Articles | Volume 28, issue 20
https://doi.org/10.5194/hess-28-4577-2024
© Author(s) 2024. 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-28-4577-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Oct 2024
Research article |
| 23 Oct 2024
| 23 Oct 2024
Catchment response to climatic variability: implications for root zone storage and streamflow predictions
Nienke Tempel
CORRESPONDING AUTHOR
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Laurène Bouaziz
Department Catchment and Urban Hydrology, Deltares, Boussinesqweg 1, 2629 HV Delft, the Netherlands
Riccardo Taormina
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Ellis van Noppen
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Jasper Stam
Ministry of Infrastructure and Water Management, Zuiderwagenplein 2, 8224 AD Lelystad, the Netherlands
Eric Sprokkereef
Ministry of Infrastructure and Water Management, Zuiderwagenplein 2, 8224 AD Lelystad, the Netherlands
Markus Hrachowitz
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
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Muhammad Ibrahim, Markus Hrachowitz, Miriam Coenders-Gerrits, and Ruud van der Ent
EGUsphere, https://doi.org/10.5194/egusphere-2026-4226, https://doi.org/10.5194/egusphere-2026-4226, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Root zone storage capacity is an important parameter in hydrological and land-surface models and is often estimated using the memory method rather than through model calibration. We evaluated the uncertainty associated with this widely used method across thousands of catchments worldwide. Our results show that uncertainty varies systematically with climate, while memory method estimates remain in close agreement with calibrated values, supporting the commonly used 20-year return period.
Julia M. Rudlang, Thiago V. M. do Nascimento, Ruud van der Ent, Fabrizio Fenicia, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 30, 4481–4508, https://doi.org/10.5194/hess-30-4481-2026, https://doi.org/10.5194/hess-30-4481-2026, 2026
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River flow (streamflow) behaviour varies across Europe and is shaped by climate and landscape. Using data from more than 7000 European catchments, we identified 10 hydrological response types based on flow magnitude, timing, and seasonality. At the continental scale, streamflow behaviour is mainly driven by climate, but landscape features are equally or more influential in several regions. These results show that streamflow behaviour emerges from the combined effects of climate and landscape.
Roberto Bentivoglio, Sebastiaan Nicolas Jonkman, Elvin Isufi, and Riccardo Taormina
Nat. Hazards Earth Syst. Sci., 26, 2089–2109, https://doi.org/10.5194/nhess-26-2089-2026, https://doi.org/10.5194/nhess-26-2089-2026, 2026
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Obtaining probabilistic flood maps with numerical models is very time-consuming. Deep learning models can speed this up, but their predictions are difficult to verify without reference data, and they ignore structures like dikes or canals. This work introduces a mass-based validation measure to assess prediction plausibility and adapts a graph-based model to include hydraulic structures, enabling realistic, large-scale probabilistic flood mapping in the Netherlands.
Hatice Türk, Christine Stumpp, Markus Hrachowitz, Peter Strauss, Günter Blöschl, and Michael Stockinger
Hydrol. Earth Syst. Sci., 30, 1053–1076, https://doi.org/10.5194/hess-30-1053-2026, https://doi.org/10.5194/hess-30-1053-2026, 2026
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This study shows that streamflow isotope data in a catchment-scale isotope-based transport model were sufficient to isolate preferential flow in the unsaturated zone but insufficient to isolate the preferential release of young groundwater, as isotope signals were strongly damped by large passive groundwater storage. As a result, groundwater age-selection assumptions affected estimates of long transit times, making it difficult to quantify the fraction of stream water older than 100 days.
Thiago V. M. do Nascimento, Julia Rudlang, Sebastian Gnann, Jan Seibert, Markus Hrachowitz, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 29, 7173–7200, https://doi.org/10.5194/hess-29-7173-2025, https://doi.org/10.5194/hess-29-7173-2025, 2025
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We show that geological maps with varying levels of detail may influence the identification of geology–streamflow relationships across European catchments at multiple scales. At the large scale, controls varied between basins, with no map consistently superior. At the intermediate and small scales, however, higher geological detail consistently strengthened correlations, particularly for baseflow signatures, with the regional map highlighting controls more consistent with process understanding.
