resilience)
1.0 to 2.0%
2
2
0.5 to 1.0%
3
3
Less than 0.5%
4 (highest resilience)
4 (highest
resilience)
478
-------
#1171: Percentage 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 percentage of population affected by food poisoning
(i.e., Salmonella spp., unsafe drinking water).
Grouped with Indicators: #322, #676
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance: Yes Importance Weight: N/A Proposed
Resilience Score: 2
Thresholds:
Greater than 20%
Threshold-Based Score: N/A
1 (lowest resilience)
Your Score: 1
1 (lowest
resilience)
15 to 20%
10 to 15%
Less than 10%
2
3
4 (highest resilience)
2
3
4 (highest
resilience)
479
-------
L.6. Telecommunications
The indicators below have been developed for the telecommunication 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 nongrouped 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 sets 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 telecommunication 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
data set(s).
2. When possible, data sets for Washington, DC are provided where data were available.
In some cases, no data sets were identified. Please suggest data sets that may be better
than the data sets 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
480
-------
PRIMARY INDICATORS AND NONGROUPED 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
Data Set(s):
No data sets have been identified.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance:
Yes
Please suggest data sets that might be appropriate.
Importance Weight:
Proposed Resilience
Score: N/A
Thresholds:
Greater than 70%
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)
481
-------
#1434: Baseline percentage of water supply for telecommunications systems that comes
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
Data Set(s):
No data sets have been identified.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance:
Yes
Please suggest data sets that might be appropriate.
Importance Weight:
Proposed Resilience
Score: N/A
Thresholds:
Greater than 50%
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
4 (highest
resilience)
482
-------
#1435: Baseline percentage of energy supply for telecommunications systems that comes
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
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance:
Yes
Importance Weight:
Proposed
Resilience Score: 4
Thresholds:
Greater than 60%
30 to 60%
10 to 30%
Less than 10%
Threshold-Based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 3
1 (lowest
resilience)
2
3
4 (highest
resilience)
483
-------
#1441: Percentage 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: Percentage of community with access to FEMA emergency radio broadcasts.
Grouped with Indicators: N/A
Data Set(s):
No data sets have been identified.
Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance:
Importance Weight:
Proposed
Yes
4
Resilience Score: 3
Thresholds:
Threshold-Based Score: N/A
Your Score: 2
Less than 80%
1 (lowest resilience)
1 (lowest
resilience)
80 to 88%
2
2
88 to 96%
3
3
Greater than 96%
4 (highest resilience)
4 (highest
resilience)
484
-------
L.7. 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 nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
485
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#985: Transport system user satisfaction
Definition: This indicator reflects the overall user satisfaction with the transport system. It is
defined as the average user satisfaction with bus service, rail service, and the accuracy of
passenger information displays.
Grouped with Indicators: N/A
Data Set(s):
(1) U.S. Census Bureau—Fact Finder
(http://factfinder2.census.gOv/faces/tableservices/j sf/pages/productview.xhtml?pid=ACS_ll_l
YR_S0802&prodType=tabl e)
(2) U.S. Census Bureau—Fact Finder
(http://factfinder2.census.gOv/faces/tableservices/j sf/pages/productview.xhtml?pid=ACS_ll_l
YR_S0801&prodType=table)
(3) Federal Highway Administration—PARTNERS IN MOTION AND CUSTOMER
SATISFACTION IN THE WASHINGTON, D C METROPOLITAN AREA
(http://ntl.bts.gov/lib/jpodocs/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_4CCustomerSur
vey.pdf)
Notes on Data Set(s):
(1) Census data indicates 30.1 min travel time to work and 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
Relevance
No
Importance Proposed Resilience Score: 3
Weight:
N/A
486
-------
Thresholds:
Threshold-Based Score: 3
Your Score: N/A
0 to 20 (very or totally
1 (lowest resilience)
1 (lowest
dissatisfied)
resilience)
21 to 60 (somewhat dissastisfied)
2
2
61 to 80 (somewhat satisfied)
3
3
81 to 100 (very or totally
4 (highest resilience)
4 (highest
satisfied)
resilience)
#988: Walkability score
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a 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
Data Set(s):
WalkScore—Washington DC Score (http://www.walkscore.eom/DCAVashington_D.C.)
Notes on Data Set(s):
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: 3
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)
487
-------
#991: Percentage transport diversity
Definition: Highest public expenditure for a single mode of transprotation as a percentage of
the total expenditures for all transportation modes.
Grouped with Indicators: N/A
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
No data available.
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
488
-------
#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 dollars). 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
Data Set(s):
Texas A&M—Urban Mobility Report
Notes on Data Set(s):
In the 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:
$495 per person
Relevance:
Importance Weight:
Proposed
Yes
4
Resilience Score: 3
Thresholds:
Threshold-Based Score: 4
Your Score: 4
$2 to less than $10 per person
1 (lowest resilience)
1 (lowest
resilience)
$10 to less than $18 per person
2
2
$18 to less than $32 per person
3
3
Greater than or equal to $32
4 (highest resilience)
4 (highest
per person
resilience)
489
-------
#1010: Community Livability Index
Definition: The Community Livability Index is the equally weighted average of the
Community Service Indicator, the Crime Indicator, the Retail Opportunity Indicator, the
Educational Indicator, the Environmental Quality Indicator, the Housing Affordability
Indicator, and the Transit Livability Indicator. Details of the calculation are provided in
Ripplinger et al. (2012; http://www.ugpti.org/pubs/pdf/DP262.pdf)
Grouped with Indicators: N/A
Data Set(s):
The Economist Intelligence Unit Global Liveability Ranking and Report August 2013
(http://www.economist.com/blogs/graphicdetail/2013/08/daily-chart-19)
Notes on Data Set(s):
Washington, DC has a score of 91.2 out of 100.
Indicator Value:
91.2
Relevance:
Yes
Importance Weight:
Proposed
Resilience Score: 3
Thresholds:
Less than 60 (most aspects of
living are substantially
constrained or severely
restricted)
61 to 70 (negative factors have
an impact on day-to-day living)
71 to 80 (day-to-day living is
fine, in genera, but some aspects
of life may entail problems)
81 to 100 (there are few, if any
challenges to living standards)
Threshold-Based Score: 4
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 3
1 (lowest
resilience)
2
3
4 (highest
resilience)
490
-------
#1399: Number of roadway/rail miles, or other transportation facilities within 10 feet of
coast
Definition: Miles of unarmored or unreinforced roadway or miles of rail lines that are within
10 vertical feet of the mean high water elevation.
Grouped with Indicators: N/A
Data Set(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
Notes on Data Set(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
GIS 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
Relevance:
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights: 2
1 (not very important)
2
3
4 (very important)
Proposed Resilience Score:
N/A
Your Score: Score not yet
assigned
1 (lowest resilience)
2
4 (highest resilience)
491
-------
#1400: Percentage of roads and railroads within the city in the 500-year floodplain
Definition: This indicator measures the percentage of roadway miles and rail line miles that
are within the 500-year floodplain..
Grouped with Indicators: N/A
Data Set(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 1400JD1401 MapFloodRail Street DC
Notes on Data Set(s):
(1) The FEMA maps website takes you to the home page. Enter the location.
(2) DC mapping tool allows overlay of floodplain 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 floodplain
Road =194 miles that intersect with the 500-year floodplain
Indicator Value:
10%
Relevance:
Yes
Importance Weight:
4
Proposed
Resilience Score: 2
Thresholds:
Threshold-Based Score: 1
Your Score: 1
Greater than 5%
1 (lowest resilience)
1 (lowest
resilience)
2 to 5%
2
2
1 to 2%
3
3
Less than 1%
4 (highest resilience)
4 (highest
resilience)
492
-------
#1401: Percentage of roads and railroads within the city in the 100-year floodplain
Definition: This indicator measures the percentage of roadway miles and rail line miles that
are within the 100-year floodplain.
Grouped with Indicators: N/A
Data Set(s):
DC.gov—AtlasPlus (http://atlasplus. dcgi s. dc. gov/)
Notes on Data Set(s):
N/A
Indicator Value:
11%
Relevance:
Yes
Importance Weight:
3
Proposed
Resilience Score: 2
Thresholds:
Threshold-Based Score: 2
Your Score: 2
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
10 to 20%
2
2
5 to 10%
3
3
Less than 5%
4 (highest resilience)
4 (highest
resilience)
493
-------
#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
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance: Yes
Importance Weight: 4
Proposed
Resilience Score: 1
Thresholds:
Threshold-Based Score: 2
Your Score: 2
Greater than 6 hours
1 (lowest resilience)
1 (lowest
resilience)
3 to 6 hours
2
2
1 to 3 hours
3
3
Less than 1 hour
4 (highest resilience)
4 (highest
resilience)
494
-------
#1404: Percentage 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 percentage 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
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance: Yes
Importance Weight: 4
Proposed Resilience
Score: N/A
Thresholds:
Threshold-Based Score: 2
Your Score: 2
Less than 70%
1 (lowest resilience)
1 (lowest
resilience)
70 to 85%
2
2
85 to 95%
3
3
Greater than 95%
4 (highest resilience)
4 (highest
resilience)
495
-------
#1406: Percentage decline in repeat maintenance events
Action Needed:
(1) Please assign an importance weight.
(2) Please review the indicator and decide if you agree with the threshold-based score.
Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator measures the percentage 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
Data Set(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. bl s. gov/pub/speci al. requests/cpi/cpi ai. txt)
Notes on Data Set(s):
Summed the "preventive and 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
percentage 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 percentage change: + 7%
Indicator Value:
47%
Relevance: Yes
Importance Weight: 1
Proposed Resilience Score: N/A
Thresholds:
Threshold-Based Score: 3
Your Score: Score not vet assigned
Less than 10%
1 (lowest resilience)
1 (lowest resilience)
10 to 25%
2
2
25 to 50%
3
3
Greater than 50%
4 (highest resilience)
4 (highest resilience)
496
-------
#1408: Percentage of bridges that are structurally deficient, (source: National Bridge
Inventory)
Definition: This indicator measures the percentage of bridges that are structurally deficient.
Bridges are considered structurally deficient if significant load-carrying elements are found to
be in poor or worse condition due to deterioration 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.
Grouped with Indicators: N/A
Data Set(s):
(1) Transportation for America—The Fix We're in for: The State of Our Bridges
(http://t4america.Org/resources/b ridges/#?latlng=3 8.90723089999999,-
77.03646409999999&bridge_id= )
(2) Transportation for America—The Fix We're in for: The State of Our Bridges
(http://t4am erica, org/resources/b ridges/states/? state=dc)
Notes on Data Set(s):
(1) Bridges are located on the map. Those structurally deficient are in red.
(2) 31 bridges were 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
Relevance:
Yes
Importance Weight:
4
Proposed
Resilience Score: 1
Thresholds:
Threshold-Based Score: 1
Your Score: 1
Greater than 10%
1 (lowest resilience)
1 (lowest
resilience)
5 to 10%
2
2
2 to 5%
3
3
Less than 2%
4 (highest resilience)
4 (highest
resilience)
497
-------
#1411: Roadway connectivity (number of intersections per square mile)
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator measures the number of intersections per square mile.
Grouped with Indicators: N/A
Data Set(s):
DC.gov—DC GIS Data Clearinghouse/Catalog
Notes on Data Set(s):
Roads GIS layer indicates there are 7,385 intersections in DC. The size of DC is 68.3 sq. mi.
The number of intersections per sq. mi. is 7,385 ^ 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
square mile
resilience)
80 to 250 intersections per
2
2
square mile
250 to 290 intersections per
3
3
square mile
Greater than 290 intersections
4 (highest resilience)
4 (highest
per square mile
resilience)
498
-------
#1412: Miles of pedestrian facilities per street mile
Definition: This indicator measures the miles of pedestrian facilities (sidewalks) per street
mile.
Grouped with Indicators: #1413
Data Set(s):
(1) Washington, DC GIS database
(2) Federal Highway Administration—Highway Statistics 2009, Public Road Length 2009
Miles by Ownership (http://www.fhwa.dot.gov/policyinformation/statistics/2009/hml0.cfm)
Notes on Data Set(s):
(1) Raw value determined based on 743,991.81 meters of sidewalk in DC (2,326.41 miles).
Divide SHAPE LEN field by 2, which gives a reliable estimate of the of length of each
sidewalk.
(2) Total miles of public roads in DC = 1,505 miles.
Therefore, miles of sidewalk per street mile = 2,326.41 ^ 1,505 = 1.55.
Indicator Value:
1.55 miles of sidewalk per street mile
Relevance:
Yes
Importance Weight:
Proposed
Resilience Score: 3
Thresholds:
Less than 0.5 miles of sidewalk
to street miles
0.5 to 1.0 miles of sidewalk to
street miles
1.0 to 2.0 miles of sidewalk to
street miles
Greater than 2.0 miles of
sidewalk to street miles
Threshold-Based Score: 3
1 (lowest resilience)
Your Score: 3
1 (lowest
resilience)
2
4 (highest resilience)
4 (highest
resilience)
499
-------
#1420: Intermodal passenger connectivity (percentage of terminals with at least one
intermodal connection for the most common mode)
Definition: This indicator measures the percentage of active passenger terminals for the most
common mode (e.g., rail, air) with at least one intermodal passenger connection. Intermodal
connections allow passengers to use a combination of modes and give travelers additional
transportation alternatives that unconnected, parallel systems do not offer.
Grouped with Indicators: #1419
Data Set(s):
Research and Innovative Technology Administration Bureau of Transportation Statistics,
Passenger Connectivity
(http://www.transtats.bts.gov/DL_SelectFields.asp7Table_IENl 180&DB_Short_Name=Transn
et)
Notes on Data Set(s):
Downloaded the dataset with the State, City, and ModesServing fields. Divided the number of
facilities in DC with ModesServing > 1 by the total number of facilities in DC.
Facilities with ModesServing > 1 = 44
Total facilities = 49
44 - 49 = 89.8%
Indicator Value:
89.8%
Relevance:
Yes
Importance Weight:
4
Proposed
Resilience Score: 4
Thresholds:
Threshold-Based Score: 4
Your Score: 4
Less than 55%
1 (lowest resilience)
1 (lowest
resilience)
55 to 70%
2
2
70 to 85%
3
3
Greater than 85%
4 (highest resilience)
4 (highest
resilience)
500
-------
#1422: Average distance of all nonwork trips
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 distance from a given home to the nearest
grocery store, high school, and health care facility (i.e., nonwork trips).
Grouped with Indicators: N/A
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance: Yes
Importance Weight: 3
Proposed
Resilience Score:
N/A
Thresholds:
Less than 5 miles
5 to 10 miles
10 to 30 miles
Greater than 30 miles
Threshold-Based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 4
1 (lowest
resilience)
2
3
4 (highest
resilience)
501
-------
#1426: City congestion rank
Definition: This indicator measures the congestion rank of the metropolitan area relative to all
U.S. metropolitan areas.
Grouped with Indicators: N/A
Data Set(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
(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 Data Set(s):
(1) INRIX scorecard ranks DC 13th most congested metro in U.S.
(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 U.S.
Relevance:
Yes
Importance Weight:
3
Proposed
Resilience Score: 4
Thresholds:
Threshold-Based Score: 1
Your Score: 1
1 to 25 (unitless rank)
1 (lowest resilience)
1 (lowest
resilience)
26 to 50 (unitless rank)
2
2
51 to 75 (unitless rank)
3
3
76 to 100 (unitless rank)
4 (highest resilience)
4 (highest
resilience)
502
-------
#1429: Telework rank
Definition: This indicator measures the telework rank of the mtropolitan area relative to all
other extra-large metropolitan areas in the U.S. The rank is based on the percentage of jobs
within the metropolitan region that could be accomplished by telecommuting if employer
policies were to permit it.
Grouped with Indicators: N/A
Data Set(s):
(1) Sperlings Best Places—Washington, DC 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_4
04_1194_815&f=00&su=p284.13.342.ip_p554.23,342.ip_&tt=2&bt=9&bts=9&zu=http%3 A//
www.bestplaces.net/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 Data Set(s):
(1) Sperlings Best Places ranks DC as the number one teleworking extra large metro area in the
U.S. (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 website (http://www.bestplaces.net/docs/datasource.aspx).
Raw value based on ranking.
(2) Site mentions Washington Post article about value of telecommute particularly during
emergencies and notes Telework Act of 2010.
Indicator Value:
1st for extralarge metro areas
Relevance:
Yes
Importance Weight:
3
Proposed
Resilience Score: 2
Thresholds:
Threshold-Based Score: 4
Your Score: 4
13 to 16 (unitless rank)
1 (lowest resilience)
1 (lowest
resilience)
9 to 12 (unitless rank)
2
2
5 to 8 (unitless rank)
3
3
1 to 4 (unitless rank)
4 (highest resilience)
4 (highest
resilience)
503
-------
SECONDARY INDICATORS
#987: Employment accessibility (mean travel time to work relative to national average)
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
Data Set(s):
(1) U.S. Census Bureau—State and County Quick Facts
(2) The Brookings Institute—Washington-Arlington-Alexandria, DC-VA-MD-WV Metro
(http://www.brookings.edU/~/media/Series/jobs%20and%20transitAVashingtonDC.PDF)
Notes on Data Set(s):
(1) Census—Mean travel time to work (minutes), workers age 16 +, 2007-2011: 29.6 min
(25.4 for U.S.); so mean travel time as a ratio with national 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
Relevance:
Yes
Importance Weight:
Proposed
Resilience Score: 4
Thresholds:
Threshold-Based Score: 2
Greater than 1.18 (unitless ratio) 1 (lowest resilience)
0.98 to 1.18 (unitless ratio)
0.79 to less than 0.98 (unitless
ratio)
Less than 0.79 (unitless ratio)
2
3
4 (highest resilience)
Your Score: 2
1 (lowest
resilience)
2
3
4 (highest
resilience)
504
-------
#1396: Access to transportation stops
Definition: This indicator reflects the percentage of the population that is near a transit stop.
