Articles | Volume 27, issue 14
https://doi.org/10.5194/hess-27-2607-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-27-2607-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Opinion article
| 
18 Jul 2023
Opinion article |
| 18 Jul 2023
| 18 Jul 2023
HESS Opinions: Are soils overrated in hydrology?
Hongkai Gao
CORRESPONDING AUTHOR
School of Geographic Sciences, East China Normal University, Shanghai, China
Key Laboratory of Geographic Information Science (Ministry of Education of China), East China Normal University, Shanghai, China
State Key Laboratory of Tibetan Plateau Earth System and Resources Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
Fabrizio Fenicia
Eawag, Swiss Federal Institute of Aquatic Science and Technology,
Dubendorf, Switzerland
Hubert H. G. Savenije
Water Resources Section, Delft University of Technology, Delft, the
Netherlands
Related authors
Leilei Yong, Yahui Wang, Batsuren Dorjsuren, Zheng Duan, and Hongkai Gao
EGUsphere, https://doi.org/10.5194/egusphere-2025-3062, https://doi.org/10.5194/egusphere-2025-3062, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Topography and vegetation critically influence hydrology but are often underrepresented in models, especially in cold, data-scarce regions like Mongolia. Using a stepwise FLEX framework, we assessed their roles in two river basins. Distributed (FLEXD) and landscape-based (FLEXT) models outperformed lumped versions. High elevations showed delayed melt sustaining flow, while low elevations responded rapidly to rain. Study confirms topography/vegetation as key hydrological controls.
Hongkai Gao, Markus Hrachowitz, Lan Wang-Erlandsson, Fabrizio Fenicia, Qiaojuan Xi, Jianyang Xia, Wei Shao, Ge Sun, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 4477–4499, https://doi.org/10.5194/hess-28-4477-2024, https://doi.org/10.5194/hess-28-4477-2024, 2024
Short summary
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The concept of the root zone is widely used but lacks a precise definition. Its importance in Earth system science is not well elaborated upon. Here, we clarified its definition with several similar terms to bridge the multi-disciplinary gap. We underscore the key role of the root zone in the Earth system, which links the biosphere, hydrosphere, lithosphere, atmosphere, and anthroposphere. To better represent the root zone, we advocate for a paradigm shift towards ecosystem-centred modelling.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
Short summary
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An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Jiaxing Liang, Hongkai Gao, Fabrizio Fenicia, Qiaojuan Xi, Yahui Wang, and Hubert H. G. Savenije
EGUsphere, https://doi.org/10.5194/egusphere-2024-550, https://doi.org/10.5194/egusphere-2024-550, 2024
Preprint archived
Short summary
Short summary
The root zone storage capacity (Sumax) is a key element in hydrology and land-atmospheric interaction. In this study, we utilized a hydrological model and a dynamic parameter identification method, to quantify the temporal trends of Sumax for 497 catchments in the USA. We found that 423 catchments (85 %) showed increasing Sumax, which averagely increased from 178 to 235 mm between 1980 and 2014. The increasing trend was also validated by multi-sources data and independent methods.
Hongkai Gao, Chuntan Han, Rensheng Chen, Zijing Feng, Kang Wang, Fabrizio Fenicia, and Hubert Savenije
Hydrol. Earth Syst. Sci., 26, 4187–4208, https://doi.org/10.5194/hess-26-4187-2022, https://doi.org/10.5194/hess-26-4187-2022, 2022
Short summary
Short summary
Frozen soil hydrology is one of the 23 unsolved problems in hydrology (UPH). In this study, we developed a novel conceptual frozen soil hydrological model, FLEX-Topo-FS. The model successfully reproduced the soil freeze–thaw process, and its impacts on hydrologic connectivity, runoff generation, and groundwater. We believe this study is a breakthrough for the 23 UPH, giving us new insights on frozen soil hydrology, with broad implications for predicting cold region hydrology in future.
Hongkai Gao, Chuntan Han, Rensheng Chen, Zijing Feng, Kang Wang, Fabrizio Fenicia, and Hubert Savenije
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-264, https://doi.org/10.5194/hess-2021-264, 2021
Manuscript not accepted for further review
Short summary
Short summary
Permafrost hydrology is one of the 23 major unsolved problems in hydrology. In this study, we used a stepwise modeling and dynamic parameter method to examine the impact of permafrost on streamflow in the Hulu catchment in western China. We found that: topography and landscape are dominant controls on catchment response; baseflow recession is slower than other regions; precipitation-runoff relationship is non-stationary; permafrost impacts on streamflow mostly at the beginning of melting season.
Leilei Yong, Yahui Wang, Batsuren Dorjsuren, Zheng Duan, and Hongkai Gao
EGUsphere, https://doi.org/10.5194/egusphere-2025-3062, https://doi.org/10.5194/egusphere-2025-3062, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Topography and vegetation critically influence hydrology but are often underrepresented in models, especially in cold, data-scarce regions like Mongolia. Using a stepwise FLEX framework, we assessed their roles in two river basins. Distributed (FLEXD) and landscape-based (FLEXT) models outperformed lumped versions. High elevations showed delayed melt sustaining flow, while low elevations responded rapidly to rain. Study confirms topography/vegetation as key hydrological controls.
