Evaluating the affecting factors of glacier mass balance in Tanggula Mountains using explainable machine learning and the open global glacier model

Glacier mass balance is a key indicator of glacier health and climate change sensitivity. Influencing factors include both climatic and nonclimatic elements, forming a complex set of drivers. There is a lack of quantitative analysis of these composite factors, particularly in climate-typical regions like the Tanggula Mountains on the central Tibetan Plateau. We collected data on various factors affecting glacier mass balance from 2000 to 2020, including climate variables, topographic variables, geometric parameters, and glacier dynamics. We utilized linear regression models, ensemble learning models, and Open Global Glacier Model (OGGM) to analyze glacier mass balance changes in the Tanggula Mountains. Results indicate that linear models explain 58% of the variance in glacier mass balance, with seasonal temperature and precipitation having significant impacts. Our findings show that ensemble learning models made the explanations 5.2% more accurate by including the impact of topographic and geometric factors such as the average glacier height, the slope of the glacier tongue, the speed of the ice flow, and the area of the glacier. Interpretable machine learning identified the spatial distribution of positive and negative impacts of these characteristics and the interaction between glacier topography and ice dynamics. Finally, we predicted the responses of glaciers of different sizes to future climate change based on the results of interpretable machine learning. It was found that relatively large glaciers (>1 km2) are likely to persist until the end of this century under low emission scenarios, whereas small glaciers (<1 km2) are expected to nearly disappear by 2080 under any emission scenario. Our research provides technical support for improving glacier change modeling and protection on the Tibetan Plateau.