Sensitivity of compound flooding potential to idealized large-scale tropical cyclone environments
In low-lying estuarine regions worldwide, compound flooding (CF) caused by the co-occurrence of extreme precipitation, river flooding, storm surge, and extreme sea level rise, are expected to increase under future climate change. The flood hazard during a compound event is often much more significant than the processes occurring in isolation and can be even more dramatic for a strongly convergent estuary like Delaware Bay. In convergent systems, the surge amplitude increases along the estuary due to rapid width decrease and then conjugate with high river discharge to significantly elevate the upstream water levels. In recent decades, there has been a rise in the frequency and intensity of CF events in different parts of the U.S. due to sea-level rise (SLR) and an increase in the frequency of intense precipitation and storm surge events. Consequently, understanding how the sensitivity of CF may respond to a changing climate through joint considerations of SLR and tropical cyclone environment (e.g., atmospheric moisture, rainfall, storm intensity, etc.) is essential for flood mitigation and risk reduction, especially in converging estuaries. This study uses the Risk Analysis Framework for Tropical Cyclones (RAFT) to construct a series of synthetic storms with perturbed SLR and environmental conditions of a Hurricane Irene-like event using reanalysis data and climate model output. A Hurricane Irene (2011) like event was selected as it produced most extreme compound flooding over the past two decades in the Delaware River watershed. Then, we used RAFT generated synthetic storms characterized by varying storm conditions to drive process-based terrestrial-coastal models (DHSVM-FVCOM) to assess how the projected storm conditions and SLR interact with the hydrological and hydrodynamic processes in their control of CF characteristics in the Delaware Bay. Ultimately, the study highlights the importance of process-based understanding of CF via sensitivity analysis which can aid the local level planning and adaptation under a changing climate.