Nathalie Rombeek, Markus Hrachowitz, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 29, 6715–6733, https://doi.org/10.5194/hess-29-6715-2025, https://doi.org/10.5194/hess-29-6715-2025, 2025
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On 29 October 2024 Valencia (Spain) was struck by torrential rainfall, triggering devastating floods in this area. In this study, we quantify and describe the spatial and temporal structure of this rainfall event using personal weather stations (PWSs). These PWSs provide near real-time observations at a temporal resolution of ~5 min. This study shows the potential of PWSs for real-time rainfall monitoring and potentially flood early warning systems by complementing dedicated rain gauge networks.
Nathalie Rombeek, Markus Hrachowitz, Arjan Droste, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 29, 4585–4606, https://doi.org/10.5194/hess-29-4585-2025, https://doi.org/10.5194/hess-29-4585-2025, 2025
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Rain gauge networks from personal weather stations (PWSs) have a network density 100 times higher than dedicated rain gauge networks in the Netherlands. However, PWSs are prone to several sources of error, as they are generally not installed and maintained according to international guidelines. This study systematically quantifies and describes the uncertainties arising from PWS rainfall estimates. In particular, the focus is on the highest rainfall accumulations.
Hatice Türk, Christine Stumpp, Markus Hrachowitz, Karsten Schulz, Peter Strauss, Günter Blöschl, and Michael Stockinger
Hydrol. Earth Syst. Sci., 29, 3935–3956, https://doi.org/10.5194/hess-29-3935-2025, https://doi.org/10.5194/hess-29-3935-2025, 2025
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Using advances in transit time estimation and tracer data, we tested if fast-flow transit times are controlled solely by soil moisture or if they are also controlled by precipitation intensity. We used soil-moisture-dependent and precipitation-intensity-conditional transfer functions. We showed that a significant portion of event water bypasses the soil matrix through fast flow paths (overland flow, tile drains, preferential-flow paths) in dry soil conditions for both low- and high-intensity precipitation.
Magali Ponds, Sarah Hanus, Harry Zekollari, Marie-Claire ten Veldhuis, Gerrit Schoups, Roland Kaitna, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 3545–3568, https://doi.org/10.5194/hess-29-3545-2025, https://doi.org/10.5194/hess-29-3545-2025, 2025
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This research examines how future climate changes impact root zone storage, a key hydrological model parameter. Root zone storage – the soil water accessible to plants – adapts to climate but is often kept constant in models. We estimated climate-adapted storage in six Austrian Alps catchments. While storage increased, streamflow projections showed minimal change, which suggests that dynamic root zone representation is less critical in humid regions but warrants further study in arid areas.
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1703–1723, https://doi.org/10.5194/hess-29-1703-2025, https://doi.org/10.5194/hess-29-1703-2025, 2025
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The quantification of precipitation into evaporation and runoff is vital for water resources management. The Budyko framework, based on aridity and evaporative indices of a catchment, can be an ideal tool for that. However, recent research highlights deviations of catchments from the expected evaporative index, casting doubt on its reliability. This study quantifies deviations of 2387 catchments, finding them minor and predictable. Integrating these into predictions upholds the framework's efficacy.
Wouter R. Berghuijs, Ross A. Woods, Bailey J. Anderson, Anna Luisa Hemshorn de Sánchez, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1319–1333, https://doi.org/10.5194/hess-29-1319-2025, https://doi.org/10.5194/hess-29-1319-2025, 2025
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Water balances of catchments will often strongly depend on their state in the recent past, but such memory effects may persist at annual timescales. We use global data sets to show that annual memory is typically absent in precipitation but strong in terrestrial water stores and also present in evaporation and streamflow (including low flows and floods). Our experiments show that hysteretic models provide behaviour that is consistent with these observed memory behaviours.
Roberto Bentivoglio, Elvin Isufi, Sebastiaan Nicolas Jonkman, and Riccardo Taormina
Nat. Hazards Earth Syst. Sci., 25, 335–351, https://doi.org/10.5194/nhess-25-335-2025, https://doi.org/10.5194/nhess-25-335-2025, 2025
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Deep learning methods are increasingly used as surrogates for spatio-temporal flood models but struggle with generalization and speed. Here, we propose a multi-resolution approach using graph neural networks that predicts dike breach floods across different meshes, topographies, and boundary conditions with high accuracy and up to 1000× speed-ups. The model also generalizes to larger more complex case studies with just one additional simulation for fine-tuning.
