Flood exposure is increasing in coastal communities due to rising sea levels. Understanding the effects of sea level rise (SLR) on frequency and consequences of coastal flooding and subsequent social and economic impacts is of utmost importance for policymakers to implement effective adaptation strategies. Effective strategies may consider impacts from cumulative losses from minor flooding as well as acute losses from major events. In the present study, a statistically coherent Mixture Normal‐Generalized Pareto Distribution model was developed, which reconciles the probabilistic characteristics of the upper tail as well as the bulk of the sea level data. The nonstationary sea level condition was incorporated in the mixture model using Quantile Regression method to characterize variable Generalized Pareto Distribution thresholds as a function of SLR. The performance validity of the mixture model was corroborated for 68 tidal stations along the Contiguous United States (CONUS) coast with long‐term observed data. The method was subsequently employed to assess existing and future coastal minor and major flood frequencies. The results indicate that the frequency of minor and major flooding will increase along all CONUS coastal regions in response to SLR. By the end of the century, under the “Intermediate” SLR scenario, major flooding is anticipated to occur with return period less than a year throughout the coastal CONUS. However, these changes vary geographically and temporally. The mixture model was reconciled with the property exposure curve to characterize how SLR might influence Average Annual Exposure to coastal flooding in 20 major CONUS coastal cities.

Changes in climate, land use, and population can increase annual and interannual variability of socioeconomic droughts in water-scarce regions. This study develops a probabilistic approach to improve characterization of sub-annual socioeconomic drought intensity-duration-frequency (IDF) relationships under shifts in water supply and demand conditions. A mixture Gamma-Generalized Pareto (Gamma-GPD) model is proposed to enhance characterization of both the non-extreme and extreme socioeconomic droughts. Subsequently, the mixture model is used to determine sub-annual socioeconomic drought intensity-duration-frequency (IDF) relationships, return period, amplification factor, and drought risk. The application of the framework is demonstrated for the City of Fort Collins (Colorado, USA) water supply system. The water demand and supply time series for the 1985–2065 are estimated using the Integrated Urban water Model (IUWM) and the Soil and Water Assessment Tool (SWAT), respectively, with climate forcing from statistically downscaled CMIP5 projections. The results from the case study indicate that the mixture model leads to enhanced estimation of sub-annual socioeconomic drought frequencies, particularly for extreme events. The probabilistic approach presented in this study provides a procedure to update sub-annual socioeconomic drought IDF curves while taking into account changes in water supply and demand conditions.

Coastal cities are exposed to multiple flood drivers such as extreme coastal high tide, storm surge, extreme precipitation, and high river flow. The interaction among these flood drivers may cause a compound flood event (Moftakhari et al., 2017; that could exacerbate flood impacts and cause huge social and economic losses (Hemmati et al., 2020;Zscheischler et al., 2018). In regions where the flood level is influenced by both extreme sea levels (SLs; either from tide or storm surge) and river flows, considering the cooccurrence of these flood drivers is important to predict the potential of high-impact compound flood events (Moftakhari et al., 2019).

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Compound dry-hot extreme (CDHE) events pose greater risks to the environment, society, and human health than their univariate counterparts. Here, we project decadal-length changes in the frequency and duration of CDHE events for major U.S. cities during the 21st century. Using the Weather Research and Forecasting (WRF) model coupled to an urban canopy parameterization, we find a considerable increase in the frequency and duration of future CDHE events across all U.S. major cities under the compound effect of high-intensity GHG- and urban development-induced warming. Our results indicate that while GHG-induced warming is the primary driver of the increased frequency and duration of CDHE events, urban development amplifies this effect and should not be neglected. Furthermore, We show that the highest frequency amplification of major CDHE events is expected for U.S. cities across the Great Plains South, Southwest, and the southern part of the Northwest National Climate Assessment regions.