Designing Resilient Multipurpose Reservoir Operation Policies in Presence of Internal Climate Variability - Upadhyay, Divya; Bhatia, Udit (Water resources research, 2025)
Adaptation planning for water resource systems is fraught with significant challenges, arising from uncertainties associated with diverse climate change scenarios, varying model structures, and Internal Climate Variability (ICV), often captured through multiple initial condition runs. ICV, typically considered irreducible, has received significant attention for state and derived hydrological variables. However, its implications and role in regional decision‐making remain elusive. Here, we develop an integrated framework to incorporate uncertainties through hydrological modeling combined with a suite of multi‐objective stochastic optimization techniques. This approach is applied to design optimal operating policies for the Sardar Sarovar Dam in Gujarat, India, a multipurpose infrastructure of national importance to meet flood control, hydroelectric generation and domestic, industrial, and irrigation water demands while accounting for two future climate change scenarios, SSP245 and SSP585, with 49 different initializations of each scenario to represent the ICV. We employ Sampling Stochastic Dynamic Programming to incorporate ICV by considering multiple initializations simultaneously, in contrast to Stochastic Dynamic Programming, which evaluates realizations individually. We show that despite the wide range of uncertainties, optimal operating policies can be designed to meet the various demands with reliability of 100%, 59%, and 27% for domestic, irrigation, and industrial water demand, respectively, when all scenarios are considered simultaneously. Our study advocates for the systematic inclusion of a wide array of climate model outputs, emphasizing that such integration is essential not only for crafting robust operating policies, but also for the reliability assessment of current operating policies in light of changing climate and demand scenarios. Plain Language Summary Reservoirs are crucial to meet water demand, control floods, and generate electricity. However, planning how to operate these reservoirs under changing climate is complicated by the natural ups and downs in the climate system, known as Internal Climate Variability (ICV). Although ICV captured through multiple initial runs of an Earth System Model has been widely studied for temperature, rainfall, and streamflow, its effects on real‐world decisions, such as how best to release and store water, are less understood. To address this gap, we developed an end‐to‐end framework that incorporates ICV in the design of optimal operating policies for a multi‐purpose reservoir. Specifically, we applied our method to the Sardar Sarovar Dam in Gujarat, India, which also serves several neighboring states by providing flood control, hydroelectric power, and water for households, industries, and agriculture. We also accounted for uncertainty in future irrigation demands to reflect additional real‐world variability in water needs. By using multiobjective optimization and hydrological modeling, we tested how different reservoir policies perform under a range of climate scenarios and multiple initial climate conditions. Our results show that considering multiple initial climate conditions not only captures a broader range of natural fluctuations but also improves our ability to estimate how the reservoir transitions between different states (e.g., storage levels). This leads to more reliable and resilient operation policies that consistently deliver water, generate hydropower, and manage flood risks. By systematically accounting for natural variability and other uncertainties, reservoirs can continue meeting critical needs despite an evolving climate. Key Points A Sampling Stochastic Dynamic Programming (SSDP) integrates Internal Climate Variability to design robust multipurpose reservoir policies Simultaneously considering multiple flow realizations yields more reliable outcomes than single‐scenario approaches Refined transition probabilities inform SSDP, enabling resilient reservoir policies under uncertainty