Jordy Salmon-Monviola, Ophélie Fovet, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 127–158, https://doi.org/10.5194/hess-29-127-2025, https://doi.org/10.5194/hess-29-127-2025, 2025
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To increase the predictive power of hydrological models, it is necessary to improve their consistency, i.e. their physical realism, which is measured by the ability of the model to reproduce observed system dynamics. Using a model to represent the dynamics of water and nitrate and dissolved organic carbon concentrations in an agricultural catchment, we showed that using solute-concentration data for calibration is useful to improve the hydrological consistency of the model.
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
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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.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
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Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Tim Hans Martin van Emmerik, Tim Willem Janssen, Tianlong Jia, Thank-Khiet L. Bui, Riccardo Taormina, Hong-Q. Nguyen, and Louise Jeanne Schreyers
EGUsphere, https://doi.org/10.5194/egusphere-2024-2270, https://doi.org/10.5194/egusphere-2024-2270, 2024
Preprint archived
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Plastic pollution has negative effects on the environment. Tropical rivers around the world suffer from both plastic pollution and invasive water hyacinths. Water hyacinths grow rapidly and form dense floating mats. Using drones, cameras and AI, we show that along the Saigon river, Vietnam, the majority of floating plastic pollution is carried by water hyacinths. Better understanding water hyacinth-plastic trapping supports improving pollution monitoring and management strategies.
Fransje van Oorschot, Ruud J. van der Ent, Andrea Alessandri, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 2313–2328, https://doi.org/10.5194/hess-28-2313-2024, https://doi.org/10.5194/hess-28-2313-2024, 2024
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Vegetation plays a crucial role in regulating the water cycle by transporting water from the subsurface to the atmosphere via roots; this transport depends on the extent of the root system. In this study, we quantified the effect of irrigation on roots at a global scale. Our results emphasize the importance of accounting for irrigation in estimating the vegetation root extent, which is essential to adequately represent the water cycle in hydrological and climate models.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
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We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Roberto Bentivoglio, Elvin Isufi, Sebastiaan Nicolas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci., 27, 4227–4246, https://doi.org/10.5194/hess-27-4227-2023, https://doi.org/10.5194/hess-27-4227-2023, 2023
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To overcome the computational cost of numerical models, we propose a deep-learning approach inspired by hydraulic models that can simulate the spatio-temporal evolution of floods. We show that the model can rapidly predict dike breach floods over different topographies and breach locations, with limited use of ground-truth data.
Fransje van Oorschot, Ruud J. van der Ent, Markus Hrachowitz, Emanuele Di Carlo, Franco Catalano, Souhail Boussetta, Gianpaolo Balsamo, and Andrea Alessandri
Earth Syst. Dynam., 14, 1239–1259, https://doi.org/10.5194/esd-14-1239-2023, https://doi.org/10.5194/esd-14-1239-2023, 2023
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Vegetation largely controls land hydrology by transporting water from the subsurface to the atmosphere through roots and is highly variable in space and time. However, current land surface models have limitations in capturing this variability at a global scale, limiting accurate modeling of land hydrology. We found that satellite-based vegetation variability considerably improved modeled land hydrology and therefore has potential to improve climate predictions of, for example, droughts.
Siyuan Wang, Markus Hrachowitz, Gerrit Schoups, and Christine Stumpp
Hydrol. Earth Syst. Sci., 27, 3083–3114, https://doi.org/10.5194/hess-27-3083-2023, https://doi.org/10.5194/hess-27-3083-2023, 2023
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This study shows that previously reported underestimations of water ages are most likely not due to the use of seasonally variable tracers. Rather, these underestimations can be largely attributed to the choices of model approaches which rely on assumptions not frequently met in catchment hydrology. We therefore strongly advocate avoiding the use of this model type in combination with seasonally variable tracers and instead adopting StorAge Selection (SAS)-based or comparable model formulations.
Pau Wiersma, Jerom Aerts, Harry Zekollari, Markus Hrachowitz, Niels Drost, Matthias Huss, Edwin H. Sutanudjaja, and Rolf Hut
Hydrol. Earth Syst. Sci., 26, 5971–5986, https://doi.org/10.5194/hess-26-5971-2022, https://doi.org/10.5194/hess-26-5971-2022, 2022
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We test whether coupling a global glacier model (GloGEM) with a global hydrological model (PCR-GLOBWB 2) leads to a more realistic glacier representation and to improved basin runoff simulations across 25 large-scale basins. The coupling does lead to improved glacier representation, mainly by accounting for glacier flow and net glacier mass loss, and to improved basin runoff simulations, mostly in strongly glacier-influenced basins, which is where the coupling has the most impact.