Thiago Victor Medeiros do Nascimento, Julia Rudlang, Sebastian Gnann, Jan Seibert, Markus Hrachowitz, and Fabrizio Fenicia
EGUsphere, https://doi.org/10.5194/egusphere-2025-739, https://doi.org/10.5194/egusphere-2025-739, 2025
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Large-sample hydrological studies often overlook the importance of detailed landscape data in explaining river flow variability. Analyzing over 4,000 European catchments, we found that geology becomes a dominant factor—especially for baseflow—when using detailed regional maps. This highlights the need for high-resolution geological data to improve river flow regionalization, particularly in non-monitored areas.
Daniel Klotz, Peter Miersch, Thiago V. M. do Nascimento, Fabrizio Fenicia, Martin Gauch, and Jakob Zscheischler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-450, https://doi.org/10.5194/essd-2024-450, 2025
Preprint under review for ESSD
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Data availability is central to hydrological science. It is the basis for advancing our understanding of hydrological processes, building prediction models, and anticipatory water management. We present a data-driven daily runoff reconstruction product for natural streamflow. We name it EARLS: European aggregated reconstruction for large-sample studies. The reconstructions represent daily simulations of natural streamflow across Europe and cover the period from 1953 to 2020.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci., 28, 4971–4988, https://doi.org/10.5194/hess-28-4971-2024, https://doi.org/10.5194/hess-28-4971-2024, 2024
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The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.
Hongkai Gao, Markus Hrachowitz, Lan Wang-Erlandsson, Fabrizio Fenicia, Qiaojuan Xi, Jianyang Xia, Wei Shao, Ge Sun, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 4477–4499, https://doi.org/10.5194/hess-28-4477-2024, https://doi.org/10.5194/hess-28-4477-2024, 2024
Short summary
Short summary
The concept of the root zone is widely used but lacks a precise definition. Its importance in Earth system science is not well elaborated upon. Here, we clarified its definition with several similar terms to bridge the multi-disciplinary gap. We underscore the key role of the root zone in the Earth system, which links the biosphere, hydrosphere, lithosphere, atmosphere, and anthroposphere. To better represent the root zone, we advocate for a paradigm shift towards ecosystem-centred modelling.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
Short summary
Short summary
An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
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The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Hubert H. G. Savenije
Proc. IAHS, 385, 1–4, https://doi.org/10.5194/piahs-385-1-2024, https://doi.org/10.5194/piahs-385-1-2024, 2024
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Hydrology is the bloodstream of the Earth, acting as a living organism, with the ecosystem as its active agent. The ecosystem optimises its survival within the constraints of energy, water, climate and nutrients. It is capable of adjusting the hydrological system and, through evolution, adjust its efficiency of carbon sequestration and moisture uptake. In trying to understand future functioning of hydrology, we have to take into account the adaptability of the ecosystem.
Jiaxing Liang, Hongkai Gao, Fabrizio Fenicia, Qiaojuan Xi, Yahui Wang, and Hubert H. G. Savenije
EGUsphere, https://doi.org/10.5194/egusphere-2024-550, https://doi.org/10.5194/egusphere-2024-550, 2024
Preprint archived
Short summary
Short summary
The root zone storage capacity (Sumax) is a key element in hydrology and land-atmospheric interaction. In this study, we utilized a hydrological model and a dynamic parameter identification method, to quantify the temporal trends of Sumax for 497 catchments in the USA. We found that 423 catchments (85 %) showed increasing Sumax, which averagely increased from 178 to 235 mm between 1980 and 2014. The increasing trend was also validated by multi-sources data and independent methods.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
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CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Hubert T. Samboko, Sten Schurer, Hubert H. G. Savenije, Hodson Makurira, Kawawa Banda, and Hessel Winsemius
Geosci. Instrum. Method. Data Syst., 12, 155–169, https://doi.org/10.5194/gi-12-155-2023, https://doi.org/10.5194/gi-12-155-2023, 2023
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The study investigates how low-cost technology can be applied in data-scarce catchments to improve water resource management. More specifically, we investigate how drone technology can be combined with low-cost real-time kinematic positioning (RTK) global navigation satellite system (GNSS) equipment and subsequently applied to a 3D hydraulic model so as to generate more physically based rating curves.
Nutchanart Sriwongsitanon, Wasana Jandang, James Williams, Thienchart Suwawong, Ekkarin Maekan, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 27, 2149–2171, https://doi.org/10.5194/hess-27-2149-2023, https://doi.org/10.5194/hess-27-2149-2023, 2023
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We developed predictive semi-distributed rainfall–runoff models for nested sub-catchments in the upper Ping basin, which yielded better or similar performance compared to calibrated lumped models. The normalised difference infrared index proves to be an effective proxy for distributed root zone moisture capacity over sub-catchments and is well correlated with the percentage of evergreen forest. In validation, soil moisture simulations appeared to be highly correlated with the soil wetness index.
Henry Zimba, Miriam Coenders-Gerrits, Kawawa Banda, Bart Schilperoort, Nick van de Giesen, Imasiku Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 27, 1695–1722, https://doi.org/10.5194/hess-27-1695-2023, https://doi.org/10.5194/hess-27-1695-2023, 2023
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Miombo woodland plants continue to lose water even during the driest part of the year. This appears to be facilitated by the adapted features such as deep rooting (beyond 5 m) with access to deep soil moisture, potentially even ground water. It appears the trend and amount of water that the plants lose is correlated more to the available energy. This loss of water in the dry season by miombo woodland plants appears to be incorrectly captured by satellite-based evaporation estimates.
