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<p>The ablation of glaciers on the Tibetan Plateau has accelerated over the past decades. Understanding the mechanism behind glacier evolution and projecting future variations based on physical processes is crucial for managing changes in glaciers and their impacts. However, current models rely on empirical relationships, which oversimplify the hydrothermal processes of glaciers and neglect the spatial heterogeneity of glacier variability caused by topography, albedo, and radiation, leading to significant uncertainties in simulation. To better understand glacier variations in unobserved regions, we established a distributed glacier mass-energy balance model that fully considers the impact of topography on solar radiation and determines glacial surface albedo through deep learning. Additionally, it is coupled with glacier dynamics to create a 3D Quasi-physical Process Glacier evolution Model (QPGM). With the Laohugou No. 12 glacier in the Qilian Mountains as an example, the strong applicability of the QPGM in simulating alpine glacier variations is demonstrated. The model’s projections suggest that the glacier could lose 60% of its mass by the end of this century under SSP2-4.5 and could completely disappear under SSP5-8.5. Thus, the QPGM represents a new and improved approach to glacier simulation and projection.</p>

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Glacier evolution model based on physical processes: Application to alpine glacier Laohugou No. 12, Qilian Mountains

  • 期刊论文

  • 发表日期：

    2025年4月8日

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  • Keqin Duan,
  • Qiong Wang,
  • Tandong Yao,
  • Ninglian Wang,
  • Jinping He,
  • Wei Shang,
  • Jiajia Jiang

摘要

The ablation of glaciers on the Tibetan Plateau has accelerated over the past decades. Understanding the mechanism behind glacier evolution and projecting future variations based on physical processes is crucial for managing changes in glaciers and their impacts. However, current models rely on empirical relationships, which oversimplify the hydrothermal processes of glaciers and neglect the spatial heterogeneity of glacier variability caused by topography, albedo, and radiation, leading to significant uncertainties in simulation. To better understand glacier variations in unobserved regions, we established a distributed glacier mass-energy balance model that fully considers the impact of topography on solar radiation and determines glacial surface albedo through deep learning. Additionally, it is coupled with glacier dynamics to create a 3D Quasi-physical Process Glacier evolution Model (QPGM). With the Laohugou No. 12 glacier in the Qilian Mountains as an example, the strong applicability of the QPGM in simulating alpine glacier variations is demonstrated. The model’s projections suggest that the glacier could lose 60% of its mass by the end of this century under SSP2-4.5 and could completely disappear under SSP5-8.5. Thus, the QPGM represents a new and improved approach to glacier simulation and projection.