Judith Uwihirwe, Alessia Riveros, Hellen Wanjala, Jaap Schellekens, Frederiek Sperna Weiland, Markus Hrachowitz, and Thom A. Bogaard
Nat. Hazards Earth Syst. Sci., 22, 3641–3661, https://doi.org/10.5194/nhess-22-3641-2022, https://doi.org/10.5194/nhess-22-3641-2022, 2022
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This study compared gauge-based and satellite-based precipitation products. Similarly, satellite- and hydrological model-derived soil moisture was compared to in situ soil moisture and used in landslide hazard assessment and warning. The results reveal the cumulative 3 d rainfall from the NASA-GPM to be the most effective landslide trigger. The modelled antecedent soil moisture in the root zone was the most informative hydrological variable for landslide hazard assessment and warning in Rwanda.
Roberto Bentivoglio, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci., 26, 4345–4378, https://doi.org/10.5194/hess-26-4345-2022, https://doi.org/10.5194/hess-26-4345-2022, 2022
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Deep learning methods have been increasingly used in flood management to improve traditional techniques. While promising results have been obtained, our review shows significant challenges in building deep learning models that can (i) generalize across multiple scenarios, (ii) account for complex interactions, and (iii) perform probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in deep learning to flood mapping.
Judith Uwihirwe, Markus Hrachowitz, and Thom Bogaard
Nat. Hazards Earth Syst. Sci., 22, 1723–1742, https://doi.org/10.5194/nhess-22-1723-2022, https://doi.org/10.5194/nhess-22-1723-2022, 2022
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This research tested the value of regional groundwater level information to improve landslide predictions with empirical models based on the concept of threshold levels. In contrast to precipitation-based thresholds, the results indicated that relying on threshold models exclusively defined using hydrological variables such as groundwater levels can lead to improved landslide predictions due to their implicit consideration of long-term antecedent conditions until the day of landslide occurrence.
Elisa Ragno, Markus Hrachowitz, and Oswaldo Morales-Nápoles
Hydrol. Earth Syst. Sci., 26, 1695–1711, https://doi.org/10.5194/hess-26-1695-2022, https://doi.org/10.5194/hess-26-1695-2022, 2022
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We explore the ability of non-parametric Bayesian networks to reproduce maximum daily discharge in a given month in a catchment when the remaining hydro-meteorological and catchment attributes are known. We show that a saturated network evaluated in an individual catchment can reproduce statistical characteristics of discharge in about ~ 40 % of the cases, while challenges remain when a saturated network considering all the catchments together is evaluated.
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.
Roberto Bentivoglio, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-614, https://doi.org/10.5194/hess-2021-614, 2021
Manuscript not accepted for further review
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Deep Learning methods have been increasingly used in flood mapping as an alternative to traditional modeling techniques. While promising results have been obtained, our review shows significant challenges in building Deep Learning models that can generalize across multiple scenarios, account for complex interactions, and provide probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in Deep Learning.
Markus Hrachowitz, Michael Stockinger, Miriam Coenders-Gerrits, Ruud van der Ent, Heye Bogena, Andreas Lücke, and Christine Stumpp
Hydrol. Earth Syst. Sci., 25, 4887–4915, https://doi.org/10.5194/hess-25-4887-2021, https://doi.org/10.5194/hess-25-4887-2021, 2021
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Deforestation affects how catchments store and release water. Here we found that deforestation in the study catchment led to a 20 % increase in mean runoff, while reducing the vegetation-accessible water storage from about 258 to 101 mm. As a consequence, fractions of young water in the stream increased by up to 25 % during wet periods. This implies that water and solutes are more rapidly routed to the stream, which can, after contamination, lead to increased contaminant peak concentrations.
Cited articles
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017.
Andréassian, V., Mander, Ü., and Pae, T.: The Budyko hypothesis before Budyko: the hydrological legacy of Evald Oldekop, J. Hydrol., 535, 386–391, 2016.
Berghuijs, W. R. and Woods, R. A.: A simple framework to quantitatively describe monthly precipitation and temperature climatology, Int. J. Climatol., 36, 3161–3174, 2016a.
Berghuijs, W. R. and Woods, R. A.: Correspondence: Space-time asymmetry undermines water yield assessment, Nat. Commun., 7, 11603, https://doi.org/10.1038/ncomms11603, 2016b.
