Reply on RC2

Abstract

Abstract. Flood risk management is increasingly challenged by climate change, rapid urbanization, and the growing complexity of hydrological systems. Reservoirs play a vital role in flood mitigation, yet traditional rule-curve operations based on historical stationarity are becoming inadequate under non-stationary and compound flood conditions. This review synthesizes advances in reservoir operation, hydrodynamic flood modelling, and optimization frameworks for downstream flood risk management, drawing on literature published between 2000 and 2025. Existing studies are classified into four thematic areas: reservoir operation strategies, hydrodynamic modelling, optimization methods, and compound flooding processes. Results reveal a shift from static rule-based operations toward adaptive, forecast-informed, and data-driven approaches that improve flood control performance and reduce peak discharge. Coupled 1D–2D hydrodynamic models better capture floodplain dynamics, while evolutionary algorithms and reinforcement learning support efficient multi-objective optimization. However, a critical knowledge gap persists, as spatial flood inundation metrics and compound flood drivers remain rarely incorporated into optimization frameworks. Using the Dong Nai–Sai Gon river basin as a representative tropical, tide-influenced system, we propose an integrated framework linking reservoir operations, hydrodynamic simulation, and compound flooding processes. Future research should prioritize hybrid physics–AI approaches, real-time data integration, and climate-resilient reservoir management.