Grouped with Indicators: #987, #988, #1417
Data Set(s):
(1) U.S. Census Bureau—State and County Quick Facts
(2) The Brookings Institute—Missed Opportunity: Transit and Jobs in Metropolitan
America (http://www.brookings.edu/research/reports/2011/05/12-jobs-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
(https://www.mwcog.org/about-us/newsroom/2012/03/23/customer-satisfaction-demand-for-
rolldc-continues-to-grow-specialized-transportation/)
(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 Data Set(s):
(1) Mean travel time to work (minutes), workers age 16 +, 2007-2011: 29.6 min (25.4 for
U.S.).
(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 U.S. 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 the bottom of the page under "Browse Catalog" and get the shapefile appropriate for
the 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
Relevance: Importance Weight: Proposed Resilience Score: 4
Yes 4
Thresholds:
Threshold-Based Score: 4
Your Score: 3
23 to 47%
1 (lowest resilience)
1 (lowest resilience)
48 to 63%
2
2
64 to 75%
3
3
76 to 100%
4 (highest resilience)
4 (highest resilience)
505
-------
#1403: Percentage 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 percentage of current culverts that cross
transportation facilities in the metropolitan region that are sized to meet current stormwater
capacity requirements.
Grouped with Indicators: #1404
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance: Yes
Importance Weight: 4
Proposed Resilience
Score: N/A
Thresholds:
Less than 75%
75 to 90%
90 to 95%
Greater than 95%
Threshold-Based Score: N/A
1 (lowest resilience)
2
3
4 (highest resilience)
Your Score: 1
1 (lowest
resilience)
2
3
4 (highest
resilience)
506
-------
#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)
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
No data available
Proposed Resilience Score: Your Score:
2 1 (lowest resilience)
2
3
4 (highest resilience)
507
-------
#1413: Percentage of short walkable sidewalks in urban areas
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator measures the percentage of sidewalks within the urban area that are
less than 330 feet.
Grouped with Indicators: #1412
Data Set(s):
(1) DC.gov—Data Catalog (data.dc.gov)
(2) Transportation for America—Dangerous by Design: Metro Area Pedestrian Safety
Rankings by State
(3) WalkScore—Website on Walkability (http://www.walkscore.eom/DCAVashington_D.C.)
Notes on Data Set(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 the search at the bottom of
the page under "Browse Catalog" and get the shapefile 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: 2
Less than 60%
1 (lowest resilience)
1 (lowest
resilience)
60 to 75%
2
2
75 to 90%
3
3
Greater than 90%
4 (highest resilience)
4 (highest
resilience)
508
-------
#1417: Percentage 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 and
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)
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
No data available
Proposed Resilience Score: Your Score:
3 1 (lowest resilience)
2
3
4 (highest resilience)
509
-------
#1419: Intermodal freight connectivity (ratio of intermodal connections used per year to
individual 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 indicator measures the 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
Data Set(s):
No data sets have been identified. Please suggest data sets that might be appropriate.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance: No
Importance Weight: N/A
Proposed
Resilience Score: 4
Thresholds:
Less than 0.5 ratio of
intermodal containers to
individual carloads
0.5 to 1.0 ratio of intermodal
containers to individual carloads
1 to 2 ratio of intermodal
containers to individual carloads
Greater than 2 ratio of
intermodal containers to
individual carloads
Threshold-Based Score: N/A
1 (lowest resilience)
Your Score: Score
not yet assigned
1 (lowest
resilience)
4 (highest resilience)
4 (highest
resilience)
510
-------
L.8. 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
nongrouped 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 sets 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 data set(s).
2. When possible, data sets for Washington, DC are provided where data were available. In some
cases, no data sets were identified. Please suggest data sets that may be better than the data sets
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
511
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PRIMARY INDICATORS AND NONGROUPED INDICATORS
#437: Percentage change in streamflow divided by percentage change in precipitation
Action Needed:
Stormwater experts need to review the indicator and provide an importance weight and
resilience score.
Definition: This indicator reflects percentage change in streamflow (Q) divided by percentage
change in precipitation (P) for 1,291 gauged watersheds across the continental U.S. from
1931 to 1988.
Grouped with Indicators: #1369
Data Set(s):
(1) USGS Hydro-Climatic Data Network (HCDN) data set 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).
Notes on Data Set(s):
(1) Includes data on mean annual streamflow (cfs) from 1931 to 1988. Calculate percentage
change in streamflow and precipitation. Divide percentage change in streamflow by
percentage change in precipitation.
(2) Includes total precipitation (in) from 1871-2013.
Indicator Value:
-14.36
Relevance: Not sure—remind me Importance Weight: N/A
Proposed Resilience
later
Score: N/A
Thresholds:
Threshold-Based Score: 1
Your Score: Score
not yet assigned
Greater than 3.0 (unitless
1 (lowest resilience)
1 (lowest
ratio)
resilience)
2.0 to 3.0 (unitless ratio)
2
2
1.0 to 2.0 (unitless ratio)
3
3
Less than 1.0 (unitless ratio)
4 (highest resilience)
4 (highest
resilience)
512
-------
#1346: Percentage of infiltration and inflow (I/I) in wastewater
Action Needed:
(1) Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
(2) Please review the importance weight given that definition has been amended.
Definition: Water that enters the wastewater system through infiltration and inflow (I/I) as a
percentage 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, cross-connections with storm drains, downspouts, and through holes in
manhole covers.
Grouped with Indicators: N/A
Data Set(s):
DC Water—Wastewater Treatment (http://www.dcwater.com/wastewater/default.cfm)
Notes on Data Set(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 -h 330 MGD = 1.1212.
Indicator Value:
1.1212
Relevance: Yes
Importance Weight: 2
Proposed
Resilience Score: 3
Thresholds:
Threshold-Based Score: 4
Your Score: Score
not yet assigned
Greater than 50%
1 (lowest resilience)
1 (lowest
resilience)
35 to 50%
2
2
20 to 35%
3
3
Less than 20%
4 (highest resilience)
4 (highest
resilience)
513
-------
#1347: Wet weather flow bypass volume relative to the 5-year average
Action Needed:
(1) Please review the indicator and decide if you agree with the threshold-based score.
Provide an explanation if a threshold-based score is not chosen.
(2) Please review the 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
Data Set(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 Data Set(s):
This document states that DC Water 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:
Threshold-Based Score: 1
Your Score: Score
not yet assigned
Greater than 2 (unitless ratio)
1 (lowest resilience)
1 (lowest
resilience)
1 to 2 (unitless ratio)
2
2
1 (unitless ratio)
3
3
Less than 1 (unitless ratio)
4 (highest resilience)
4 (highest
resilience)
514
-------
#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
etal., 1999).
Grouped with Indicators: #437
Data Set(s):
USGS Hydro-Climatic Data Network (HCDN) data set for 1931-1988, POTOMAC River
(http://pubs.usgs.gov/wri/wri934076/stations/01646502.html)
Notes on Data Set(s):
USGS HCDN has one 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
October 1 to September 30). See file ID437_HCDN_Streamflowdata_DC.xlsx.
Indicator Value:
1.221
Relevance: Not sure—remind me Importance Weight: N/A
Proposed Resilience
later
Score: N/A
Thresholds:
Threshold-Based Score: 1
Your Score: Score
not yet assigned
Greater than 0.60 (unitless
1 (lowest resilience)
1 (lowest
ratio)
resilience)
0.40 to 0.60 (unitless ratio)
2
2
0.20 to 0.40 (unitless ratio)
3
3
Less than 0.20 (unitless ratio)
4 (highest resilience)
4 (highest
resilience)
515
-------
#1428: Total number of Safe Drinking Water Act (SDWA) violations
Action Needed:
(1) Please review the indicator and decide if you agree with the threshold-based score.
Provide an explanation if a threshold-based score is not chosen.
(2) Please review the 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
Data Set(s):
U.S. Environmental Protection Agency—EnviroFacts: D.C. Water and Sewer Authority
Notes on Data Set(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: 3
Proposed
Resilience Score: 3
Thresholds:
Threshold-Based Score: 4
Your Score: 4
Greater than 4 violations
1 (lowest resilience)
1 (lowest
resilience)
3 to 4 violations
2
2
1 to 2 violations
3
3
0 violations
4 (highest resilience)
4 (highest
resilience)
516
-------
#1442: Ratio of water consumption to water availability
Action Needed:
Please decide if the threshold-based score or score from a previous meeting is more
appropriate. Provide an explanation if a threshold-based score is not chosen.
Definition: This indicator measures the fraction of available water that is currently consumed.
It is calculated by dividing total water consumption by the total available water from surface
water and groundwater sources.
Grouped with Indicators: N/A
Data Set(s):
(1) Metropolitan Washington Council of Governments—History and Background: Drought
Monitoring in the Metropolitan Washington Region
(http://www.mwcog.org/uploads/committee-documents/kllbW19d20130409105942.pdf)
(2) U.S. Army Corps of Engineers—Washington Aqueduct Annual Financial Report FY2012
(http://www.nab.usace.army.mil/Portals/63/docs/Washington_Aqueduct/FY_2012_Washingt
on_Aqueduct_Annual_F i nanci alReport. pdf)
Notes on Data Set(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 -h 139.59 MGD = 2.722
Indicator Value:
2.722
Relevance: No
Importance Weight: N/A
Proposed
Resilience Score: 4
Thresholds:
Threshold-Based Score: 1
Your Score: Score
not yet assigned
Greater than 0.20 (unitless ratio)
1 (lowest resilience)
1 (lowest
resilience)
0.13 to 0.20 (unitless ratio)
2
2
0.06 to 0.13 (unitless ratio)
3
3
517
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L.9. Thresholds
Thresholds
Indicator ID#
Indicator Name
Score 1
(lowest
resilience)
Score 2
Score 3
Score 4
(highest
resilience)
i. Economy
709
Percentage of owned
housing units that are
affordable
0 to 30%
30 to 45%
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%
111
Percentage access to
health insurance of
noninstitutionalized
population
Less than 85%
85 to 90%
90 to 95%
Greater than
95%
722
Percentage change in
homeless population
Greater than
10%
Oto 10%
negative 10
to 0%
Less than
negative 10%
1375
Percentage of
population living
below the poverty line
Greater than
20%
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
3.0 to 4.0
tons of oil
equivalent
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
2,000 to
3,000 Btu per
dollar
1,500 to
2,000 Btu per
dollar
Less than
1,500 Btu per
dollar
949
Percentage energy
consumed for
electricity
N/A
N/A
N/A
N/A
950
Percentage of
electricity generation
from noncarbon
sources
Less than 25%
25 to 50%
50 to 75%
Greater than
75%
951
Percentage of total
energy use from
renewable sources
Less than 20%
20 to 40%
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
megawatts
per capita
2.0 to 5.0
megawatts
per capita
Greater than
5.0 megawatts
per capita
518
-------
Thresholds
Score 1 Score 4
(lowest (highest
Indicator ID# Indicator Name resilience) Score 2 Score 3 resilience)
970
Average capacity of a
decentralized energy
source
Less than
5,000
megawatts per
square mile
5,000 to
10,000
megawatts
per square
mile
10,000 to
15,000
megawatts
per square
mile
Greater than
15,000
megawatts per
square mile
971
Energy source
capacity per unit area
Less than 10
megawatts per
square mile
10 to 50
megawatts
per square
mile
50 to 100
megawatts
per square
mile
Greater than
100 megawatts
per square mile
983
Average customer
energy outage (hours)
in recent major storm
Greater than
40 hours
20 to 40
hours
10 to 20 hours
Less than 10
hours
iii.
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
Percentage of natural
area that is in small
natural patches
Greater than
80%
60 to 80%
40 to 60%
Less than 40%
254
Ratio of perimeter to
area of natural patches
Greater than
0.025 (unitless
ratio)
0.015 to
0.025
(unitless
ratio)
0.005 to 0.015
(unitless
ratio)
Less than
0.005 (unitless
ratio)
825
Percentage change in
impervious cover
Greater than
1%
0 to 1%
Negative 1 to
0%
Less than
negative 1%
1436
Percentage of city area
in 100-year floodplain
Greater than
20%
5 to 20%
1 to 5%
Less than 1%
1437
Percentage of city area
in 500-year floodplain
Greater than
30%
10 to 30%
2 to 10%
Less than 2%
1438
Percentage of city
population in 100-year
floodplain
Greater than
20%
5 to 20%
1 to 5%
Less than 1%
1439
Percentage of city
population in 500-year
floodplain
Greater than
30%
10 to 30%
2 to 10%
Less than 2%
1440
Palmer Drought
Severity Index
Less than or
equal to
negative 4.0
(extreme
drought)
Negative 3.99
to negative
3.0 (severe
drought)
Negative 2.99
to negative
2.0 (moderate
drought)
Greater than or
equal to
negative 1.99
(mild or no
drought)
519
-------
iv.
Natural Environment
17
Altered wetlands
Greater than
40 to 60%
20 to 40%
Less than 20%
(percentage of
60%
wetlands lost)
66
Percentage change in
Greater than
50 to 100%
10 to 50%
Less than 10%
disruptive species
100%
273
Percentage of total
Greater than
5 to 20%
1 to 5%
Less than 1%
wildlife species of
20%
greatest conservation
need
284
Physical Habitat Index
0 to 50
51 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
Less than 02
0.2 to 0.4
0.4 to 0.6
Greater than
species diversity from
Shannon
Shannon
Shannon
0.60 Shannon
pre-European
Diversity
Diversity
Diversity
Diversity Index
settlement
Index
Index
Index
680
Ecological
Less than 10%
10 to 25%
25 to 50%
Greater than
connectivity
50%
(percentage of area
classified as hub or
corridor)
681
Relative ecological
Less than 120
120 to 180
180 to 230
Greater than
condition of
White and
White and
White and
230 White and
undeveloped land
Maurice Index
Maurice
Maurice
Maurice Index
score
Index score
Index score
score
682
Percentage change in
Less than
Negative 66
0 to 66%
Greater than
bird population
negative 66%
to 0%
66%
v.
People
209
Percentage of
Greater than
10 to 30%
2 to 10%
Less than 2%
population living
30%
within the 500-year
floodplain
322
Percentage of
Greater than
1 to 2%
0 to 1%
0%
population affected by
2%
waterborne diseases
393
Percentage of
Greater than
20 to 30%
10 to 20%
Less than 10%
vulnerable population
30%
that is homeless
675
Asthma prevalence
Greater than
9 to 12%
6 to 9%
Less than 6%
(percentage of
12%
population affected by
asthma)
520
-------
676
Percentage of
Greater than 3
2 to 3%
1 to 2%
Less than 1%
population affected by
to 4%
notifiable diseases
690
Emergency medical
Greater than
10 to 12
8 to 10
Less than 8
service response times
12 minutes
minutes
minutes
minutes
725
Number of physicians
Less than 0.02
0.02 to 0.03
0.03 to 0.04
Greater than
per capita
physicians per
physicians
physicians per
0.04 physicians
capita
per capita
capita
per capita
728
Adult care (homes per
Less than
0.00010 to
0.00020 to
Greater than
capita)
0.00010 adult
0.00020 adult
0.00040 adult
0.00040 adult
homes per
homes per
homes per
homes per
capita of
capita of
capita of
capita of
elderly
elderly
elderly
elderly
population
population
population
population
757
Average police
Greater than
10 to 12
8 to 10
Less than 8
response time
12 minutes
minutes
minutes
minutes
784
Number of sworn
Less than 0.10
0.10 to 0.20
0.20 to 0.50
Greater than
police officers per
police officers
police
police officers
0.50 police
capita
per capita
officers per
per capita
officers per
capita
capita
798
Percentage of fire
Less than 85%
85 to 90%
90 to 95%
Greater than
response times less
95%
than 6.5 minutes
1157
Percentage of housing
Less than 70%
70 to 88%
88 to 94%
Greater than
units with air
94%
conditioning
1170
Percentage of
Greater than
1.0 to 2.0%
0.5 to 1.0%
Less than 0.5%
population
2.0%
experiencing heat-
related deaths
1171
Percentage of
Greater than
15 to 20%
10 to 15%
Less than 10%
population affected by
20%
food poisoning
1376
Percentage of
Greater than
15 to 20%
10 to 15%
Less than 10%
population that is
20%
disabled
1387
Percentage of
Greater than
15 to 20%
10 to 15%
Less than 10%
population vulnerable
20%
due to age
1390
Percentage of
Greater than
20 to 30%
10 to 20%
Less than 10%
population that is
30%
living alone
1443
Deaths from extreme
Greater than
100 to 150
50 to 100
Less than 50
weather events
150 deaths
deaths
deaths
deaths
1433
Percentage of
Greater than
50 to 70%
30 to 50%
Less than
system capacity
70%
30%
needed to carry
baseline level of
traffic
521
-------
1434
Baseline
Greater than
20 to 50%
5 to 20%
Less than 5%
percentage of
50%
water supply for
telecommunication
systems that
comes from
outside the
metropolitan area
1435
Baseline
Greater than
30 to 60%
10 to 30%
Less than
percentage of
60%
10%
energy supply for
telecommunication
systems that
comes from
outside the
metropolitan area
1441
Percentage of
Less than 80%
80 to 88%
88 to 96%
Greater than
community with
96%
access to FEMA
emergency radio
broadcasts
vi.
