Combining Machine Learning and Process-Based Modelling for Sediment Load Estimation in the Data-Scarce Kessie Watershed, Upper Blue Nile Basin

Abstract

The Upper Blue Nile Basin contributes about 60% of the Nile River’s annual streamflow but faces severe sediment-related challenges driven by intense monsoonal erosion and reservoir siltation. Accurate estimation of suspended sediment concentration (SSC) and sediment load in large, data-scarce watersheds remains difficult due to sparse monitoring and complex supply-limited transport dynamics. This study develops a hybrid machine learning (ML) and process-based approach for the Kessie watershed (65,784 km2), a major sediment source upstream of the GERD. The approach combines Random Forest (RF) based SSC reconstruction from 251 intermittent samples spanning 1995–2011, approximately 70% collected during the wet season (June–October) and 94% concentrated in 2008–2011, covering a wide range of observed streamflow conditions at the time of sampling (120–5897 m3/s), with a two-stage calibration of the WASA-SED model. Using hydrologically informed predictors, the RF algorithm substantially outperformed the bias-corrected traditional sediment rating curve and other ML algorithms, increasing the validation coefficient of determination (R2) from 0.274 to 0.693. The reconstructed daily SSC yielded a mean annual sediment load of 180.7 Mt/yr. The model performed well, particularly at monthly scales for 1995–2011, achieving good to very good performance (NSE up to 0.83/0.71 for streamflow and 0.86/0.63 for sediment load, calibration/validation, respectively) and reproducing dominant hydrological and sediment regimes using duration curves. Mann–Kendall trend analysis (α = 0.05) indicated no statistically significant monotonic trends in annual rainfall (p = 0.90), mean annual streamflow (p = 0.24 observed; p = 0.84 simulated), or mean annual sediment load (p = 0.66 simulated); the reconstructed sediment load series showed a near-significant increasing tendency (p = 0.06) that falls below the accepted significance threshold and should be interpreted with caution given the short 17-year record. This hybrid approach effectively captures monsoon-driven sediment fluxes and provides model-based daily-to-monthly sediment load estimates with quantified uncertainty. It supports improved reservoir sedimentation assessment, erosion-risk evaluation, and transboundary water-resources planning in data-scarce tropical highlands.