How Probable Is Widespread Flooding in the United States?

Abstract

Widespread flooding can cause major damages and substantial recovery costs. Still, estimates of how susceptible a region is to widespread flooding are largely missing mainly because of the sparseness of widespread flood events in records. The aim of this study is to assess the seasonal susceptibility of regions in the United States to widespread flooding using a stochastic streamflow generator, which enables simulating a large number of spatially consistent flood events. Furthermore, we ask which factors influence the strength of regional flood susceptibilities. We show that susceptibilities to widespread flooding vary regionally and seasonally. They are highest in regions where catchments show regimes with a strong seasonality, that is, the Pacific Northwest, the Rocky Mountains, and the Northeast. In contrast, they are low in regions where catchments are characterized by a weak seasonality and intermittent regimes such as the Great Plains. Furthermore, susceptibility is found to be the highest in winter and spring when spatial flood dependencies are strongest because of snowmelt contributions and high soil moisture availability. We conclude that regional flood susceptibilities emerge in river basins with catchments sharing similar streamflow and climatic regimes.

A multi-source 120-year US flood database with a unified common format and public access

“…Insurance companies collectively offer valuable information on flood damages and people affected from a financial perspective (Swiss Re, 2010). Until recently, the increasing engagement of social scientists has greatly supported near-real-time flood reporting with web or mobile applications (Chen et al, 2016;de Bruijn et al, 2019), although these reports are often confined to populated urban areas. In addition to human-led reporting, stream gage and opportunistic sensors (e.g., surveillance cameras, ground radars, and satellites) can also augment flood monitoring in real time (Hall et al, 2015;Shen et al, 2019).…”

A Vine Copula‐Based Polynomial Chaos Framework for Improving Multi‐Model Hydroclimatic Projections at a Multi‐Decadal Convection‐Permitting Scale

“…Such different changes in the projected annual maximum streamflow lead to the difference in flood risk assessments (see Figures 13e and 13f). The flood magnitudes are estimated by first defining the Peaks over Threshold (POT) samples using the tenth percentile of the annual maximum streamflow as a threshold and then fitting the generalized Pareto distribution (GP) (see details in Brunner et al., 2020). In general, the Longchuan station is projected to experience substantial increases in the flood magnitudes for the future period 2076–2099 compared to the historical period 1982–2005, but such increases are not significant for the Boluo station.…”

Snow-influenced floods are more strongly connected in space than purely rainfall-driven floods

“…For example, in the Columbia River basin, there are first indications that a decrease in snowmelt contributions may lead to a decreasing synchronicity between high flows in snowmeltdominated basins (Rupp et al 2021). Such changes in the spatial dependence have important implications for regional flood hazard and risk, which are modulated by the strength of spatial flood dependencies (Thieken et al 2015, Brunner et al 2020a. The exact direction and magnitude of change in spatial flood connectedness and associated hazard have to be assessed using targeted modeling experiments, e.g.…”

“…Widespread or regional floods affecting more than one catchment are associated with large damages and costs, as illustrated by the 2021 flood events in Germany (Kreienkamp et al 2021) or the lower Mississippi flood in 2019 (Pal et al 2020). Regional as opposed to local floods develop in regions where different catchments are likely to be jointly affected by flooding because of common flood generation mechanisms (Brunner et al 2020a). We need to better understand the mechanisms causing large-scale and spatially connected floods to improve the prediction of the magnitude of regional floods and to assess future changes in their likelihood.…”