1.电子科技大学资源与环境学院,成都 611731
2.瑞士联邦森林雪与景观研究所,苏黎世 8903
邓诗宇(1997—),女,博士研究生,主要从事森林与气候变化对水文过程的影响研究。E-mail:shiyudeng@std.uestc.edu.cn
张明芳(1982—),女,博士,教授,主要从事土地利用/覆被变化及气候变化对水资源的影响、流域生态系统保护与管理研究。E-mail:mingfangzhang@uestc.edu.cn
收稿:2026-03-02,
修回:2026-04-02,
录用:2026-04-10,
网络首发:2026-07-20,
纸质出版:2026-10-28
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邓诗宇,张明芳,侯怡萍.中国流域陆地水储量变化驱动模式的空间分异规律[J].水土保持学报,2026,40(5):301-313.
Deng Shiyu, Zhang Mingfang, Hou Yiping. Spatial differentiation patterns of driving modes of terrestrial water storage variations in China′s river basins[J]. Journal of Soil and Water Conservation,2026,40(5):301-313.
邓诗宇,张明芳,侯怡萍.中国流域陆地水储量变化驱动模式的空间分异规律[J].水土保持学报,2026,40(5):301-313. DOI: 10.13870/j.cnki.stbcxb.2026.05.015. CSTR: 32310.14.stbcxb.2026.05.015.
Deng Shiyu, Zhang Mingfang, Hou Yiping. Spatial differentiation patterns of driving modes of terrestrial water storage variations in China′s river basins[J]. Journal of Soil and Water Conservation,2026,40(5):301-313. DOI: 10.13870/j.cnki.stbcxb.2026.05.015. CSTR: 32310.14.stbcxb.2026.05.015.
目的
2
针对我国流域陆地水储量变化的驱动模式分类不明和空间分异规律认识不清的问题,本研究旨在定量识别多因素对陆地水储量变化的贡献,揭示陆地水储量变化的驱动模式及其空间格局,为全球变化背景下流域水资源的可持续利用与管理提供科学支撑。
方法
2
采用Mann-Kendall趋势分析、偏相关分析和随机森林模型,结合重力恢复与气候试验(GRACE)卫星数据、气象站点、流域特征数据和人类活动数据,量化气候、人类活动和流域特征对我国214个三级流域陆地水储量变化的相对贡献,并采用K-means聚类分析法进一步揭示流域陆地水储量变化驱动模式及其空间分异规律。
结果
2
1)我国气候相对贡献率(Con
c
)高值区主要分布于东南沿海、东北及青藏高原等气候极端地区,如东南诸河流域Con
c
为64.82%,是全国平均Con
c
的1.5倍。流域特征贡献率(Con
w
)高值区集中在森林覆盖率高、地形复杂的地区(如西南诸河等Con
w
为30.27%,为全国平均Con
w
的2.6倍)。人类活动相对贡献率(Con
h
)较高的地区主要集中在西北诸河、海河和黄河流域等干旱-半干旱区及人口稠密的长江中下游流域,上述四大流域Con
h
平均值(55.84%)较全国平均Con
h
值高20%。2)中国流域陆地水储量变化驱动模式可
分为气候主导、人类活动主导、流域特征主导、气候-人类活动协同和气候-流域特征协同5类。松花江流域、辽河流域及青藏高原流域为气候主导模式;西北诸河流域、华北的海河流域、珠江流域中上游和长江流域下游则为人类活动主导模式。淮河下游和西南诸河流域为气候-流域特征协同驱动模式,长江流域中游、黄河流域中上游和西北诸河南部流域则为气候-人类活动协同驱动模式。
结论
2
我国未来水安全保障规划应该基于流域陆地水储量变化驱动模式的空间分异规律分区施策。在气候主导区提升生态系统的水文调节能力和水利工程设施的韧性,增强气候变化适应性;在人类活动主导区实施节水与生态保护协同管理;在气候-流域特征协同驱动区加强国土生态空间优化和流域生态修复,促进林水资源的协调性和气候韧性;气候-人类活动协同驱动区强化水资源的适应性管理,协调好生态用水和经济社会用水。本研究可为我国构建全球变化下流域水资源协同治理体系、保障国家水安全提供科学支撑。
Objective
2
To address the unclear classification of driving modes and the insufficient understanding of spatial differentiation patterns of terrestrial water storage (TWS) variations in China′s river basins, this study aims to quantitatively identify the contributions of multiple factors to TWS variations, reveal the driving modes and their spatial distributions, and provide scientific support for sustainable utilization and management of water resources in river basins under global change.
Methods
2
This study employed the Mann-Kendall trend analysis, partial correlation analysis, and random forest models, combined with multi-source datasets including Gravity Recovery and Climate Experiment (GRACE) satellite data, meteorological station observations, basin characteristics, and human activity data. The relative contributions of climate, human activities, and basin characteristics to TWS variations in 214 third-order basins in China were quantified. K-means clustering analysis was further applied to reveal the driving modes of TWS variations and their spatial differentiation patterns.
Results
2
1) Regions with high relative contribution of climate (Con
c
) were mainly distributed in climate-extreme areas, including the southeastern coast, northeastern China, and the Qinghai-Xizang Plateau. Specifically, Con
c
in the southeastern river basins was 64.82%, which was 1.5 times the national average
.
Re
gions with high contribution of basin characteristics (Con
w
) were concentrated in areas with high forest coverage and complex terrain (e.g., Con
w
in the southwestern rivers was 30.27%, which was 2.6 times the national average). Regions with high relative contribution of human activities (Conₕ) were mainly distributed in arid and semi-arid regions, including the northwestern river basins, the Haihe River basin, and the Yellow River basin, as well as in densely populated regions such as the middle and lower reaches of the Yangtze River. The average Conₕ in these four regions was 55.84%, which was 20% higher than the national average. 2) The driving modes of TWS variations in China′s river basins were classified into five types: climate-dominated, human activity-dominated, basin characteristic-dominated, climate-human activity co-driven, and climate-basin characteristic co-driven. The Songhua River basin, Liaohe River basin, and Qinghai-Xizang Plateau river basins were climate-dominated. The northwestern river basins, the Haihe River basin in North China, the middle and upper reaches of the Pearl River basin, and the lower reaches of the Yangtze River basin were human activity-dominated. The lower Huaihe River basin and the southwestern river basins were climate-basin characteristic co-driven. The middle Yangtze River basin, the middle and upper Yellow River basin, and the southern part of the northwestern river basins were climate-human activity co-driven.
Conclusion
2
Future water security planning in China should implement region-specific strategies based on the spatial differentiation pattern of driving modes of TWS variations. In climate-dominated regions, efforts should focus on enhancing hydrological regulation capacity of ecosystems and the resilience of water infrastructure to strengthen climate change adaptability. In human activity-dominated regions, coordinated management of water conservation and ecological protection should be implemented. In climate-basin characteristic co-driven regions, ecological spatial optimization and basin ecological restoration should be strengthened to promote the coordination of forest and water resources and climate resilience. In climate-human co-driven regions, adaptive water resource management should be strengthened to coordinate ecological water use and socioeconomic water use. This study provides scientific support for the construction of a coordinated governance system for basin water resources and for ensuring national water security under global change.
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