Inland Flooding and Rainfall from Hurricane Irene and Tropical Storm Lee (2011): Coupled Atmosphere–Wave–Ocean Model Simulations and Remote Sensing and In Situ Observations with a Machine Learning Tool

Abstract

Abstract Inland flooding from landfalling tropical cyclones (TCs) is a major cause of death and damage to property and infrastructure worldwide. The mid-Atlantic region of the United States was devastated by Hurricane Irene and Tropical Storm Lee during late August–early September 2011, when the two storms produced sequential heavy rainfall and record flooding. Many rivers and streams reached their all-time record discharge to date. This study aims at 1) better understanding and predicting TC rainfall using various observed rainfall products and a high-resolution coupled atmosphere–wave–ocean model, namely, the Unified Wave Interface-Coupled Model (UWIN-CM), 2) characterizing inland flooding using streamflow data, and 3) improving prediction of TC-induced inland flooding using UWIN-CM and a machine learning K-nearest-neighbor (KNN) model. The results show that there is a wide range of uncertainty in satellite and radar–gauge-observed rainfall products in terms of rain-rate distribution and cumulative rainfall over the mid-Atlantic region. UWIN-CM rainfall is closer to the radar–gauge data than satellite data over land. Streamflow in most large rivers (>500 cfs) peaked after Lee, which reflects the sequential rainfall contributions of the two storms. The rainfall–streamflow–discharge response times were dependent on the size of the stream and the peak rain rates. To better predict rainfall and flooding, UWIN-CM and observed rainfall are used with the machine learning KNN regression model for prediction of severity of TC-induced inland flooding hazard. These results demonstrate the value of a stepped approach for rainfall and flood prediction toward a fully coupled atmosphere–ocean–land/hydrology model in the future.