Marvin Höge, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 26, 5085–5102, https://doi.org/10.5194/hess-26-5085-2022, https://doi.org/10.5194/hess-26-5085-2022, 2022
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Neural ODEs fuse physics-based models with deep learning: neural networks substitute terms in differential equations that represent the mechanistic structure of the system. The approach combines the flexibility of machine learning with physical constraints for inter- and extrapolation. We demonstrate that neural ODE models achieve state-of-the-art predictive performance while keeping full interpretability of model states and processes in hydrologic modelling over multiple catchments.
Hongkai Gao, Chuntan Han, Rensheng Chen, Zijing Feng, Kang Wang, Fabrizio Fenicia, and Hubert Savenije
Hydrol. Earth Syst. Sci., 26, 4187–4208, https://doi.org/10.5194/hess-26-4187-2022, https://doi.org/10.5194/hess-26-4187-2022, 2022
Short summary
Short summary
Frozen soil hydrology is one of the 23 unsolved problems in hydrology (UPH). In this study, we developed a novel conceptual frozen soil hydrological model, FLEX-Topo-FS. The model successfully reproduced the soil freeze–thaw process, and its impacts on hydrologic connectivity, runoff generation, and groundwater. We believe this study is a breakthrough for the 23 UPH, giving us new insights on frozen soil hydrology, with broad implications for predicting cold region hydrology in future.
Henry Zimba, Miriam Coenders-Gerrits, Kawawa Banda, Petra Hulsman, Nick van de Giesen, Imasiku Nyambe, and Hubert Savenije
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-114, https://doi.org/10.5194/hess-2022-114, 2022
Manuscript not accepted for further review
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We compare performance of evaporation models in the Luangwa Basin located in a semi-arid and complex Miombo ecosystem in Africa. Miombo plants changes colour, drop off leaves and acquire new leaves during the dry season. In addition, the plant roots go deep in the soil and appear to access groundwater. Results show that evaporation models with structure and process that do not capture this unique plant structure and behaviour appears to have difficulties to correctly estimating evaporation.
Laurène J. E. Bouaziz, Emma E. Aalbers, Albrecht H. Weerts, Mark Hegnauer, Hendrik Buiteveld, Rita Lammersen, Jasper Stam, Eric Sprokkereef, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 26, 1295–1318, https://doi.org/10.5194/hess-26-1295-2022, https://doi.org/10.5194/hess-26-1295-2022, 2022
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Assuming stationarity of hydrological systems is no longer appropriate when considering land use and climate change. We tested the sensitivity of hydrological predictions to changes in model parameters that reflect ecosystem adaptation to climate and potential land use change. We estimated a 34 % increase in the root zone storage parameter under +2 K global warming, resulting in up to 15 % less streamflow in autumn, due to 14 % higher summer evaporation, compared to a stationary system.
Hubert T. Samboko, Sten Schurer, Hubert H. G. Savenije, Hodson Makurira, Kawawa Banda, and Hessel Winsemius
Geosci. Instrum. Method. Data Syst., 11, 1–23, https://doi.org/10.5194/gi-11-1-2022, https://doi.org/10.5194/gi-11-1-2022, 2022
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The study was conducted along the Luangwa River in Zambia. It combines low-cost instruments such as UAVs and GPS kits to collect data for the purposes of water management. A novel technique which seamlessly merges the dry and wet bathymetry before application in a hydraulic model was applied. Successful implementation resulted in water authorities with small budgets being able to monitor flows safely and efficiently without significant compromise on accuracy.
Marco Dal Molin, Dmitri Kavetski, and Fabrizio Fenicia
Geosci. Model Dev., 14, 7047–7072, https://doi.org/10.5194/gmd-14-7047-2021, https://doi.org/10.5194/gmd-14-7047-2021, 2021
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This paper introduces SuperflexPy, an open-source Python framework for building flexible conceptual hydrological models. SuperflexPy is available as open-source code and can be used by the hydrological community to investigate improved process representations, for model comparison, and for operational work.
Hongkai Gao, Chuntan Han, Rensheng Chen, Zijing Feng, Kang Wang, Fabrizio Fenicia, and Hubert Savenije
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-264, https://doi.org/10.5194/hess-2021-264, 2021
Manuscript not accepted for further review
Short summary
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Permafrost hydrology is one of the 23 major unsolved problems in hydrology. In this study, we used a stepwise modeling and dynamic parameter method to examine the impact of permafrost on streamflow in the Hulu catchment in western China. We found that: topography and landscape are dominant controls on catchment response; baseflow recession is slower than other regions; precipitation-runoff relationship is non-stationary; permafrost impacts on streamflow mostly at the beginning of melting season.
Laurène J. E. Bouaziz, Fabrizio Fenicia, Guillaume Thirel, Tanja de Boer-Euser, Joost Buitink, Claudia C. Brauer, Jan De Niel, Benjamin J. Dewals, Gilles Drogue, Benjamin Grelier, Lieke A. Melsen, Sotirios Moustakas, Jiri Nossent, Fernando Pereira, Eric Sprokkereef, Jasper Stam, Albrecht H. Weerts, Patrick Willems, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 25, 1069–1095, https://doi.org/10.5194/hess-25-1069-2021, https://doi.org/10.5194/hess-25-1069-2021, 2021
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We quantify the differences in internal states and fluxes of 12 process-based models with similar streamflow performance and assess their plausibility using remotely sensed estimates of evaporation, snow cover, soil moisture and total storage anomalies. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Therefore, we invite modelers to evaluate their models using multiple variables and to rely on multi-model studies.
