Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Sciences, Chinese Academy of Sciences, Lanzhou 730000, China.
Sci Total Environ. 2019 Feb 10;650(Pt 1):661-670. doi: 10.1016/j.scitotenv.2018.08.398. Epub 2018 Aug 28.
The Qinghai-Tibet Plateau (QTP), where is underlain by the highest and most extensive mid-altitude permafrost, is undergoing more dramatic climatic warming than its surrounding regions. Mapping the distribution of permafrost is of great importance to assess the impacts of permafrost changes on the regional climate system. In this study, we applied logistic regression model (LRM) and multi-criteria analysis (MCA) methods to map the decadal permafrost distribution on the QTP and to assess permafrost dynamics from the 1980s to 2000s. The occurrence of permafrost and its impacting factors (i.e., climatic and topographic elements) were constructed from in-situ field investigation-derived permafrost distribution patterns in 4 selected study regions. The validation results indicate that both LRM and MCA could efficiently map the permafrost distribution on the QTP. The areas of permafrost simulated by LRM and MCA are 1.23 × 10 km and 1.20 × 10 km, respectively, between 2008 and 2012. The LRM and MCA modeling results revealed that permafrost area has significantly decreased at a rate of 0.066 × 10 km decade over the past 30 years, and the decrease of permafrost area is accelerating. The sensitivity test results indicated that LRM did well in identifying the spatial distribution of permafrost and seasonally frozen ground, and MCA did well in reflecting permafrost dynamics. More parameters such as vegetation, soil property, and soil moisture are suggested to be integrated into the models to enhance the performance of both models.
青藏高原（QTP）是中海拔永久冻土分布最广、海拔最高的地区，其气候变暖比周边地区更为显著。绘制永久冻土的分布对评估永久冻土变化对区域气候系统的影响非常重要。本研究应用逻辑回归模型（LRM）和多准则分析（MCA）方法，绘制了青藏高原近 30 年来（20 世纪 80 年代至 2000 年代）的永久冻土分布图，并评估了永久冻土的动态变化。从 4 个选定的研究区域的实地调查中，构建了永久冻土的发生及其影响因素（即气候和地形要素）的分布模式。验证结果表明，LRM 和 MCA 都能有效地绘制青藏高原的永久冻土分布。2008-2012 年，LRM 和 MCA 模拟的永久冻土面积分别为 1.23×105km2和 1.20×105km2。LRM 和 MCA 建模结果表明，过去 30 年来，永久冻土面积以每年 0.066×105km2的速度显著减少，而且减少的速度正在加快。敏感性测试结果表明，LRM 能很好地识别永久冻土和季节性冻土的空间分布，MCA 能很好地反映永久冻土的动态变化。建议将更多的参数（如植被、土壤性质和土壤湿度）纳入模型，以提高两种模型的性能。