Beven, K.: Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication, Hydrolog. Sci. J., 61, 1652–1665, 2016.
Bouaziz, L., Weerts, A., Schellekens, J., Sprokkereef, E., Stam, J., Savenije, H., and Hrachowitz, M.: Redressing the balance: quantifying net intercatchment groundwater flows, Hydrol. Earth Syst. Sci., 22, 6415–6434, https://doi.org/10.5194/hess-22-6415-2018, 2018.
Bouaziz, L. J., Steele-Dunne, S. C., Schellekens, J., Weerts, A. H., Stam, J., Sprokkereef, E., and Hrachowitz, M.: Improved understanding of the link between catchment-scale vegetation accessible storage and satellite-derived Soil Water Index, Water Resour. Res., 56, e2019WR026365, https://doi.org/10.1029/2019WR026365, 2020.
Bouaziz, L. J. E., Fenicia, F., Thirel, G., de Boer-Euser, T., Buitink, J., Brauer, C. C., De Niel, J., Dewals, B. J., Drogue, G., Grelier, B., Melsen, L. A., Moustakas, S., Nossent, J., Pereira, F., Sprokkereef, E., Stam, J., Weerts, A. H., Willems, P., Savenije, H. H. G., and Hrachowitz, M.: Behind the scenes of streamflow model performance, Hydrol. Earth Syst. Sci., 25, 1069–1095, https://doi.org/10.5194/hess-25-1069-2021, 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.
Brown, A. E., Zhang, L., McMahon, T. A., Western, A. W., and Vertessy, R. A.: A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation, J. Hydrol., 310, 28–61, 2005.
Brunner, M. I., Farinotti, D., Zekollari, H., Huss, M., and Zappa, M.: Future shifts in extreme flow regimes in Alpine regions, Hydrol. Earth Syst. Sci., 23, 4471–4489, https://doi.org/10.5194/hess-23-4471-2019, 2019.
Budyko, M. I. and Miller, D. H.: Climate and life, Academic Press, New York, 508 pp., 1974.
Coenders-Gerrits, A. M. J., Van der Ent, R. J., Bogaard, T. A., Wang-Erlandsson, L., Hrachowitz, M., and Savenije, H. H. G.: Uncertainties in transpiration estimates, Nature, 506, E1–E2, 2014.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J., and Jones, P. D.: An ensemble version of the E-OBS temperature and precipitation data sets, J. Geophys. Res.-Atmos., 123, 9391–9409, 2018.
Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, 2020.
Clark, M. P., Rupp, D. E., Woods, R. A., Zheng, X., Ibbitt, R. P., Slater, A. G., and Uddstrom, M. J.: Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model, Adv. Water Resour., 31, 1309–1324, 2008.
de Boer-Euser, T., McMillan, H. K., Hrachowitz, M., Winsemius, H. C., and Savenije, H. H.: Influence of soil and climate on root zone storage capacity, Water Resour. Res., 52, 2009–2024, 2016.
De Wit, M. J., Van Den Hurk, B. J., Warmerdam, P. M., Torfs, P. J., Roulin, E., and Van Deursen, W. P.: Impact of climate change on low-flows in the river Meuse, Climatic Change, 82, 351–372, 2007.
Dirkse, G. M. and Daamen, W. P.: Dutch forest monitoring network, design and results, Commun. Ecol., 5, 115–120, 2004.
Donohue, R. J., Roderick, M. L., and McVicar, T. R.: On the importance of including vegetation dynamics in Budyko's hydrological model, Hydrol. Earth Syst. Sci., 11, 983–995, https://doi.org/10.5194/hess-11-983-2007, 2007.
Donohue, R. J., Roderick, M. L., and McVicar, T. R.: Roots, storms and soil pores: Incorporating key ecohydrological processes into Budyko's hydrological model, J. Hydrol., 436, 35–50, 2012.
Dralle, D. N., Hahm, W. J., Chadwick, K. D., McCormick, E., and Rempe, D. M.: Technical note: Accounting for snow in the estimation of root zone water storage capacity from precipitation and evapotranspiration fluxes, Hydrol. Earth Syst. Sci., 25, 2861–2867, https://doi.org/10.5194/hess-25-2861-2021, 2021.