Petra Hulsman, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 25, 957–982, https://doi.org/10.5194/hess-25-957-2021, https://doi.org/10.5194/hess-25-957-2021, 2021
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Satellite observations have increasingly been used for model calibration, while model structural developments largely rely on discharge data. For large river basins, this often results in poor representations of system internal processes. This study explores the combined use of satellite-based evaporation and total water storage data for model structural improvement and spatial–temporal model calibration for a large, semi-arid and data-scarce river system.
César Dionisio Jiménez-Rodríguez, Miriam Coenders-Gerrits, Bart Schilperoort, Adriana del Pilar González-Angarita, and Hubert Savenije
Hydrol. Earth Syst. Sci., 25, 619–635, https://doi.org/10.5194/hess-25-619-2021, https://doi.org/10.5194/hess-25-619-2021, 2021
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During rainfall events, evaporation from tropical forests is usually ignored. However, the water retained in the canopy during rainfall increases the evaporation despite the high-humidity conditions. In a tropical wet forest in Costa Rica, it was possible to depict vapor plumes rising from the forest canopy during rainfall. These plumes are evidence of forest evaporation. Also, we identified the conditions that allowed this phenomenon to happen using time-lapse videos and meteorological data.
Bart Schilperoort, Miriam Coenders-Gerrits, César Jiménez Rodríguez, Christiaan van der Tol, Bas van de Wiel, and Hubert Savenije
Biogeosciences, 17, 6423–6439, https://doi.org/10.5194/bg-17-6423-2020, https://doi.org/10.5194/bg-17-6423-2020, 2020
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With distributed temperature sensing (DTS) we measured a vertical temperature profile in a forest, from the forest floor to above the treetops. Using this temperature profile we can see which parts of the forest canopy are colder (thus more dense) or warmer (and less dense) and study the effect this has on the suppression of turbulent mixing. This can be used to improve our knowledge of the interaction between the atmosphere and forests and improve carbon dioxide flux measurements over forests.
Justus G. V. van Ramshorst, Miriam Coenders-Gerrits, Bart Schilperoort, Bas J. H. van de Wiel, Jonathan G. Izett, John S. Selker, Chad W. Higgins, Hubert H. G. Savenije, and Nick C. van de Giesen
Atmos. Meas. Tech., 13, 5423–5439, https://doi.org/10.5194/amt-13-5423-2020, https://doi.org/10.5194/amt-13-5423-2020, 2020
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In this work we present experimental results of a novel actively heated fiber-optic (AHFO) observational wind-probing technique. We utilized a controlled wind-tunnel setup to assess both the accuracy and precision of AHFO under a range of operational conditions (wind speed, angles of attack and temperature differences). AHFO has the potential to provide high-resolution distributed observations of wind speeds, allowing for better spatial characterization of fine-scale processes.
Renaud Hostache, Dominik Rains, Kaniska Mallick, Marco Chini, Ramona Pelich, Hans Lievens, Fabrizio Fenicia, Giovanni Corato, Niko E. C. Verhoest, and Patrick Matgen
Hydrol. Earth Syst. Sci., 24, 4793–4812, https://doi.org/10.5194/hess-24-4793-2020, https://doi.org/10.5194/hess-24-4793-2020, 2020
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Our objective is to investigate how satellite microwave sensors, particularly Soil Moisture and Ocean Salinity (SMOS), may help to reduce errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. We assimilated a long time series of SMOS observations into a hydro-meteorological model and showed that this helps to improve model predictions. This work therefore contributes to the development of faster and more accurate drought prediction tools.
Cited articles
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark,
M. P.: A ranking of hydrological signatures based on their predictability in
space, Water Resour. Res., 54, 8792–8812,
https://doi.org/10.1029/2018WR022606, 2018.
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J.,
Srinivasan, R., Santhi, C., Harmel, R. D., Van Griensven, A., Van Liew, M. W.,
Kannan, N., and Jha, M. K.: SWAT: Model use, calibration, and validation,
T. ASABE, 55, 1491–1508, https://doi.org/10.13031/2013.42256, 2012.
Banwart, S. A., Bernasconi, S. M., Blum, W. E. H., de Souza, D. M., Chabaux, F.,
Duffy, C., Kercheva, M., Krám, P., Lair, G. J., Lundin, L., Menon, M.,
Nikolaidis, N., Novak, M., Panagos, P., Ragnarsdottir, K. V., Robinson, D. A.,
Rousseva, S., de Ruiter, P., van Gaans, P., Weng, L., White, T., and Zhang,
B.: Soil functions in Earth's critical zone: Key results and conclusions,
Adv. Agron., 142, 1–27, https://doi.org/10.1016/bs.agron.2016.11.001, 2017.
Bastiaanssen, W. G. M., Cheema, M. J. M., Immerzeel, W. W., Miltenburg, I.
J., and Pelgrum, H.: Surface energy balance and actual evapotranspiration of
the transboundary Indus Basin estimated from satellite measurements and the
ETLook model, Water Resour. Res., 48, W11512, https://doi.org/10.1029/2011WR010482, 2012
Beekman, W., Caljé, R., Schaars, F., and Heijkers, J.: Vergelijking van
enkele schattingsmethoden voor de actuele verdamping (Comparison between
several methods to estimate actual evaporation), Stromingen, 20, 39–46,
2014.
Beven, K.: A century of denial: Preferential and nonequilibrium water flow
in soils, Vadose Zone J., 17, 1–17, https://doi.org/10.2136/vzj2018.08.0153,
2018.
Beven, K. and Germann, P.: Macropores and water flow in soils revisited,
Water Resour. Res., 49, 3071–3092, https://doi.org/10.1002/wrcr.20156,
2013.
