Publication
Hummel MA, Wood NJ, Schweikert A, Stacey MT, Jones J, Barnard PL, Erikson L (2017) 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. Regional Environmental Change (online): doi: 10.1007/s10113-017-1267-5
View a read-only version here.
Summary
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 increasingly 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 and compare exposure variables related to residents, employees, and structures for six hazard scenario combinations of sea level rise and storms.
Results
Clustering based on demographic variables for residents in flood-hazard zones suggests six community groups defined by ethnicity, race, age, group quarters, and renter-occupied households. In some cases, collaborations between neighboring communities are useful. For example, Larkspur, Corte Madera, Tiburon, Belvedere, and Sausalito all have relatively high numbers of people who are under 5 or over 65 years in age, suggesting that these communities would benefit from developing evacuation plans that consider these age groups. As neighbors, these communities may have already developed partnerships that would facilitate sharing these strategies. In other cases, cluster analysis identifies communities that are separated geographically but have similar exposure and could thus benefit from collaboration. For example, San Rafael, which has a relatively high number of renter-occupied households and residents that identify as Hispanic in hazards zones, is more similar to communities in the South Bay, including Menlo Park, East Palo Alto, Mountain View, and Sunnyvale, than it is to neighboring communities in the North Bay. These regional-scale similarities, which may typically be overlooked, are easier to identify with cluster analysis.
When considering the type of businesses in hazard zones, four community groups emerge. Again, the potential benefits of cross-regional collaborations are evident, since communities located in Marin County are frequently clustered with communities in the South Bay. However, the cluster membership has changed now that business types are being considered instead of demographics. These shifts in membership based on the variables considered (i.e. business types versus demographics) demonstrates the importance of taking a more nuanced approach to evaluating similarities in exposure between communities based on specific variables of interest, rather than simply clustering or ranking at an aggregate level using all socioeconomic variables.
In addition to shifts in cluster compositions based on variables, results also demonstrate that cluster compositions shift across sea level rise (SLR) scenarios. For example, assuming 50 cm of SLR in flood-hazard zones, San Mateo and unincorporated areas of Sonoma County are in their own groups due to relatively high amounts of critical facilities (San Mateo) or roads and rails (Sonoma County) in hazard zones. If SLR assumptions are increased to 150 cm, Sonoma County is joined by San Mateo and Redwood City in Group 4, with the highest exposure, and 11 other communities join Group 3 due to high amounts of roads in hazard zones. The expansion of group size with increasing SLR scenarios for infrastructure suggests that some collaborations may focus on short-term mitigation efforts to protect existing infrastructure or facilities (e.g., groups assuming 50 cm SLR) whereas others may focus on long-term infrastructure investment planning (e.g., groups assuming 150 cm SLR). At the same time, recognition of the expansion of clusters may provide an incentive for communities that join high-exposure groups under higher SLR scenarios to learn from communities that face similar risks in the short-term and to participate in the development of adaptation or risk-management strategies now, with the knowledge that early investment in these collaborations may enhance future planning and mitigation efforts.
Conclusions
This analysis provides a means for communities to develop informed, knowledge-based networks to address similar challenges related to sea level rise adaptation. Based on our results, we reach several conclusions that may influence future community-exposure studies and outreach related to climate-driven coastal hazards.
- Cluster analysis provides a quantitative method for identifying community groupings with similar hazard exposure, considering multiple hazard scenarios and societal variables of interest.
- Cluster compositions change based on the selected societal variables and SLR scenarios, demonstrating the importance of developing multiple cluster models that allowing managers to seek out adaptation networks that match their risk tolerance or planning horizons.
- Shifts in cluster compositions due to selected societal variables and SLR scenarios suggest that a community could participate in multiple networks to share best practices and leverage resources to target specific issues or policy interventions.
These conclusions demonstrate that cluster analysis provides an effective, data-driven tool for understanding community exposure and developing collaboration networks that facilitate adaptation planning and investment prioritization in the face of uncertain future conditions.