Telecommunications
1433
Percentage of system
Greater than
50 to 70%
30 to 50%
Less than 30%
capacity needed to
70%
carry baseline level of
traffic
1434
Baseline percentage of
Greater than
20 to 50%
5 to 20%
Less than 5%
water supply for
50%
telecommunication
systems that comes
from outside the
metropolitan area
1435
Baseline percentage of
Greater than
30 to 60%
10 to 30%
Less than 10%
energy supply for
60%
telecommunication
systems that comes
from outside the
metropolitan area
1441
Percentage of
Less than 80%
80 to 88%
88 to 96%
Greater than
community with
96%
access to FEMA
emergency radio
broadcasts
vii.
Transportation
Transport system user
0 to 20 (very
21 to 60
61 to 80
81 to 100 (very
985
satisfaction
or totally
(somewhat
(somewhat
or totally
dissatisfied)
dissatisfied)
satisfied)
satisfied)
522
-------
1010
Community Livability
Index
Less than 60
(most aspects
of living are
substantially
constrained or
severely
restricted)
61 to 70
(negative
factors have
an impact on
day-to-day
living)
71 to 80 (day-
to-day living
is fine, in
general, but
some aspects
of life may
entail
problems)
81 to 100
(there are few,
if any,
challenges to
living
standards)
1396
Percentage access to
transportation stops
23 to 47%
48 to 63%
64 to 75%
76 to 100%
1399
Percentage of roads
and railroads within
the city that are
located within 10 feet
of water
N/A
N/A
N/A
N/A
1400
Percentage of roads
and railroads within
the city in the 500-
year floodplain
Greater than
5%
2 to 5%
1 to 2%
Less than 1%
1401
Percentage of roads
and railroads within
the city in the 100-
year floodplain
Greater than
20%
10 to 20%
5 to 10%
Less than 5%
1402
Total annual hours of
rail line closure due to
heat and maintenance
problems
Greater than 6
hours
3 to 6 hours
1 to 3 hours
Less than 1
hour
1403
Percentage of city
culverts that are sized
to meet current
stormwater capacity
requirements
Less than 75%
75 to 90%
90 to 95%
Greater than
95%
1404
Percentage of city
culverts that are sized
to meet future
stormwater capacity
requirements
Less than 70%
70 to 85%
85 to 95%
Greater than
95%
1406
Percentage decline in
repeat maintenance
events
Less than 10%
10 to 25%
25 to 50%
Greater than
50%
1408
Percentage of bridges
that are structurally
deficient
Greater than
10%
5 to 10%
2 to 5%
Less than 2%
1411
Roadway connectivity
Less than 80
80 to 250
250 to 290
Greater than
(number of
intersections
intersections
intersections
290
intersections per
square mile)
per square
mile
per square
mile
per square
mile
intersections
per square mile
1412
Miles of pedestrian
Less than 0.5
0.5 to 1.0
1.0 to 2.0
Greater than
facilities per street
miles of
miles of
miles of
2.0 miles of
mile
523
-------
sidewalk to
sidewalk to
sidewalk to
sidewalk to
street miles
street miles
street miles
street miles
1413
Percentage of short
Less than 60%
60 to 75%
75 to 90%
Greater than
walkable sidewalks in
90%
urban areas
1419
Intermodal freight
Less than 0.5
0.5 to 1.0
1 to 2 ratio of
Greater than 2
connectivity (ratio of
ratio of
ratio of
intermodal
ratio of
intermodal
intermodal
intermodal
containers to
intermodal
connections used per
containers to
containers to
individual
containers to
year to individual
individual
individual
carloads
individual
modes)
carloads
carloads
carloads
1420
Intermodal passenger
Less than 55%
55 to 70%
70 to 85%
Greater than
connectivity
85%
(percentage of
terminals with at least
one intermodal
connection for the
most common mode)
1422
Average distance of all
Greater than
10 to 30
5 to 10 miles
Less than 5
nonwork trips
30 miles
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)
viii. Water
437
Percentage change in
Greater than
2.0 to 3.0
1.0 to 2.0
Less than 1.0
streamflow divided by
3.0 (unitless
(unitless
(unitless
(unitless ratio)
percentage change in
ratio)
ratio)
ratio)
precipitation
1346
Percentage of
Greater than
35 to 50%
20 to 35%
Less than 20%
infiltration and inflow
50%
(I/I) in wastewater
1347
Wet weather flow
Greater than 2
1 to 2
1 (unitless
Less than 1
bypass volume relative
(unitless ratio)
(unitless
ratio)
(unitless ratio)
to the 5-year average
ratio)
1369
Annual CV of
Greater than
0.40 to 0.60
0.20 to 0.40
Less than 0.20
unregulated
0.60 (unitless
(unitless
(unitless
(unitless ratio)
streamflow
ratio)
ratio)
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
Greater than
0.13 to 0.20
0.06 to 0.13
Less than 0.06
consumption to water
0.20 (unitless
(unitless
(unitless
(unitless ratio)
availability
ratio)
ratio)
ratio)
524
-------
APPENDIX M. QUALITATIVE INDICATORS: WORCESTER, MA
A complete set of the qualitative indicators by sector developed for the tool.
M.l. 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 Worcester, MA 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
525
-------
#1: Is the economy of the urban area largely independent, or is it largely dependent on
economic activity in other urban areas?
Relevance N/A
Importance Weights N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
Largely dependent
1 (lowest resilience)
Somewhat dependent
2
Somewhat independent
3
Largely independent
4 (highest resilience)
#2: Does the urban area have mechanisms to help businesses quickly return to normal
operations?
Relevance N/A
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
No
1 (lowest resilience)
Yes
3 (highest resilience)
526
-------
#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?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
No
Yes
Resilience Score N/A
1 (lowest resilience)
3 (highest resilience)
#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 N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 3
1 (lowest resilience)
4 (highest resilience)
527
-------
#5: Has the urban area's resilience to major changes in energy policy/prices been assessed?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#6: Is funding available for adaptive development projects that could also serve as
recreation areas (e.g., retention areas along waterways that could also serve as parks)? Are
such multipurpose projects required or are there incentives for these projects?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
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
1 (lowest resilience)
3 (highest resilience)
528
-------
#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?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Answer N/A
No
Yes
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Resilience Score N/A
1 (lowest resilience)
3 (highest resilience)
#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 N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
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.
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
529
-------
#9: Is comprehensive adaptation planning possible with the urban area's current
resources? If so, is adaptation planning already occurring?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
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 3
1 (lowest resilience)
4 (highest resilience)
#10: Is planning for climate change adaptation in the urban area incorporated into one
office within the local government or is planning spread out across several offices within
the government?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 2
1 (not very important)
2
3
4 (very important)
Answer
Adaptation planning responsibilities are not incorporated
into any offices within the local government.
Adaptation planning responsibilities are spread out
over multiple offices within the local government.
Adaptation planning is shared between two or three offices
within the local government.
Adaptation planning is incorporated into one office within
the local government.
Resilience Score 2
1 (lowest resilience)
4 (highest resilience)
530
-------
#11: How flexible are planning processes for short-term and long-term responses? For
example, is there flexibility in changing planning priorities if necessary?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
Planning processes are fairly inflexible.
Planning processes are somewhat flexible.
Planning processes are moderately flexible.
Planning processes are very flexible.
Resilience Score 3
1 (lowest resilience)
2
4 (highest resilience)
#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?
Relevance N/A
Yes
No (not relevant)
Not sure—remind me later
Importance Weight 4
1
2
3
4 (very important)
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 3
1 (lowest resilience)
4 (highest resilience)
531
-------
#13: Does adaptation planning for the urban area consider the costs and benefits of
possible decisions, and does it encourage both pre-event and postevent evaluations of the
effectiveness of adaptation measures?
Relevance N/A
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
Adaptation planning does not consider costs and benefits
1 (lowest resilience)
and does not encourage pre-event or postevent
effectiveness evaluations.
Adaptation planning does consider costs and benefits but
2
does not encourage pre-event or postevent effectiveness
evaluations.
Adaptation planning does consider costs and benefits
3
and encourages pre-event or postevent effectiveness
evaluations.
Adaptation planning does consider costs and benefits and
4 (highest resilience)
requires pre-event or postevent effectiveness evaluations.
532
-------
#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 is: in coastal cities, some areas can be protected by a
seawall, or households and institutions in vulnerable areas can be moved inland. Do
current adaptation plans account for the resilience-cost tradeoffs in this decision?)
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
Adaptation plans do not explicitly consider resilience-cost
tradeoffs or no adaptation plans exist.
Adaptation plans consider one or two resilience-cost
tradeoffs.
Adaptation plans consider some resilience-cost tradeoffs.
Adaptation plans consider many resilience-cost tradeoffs.
Resilience Score N/A
1 (lowest resilience)
4 (highest resilience)
#165: What financial capacity or credit risk is indicated by the city's bond rating(s)?
Relevance
Yes (relevant)
No (not relevant)
Not sure—remind me later
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.
Importance Weight 4
1 (not very important)
2
3
4 (very important)
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
533
-------
M.2. 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 Worcester, MA 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.
534
-------
#15: Do you have a diverse energy portfolio?
Relevance N/A
Imvortance Weights 3
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
No
1 (lowest resilience)
Yes
3 (highest resilience)
#16: Are there redundant systems in place for coping with extreme events?
Relevance N/A
Imvortance Weights 2
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
No, redundant energy systems are not in place.
1 (lowest resilience)
Yes, but these redundant energy systems have only a
2
small 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
4 (highest resilience)
capacity necessary.
535
-------
#17: To what extent do energy supplies come from outside the metropolitan area?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
They come exclusively from outside the area.
To a great extent
To a moderate extent
Only to a small extent
Resilience Score 3
1 (lowest resilience)
2
4 (highest resilience)
#18: Is the availability of energy goods and services at risk if other city goods and services
(e.g., water, transportation, telecommunications) are affected by extreme climatic events or
gradual climatic changes?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
Availability of energy resources is at significant risk if
other city services are affected by climatic events or
changes.
Availability of energy resources is at moderate risk if other
city services are affected by climatic events or changes.
Availability of energy resources is at some risk if other city
services are affected by climatic events or changes.
Availability of energy resources is at minimal risk if other
city services are affected by climatic events or changes.
Resilience Score N/A
1 (lowest resilience)
4 (highest resilience)
536
-------
#19: How many minutes per year or hours per year do you have power outages?
Relevance N/A
Imvortance Weight N/A
Yes (relevant)
1
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
More than 1 day per year for all outage events
1 (lowest resilience)
More than 1 hour to 1 day per year for all outage events
2
More than 30 minutes to 1 hour per year for all outage
3
events
Less than 30 minutes per year for all outage events
4 (highest resilience)
#20: What is the response time to restore electrical power after an outage?
Relevance N/A
Imvortance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 1
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)
537
-------
#21: Does capacity exist to handle a higher peak demand or peaks at different times?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
Electricity generation capacity cannot handle higher
peak demands or peaks at different times than
currently experienced.
Electricity generation capacity can handle higher peak
demands or peaks at different times than currently
experienced.
Resilience Score 1
1 (lowest resilience)
3 (highest resilience)
#22: To what extent have efforts been made to reduce energy demand?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
Few to no efforts have been made to reduce energy
demand.
Fair efforts have been made to reduce energy demand.
Moderate efforts have been made to reduce energy
demand.
Significant efforts have been made to reduce energy
demand.
Resilience Score 3
1 (lowest resilience)
2
3
4 (highest resilience)
538
-------
#23: What are the opportunities for distributed generation sources (i.e., different capacity
for energy generation from different sources including renewable)?
Relevance N/A
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
Political and technical capacity do not allow for generation
1 (lowest resilience)
from multiple sources.
Political and technical capacity could allow for
2
generation from multiple sources, but such diversified
generation is not currently occurring.
Political and technical capacity currently provide for
3
generation from multiple sources, not including
renewables.
Political and technical capacity currently provide for
4 (highest resilience)
generation from multiple sources, including renewables.
#24: Are there smart grid opportunities to manage demand?
Relevance N/A
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 1
No
1 (lowest resilience)
Yes
3 (highest resilience)
539
-------
#147: Do municipal managers draw on past data/experiences of extreme weather events to
assess the effects of these events on oil and gas availability and pricing? (DOE, 2013)
Relevance N/A Importance Weight N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#148: Has the city consulted with local power companies to develop plans for potential
increases in electricity demand for summer cooling? (DOE, 2013)
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
The city has not consulted with local power companies and
is not developing plans for potential increase in electricity
for cooling.
The city has consulted with local power companies
regarding 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
developing plans for potential increase in electricity for
cooling.
The city has consulted with local power companies and
developed plans for potential increase in electricity for
cooling.
Resilience Score N/A
1 (lowest resilience)
4 (highest resilience)
540
-------
#149: Has the city coordinated with local water suppliers and power generation facilities to
discuss potential climate-induced water shortages and their impacts on cooling the power
generation facilities?(DOE, 2013)
Relevance N/A Importance Weight N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#150: Do municipal managers in coastal areas consider the impacts of sea level rise on
power generation facilities?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
No
Yes, but these considerations are not incorporated into
planning for these facilities.
Yes, and these considerations are being incorporated into
planning for these facilities.
Yes, and these considerations are incorporated into
planning for these facilities.
Resilience Score N/A
1 (lowest resilience)
2
4 (highest resilience)
541
-------
M.3. 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 Worcester, MA 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
542
-------
#25: Can resilience planning/adaptation be incorporated into existing programs that
communities engage in regularly (e.g., zoning, hazard mitigation plans)?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
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 3
1 (lowest resilience)
4 (highest resilience)
#26: Has the city made efforts to use urban forms to mitigate climate change impacts and
to maximize benefits (e.g., urban tree canopy cover)?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 2
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 2
1 (lowest resilience)
4 (highest resilience)
543
-------
#27: Are urban forms used that address (lessen) urban heat island effects (e.g., through
increasing evapotranspiration or increasing urban ventilation)?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 2
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
1 (lowest resilience)
4 (highest resilience)
#28: Does zoning encourages green roofs or other practices that reduce urban heat?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
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 N/A
1 (lowest resilience)
4 (highest resilience)
544
-------
#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 N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
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 3
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 N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
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 3
1 (lowest resilience)
4 (highest resilience)
545
-------
#31: What percentage of open/green space is required for new development (to encourage
increases in such space)?
Relevance N/A Importance Weights 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No open/green space is required for new development. 1 (lowest resilience)
A small percentage of open/green space is required for new 2
development.
A moderate percentage of open/green space is required for 3
new development.
A high percentage of open/green space is required for new 4 (highest resilience)
development.
#32: Are there mechanisms for the local government to purchase land that is unfavorable
for redevelopment due to the results of extreme events (e.g., flooding from a hurricane)? If
so, what are those mechanisms?
Relevance N/A
Importance Weights 1
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
No, such mechanisms do not exist.
1 (lowest resilience)
Yes, there are such mechanisms, but they are only
2
preliminary and are slightly helpful.
Yes, there are such mechanisms and they are somewhat
3
helpful.
Yes, there are such mechanisms and they are helpful.
4 (highest resilience)
546
-------
#33: Are there policies or zoning practices in place that allow transfer of ownership of
undevelopable land subject to flooding or excessive erosion to the city (or allow non-
permanent structures only)? Are these policies or zoning practices enforced?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 1
1 (not very important)
2
3
4 (very important)
Answer N/A
Policies do not allow ownership transfer.
Policies allow ownership transfer, but these policies are
enforced only rarely.
Policies allow ownership transfer, but these policies are
only enforced some of the time.
Policies allow ownership transfers, and these policies are
enforced.
Resilience Score N/A
1 (lowest resilience)
2
4 (highest resilience)
#34: Where developed land is located in areas vulnerable to extreme events, are resilient
retrofits being implemented or planned?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
547
-------
#35: Are there codes to prevent development in flood-prone areas?
Relevance N/A Importance Weights 1
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#36: Are there regulations in place regarding whether communities that are affected by
floods will be rebuilt in the same location?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 1
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
1 (lowest resilience)
4 (highest resilience)
548
-------
#37: Have the regulations regarding rebuilding of communities affected by floods been
enforced to date?
Relevance N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#38: Do incentives exist to integrate green stormwater infrastructure into infrastructure
planning to mitigate flooding?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#39: Are there incentives to reduce the amount of impervious surface, to prevent
development in floodplains, to use urban forestry to reduce impacts, to use green
infrastructure for stormwater management, etc.?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No, such incentives do not exist. 1 (lowest resilience)
Yes, incentives exist to promote green infrastructure- 3 (highest resilience)
oriented solutions to stormwater management.
549
-------
#40: To what extent was green infrastructure selected to provide the maximum ecological
benefits?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 2
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 2
1 (lowest resilience)
4 (highest resilience)
#41: Has green infrastructure maintenance been built into the budget?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
550
-------
#142: Are coastal hazard maps with 1-meter altitude contours available, and are these
maps used in planning?
Relevance N/A Importance Weight N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
Such maps have not been developed and are not planned to 1 (lowest resilience)
be developed.
Plans exist to develop such maps OR such maps exist but 2
are not used in planning.
Such maps are being developed and these maps are used or 3
will be used in planning.
Such maps exist and these maps are used in planning. 4 (highest resilience)
#151: Have institutional land practices (i.e., zoning, land use planning) potentially been
hindered by other government agencies seeking to shift financial resources when it comes
to climate change planning?
