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中国黄土区影响地下水干旱的因素分析

Analysis of factors influencing groundwater drought in the Loess zone of China.

作者信息

Qiu Yunxiao, He Zimiao, Yu Xinxiao, Jia Guodong, Wang Yusong

机构信息

Beijing Forestry University, Key Laboratory of Soil and Water Conservation and Desertification Combating of Ministry of Education, Beijing 100083, P.R. China.

North China University of Water Resources and Electric Power, College of Surveying and Geo-informatics, Zhengzhou 450046, P.R. China.

出版信息

iScience. 2024 Sep 11;27(10):110929. doi: 10.1016/j.isci.2024.110929. eCollection 2024 Oct 18.

DOI:10.1016/j.isci.2024.110929
PMID:39635120
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11615186/
Abstract

Understanding the characteristics and factors influencing groundwater resources is important for regional water resources management. The Gravity Recovery and Climate Experiment (GRACE)-based groundwater conditions were used to analyze the spatiotemporal characteristics of and the factors influencing groundwater storage (GWS) distribution in the Loess zone of the Yellow River Basin. The results revealed that the spatiotemporal distribution of GWS anomalies in the Loess zone of China was best explained by the first three components of the empirical orthogonal function (EOF), representing 85.6% of the total variance. The normalized difference vegetation index (NDVI) was significantly correlated with groundwater drought ( < 0.05). In addition, NDVI and evapotranspiration (ET) were the dominant factors influencing groundwater drought. NDVI was the dominant influencing factor in 67% and 80% of the total study area between 2002-2014 and 2015-2021, respectively. This study provides important guidance for a future ecological restoration plan in the Loess zone.

摘要

了解影响地下水资源的特征和因素对于区域水资源管理至关重要。基于重力恢复与气候实验（GRACE）的地下水状况被用于分析黄河流域黄土区地下水储量（GWS）分布的时空特征及其影响因素。结果表明，中国黄土区GWS异常的时空分布最好由经验正交函数（EOF）的前三个分量来解释，占总方差的85.6%。归一化植被指数（NDVI）与地下水干旱显著相关（<0.05）。此外，NDVI和蒸散量（ET）是影响地下水干旱的主要因素。在2002 - 2014年和2015 - 2021年期间，NDVI分别是整个研究区域的67%和80%的主要影响因素。该研究为黄土区未来的生态恢复计划提供了重要指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/0505e1d6bffb/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/a2ff3761fd10/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/d0ac1d66a25e/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/778538973e80/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/89d7458e46ae/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/f9aaf6303932/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/d6d46b67a23a/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/9367fdc59700/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/06b3c3b2787e/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/15d6937edb5c/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/0505e1d6bffb/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/a2ff3761fd10/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/d0ac1d66a25e/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/778538973e80/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/89d7458e46ae/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/f9aaf6303932/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/d6d46b67a23a/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/9367fdc59700/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/06b3c3b2787e/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/15d6937edb5c/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6200/11615186/0505e1d6bffb/gr9.jpg

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本文引用的文献

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The changing nature of groundwater in the global water cycle.全球水循环中地下水性质的变化。
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Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California.基于机器学习的加利福尼亚中央谷地GRACE估算地下水降尺度分析
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Mapping groundwater-dependent ecosystems in arid Central Asia: Implications for controlling regional land degradation.绘制干旱中亚地区依赖地下水的生态系统图谱：对控制区域土地退化的启示。
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