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Clusters of community exposure to coastal flooding hazards based on storm and sea level rise scenarios—implications for adaptation networks in the San Francisco Bay region

DOI:10.1007/s10113-017-1267-5
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Michelle A. Hummel
Michelle A. Hummel
Michelle A. Hummel
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Nathan J. Wood at United States Geological Survey
Nathan J. Wood
Amy Schweikert at Colorado School of Mines
Amy Schweikert
Mark T. Stacey
Mark T. Stacey
Mark T. Stacey
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Abstract and Figures

Sea level is projected to rise over the coming decades, further increasing the extent of flooding hazards in coastal communities. Efforts to address potential impacts from climate-driven coastal hazards have called for collaboration among communities to strengthen the application of best practices. However, communities currently lack practical tools for identifying potential partner communities based on similar hazard exposure characteristics. This study uses statistical cluster analysis to identify similarities in community exposure to flooding hazards for a suite of sea level rise and storm scenarios. We demonstrate this approach using 63 jurisdictions in the San Francisco Bay region of California (USA) and compare 21 distinct exposure variables related to residents, employees, and structures for six hazard scenario combinations of sea level rise and storms. Results indicate that cluster analysis can provide an effective mechanism for identifying community groupings. Cluster compositions changed based on the selected societal variables and sea level rise scenarios, suggesting that a community could participate in multiple networks to target specific issues or policy interventions. The proposed clustering approach can serve as a data-driven foundation to help communities identify other communities with similar adaptation challenges and to enhance regional efforts that aim to facilitate adaptation planning and investment prioritization.
Graphs of variable z-scores by cluster and associated maps of community clusters assuming 50 cm of SLR and a 100-year storm based on a the number and percentage of residents and employees in hazard zones, b the length of critical infrastructure and number of facilities in hazard zones, c demographic attributes of residents in hazard zones, and d the number and percentage of business types in hazard zones. Clusters in a and b represent increasing levels of exposure from left to right; therefore, group colors of yellow to orange to red were chosen to reflect these increases. Variables in c and d do not have internal prioritization; therefore, group colors match the color of the variable that has the highest z-score in that group. Communities with noteworthy exposure are identified on the maps and graphs with numbering based on the study-area map in Online Resource 1
Graphs of variable z-scores by cluster and associated maps of community clusters assuming 50 cm of SLR and a 100-year storm based on a the number and percentage of residents and employees in hazard zones, b the length of critical infrastructure and number of facilities in hazard zones, c demographic attributes of residents in hazard zones, and d the number and percentage of business types in hazard zones. Clusters in a and b represent increasing levels of exposure from left to right; therefore, group colors of yellow to orange to red were chosen to reflect these increases. Variables in c and d do not have internal prioritization; therefore, group colors match the color of the variable that has the highest z-score in that group. Communities with noteworthy exposure are identified on the maps and graphs with numbering based on the study-area map in Online Resource 1
… 
Cluster analysis of population exposure to coastal-flooding hazards assuming no storm and a 50 cm of SLR and b 150 cm of SLR. Variables in each graph of z-scores and clustering group assignments include the number and community percentages of residents and employees in hazard zones. Increasing group numbers denote increasing population exposure (e.g., population exposure is highest for group 4 communities). Communities in groups 2–4 are identified on the maps. All other community names can be found in Online Resource 1
Cluster analysis of population exposure to coastal-flooding hazards assuming no storm and a 50 cm of SLR and b 150 cm of SLR. Variables in each graph of z-scores and clustering group assignments include the number and community percentages of residents and employees in hazard zones. Increasing group numbers denote increasing population exposure (e.g., population exposure is highest for group 4 communities). Communities in groups 2–4 are identified on the maps. All other community names can be found in Online Resource 1
… 
Cluster analysis of built-environment exposure to coastal-flooding hazards assuming no storm and a 50 cm of SLR and b 150 cm of SLR. Variables in each graph of z-scores and clustering group assignments include the length of critical infrastructure and number of critical facilities in hazard zones. Communities in groups 2–4 are identified on the maps. All other community names can be found in Online Resource 1
Cluster analysis of built-environment exposure to coastal-flooding hazards assuming no storm and a 50 cm of SLR and b 150 cm of SLR. Variables in each graph of z-scores and clustering group assignments include the length of critical infrastructure and number of critical facilities in hazard zones. Communities in groups 2–4 are identified on the maps. All other community names can be found in Online Resource 1
… 
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ORIGINAL ARTICLE
Clusters of community exposure to coastal flooding hazards based
on storm and sea level rise scenarios—implications for adaptation
networks in the San Francisco Bay region
Michelle A. Hummel
1
&Nathan J. Wood
2
&Amy Schweikert
2
&Mark T. Stacey
1
&Jeanne Jones
2
&Patrick L. Barnard
3
&
Li Erikson
3
Received: 20 February 2017 /Accepted: 28 November 2017 /Published online: 7 December 2017
#Springer-Verlag GmbH Germany, part of Springer Nature 2017
Abstract
Sea level is projected to rise over the coming decades, further increasing the extent of flooding hazards in coastal communities. Efforts
to address potential impacts from climate-driven coastal hazards have called for collaboration among communities to strengthen the
application of best practices. However, communities currently lack practical tools for identifying potential partner communities based
on similar hazard exposure characteristics. This study uses statistical cluster analysis to identify similarities in community exposure to
flooding hazards for a suite of sea level rise and storm scenarios. We demonstrate this approach using 63 jurisdictions in the San
Francisco Bay region of California (USA) and compare 21 distinct exposure variables related to residents, employees, and structures for
six hazard scenario combinations of sea level rise and storms. Results indicate that cluster analysis can provide an effective mechanism
for identifying community groupings. Cluster compositions changed based on the selected societal variables and sea level rise
scenarios, suggesting that a community could participate in multiple networks to target specific issues or policy interventions. The
proposed clustering approach can serve as a data-driven foundation to help communities identify other communities with similar
adaptation challenges and to enhance regional efforts that aim to facilitate adaptation planning and investment prioritization.