Relevance N/A Importance Weight N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
Yes 1 (lowest resilience)
No 3 (highest resilience)
551
-------
#152: Does knowledge of historical land use/land cover changes contribute to planners'
understanding of climate stresses?
Relevance N/A Importance Weight 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#153: Have specific historical land use/land cover changes been recognized as increasing or
decreasing vulnerability to climate stresses?
Relevance N/A Importance Weight N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
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?
Relevance N/A Importance Weight 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
552
-------
#167: In general, what is the monetary value of infrastructure located within the 500-year
floodplain in the city?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 4
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)
553
-------
M.4. 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 Worcester, MA 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
554
-------
#42: Is the availability of environmental/ecosystem goods and services at risk if other city
goods and services (e.g., power, water, telecommunications) are affected by extreme
climatic events or gradual climatic changes?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
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 2
1 (lowest resilience)
4 (highest resilience)
#43: What regulatory and planning tools related to air quality, water quality, and land use
are already available locally? For example, does the urban area have invasive plant
ordinances or tree planting requirements?
Relevance N/A Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
The urban area does not have regulatory and planning tools 1 (lowest resilience)
for air and water quality and land use.
The urban area has few regulatory and planning tools for 2
air and water quality and land use.
The urban area has several regulatory and planning 3
tools for air and water quality and land use.
The urban area has many regulatory and planning tools for 4 (highest resilience)
air and water quality and land use.
555
-------
#44: Do plans exist for increasing open and green space?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#45: Has the continuity of open or green spaces been assessed and addressed in planning
efforts?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 4
1 (lowest resilience)
4 (highest resilience)
556
-------
#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 N/A
Importance Weights 3
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
Native species lists do not exist and are not being
1 (lowest resilience)
developed.
Native species lists exist, but green infrastructure uses
2
mostly nonnative species OR native species lists are under
development.
Native species lists exist and green infrastructure uses
3
mostly these species.
Native species lists exist and green infrastructure uses only
4 (highest resilience)
these species.
#47: Does the urban area coordinate with other nearby entities on water quality?
Relevance N/A
Importance Weights 3
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
No
1 (lowest resilience)
Yes
3 (highest resilience)
557
-------
#48: To what degree do local versus distant sources influence air quality?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
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 N/A
1 (lowest resilience)
4 (highest resilience)
#49: Does the urban area have air quality districts?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
No
Yes
Resilience Score N/A
1 (lowest resilience)
3 (highest resilience)
558
-------
#50: Has an air quality analysis been completed at multiple scales/resolutions?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
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 N/A
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 N/A Importance Weights 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
559
-------
#52: Has an analysis of areas with good ventilation (e.g., aligned with prevailing breezes,
good tree canopy cover) been completed?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 2
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 3
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 N/A Importance Weights 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
560
-------
#54: Does the urban area have a district-scale (i.e., higher resolution than city scale)
thermal comfort index?
Relevance N/A Importance Weights 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
561
-------
M.5. 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 Worcester, MA 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
562
-------
#55: How available and how comprehensive are your planning resources for responding to
extreme events?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
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 N/A
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 N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
No
Yes
Resilience Score N/A
1 (lowest resilience)
3 (highest resilience)
563
-------
#57: Do policies and outreach/education programs promote behavioral changes that
facilitate climate change adaptation?
Relevance N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#58: Are emergency response staff well trained to respond to large-scale extreme weather
events?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
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 N/A
1 (lowest resilience)
4 (highest resilience)
564
-------
#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 N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
The distribution of such services could use improvement.
Yes, such services are well-distributed.
Resilience Score N/A
1 (lowest resilience)
3 (highest resilience)
#60: Is there sufficient capacity in public health and emergency response systems for
responding to extreme events?
Relevance N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#61: Does the city have the capacity to provide public transportation for emergency
evacuations?
Relevance N/A
Importance Weights N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
Insufficient capacity
1 (lowest resilience)
Fair capacity
2
Moderate capacity
3
Extensive capacity
4 (highest resilience)
565
-------
#62: What evacuation and shelter-in-place options are available to residents in the event of
a heat wave?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
No evacuation or shelter-in-place options are available to
residents in the event of a heat wave.
One to two evacuation and shelter-in-place options are
available to residents in the event of a heat wave.
Several evacuation and shelter-in-place options are
available to residents in the event of a heat wave.
Many evacuation and shelter-in-place options are available
to residents in the event of a heat wave.
Resilience Score N/A
1 (lowest resilience)
4 (highest resilience)
#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, using fire
hydrants for cooling), given predicted climatic changes?
Relevance N/A
Importance Weights N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
Plans do not exist to provide heat adaptation strategies.
1 (lowest resilience)
Plans exist to provide one or a few heat adaptation
2
strategies.
Plans exist to provide some heat adaptation strategies.
3
Plans exist to provide many heat adaptation strategies.
4 (highest resilience)
566
-------
#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 N/A
Importance Weights N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
The healthcare community is poorly prepared.
1 (lowest resilience)
The healthcare community's level of preparation is fair.
2
Yes, the healthcare community is moderately prepared.
3
Yes, the healthcare community is well-prepared.
4 (highest resilience)
#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 N/A
Importance Weights N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
Availability of public health resources is at significant risk
1 (lowest resilience)
if other city services are affected by climatic events or
changes.
Availability of public health resources is at moderate risk if
2
other city services are affected by climatic events or
changes.
Availability of public health resources is at some risk if
3
other city services are affected by climatic events or
changes.
Availability of public health resources is at minimal risk if
4 (highest resilience)
other city services are affected by climatic events or
changes.
567
-------
#66: Do public health programs incorporate longer time frames (e.g., 10 or more years),
and do they address climate change-related health issues (e.g., movement of deer ticks to
more northerly locations)?
Relevance N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
Public health programs are not designed to address climate- 1 (lowest resilience)
related health issues.
Public health programs incorporate long-term timeframes 3 (highest resilience)
and are address climate-related health issues.
#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 N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
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 N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
568
-------
#69: Do planners in the urban area know the demographic characteristics of populations
vulnerable to climate change?
Relevance N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#70: Do planners in the urban area know the locations of populations most vulnerable to
climate change effects?
Relevance N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
569
-------
#71: Are there services and emergency responses aimed at quickly reaching vulnerable
populations during power outages?
Relevance N/A
Importance Weights N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
Services and emergency responses are not made especially
1 (lowest resilience)
available to vulnerable populations during power outages.
Yes, but these services and responses are provided slower
2
than they are needed.
Yes, and these services and responses are provided
3
somewhat rapidly.
Yes, and these services and responses are provided rapidly.
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 N/A
Importance Weights N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
No
1 (lowest resilience)
Yes
3 (highest resilience)
570
-------
#73: Are policies and programs to promote adaptive behavior designed and implemented in
ways that promote the health and well-being of vulnerable populations?
Relevance N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#74: Are policies and programs to promote adaptive behavior evaluated in ways that take
into account vulnerable populations?
Relevance N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
571
-------
#108: How accessible are different modes of transportation (e.g., to what proportion of the
population, what subpopulations [vulnerable people])?
Relevance N/A Importance Weight N/A
Yes (relevant) 1
No (not relevant) 2
Not sure—remind me later 3
4
Answer N/A
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 N/A
1 (lowest resilience)
4 (highest resilience)
572
-------
#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 (e.g., due to reduced access to specialized
medical care or equipment)?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
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 N/A
1 (lowest resilience)
4 (highest resilience)
#143: Are early warning systems for meteorological extreme events available?
Relevance N/A Importance Weight N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
573
-------
#158: Do municipal managers consider local stakeholder knowledge and local resources
(e.g., libraries, archives) in climate change resilience planning?
Relevance N/A
Importance Weight N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
i.
J
4 (very important)
Answer N/A
Resilience Score N/A
No
1 (lowest resilience)
Yes
3 (highest resilience)
574
-------
M.6. Telecommunications
The questions below have been developed for the telecommunication 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 telecommunication 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 Worcester, MA 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
575
-------
#75: What natural disasters has the area experienced in the past, and what services were
retained or largely unaffected despite these disasters?
Relevance N/A
Importance Weights 3
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
Area has either not experienced many natural disasters in
1 (lowest resilience)
recent history, or services were significantly impaired
during recent natural disasters.
Area has experienced some extreme weather or other
2
natural disasters, but some services were significantly
affected.
Area has experienced some extreme weather or other
3
natural disasters, and most services were unaffected or
affected in minor ways.
Area has experienced major extreme weather events or
4 (highest resilience)
other natural disasters, and majority of services were
retained or were largely unaffected.
#76: How would a temporary loss of telecommunication infrastructure affect the local and
regional economies?
Relevance N/A
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
Major effect
1 (lowest resilience)
Moderate effect
2
Small effect
3
Little to no effect
4 (highest resilience)
576
-------
#77: Are data centers located within or outside of the urban area?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
Within 1 (lowest resilience)
Mostly within the urban area but somewhat outside the 2
urban area.
Mostly outside the urban area but somewhat within the 3
urban area.
Outside 4 (highest resilience)
#78: For each telecommunication service, are there key nodes whose failure would severely
affect the service?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 3
1
2
3
4
Answer
Resilience Score 3
There are many key nodes whose failure would severely
1 (lowest resilience)
affect service.
There are some key nodes whose failure would severely
2
affect service.
There are a few key nodes whose failure would severely
3
affect service.
No, there are no nodes whose failure would severely affect
4 (highest resilience)
service.
577
-------
#79: How robust is the telecommunication network in terms of resilience to damage to or
failure of key nodes?
Relevance N/A
Importance Weights 2
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
The telecommunication network is not resilient to damage
1 (lowest resilience)
or failure of key nodes.
The telecommunication network is slightly resilient to
2
damage or failure of key nodes.
The telecommunication network is somewhat resilient
3
to damage or failure of key nodes.
The telecommunication network is very resilient to damage
4 (highest resilience)
or failure of key nodes.
#80: Are there parts of the telecommunication infrastructure that are particularly
vulnerable to high temperatures or prolonged high temperatures?
Relevance N/A
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
No
1 (lowest resilience)
Yes
3 (highest resilience)
578
-------
#81: Are there satellite-based communications on frequency bands (e.g., the Ka band) that
are vulnerable to wet-weather disruption?
Relevance N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#82: Are your telecommunication infrastructure components located wisely with respect to
your anticipated climate stressors (i.e., aboveground, underground, or serviced by
satellite)?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
579
-------
#83: Are aboveground infrastructure components vulnerable to wind (e.g., cell towers)?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
All aboveground infrastructure components are vulnerable
to expected winds.
Some aboveground infrastructure components are
vulnerable to expected winds.
Few aboveground infrastructure components are
vulnerable to expected winds.
No aboveground infrastructure components are vulnerable
to expected winds.
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
#84: Are belowground infrastructure components vulnerable to rising water or salt water
intrusion?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 2
1 (not very important)
2
3
4 (very important)
Answer
All belowground infrastructure components are vulnerable
to expected rises in groundwater levels or from salt water
intrusion.
Some belowground infrastructure components are
vulnerable to expected rises in groundwater levels or from
salt water intrusion.
Few belowground infrastructure components are
vulnerable to expected rises in groundwater levels or
from salt water intrusion.
No belowground infrastructure components are vulnerable
to expected rises in groundwater levels or from salt water
intrusion.
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
580
-------
#85: If the area has satellite-based communications that are vulnerable to wet-weather
disruption, does the area have a backup tower network?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight N/A
1
2
3
4
Answer N/A
The area does not have a tower network that could provide
backup.
The area has a tower network that could provide a small
amount of backup.
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 N/A
1 (lowest resilience)
4 (highest resilience)
#86: Does your community have sufficient access to backup telecommunication systems?
What is the capacity of the telecommunication infrastructure?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
There are no backup systems. Capacity of the
telecommunication infrastructure is low.
There are some minimal backup systems, but
telecommunication 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
telecommunication systems is high.
Resilience Score 4
1 (lowest resilience)
4 (highest resilience)
581
-------
#87: Is backup power for telecommunication systems provided? If so, is it provided by
diesel generators?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
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 4
1 (lowest resilience)
2
4 (highest resilience)
#88: What is the extent of telecommunication redundancy? Do first responders and the
public have multiple communication options, served by different infrastructure?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
There is little to no redundancy.
There is a small amount of redundancy.
There is a moderate amount of redundancy. There are
more than one communications options, served by
different infrastructure.
There is a great deal of redundancy. There are multiple
communications options, served by different infrastructure.
Resilience Score 3
1 (lowest resilience)
2
4 (highest resilience)
582
-------
#89: What percentage of telecommunication system capacity is required for the baseline
level of use?
Relevance N/A
Importance Weight 2
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
Greater than 85%
1 (lowest resilience)
70 to 85%
2
60 to 70%
3
Less than 60%
4 (highest resilience)
#90: Does telecommunication infrastructure have the capacity for increased public deman
in an emergency?
Relevance N/A
Importance Weights N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
No
1 (lowest resilience)
Yes
3 (highest resilience)
583
-------
#91: Do local authorities have established relations with telecommunication infrastructure
service providers? Are emergency protocols and plans in place?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#92: Do local private-sector telecommunication infrastructure service providers have the
authority and resources to make quick decisions and implement them in and after an
emergency?
Relevance N/A Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#93: Can local authorities and telecommunication providers give first responder and
decision-maker communications priority during an expected surge in traffic in emergency
situations?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
584
-------
#94: Are public-address systems (e.g., loud speakers, text messages, radio broadcasts,
emergency television broadcasts) in place to provide instructions to the public in case of an
emergency?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight!
1
2
3
4
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 3
1 (lowest resilience)
2
4 (highest resilience)
#95: What modes do authorities in the urban area use to communicate emergency
information and alerts? Are these modes low- or high-bandwidth?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
Authorities do not use multiple modes (e.g., text
messaging, email, phone calls), or none of the modes used
is low bandwidth.
Authorities use one to two modes (e.g., text messaging,
email, phone calls) and one or two of these modes is low
bandwidth.
Authorities use multiple modes (e.g., text messaging,
email, phone calls) and one or two of these modes are low
bandwidth.
Authorities use multiple modes (e.g., text messaging,
email, phone calls) and some of these modes are low
bandwidth.
Resilience Score 4
1 (lowest resilience)
4 (highest resilience)
585
-------
#96: What is the likelihood that the capacity of local first responder communication
systems would be exceeded during a disaster?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 4
1 (lowest resilience)
4 (highest resilience)
#97: Does the area have access to backup emergency call/response (911) networks if the
primary networks fail or are overloaded?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 4
1 (lowest resilience)
4 (highest resilience)
586
-------
#98: Is the availability of telecommunication 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 N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
Availability of telecommunication resources is at
significant risk if other city services are affected by
climatic events or changes.
Availability of telecommunication resources is at moderate
risk if other city services are affected by climatic events or
changes.
Availability of telecommunication resources is at some
risk if other city services are affected by climatic events
or changes.
Availability of telecommunication resources is at minimal
risk if other city services are affected by climatic events or
changes.
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
#99: Do telecommunication systems have enough energy and water supply to handle extra
load in the case of sudden natural disasters?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 4
1 (not very important)
2
3
4 (very important)
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.
Resilience Score 4
1 (lowest resilience)
4 (highest resilience)
587
-------
#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?
Relevance N/A Importance Weight 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
588
-------
M.7. 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 Worcester, MA 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
589
-------
#100: Is the availability of transportation goods and services at risk if other city goods and
services (e.g., power, water, telecommunications) are affected by extreme climatic events or
gradual climatic changes?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
Availability of transportation resources is at significant risk
if other city services are affected by climatic events or
changes.
Availability of transportation resources is at moderate risk
if other city services are affected by climatic events or
changes.
Availability of transportation resources is at some risk
if other city services are affected by climatic events or
changes.
Availability of transportation resources is at minimal risk if
other city services are affected by climatic events or
changes.
Resilience Score 3
1 (lowest resilience)
4 (highest resilience)
#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 N/A
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
None
1 (lowest resilience)
Low
2
Medium
3
High
4 (highest resilience)
590
-------
#102: How resistant to potential impacts of climate change are critical transportation
facilities (e.g., high-traffic vehicle or rail bridges, tunnels)?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weight 4
1
2
3
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 3
1 (lowest resilience)
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 N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 3
1 (lowest resilience)
4 (highest resilience)
591
-------
#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 N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
More than 1 week 1 (lowest resilience)
Approximately 1 week 2
4 to 6 days 3
1 to 3 days 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 N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
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 N/A
1 (lowest resilience)
4 (highest resilience)
592
-------
#106: To what extent is the area dependent on long-range transportation of goods and
services versus locally available goods and services (food, energy, etc.)?
Relevance N/A
Importance Weights 3
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
90-100% dependent on long-range transportation of goods
1 (lowest resilience)
and services
50-90% dependent on long-range transportation of
2
goods and services
10-50%) dependent on long-range transportation of goods
3
and services
0-10%o dependent on long-range transportation of goods
4 (highest resilience)
and services
#107: What flexibility has been built into the transportation system (different modes)?
Relevance N/A
Importance Weights 2
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 2
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)
593
-------
#108: How accessible are different modes (e.g., to what proportion of the population, what
subpopulations [vulnerable people])?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
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 N/A
1 (lowest resilience)
4 (highest resilience)
594
-------
#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 (e.g., due to reduced access to specialized
medical care or equipment)?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
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 N/A
1 (lowest resilience)
4 (highest resilience)
#110: How familiar is the community with evacuation procedures?
