Recession of Gya Glacier and the 2014 glacial lake outburst flood in the Trans-Himalayan region of Ladakh, India.

ABSTRACT

This study assessed spatiotemporal changes at Gya Glacier, the associated development of a proglacial lake, and reconstructed the 2014 outburst flood that struck Gya Village in the Trans-Himalayan region of Ladakh, India. This study analyzed and for the first time modeled a Glacial Lake Outburst Flood (GLOF) event in the Trans-Himalayan region of Ladakh. Glacier and glacial lakes changes were quantified using remote sensing data supplemented with field observations. Glacier ice-thickness and glacier-bed overdeepenings were modeled using a shear-stress based model, GlabTop (Glacier-bed Topography). The reconstruction of the 2014 GLOF and the potential hazard assessment of Gya Lake were carried out using the hydrodynamic model HEC-RAS; results were validated against ground-collected data. Temporal evaluation of satellite data revealed a 45.6% loss in the total glacier area between 1969 and 2019. The earliest snow-free image available for the region shows that a proglacial lake existed as early as 1969 with an area of 3.06 ha. The lake has expanded to ~11 ha in 2019. Results from the GlabTop model suggest that the lake could grow further up to 12 ha in the future. Field-based geomorphic indicators suggest that the 2014 GLOF event resulted from a piping failure of the frontal moraine destroying numerous agricultural fields, some buildings, downstream infrastructure, and eroded natural channel embankments. The reconstruction of the event revealed that 25% of the lake waters drained out with a peak discharge of 470 m3s-1, inundating an area of ~4 km2 around Gya Village. However, a complete breaching of the terminal moraine could result in an event that would be 5.5 times larger than the 2014 GLOF. Therefore, this study could be useful not only in planning disaster-resilient infrastructure around proglacial lake environments in the cold-arid Ladakh but also in framing mitigation plans to reduce risk for vulnerable downstream communities.