A hybrid statistical–dynamical framework for compound coastal flooding analysis

Abstract Compound coastal flooding due to astronomic, atmospheric, oceanographic, and hydrologic drivers poses severe threats to coastal communities. While physics-driven approaches are able to dynamically simulate temporally and spatially varying compound flooding generated by multiple drivers with correlations between some of them, computational burdens limit their capability to explore the full range of conditions that contribute to compound coastal hazards. Data-driven statistical approaches address some of these computational challenges; however, they are also unable to explore all possible forcing combinations due to short observational records, and projections are typically limited to a few locations. This study proposes a hybrid statistical–dynamical framework for compound coastal flooding analysis that integrates a stochastic generator of compound flooding drivers, a hydrodynamic model, and machine learning-based surrogate models. The framework was demonstrated in San Francisco Bay (SF) over the past 500 years with accuracy similar to the physics-driven approach but with much higher computational efficiency. The stochastic generator of compound flooding drivers is developed by coupling a sea surface temperature (SST) reconstruction model with a climate emulator, weather generator, and model of the hydrological and reservoir system. Using reconstructed SSTs as input, the generator of compound flooding drivers is employed to simulate time series of the forcing factors contributing to compound flooding (e.g. surge, waves, river discharge, etc) in SF Bay. A process-based hydrodynamic model is built to predict total water levels varying in time and space throughout SF Bay based on stochastically generated drivers. The machine learning-based surrogate models are then developed from a relatively small library (several hundred) of hydrodynamic model simulations to efficiently predict water levels for compound flooding analysis under the full range of stochastic drivers. This study contributes a hybrid statistical–dynamical framework to better understand the spatial distribution and temporal evolution of compound coastal-fluvial flooding, along with the relative contributions of drivers in complex nearshore, estuarine, and river environments for centennial timescales under past, present, and future climates.