Bonetti, S., Wei, Z., and Or, D.: A framework for quantifying hydrologic
effects of soil structure across scales, Commun. Earth Environ., 2, 107,
https://doi.org/10.1038/s43247-021-00180-0, 2021.
Bouaziz, L. J. E., Aalbers, E. E., Weerts, A. H., Hegnauer, M., Buiteveld, H., Lammersen, R., Stam, J., Sprokkereef, E., Savenije, H. H. G., and Hrachowitz, M.: Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters, Hydrol. Earth Syst. Sci., 26, 1295–1318, https://doi.org/10.5194/hess-26-1295-2022, 2022.
Brutsaert, W.: Long-term groundwater storage trends estimated from streamflow
records: Climatic perspective, Water Resour. Res., 44, W02409,
https://doi.org/10.1029/2007WR006518, 2008.
Chapin III, F. S., Matson, P. A., Vitousek, P., and Chapin, M. C.: Principles
of Terrestrial Ecosystem Ecology, 2 edn., Springer, 233 Spring Street, New York, NY 10013, USA, https://doi.org/10.1007/978-1-4419-9504-9, 2011.
Dal Molin, M., Schirmer, M., Zappa, M., and Fenicia, F.: Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment, Hydrol. Earth Syst. Sci., 24, 1319–1345, https://doi.org/10.5194/hess-24-1319-2020, 2020.
Davies, J., Beven, K., Rodhe, A., Nyberg, L., and Bishop, K.: Integrated
modeling of flow and residence times at the catchment scale with multiple
interacting pathways, Water Resour. Res., 49, 4738–4750,
https://doi.org/10.1002/wrcr.20377, 2013.
De Boer-Euser, T., McMillan, H. K., Hrachowitz, M., Winsemius, H. C., and
Savenije, H. H. G.: Influence of soil and climate on rootzone storage
capacity, Water Resour. Res., 52, 2009–2024,
https://doi.org/10.1002/2015WR018115, 2016.
De Roo, A. P. J., Wesseling, C. G., and Ritsema, C. J: LISEM: A Single-event
Physically Based Hydrological and Soil Erosion Model for Drainage Basins. I:
Theory, Input and Output, Hydrol. Process., 10, 1107–1117,
https://doi.org/10.1002/(SICI)1099-1085(199608)10:8<1107::AID-HYP415>3.0.CO;2-4, 1996.
Domeignoz-Horta, L. A., Shinfuku, M., Junier, P., Poirier, S., Verrecchia,
E., Sebag, D., and DeAngelis, K. M.: Direct evidence for the role of
microbial community composition in the formation of soil organic matter
composition and persistence, ISME Commun., 1, 64,
https://doi.org/10.1038/s43705-021-00071-7, 2021.
Drewniak, B. A.: Simulating dynamic roots in the energy Exascale Earth
system land model, J. Adv. Model. Earth Sy., 11, 338–359,
https://doi.org/10.1029/2018MS001334, 2019.
Duffy, C. J.: A two-state integral-balance model for soil moisture and
groundwater dynamics in complex terrain, Water Resour. Res., 32, 2421–2434,
https://doi.org/10.1029/96WR01049, 1996.
Fatichi, S., Or, D., Walko, R., Vereecken, H., Young, M. H., Ghezzehei, T.
A., Hengl, T., Kollet, S., Agam, N., and Avissar, R.: Soil structure is an
important omission in Earth System Models, Nat. Commun., 11, 522,
https://doi.org/10.1038/s41467-020-14411-z, 2020.
Fenicia, F., Kavetski, D., and Savenije, H. H. G.: Elements of a flexible
approach for conceptual hydrological modeling: 1. Motivation and theoretical
development, Water Resour. Res., 47, W11510,
https://doi.org/10.1029/2010WR010174, 2011.
Fenicia, F., Kavetski, D., Savenije, H. H. G., and Pfister, L.: From
spatially variable streamflow to distributed hydrological models: Analysis
of key modeling decisions, Water Resour. Res., 52, 954–989,
https://doi.org/10.1002/2015WR017398, 2016.
Fenicia, F., Meissner, D., and McDonnell, J. J.: Modeling streamflow
variability at the regional scale: (2) Development of a bespoke distributed
conceptual model, J. Hydrol., 605, 127286,
https://doi.org/10.1016/j.jhydrol.2021.127286, 2022.
Freeze, R. A. and Harlan, R. L.: Blueprint for a physically-based,
digitally-simulated hydrologic response model, J. Hydrol., 9, 237–258,
https://doi.org/10.1016/0022-1694(69)90020-1, 1969.
Gao, H., Hrachowitz, M., Schymanski, S. J., Fenicia, F., Sriwongsitanon, N.,
and Savenije, H. H. G.: Climate controls how ecosystems size the rootzone
storage capacity at catchment scale, Geophys. Res. Lett., 41, 7916–7923,
https://doi.org/10.1002/2014GL061668, 2014.
Gao, H., Sabo, J. L., Chen, X., Liu, Z., Yang, Z., Ren, Z., and Liu, M.:
Landscape heterogeneity and hydrological processes: a review of
landscape-based hydrological models, Landsc. Ecol., 33, 1461–1480,
https://doi.org/10.1007/s10980-018-0690-4, 2018.
Gao, H., Birkel, C., Hrachowitz, M., Tetzlaff, D., Soulsby, C., and Savenije, H. H. G.: A simple topography-driven and calibration-free runoff generation module, Hydrol. Earth Syst. Sci., 23, 787–809, https://doi.org/10.5194/hess-23-787-2019, 2019.