Relevance N/A Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
Unfamiliar 1 (lowest resilience)
Only slightly familiar (or only some subpopulations are 2
familiar)
Somewhat familiar 3
Very familiar 4 (highest resilience)
595
-------
#111: What length of time would be required to restore major passenger rail
transportation facilities in the urban area if they experience a failure?
Relevance N/A Importance Weights 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
More than 1 week 1 (lowest resilience)
Approximately 1 week 2
4 to 6 days 3
1 to 3 days 4 (highest resilience)
#112: What length of time would be required to restore major freight rail transportation
facilities in the urban area if they experience a failure?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 1
1 (not very important)
2
3
4 (very important)
Answer
More than 1 week
Approximately 1 week
4 to 6 days
1 to 3 days
Resilience Score 1
1 (lowest resilience)
2
3
4 (highest resilience)
596
-------
#113: What length of time would be required to restore major bicycle and pedestrian
transportation links in the urban area if they experience a failure?
Relevance N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
Approximately 1 week 1 (lowest resilience)
4 to 6 days 2
1 to 3 days 3
Less than 1 day 4 (highest resilience)
#114: Are urban areas set up to provide accessibility (e.g., to jobs) if mobility is interrupted
or impeded?
Relevance N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
597
-------
#115: Do current planning regimes include proactive resilience building, or is only reactive
disaster response being addressed?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
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 2
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 N/A Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
No funding mechanisms exist to adapt transportation 1 (lowest resilience)
systems to climatic changes, and none could be established.
No funding mechanisms exist to adapt transportation 2
systems to climatic changes, but mechanisms could be
established.
Funding mechanisms are being developed to adapt 3
transportation systems to climatic changes.
Funding mechanisms exist to adapt transportation systems 4 (highest resilience)
to climatic changes.
598
-------
#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 N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
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 2
1 (lowest resilience)
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 N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No currently used materials are compatible with anticipated 1 (lowest resilience)
changes in temperature.
A few currently used materials are compatible with 2
anticipated changes in temperature.
Some currently used materials are compatible with 3
anticipated changes in temperature.
All currently used materials are compatible with anticipated 4 (highest resilience)
changes in temperature.
599
-------
#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 N/A
Importance Weights N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
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 N/A
Importance Weights N/A
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer N/A
Resilience Score N/A
Not implemented or planned
1 (lowest resilience)
Planned but not yet implemented
2
Some implementation with further green infrastructure
3
planned
Widespread implementation with additional projects
4 (highest resilience)
planned
600
-------
#162: Have municipalities considered new methods of designing roads/bridges to prepare
for heavily traveled routes during an extreme climate event (e.g., coastal evacuation
routes)?
Relevance N/A Importance Weight N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
601
-------
M.8. 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 Worcester, MA 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
602
-------
#121: Does the water supply draw from a diversity of sources?
Relevance N/A Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#122: To what extent do water supplies come from outside the metropolitan area?
Relevance N/A Importance Weights 1
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 2
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)
#123: Is there a recharge plan in place for groundwater supplies?
Relevance N/A Importance Weights 2
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No 1 (lowest resilience)
Yes 3 (highest resilience)
603
-------
#124: Do programs for long-term maintenance of water supplies (e.g., erosion control
methods, reforestation of the watershed) exist?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
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 N/A Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
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 N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
604
-------
#127: Are water and wastewater treatment plants located in a flood zone?
Relevance N/A
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
At least 50% of water and wastewater treatment plant
1 (lowest resilience)
capacity is located in a flood zone.
30% to 49%) of water and wastewater treatment plant
2
capacity is located in a flood zone.
10% to 29% of water and wastewater treatment plant
3
capacity is located in a flood zone.
Less than 10%> of water and wastewater treatment plant
4 (highest resilience)
capacity is located in a flood zone.
#128: Are groundwater supplies susceptible to salt water intrusion and sea level rise?
Relevance N/A
Importance Weights 1
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 4
Groundwater supplies are very susceptible to salt water
1 (lowest resilience)
intrusion given anticipated sea level rise.
Groundwater supplies are moderately susceptible to salt
2
water intrusion given anticipated sea level rise.
Groundwater supplies are slightly susceptible to salt water
3
intrusion given anticipated sea level rise.
No, groundwater supplies are not susceptible to salt
4 (highest resilience)
water intrusion and sea level rise.
605
-------
#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 N/A Importance Weights N/A
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer N/A Resilience Score N/A
No 1 (lowest resilience)
Yes 3 (highest resilience)
#130: Does treatment capacity exist to accommodate nutrient loading?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 2
1 (not very important)
2
3
4 (very important)
Answer
Drinking water treatment capacity cannot
accommodate nutrient loading in source water.
Drinking water treatment capacity can accommodate
expected levels of nutrient loading in source water.
Resilience Score 1
1 (lowest resilience)
3 (highest resilience)
#131: Does the drinking water treatment plant have redundant treatment chemical
suppliers?
Relevance N/A Importance Weight 3
Yes (relevant) 1
No (not relevant) 2
Not sure—remind me later 3
4
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
606
-------
#132: Are there redundant drinking water systems in place for coping with extreme events,
including supply, treatment, and distribution systems?
Relevance N/A
Importance Weights 2
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
No, redundant drinking water systems are not in place.
1 (lowest resilience)
Yes, but these redundant drinking water systems have only
2
a small amount of the capacity necessary.
Yes, and these redundant drinking water systems have
3
some of the capacity necessary.
Yes, and these redundant drinking water systems have all
4 (highest resilience)
the capacity necessary.
#133: Is backup power for water supply, treatment, and distribution systems provided?
Relevance N/A
Importance Weights 4
Yes (relevant)
1 (not very important)
No (not relevant)
2
Not sure—remind me later
3
4 (very important)
Answer
Resilience Score 3
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)
607
-------
#134: How diverse are individual properties (i.e., are they equipped to harvest rainwater or
recharge groundwater so they can create or augment local water supplies)?
Relevance N/A Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 1
No individual properties are equipped to either harvest 1 (lowest resilience)
rainwater 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.
#135: Are there redundant wastewater and stormwater systems in place for coping with
extreme events, including collection systems and wastewater treatment systems?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights N/A
1 (not very important)
2
3
4 (very important)
Answer N/A
No, redundant wastewater and stormwater systems are not
in place.
Yes, but these redundant wastewater and stormwater
systems have only a small amount of the capacity
necessary.
Yes, and these redundant wastewater and stormwater
systems have some of the capacity necessary.
Yes, and these redundant wastewater and stormwater
systems have all the capacity necessary.
Resilience Score N/A
1 (lowest resilience)
4 (highest resilience)
608
-------
#136: Does a water/wastewater agency response network provide technical
resources/support to the urban area's water system during emergencies?
Relevance N/A Importance Weights 4
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
#137: Have storm sewers and drains to storm sewers been inventoried, and are these
inventories used in planning?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 3
1 (not very important)
2
3
4 (very important)
Answer
Storm sewers and drains to storm sewers are not
inventoried and are not planned to be inventoried.
Plans exist to inventory storm sewers and drains to storm
sewers OR these inventories exist but are not used in
planning.
Storm sewers and drains to storm sewers are being
inventoried and these inventories are used or will be used
in planning.
Storm sewers and drains to storm sewers have been
inventoried and these inventories are used in planning.
Resilience Score 4
1 (lowest resilience)
4 (highest resilience)
609
-------
#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 N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 4
1 (not very important)
2
3
4 (very important)
Answer
Availability of water resources is at significant risk if
other city services are affected by climatic events or
changes.
Availability of water resources is at moderate risk if other
city services are affected by climatic events or changes.
Availability of water resources is at some risk if other city
services are affected by climatic events or changes.
Availability of water resources is at minimal risk if other
city services are affected by climatic events or changes.
Resilience Score 1
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 N/A Importance Weights 1
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
No 1 (lowest resilience)
Yes 3 (highest resilience)
610
-------
#140: To what extent have efforts been made to reduce water demand?
Relevance N/A Importance Weights 3
Yes (relevant) 1 (not very important)
No (not relevant) 2
Not sure—remind me later 3
4 (very important)
Answer Resilience Score 3
Few to no efforts have been made to reduce water demand. 1 (lowest resilience)
Fair efforts have been made to reduce water demand. 2
Moderate efforts have been made to reduce water 3
demand.
Significant efforts have been made to reduce water 4 (highest resilience)
demand.
#141: Are customers familiar with water conservation measures, and are they willing to
implement these measures?
Relevance N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights 1
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 2
1 (lowest resilience)
4 (highest resilience)
611
-------
APPENDIX N. QUANTITATIVE INDICATORS: WORCESTER, MA
A complete set of the quantitative indicators by sector developed for the tool.
N.l. 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
nongrouped 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
Worcester, MA, data sets 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 Worcester, MA or based on any
other criteria. Secondary Indicators may be considered if the primary indicator is not
adequately defined or does not have available data set(s).
2. When possible, data sets for Worcester, MA are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
612
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#709: Percentage of owned housing units that are affordable
Definition: This indicator measures (1) the percentage of owned housing units where selected
monthly ownership costs (rent, mortgages, real estate taxes, insurance, utilities, fuel, fees) as
a percentage of household income (SMOCAPI) exceeds 35% or (2) the percentage of rented
housing units where gross rent as a percentage of household income (GRAPI) exeeds 35%.
Grouped with Indicators: N/A
Data Set(s):
Worcester Regional Research Bureau—Worcester by the Numbers: Housing and Land Use
(http://www.wrrb.org/files/downloads/reports/eco_dev/2013/worcester-by-the-numbers-
housing-and-land-report.pdf)
Notes on Data Set(s):
This source states that 50.9% of households are spending more than 30% of their income on
rent, (based on data from the U.S. Census Bureau, 2007-2011 American Community Survey
5-Year Estimates).
Based on those data, (100-50.9) or 49.1% of households have ownership/rental costs of 30%
or less of their income.
Indicator Value:
49.1%
Relevance: N/A
Importance Weight: 2
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 3
Your Score 3
0 to 30%
1 (lowest resilience)
1 (lowest
resilience)
30 to 45%
2
2
45 to 60%
3
3
Greater than 60%
4 (highest resilience)
4 (highest
resilience)
613
-------
#711: Overall 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
Data Set(s):
NAICS (American FactFinder)—EC0700A1: All sectors: Geographic Area Series: Economy-
Wide Key Statistics: 2007
(http://factfinder2.census.gov/faces/nav/jsf/pages/community_facts.xhtml)
Notes on Data Set(s):
Employment data are available for 12 NAICS codes (some are aggregate codes). One
NAICS code employs less than 1% of the employed population (as represented in the NAICS
table); therefore, this source was excluded from the calculation. All 11 of the remaining
NAICS codes employ between 1 and 40% of the employed population. Therefore,
11^-11 = 100% of the sectors employ < 40% of the population.
Indicator Value:
100%
Relevance: Importance Weight: Proposed
N/A 4 Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 4
Your Score: N/A
0 to less than 83%
1 (lowest resilience)
1 (lowest
resilience)
83 to less than 91%
2
2
91 to less than 100%
3
3
100%
4 (highest resilience)
4 (highest
resilience)
614
-------
#717: Percentage access to health insurance of noninstitutionalized population
Definition: This indicator measures the percentage of noninstitutionalized residents with
health insurance.
Grouped with Indicators: #725
Data Set(s):
Massachusetts Department of Health—Regional Health Status Indicators
Massachusetts (http://www.mass.gov/eohhs/docs/dph/research-epi/central-region-report.pdf)
Notes on Data Set(s):
See pp. 25-26, 2005 data for Central Massachusetts. 30% of adults have access to health
insurance as of 2005. (The source does not state whether this value considers the population
as "noninstitutionalized").
Indicator Value:
30%
Relevance: N/A
Importance Weight: 2
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 1
Your Score: N/A
Less than 85%
1 (lowest resilience)
1 (lowest
resilience)
85 to 90%
2
2
90 to 95%
3
3
Greater than 95%
4 (highest resilience)
4 (highest
resilience)
615
-------
#722: Percentage change in homeless population
Definition: This indicator measures the percentage change in the homeless population.
Grouped with Indicators: N/A
Data Set(s):
(1) Central MA Housing Alliance—2012 Point-in-Time survey
(http://www.cmhaonline.org/documents/point_in_time/2012/2012_City_of_W orcester_Point
%20in%20Time%20Survey.pdf).
(2) Central MA Housing Alliance—2013 Point-in-Time survey:
(http://www.cmhaonline.Org/documents/point_in_time/2013/2013_PIT_City_of_Worcester.p
Notes on Data Setfs):
(1) 1,144 homeless in Worcester in January 2012
(2) 1,202 homeless in Worcester in January 2013
Therefore, percentage change in homeless population = (1,202 - 1,144) ^ 1,144 = 5.1%
increase in homeless population.
Indicator Value:
df)
5.1%
Relevance: N/A
Importance Weight: 3
Proposed
Resilience Score:
N/A
Thresholds:
Greater than 10%
Threshold-Based Score: 2
1 (lowest resilience)
Your Score: N/A
1 (lowest
Oto 10%
Negative 10 to 0%
Less than negative 10%
2
3
4 (highest resilience)
resilience)
2
3
4 (highest
resilience)
616
-------
#1375: Percentage of population living below the poverty line
Definition: This indicator measures the percentage of the population living below the poverty
line.
Grouped with Indicators: N/A
Data Set(s):
Census Bureau: State and County QuickFacts, Worcester City
Notes on Data Set(s):
Based on 2007-2011 data, 19% of the population of Worcester is below the poverty line
(from the U.S. Census Bureau's American Community survey).
Indicator Value:
19%
Relevance: N/A Importance Weight: 3 Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 2
Your Score: N/A
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
16 to 20%
2
2
12 to 16%
3
3
Less than 12%
4 (highest resilience)
4 (highest
resilience)
617
-------
N.2. 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
nongrouped 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
Worcester, MA, data sets 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 Worcester, MA or based on any
other criteria. Secondary Indicators may be considered if the primary indicator is not
adequately defined or does not have available data set(s).
2. When possible, data sets for Worcester, MA are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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.
Note: No data sets for the enersv sector were identified during the Worcester case study. For
information on the quantitative indicators for this section. please see Appendix H.
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
618
-------
N.3. 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 nongrouped 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
Worcester, MA, data sets 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:
5. 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 Worcester, MA or
based on any other criteria. Secondary Indicators may be considered if the primary indicator
is not adequately defined or does not have available data set(s).
6. When possible, data sets for Worcester, MA are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets identified or where data gaps exist.
7. For indicators selected as .yes (relevant), discuss an importance weight, where 1 = not very
important and 4 = very important.
8. 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
619
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#51: Coastal Vulnerability Index Rank
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), and f = mean wave
height (m).
Grouped with Indicators: N/A
Data Set(s):
No data available. Please suggest alternate data.
Notes on Data Set(s):
N/A
Indicator Value:
N/A
Relevance: N/A Importance Weight: N/A Proposed
Resilience Score:
N/A
Thresholds:
5 (very high vulnerability)
Threshold-Based Score: N/A
1 (lowest resilience)
Your Score: N/A
1 (lowest
4 (high vulnerability)
3 (moderate vulnerability)
1 or 2 (low or no vulnerability)
2
3
4 (highest resilience)
resilience)
2
3
4 (highest
resilience)
620
-------
#194: Percentage of natural area that is in small natural patches
Definition: This indicator measures the percentage of the total natural area in a city that is in
patches of less than 10 acres. 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
Data Set(s):
(1) MassGIS data layers: BioMap2 (BM2COREH AB IT AT)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/biomap2.html)
(2) MassGIS data layers: Community Boundaries (TOWNSPOLYM)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/towns.html)
Notes on Data Set(s):
Clip the core habitat shape file (BM2 C0RE HABITAT) by the Worcester town boundary.
Calculate the clipped acreage of the patches as the product of the field ACRES and the ratio
of the new ShapeArea to the original ShapeArea. Sum the clipped acreage to get the total
natural area in Worcester. Sum the clipped acreage of all patches with clipped acreage less
than 10 acres to get the area in natural patches. Divide area of all patches less than 10 acres
by the total area of all patches in Worcester.
Total area of all patches less than 10 acres = 6.75 acres
Total area of all natural patches in Worcester = 1,478.0 acres
Percentage of natural area in small natural patches = 0.46%
Indicator Value:
0.46%
Relevance: N/A Importance Weight: N/A Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 4
Your Score: N/A
Greater than 80%
1 (lowest resilience)
1 (lowest
resilience)
60 to 80%
2
2
40 to 60%
3
3
Less than 40%
4 (highest resilience)
4 (highest
resilience)
621
-------
#254: Ratio of perimeter to area of natural patches
Definition: This indicator is calculated as the average ratio of the perimeter to area.
Grouped with Indicators: N/A
Data Set(s):
(1) MassGIS data layers: BioMap2 (BM2COREHAB IT AT)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/biomap2.html)
(2) MassGIS data layers: Community Boundaries (TOWNS POLYM)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/towns.html)
Notes on Data Set(s):
Clip the core habitat shapefile (BM2 C0RE HABITAT) by the Worcester town boundary.
Divide the original (preclipping) shape length by the original (preclipping) shape area for all
BioMap polygons in Worcester. Take the average perimeter to area ratio.
Average perimeter to area ratio: 0.0154
Indicator Value:
0.0154
Relevance: N/A Importance Weight: 3 Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 1
Your Score: N/A
Greater than 0.025 (unitless
1 (lowest resilience)
1 (lowest
ratio)
resilience)
0.015 to 0.025 (unitless ratio)
2
2
0.005 to 0.015 (unitless ratio)
3
3
Less than 0.005 (unitless ratio)
4 (highest resilience)
4 (highest
resilience)
622
-------
#437: Percentage change in streamflow divided by percentage 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 U.S. from 1931 to 1988.
