Li et al. (2025) 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

Identification

• Journal: Water
• Year: 2025
• Date: 2025-10-09
• Authors: Qin Li, Song Ye, Ying‐Ping Wang, QU Ying-jie, Zhu Yao, B Liao, Junke Wang
• DOI: 10.3390/w17192914

Research Groups

• Spatial Information Technology Application Department, Changjiang River Scientific Research Institute, Wuhan, China
• Key Lab of Basin Water Resource and Eco-Environmental Science in Hubei Province, Wuhan, China

Short Summary

This study quantifies the individual and interactive effects of natural and anthropogenic factors on long-term terrestrial water storage anomaly (TWSA) trends across the Yangtze River Basin (YRB) using multisource data and the Geographical Detector method. It reveals spatially heterogeneous and synergistic drivers, highlighting the critical role of both climate variability and human activities in regional water storage dynamics.

Objective

• To identify the key driving factors and their interactions that dominate the trends of terrestrial water storage anomaly (TWSA) changes in the Yangtze River Basin (YRB) and to analyze their spatial differentiation across its upper, middle, and lower reaches.

Study Configuration

• Spatial Scale: Yangtze River Basin (YRB), covering approximately 1.8 million square kilometers, divided into upper, middle, and lower reaches. Data resampled to a uniform spatial resolution of 0.25° × 0.25°.
• Temporal Scale: Long-term trends from 2002 to 2017, with data aggregated to a monthly scale.

Methodology and Data

• Models used:
  • Geographical Detector (GeoDetector) model (factor detector and interaction detector modules)
  • Lindemann, Merenda, and Gold (LMG) method (for comparison)
• Data sources:
  • Terrestrial Water Storage Anomaly (TWSA): GRACE CSR-M mascon product
  • Soil Moisture Storage (SMS): FLDAS Noah
  • Evapotranspiration (ET) & Potential Evapotranspiration (PET): GLEAM
  • Precipitation (PRE): CN05.1
  • Temperature (TEM) & Snow Water Equivalent (SWE): ERA5 Land
  • Runoff (RO): CNRD v1.0
  • Normalized Difference Vegetation Index (NDVI): 5 km resolution dataset of monthly NDVI product of China
  • Nighttime-light (LIGHT): A prolonged artificial nighttime-light dataset of China
  • Reservoir Water Storage (RWS): WaterGAP Global Hydrology Model (WGHM)

Main Results

• Overall TWSA Trend: The YRB generally shows an increasing TWSA trend, with rates exceeding 5 millimeters per year in the eastern upper and middle reaches. However, the central-western upper reaches exhibit a reduction of approximately -5 millimeters per year due to glacier melting.
• Upper YRB: Temperature (TEM, Q=0.3802), Snow Water Equivalent (SWE, Q=0.3669), Normalized Difference Vegetation Index (NDVI, Q=0.234), Precipitation (PRE, Q=0.1671), and Reservoir Water Storage (RWS, Q=0.1531) are the primary drivers. All pairwise interactions enhance explanatory power, with TEM and SWE showing particularly strong synergistic effects.
• Middle YRB: Precipitation (PRE, Q=0.5369), Temperature (TEM, Q=0.5174), Soil Moisture Storage (SMS, Q=0.5065), and Runoff (RO, Q=0.3937) are the dominant factors. Interactions between TEM and SMS (71.1%) and RO and TEM (66.3%) show high explanatory power. Nighttime light (LIGHT), while weak alone (Q=0.0194), exhibits strong nonlinear enhancement when interacting with PRE (56.3%), TEM (53.9%), and SMS (53.3%).
• Lower YRB: Precipitation (PRE, Q=0.575) and Runoff (RO, Q=0.3191) are the most significant drivers. Soil Moisture Storage (SMS, Q=0.2547) and Nighttime-light (LIGHT, Q=0.2206) also show notable influence. All two-factor interactions enhance explanatory power, with PRE combined with any other factor exceeding 60% (e.g., PRE with SWE at 70.5%, PRE with LIGHT at 64.8%). Nonlinear enhancement dominates interactions.

Contributions

• Provides a quantitative identification of individual and interactive effects of multiple natural and anthropogenic drivers on long-term TWSA trends across the YRB's sub-basins.
• Offers region-specific insights into the spatially heterogeneous and synergistic nature of TWSA drivers, which is crucial for sustainable water resource management.
• Introduces an integrated application of the GeoDetector model as a novel framework for disentangling multi-source drivers of water storage dynamics in large river basins.

Funding

• Fundamental Research Funds for Central Public Welfare Research Institutes of China (Grant No. CKSF20241028/KJ, No. CKSF2025703/KJ, No. CKSF2025727/KJ).

Citation

@article{Li2025Identification,
  author = {Li, Qin and Ye, Song and Wang, Ying‐Ping and Ying-jie, QU and Yao, Zhu and Liao, B and Wang, Junke},
  title = {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},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w17192914},
  url = {https://doi.org/10.3390/w17192914}
}