4.7 Article

Spatial distribution and changes of permafrost on the Qinghai-Tibet Plateau revealed by statistical models during the period of 1980 to 2010

Journal

SCIENCE OF THE TOTAL ENVIRONMENT
Volume 650, Issue -, Pages 661-670

Publisher

ELSEVIER
DOI: 10.1016/j.scitotenv.2018.08.398

Keywords

Permafrost distribution; Permafrost changes; Qinghai-Tibet Plateau; Logistic regression model; Multi-criteria analysis

Funding

  1. Chinese Academy of Sciences [XDA20100103, ZDRW-ZS-2017-4]
  2. State Key Laboratory of Cryosphere Sciences [SKLCS-ZZ-2018]
  3. National Natural Science Foundation of China [41721091, 41690142, 41771076]

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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) andmulti-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 x 10(6) km(2) and 1.20 x 10(6) km(2), respectively, between 2008 and 2012. The LRM and MCA modeling results revealed that permafrost area has significantly decreased at a rate of 0.066 x 10(6) km(2) decade(-1) 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. (C) 2018 Published by Elsevier B.V.

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