Research Interests

In the intricate tapestry of natural systems, my research interest lies at the confluence of snow avalanches, precipitation extremes, hydro-climatology, and the impact of climate change. This multidisciplinary pursuit is not merely driven by curiosity but by a profound understanding of the critical role these elements play in shaping our planet's hydrological processes and, consequently, our environment's resilience. Snow avalanches, as a manifestation of complex snowpack dynamics, serve as both a natural hazard and a lens through which we can better comprehend the broader hydrological system's behavior. Likewise, precipitation extremes, characterized by their intensity, duration, and spatial distribution, are integral to understanding the variability of water resources and the implications for ecosystems and human societies. Within this framework, hydro-climatology provides the theoretical foundation to analyze the interactions between atmospheric and hydrological processes, offering insights into the drivers behind precipitation patterns, extreme events, and their spatial-temporal variability.

In the context of climate change, these pursuits take on added urgency. As our planet undergoes unprecedented shifts in temperature, precipitation patterns, and glacial dynamics, it becomes imperative to anticipate and adapt to the resulting hydrological alterations. Here, the integration of machine learning techniques offers a powerful toolset to enhance our predictive capabilities, enabling the identification of non-linear relationships, pattern recognition in large datasets, and the development of robust forecasting models. Through the application of advanced algorithms and data-driven methodologies, we can extract valuable insights from observational and simulation data, facilitating a deeper understanding of the complex interplay between climate drivers, hydrological processes, and environmental responses.

Central to this endeavor is the development and refinement of hydrological and hydrodynamic models. These computational frameworks serve as virtual laboratories, allowing us to simulate and analyze the behavior of water systems under various climatic scenarios and management interventions. By coupling physical principles with empirical data and machine learning-derived insights, we can improve the fidelity and accuracy of these models, enabling more informed decision-making in water resource management, flood forecasting, ecosystem conservation, and infrastructure planning.

In essence, my research journey is guided by a relentless pursuit of knowledge aimed at unraveling the intricacies of Earth's hydrological systems and their response to climate change. By leveraging interdisciplinary approaches, cutting-edge methodologies, and a steadfast commitment to scientific inquiry, I aspire to contribute meaningfully to our collective efforts towards building a more resilient and sustainable future for generations to come.

Bansal, J. K. and Goswami, A.: Snow Avalanche Hazard Zonation and Climate Change Trends in Kullu Region of Himachal Pradesh, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-551, https://doi.org/10.5194/egusphere-egu25-551, 2025. 

Jha, A., Bansal, J. K., & Gupta, N. C. (2025). Assessing the impact on Future Discharge on Gandak River Basin Using SWAT Model and Machine Learning Techniques (No. EGU25-835). Copernicus Meetings. 

Joshal K. Bansal, Ajanta Goswami (2024). Enhancing Discharge Predictions in the Lower Mahanadi River Basin Using Machine Learning and the SWAT Model. In ISFF8 2024, 8th International Symposium on Flash Floods in Wadi System, Al Ain, UAE, 3-6 Nov, 2024.

Joshal Kumar Bansal, Ajanta Goswami, Raaj Ramsankaran, Vaishnavi Singh and Gargi Kulshrestha (2023). Application of Machine Learning Techniques combined with the Stumpf model for bathymetry extraction and GLOF modelling at Drang Drung Glacial Lake, Ladakh, India. In NCPS 2023, National Conference of Polar Sciences, NCPS 2023 at National Council for Polar and Ocean Research, Vasco-da-Gama, Goa, India, 16-20 May, 2023.

Joshal Kumar Bansal, Deepti Rani, Vishal Singh, Manish Kumar Nema, Sanjay Kumar Jain (2022). Generation of a Hybrid Rainfall Dataset for Mizoram State Using Data Assimilation Techniques. In Roorkee Water Conclave 2022, 02 – 04 March, 2022. (Under review for publication in Water Security, Elsevier)

Joshal Kumar Bansal, Pankaj R Dhote, Vaibhav Garg and Praveen K Thakur (2021). Hydrodynamic Modeling and Satellite Altimeter based Establishment of Virtual Gauging Network in Flood Prone River Basin. In HYDRO 2021, 26th International Conference on Hydraulics, Water Resources and Coastal Engineering, HYDRO 2021 INTERNATIONAL at SVNIT Surat, Gujarat, India, 23 – 25 December, 2021. 