Grouped with Indicators: #1369
Data Set(s):
USGS—Annual Statistics for the Nation USGS 01111212 Blackstone River discharge data
(http://nwis.waterdata.usgs.gov/nwis/annual?site_no=01111212&por_01111212_2=12680
96,00060,2,2006,2013&start_dt=2006&end_dt=2013&year_type=W&form
at=html_tabl e& amp; date_format=YY Y Y-MM-
DD&rdb_compression=file&submitted_form=parameter_selection_list);
Weather Underground—Worcester, MA
(http://www.wunderground.eom/history/airport/K ORH/2012/l/l/CustomHistory.html?dayend=
31 &monthend= 12&yearend=2012&req_city=NA&req_state=NA&req_statename=NA)
Notes on Data Set(s):
Streamflow—USGS Blackstone River—Annual Percentage Change CFS between 2007 and
2012 = -7%
Precipitation—Weather Underground—Annual Percentage Change in total precipitation from
2007 to 2012 = -5%
Percentage change streamflow ^ percentage change precipitation =1.4
Indicator Value:
1.4
Relevance: Importance Weight: Proposed
N/A N/A Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 3
Your Score: N/A
Greater than 3.0 (unitless ratio)
1 (lowest resilience)
1 (lowest
resilience)
2.0 to 3.0 (unitless ratio)
2
2
1.0 to 2.0 (unitless ratio)
3
3
Less than 1.0 (unitless ratio)
4 (highest resilience)
4 (highest
resilience)
623
-------
#825: Percentage change in impervious cover
Definition: This indicator reflects the change in the percentage of the metropolitan area that is
impervious surface (roads, buildings, sidewalks, parking lots, etc.).
Grouped with Indicators: #303, #308
Data Set(s):
NLCD 2001/2006 Percentage Developed Imperviousness Change
(http://www.mrlc.gov/nlcd06_data.php)
Notes on Data Set(s):
Clip the raster file to the town boundary, then calculate the product of the Count and Red (the
percentage change in imperviousness) fields. Sum this product and divide by the sum of the
Count field.
Percentage change in impervious surface cover = 1.8% increase
Indicator Value:
1.8%
Relevance: N/A
Importance Weight: 4
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 1
Your Score: N/A
Greater than 1%
1 (lowest resilience)
1 (lowest
resilience)
0 to 1%
2
2
Negative 1 to 0%
3
3
Less than negative 1%
4 (highest resilience)
4 (highest
resilience)
624
-------
#1436: Percentage of city area in 100-year floodplain
Definition: This indicator reflects the percentage of the metropolitan area that lies within the
100-year floodplain.
Grouped with Indicators: #1437, #1438, #1439
Data Set(s):
(1) MassGIS data layers: FEMA National Flood Hazard layer (FEMANFHLPOLY)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/nfhl.html)
(2) MassGIS data layers: Community Boundaries (TOWNSPOLYM)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/towns.html)
(3) MassGIS data layers: Land Use (2005) (LANDUSE2005_POLY_WORN and
LANDUSE2005_POLY_WORS) (http://www.mass.gov/anf/research-and-tech/it-serv-and-
support/application-serv/office-of-geographic-information-massgis/datalayers/lus2005.html)
Notes on Data Set(s):
Clip all layers by Worcester town boundary. Select Flood Codes "A, AE, AH, AO, and VE"
polygons from FEMA layer, then dissolve to create the 100-year floodplain. Select "water"
category in the 2005 Land Use layer, dissolve the selection, and union this layer with the
Worcester town layer. Select the parts of the new layer after the union not in water
(FID_WaterLayer=-l) and dissolve to create a layer for Worcester with water area removed.
Calculate the total area of this layer before creating a union of this layer with the 100-year
floodplain layer created above. Then select the parts of the nonwater Worcester layer that are
in the 100-year floodplain and divide this by the total nonwater area calculated above.
Total Worcester nonwater area: 96,678,783 m2
Total Worcester nonwater area in the 100-year floodplain: 3,577,250 m2
Percentage of city area in 100-year floodplain: 3.7%
Indicator Value:
3.70%
Relevance: N/A Importance Weight: 4 Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 4
Your Score: N/A
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
5 to 20%
2
2
1 to 5%
3
3
Less than 1%
4 (highest resilience)
4 (highest
resilience)
625
-------
SECONDARY INDICATORS
#308: Percentage 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 presettlement 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
Data Set(s):
2010 Mass. Department of Transportation:
http://www.massdot.state.ma.us/planning/Main/MapsDataandReports/Data/GISData/UrbanBoundaries.aspx
Notes on Data Set(s):
The shapefile for source 2 (2010 Mass. Urban Boundaries) indicates that Worcester city is 100% urban.
Indicator Value:
100%
Relevance: N/A Importance Weight: 3 Proposed Resilience
Score: N/A
Thresholds:
Threshold-Based Score: 1
Your Score: N/A
Greater than 90%
1 (lowest resilience)
1 (lowest
resilience)
75 to 90%
2
2
60 to 75%
3
3
Less than 60%
4 (highest resilience)
4 (highest
resilience)
626
-------
#1437: Percentage of city area in 500-year floodplain
Definition: This indicator reflects the percentage of the metropolitan area that lies within the
500-year floodplain.
Grouped with Indicators: #1436, #1438, #1439
Data Set(s):
(1) MassGIS data layers: FEMA National Flood Hazard layer (FEMANFHLPOLY)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/nfhl.html)
(2) MassGIS data layers: Community Boundaries (TOWNSPOLYM)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/towns.html)
(3) MassGIS data layers: Land Use (2005) (LANDUSE2005_POLY_WORN and
LANDUSE2005_POLY_WORS) (http://www.mass.gov/anf/research-and-tech/it-serv-and-
support/application-serv/office-of-geographic-information-massgis/datalayers/lus2005.html)
Notes on Data Set(s):
Clip all layers by Worcester town boundary. Select Flood Codes "A, AE, AH, AO, and VE"
polygons from FEMA layer, then dissolve to create the 100-year floodplain. Select "water"
category in the 2005 Land Use layer, dissolve the selection, and union this layer with the
Worcester town layer. Select the parts of the new layer after the union not in water
(FID_WaterLayer=-l) and dissolve to create a layer for Worcester with water area removed.
Calculate the total area of this layer before creating a union of this layer with the 500-year
floodplain layer created above. Then select the parts of the nonwater Worcester layer that are
in the 100-year floodplain, and divide this by the total nonwater area calculated above.
Total Worcester nonwater area: 96,678,783 m2
Total Worcester nonwater area in the 500-year floodplain: 4,418,906 m2
Percentage of city area in
Indicator Value:
4.6%
Relevance: N/A
500-year floodplain: 4.6%
Importance Weight: 3
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 3
Your Score: N/A
Greater than 30%
1 (lowest resilience)
1 (lowest
resilience)
10 to 30%
2
2
2 to 10%
3
3
Less than 2%
4 (highest resilience)
4 (highest
resilience)
627
-------
#1438: Percentage of city population in 100-year floodplain
Definition: This indicator reflects the percentage of the city population living within the 100-
year floodplain.
Grouped with Indicators: #1436, #1437, #1439
Data Set(s):
(1) MassGIS data layers: FEMA National Flood Hazard layer (FEMANFHLPOLY)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/nfhl.html)
(2) MassGIS data layers: U.S. Census 2010 Blocks (CENSUS201OBLOCKS POLY)
http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/census2010.html
(3) MassGIS data layers: Community Boundaries (TOWNS POLYM)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/towns.html)
(4) MassGIS data layers: Land Use (2005) (LANDUSE2005_POLY_WORN and
LANDUSE2005_POLY_WORS) (http://www.mass.gov/anf/research-and-tech/it-serv-and-
support/application-serv/office-of-geographic-information-massgis/datalayers/lus2005.html)
Notes on Data Set(s):
Clip all three layers by Worcester town boundary. Select Flood Codes "0.2 PCT Annual
Chance Flood Hazard (X), A, AE, AH, AO, and VE" polygons from FEMA layer. These
represent the 500- and 100-year floodplains, respectively. Select the "water" category in the
2005 Land Use layer, and erase these water polygons from the U.S. census block groups
layer. Select from these U.S. census blocks that intersect with the 100- OR 500-year
floodplains. Determine the area of overlap between the flood zones and census blocks.
Divide this area by the total area of the census block to develop a percentage overlap value.
Multiply this percentage overlap value by the total population of the census block. This
assumes that population within each census block is equally spatially distributed. Sum this
value.
Total Worcester population: 181,045
Area-weighted population: 3,798
Percentage population living in 100-year floodplain = 2.1%
Indicator Value:
2.10%
Relevance: N/A Importance Weight: 3 Proposed Resilience Score: N/A
Thresholds:
Threshold-Based Score: 3
Your Score: N/A
Greater than 20%
1 (lowest resilience)
1 (lowest resilience)
5 to 20%
2
2
1 to 5%
3
3
Less than 1%
4 (highest resilience)
4 (highest resilience)
628
-------
#1439: Percentage of city population in 500-year floodplain
Definition: This indicator reflects the percentage of the city population living within the 500-
year floodplain.
Grouped with Indicators: #1436, #1437, #1438
Data Set(s):
(1) MassGIS data layers: FEMA National Flood Hazard layer (FEMANFHLPOLY)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/nfhl.html)
(2) MassGIS data layers: U.S. Census 2010 Blocks (CENSUS201OBLOCKS POLY)
http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/census2010.html
(3) MassGIS data layers: Community Boundaries (TOWNS POLYM)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/towns.html)
(4) MassGIS data layers: Land Use (2005) (LANDUSE2005_POLY_WORN and
LANDUSE2005_POLY_WORS) (http://www.mass.gov/anf/research-and-tech/it-serv-and-
support/application-serv/office-of-geographic-information-massgis/datalayers/lus2005.html)
Notes on Data Set(s):
Clip all three layers by Worcester town boundary. Select Flood Codes "0.2 PCT Annual
Chance Flood Hazard (X), A, AE, AH, AO, and VE" polygons from FEMA layer. These
represent the 500- and 100-year floodplains, respectively. Select the "water" category in the
2005 Land Use layer, and erase these water polygons from the U.S. census block groups
layer. Select from these U.S. census blocks that intersect with the 100- OR 500-year
floodplains. Determine the area of overlap between the flood zones and census blocks.
Divide this area by the total area of the census block to develop a percentage overlap value.
Multiply this percentage overlap value by the total population of the census block. This
assumes that population within each census block is equally spatially distributed. Sum this
value.
Total Worcester population: 181,045
Area-weighted population: 4,653
Percentage population living in 500-year floodplain = 2.6%
Indicator Value:
2.6%
Relevance: N/A Importance Weight: 4 Proposed Resilience Score: N/A
Thresholds:
Threshold-Based Score: 3
Your Score: N/A
Greater than 30%
1 (lowest resilience)
1 (lowest resilience)
10 to 30%
2
2
2 to 10%
3
3
Less than 2%
4 (highest resilience)
4 (highest resilience)
629
-------
N.4. 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 nongrouped 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
Worcester, MA, data sets 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 Worcester, MA or
based on any other criteria. Secondary Indicators may be considered if the primary indicator
is not adequately defined or does not have available data set(s).
2. When possible, data sets for Worcester, MA are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
630
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#326: Wetland species at risk (number of species)
Definition: Number of wetland and freshwater species at risk (rare, threatened, or
endangered).
Grouped with Indicators: N/A
Data Set(s):
Natural Heritage and Endangered Species Fund—Biomap of Worcester
(http://maps.massgis.state.ma.us/dfg/biomap/pdf/town_coreAVorcester.pdf)
Notes on Data Set(s):
One species of concern is associated with the "aquatic core." Vasey's pondweed (plant).
Indicator Value:
1 species
Relevance: N/A Importance Weight: 1 Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: N/A
Your Score: N/A
Greater than 160 species at risk 1 (lowest resilience)
1 (lowest
100 to 160 species at risk 2
50 to less than 100 species at 3
risk
Less than 50 species at risk 4 (highest resilience)
2
3
resilience)
2
3
4 (highest
resilience)
631
-------
N.5. 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
nongrouped 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
Worcester, MA, data sets 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 Worcester, MA or based on any
other criteria. Secondary Indicators may be considered if the primary indicator is not
adequately defined or does not have available data set(s).
2. When possible, data sets for Worcester, MA are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
632
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#690: Emergency medical service response times
Definition: This indicator measures average annual response times (in minutes) for
emergency medical service calls.
Grouped with Indicators: #757, #784, #798
Data Set(s):
Worcester Regional Research Bureau—Benchmarking Public Safety
in Worcester: 2012
(http://www.wrrb.org/files/downloads/ongoing/benchmarking/pub_safety/2012/benchmarkin
g-public-safety-in-worcester-2012.pdf)
Notes on Data Set(s):
EMS response times shown in Graph 9 for years 2000-2011.
Indicator Value:
5 minutes
Relevance: N/A
Importance Weight: 4
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 4
Your Score: N/A
Greater than 12 minutes
1 (lowest resilience)
1 (lowest
resilience)
10 to 12 minutes
2
2
8 to 10 minutes
3
3
Less than 8 minutes
4 (highest resilience)
4 (highest
resilience)
633
-------
#725: Number of physicians per capita
Definition: This indicator reflects the total number of M.D. and D.O. physicians per capita.
Grouped with Indicators: #717
Data Set(s):
(1) SNR Denton and Lewin Group, prepared for the American Medical Association (using
AMA masterfile)—The Economic Impact of Office-Based Physicians in Massachusetts
(https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=l&ved=0CC4QFjA
A&url=http%3A%2F%2Fwww.massmed.org%2FAdvocacy%2FState-Advocacy%2FThe-
Economic-Impact-of-Office-Based-Physicians-in-Massachusetts-
(pdf)%2F&ei=fmU4UvDbIsj 64APb0oDYCw&usg=AFQj CNGESacsZacFuaV3DKgM-
LdUl_oAsg&sig2=YKlwol lXNGxseR-dcOVrRw&bvm=bv. 52164340,d.dmg&cad=rja)
(2) U.S. Census Bureau—Worcester—Census 2010
Notes on Data Set(s):
(1) Number of physicians in Worcester metropolitan statistical area (see pg. 6, Table 2):
1,966 physicians (2009).
(2) Total population = 181,045
Indicator Value:
0.0109 physicians per capita
Relevance: N/A Importance Weight: 2 Proposed
Resilience Score:
N/A
Thresholds: Threshold-Based Score: 1 Your Score: N/A
Less than 0.02 physicians per
1 (lowest resilience)
1 (lowest
capita
resilience)
0.02 to 0.03 physicians per
2
2
capita
0.03 to 0.04 physicians per
3
3
capita
Greater than 0.04 physicians per
4 (highest resilience)
4 (highest
capita
resilience)
634
-------
#1376: Percentage of population that is disabled
Definition: This indicator reflects the percentage of the noninstitutionalized 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
Data Set(s):
U.S. Census Bureau—Worcester American Community Survey 2009-2011 3 Year Estimates
DP02: Selected Social Characteristics in the United States
(http://factfinder2.census.gOv/faces/tableservices/j sf/pages/productview.xhtml?pid=ACS_ll_
3YR_DP02&prodType=table)
Notes on Data Set(s):
Percentage of total noninstitutionalized population with a disability = 12.5% (± 0.8%)
Indicator Value:
12.5%
Relevance: N/A
Importance Weight: 3
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 3
Your Score: N/A
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
15 to 20%
2
2
10 to 15%
3
3
Less than 10%
4 (highest resilience)
4 (highest
resilience)
635
-------
#1387: Percentage of population vulnerable due to age
Definition: This indicator reflects percentage of population above 65 or under 5 years old.
Grouped with Indicators: #393, #728, #1157, #1170
Data Set(s):
U.S. Census Bureau—State and County Quick Facts: Worcester (city), Massachusetts
Notes on Data Set(s):
Percentage of people under 5 years and over 65 years from 2010 census
Indicator Value:
18.3%
Relevance: N/A
Importance Weight: 3
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 2
Your Score: N/A
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
15 to 20%
2
2
10 to 15%
3
3
Less than 10%
4 (highest resilience)
4 (highest
resilience)
636
-------
#1390: Percentage of population that is living alone
Definition: This indicator reflects the percentage of population that is 65 years or older and
living alone.
Grouped with Indicators: N/A
Data Set(s):
(1) U.S. Census Bureau—Worcester—American Community Survey 2009-2011 5 Year
DP02: Selected Social Characteristics in the United States
(http://factfinder2.census.gOv/faces/tableservices/j sf/pages/productview.xhtml?pid=ACS_ll
5YR_DP02&prodType=table)
(2) U.S. Census Bureau—Worcester Census 2010
Notes on Data Set(s):
(1) ACS 2011 5-year data set of households with people 65 years or older living
alone = 8,114 (±457)
(2) 2010 census total population of Worcester = 181,045
Therefore, the population that is 65 years and older living alone = 8,114 ^ 181,045 = 4.48%
Indicator Value:
4.48%
Relevance: N/A Importance Weight: 4 Proposed
Resilience Score:
N/A
Thresholds:
Greater than 30%
Threshold-Based Score: 4
1 (lowest resilience)
Your Score: N/A
1 (lowest
20 to 30%
10 to 20%
Less than 10%
2
3
4 (highest resilience)
resilience)
2
3
4 (highest
resilience)
637
-------
#1439: Percentage of city population in 500-year floodplain
Definition: This indicator reflects the percentage of the city population living within the 500-
year floodplain.
