Disentangling Atmospheric, Hydrological, and Coupling Uncertainties in Compound Flood Modeling within a Coupled Earth System Model

Abstract. Compound riverine and coastal flooding is usually driven by complex interactions among meteorological, hydrological, and ocean extremes. However, existing efforts of modeling this phenomenon often rely on models that do not integrate hydrological processes across atmosphere-land-river-ocean systems, leading to substantial uncertainties that have not been fully examined. To bridge the gap, we leverage the new capabilities of the Energy Exascale Earth System Model (E3SM) that enable a multi-component framework that integrates coastal-refined atmospheric, terrestrial, and oceanic components. We evaluate compound uncertainties arising from two-way land-river-ocean coupling in E3SM, and track the cascading meteorological and hydrological uncertainties through ensemble simulations over the Delaware River basin and estuary during Hurricane Irene (2011). Our findings highlight the importance of two-way river-ocean coupling to compound flood modeling and demonstrate E3SM’s effectiveness in handling multivariate flooding on the coast. Our study shows the growing uncertainties that transition from atmospheric forcings to flood distribution and severity. Furthermore, an Artificial Neural Network based analysis is used to assess the roles of some understudied hydrological drivers, such as infiltration and soil moisture, in the generation of compound flooding. The response of compound floods to tropical cyclones (TCs) is found to be susceptible to these often overlooked drivers. For instance, flood damage could be tripled if Hurricane Irene was preceded by an extreme antecedent soil moisture condition (AMC). The results not only support the use of a multi-component framework for interactive flooding processes, but also underscore the necessity of broader definitions of compound flooding that encompasses the simultaneous occurrence of intense precipitation, storm surge, and high AMC during TCs.