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. 2025 Apr 24;15(1):14319.
doi: 10.1038/s41598-025-98339-8.

Quantifying soil wind erosion attribution in Inner Mongolia's desert grassland

Hongtao Jiang  1   2   3 , Wenbang Gao  4   5 , Baoyuan Liu  6 , An Chang  1   2 , Qiankun Guo  7
Affiliations

Quantifying soil wind erosion attribution in Inner Mongolia's desert grassland

Hongtao Jiang et al. Sci Rep. .

Abstract

Wind erosion is a major ecological challenge in the Inner Mongolia desert grassland, directly impacting regional ecological stability and sustainable development. To gain a deeper understanding of the wind erosion process and its key driving factors, this study carefully assembled 21 indicators and quantified each factor's relative contribution to wind erosion using 137Cs tracing technology and geographic detectors. Data collection methods included the acquisition of soil samples, meteorological data, and vegetation cover information through remote sensing technology, along with field measurements taken at several sampling points within the study area. Data analysis was conducted using geographic detectors, which spatially identified the key factors influencing wind erosion and quantitatively assessed their contributions. Additionally, the spatial distribution of risk zones was accurately identified, analyzing wind erosion intensity and distribution patterns across different regions. The results showed that soil organic matter (SOM), vegetation height (VH), average annual precipitation (PPT), average annual temperature (AAT), and potential evaporation (PE) were the primary contributors to wind erosion, with their explanatory powers for the wind erosion modulus being 0.60, 0.51, 0.51, 0.48, and 0.44, respectively. This study provides important theoretical support for regional wind erosion control and proposes targeted management strategies to improve management efficiency and promote ecological protection and sustainable development.

Keywords: 137Cs tracing; Geographic detectors; Impact factor; Inner Mongolia desert grassland; Quantitative attribution; Soil wind erosion.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Spatial distribution distribution map of sampling points (Cartographic software: ArcGIS 10.5 https://www.esri.com/zh-cn).
Fig. 2
Fig. 2
Distribution map of background activity profile for 137Cs (Cartographic software: OriginLab origin2022 https://www.originlab.com).
Fig. 3
Fig. 3
Spatial distribution of soil erosion (Cartographic software: ArcGIS 10.5 https://www.esri.com/zh-cn).
Fig. 4
Fig. 4
Explanatory power of factors on the spatial distribution of erosion (Cartographic software: OriginLab origin2022 https://www.originlab.com).
Fig. 5
Fig. 5
Spatial distribution of factors with q ≥ 0.4 (Cartographic software: ArcGIS 10.5 https://www.esri.com/zh-cn).
Fig. 6
Fig. 6
q value of each indicator factor after interaction (Cartographic software: OriginLab origin2022 https://www.originlab.com).
Fig. 7
Fig. 7
Distribution of corresponding erosion risk levels for each factor (Cartographic software: ArcGIS 10.5 https://www.esri.com/zh-cn).
Fig. 8
Fig. 8
High explanatory power factor (q > 0.4) interaction results (Cartographic software: ArcGIS 10.5 https://www.esri.com/zh-cn).
See this image and copyright information in PMC

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