在北极的永久土景观中使用机器学习模型进行热湖易感性评估
Rui Wang1, Lanlan Guo1, Yuting Yang2
1Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing, 100875, China; Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
北极的变暖导致永久土融化和热湖形成. 机器学习地图预测了当前和未来的热湖易感性,确定斜率作为关键因素,并突出了气候场景下的区域变化.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 气候科学 气候科学
背景情况:
- 北极的变暖正在迅速融化富含冰的永久土.
- 永久土的融化导致地面沉降和热湖的形成.
- 热岩湖显著改变北极表面的能量平衡,影响地形学,水文学,生态学,基础设施和温室气体排放.
研究的目的:
- 在北极地区开发热岩湖易感性 (TLS) 的空间显式地图.
- 确定驱动热石岩湖分布的关键环境因素.
- 在不同的气候变化场景下预测未来的TLS变化.
主要方法:
- 利用机器学习算法,对高分辨率的环境数据和现有的热岩湖库存进行训练.
- 生成了热岩湖 (TLS) 的当前和未来易感性地图,面积为北60度以北1.8×10^6平方公里.
- 采用概率校准曲线和变化系数 (CV) 来严格验证TLS地图可靠性.
主要成果:
- 高和非常高的TLS区域占研究区域的约10.4%,主要是在永久土低地.
- 在代表性度路径 (RCP) 场景下,预计至少23.9%的当前TLS区域将消失.
- 预计加拿大北部的易感性增加,表面的斜率被确定为热湖发生的最关键的环境因素.
结论:
- 该研究提供了一种可靠的方法来评估环北极热岩湖的分布和动态.
- 结果提高了对永久土景观应对气候变化的理解.
- 开发的TLS地图对于北极环境管理和基础设施规划至关重要.
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