Increased Flood Exposure Due to Climate Change and Population Growth in the United States

Abstract

Precipitation extremes are increasing globally due to anthropogenic climate change. However, there remains uncertainty regarding impacts upon flood occurrence and subsequent population exposure. Here, we quantify changes in population exposure to flood hazard across the contiguous United States. We combine simulations from a climate model large ensemble and a high-resolution hydrodynamic flood model-allowing us to directly assess changes across a wide range of extreme precipitation magnitudes and accumulation timescales. We report a mean increase in the 100-year precipitation event of~20% (magnitude) and >200% (frequency) in a high warming scenario, yielding ã 30-127% increase in population exposure. We further find a nonlinear increase for the most intense precipitation events-suggesting accelerating societal impacts from historically rare or unprecedented precipitation events in the 21st century. Plain Language Summary Heavy rainfall is increasing globally due to human-caused global warming. However, it is still unclear how these increases in heavy rainfall might affect flood risk. In this paper, we investigate how global warming and population changes together may be affecting the number of people at risk from floods in the United States. We combine simulations from a climate model and flood model-allowing us to consider a wide range of heavy rainfall events. We report a~20% increase in the size and a >200% increase in the frequency of very heavy and rare rainfall events, which leads to a~30-127% increase in the number of people at risk from floods. Finally, we find that the heaviest rainfall events increase by the widest margin-suggesting the possibility of major increases in damage and disruption caused by severe floods in the 21st century.

Detectable Increases in Sequential Flood‐Heatwave Events Across China During 1961–2018

“…An improved identification of floods could be achieved through incorporating observed discharge data (Zhang & Villarini, 2020) or hydrodynamic models' outputs (Swain et al, 2020), and is further cross-validated with local disaster database. Though the assumption-floods are influenced little by mean-warming-for the PDF-shifting method generally holds at the current warming level ( Figures S9c and S9d), it will not necessarily do at higher levels when warming-induced intensification of precipitation extremes emerges from the range of internal variability even at the regional scale (Li et al, 2018;Swain et al, 2020). A generalized statistical framework that accounts for contributions from independent changes in each variable (precipitation & temperature) as well as their changing dependence (Zscheischler & Seneviratne, 2017;Zscheischler et al, 2018) is still warranted.…”

Floodplains in the Anthropocene: A Global Analysis of the Interplay Between Human Population, Built Environment, and Flood Severity

“…Increasing population in floodplains (Di Baldassarre et al., 2013; Hu et al., 2018), along with climatic alteration of the magnitude and timing of floods (Blöschl et al., 2017, 2019; Ji et al., 2015), might increase flood risk in many places around the world (Bouwer et al., 2010; Jongman et al., 2012, 2014; Swain et al., 2020). One strategy to reduce flood risk consists of reducing human presence in flood‐prone areas or relocate further away from the river or to upland areas (Hino et al., 2017; Penning‐Rowsell et al., 2013).…”

Peak Runoff Timing Is Linked to Global Warming Trajectories

“…confidence that the frequency of extreme floods associated with annual streamflow maxima has increased over most regions, and this trend is likely to continue in the future (Arnell & Gosling, 2016;Hirabayashi et al, 2013;Hirsch & Archfield, 2015;Milly et al, 2002;Slater & Villarini, 2016;Swain et al, 2020). A number of studies have also addressed the question of streamflow seasonality shifts due to impact of nonstationary climate on maximum annual streamflow occurrence (Bloschl et al, 2017;Clow, 2010;Cunderlik & Ouarda, 2009;Dudley et al, 2017;Villarini, 2016).…”