Assessing the Compound Flooding Risk and Impacts across the Coastal Areas of the United States

Compound floods, particularly in estuaries and coastal areas, are gaining increasing attention among the recent extreme climatic events. Understanding which driver dominates inundation depth (ID) is still an open question. In this study, a detailed and extended assessment of flood damages from 2009 to 2022 is conducted across the USA coast, based on the National Flood Insurance Program (NFIP) insurance claims records and historical storm events that occurred during the corresponding period.

To identify the relative importance of the driving mechanisms (inland vs. coastal flows) for a particular location, we propose an index [hereafter named D-Index] that identifies the topology of the local draining potentials to either the closest river, or to the coast. The D-Index captures the topographic control over ID, and it considers the vertical hydrologic distance between a location and its nearest water body, either a river stream, or the coastline.

The D-index was initially developed and validated considering 1051 simulations of historical flood events covering a time span of 40 years in Connecticut (CT), USA, and several simulated storms associated with future climate scenarios. For the analysis, we simulated river discharge time series for each event using a physically based distributed hydrological model and retrieved the storm surge from tidal stations. These time series are used as upstream and downstream boundary conditions for 2D hydrodynamic simulations. We focused the analysis on seven locations along the coast of CT for which we had available LIDAR-derived 1m DEM. To capture the variability of inundation characteristics over the full-scale gradient from river to coast, we highlight the correlation of ID to different drivers in distinct categories of the D-Index. We identified thresholds of standard deviation of the D-Index to identify areas where ID strongly correlates with either of the flood drivers. For validation, we demonstrated that it is possible to use the results obtained from the 1 m analysis to generalize the findings using coarser (still high quality) resolution DEM for the entire CT coast to derive zones dominated by surge, river flow, or the compound effect of both. The areas mapped as surge dominated based on the D-index overlap well with the SLOSH ranking.

We demonstrated the actual impacts of major events, e.g., Irene (2011) and Sandy (2012), to analyze the differences in the corresponding claims data by detecting the underlying flood drivers. To date, the claim records have been investigated based on individual drivers, for example flood caused either by excessive river flow or by coastal flooding. Hence, it is crucial to assess how compound flooding reflect on insurance flood claim records. The results obtained in this study demonstrate the potential of integrating a flood type-specific mapping system into a compound flood impact estimation. The outcome of this study will be helpful for the coastal communities to better understand their risk to the compounding impacts of various environmental forcings (heavy precipitation, surge, and the effect of sea level rise), which is important for increasing their resilience to future compound flooding events.