Simulation of Spatiotemporal Distribution and Variation of 30 m Resolution Permafrost in Northeast China from 2003 to 2021

Chengcheng Zhang, Wei Shan (), Shuai Liu, Ying Guo and Lisha Qiu
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Chengcheng Zhang: Institute of Cold Regions Science and Engineering, Northeast Forestry University, Harbin 150040, China
Wei Shan: Institute of Cold Regions Science and Engineering, Northeast Forestry University, Harbin 150040, China
Shuai Liu: Institute of Cold Regions Science and Engineering, Northeast Forestry University, Harbin 150040, China
Ying Guo: Institute of Cold Regions Science and Engineering, Northeast Forestry University, Harbin 150040, China
Lisha Qiu: Institute of Cold Regions Science and Engineering, Northeast Forestry University, Harbin 150040, China

Sustainability, 2023, vol. 15, issue 19, 1-24

Abstract: The high-resolution permafrost distribution maps have a closer relationship with engineering applications in cold regions because they are more relative to the real situation compared with the traditional permafrost zoning mapping. A particle swarm optimization algorithm was used to obtain the index η with 30 m resolution and to characterize the distribution probability of permafrost at the field scale. The index consists of five environmental variables: slope position, slope, deviation from mean elevation, topographic diversity, and soil bulk density. The downscaling process of the surface frost number from a resolution of 1000 m to 30 m is achieved by using the spatial weight decomposition method and index η. We established the regression statistical relationship between the surface frost number after downscaling and the temperature at the freezing layer that is below the permafrost active layer base. We simulated permafrost temperature distribution maps with 30 m resolution in the four periods of 2003–2007, 2008–2012, 2013–2017, and 2018–2021, and the permafrost area is, respectively, 28.35 × 10 4 km 2 , 35.14 × 10 4 km 2 , 28.96 × 10 4 km 2 , and 25.21 × 10 4 km 2 . The proportion of extremely stable permafrost (<−5.0 °C), stable permafrost (−3.0~−5.0 °C), sub-stable permafrost (−1.5~−3.0 °C), transitional permafrost (−0.5~−1.5 °C), and unstable permafrost (0~−0.5 °C) is 0.50–1.27%, 6.77–12.45%, 29.08–33.94%, 34.52–39.50%, and 19.87–26.79%, respectively, with sub-stable, transitional, and unstable permafrost mainly distributed. Direct and indirect verification shows that the permafrost temperature distribution maps after downscaling still have high reliability, with 83.2% of the residual controlled within the range of ±1 °C and the consistency ranges from 83.17% to 96.47%, with the identification of permafrost sections in the highway engineering geological investigation reports of six highway projects. The maps are of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future in Northeast China.

Keywords: MODIS land surface temperature; surface frost number; permafrost temperature; downscaling; 30 m spatial resolution; Northeast China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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