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The impacts of vegetation on the soil surface freezing-thawing processes at permafrost southern edge simulated by an improved process-based ecosystem model

Authors :
Zhenhai Liu
Weibo Shi
Xiaobo Wang
Hui Li
Tongtong Zhu
Shaoqiang Wang
Jinghua Chen
Yongkai Tu
Qinyi Wang
Junbang Wang
Yuanyuan Liu
Zhaosheng Wang
Mei Huang
Bin Chen
Source :
Ecological Modelling. 456:109663
Publication Year :
2021
Publisher :
Elsevier BV, 2021.

Abstract

Permafrost degradation due to climate warming would potentially increase the release of previously frozen soil carbon and change the carbon budget of the cold region ecosystem. The underlying permafrost degradation would be effectively mediated by soil surface freezing-thawing (FT) processes. Aboveground vegetation can regulate soil FT processes, however its effects on ground thermal transfer have not been well represented by ecosystem models. In this study, we improved the hydrothermal module of the Boreal Ecosystem Productivity Simulator (BEPS) through more careful parameterization of snowpack density, puddled water, soil organic matter and super-cooled soil water. The impacts of vegetation on the soil surface FT processes have also been investigated using the improved BEPS model and the measured soil temperature data at forest and grassland sites on the southern edge of permafrost region in Mongolia and northeastern China. The improved BEPS model performs better than the original model in simulations of soil temperature and soil FT processes. Smaller amplitudes of soil diurnal FT cycles were found in forest sites compared to grassland sites. Forest sites have delayed soil thaw timing and similar soil freezing time compared to grassland sites. Differences in snow depths and soil organic matter content due to distinct vegetation community structures have considerable influences on the disparity in soil FT processes. Thus, it is important to improve the simulation of the impacts of vegetation on soil surface FT processes for better forecasting the permafrost degradation.

Details

ISSN :
03043800
Volume :
456
Database :
OpenAIRE
Journal :
Ecological Modelling
Accession number :
edsair.doi...........0984d43076b3c4833f6c2ea90d212ffd
Full Text :
https://doi.org/10.1016/j.ecolmodel.2021.109663