Field expedition reveals key insights into GLOF risks in J&K

SRINAGAR, AUGUST 02: On the directions of Chief Secretary, Atal Dulloo, a team of Department of Geography & Disaster Management, University of Kashmir with the facilitation of Department of Disaster Management, Relief, Rehabilitation & Reconstruction (DMRRR) recently conducted the Geographical Field Expedition of Sheeshnag and Sonasar Glacial lakes in order to prepare their Glacial Lake Outburst Flood (GLOF) hazard assessment.

The team was headed by Prof. Pervez Ahmed, Head Department of Geography & Disaster Management, University of Kashmir, who is the member of Focused Glacial Lake Outburst Flood Monitoring Committee (FGMC) constituted by the Government of Jammu and Kashmir recently to focus on mitigation measures due to overflow of glacial lakes. The FGMC is headed by Principal Secretary Home, Chandraker Bharti.

A detailed presentation was given to FGMC after the field expedition highlighting the major findings and future possibilities. Subsequently, a comprehensive preliminary report was submitted to the Chairman, FGMC for deliberations and future course of action.

In the next phase, DMRR&R has planned to install Early Warning Systems for generating real-time alerts at most of at-risk glacial lakes in UT of J&K in collaboration with National Disaster Management Authority and a technical partner.

The Himalayan glaciers have been rapidly melting due to accelerated climate change, with significant warming observed in the 21st century. This trend has led to the formation and expansion of glacial lakes, which are increasingly prone to catastrophic outburst floods. The projections indicate that up to 65% of the ice mass in High Mountain Asia could disappear by the end of the century, exacerbating the risks of GLOFs.

This expedition underscores the urgent need for enhanced monitoring and risk management strategies to address the growing threat of GLOFs in the Himalayas.

Efforts to expand the mitigation programme are being expedited. Further, this region is known for highly localised heavy rainfall events. Therefore, efforts to improve the predictive ability for such events will also be intensified in collaboration with relevant agencies.