This study examines how avalanches striking a glacial lake can cause sudden floods downstream in Nepal's Manaslu region. We used computer simulations to simulate various avalanche sizes and their effects on the lake. These simulations revealed that medium and large avalanches could lead to severe flooding in a short amount of time. This highlights the pressing need for early warning systems and better disaster preparedness to protect at-risk communities.

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.

We have estimated hydropower potential in the two glacierized river basins of the Nepalese Himalayas. The Glacio-hydrological Degree-Day Model (GDM) was used with different geospatial criteria. In order to force the model simulation and to assess potential future hydrological regimes, a variety of climate variables were combined and used. The sensitivity of climate variables and their impact on hydropower potential were investigated with a combination of different climate variables.

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.

Glacial lake outburst flood (GLOF) modeling of Tsho Rolpa showed that, even if the lake breaches by 20 m in 40 years (from 2021), there will be a sufficient lead time of more than 7 h for early warning and human evacuations in the downstream areas. However, precautionary measures such as community-based GLOF early-warning systems and mechanisms allowing close observation in the case of GLOF events should be established in GLOF-prone regions.