[A-CC26] Glaciology

[ACC26-P03] Estimating optimized thermal resistance distribution from the mass loss inversion method on debris-covered glacier in the Himalayas

*Yota Sato¹, Koji Fujita², Hiroto Nagai³, Sojiro Sunako⁴, Tika Ram Grung⁵, Takayuki Nuimura⁶, Akiko Sakai², Rijan Kayastha⁷, Masayuki Takigawa¹, Keiko Konya¹ (1.Japan Agency for Marine-Earth Science and Technology, 2.Graduate School of Environmental Studies, Nagoya University, 3.Rissho University, 4.Snow and Ice Research Center, National Research Institute for Earth Science and Disaster Resilience (NIED), 5.International Center for Integrated Mountain Development (ICIMOD), 6.Tokyo Denki University, 7.Kathmandu University)

Keywords:Glacier, Himalaya, Energy balance

Glaciers in High Mountain Asia are essential water resources and have been shrinkage in recent decades. Particularly in the Himalayan region, more than 15% of glaciers are debris-covered (Herreid and Pellicciotti, 2020). A thick debris mantle insulates the sub-debris ice, whereas a thin debris layer can enhance ice ablation (e.g., Pratap et al., 2023). Because debris thickness is spatially heterogeneous, debris-covered glaciers exhibit complex melting patterns. To quantify the sub-debris melt from an energy balance approach, it is essential to estimate the thermal properties of the debris layer. Thermal resistance estimated from satellite-based thermal infrared sensors has been used to estimate the spatial distribution of thermal properties of debris layers (e.g., Suzuki et al. 2006). However, due to the spatial pattern of thermal resistance being challenging to validate, its validity as a robust parameter has yet to be fully explored. In this study, we combined in-situ meteorological data, high-resolution digital elevation models, and an energy balance model to estimate the optimal thermal resistance distribution and its characteristics in a Himalayan debris-covered glacier.
Our target debris-covered Trakarding Glacier (27.9°N, 86.5°E; 2.9 km²; debris-covered area spanning 4,500–5,000 m a.s.l.) is located in the Rolwaling region, eastern Nepal Himalaya. We have conducted in-situ measurements since 2016 and have measured meteorological data using an automatic weather station beside the glacier. We also conducted airborne-based photogrammetry surveys to obtain high-resolution digital elevation models. First, we estimated mass balance distribution from airborne-based surface elevation change with ice flow dynamics. Then, we calculated the energy balance on the debris-covered surface using the energy balance model based on thermal resistance methods with 90 m spatial resolution (Fujita and Sakai, 2014). Finally, assuming that these two independent methods calculate equal mass balances, we inversely estimated the optimal distribution of thermal resistance on the glacier. We will discuss the characteristics of the optimized inversely-estimated thermal resistance distribution and the difference with satellite-based ones.