• Description

Governments at all levels have a responsibility to help communities adapt to increasing climate risks. Local governments are on the front lines, as they regulate and incentivize the location of new housing and commercial development, develop and operate transportation and water infrastructure, and oversee emergency preparedness and response. The rapidly growing field of climate analytics can help local governments adopt a more proactive approach by identifying risks, developing climate action plans, and implementing strategies that limit the harms of both chronic and acute climate stresses, from intense storms to wildfires to extreme heat.

The goal of this project is to illustrate how local governments can use geographically granular climate risk data to map local hazards and plan community-based adaptation strategies, while highlighting some of the challenges in working with this data. We also discuss areas where regional, state, and federal agencies can support their local colleagues in these efforts. This analysis is intended to be useful for local governments—including elected officials and career staff—as well as utilities, regional planning agencies, private sector firms, and civic organizations engaged with built environment planning.

To illustrate the potential uses and challenges of geographically granular climate risk information, we analyze data created by First Street Foundation that measures heat, wildfire, and flood risk. Focusing on the city of San Diego, we create risk maps at several levels of geography—city, neighborhood, and parcel—to illustrate how risk varies across geography, over time, and by climate risk category. These metrics primarily capture physical risk; when possible, we look at overlaps with social and economic characteristics that affect community vulnerability. Case studies of three neighborhoods with particularly high risks show the usefulness—and some cautions—of parcel-level analysis.

How climate risk data can help communities become more resilient: Insights from San Diego