Keywords Climate change .Adaptation .Flooding .Exposure .Cluster analysis
Introduction
Coastal communities in low-lying areas are continually vul-
nerable to flooding, and future threats are likely to increase
due to the influence of projected sea level rise (SLR) (Deconto
and Pollard 2016), potentially displacing millions of people
and causing up to $1 trillion in damages (Nicholls 2004;
Hinkel et al. 2013). Projected increases in sea level can lead
to more frequent and persistent nuisance flooding (Sweet et al.
2014), permanent inundation of low-lying areas, and increases
Editor: Christopher Reyer.
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s10113-017-1267-5) contains supplementary
material, which is available to authorized users.
*Michelle A. Hummel
mhummel@berkeley.edu
Nathan J. Wood
nwood@usgs.gov
Amy Schweikert
aschweikert@usgs.gov
Mark T. Stacey
mstacey@berkeley.edu
Jeanne Jones
jmjones@usgs.gov
Patrick L. Barnard
pbarnard@usgs.gov
Li Erikson
LErikson@usgs.gov
1
Civil and Environmental Engineering, University of California,
Berkeley, 205 O’Brien Hall, Berkeley, CA 94720, USA
2
Western Geographic Science Center, U.S. Geological Survey, Menlo
Park, CA, USA
3
Pacific Coastal and Marine Science Center, U.S. Geological Survey,
Santa Cruz, CA, USA
Regional Environmental Change (2018) 18:1343–1355
https://doi.org/10.1007/s10113-017-1267-5
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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Article
  • Sep 2015
Quantitative assessment of the vulnerability and adaptation options of road infrastructure and economic impacts of climate change is essential to building a more robust and resilient transportation network. To date, most research has focused on qualitative statements and broad findings or on location-specific case studies. This study details a quantitative, engineering-based analysis of the impacts of specific climate stressors on types of road infrastructure. The results are designed to be utilized by transportation planners to understand the vulnerability, risk, and adaptation options for creating a climate-resilient road network by providing specific design changes and fiscal cost analysis. The current study aims to build on previous work and addresses several gaps: use of all climate models approved by the Intergovernmental Panel on Climate Change to provide guidance despite uncertainty, provision of results similar to existing risk and vulnerability analyses to allow for implementation in existing planning processes, and introduction of a methodology requiring only routinely available road network information to allow for replicability across the United States. California is used as an illustrative case study that helps identify the existing vulnerabilities of the road network to climate change and the fiscal savings possible through proactive adaptation strategies. Findings show that for the higher-impact model (95th percentile), California could save $1.9 billion between 2015 and 2050 by proactive adaptation. The contribution of this research is to move beyond the identification of vulnerabilities to a quantitative assessment of specific adaptation options that reduce a community's or region's vulnerability to climate change.
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Taxonomy of USA east coast fishing communities in terms of social vulnerability and resilience
Article
Increased concern with the impacts that changing coastal environments can have on coastal fishing communities led to a recent effort by NOAA Fisheries social scientists to develop a set of indicators of social vulnerability and resilience for the U.S. Southeast and Northeast coastal communities. A goal of the NOAA Fisheries social vulnerability and resilience indicator program is to support time and cost effective use of readily available data in furtherance of both social impact assessments of proposed changes to fishery management regulations and climate change adaptation planning. The use of the indicators to predict the response to change in coastal communities would be enhanced if community level analyses could be grouped effectively. This study examines the usefulness of combining 1130 communities into 35 relevant subgroups by comparing results of a numerical taxonomy with data collected by interview methods, a process herein referred to as "ground-truthing." The validation of the taxonomic method by the method of ground-truthing indicates that the clusters are adequate to be used to select communities for in-depth research.
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