Gao, H., Dong, J., Chen, X., Cai, H., Liu, Z., Jin, Z., Mao, D., Yang, Z.,
and Duan, Z.: Stepwise modeling and the importance of internal variables
validation to test model realism in a data scarce glacier basin, J. Hydrol.,
591, 125457, https://doi.org/10.1016/j.jhydrol.2020.125457, 2020.
Gao, H., Han, C., Chen, R., Feng, Z., Wang, K., Fenicia, F., and Savenije, H.: Frozen soil hydrological modeling for a mountainous catchment northeast of the Qinghai–Tibet Plateau, Hydrol. Earth Syst. Sci., 26, 4187–4208, https://doi.org/10.5194/hess-26-4187-2022, 2022.
Germann, P. F.: Preferential flow: Stokes approach to infiltation and
drainage, Geographica Bernensia, Bern, Switzerland, ISBN 978-3-905835-34-2,
2014.
Gerrits, A. M. J., Pfister, L., and Savenije, H. H. G.: Spatial and temporal
variability of canopy and forest floor interception in a beech forest,
Hydrol. Process., 24, 3011–3025, https://doi.org/10.1002/hyp.7712, 2010.
Gharari, S., Hrachowitz, M., Fenicia, F., and Savenije, H. H. G.: Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment, Hydrol. Earth Syst. Sci., 15, 3275–3291, https://doi.org/10.5194/hess-15-3275-2011, 2011.
Gharari, S., Hrachowitz, M., Fenicia, F., Gao, H., and Savenije, H. H. G.: Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration, Hydrol. Earth Syst. Sci., 18, 4839–4859, https://doi.org/10.5194/hess-18-4839-2014, 2014.
Gupta, S., Hengl, T., Lehmann, P., Bonetti, S., and Or, D.: SoilKsatDB: global database of soil saturated hydraulic conductivity measurements for geoscience applications, Earth Syst. Sci. Data, 13, 1593–1612, https://doi.org/10.5194/essd-13-1593-2021, 2021.
Gutmann, E. D. and Small, E. E.: A comparison of land surface model soil
hydraulic properties estimated by inverse modeling and pedotransfer
functions, Water Resour. Res., 43, W05418,
https://doi.org/10.1029/2006WR005135, 2007.
Haghverdi, A., Öztürk, H. S., and Durner, W: Studying Unimodal,
Bimodal, PDI and Bimodal-PDI Variants of Multiple Soil Water Retention
Models: II. Evaluation of Parametric Pedotransfer Functions Against Direct
Fits, Water, 12, 896, https://doi.org/10.3390/w12030896, 2020.
Hohenbrink, T. L., Jackisch, C., Durner, W., Germer, K., Iden, S. C., Kreiselmeier, J., Leuther, F., Metzger, J. C., Naseri, M., and Peters, A.: Soil water retention and hydraulic conductivity measured in a wide saturation range, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-74, in review, 2023.
Huggett, R.: Soil as part of the Earth system, Prog. Phys. Geog., 47,
454–466, https://doi.org/10.1177/03091333221147655, 2023.
Hulsman, P., Savenije, H. H. G., and Hrachowitz, M.: Learning from satellite observations: increased understanding of catchment processes through stepwise model improvement, Hydrol. Earth Syst. Sci., 25, 957–982, https://doi.org/10.5194/hess-25-957-2021, 2021a.
Hulsman, P., Hrachowitz, M., and Savenije, H. H. G.: Improving the
representation of long-term storage variations with conceptual hydrological
models in data-scarce regions, Water Resour. Res., 57, e2020WR028837,
https://doi.org/10.1029/2020WR028837, 2021b.
Jackisch, C., Hassler, S. K., Hohenbrink, T. L., Blume, T., Laudon, H., McMillan, H., Saco, P., and van Schaik, L.: Preface: Linking landscape organisation and hydrological functioning: from hypotheses and observations to concepts, models and understanding, Hydrol. Earth Syst. Sci., 25, 5277–5285, https://doi.org/10.5194/hess-25-5277-2021, 2021.
Jarvis, N., Koestel, J., and Larsbo, M.: Understanding Preferential Flow in
the Vadose Zone: Recent Advances and Future Prospects, Vadose Zone J., 15,
1–11, https://doi.org/10.2136/vzj2016.09.0075, 2016.
Kishné, A. S., Yimam, Y. T., Morgan, C. L. S., and Dornblaser, B. C.:
Evaluation and improvement of the default soil hydraulic parameters for the
Noah Land Surface model, Geoderma, 285, 247–259, https://doi.org/10.1016/j.geoderma.2016.09.022, 2017.
Kleidon, A.: Global datasets of rooting zone depth inferred from inverse
methods, J. Climate, 17, 2714–2722, https://doi.org/10.1175/1520-0442(2004)017<2714:GDORZD>2.0.CO;2, 2004.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C.,
Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D.,
Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J.,
Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A.,
Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P.,
Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B.,
Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox,
R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey,
A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson,
B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Martin, M. V., and
Zeng, X.: The Community Land Model version 5: Description of new features,
benchmarking, and impact of forcing uncertainty, J. Adv. Model. Earth Syst.,
11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019.
Lin, H., Bouma, J., Pachepsky, Y., Western, A., Thompson, J., van Genuchten,
R., Vogel, H.-J., and Lilly, A.: Hydropedology: Synergistic integration of
pedology and hydrology, Water Resour. Res., 42, W05301,
https://doi.org/10.1029/2005WR004085, 2006.
