PREDICTIVE SEDIMENT TRANSPORT MODELS FOR SUSTAINABLE RESERVOIR MANAGEMENT: AN EXPERT REVIEW OF METHODOLOGIES AND APPLICATIONS

Abstract

Reservoir sedimentation is an escalating global concern that threatens water security, infrastructure longevity, and ecological sustainability. Sediment transport modeling (STM) has become a pivotal tool for predicting reservoir sediment accumulation, informing operational strategies, and optimizing long-term sustainable water resource management (SWRM). This review comprehensively examines empirical, semi-empirical, process-based, and data-driven sediment transport models, highlighting their applications, strengths, and limitations. Empirical models, such as trapping efficiency-based approaches, provide rapid bulk sediment estimates but lack spatial resolution. Process-based models (1D, 2D, 3D) solve governing hydro-morphodynamic equations, including mass and momentum conservation, the Exner equation, and sediment transport relations, capturing detailed sediment dynamics for complex reservoirs. Advanced physics considerations, such as non-uniform sediment transport, armoring, and turbidity currents, are critical for accurate predictions. Moreover, machine learning (ML) and hybrid frameworks enhance forecasting capabilities, particularly for suspended sediment concentrations and real-time operational decision-making. The integration of these predictive approaches into SWRM enables the design of optimized structural measures, operational rule curves, and watershed management strategies. The review emphasizes the necessity of uncertainty quantification, data assimilation, and long-term climate adaptation to ensure the effectiveness of sediment management interventions, advocating a holistic, multi-scale modeling approach for sustainable reservoir operations.