Project 2.3: Novel Approaches To Integrated Flood Risk Management In Multi-Purpose Multi-Reservoir Systems And Flood Inundation Mapping

Flood risk management in multi-purpose, multi-reservoir systems is a critical challenge under changing climatic conditions. Conventional reservoir operation policies often treat reservoirs in isolation, leading to sub-optimal outcomes. This study proposes an integrated approach using Elitist-Mutated Multi-Objective Particle Swarm Optimization (EM-MOPSO) combined with hedging rules to optimize reservoir operations across a river basin. By systematically quantifying inflow uncertainties, demand variations, and climate-emission scenarios from the IPCC AR6, the methodology aims to minimize flood risks while balancing multiple objectives such as water supply, hydropower, and ecological needs. The Krishna Basin serves as a test case to evaluate the effectiveness of EM-MOPSO with hedging rules compared to other meta-heuristic methods, highlighting its potential as a climate change adaptation strategy. Complementing reservoir optimization, the research introduces a novel approach for flood inundation mapping corresponding to Probable Maximum Flood (PMF) and other recurrence intervals. Digital Elevation Models (DEMs) of varying spatial resolutions, topographic indices (TI), and machine learning algorithms will be integrated to delineate flood-prone areas with improved accuracy. This approach builds on established correlations between TI and inundation susceptibility, while leveraging advanced terrain analysis and data-driven techniques to enhance predictive reliability. The expected outcomes include robust optimization models for reservoir operation, innovative flood hazard mapping methodologies, and practical applications to major Indian river basins such as Krishna, Mahanadi, and Godavari. The research will contribute to improved flood risk assessment, climate-resilient reservoir management, and actionable strategies for disaster mitigation, while fostering interdisciplinary collaboration across hydrology, climate science, and optimization.