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and
Bergström, S.: Development and test of the distributed HBV-96
hydrological model, J. Hydrol., 201, 272–288,
https://doi.org/10.1016/S0022-1694(97)00041-3, 1997.
Lu, H., Yuan, W., and Chen, X.: A processes-based dynamic root growth model
integrated into the ecosystem model, J. Adv. Model. Earth Syst., 11,
4614–4628, https://doi.org/10.1029/2019MS001846, 2019.
McCormick, E. L., Dralle, D. N., Hahm, W. J., Tune, A. K., Schmidt, L. M.,
Chadwick, K. D., and Rempe, D. M.: Widespread woody plant use of water
stored in bedrock, Nature, 597, 225–229,
https://doi.org/10.1038/s41586-021-03761-3, 2021.
McDonnell, J. J., Sivapalan, M., Vache, K., Dunn, S., Grant, G., Haggerty,
R., Hinz, C., Hooper, R., Kirchner, J., Roderick, M. L., Selker, J., and
Weiler, M.: Moving beyond heterogeneity and process complexity: A new vision
for watershed hydrology, Water Resour. Res., 43, W07301,
https://doi.org/10.1029/2006WR005467, 2007.
McDonnell, J. J., Spence, C., Karran, D. J., van Meerveld, H. J., and
Harman, C. J.: Fill-and-spill: A process description of runoff generation at
the scale of the beholder, Water Resour. Res., 57, e2020WR027514,
https://doi.org/10.1029/2020WR027514, 2021.
Moore, R. J.: The PDM rainfall-runoff model, Hydrol. Earth Syst. Sci., 11, 483–499, https://doi.org/10.5194/hess-11-483-2007, 2007.
Nijzink, R., Hutton, C., Pechlivanidis, I., Capell, R., Arheimer, B., Freer, J., Han, D., Wagener, T., McGuire, K., Savenije, H., and Hrachowitz, M.: The evolution of root-zone moisture capacities after deforestation: a step towards hydrological predictions under change?, Hydrol. Earth Syst. Sci., 20, 4775–4799, https://doi.org/10.5194/hess-20-4775-2016, 2016.
Or, D.: The tyranny of small scales–On representing soil processes in
global land surface models, Water Resour. Res., 56, 1–9,
https://doi.org/10.1029/2019WR024846, 2020.
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a
parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289,
https://doi.org/10.1016/S0022-1694(03)00225-7, 2003.
Ponge, J.: The soil as an ecosystem, Biol. Fertil. Soils, 51, 645–648,
https://doi.org/10.1007/s00374-015-1016-1, 2015.
Refsgaard, J. C., Stisen, S., and Koch, J.: Hydrological process knowledge
in catchment modelling–Lessons and perspectives from 60 years development,
Hydrol. Process., 36, e14463, https://doi.org/10.1002/hyp.14463, 2022.
Reggiani, P., Sivapalan, M., and Majid Hassanizadeh, S.: A unifying
framework for watershed thermodynamics: balance equations for mass,
momentum, energy and entropy, and the second law of thermodynamics, Adv.
Water Resour., 22, 367–398, https://doi.org/10.1016/S0309-1708(98)00012-8,
1998.
Rodríguez-Iturbe, I. and Porporato, A.: Ecohydrology of
water-controlled ecosystems: Soil moisture and plant dynamics, Cambridge
University Press, The Edinburgh Building, Cambridge, UK, https://doi.org/10.1017/CBO9780511535727, 2004.
Savenije, H. H. G.: HESS Opinions “Topography driven conceptual modelling (FLEX-Topo)”, Hydrol. Earth Syst. Sci., 14, 2681–2692, https://doi.org/10.5194/hess-14-2681-2010, 2010.
Savenije, H. H. G. and Hrachowitz, M.: HESS Opinions “Catchments as meta-organisms – a new blueprint for hydrological modelling”, Hydrol. Earth Syst. Sci., 21, 1107–1116, https://doi.org/10.5194/hess-21-1107-2017, 2017.
Savenije, H. H. G.: HESS Opinions: Linking Darcy's equation to the linear reservoir, Hydrol. Earth Syst. Sci., 22, 1911–1916, https://doi.org/10.5194/hess-22-1911-2018, 2018.
Schoener, G., Stone, M. C., and Thomas, C.: Comparison of seven simple loss
models for runoff prediction at the plot, hillslope and catchment scale in
the semiarid southwestern U.S, J. Hydrol., 598, 126490,
https://doi.org/10.1016/j.jhydrol.2021.126490, 2021.
Shi, P., Tun, B., Cheng, G., and Luo, J.: Water retention capacity
evaluation of main forest vegetation types in the Upper Yangtze Basin, J.
Nat. Resourc., 19, 351–360, https://doi.org/10.11849/zrzyxb.2004.03.012, 2004.
Singh, C., Wang-Erlandsson, L., Fetzer, I., Rockström, J., and van der
Ent, R.: Rootzone storage capacity reveals drought coping strategies along
rainforest-savanna transitions, Environ. Res. Lett., 15, 124021,
https://doi.org/10.1088/1748-9326/abc377, 2020.
Sivapalan, M., Bloschl, G., Zhang, L., and Vertessy, R.: Downward approach
to hydrological prediction, Hydrol. Process., 17, 2101–2111,
https://doi.org/10.1002/hyp.1425, 2003.
Sternagel, A., Loritz, R., Klaus, J., Berkowitz, B., and Zehe, E.: Simulation of reactive solute transport in the critical zone: a Lagrangian model for transient flow and preferential transport, Hydrol. Earth Syst. Sci., 25, 1483–1508, https://doi.org/10.5194/hess-25-1483-2021, 2021.
