Spatial differentiation and influencing factors of active layer thickness in the Da Hinggan Ling Prefecture

• Published in: Frontiers in Earth Science
• Main Authors: Zhan, Daqing, Li, Miao, Xiao, Yang, Man, Haoran, Zang, Shuying
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Frontiers Media SA 2023
• Subjects: Active layer thickness; permafrost
• Online Access: http://dx.doi.org/10.3389/feart.2022.1066662; https://www.frontiersin.org/articles/10.3389/feart.2022.1066662/full

Active layer thickness (ALT) of permafrost changes significantly under the combined influence of human activities and climate warming, which has a significant impact on the ecological environment, hydrology, and engineering construction in cold regions. The spatial differentiation of Active layer thickness and its influencing factors have become one of the hot topics in the field of cryopedology in recent years, but there are few studies in the Da Hinggan Ling Prefecture (DHLP). In this study, the Stefan equation was used to simulate the Active layer thickness in the Da Hinggan Ling Prefecture, and the factor detection and interaction detection functions of geodetector were used to analyze the factors affecting the spatial differentiation of Active layer thickness from both natural and humanity aspects. The results showed that Active layer thickness in the Da Hinggan Ling Prefecture ranges from 58.82 cm to 212.55 cm, the determinant coefficient R 2 , MAE, RMSE between simulation results and the sampling points data were 0.86, 11.25 (cm) and 13.25 (cm), respectively. Lower Active layer thickness values are mainly distributed higher elevations in the west, which are dominated by forest (average ALT: 136.94 cm) and wetlands (average ALT: 71.88 cm), while the higher values are distributed on cultivated land (average ALT: 170.35 cm) and construction land (average ALT: 176.49 cm) in the southeast. Among the influencing factors, elevation is significantly negatively correlated with ALT. followed by summer mean LST, SLHF and snow depth. NDVI and SM has the strong explanation power for the spatial differentiation of ALT in factor detection. Regarding interactions, the explanatory power of slope ∩ snow depth is the highest of 0.83, followed by the elevation ∩ distance to settlements. The results can provide reference for the formulation of ecological environmental protection and engineering construction policies in cold regions.