Mon Feb 16, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Ali, Shoaib, Ran, Jiangjun, Tangdamrongsub, Natthachet, Khorrami, Behnam, Ferreira, Vagner, Shi, Haiyun, and Zhang, Wenmin, 2025. Weight–Supported Random Forest Downscaled GRACE (–FO) Data Uncovers Groundwater Depletion Linked to Winter Wheat Cultivation in the North China Plain. Earth Systems and Environment, .
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025ESE...tmp..415A,
author = {{Ali}, Shoaib and {Ran}, Jiangjun and {Tangdamrongsub}, Natthachet and {Khorrami}, Behnam and {Ferreira}, Vagner and {Shi}, Haiyun and {Zhang}, Wenmin},
title = "{Weight-Supported Random Forest Downscaled GRACE (-FO) Data Uncovers Groundwater Depletion Linked to Winter Wheat Cultivation in the North China Plain}",
journal = {Earth Systems and Environment},
keywords = {North china plain, GRACE (-FO), GWSA, RF$_{SW}$, Downscaling, Winter wheat},
year = 2025,
month = dec,
abstract = "{Groundwater is a critical resource for sustainable development,
particularly in arid regions facing water scarcity. The Gravity
Recovery and Climate Experiment (GRACE) and its Follow-On,
GRACE-FO, offer valuable data on groundwater storage anomalies
(GWSA). However, while their coarse resolution has been improved
using machine learning approaches such as the global random
forest (RF$_{G}$) model, the aspatial nature of the RF$_{G}$
model limits its ability to capture spatial heterogeneity when
downscaling GRACE (-FO) data. Downscaling GWSA data to higher
resolutions is crucial for assessing small-scale groundwater
variations. To address this, a novel spatially weighted random
forest (RF$_{SW}$) model has been proposed to downscale GWSA to
a high resolution (0.1{\textdegree}) across the North China
Plain (NCP) from 2003 to 2023. We found that the RF$_{SW}$ model
outperforms the RF$_{G}$ model, reducing RMSE by 44.44\% and
residuals by 43.57\%. The downscaled GWSA data strongly
correlate with in-situ measurements from 559 monitoring wells
(correlation coefficients: 0.52-0.85), revealing significant
groundwater depletion in the Piedmont Plain (PP) and East-
Central Plain (ECP) sub-regions, with the most severe losses in
Shijiazhuang (17.08), Xingtai (16.67), and Handan (16.02 mm/yr),
respectively. The winter wheat area doubling from 2.5 million to
5.8 million hectares, reducing GWSA from 180 mm to 480 mm. This
improved downscaling technique enhances our understanding of
local groundwater dynamics and their relationship to
agricultural practices. This method's high-resolution GWSA data
can inform more targeted and effective water management
strategies in water-stressed regions worldwide.}",
doi = {10.1007/s41748-025-00976-6},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025ESE...tmp..415A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Mon Feb 16, F. Flechtner