草地学报2026，Vol.34Issue(3)：984-997,14.DOI:10.11733/j.issn.1007-0435.2026.03.022
青海湖流域土壤水分空间格局及模拟研究
Spatial Patterns and Modeling of Soil Moisture in the Qinghai Lake Basin
摘要
Abstract
Spatial patterns of soil water content are fundamental to regional ecosystem stability and the sustain-able use of water resources.Focusing on the Qinghai Lake Basin,this study integrated field measurements and remote-sensing imagery with statistical and geospatial analyses to investigate the spatial distributions of soil water content and soil water storage at multiple depths.Machine-learning models—Random forest(RF)and Extreme gradient boosting(XGBoost)—were applied to simulate soil water content and storage at varying depths across the basin.The results indicated that soil moisture content in the Qinghai Lake Basin gradually decreased with increasing soil depth(32.06%,27.17%,and 24.99%,respectively),while soil water storage increased with depth(29.72 mm,32.07 mm,and 32.34 mm,respectively).Spatially,soil moisture in the basin exhibited a general pattern of high values in the north and east and low values in the south and west,with higher values in the upper reaches of rivers than in the lower reaches,and higher values in the mountainous areas surrounding the lake than in the lakeshore zones.Across different vegetation types,alpine meadows had the highest soil moisture content and water storage.Model simulation results revealed that air temperature,pre-cipitation,and the Normalized Difference Vegetation Index were the primary influencing factors of the soil mois-ture spatial model.This study elucidates the spatial distribution patterns and environmental response mecha-nisms of soil moisture in the Qinghai Lake Basin,providing a scientific basis for regional ecological restoration and water resource management.关键词
土壤含水量/土壤储水量/空间分布/机器学习Key words
Soil water content/Soil water storage/Spatial distribution/Machine learning分类
农业科技引用本文复制引用
丁辰深,曹生奎,张富玲,王江,侯瑶芳,雷义珍,裴若颖..青海湖流域土壤水分空间格局及模拟研究[J].草地学报,2026,34(3):984-997,14.基金项目
青海省自然科学基金项目(2023-ZJ-924M)资助 （2023-ZJ-924M）
