Reconstructing Long‐Term Reservoir Storage in India Using Hydrological Modeling and Machine Learning

Abstract

India has one of the largest networks of large dams in the world, where reservoirs play a crucial role in regulating monsoon‐driven water availability for irrigation, hydropower, flood control, and water supply. However, long‐term daily reservoir storage observations are available only from the year 2000 onwards. In this study, we reconstruct long‐term daily live storage for major reservoirs across India by integrating climate variables, hydrological model simulations, and machine learning approaches. We used model‐simulated reservoir storage as a baseline predictor and applied Random Forest (RF) and XGBoost (XGB) with meteorological and hydrological variables, substantially improving accuracy compared to the raw simulations. The trained models over the observation period were then applied to the pre‐2000 period to reconstruct reservoir storage. Using the reconstructed data, we examined long‐term changes in reservoir storage variability and the timing of peak variability in reservoir storage during both the summer monsoon (June–September) filling and dry‐season release periods. We show that despite increasing reservoir capacity, normalized storage shows a moderate long‐term decline, reflecting an increase in water withdrawals. Pre‐monsoon storage exhibits larger and more spatially variable trends than post‐monsoon storage, while the frequency of low‐storage conditions has declined and high‐storage states have become more common. Peak storage variability typically occurs during late July‐mid August and around January, corresponding to peak monsoon inflows and irrigation demand. These findings provide insights into evolving reservoir storage dynamics and their implications for water resources management under changing hydroclimatic conditions in India.