Duethmann, D., Blöschl, G., and Parajka, J.: Why does a conceptual hydrological model fail to correctly predict discharge changes in response to climate change?, Hydrol. Earth Syst. Sci., 24, 3493–3511, https://doi.org/10.5194/hess-24-3493-2020, 2020.
Eagleson, P. S.: Ecological optimality in water-limited natural soil-vegetation systems: 1. Theory and hypothesis, Water Resour. Res., 18, 325–340, 1982.
ECA&D: E-OBS gridded dataset, ECA&D [data set], https://www.ecad.eu/download/ensembles/download.php (last access: 22 October 2024), 2024.
European Environment Agency: Corine Land Cover (CLC) 2018, Version 2020 20u1, European Environment Agency (EEA) under the framework of the Copernicus programme, http://land.copernicus.eu/pan-european/corine-land-cover/ (last access: 7 June 2019), 2018.
Euser, T., Hrachowitz, M., Winsemius, H. C., and Savenije, H. H.: The effect of forcing and landscape distribution on performance and consistency of model structures, Hydrol. Process., 29, 3727–3743, 2015.
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B., and Otero-Casal, C.: Hydrologic regulation of plant rooting depth, P. Natl. Acad. Sci. USA, 114, 10572–10577, 2017.
Fenicia, F., Savenije, H. H. G., Matgen, P., and Pfister, L.: Is the groundwater reservoir linear? Learning from data in hydrological modelling, Hydrol. Earth Syst. Sci., 10, 139–150, https://doi.org/10.5194/hess-10-139-2006, 2006.
Fenicia, F., Savenije, H. H. G., and Avdeeva, Y.: Anomaly in the rainfall-runoff behaviour of the Meuse catchment. Climate, land-use, or land-use management?, Hydrol. Earth Syst. Sci., 13, 1727–1737, https://doi.org/10.5194/hess-13-1727-2009, 2009.
Flo, V., Martínez-Vilalta, J., Mencuccini, M., Granda, V., Anderegg, W. R., and Poyatos, R.: Climate and functional traits jointly mediate tree water-use strategies, New Phytol., 231, 617–630, 2021.
Fowler, K., Knoben, W., Peel, M., Peterson, T., Ryu, D., Saft, M., and Western, A.: Many commonly used rainfall-runoff models lack long, slow dynamics: Implications for runoff projections, Water Resour. Res., 56, e2019WR025286, https://doi.org/10.1029/2019WR025286, 2020.
Fu, B. P.: On the calculation of the evaporation from land surface, Sci. Atmos. Sin., 5, 23–31, 1981.
Gao, H., Hrachowitz, M., Schymanski, S. J., Fenicia, F., Sriwongsitanon, N., and Savenije, H. H.: Climate controls how ecosystems size the root zone storage capacity at catchment scale, Geophys. Res. Lett., 41, 7916–7923, 2014.
Gao, H., Hrachowitz, M., Sriwongsitanon, N., Fenicia, F., Gharari, S., and Savenije, H. H.: Accounting for the influence of vegetation and landscape improves model transferability in a tropical savannah region, Water Resour. Res., 52, 7999–8022, 2016.
Gentine, P., D'Odorico, P., Lintner, B. R., Sivandran, G., and Salvucci, G.: Interdependence of climate, soil, and vegetation as constrained by the Budyko curve, Geophys. Res. Lett., 39, 2–7, https://doi.org/10.1029/2012GL053492,, 2012.
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.
Gudmundsson, L., Greve, P., and Seneviratne, S. I.: The sensitivity of water availability to changes in the aridity index and other factors – A probabilistic analysis in the Budyko space, Geophys. Res. Lett., 43, 6985–6994, 2016.
Guerrero-Ramírez, N. R., Mommer, L., Freschet, G. T., Iversen, C. M., McCormack, M. L., Kattge, J., and Weigelt, A.: Global root traits (GRooT) database, Global Ecol. Biogeogr., 30, 25–37, 2021.
Guswa, A. J.: The influence of climate on root depth: A carbon cost-benefit analysis, Water Resour. Res., 44, W02427, https://doi.org/10.1029/2007WR006384, 2008.
Hakala, K., Addor, N., Gobbe, T., Ruffieux, J., and Seibert, J.: Risks and opportunities for a Swiss hydroelectricity company in a changing climate, Hydrol. Earth Syst. Sci., 24, 3815–3833, https://doi.org/10.5194/hess-24-3815-2020, 2020.
