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Journal of Desert Research ›› 2024, Vol. 44 ›› Issue (5): 133-142.DOI: 10.7522/j.issn.1000-694X.2024.00047

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Relationship between soil moisture and topography and vegetation in the Tengger Desert

Ting Ning1(), Dinghai Zhang1(), Youyi Zhao1, Jing Jiang2   

  1. 1.Center for Quantitative Biology,College of Science /, Gansu Agricultural University,Lanzhou 730070,China
    2.College of Management, Gansu Agricultural University,Lanzhou 730070,China
  • Received:2024-01-31 Revised:2024-04-25 Online:2024-09-20 Published:2024-10-15
  • Contact: Dinghai Zhang

Abstract:

Soil moisture critically limits the growth of sand-binding vegetation.Analysis of soil moisture distribution patterns in sandy areas, and their correlation with topography and vegetation factors, provides a scientific basis for desertification control. This study focuses on soil moisture in fixed sand dunes within the Hongwei natural vegetation area of the Tengger Desert. Employing methodologies including generalized linear models (GLM), generalized additive models (GAM), and random forest (RF), the research explores the impacts of topography and vegetation factors on soil moisture at various depths. Findings indicate distinct layers within the 0-300 cm soil depth, specifically a surface layer (0-40 cm), a middle layer (40-200 cm), and a deep layer (200-300 cm), each showing significant differences. The RF model exhibits superior accuracy compared to the GLM and GAM models, suggesting that soil moisture decreases with increasing differences in terrain height. Moreover, surface and middle soil moisture levels rise with higher herbaceous coverage, while middle and deep soil moisture levels initially decrease and then increase with greater shrub abundance and biomass. The study suggests that future revegetation and sand fixation efforts should focus on controlling the density and placement of sand-binding shrubs and herbaceous plants to facilitate the restoration of sand-binding vegetation in the study area.

Key words: soil moisture, topography and vegetation factors, generalized linear model, generalized additive model, random forest model

CLC Number: