Guwahati - Researchers from the Indian Institute of Technology, Guwahati, have developed an advanced predictive framework to identify areas where new glacial lakes are likely to form in the Eastern Himalayas. The study provides important insights for disaster risk reduction and long-term resource management in high mountain regions.
Due to the climate change, rapid glacier, melting in the Himalayas is leading to the formation and expansion of glacial lakes, increasing the risk of glacial lake outburst floods (GLOFs). Such floods can cause severe damage to infrastructure settlements and natural ecosystems as seen in the past , disasters in the himalayan region. To address this challenge, the IIT Guwahati research team developed a probabilistic model using high resolution satellite imagery and digital elevation data to better capture complex landscape features. Unlike earlier models that focused mainly on climatic factors, the new framework also considers topography, including bowl-shaped, depression, valleys, and glacier formed lakes making predictions more realistic and reliable.
The team tested multiple predictive methods, including logistic Regression, Artificial Neural networks and Bayesin Neural Networks. Among these, the Bayesin Neural Network approach was found to be the most accurate and predicting potential glacial lake formation sites.Using the new framework, the researchers identified 492 locations in the Eastern Himalayas where new glacial lakes are likely to form. These areas will require careful monitoring and preventive measures to reduce future disaster risks.
Speaking on the significance of the research, Prof. Ajay Dashora, Assistant Professor, Department of Civil Engineering, IIT Guwahati, said that the framework can support early-warning systems for glacial lake outburst floods, help in safer planning of roads, hydropower projects and settlements, and contribute to climate-resilient development in the Himalayan region
The research findings have been published in the international journal Scientific Reports. The team plans to further improve the model by adding field based validation and additional geological data to enhance accuracy and expand its use for large scale monitoring of glacial hazards.
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