This study reports a new and significantly enhanced analysis of US flood hazard at 30 m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel ...depth, more rigorous flood frequency analysis, output downscaling to property tract level, and inclusion of the impact of local interventions in the flooding system. For the first time, we consider pluvial, fluvial, and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centered on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high‐quality local models and the entire catalog of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69–0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high‐frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national‐scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near‐future time horizon, we project clear patterns of changing flood hazard (3σ changes in 100 years inundated area of −3.8 to +16% at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defense protection may become compromised by this time.
Plain Language Summary
We develop a method to estimate past, present, and future flood risk for all properties in the conterminous United States whether affected by river, coastal or rainfall flooding. The analysis accounts for variability within environmental factors including changes in sea level rise, hurricane intensity and landfall locations, precipitation patterns, and river discharge. We show that even for a conservative climate change trajectory we can expect locally significant changes in the land area at risk from floods by 2050, and by this time defenses protecting 2,200 km2 of land may be compromised. The complete dataset has been made available via a website (https://floodfactor.com/) created by the First Street Foundation in order to increase public awareness of the threat posed by flooding to safety and livelihoods.
Key Points
First complete high‐resolution flood hazard analysis of conterminous US flood risk from all major sources (fluvial, pluvial, and coastal)
In validation tests the model achieved Critical Success Index scores of 0.69–0.82, similar to many local custom‐built 2D models
By 2050, flood hazard increases for the Eastern seaboard and Western states, but decreases or changes little for the center and South‐West
Typical flood models do not take into consideration the spatial structure of flood events, which can lead to errors in the estimation of flood risk at regional to continental scales. Large‐scale ...stochastic flood models can simulate synthetic flood events with a realistic spatial structure, although this method is limited by the availability of gauge data. Simulated discharge from global hydrological models has been successfully used to drive stochastic modeling in data‐rich areas. This research evaluates the use of discharge hindcasts from global hydrological models in building stochastic river flood models globally: synthetic flood events in different regions of the world (Australia, South Africa, South America, Malaysia, Thailand, and Europe) are simulated using both gauged and modeled discharge. By comparing them, we analyze how a model‐based approach can simulate spatial dependency in large‐scale flood modeling. The results show a promising performance of the model‐based approach, with errors comparable to those obtained over data‐rich sites: a model‐based approach simulates the joint occurrence of relative flow exceedances at two given locations similarly to when a gauge‐based statistical model is used. This suggests that a network of synthetic gauge data derived from global hydrological models would allow the development of a stochastic flood model with detailed spatial dependency, generating realistic event sets in data‐scarce regions and loss exceedance curves where exposure data are available.
Key Points
Large scale stochastic flood models can simulate synthetic flood events with a realistic spatial structure even in data‐poor regions
Using a model‐based multivariate extreme model provides more robust dependence estimates than empirical distance decay functions
Modeled flow can be used in data poor‐regions to characterize dependence in large‐scale stochastic flood models and estimate flood risk
Flood event set generation, as employed in catastrophe risk models, relies on gauge information that is not available in data‐scarce regions. To overcome this limitation, we develop a stochastic ...fluvial and pluvial flood model of Southeast Asia, using freely and globally available discharge data from the global hydrological model GloFAS and rainfall from the ERA5 reanalysis. We use a conditional multivariate statistical model to produce a synthetic catalog of 10,000 years of flood events. We calculate the flood population exposure associated with each flood event using freely available population data from WorldPop and generate exposure probability exceedance curves. We validate the population exposure curves against observed flood disaster data from EM‐DAT, showing that our methodology provides exposure estimates that are in line with historical observations. We find that there is a 1% probability that more than 30 million people will be exposed to flooding in a given year according to our event set. This number is roughly half the population living in the 100‐year return period flood zone of Fathom's hazard maps, suggesting most studies based on static flood maps overestimate exposure. This analysis provides significant progress over previous non‐stochastic studies which are only able to compute total or average exposure within a given floodplain area and demonstrates that a reanalysis‐based stochastic flood model can be designed to generate reliable estimates of population exposure probability exceedance. This study is a step toward a fully global catastrophe model for floods capable of providing exposure and loss estimates worldwide.
Key Points
Global hydrological models can be used to drive a large‐scale stochastic flood inundation model in Southeast Asia
A reanalysis‐based stochastic flood model generates realistic flood events
The computed flood exposure exceedance curve for Southeast Asia compares well to the EM‐DAT database
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