Integrating WRF-Hydro and DART for Ensemble Streamflow Forecasting in a Highly Regulated Basin with Anthropogenic Intervention

Streamflow forecasting in anthropogenically altered river basins presents substantial challenges, particularly where dam and weir operations strongly modify natural flow regimes. In such systems, conventional hydrologic models often have limited capability to represent the non-linear effects of reservoir regulation, resulting in rapid degradation of forecast skill during extreme events. This study evaluates an ensemble-based hydrologic data assimilation (DA) framework applied to the Nakdong River Basin, South Korea, a highly regulated river system characterized by a dense network of dams and multi-functional weirs. We implement a coupled modeling framework integrating the WRF-Hydro system with the Data Assimilation Research Testbed (DART) to investigate the applicability of DA in a managed hydrologic environment. The WRF-Hydro reservoir module is used to explicitly represent storage and release processes, while DART provides ensemble Kalman filter–based assimilation. A central challenge is the mismatch between modeled natural flows and observed regulated discharges. To address this, streamflow and dam storage (or water level) observations are assimilated to update both natural hydrologic states and managed infrastructure states. The framework is evaluated for an extreme rainfall event in August 2022, demonstrating that joint updating of streamflow and reservoir states improves the ensemble representation of human-induced timing and magnitude. Remaining challenges related to error covariance specification in operationally controlled systems are discussed, underscoring the importance of explicitly accounting for anthropogenic intervention in hydrologic DA systems to improve flood forecasting in regulated basins.