NOAA's National Centers for Environmental Information
Disaster and Risk Mapping
Use the interactive mapping tools below to better visualize the frequency and cost of billion-dollar weather and climate events.
State Cost and Frequency
County Risk Assessment
Risk Score disaster types:
Historic and Future Risks
The maps above reflect analysis from the FEMA National Risk Index (NRI), the Social Vulnerability Index (SoVI) and spatial distributions of projected damages for select hazards and socioeconomic metrics. The NRI leverages nationwide datasets on hazard frequency and exposures at risk. The maps also reference the SoVI that is "a location-specific assessment of social vulnerability that utilizes 29 socioeconomic variables deemed to contribute to a community's reduced ability to prepare for, respond to, and recover from hazards." For future perspective, these maps also reference county-level median values for average 2080 to 2099 RCP8.5 high-emission scenarios and impacts. Impacts are changes relative to counterfactual 'no additional climate change' trajectories and include: Percent change in yields, area-weighted average for corn, wheat, soybeans, and cotton; Change in all-cause mortality rates, across all age groups; Change in electricity demand; Change in labor supply of full-time-equivalent workers for high-risk jobs where workers are heavily exposed to outdoor temperatures; Change in damages from coastal storms; and Median total direct economic damage across all sectors.
This mapping tool is intended to provide U.S. communities with information and guidance on their susceptibility to weather and climate hazards, as well as potential future impacts from them. This tool could assist with: Enhancing hazard preparation and mitigation for homeowners, community planners and emergency managers; Encouraging community-level risk communication and planning; Guiding communities' building codes and standards; Informing disaster resilience and recovery plans in the long term. This mapping tool gives broad, nationwide comparisons reflecting a county's combined physical exposure, vulnerability and resilience to hazard risks. These county maps may not be as accurate as locally available data, and users with access to local data for each risk component should consider substituting those data to calculate a more precise annual loss values at the local levels. For example, finer detailed risk analysis would require more spatially and temporally granular data for hazard exposure, annualized frequency, and historic loss measurements. This mapping also does not consider the complex economic and physical interdependencies that exist across geographic regions. For example, a disaster's impact on one location can cause indirect losses in surrounding counties. Given these caveats, the county risk mapping should be considered a baseline measurement and a guideline for determining natural hazard risk but should not be used as an absolute measurement of risk.
Historically, the U.S. South, Central and Southeast regions have experienced a higher frequency and cost from billion-dollar disaster events (see top map metrics). These same U.S. regions are also projected to have the most negative future impacts across several socioeconomic metrics (see county map metrics). Comparing present and future risk maps above with county social vulnerability (SoVI) helps visualize the potential for different present and future hazard risks.
The hazard risk scores should be considered a guideline for determining hazard risk but should not be used as an absolute measurement of risk. All scores are relative, as each county's score is evaluated in comparison with all other counties. The hazard risk score combines a county's risk to natural hazards representing several factors: the annualized hazard frequency; the potential hazard cost related to building value, crop value and population exposure; social vulnerability and resilience to recover from hazard impacts based on dozens of socioeconomic variables.
County-Risk Mapping Source Metadata and References
The county hazard risk maps reflect analysis from the FEMA National Risk Index (NRI), the Social Vulnerability Index (SoVI) and spatial distributions of projected damages for select hazards and socioeconomic metrics. The integration of this new hazard risk data merges different forms of county-level, socio-economic risk noting regions of high vulnerability and lower resilience to disaster impacts, in relation to the frequency and spatial distribution of U.S. billion-dollar weather and climate disaster events.
Below is some additional background:-
According to the National Risk Index, higher expected annual loss, higher social vulnerability, and/or lower community resilience increase your overall risk. The annualized natural hazard frequency is defined as the expected frequency or probability of an event happening per year. Frequency is derived either from the number of recorded events each year over a given period or the modeled probability of an event occurring each year.
The NRI "considers that natural hazards can occur in places where they may have not yet been recorded to-date and that hazards may have occurred in locations without being recorded. Therefore, the NRI has built-in minimum representative frequency values for certain geographical areas and hazards, such as hurricane, ice storm and tornado. The surrounding area's loss ratios have the greatest influence on the historic loss ratio of a county for which the largest weighting factor contributor is the surrounding-level data. Counties that have experienced few loss-causing event-days or have widely varying loss ratios get the most influence from regional or national-level loss data."
"In the National Risk Index, risk is defined as the potential for negative impacts, because of natural hazard. The risk equation behind the NRI includes three components: a natural hazards component, a consequence enhancing component, and a consequence reduction component. Expected annual loss is the natural hazards risk component, measuring the expected loss of building value, population, and/or agricultural value each year due to natural hazards. Social vulnerability is the consequence enhancing component and analyzes demographic characteristics to measure a community's susceptibility of social groups to the adverse impacts of natural hazards. Community resilience is the consequence reduction component and uses demographic characteristics to measure a community's ability to prepare for, adapt to, withstand, and recover from the effects of natural hazards."
"An overall composite Risk Index score and individual hazard Risk Index scores are calculated for each county and Census tract included in the NRI. A composite Risk Index score measures the relative risk of a location considering each natural hazard included in the index. An individual hazard Risk Index score measures the relative natural hazard risk of a location for a single natural hazard. All scores are relative as each county's score is evaluated in comparison with all other counties."
