Leveraging Synthetic Aperture Radar (SAR) to improve above-normal flow prediction in ungauged basins

Effective flood prediction significantly enhances risk management and response strategies, yet remains challenging, particularly in ungauged basins.This study investigates the capacity for integrating streamflow derived from Synthetic Aperture Radar (SAR) and U.S. National Water Model (NWM) output to provide enhanced predictions of above-normal flow (ANF).Leveraging the Global Flood Detection System (GFDS) and Principal Component Regression (PCR) of SAR data, we apply the Spatial-temporal Hierarchical model (STHM) for ANF prediction replacing antecedent streamflow with SAR-derived flow.Our evaluation shows promising results, with STHM-SAR significantly improving prediction accuracy of NWM, especially coastal regions where approximately 60% of sites demonstrated enhanced performance compared to previous efforts.Spatial and temporal validations underscore the model's robustness, with SAR data contributing to explained variance by 24% on average.This approach not only streamlines post-processing modeling but also uniquely combines existing data, showcasing its potential to improve hydrological modeling, particularly in regions with limited measurements. Hosted fileRS_main.docx available at https://authorea.com/users/577650/articles/737256-leveragingsynthetic-aperture-radar