Han, J., Yang, Y., Roderick, M. L., McVicar, T. R., Yang, D., Zhang, S., and Beck, H. E.: Assessing the steady-state assumption in water balance calculation across global catchments, Water Resour. Res., 56, e2020WR027392, https://doi.org/10.1029/2020WR027392, 2020.
Hanus, S., Hrachowitz, M., Zekollari, H., Schoups, G., Vizcaino, M., and Kaitna, R.: Future changes in annual, seasonal and monthly runoff signatures in contrasting Alpine catchments in Austria, Hydrol. Earth Syst. Sci., 25, 3429–3453, https://doi.org/10.5194/hess-25-3429-2021, 2021.
Hooghart, J. and Lablans, W.: Van Penman naar Makkink: een nieuwe berekeningswijze voor de klimatologische verdampingsgetallen, De Bilt, KNMI – Royal Netherlands Meteorological Institute, De Bilt, the Netherlands, 1988.
Hrachowitz, M. and Clark, M. P.: HESS Opinions: The complementary merits of competing modelling philosophies in hydrology, Hydrol. Earth Syst. Sci., 21, 3953–3973, https://doi.org/10.5194/hess-21-3953-2017, 2017.
Hrachowitz, M., Stockinger, M., Coenders-Gerrits, M., van der Ent, R., Bogena, H., Lücke, A., and Stumpp, C.: Reduction of vegetation-accessible water storage capacity after deforestation affects catchment travel time distributions and increases young water fractions in a headwater catchment, Hydrol. Earth Syst. Sci., 25, 4887–4915, https://doi.org/10.5194/hess-25-4887-2021, 2021.
Hulsman, P., Hrachowitz, M., and Savenije, H. H.: 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, 2021.
Ibrahim, M., Coenders-Gerrits, M., van der Ent, R., and Hrachowitz, M.: Catchments do not strictly follow Budyko curves over multiple decades but deviations are minor and predictable, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2024-120, in review, 2024.
Jaramillo, F., Cory, N., Arheimer, B., Laudon, H., van der Velde, Y., Hasper, T. B., Teutschbein, C., and Uddling, J.: Dominant effect of increasing forest biomass on evapotranspiration: interpretations of movement in Budyko space, Hydrol. Earth Syst. Sci., 22, 567–580, https://doi.org/10.5194/hess-22-567-2018, 2018.
Jaramillo, F., Piemontese, L., Berghuijs, W. R., Wang-Erlandsson, L., Greve, P., and Wang, Z.: Fewer basins will follow their Budyko curves under global warming and fossil-fueled development, Water Resour. Res., 58, e2021WR031825, https://doi.org/10.1029/2021WR031825, 2022.
Jasechko, S.: Plants turn on the tap, Nat. Clim. Change, 8, 562–563, 2018.
Kleidon, A.: Global datasets of rooting zone depth inferred from inverse methods, J. Climate, 17, 2714–2722, 2004.
Liu, Z., Cheng, L., Zhou, G., Chen, X., Lin, K., Zhang, W., and Zhou, P.: Global response of evapotranspiration ratio to climate conditions and watershed characteristics in a changing environment, J. Geophys. Res.-Atmos., 125, e2020JD032371, https://doi.org/10.1029/2020JD032371, 2020.
Ma, H., Mo, L., Crowther, T. W., Maynard, D. S., van den Hoogen, J., Stocker, B. D., and Zohner, C. M.: The global distribution and environmental drivers of aboveground versus belowground plant biomass, Nat. Ecol. Evol., 5, 1110–1122, 2021.
Maxwell, R. M., Condon, L. E., and Kollet, S. J.: A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3, Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, 2015.
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, 2021.
McMillan, H. K., Westerberg, I. K., and Krueger, T.: Hydrological data uncertainty and its implications, Wiley Interdisciplin. Rev.: Water, 5, e1319, https://doi.org/0.1002/wat2.1319, 2018.
Merz, R., Parajka, J., and Blöschl, G.: Time stability of catchment model parameters: Implications for climate impact analyses, Water Resour. Res., 47, 1–17, https://doi.org/10.1029/2010WR009505, 2011.
Milly, P. C. D.: Climate, interseasonal storage of soil water, and the annual water balance, Adv. Water Resour., 17, 19–24, 1994.
