Machine Learning-based Downscaling of GRACE Data to Enhance Assessment of Spatiotemporal Evolution of Coastal Plain Groundwater Storage

Chengcheng Wu, Chengpeng Lu (), Jingya Hu, Bo Liu, Longcang Shu and Yong Zhang
Additional contact information
Chengcheng Wu: Hohai University
Chengpeng Lu: Hohai University
Jingya Hu: Water Conservancy Bureau of Pinghu
Bo Liu: Hohai University
Longcang Shu: Hohai University
Yong Zhang: University of Alabama

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 12, No 17, 6377-6397

Abstract: Abstract Groundwater storage is vital for regional water security, particularly in hydrologically complex coastal plains. However, the coarse spatial resolution of GRACE satellite data limits its application in local-scale groundwater studies. This study presents a machine learning–based framework to downscale GRACE-derived terrestrial water storage anomalies (TWSA) using high-resolution (4 km × 4 km) TerraClimate datasets over the Hang-Jia-Hu (HJH) Plain in eastern China. Three machine learning models—Random Forest (RF), XGBoost (XGB), and LightGBM (LGBM) —were evaluated, with RF showing the best performance. The downscaled groundwater storage anomaly (GWSA) data were validated against 68 in-situ groundwater level (GWL) time series, resulting in an average correlation improvement of 16% at 45% of unconfined aquifer stations and 23% at 69% of confined aquifer stations. The analysis revealed a sharp GWS decline before 2011 due to over-extraction, followed by gradual recovery after groundwater regulation. Shapely Additive Explanations (SHAP) analysis identified the Palmer Drought Severity Index (PDSI), actual evapotranspiration (AET), and vapor pressure (VAP) as key model drivers. A leakage intensity index was developed using downscaled GWSA and GWL data, highlighting spatial variations in aquifer connectivity and identifying zones with strong vertical recharge that may buffer seawater intrusion. These results demonstrate that the proposed downscaling approach not only enhances the spatial resolution and accuracy of GRACE data but also enables improved assessment of groundwater system behavior in coastal regions, supporting more informed water resource management.

Keywords: Groundwater storage; GRACE satellite; Machine learning; Coastal plain; Leakage recharge (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-025-04254-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:39:y:2025:i:12:d:10.1007_s11269-025-04254-y

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-025-04254-y

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().