Towards a Robust Hydrologic Data Assimilation System for Hurricane-induced River Flow Forecasting

Abstract. The Hybrid Ensemble and Variational Data Assimilation framework for Environmental Systems (HEAVEN) is a method developed to enhance hydrologic model predictions while accounting for different sources of uncertainties involved in various layers of model simulations. While the effectiveness of this data assimilation in forecasting streamflow have been proven in previous studies, its potential to improve flood forecasting during extreme events remains unexplored. This study aims to demonstrate this potential by employing HEAVEN to assimilate streamflow data from USGS stations into a conceptual hydrologic model to enhance its capability to forecast hurricane-induced floods across multiple locations within three watersheds in the Southeastern United States. The SAC-SMA hydrologic model is driven by two variables: precipitation and Potential Evapotranspiration (PET), collected from phase 2 of the North American Land Data Assimilation System (NLDAS-2) and MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data, respectively. We have validated the probabilistic streamflow predictions during five instances of hurricane-induced flooding across three regions. The results show that this data assimilation approach significantly improves hydrologic model’s ability to forecast extreme river flows. By accounting for different sources of uncertainty in model predictions—in particular model structural uncertainty in addition to model parameter uncertainty, and atmospheric forcing data uncertainty, the HEAVEN emerges as a powerful tool for enhancing flood prediction accuracy.