Coastal flooding from landfalling tropical cyclones (TCs) and extra-tropical cyclones is a major hazard with increasing severity in a warming climate and sea level rise. It is difficult to predict because of highly complex compound effects of storm induced heavy rainfall, storm surge, stream flow and river discharge. Furthermore, built environment including land use and poor drainage system can increase the flood risk. These present a grand challenge for coastal regions. Traditional approach to study coastal flooding has been siloed into separated disciplines. The rainfall is studied by the atmospheric scientists, storm surge by oceanographers, stream flow and river discharge by hydrologists, and built environment by engineers. In addition to physical sciences, societal response and decision making require engaging social behavior sciences. Coastal flooding often affects poor and vulnerable communities disproportionally worldwide. To meet this grand challenge, we must break these disciplinary and socio-economic barriers and take a holistic system-level approach.
In this talk, we will present examples of recent advancement in multi-scale Earth system modeling and observing capabilities over the coastal regions from flooding events of Superstorm Sandy (2012) and Hurricane Ida (2021) in New York, to the record flooding from sequential storms of Hurricane Irene and Tropical Storm Lee (2011) in the Mid-Atlantic region. We focus on better understanding and improving prediction of the compound effects of rain, storm surge, and river discharge using high-resolution, coupled Earth system models and observations from various satellite and radar-gauge rainfall products, streamflow data, NDBC buoys, NOAA tide gauges, and USGS estuary sites. AI/ML methods are used to connect the model predictions and observations to flood risks with coastal natural and built environmental conditions. These results have important implications in decision making relevant to building resiliency to coastal flooding.