Articles | Volume 30, issue 10
https://doi.org/10.5194/hess-30-3061-2026
© 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-30-3061-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Rock fracture characteristics regulate water storage and seasonal tree water uptake in karst
Xiuqiang Liu
School of Earth System Science, Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China
Xi Chen
CORRESPONDING AUTHOR
School of Earth System Science, Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin 300072, China
Zhicai Zhang
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Weihan Liu
School of Earth System Science, Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China
Tao Peng
State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China
Puding Karst Ecosystem Research Station, Chinese Academy of Sciences, Puding 562100, China
Jeffrey J. McDonnell
Global Institute for Water Security, School of Environment and Sustainability, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
North China University of Water Resources and Electric Power, Zhengzhou, China
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
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We developed the first high-resolution, integrated surface water–groundwater hydrologic model of the entirety of continental China using ParFlow. The model shows good performance in terms of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision-making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
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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.
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Hydrol. Earth Syst. Sci., 26, 5515–5534, https://doi.org/10.5194/hess-26-5515-2022, https://doi.org/10.5194/hess-26-5515-2022, 2022
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We developed a coupled flow–tracer model to understand the effects of passive storage on modeling hydrological function and isotope dynamics in a karst flow system. Models with passive storages show improvement in matching isotope dynamics performance, and the improved performance also strongly depends on the number and location of passive storages. Our results also suggested that the solute transport is primarily controlled by advection and hydrodynamic dispersion in the steep hillslope unit.
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Short summary
Trees in rocky landscapes often depend on hidden water deep underground, yet the role of bedrock cracks remains unclear. In a karst forest in southwest China, we traced water movement and seasonal use by trees using stable isotopes. We found that large soil-filled fractures can store water for months, providing a crucial supply in the early growing season and helping forests cope with wet–dry changes in rainfall patterns.
Trees in rocky landscapes often depend on hidden water deep underground, yet the role of bedrock...