Optimizing environmental flow trade-offs for sustainable hydropower in a Himalayan reservoir, Dudhkoshi storage hydroelectric project

The increasing population and intensifying climate change in the central Himalayas necessitate sustainable water resources management. Optimizing reservoir operations is crucial for managing water effectively and balancing objectives like energy generation, irrigation, flood control, and ecosystem protection. This study evaluates the reservoir operation strategies in the Dudhkoshi Storage Hydroelectric Project (DKSHEP) for maximizing energy while ensuring the environmental flow (e-flow) requirement. Using the hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO) algorithm, this study analyzes the optimized operation under seven choices for the e-flow assessment method. The results show that the choice of e-flow method greatly affects reservoir operation and energy yield. Static approaches such as the Department of Electricity Development (DoED) guideline and the Monthly Minimum Flow Method (MMFM) maximize annual energy output (3318 and 3291 GWh, respectively) but increase ecological risk. In contrast, Tessman ensures better ecological protection but reduces energy generation by about 35% (relative to the DoED baseline). The Annual Distribution Method (ADM) offers balanced outcomes, maintaining energy efficiency while safeguarding downstream ecosystems. Dam operation is likely to alter the downstream flow, causing a reduction of up to 38% in June (DoED method) until the confluence with the Sunkoshi River. Our results demonstrate that adaptive and balanced e-flow strategies supported by optimization can aid in decision-making for sustainable reservoir management. This study highlights the importance of a detailed analysis of hydrology, downstream environmental impacts, and aquatic biology, considering the water, energy, and environment nexus in the central Himalayas.