Articles | Volume 30, issue 12
https://doi.org/10.5194/hess-30-3763-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-3763-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jun 2026
Research article |
| 23 Jun 2026
| 23 Jun 2026
Quantification of delayed recharge by soil surface and riverbed infiltration in a deep groundwater depression zone in the North China Plain
Shenghao Xu
State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin 300350, China
Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
Xinwang Li
Hebei Institute of Water Science (Hebei province dam safety technology center, Hebei province levee sluice technology center), Shijiazhuang 050051, China
Jianzhu Li
State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin 300350, China
Wenhao Shi
Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
Shaowei Lian
Hebei Provincial Hydrologic Survey and Research Center, Shijiazhuang 050051, China
Lei Li
Ninth Geological Brigade of Hebei Bureau of Geology and Mineral Resources, Xingtai 054000, China
Lutz Weihermüller
Agrosphere Institute IBG-3, Forschungszentrum Jülich GmbH, Jülich 52428, Germany
Marcel Schaap
Department of Environmental Science, University of Arizona, Tucson 85721, USA
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Hydrol. Earth Syst. Sci., 30, 2523–2542, https://doi.org/10.5194/hess-30-2523-2026, https://doi.org/10.5194/hess-30-2523-2026, 2026
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Wenhong Wang, Shiao Feng, Yonggen Zhang, Zhongwang Wei, Jianzhi Dong, Lutz Weihermüller, Cong-Qiang Liu, and Harry Vereecken
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Ting Zhang, Ran Zhang, Jianzhu Li, and Ping Feng
Hydrol. Earth Syst. Sci., 29, 5955–5974, https://doi.org/10.5194/hess-29-5955-2025, https://doi.org/10.5194/hess-29-5955-2025, 2025
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This study presents a model integrating attention mechanisms and physical constraints to improve flood prediction. It forecasts floods up to 6 h in advance. The model enhances accuracy by focusing on critical input features and historical patterns. Results demonstrate its superior performance compared to other models, offering improved flood prediction with greater interpretability and alignment with physical laws.
Manuela S. Kaufmann, Anja Klotzsche, Jan van der Kruk, Anke Langen, Harry Vereecken, and Lutz Weihermüller
SOIL, 11, 267–285, https://doi.org/10.5194/soil-11-267-2025, https://doi.org/10.5194/soil-11-267-2025, 2025
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To use fertilizers more effectively, non-invasive geophysical methods can be used to understand nutrient distributions in the soil. We utilize, in a long-term field study, geophysical techniques to study soil properties and conditions under different fertilizer treatments. We compared the geophysical response with soil samples and soil sensor data. In particular, electromagnetic induction and electrical resistivity tomography were effective in monitoring changes in nitrate levels over time.
Yan Liu, Ting Zhang, Yi Ding, Aiqing Kang, Xiaohui Lei, and Jianzhu Li
Hydrol. Earth Syst. Sci., 28, 5541–5555, https://doi.org/10.5194/hess-28-5541-2024, https://doi.org/10.5194/hess-28-5541-2024, 2024
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Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
Hydrol. Earth Syst. Sci., 28, 3391–3433, https://doi.org/10.5194/hess-28-3391-2024, https://doi.org/10.5194/hess-28-3391-2024, 2024
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Pedotransfer functions (PTFs) are used to predict parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically relies on the nature of the datasets for training the PTFs and the physical comprehensiveness of the models. This roadmap paper is addressed to PTF developers and users and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTF research.
Siyao Zhang, Jianzhu Li, Ting Zhang, and Ping Feng
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-185, https://doi.org/10.5194/hess-2024-185, 2024
Manuscript not accepted for further review
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Flash drought (FD) in the North China Plain (NCP) from 1981 to 2022 was identified by three methods. It occurs more frequently in central and northern NCP than southern, with longer duration in southern and eastern NCP. FD shows seasonal variations. NCP is prone to severe FDs. There is a decreasing trend in FD, with three hotspots in northwestern, eastern and southwestern NCP. The FD frequency is affected by thresholds in the identification methods. This study may be valuable for FD mitigation.
Yanchen Zheng, Gemma Coxon, Ross Woods, Daniel Power, Miguel Angel Rico-Ramirez, David McJannet, Rafael Rosolem, Jianzhu Li, and Ping Feng
Hydrol. Earth Syst. Sci., 28, 1999–2022, https://doi.org/10.5194/hess-28-1999-2024, https://doi.org/10.5194/hess-28-1999-2024, 2024
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Reanalysis soil moisture products are a vital basis for hydrological and environmental research. Previous product evaluation is limited by the scale difference (point and grid scale). This paper adopts cosmic ray neutron sensor observations, a novel technique that provides root-zone soil moisture at field scale. In this paper, global harmonized CRNS observations were used to assess products. ERA5-Land, SMAPL4, CFSv2, CRA40 and GLEAM show better performance than MERRA2, GLDAS-Noah and JRA55.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Benjamin Guillaume, Hanane Aroui Boukbida, Gerben Bakker, Andrzej Bieganowski, Yves Brostaux, Wim Cornelis, Wolfgang Durner, Christian Hartmann, Bo V. Iversen, Mathieu Javaux, Joachim Ingwersen, Krzysztof Lamorski, Axel Lamparter, András Makó, Ana María Mingot Soriano, Ingmar Messing, Attila Nemes, Alexandre Pomes-Bordedebat, Martine van der Ploeg, Tobias Karl David Weber, Lutz Weihermüller, Joost Wellens, and Aurore Degré
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Measurements of soil water retention properties play an important role in a variety of societal issues that depend on soil water conditions. However, there is little concern about the consistency of these measurements between laboratories. We conducted an interlaboratory comparison to assess the reproducibility of the measurement of the soil water retention curve. Results highlight the need to harmonize and standardize procedures to improve the description of unsaturated processes in soils.
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Short summary
The North China Plain, home to over 300 million people, faces groundwater decline from irrigation, creating 80-meter-deep zones threatening supplies. We modeled rain and river infiltration using borehole and weather records to study restoration. Rain recharge averages 446 days, varying nearly 140 times: 10 days in sandy western foothills to 1395 days in clayey central/eastern plains. River recharge is faster at 91 days, suggesting flood basins could accelerate recovery for sustainable farming.
The North China Plain, home to over 300 million people, faces groundwater decline from...
