日本地球惑星科学連合2024年大会

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[J] ポスター発表

セッション記号 A (大気水圏科学) » A-CC 雪氷学・寒冷環境

[A-CC26] 雪氷学

2024年5月29日(水) 17:15 〜 18:45 ポスター会場 (幕張メッセ国際展示場 6ホール)

コンビーナ:砂子 宗次朗(防災科学技術研究所)、谷川 朋範(気象庁気象研究所)、大沼 友貴彦(宇宙航空研究開発機構)、渡邊 達也(北見工業大学)

17:15 〜 18:45

[ACC26-P03] ヒマラヤ地域のデブリ氷河を対象とした最適な熱抵抗値分布の推定

*佐藤 洋太1藤田 耕史2永井 裕人3砂子 宗次朗4、Gurung Tika5縫村 崇行6、坂井 亜規子2、Kayastha Rijan7滝川 雅之1紺屋 恵子1 (1.海洋研究開発機構(JAMSTEC) 北極環境変動総合研究センター、2.名古屋大学環境学研究科、3.立正大学地球環境学部、4.防災科学技術研究所 雪氷防災センター、5.International Center for Integrated Mountain Development (ICIMOD)、6.東京電機大学、7.カトマンズ大学)

キーワード:氷河、ヒマラヤ、熱収支

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.