A hybrid physics–ML framework for integrating groundwater dynamics into land surface modeling: a ParFlow-based demonstration in headwater watersheds of the Pearl River Basin, China
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Hui Huang
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Zeyu Tang
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Yongjiu Dai
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Xiaobin Chang
School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, China
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Views and downloads (calculated since 20 May 2026)
Cumulative views and downloads
(calculated since 20 May 2026)
Total article views: 425 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
423
0
2
425
0
0
HTML: 423
PDF: 0
XML: 2
Total: 425
BibTeX: 0
EndNote: 0
Views and downloads (calculated since 20 May 2026)
Cumulative views and downloads
(calculated since 20 May 2026)
Viewed (geographical distribution)
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 425 (including HTML, PDF, and XML)
Thereof 412 with geography defined
and 13 with unknown origin.
Total article views: 425 (including HTML, PDF, and XML)
Thereof 412 with geography defined
and 13 with unknown origin.
Groundwater strongly influences exchanges of water and energy between land and atmosphere, but detailed simulation is computationally expensive. We developed a faster approach that preserves groundwater–land surface interactions. In a representative watershed within China's Pearl River Basin, it ran 18.24 times faster while closely matching the physics-based model, offering a practical way to represent groundwater in large-scale environmental modeling.
Groundwater strongly influences exchanges of water and energy between land and atmosphere, but...