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© 2025. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Large-scale hydrologic modeling at the national scale is an increasingly important effort worldwide to tackle ecohydrologic issues induced by global water scarcity. In this study, a surface water–groundwater integrated hydrologic modeling platform was built using ParFlow, covering the entirety of continental China with a resolution of 30 arcsec. This model, CONCN 1.0, offers a full treatment of 3D variably saturated groundwater by solving Richards' equation, along with the shallow-water equation at the ground surface. The performance of CONCN 1.0 was rigorously evaluated using both global data products and observations. RSR values (the ratio of the root mean squared error to the standard deviation of observations) show satisfying performance with regard to streamflow, yet the streamflow is lower in the endorheic, Hai, and Liao rivers due to uncertainties in potential recharge. RSR values also indicate satisfying performance in terms of the water table depth of the CONCN model. This is an intermediate performance compared to two global groundwater models, highlighting the uncertainties that persist in current large-scale groundwater modeling. Our modeling work is also a comprehensive evaluation of the current workflow for continental-scale hydrologic modeling using ParFlow and could be a good starting point for modeling in other regions worldwide, even when using different modeling systems. More specifically, the vast arid and semi-arid regions in China with substantial sinks (i.e., the endpoints of endorheic rivers) and the large uncertainties in potential recharge pose challenges for the numerical solution and model performance, respectively. Incompatibilities between data and the model, such as the mismatch of spatial resolutions between models and products and the shorter, less frequent observation records, necessitate further refinement of the workflow to enable fast modeling. This work not only establishes the first integrated hydrologic modeling platform in China for efficient water resources management but will also benefit the improvement of next-generation models worldwide.

Details

Title
CONCN: a high-resolution, integrated surface water–groundwater ParFlow modeling platform of continental China
Author
Chen, Yang 1 ; Jia, Zitong 2 ; Xu, Wenjie 3 ; Zhongwang Wei 1 ; Zhang, Xiaolang 4 ; Zou, Yiguang 5   VIAFID ORCID Logo  ; McDonnell, Jeffrey 6 ; Condon, Laura 7   VIAFID ORCID Logo  ; Dai, Yongjiu 1 ; Reed, Maxwell 8   VIAFID ORCID Logo 

 School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China 
 College of Water Sciences, Beijing Normal University, Beijing, China 
 Institute of Geological Survey, China University of Geosciences, Wuhan, China 
 Department of Geosciences, Florida Atlantic University, Boca Raton, USA 
 Department of Geography, National University of Singapore, Singapore, Singapore 
 School of Environment and Sustainability, Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada; School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham, UK; North China University of Water Resources and Electric Power, Zhengzhou, China 
 Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, USA 
 Department of Civil and Environmental Engineering, High Meadows Environmental Institute, Integrated GroundWater Modeling Center, Princeton University, Princeton, USA 
Pages
2201-2218
Publication year
2025
Publication date
2025
Publisher
Copernicus GmbH
ISSN
10275606
e-ISSN
16077938
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3202667009
Copyright
© 2025. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.