Identification of Driving Factors of Long-Term Terrestrial Water Storage Anomaly Trend Changes in the Yangtze River Basin Based on Multisource Data and Geographical Detector Method

Terrestrial water storage anomaly (TWSA) plays a vital role in regulating the global water cycle and freshwater availability. Understanding the drivers behind long-term TWSA changes is critical, yet disentangling natural and anthropogenic influences remains challenging. This study employs the Geographical Detector method and multisource data to quantify the individual and interactive effects of multiple drivers on TWSA trends across the upper, middle, and lower reaches of the Yangtze River Basin (YRB). In the upper YRB, temperature, snow water equivalent, vegetation, precipitation, and reservoir storage are the primary contributors. In the middle YRB, precipitation, temperature, and soil moisture dominate. Although nighttime light (a proxy for urbanization) alone explains only 1.94% of the variation in this region, its interaction with precipitation increases explanatory power to 56.3%, highlighting a strong nonlinear effect. In the lower YRB, precipitation and runoff are the leading factors, while nighttime light again exhibits enhanced influence through interactions. These findings reveal the spatial heterogeneity and synergistic nature of TWSA drivers and underscore the need to consider both natural variability and human-induced processes when assessing long-term water storage dynamics. The results offer valuable insights for sustainable water resource management in the context of climate change and rapid urban development.

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Publication Details

Journal
Water
Published
2025-10-09
DOI
https://doi.org/10.3390/w17192914
Primary Topic
Hydrology and Watershed Management Studies
Type
article
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article

Identification of Driving Factors of Long-Term Terrestrial Water Storage Anomaly Trend Changes in the Yangtze River Basin Based on Multisource Data and Geographical Detector Method

Ying‐Ping Wang, QU Ying-jie, Zhu Yao, Junke Wang et al.
Water
Hydrology and Watershed Management Studies
article

Identification of Driving Factors of Long-Term Terrestrial Water Storage Anomaly Trend Changes in the Yangtze River Basin Based on Multisource Data and Geographical Detector Method

Ying‐Ping Wang, QU Ying-jie, Zhu Yao, Junke Wang, Qin Li, Song Ye, B Liao
article en

Abstract

Terrestrial water storage anomaly (TWSA) plays a vital role in regulating the global water cycle and freshwater availability. Understanding the drivers behind long-term TWSA changes is critical, yet disentangling natural and anthropogenic influences remains challenging. This study employs the Geographical Detector method and multisource data to quantify the individual and interactive effects of multiple drivers on TWSA trends across the upper, middle, and lower reaches of the Yangtze River Basin (YRB). In the upper YRB, temperature, snow water equivalent, vegetation, precipitation, and reservoir storage are the primary contributors. In the middle YRB, precipitation, temperature, and soil moisture dominate. Although nighttime light (a proxy for urbanization) alone explains only 1.94% of the variation in this region, its interaction with precipitation increases explanatory power to 56.3%, highlighting a strong nonlinear effect. In the lower YRB, precipitation and runoff are the leading factors, while nighttime light again exhibits enhanced influence through interactions. These findings reveal the spatial heterogeneity and synergistic nature of TWSA drivers and underscore the need to consider both natural variability and human-induced processes when assessing long-term water storage dynamics. The results offer valuable insights for sustainable water resource management in the context of climate change and rapid urban development.

WaterVol. 17(19)
Changjiang Water Resources Commission (CN)
Openalex Percentile: Top 74%
Hydrology and Watershed Management Studies
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Identification of Driving Factors of Long-Term Terrestrial Water Storage Anomaly Trend Changes in the Yangtze River Basin Based on Multisource Data and Geographical Detector Method — Ying‐Ping Wang, QU Ying-jie, et al. · Water (2025) | TGRS Research Map | TGRS