Grouped with Indicators: #1436, #1437, #1438
Data Set(s):
(1) MassGIS data layers: FEMA National Flood Hazard layer (FEMANFHLPOLY)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/nfhl.html)
(2) MassGIS data layers: U.S. Census 2010 Blocks (CENSUS201OBLOCKS POLY)
http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/census2010.html
(3) MassGIS data layers: Community Boundaries (TOWNS POLYM)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/towns.html)
(4) MassGIS data layers: Land Use (2005) (LANDUSE2005_POLY_WORN and
LANDUSE2005_POLY_WORS) (http://www.mass.gov/anf/research-and-tech/it-serv-and-
support/application-serv/office-of-geographic-information-massgis/datalayers/lus2005.html)
Notes on Data Set(s):
Clip all three layers by Worcester town boundary. Select Flood Codes "0.2 PCT Annual
Chance Flood Hazard (X), A, AE, AH, AO, and VE" polygons from FEMA layer. These
represent the 500- and 100-year floodplains, respectively. Select the "water" category in the
2005 Land Use layer, and erase these water polygons from the U.S. census block groups
layer. Select from these U.S. census blocks that intersect with the 100- OR 500-year
floodplains. Determine the area of overlap between the flood zones and census blocks.
Divide this area by the total area of the census block to develop a percentage overlap value.
Multiply this percentage overlap value by the total population of the census block. This
assumes that population within each census block is equally spatially distributed. Sum this
value.
Total Worcester population: 181,045
Area-weighted population: 4,653
Percentage population living in 500-year floodplain = 2.6%
Indicator Value:
2.6%
Relevance: N/A
Importance Weight: 1
Proposed Resilience Score: N/A
Thresholds:
Threshold-Based Score: 3
Your Score: N/A
Greater than 30%
1 (lowest resilience)
1 (lowest resilience)
10 to 30%
2
2
2 to 10%
3
3
Less than 2%
4 (highest resilience)
4 (highest resilience)
638
-------
SECONDARY INDICATORS
#728: Adult care (homes per capita)
Definition: The number of adult day care homes and assisted living homes per capita of
population over 65 years.
Grouped with Indicators: #393. #1157, #1170,
#1387
Data Set(s):
(1) Senior Connection—Guide to Elderly Services
(http: //www. seni orconnecti on. org/ search. htp)
(2) U.S. Census Bureau—Worcester—Census 2010
Notes on Data Set(s):
(1) Database is searchable by city and type of adult care facilities. Thirteen adult day cares
and five assisted living facilities are found in Worcester.
(2) 2010 census: population 65 years and over = 21,158
Indicator Value:
0.000851 adult homes per capita of elderly population
Relevance: N/A Importance Weight: 3 Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 4
1 (lowest resilience)
Your Score: N/A
Less than 0.00010 adult homes
per capita of elderly population
0.00010 to 0.00020 adult homes
per capita of elderly population
2
2
1 (lowest
resilience)
0.00020 to 0.00040 adult
3
3
homes per capita of elderly
population
Greater than 0.00040 adult
homes per capita of elderly
population
4 (highest resilience)
4 (highest
resilience)
639
-------
#784: Number of sworn police officers per capita
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
Data Set(s):
(1) Worcester Regional Research Bureau—Benchmarking Public Safety in Worcester: 2012
(http://www.wrrb.org/files/downloads/ongoing/benchmarking/pub_safety/2012/benchmarkin
g-public-safety-in-worcester-2012.pdf)
(2) Worcester—Census 2010
Notes on Data Set(s):
(1) Worcester Regional Research, Table 5, 2010: Uniformed Positions (pg. 3) = 440
(2) 2010 census: total population in Worcester = 181,045
Indicator Value:
0.0024 police officers per capita
Relevance: N/A Importance Weight: 4 Proposed
Resilience Score:
N/A
Thresholds:
Less than 0.10 police officers
per capita
0.10 to 0.20 police officers per
capita
0.20 to 0.50 police officers per
capita
Greater than 0.50 police officers
per capita
Threshold-Based Score: 1
1 (lowest resilience)
4 (highest resilience)
2
3
Your Score: N/A
4 (highest
resilience)
2
3
1 (lowest
resilience)
640
-------
N.6. Telecommunications
The indicators below have been developed for the Telecommunication 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 nongrouped 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
Worcester, MA, data sets 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 telecommunication 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 Worcester, MA or
based on any other criteria. Secondary Indicators may be considered if the primary indicator
is not adequately defined or does not have available data set(s).
2. When possible, data sets for Worcester, MA are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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.
Note: No data sets for the Telecommunications sector were identified during the Worcester
case study. For information on the quantitative indicators for this section. please see
Appendix H.
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
641
-------
N.7. 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 nongrouped 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
Worcester, MA, data sets 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 Worcester, MA or based
on any other criteria. Secondary Indicators may be considered if the primary indicator is not
adequately defined or does not have available data set(s).
2. When possible, data sets for Worcester, MA are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
642
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#988: Walkability score
Definition: This indicator reflects the walkability score of the community (points out of 100).
Grouped with Indicators: #987, #1396, #1417
Data Set(s):
WalkScore—Worcester, MA (http://www.walkscore.com/MAAVorcester)
Notes on Data Set(s):
Website on walkability. Worcester scored a 60, which means that it is "somewhat walkable."
Indicator Value:
60 score "somewhat walkable"
Relevance: N/A
Importance Weight: 1
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 2
Your Score: N/A
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)
643
-------
#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 dollars). 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
Data Set(s):
(1) Texas A&M—Urban Mobility report
(2) Worcester—Census 2010
Notes on Data Set(s):
(1) Texas A&M Urban Mobility report. Table 8 page 53: $6.7 million in 2011 operational
treatment savings
(2) 2010 census: total population in Worcester = 181,045
Indicator Value:
$37/person
Relevance: N/A
Importance Weight: N/A
Proposed Resilience
Score: N/A
Thresholds:
Threshold-Based Score: 4
Your Score: N/A
$2 to less than $10 per person
1 (lowest resilience)
1 (lowest
resilience)
$10 to less than $18 per person
2
2
$18 to less than $32 per person
3
3
Greater than or equal to $32 per
4 (highest resilience)
4 (highest
person
resilience)
644
-------
#1399: Percentage of roads and railroads within the city that are located within 10 feet of
water.
Definition: Miles of unarm ored 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
Data Setfs):
(1) MassGIS data layers: 2010 U.S. Census Tiger Roads
(CENSUS2010TIGERROADS_ARC) (http://www.mass.gov/anf/research-and-tech/it-serv-
and-support/application-serv/office-of-geographic-information-
massgi s/datalayers/census2010. html)
(2) MassGIS data layers: Trains (TRAINS_ARC) (http://www.mass.gov/anf/research-and-
tech/it-serv-and-support/application-serv/office-of-geographic-information-
massgis/datalayers/trains.html)
(3) MassGIS data layers: DEP Wetlands (1:12,000) (WETLAND SDEPPOLY)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/depwetl andsll2000.html)
(4) MassGIS data layers: Community Boundaries (TOWNS POLYM)
(http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-
geographic-information-massgis/datalayers/towns.html)
Notes on Data Setfs):
Buffer from the town boundary out to 11 feet and use this buffer to clip the wetlands layer
(WETLANDSDEP POLY). Select from the clipped wetlands layer areas with wetland codes
9 and 22 (open water and barrier beach-open water), then buffer the selected water areas by
10 feet. Clip the roads layer (CENSUS2010TIGERROADS ARC) and trains layer
(TRAINSARC) to the town boundary and sum the Shape Length to get toal length of road
and rail miles in Worcester. Intersect these clipped layers with the buffered water layer from
above and sum the Shape Lengths of the resulting intersections to get the length of rails and
roads within 10 feet of open water.
Total rail miles in Worcester = 74.64 miles
Total road miles in Worcester = 671.61 miles
Total road and rail miles = 74.64 + 671.61 = 746.25
Rail miles intersecting open water buffer = 0.474 miles
Road miles intersecting open water buffer = 0.520 miles
Total road and rail miles intersecting open water buffer = 0.994 miles
0.994-746.25 = 0.13%
Relevance: N/A
Yes (relevant)
No (not relevant)
Not sure—remind me later
Importance Weights:
1 (not very important)
2
3
4 (very important)
645
-------
Indicator Value:
0.13%
Proposed Resilience Score: Your Score: N/A
N/A 1 (lowest resilience)
2
3
4 (highest resilience)
#1400: Percentage of roads and railroads within the city in the 500-year floodplain
Definition: This indicator measures the percentage of roadway miles and rail line miles that
are within the 500-year floodplain.
Grouped with Indicators: N/A
Data Set(s):
(1) MassGIS—FEMA National Flood Hazard Layer (http://www.mass.gov/anf/research-and-
tech/it-serv-and-support/application-serv/office-of-geographic-information-
mas sgi s/datal ay ers/nfhl. html)
(2) Census Bureau: TIGER 2013 Roads, Worcester County
(http://www2.census.gOv/geo/tiger/TIGER2013/ROADS/tl_2013_25027_roads.zip)
(3) Census Bureau: TIGER 2013 Railroads
(http://www2.census.gov/geo/tiger/TIGER2013/RAILS/tl_2013_us_rails.zip)
Notes on Data Set(s):
(1) FEMA NFHL data for Massachusetts. All polygons indicate 100-year floodplain, except
for those tagged as 0.2% flood chance which represent the 500-year floodplain.
(2) Road polylines
(3) Railroad polylines
45.6 km of 1,177.8 total km of roads in Worcester are in the 500-year floodplain; 3.9 km of
37.6 km of railroads in Worcester are in the 500-year floodplain.
Therefore, 49.5 km of 1,215.4 total km of roads and railroads in Worcester are in the 500-
year floodplain. 49.5 ^ 1,215.4 = 4.1%).
Indicator Value:
4.1%) of roads and railroads
Relevance: N/A
Importance Weight: 4
Proposed Resilience Score: N/A
Thresholds:
Threshold-Based Score: 2
Your Score: N/A
Greater than 5%>
1 (lowest resilience)
1 (lowest resilience)
2 to 5%
2
2
1 to 2%
3
3
Less than 1%>
4 (highest resilience)
4 (highest resilience)
646
-------
#1401: Percentage of roads and railroads within the city in the 100-year floodplain.
Definition: This indicator measures the percentage of roadway miles and rail line miles that are
within the 100-year floodplain.
Grouped with Indicators: N/A
Data Set(s):
(1) MassGIS—FEMA National Flood Hazard Layer (http://www.mass.gov/anf/research-and-
tech/it-serv-and-support/application-serv/office-of-geographic-information-
mas sgi s/datal ay ers/nfhl. html)
(2) Census Bureau: TIGER 2013 Roads, Worcester County
(http://www2.census.gOv/geo/tiger/TIGER2013/ROADS/tl_2013_25027_roads.zip)
(3) Census Bureau: TIGER 2013 Railroads
(http://www2.census.gov/geo/tiger/TIGER2013/RAILS/tl_2013_us_rails.zip)
Notes on Data Set(s):
(1) FEMA NFHL data for Massachusetts. All polygons indicate the 100-year floodplain,
except for those tagged as 0.2% flood chance which represent the 500-year floodplain.
(2) Road polylines
(3) Railroad polylines
32.0 km of 1,177.8 total km of roads in Worcester are in the 100-year floodplain; 1.3 km of
37.6 km of railroads in Worcester are in the 100-year floodplain.
Therefore, 33.3 km of 1,215.4 total km of roads and railroads in Worcester are in the 100-
year floodplain. 33.3 ^ 1,215.4 = 2.7%
Indicator Value:
2.7% of roads and railroads
Relevance: N/A
Importance Weight: 4
Proposed Resilience
Score: N/A
Thresholds:
Threshold-Based Score: 4
Your Score: N/A
Greater than 20%
1 (lowest resilience)
1 (lowest
resilience)
10 to 20%
2
2
5 to 10%
3
3
Less than 5%
4 (highest resilience)
4 (highest
resilience)
647
-------
#1408: Percentage of bridges that are structurally deficient (source: National Bridge
Inventory)
Definition: This indicator measures the percentage of bridges that are structurally deficient.
Bridges are considered structurally deficient if significant load-carrying elements are found to
be in poor or worse condition due to deterioration 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.
Grouped with Indicators: N/A
Data Set(s):
U.S. Department of Transportation—National Bridge Inventory ASCII data file for
Massachusetts (http://www.fhwa.dot.gov/bridge/nbi/2012/MA12.txt)
Notes on Data Set(s):
Extracted latitude/longitude in DMS format and status code for structural deficiency.
Converted to DD, clipped by Worcester boundary.
Indicator Value:
3.88%
Relevance: N/A Importance Weight: 1 Proposed Resilience
Score: N/A
Thresholds:
Greater than 10%
Threshold-Based Score: 3
1 (lowest resilience)
Your Score: N/A
1 (lowest
5 to 10%
2 to 5%
Less than 2%
2
3
4 (highest resilience)
resilience)
2
3
4 (highest
resilience)
648
-------
#1411: Roadway connectivity (number of intersections per square mile)
Definition: This indicator measures the number of intersections per square mile.
Grouped with Indicators: N/A
Data Set(s):
Census Bureau: TIGER 2013 Roads, Worcester County
(http://www2.census.gov/geo/tiger/TIGER2013/ROADS/tl_2013_25027_roads.zip)
Notes on Data Set(s):
Clipped roads to Worcester boundary. Intersected with self to find all node intersections.
Used summary stats to combine coincident points.
Number of intersections in Worcester = 5,413
Area of Worcester = 38.441 square miles
Indicator Value:
140.81 intersections per square mile
Relevance: N/A
Importance Weight: 1
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 2
Your Score: N/A
Less than 80 intersections per
1 (lowest resilience)
1 (lowest
square mile
resilience)
80 to 250 intersections per
2
2
square mile
250 to 290 intersections per
3
3
square mile
Greater than 290 intersections
4 (highest resilience)
4 (highest
per square mile
resilience)
649
-------
#1420: Intermodal passenger connectivity (percentage of terminals with at least one
intermodal connection for the most common mode)
Definition: This indicator measures the percentage of active passenger terminals for the most
common mode (e.g., rail, air) with at least one intermodal passenger connection. Intermodal
connections allow passengers to use a combination of modes and give travelers additional
transportation alternatives that unconnected, parallel systems do not offer.
Grouped with Indicators: #1419
Data Set(s):
Research and Innovative Technology Administration Bureau of Transportation Statistics,
Passenger Connectivity
(http://www.transtats.bts.gov/DL_SelectFields.asp7Table_IENl 180&DB_Short_Name=Transn
et)
Notes on Data Set(s):
Downloaded the data set with the State, City, and ModesServing (i.e., Count of the Number of
Transportation Modes Serving the Facility) fields. Divided the number of transit facilities in
Worcester with ModesServing > 1 by the total number of facilities in Worcester.
Facilities with ModesServing >1=4
Total facilities = 4
4-4= 100%
Indicator Value:
100%
Relevance: N/A
Importance Weight: 2
Proposed Resilience
Score: N/A
Thresholds:
Threshold-Based Score: 4
Your Score: N/A
Less than 55%
1 (lowest resilience)
1 (lowest
resilience)
55 to 70%
2
2
70 to 85%
3
3
Greater than 85%
4 (highest resilience)
4 (highest
resilience)
650
-------
#1426: City congestion rank
Definition: This indicator measures the congestion rank of the metropolitan area relative to all
U.S. metropolitan areas.
Grouped with Indicators: N/A
Data Set(s):
(1) INRIX Score Card—website (http://scorecard.inrix.com/scorecard/default.asp)
(2) INRIX Score Card—Word file—ID1426_INRIXscorecard_worcester.docx
Notes on Data Set(s):
(1) and (2) INRIX scorecard ranks Worcester the 46th most congested.
Indicator Value:
46th most congested metro in the U.S.
Relevance: N/A
Importance Weight: 1
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 2
Your Score: N/A
1 to 25 (unitless rank)
1 (lowest resilience)
1 (lowest
resilience)
26 to 50 (unitless rank)
2
2
51 to 75 (unitless rank)
3
3
76 to 100 (unitless rank)
4 (highest resilience)
4 (highest
resilience)
651
-------
SECONDARY INDICATORS
#987: Employment accessibility (mean travel time to work relative to national average)
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
Data Set(s):
(1) Worcester—Census—
Worcester_ACS-201 l-5yr_EconomicCharacteristics.xls (2) U.S. Census Bureau
—People QuickFacts 2011
Notes on Data Set(s):
(1) Worcester, MA mean travel time to work (minutes), workers age 16 +, 2007-2011: 22.7
minutes
(2) USA mean travel time to work (minutes), workers age 16 +, 2007-2011: 25.4 minutes
Therefore, mean travel time to work relative to national average = 22.7 ^ 25.4 = 0.89
Indicator Value:
0.89
Relevance: N/A Importance Weight: 2 Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 3
Your Score: N/A
Greater than 1.18 (unitless ratio) 1 (lowest resilience)
1 (lowest
0.98 to 1.18 (unitless ratio) 2
0.79 to less than 0.98 (unitless 3
ratio)
Less thank 0.79 (unitless ratio) 4 (highest resilience)
resilience)
2
3
4 (highest
resilience)
652
-------
#1396: Percent access to transportation stops
Definition: This indicator reflects the percentage of the population that is near a transit stop.
