Research Projects

Observational data for the Himalayan region are very less. However, gridded climate data is crucial for long-term climate trends and future modelling purposes, whereas biased data is critical for long-term climate trends and future modelling purposes (Wang et al., 2017). We will use ML-based hybrid of multiple grid-based datasets and producing a new dataset for further trend and hydrological analysis for the study area, which despite their significance, scientific understanding of the coupled cryosphere-hydrology system at the basin scale remains limited due to inconsistencies in climate datasets, inadequate representation of glacier processes in hydrological models, and a lack of robust validation through field-based. Using various scenario data for historical and future hydrological analyses. For future studies, data from multiple scenarios will be used to calculate glacier mass balance and volume loss. Glacier retreats are a primary reason for the development of glacier lakes, which pose a significant risk to natural hazards. This research aims to bridge these gaps by employing a multidisciplinary, integrated approach. First, a high-resolution, observation-constrained climate forcing dataset will be created utilizing ML-based hybrid approaches to combine several gridded products with ground observations. This combined data will then be fed into a physically-based hydrological model to simulate historical and future streamflow components, such as glacier and snowmelt, under various CMIP6 climate scenarios. The model will be dynamically coupled with a glacier ice thickness model to simulate variations in glacier thickness and spatial redistribution when climatic and mass balance conditions change (Linsbauer et al., 2012). To evaluate and improve the model's results, stable isotope analysis of precipitation and streamflow samples will be utilized to identify and quantify the contributions of various hydrological sources (Kendall & McDonnell, 1998; Jasechko et al., 2016). This will provide essential insights into seasonal water partitioning and boost confidence in the simulated glacier melt contributions. The study will also identify possible glacier lake formation and outburst hotspots by assessing geomorphological, glaciological, and climatic controls with remote sensing and GIS techniques. The combination of data-driven climate fusion, physically-based modelling, isotope validation, and hazard assessment makes this work both comprehensive and novel. This study will produce a solid climate forcing dataset, enhanced projections of glacier thickness and runoff, source-partitioned hydrological responses using isotopes, and hazard maps of possible GLOF zones in the study region. The work will help to advance our understanding of cryosphere-hydrology interactions in a changing climate, as well as provide useful tools for regional water resource management, climate impact assessments, and catastrophe risk mitigation in the Himalayas.

Glacier evolution, SPHY model, GlabTop Model, Hybrid datasets, GLOF, MCDM