Modeling Stream Flow and Sediment Transport With Physical and Machine Learning Models: CNN–LSTM‐Augmented SWAT for the Ethiopian Highland Watershed

Sediment yield poses a major challenge to reservoir sustainability in Ethiopia’s highlands, where land use change and intense rainfall accelerate erosion. This study assessed sediment dynamics in the Muga River catchment using the SWAT model and a hybrid CNN–LSTM framework. High‐resolution spatial data and long‐term meteorological and sediment records (1990–2025) were used for calibration and validation, achieving strong performance for streamflow ( R ²  = 0.85, NSE = 0.80) and sediment yield ( R ²  = 0.84, NSE = 0.81). The average annual sediment yield simulated by SWAT (21.94 ton/ha/year) closely matched observed values (19.73 ton/ha/year). Sensitivity analysis identified CN2, SOL_BD, and GW_DELAY as key drivers of runoff and erosion, while scenario simulations highlighted rainfall variability, water yield thresholds, and lateral flow as critical modulators of sediment generation. The CNN–LSTM model improved predictive accuracy and corrected SWAT biases, particularly for peak flows, with the hybrid model reducing RMSE by 22.4% for streamflow and 25.2% for sediment compared to standalone LSTM. These findings support targeted erosion control, scenario‐based land use planning, and adaptive reservoir management in the Muga watershed. The integrated modeling framework demonstrates potential for application in similar data‐scarce, erosion‐prone regions, though further validation across diverse catchments is recommended before broader generalization.