Abstract
In this study, the Glacier Lake Outburst Flood (GLOF) that occurred over Kedarnath in June 2013 was modeled using integrated observations from the field and Remote Sensing (RS). The lake breach parameters such as area, depth, breach, and height have been estimated from the field observations and Remote Sensing (RS) data. A number of modelling approaches, including Snow Melt Runoff Model (SRM), Modified Single Flow model (MSF), Watershed Management System (WMS), Simplified Dam Breach Model (SMPDBK) and BREACH were used to model the GLOF. Estimations from SRM produced a runoff of about 22.7 m3 during 16–17, June 2013 over Chorabari Lake. Bathymetry data reported that the lake got filled to its maximum capacity (3822.7 m3) due to excess discharge. Hydrograph obtained from the BREACH model revealed a peak discharge of about 1699 m3/s during an intense water flow episode that lasted for 10–15 minutes on 17th June 2013. Excess discharge from heavy rainfall and snowmelt into the lake increased its hydrostatic pressure and the lake breached cataclysmically.
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Acknowledgement
We thank Omer Khalid Murtaza, Fayaah Ahmad Tantray and Pushkar Nath for assistance in the field. The research work was conducted as part of the DST, Govt. of India, New Delhi sponsored research project titled “Risk Assessment for Kedarnath Glacial Lake Outburst Floods” under the national project “Mapping Your Neighborhood in Uttarakhand (MANU)” and the financial assistance received under the project to accomplish this research is thankfully acknowledged. We also thank Department of Earth sciences, University of Kashmir for their valuable support throughout the research work also the assistance by Uttarakhand Police, Disaster management cell and other government and non-government organizations during field data collection is thankfully acknowledged. We appreciated comments by the four anonymous reviewers on an earlier version of the manuscript.
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Rafiq, M., Romshoo, S., Mishra, A. et al. Modelling Chorabari Lake outburst flood, Kedarnath, India. J. Mt. Sci. 16, 64–76 (2019). https://doi.org/10.1007/s11629-018-4972-8
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DOI: https://doi.org/10.1007/s11629-018-4972-8