The Himalayan river basins have complex terrain and lack detailed hydrological and meteorological information. This has motivated us to develop a fast and distributed model named PyGDM to simulate the hydrology of this region, which is home to both glaciers and snow. PyGDM is good at simulating glacier and snow melt. Hence, the model is suitable for studying different aspects of the Himalayan region, such as the impact of climate change and hydropower scenarios.

This study explores the potential of integrating data science models to enhance the predictive capacity of a theory-guided glacier hydrological model for improved river discharge simulations in the Himalayan basins. By combining data science and physical process models, the study addresses the limitations inherent in each approach.