Article(id=1309569839786714073, tenantId=1146029695717560320, journalId=1308397196074385509, issueId=1309569821424051089, articleNumber=null, orderNo=null, doi=10.11733/j.issn.1007-0435.2026.03.022, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747843200000, receivedDateStr=2025-05-22, revisedDate=1751212800000, revisedDateStr=2025-06-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1790155380436, onlineDateStr=2026-09-23, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790155380436, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790155380436, creator=13701087609, updateTime=1790155380436, updator=13701087609, issue=Issue{id=1309569821424051089, tenantId=1146029695717560320, journalId=1308397196074385509, year='2026', volume='34', issue='3', pageStart='745', pageEnd='1136', issueExtLink='null', onlineDate='null', pubDate='1773504000000', pubDateStr='2026-03-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790155376058, creator='13701087609', updateTime=1790156773348, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309575682133422151, tenantId=1146029695717560320, journalId=1308397196074385509, issueId=1309569821424051089, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309575682133422152, tenantId=1146029695717560320, journalId=1308397196074385509, issueId=1309569821424051089, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=984, endPage=997, ext={EN=ArticleExt(id=1309569840210338779, articleId=1309569839786714073, tenantId=1146029695717560320, journalId=1308397196074385509, language=EN, title=Spatial Patterns and Modeling of Soil Moisture in the Qinghai Lake Basin, columnId=null, journalTitle=Acta Agrestia Sinica, columnName=null, runingTitle=null, highlight=null, articleAbstract=Spatial patterns of soil water content are fundamental to regional ecosystem stability and the sustainable 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， precipitation， and the Normalized Difference Vegetation Index were the primary influencing factors of the soil moisture spatial model. This study elucidates the spatial distribution patterns and environmental response mechanisms of soil moisture in the Qinghai Lake Basin， providing a scientific basis for regional ecological restoration and water resource management., authors=DING Chen-shen^1,2, CAO Sheng-kui^1,2,3, ZHANG Fu-ling⁴, WANG Jiang^1,2, HOU Yao-fang^1,2, LEI Yi-zhen^1,2, PEI Ruo-ying^1,2, authorsList=DING Chen-shen, CAO Sheng-kui, ZHANG Fu-ling, WANG Jiang, HOU Yao-fang, LEI Yi-zhen, PEI Ruo-ying, authorCompany=1 Qinghai Provincial Key Laboratory of Physical Geography and Environmental Processes, College of Geographical Science, Qinghai Normal University, Xining, Qinghai Province 810008, China;
2 Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation (Ministry of Education), Qinghai Normal University, Xining, Qinghai Province 810008, China;
3 Academy of Plateau Science and Sustainability, People's Government of Qinghai Province and Beijing Normal University, Xining, Qinghai Province 810008, China;
4 Qinghai Provincial Key Laboratory of Geospatial Information Technology and Application, Xining, Qinghai Province 810008, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309569840063538138, articleId=1309569839786714073, tenantId=1146029695717560320, journalId=1308397196074385509, language=CN, title=青海湖流域土壤水分空间格局及模拟研究, columnId=1309565024058835950, journalTitle=草地学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=土壤水分空间分布格局对区域生态稳定和水资源可持续利用至关重要。本研究以青海湖流域为研究对象，结合实测数据和遥感影像，采用数理统计和地理空间分析方法研究了青海湖流域不同深度土壤含水量和储水量的空间分布特征，并借助随机森林（Random forest， RF）和极端梯度提升（Extreme gradient boosting，XGBoost）机器学习模型模拟了流域内不同深度土壤含水量和储水量。结果表明：青海湖流域土壤含水量随土壤深度增加逐渐减少（分别为32.06%，27.17%，24.99%），土壤储水量随土壤深度增加而增加（分别为29.72，32.07，32.34 mm）；青海湖流域土壤水分空间格局整体呈北高南低、东高西低、河流上游高下游低，环湖地区山区高湖滨低的特征。不同植被类型，土壤含水量和储水量均以高寒草甸最高。模型模拟结果显示，气温、降水和归一化植被指数是土壤水分空间模型的主要影响因素。本研究揭示了青海湖流域土壤水分的空间分布规律及环境响应机制，为区域生态恢复和水资源管理提供科学依据。, authors=丁辰深^1,2, 曹生奎^1,2,3, 张富玲⁴, 王江^1,2, 侯瑶芳^1,2, 雷义珍^1,2, 裴若颖^1,2, authorsList=丁辰深, 曹生奎, 张富玲, 王江, 侯瑶芳, 雷义珍, 裴若颖, authorCompany=1 青海师范大学地理科学学院/青海省自然地理与环境过程重点实验室, 青海 西宁 810008;
2 青海师范大学青藏高原地表过程与生态保育教育部重点实验室, 青海 西宁 810008;
3 青海省人民政府-北京师范大学高原科学与可持续发展研究院, 青海 西宁 810008;
4 青海省地理空间信息技术与应用重点实验室, 青海 西宁 810008, correspAuthors=null, authorNote=丁辰深（2001-），男，汉族，河南驻马店人，硕士研究生，主要从事生态水文研究，E-mail：19840970292@163.com, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=vkosy5HWHMJlIkpxe0Y0pA==, pdfFileSize=10314703, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=青海省自然科学基金项目 (2023-ZJ-924M))}, authors=null, keywords=[Keyword(id=1309569840357139420, tenantId=1146029695717560320, journalId=1308397196074385509, articleId=1309569839786714073, language=CN, orderNo=1, keyword=土壤含水量), Keyword(id=1309569840441025501, tenantId=1146029695717560320, journalId=1308397196074385509, articleId=1309569839786714073, language=CN, orderNo=2, keyword=土壤储水量), Keyword(id=1309569840550077406, 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