Pankaj R Dhote, Joshal Bansal, Vaibhav Garg, Praveen Thakur, Ankit Agarwal (2021). Potential of Multi-Mission Satellite Altimetry Observations and Hydrodynamic Model to Establish Virtual Gauging Network in Sparsely Gauged Basin. AGU 2021 Fall Meeting, New Orleans, LA and virtual, 13-17 December, 2021. doi.org/10.1002/essoar.10509744.1

Bansal, J. K., Goswami, A., & Roy, A. (2025). Snow Avalanche in the Indian Himalayas: Hazard Zonation and Climate Change Trends in Kullu region of Himachal Pradesh, India. Physics and Chemistry of the Earth, Parts A/B/C, 103882. 

Dhote, P. R., Bansal, J. K., Garg, V., Thakur, P. K., & Agarwal, A. (2025). A framework for generating rating curves in Mahanadi River using hydrodynamic model and radar altimetry data. Hydrological Sciences Journal, 1–16. https://doi.org/10.1080/02626667.2024.2430387

Rather, A. F., Ahmed, R., Bansal, J. K., Mir, R. A., Ahmed, P., Malik, I. H., & Varade, D. (2024). Glacial lake outburst flood risk assessment of a rapidly expanding glacial lake in the Ladakh region of Western Himalaya, using hydrodynamic modeling. Geomatics, Natural Hazards and Risk, 15(1), 2413893. https://doi.org/10.1080/19475705.2024.2413893. 

Ahmed, R., Shamim, T., Bansal, J. K., Rather, A. F., Javaid, S., Wani, G. F., ... & Mir, R. A. (2024). Assessing climate trends in the Northwestern Himalayas: a comprehensive analysis of high-resolution gridded and observed datasets. Geomatics, Natural Hazards and Risk, 15(1), 2401994. https://doi.org/10.1080/19475705.2024.2401994

Rather, A. F., Ahmed, R., Ahmed, P., Bansal, J. K., Banerjee, P., Wani, T. A., ... & Mir, R. A. (2024). Examining the glacier-glacial lake interactions of potentially dangerous glacial lakes (PDGLs) under changing climate in Shyok catchment of the Upper Indus Basin. Physics and Chemistry of the Earth, Parts A/B/C, 136, 103686. https://doi.org/10.1016/j.pce.2024.103686

Singh, V., Bansal, J. K., Rani, D., Singh, P. K., Nema, M. K., Singh, S. K., & Jain, S. K. (2024). Data assimilation with machine learning for constructing gridded rainfall time series data to assess long-term rainfall changes in the northeastern regions in India. Journal of Water and Climate Change, jwc2024644. https://doi.org/10.2166/wcc.2024.644

Ahmed, R., Ahmad, S. T., Wani, G. F., Mir, R. A., Almazroui, M., Bansal, J. K., & Ahmed, P. (2022). Glacial lake changes and the identification of potentially dangerous glacial lakes (PDGLs) under warming climate in the Dibang River Basin, Eastern Himalaya, India. Geocarto International, 37(27), 17659-17685. https://doi.org/10.1080/10106049.2022.2134461

Bansal, J. K., Keshariya, A., Patle, P., & Goswami, A. Calculation of Climate indices using CMIP6 climate data over Rajasthan State of India. https://www.nwda.gov.in/upload/uploadfiles/e-book/S-3/Calculation-of-Climate-indices-using-CMIP6.pdf

Bansal, J. K., Dhote, P. R., Garg, V., & Thakur, P. K. (2021, December). Hydrodynamic Modelling and Satellite Altimeter-Based Establishment of Virtual Gauging Network in Flood-Prone River Basin. In International Conference on Hydraulics, Water Resources and Coastal Engineering (pp. 23-37). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-1890-4_3