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Influence of Soil Moisture in Semi-Fixed Sand Dunes of the Tengger Desert, China, Based on PLS-SEM and SHAP Models

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  • Haidi Qi

    (Centre for Quantitative Biology, College of Science, Gansu Agricultural University, Lanzhou 730070, China)

  • Dinghai Zhang

    (Centre for Quantitative Biology, College of Science, Gansu Agricultural University, Lanzhou 730070, China)

  • Zhishan Zhang

    (Shapotou Desert Research and Experimental Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China)

  • Youyi Zhao

    (Centre for Quantitative Biology, College of Science, Gansu Agricultural University, Lanzhou 730070, China)

  • Zhanhong Shi

    (Centre for Quantitative Biology, College of Science, Gansu Agricultural University, Lanzhou 730070, China)

Abstract

Drought stress significantly limits the function and stability of desert ecosystems. This research examines the distribution characteristics of soil moisture across different microtopographic types in the semi-fixed dunes located at the southeastern edge of the Tengger Desert. We constructed a path model to examine the direct and indirect impacts of topography, shrub vegetation, and herbaceous vegetation. The data encompassed soil moisture, topography, and vegetation variables, which were collected from field experiments to ensure their accuracy and relevance. Furthermore, SHAP models based on machine learning algorithms were utilized to elucidate the specific mechanisms through which key factors influence soil moisture. The results of the descriptive statistics indicate the highest surface soil moisture content, recorded at 1.21%, was observed at the bottom of the dunes, while the leeward slopes demonstrated elevated moisture levels in the middle and deep soil layers, with measurements of 2.25% and 2.43%, respectively. Soil moisture at different depths initially decreases and then increases with greater herbaceous cover and slope direction, while surface soil moisture follows a similar trend in terms of height difference, with 3 m serving as the boundary for trend changes. Middle and deep soil moistures initially increase and then decrease with greater biomass and shrub coverage, with 30 g and 40% serving as the boundary for trend changes respectively. This study elucidates the spatial distribution patterns and influencing factors of soil moisture in semi-fixed dunes, offering valuable references for the establishment of sand-stabilizing vegetation in desert regions.

Suggested Citation

  • Haidi Qi & Dinghai Zhang & Zhishan Zhang & Youyi Zhao & Zhanhong Shi, 2024. "Influence of Soil Moisture in Semi-Fixed Sand Dunes of the Tengger Desert, China, Based on PLS-SEM and SHAP Models," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6971-:d:1456230
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    References listed on IDEAS

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    1. Theo Dijkstra & Jörg Henseler, 2011. "Linear indices in nonlinear structural equation models: best fitting proper indices and other composites," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(6), pages 1505-1518, October.
    2. Cheng, Minghan & Jiao, Xiyun & Liu, Yadong & Shao, Mingchao & Yu, Xun & Bai, Yi & Wang, Zixu & Wang, Siyu & Tuohuti, Nuremanguli & Liu, Shuaibing & Shi, Lei & Yin, Dameng & Huang, Xiao & Nie, Chenwei , 2022. "Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning," Agricultural Water Management, Elsevier, vol. 264(C).
    3. Gong, Xinghui & Zhang, Hongbo & Ren, Chongfeng & Sun, Dongyong & Yang, Jiantao, 2020. "Optimization allocation of irrigation water resources based on crop water requirement under considering effective precipitation and uncertainty," Agricultural Water Management, Elsevier, vol. 239(C).
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