Zuzak, C., E. Goodenough, C. Stanton, M. Mowrer, N. Ranalli, D. Kealey, and J. Rozelle. 2021. National Risk Index Technical Documentation. Federal Emergency Management Agency, Washington, DC.
NCEI has integrated the hazards as follows to better match the hazards analyzed in the U.S. Billion-dollar weather and climate disaster analysis. We also allow dynamic mapping of these seven different primary hazards into 127 unique hazard map combinations:
Drought 2000-2017 = DRGT_RISKS (1/1/2000 to 12/31/2017) + HWAV_RISKS (11/12/2005 to 12/31/2017)
Flooding 1995-2019 = RFLD_RISKS (1/1/1995 to 12/31/2019)
Freeze 1946-2017 = CWAV_RISKS (11/12/2005 to 12/31/2017) + ISTM_RISKS (12/31/1946 to 2/12/2014)
Severe Storm 1986-2019 = HAIL_RISKS (1/1/1986 and 12/31/2017) + SWND_RISKS (1/1/1986 and 12/31/2017) + TRND_RISKS (1/1/1986 and 12/31/2019)
Tropical Cyclone 1851-2017 = HRCN_RISKS (1851 to 2017)
Wildfire = WFIR_RISKS (wildfires modelled via FSim)
Winter Storm 2005-2017 = WNTW_RISKS (11/12/2005 to 12/31/2017)
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The Social Vulnerability Index (SoVI) for the United States is "a location-specific assessment of social vulnerability that utilizes 29 socioeconomic variables deemed to contribute to a community's reduced ability to prepare for, respond to, and recover from hazards." The dataset was acquired from HVRI's SoVI website, and more information is available via the University of South Carolina's Hazards and Vulnerability Research Institute (HVRI).
Source:
Cutter, S., Ash, K., and C. Emrich. 2014. The geographies of community disaster resilience. Global Environmental Change, 29, 65-77. http://dx.doi.org/10.1016/j.gloenvcha.2014.08.005.
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Published scientific paper on "Estimating economic damage from climate change in the United States" (Hsiang et al., 2017)
"A distribution of empirically grounded economic impacts is computed for each joint realization of county-level daily temperature and precipitation: (iv) Econometrically derived dose-response functions estimating the nonlinear effects of temperature, rainfall, and CO2 on agriculture, mortality, labor, and energy demand are constructed via Bayesian meta-analysis. The research only employs studies that are nationally representative, spatially disaggregated, and account for temporal displacement and unobserved heterogeneity across locations, along with the additional criterion that studies statistically identify marginal distortions in the distribution of experienced daily temperatures. Econometric uncertainty is accounted for by resampling from the 26 posterior functions. County-level daily projections are mapped onto the distribution of possible responses to construct 3,143 county-level joint distributions for 15 impacts across 29,000 possible states of the world during 2000 to 2099, although for display purposes we primarily summarize 2080 to 2099 impacts here."
"A parallel approach is necessary to estimate energy demand changes and coastal impacts: Energy demand estimated is used as a partial calibration for the National Energy Modeling System (NEMS). NEMS is then run with different weather realizations to estimate energy supply costs. Cyclone exposure is simulated via analytical wind field models that force a storm surge model, with cyclogenesis and storm tracks generated via either semi-parametrically resampling historical activity or resampling from projected storm tracks and intensities. Inundation from localized probabilistic sea level rise projections interacting with storm surge and wind exposure are mapped onto a database of all coastal properties maintained by Risk Management Solutions, where engineering models predict damage."
"Finally, economic impacts are aggregated and indexed against the global mean surface temperature (GMST) in their corresponding climate realization to construct multidimensional probabilistic damage functions suitable for application in integrated assessment modeling: Direct impacts are aggregated across space or time within each sector. Monetizing the value of nonmarket impacts (deaths) using willingness-to-pay, impacts across all sectors are aggregated to compute total damages."
"Importantly, this research holds the scale and spatial distribution of the U.S. population and economy fixed at values observed in 2012, since current values are well understood and widely agreed on. Various previous analyses note that natural demographic change and economic growth may dominate climate change effects in overall magnitude, although such comparisons are not our focus here. Because we compute impacts using scale-free intensive measures (e.g., percentage changes), future expansion of the economy or population does not affect our county-level estimates, and our aggregate results will be unbiased as long as this expansion is balanced across space. If such expansion is not balanced across space, then our aggregated results will require a second-order adjustment with a sign that depends on the spatial covariance of changes in climate exposure and changes in economic or population structure."
Source:
Hsiang, S., Kopp, R.E., Jina, A., Rising, J., Delgado M., Mohan, S., Rasmussen, D.J., Muir-Wood, R., Wilson, P., Oppenheimer, M., Larsen, K., and T. Houser. 2017. Estimating economic damage from climate change in the United States. Science, 356, 1362-1369. doi:10.1126/science.aal4369.
Citing this information:
NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar Weather and Climate Disasters (2022). https://www.ncdc.noaa.gov/billions/, DOI: 10.25921/stkw-7w73