Grouped with Indicators: #987, #978, #1417
Data Set(s):
Brookings Institute—Worcester, MA Metro Area, Missed Opportunity: Transit and Jobs in
the Metropolitan Area
(http://www.brookings.edU/~/media/Series/jobs%20and%20transitAVorcesterMA.PDF)
Notes on Data Set(s):
46%) coverage (i.e., share of working age adults near a transit stop)
Indicator Value:
46%o near transit stop
Relevance: N/A
Importance Weight: 2
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 1
Your Score: N/A
23 to 47%
1 (lowest resilience)
1 (lowest
resilience)
48 to 63%
2
2
64 to 75%
3
3
76 to 100%
4 (highest resilience)
4 (highest
resilience)
653
-------
N.8. 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
nongrouped 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
Worcester, MA, data sets 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 Worcester, MA or based on any
other criteria. Secondary Indicators may be considered if the primary indicator is not
adequately defined or does not have available data set(s).
2. When possible, data sets for Worcester, MA are provided where data were available. In
some cases, no data sets were identified. Please suggest data sets that may be better than the
data sets 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
654
-------
PRIMARY INDICATORS AND NONGROUPED INDICATORS
#437: Percentage change in streamflow divided by percentage change in precipitation
Definition: This indicator reflects percentage change in streamflow (Q) divided by percentage
change in precipitation (P) for 1,291 gauged watersheds across the continental U.S. from
1931 to 1988.
Grouped with Indicators: #1369
Data Set(s):
(1) USGS—Annual Statistics for the Nation USGS 01111212 Blackstone River discharge
data
(http://nwis.waterdata.usgs.gov/nwis/annual?site_no=01111212&por_01111212_2=126
8096,00060,2,2006,2013&start_dt=2006&end_dt=2013&year_type=W&
format=html_table& date_format=YYYY -MM-
DD&rdb_compression=file&submitted_form=parameter_selection_list)
(2) Weather Underground—Worcester, MA
(http://www.wunderground.eom/history/airport/KORH/2012/l/l/CustomHistory.html7dayen
d=31 &monthend= 12&yearend=2012&req_city=NA&req_state=NA&req_statename=NA)
Notes on Data Set(s):
(1) Streamflow—USGS Blackstone River—annual percentage change CFS between 2007
and 2012 = -7%
(2) Precipitation—Weather Underground—annual percentage change in total precipitation
from 2007 to 2012 = -5%
Percentage change streamflow ^ percentage change precipitation =1.4
Indicator Value:
1.4
Relevance: N/A
Importance Weight: N/A
Proposed Resilience
Score: N/A
Thresholds:
Threshold-Based Score: 3
Your Score: N/A
Greater than 3.0 (unitless ratio)
1 (lowest resilience)
1 (lowest
resilience)
2.0 to 3.0 (unitless ratio)
2
2
1.0 to 2.0 (unitless ratio)
3
3
Less than 1.0 (unitless ratio)
4 (highest resilience)
4 (highest
resilience)
655
-------
#1428: Total number of Safe Drinking Water Act (SDWA) violations
Definition: This indicator measures the total number of SDWA violations over the last 5
years.
Grouped with Indicators: N/A
Data Set(s):
EPA—Safe Drinking Water Information System (SDWIS) Worcester DPW, Water Supply
Division
Notes on Data Set(s):
The SDWIS query shows no regulatory violations for the past 5 years.
Indicator Value:
0 SDWA violations in last 5 years
Relevance: N/A
Importance Weight: 2
Proposed
Resilience Score:
N/A
Thresholds:
Threshold-Based Score: 4
Your Score: N/A
Greater than 4 violations
1 (lowest resilience)
1 (lowest
resilience)
3 to 4 violations
2
2
1 to 2 violations
3
3
0 violations
4 (highest resilience)
4 (highest
resilience)
656
-------
#1442: Ratio of water consumption to water availability
Definition: This indicator measures the fraction of available water that is currently consumed.
It is calculated by dividing total water consumption by the total available water from surface
water and groundwater sources.
Grouped with Indicators: N/A
Data Set(s):
(1) City of Worcester Water Operations—2012 Water Quality Report
(2) City of Worcester—Water/Sewer Operations (http://www.worcesterma.gov/dpw/
water-sewer-operations)
Notes on Data Set(s):
(1) Total raw water storage is 7,379.9 MG. This does not include inactive emergency
sources.
(2) According to the city website, average water produced is 24 MGD including sales to other
towns.
7,379.9 MG available in nonemergency supply ^ (24 x 365) MG used per year = 0.842
Indicator Value:
0.842
Relevance: N/A Importance Weight: 4 Proposed
Resilience Score:
N/A
Thresholds: N/A
Threshold-Based Score: 1
Your Score: N/A
Greater than 0.20 (unitless ratio) 1 (lowest resilience)
1 (lowest
0.13 to 0.20 (unitless ratio)
0.06 to 0.13 (unitless ratio)
Less than 0.06 (unitless ratio)
2
3
4 (highest resilience)
resilience)
2
3
4 (highest
resilience)
657
-------
N.9. Thresholds
Thresholds
Indicator ID#
Indicator Name
Score 1
(lowest
resilience)
Score 2
Score 3
Score 4
(highest
resilience)
i. Economy
709
Percentage of owned
housing units that are
affordable
0 to 30%
30 to 45%
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
Percentage access to
health insurance of
noninstitutionalized
population
Less than 85%
85 to 90%
90 to 95%
Greater than
95%
722
Percentage change in
homeless population
Greater than
10%
Oto 10%
Negative 10
to 0%
Less than
negative 10%
1375
Percentage of
population living
below the poverty line
Greater than
20%
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
3.0 to 4.0
tons of oil
equivalent
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
2,000 to
3,000 Btu per
dollar
1,500 to
2,000 Btu per
dollar
Less than
1,500 Btu per
dollar
949
Percentage energy
consumed for
electricity
N/A
N/A
N/A
N/A
950
Percentage of
electricity generation
from noncarbon
sources
Less than 25%
25 to 50%
50 to 75%
Greater than
75%
951
Percentage of total
energy use from
renewable sources
Less than 20%
20 to 40%
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
megawatts
per capita
2.0 to 5.0
megawatts
per capita
Greater than
5.0 megawatts
per capita
658
-------
Thresholds
Score 1 Score 4
(lowest (highest
Indicator ID# Indicator Name resilience) Score 2 Score 3 resilience)
970
Average capacity of a
decentralized energy
source
Less than
5,000
megawatts per
square mile
5,000 to
10,000
megawatts
per square
mile
10,000 to
15,000
megawatts
per square
mile
Greater than
15,000
megawatts per
square mile
971
Energy source
capacity per unit area
Less than 10
megawatts per
square mile
10 to 50
megawatts
per square
mile
50 to 100
megawatts
per square
mile
Greater than
100 megawatts
per square mile
983
Average customer
energy outage (hours)
in recent major storm
Greater than
40 hours
20 to 40
hours
10 to 20 hours
Less than 10
hours
iii.
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
Percentage of natural
area that is in small
natural patches
Greater than
80%
60 to 80%
40 to 60%
Less than 40%
254
Ratio of perimeter to
area of natural patches
Greater than
0.025 (unitless
ratio)
0.015 to
0.025
(unitless
ratio)
0.005 to 0.015
(unitless
ratio)
Less than
0.005 (unitless
ratio)
825
Percentage change in
impervious cover
Greater than
1%
Oto 1%
Negative 1 to
0%
Less than
negative 1%
1436
Percentage of city area
in 100-year floodplain
Greater than
20%
5 to 20%
1 to 5%
Less than 1%
1437
Percentage of city area
in 500-year floodplain
Greater than
30%
10 to 30%
2 to 10%
Less than 2%
1438
Percentage of city
population in 100-year
floodplain
Greater than
20%
5 to 20%
1 to 5%
Less than 1%
1439
Percentage of city
population in 500-year
floodplain
Greater than
30%
10 to 30%
2 to 10%
Less than 2%
1440
Palmer Drought
Severity Index
Less than or
equal to
negative 4.0
(extreme
drought)
Negative 3.99
to negative
3.0 (severe
drought)
Negative 2.99
to negative
2.0 (moderate
drought)
Greater than or
equal to
negative 1.99
(mild or no
drought)
659
-------
iv.
Natural Environment
17
Altered wetlands
Greater than
40 to 60%
20 to 40%
Less than 20%
(percentage of
60%
wetlands lost)
66
Percentage change in
Greater than
50 to 100%
10 to 50%
Less than 10%
disruptive species
100%
273
Percentage of total
Greater than
5 to 20%
1 to 5%
Less than 1%
wildlife species of
20%
greatest conservation
need
284
Physical habitat index
0 to 50
51 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
Less than 0.2
0.2 to 0.4
0.4 to 0.6
Greater than
species diversity from
Shannon
Shannon
Shannon
0.60 Shannon
pre-European
Diversity
Diversity
Diversity
Diversity Index
settlement
Index
Index
Index
680
Ecological
Less than 10%
10 to 25%
25 to 50%
Greater than
connectivity
50%
(percentage of area
classified as hub or
corridor)
681
Relative ecological
Less than 120
120 to 180
180 to 230
Greater than
condition of
White and
White and
White and
230 White and
undeveloped land
Maurice Index
Maurice
Maurice
Maurice Index
score
Index score
Index score
score
682
Percentage change in
Less than
Negative 66
0 to 66%
Greater than
bird population
negative 66%
to 0%
66%
v.
People
322
Percentage of
Greater than
1 to 2%
Oto 1%
0%
population affected by
2%
waterborne diseases
393
Percentage of
Greater than
20 to 30%
10 to 20%
Less than 10%
vulnerable population
30%
that is homeless
675
Asthma prevalence
Greater than
9 to 12%
6 to 9%
Less than 6%
(percentage of
12%
population affected by
asthma)
676
Percentage of
Greater than 3
2 to 3%
1 to 2%
Less than 1%
population affected by
to 4%
notifiable diseases
660
-------
690
Emergency medical
Greater than
10 to 12
8 to 10
Less than 8
service response times
12 minutes
minutes
minutes
minutes
725
Number of physicians
Less than 0.02
0.02 to 0.03
0.03 to 0.04
Greater than
per capita
physicians per
physicians
physicians per
0.04 physicians
capita
per capita
capita
per capita
728
Adult care (homes per
Less than
0.00010 to
0.00020 to
Greater than
capita)
0.00010 adult
0.00020 adult
0.00040 adult
0.00040 adult
homes per
homes per
homes per
homes per
capita of
elderly
population
capita of
elderly
population
capita of
elderly
population
capita of
elderly
population
757
Average police
Greater than
10 to 12
8 to 10
Less than 8
response time
12 minutes
minutes
minutes
minutes
784
Number of sworn
Less than 0.10
0.10 to 0.20
0.20 to 0.50
Greater than
police officers per
police officers
police
police officers
0.50 police
capita
per capita
officers per
capita
per capita
officers per
capita
798
Percentage of fire
response times less
than 6.5 minutes
Less than 85%
85 to 90%
90 to 95%
Greater than
95%
1157
Percentage of housing
units with air
conditioning
Less than 70%
70 to 88%
88 to 94%
Greater than
94%
1170
Percentage of
population
experiencing heat-
related deaths
Greater than
2.0%
1.0 to 2.0%
0.5 to 1.0%
Less than 0.5%
1171
Percentage of
population affected by
food poisoning
Greater than
20%
15 to 20%
10 to 15%
Less than 10%
1376
Percentage of
population that is
disabled
Greater than
20%
15 to 20%
10 to 15%
Less than 10%
1387
Percentage of
population vulnerable
due to age
Greater than
20%
15 to 20%
10 to 15%
Less than 10%
1390
Percentage of
population that is
living alone
Greater than
30%
20 to 30%
10 to 20%
Less than 10%
1439
Percentage of
population living
within the 500-year
floodplain
Greater than
30%
10 to 30%
2 to 10%
Less than 2%
vi.
Telecommunications
1433
Percentage of Greater than 50 to 70%
30 to 50%
Less than 30%
system capacity 70%
needed to carry
baseline level of
traffic
661
-------
1434
Baseline Greater than 20 to 50%
5 to 20%
Less than 5%
percentage of 50%
water supply for
telecommunication
systems that
comes from
outside the
metropolitan area
1435
Baseline Greater than 30 to 60%
10 to 30%
Less than 10%
percentage of 60%
energy supply for
telecommunication
systems that
comes from
outside the
metropolitan area
1441
Percentage of Less than 80% 80 to 88%
88 to 96%
Greater than
community with
96%
access to FEMA
emergency radio
broadcasts
vii.
Transportation
985
Transport system user
0 to 20 (very
21 to 60
61 to 80
81 to 100 (very
satisfaction
or totally
(somewhat
(somewhat
or totally
dissatisfied)
dissatisfied)
satisfied)
satisfied)
987
Employment
Greater than
0.98 to 1.18
0.79 to less
Less than 0.79
accessibility (mean
1.18 (unitless
(unitless
than 0.98
(unitless ratio)
travel time to work
ratio)
ratio)
(unitless
relative to national
ratio)
average)
988
Walkability score
0 to 49 "car
50 to 69
70 to 89 "very
90 to 100
dependent"
"somewhat
walkable"
"walker's
walkablc"
paradise"
991
Percentage transport
N/A
N/A
N/A
N/A
diversity
1003
Mobility management
$2 to less than
$10 to less
$18 to less
Greater than or
(yearly congestion
$10 per person
than $18 per
than $32 per
equal to $32
costs saved by
person
person
per person
operational treatments
per capita)
1010
Community Livability
Less than 60
61 to 70
71 to 80 (day-
81 to 100
Index
(most aspects
(negative
to-day living
(there are few,
of living are
factors have
is fine, in
if any,
substantially
an impact on
general, but
challenges to
constrained or
day-to-day
some aspects
living
severely
living)
of life may
standards)
restricted)
entail
problems)
1396
Percentage access to
23 to 47%
48 to 63%
64 to 75%
76 to 100%
transportation stops
662
-------
1399
Percentage of roads
N/A
N/A
N/A
N/A
and railroads within
the city that are
located within 10 feet
of water
1400
Percentage of roads
Greater than
2 to 5%
1 to 2%
Less than 1%
and railroads within
5%
the city in the 500-
year floodplain
1401
Percentage of roads
Greater than
10 to 20%
5 to 10%
Less than 5%
and railroads within
20%
the city 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
Percentage of city
Less than 75%
75 to 90%
90 to 95%
Greater than
culverts that are sized
95%
to meet current
stormwater capacity
requirements
1404
Percentage of city
Less than 70%
70 to 85%
85 to 95%
Greater than
culverts that are sized
95%
to meet future
stormwater capacity
requirements
1406
Percentage decline in
Less than 10%
10 to 25%
25 to 50%
Greater than
repeat maintenance
50%
events
1408
Percentage of bridges
Greater than
5 to 10%
2 to 5%
Less than 2%
that are structurally
10%
deficient
1411
Roadway connectivity
Less than 80
80 to 250
250 to 290
Greater than
(number of
intersections
intersections
intersections
290
intersections per
per square
per square
per square
intersections
square mile)
mile
mile
mile
per square mile
1412
Miles of pedestrian
Less than 0.5
0.5 to 1.0
1.0 to 2.0
Greater than
facilities per street
miles of
miles of
miles of
2.0 miles of
mile
sidewalk to
sidewalk to
sidewalk to
sidewalk to
street miles
street miles
street miles
street miles
1413
Percentage of short
Less than 60%
60 to 75%
75 to 90%
Greater than
walkable sidewalks in
90%
urban areas
1419
Intermodal freight
Less than 0.5
0.5 to 1.0
1 to 2 ratio of
Greater than 2
connectivity (ratio of
ratio of
ratio of
intermodal
ratio of
intermodal
intermodal
intermodal
containers to
intermodal
connections used per
containers to
containers to
individual
containers to
year to individual
individual
individual
carloads
individual
modes)
carloads
carloads
carloads
663
-------
1420
Intermodal passenger
connectivity
(percentage of
terminals with at least
one intermodal
connection for the
most common mode)
Less than 55%
55 to 70%
70 to 85%
Greater than
85%
1422
Average distance of all
Greater than
10 to 30
5 to 10 miles
Less than 5
nonwork trips
30 miles
miles
miles
1426
City congestion rank
1 to 25
26 to 50
51 to 75
76 to 100
(unitless rank)
(unitless
rank)
(unitless rank)
(unitless rank)
1429
Telework rank
13 to 16
9 to 12
5 to 8
1 to 4 (unitless
(unitless rank)
(unitless
rank)
(unitless rank)
rank)
viii. Water
437
Percentage change in
Greater than
2.0 to 3.0
1.0 to 2.0
Less than 1.0
streamflow divided by
3.0 (unitless
(unitless
(unitless
(unitless ratio)
percentage change in
ratio)
ratio)
ratio)
precipitation
1346
Percentage of
infiltration and inflow
(I/I) in wastewater
Greater than
50%
35 to 50%
20 to 35%
Less than 20%
1347
Wet weather flow
Greater than 2
1 to 2
1 (unitless
Less than 1
bypass volume relative
(unitless ratio)
(unitless
ratio)
(unitless ratio)
to the 5-year average
ratio)
1369
Annual CV of
Greater than
0.40 to 0.60
0.20 to 0.40
Less than 0.20
unregulated
streamflow
0.60 (unitless
ratio)
(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
Greater than
0.13 to 0.20
0.06 to 0.13
Less than 0.06
consumption to water
availability
0.20 (unitless
ratio)
(unitless
ratio)
(unitless
ratio)
(unitless ratio)
664
-------
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