Theobald, D. M., Kennedy, C., Chen, B., Oakleaf, J., Baruch-Mordo, S., and Kiesecker, J.: Earth transformed: detailed mapping of global human modification from 1990 to 2017, Earth Syst. Sci. Data, 12, 1953–1972, https://doi.org/10.5194/essd-12-1953-2020, 2020.
Troch, P. A., Lahmers, T., Meira, A., Mukherjee, R., Pedersen, J. W., Roy,
T., and Valdes-Pineda, R.: Catchment coevolution: A useful framework for
improving predictions of hydrological change?, Water Resour. Res., 51,
4903–4922, https://doi.org/10.1002/2015WR017032, 2015.
Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U.,
Montzka, C., Nemes, A., Pachepsky, Y.A., Padarian, J., Schaap, M.G.,
Tóth, B., Verhoef, A., Vanderborght, J., Ploeg, M. J., Weihermüller,
L., Zacharias, S., Zhang, Y., and Vereecken, H.: Pedotransfer Functions in
Earth System Science: Challenges and Perspectives, Rev. Geophys., 55,
1199–1256, https://doi.org/10.1002/2017RG000581, 2017.
Vereecken, H., Amelung, W., Bauke, S. L., Bogena, H., Brueggemann, N.,
Montzka, C., Vanderborght, J., Bechtold, M., Bloeschl, G., Carminati, A.,
Javaux, M., Konings, A. G., Kusche, J., Neuweiler, I., Or, D., Steele-Dunne,
S., Verhoef, A., Young, M., and Zhang, Y.: Soil hydrology in the Earth
system, Nat. Rev. Earth Environ., 3, 573–587,
https://doi.org/10.1038/s43017-022-00324-6, 2022.
Wang-Erlandsson, L., Bastiaanssen, W. G. M., Gao, H., Jägermeyr, J., Senay, G. B., van Dijk, A. I. J. M., Guerschman, J. P., Keys, P. W., Gordon, L. J., and Savenije, H. H. G.: Global root zone storage capacity from satellite-based evaporation, Hydrol. Earth Syst. Sci., 20, 1459–1481, https://doi.org/10.5194/hess-20-1459-2016, 2016.
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F. P.,
Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D.,
van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B.,
Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade,
A., and Whitehead, P.: Hyperresolution global land surface modeling: Meeting
a grand challenge for monitoring Earth's terrestrial water, Water Resour.
Res., 47, W05301, https://doi.org/10.1029/2010WR010090, 2011.
Weil, R. R. and Brady, N. C.: The Nature and Properties of Soils, 15th
edition, Pearson Education, USA, ISBN 978-0133254488, 2017.
Weiler, M.: Macropores and preferential flow-a love-hate relationship,
Hydrol. Process., 31, 15–19, https://doi.org/10.1002/hyp.11074, 2017.
Winsemius, H. C., Savenije, H. H. G., van de Giesen, N. C., van den Hurk, B.
J. J. M., Zapreeva, E. A., and Klees, R.: Assessment of Gravity Recovery and
Climate Experiment (GRACE) temporal signature over the upper Zambezi, Water
Resour. Res., 42, W12201, https://doi.org/10.1029/2006WR005192, 2006.
Wu, J.: Key concepts and research topics in landscape ecology revisited: 30
years after the Allerton Park workshop, Landsc. Ecol., 28, 1–11,
https://doi.org/10.1007/s10980-012-9836-y, 2013.
Xi, Q., Zhong, H., Wang, T., He, T., Gao, H., Xia, J., Wang-Erlandsson, L.,
Tang, Q., and Liu, J.: Spatio-temporal variation of gray-green-blue storage
capacity in nine major basins of China, Chin. Sci. Bull., 66, 4437–4448,
https://doi.org/10.1360/TB-2021-0381, 2021.
Yang, Y., Donohue, R. J., and McVicar, T. R.: Global estimation of effective
plant rooting depth: Implications for hydrological modeling, Water Resour.
Res., 52, 8260–8276, https://doi.org/10.1002/2016WR019392, 2016.
Zehe, E., Loritz, R., Edery, Y., and Berkowitz, B.: Preferential pathways for fluid and solutes in heterogeneous groundwater systems: self-organization, entropy, work, Hydrol. Earth Syst. Sci., 25, 5337–5353, https://doi.org/10.5194/hess-25-5337-2021, 2021.
Zhang, B., Wu, P., Zhao, X., Gao, X., and Shi, Y.: Assessing the spatial and
temporal variation of the rainwater harvesting potential (1971–2010) on the
Chinese Loess Plateau using the VIC model, Hydrol. Process., 28, 534–544,
https://doi.org/10.1002/hyp.9608, 2014.
Zhao, R. J.: The Xinanjiang model applied in China, J. Hydrol., 135,
371–381, https://doi.org/10.1016/0022-1694(92)90096-e, 1992.
Short summary
It is a deeply rooted perception that soil is key in hydrology. In this paper, we argue that it is the ecosystem, not the soil, that is in control of hydrology. Firstly, in nature, the dominant flow mechanism is preferential, which is not particularly related to soil properties. Secondly, the ecosystem, not the soil, determines the land–surface water balance and hydrological processes. Moving from a soil- to ecosystem-centred perspective allows more realistic and simpler hydrological models.
It is a deeply rooted perception that soil is key in hydrology. In this paper, we argue that it...
Special issue