Nearing, G. S., Tian, Y., Gupta, H. V., Clark, M. P., Harrison, K. W., and Weijs, S. V.: A philosophical basis for hydrological uncertainty, Hydrolog. Sci. J., 61, 1666–1678, 2016.
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, 2016a.
Nijzink, R. C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H. H. G., and Hrachowitz, M.: The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models, Hydrol. Earth Syst. Sci., 20, 1151–1176, https://doi.org/10.5194/hess-20-1151-2016, 2016b.
Oldekop, E.: Collection of the Works of Students of the Meteorological Observatory, University of TartuJurjew-Dorpat Tartu, Estonia, p. 209, 1911.
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil, F., and Loumagne, C.: Which potential evapotranspiration input for a lumped rainfall–runoff model: Part 2 – Towards a simple and efficient potential evapotranspiration model for rainfall–runoff modelling, J. Hydrol., 303, 290–306, 2005.
Prudhomme, C., Giuntoli, I., Robinson, E. L., Clark, D. B., Arnell, N. W., Dankers, R., and Wisser, D.: Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment, P. Natl. Acad. Sci. USA, 111, 3262–3267, 2014.
Reaver, N. G. F., Kaplan, D. A., Klammler, H., and Jawitz, J. W.: Theoretical and empirical evidence against the Budyko catchment trajectory conjecture, Hydrol. Earth Syst. Sci., 26, 1507–1525, https://doi.org/10.5194/hess-26-1507-2022, 2022.
Rennó, C. D., Nobre, A. D., Cuartas, L. A., Soares, J. V., Hodnett, M. G., and Tomasella, J.: HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia, Remote Sens. Environ., 112, 3469–3481, 2008.
Roderick, M. L. and Farquhar, G. D.: A simple framework for relating variations in runoff to variations in climatic conditions and catchment properties, Water Resour. Res., 47, W00G07, https://doi.org/10.1029/2010WR009826, 2011.
Rodriguez-Iturbe, I., D'Odorico, P., Laio, F., Ridolfi, L., and Tamea, S.: Challenges in humid land ecohydrology: Interactions of water table and unsaturated zone with climate, soil, and vegetation, Water Resour. Res., 43, W09301, https://doi.org/10.1029/2007WR006073, 2007.
Rottler, E., Bronstert, A., Bürger, G., and Rakovec, O.: Projected changes in Rhine River flood seasonality under global warming, Hydrol. Earth Syst. Sci., 25, 2353–2371, https://doi.org/10.5194/hess-25-2353-2021, 2021.
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.
Schreiber, P.: Über die Beziehungen zwischen dem Niederschlag und der Wasserführung der Flüsse in Mitteleuropa, Z. Meteorol., 21, 441–452, 1904.
Schymanski, S. J., Sivapalan, M., Roderick, M. L., Beringer, J., and Hutley, L. B.: An optimality-based model of the coupled soil moisture and root dynamics, Hydrol. Earth Syst. Sci., 12, 913–932, https://doi.org/10.5194/hess-12-913-2008, 2008.
Service Public de Wallonie: Direction générale opérationnelle de la Mobilité et des Voies hydrauliques, Département des Etudes et de l'Appui à la Gestion, Direction de la Gestion hydrologique, Rapport annuel 2018, Tech. rep., Service Public de Wallonie, Namur, Belgium, https://doi.org/10.1029/2012WR012055, 2018.
Shapiro, S. S. and Wilk, M. B.: An analysis of variance test for normality (complete samples), Biometrika, 52, 591–611, 1965.
Sivandran, G. and Bras, R. L.: Identifying the optimal spatially and temporally invariant root distribution for a semiarid environment, Water Resour. Res., 48, W12525, https://doi.org/10.1029/2012WR012055, 2012.
Speich, M. J. R., Zappa, M., Scherstjanoi, M., and Lischke, H.: FORests and HYdrology under Climate Change in Switzerland v1.0: a spatially distributed model combining hydrology and forest dynamics, Geosci. Model Dev., 13, 537–564, https://doi.org/10.5194/gmd-13-537-2020, 2020.
Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., and Jackson, R. B.: Global patterns of water storage in the rooting zones of vegetation, Nat. Geosci., 16, 250–256, 2023.
Tempel, N.: Paper-Catchment-Response, Zenodo [code], https://doi.org/10.5281/zenodo.13969617, 2024.
Teuling, A. J. and Hoek van Dijke, A. J.: Forest age and water yield, Nature, 578, E16–E18, 2020.
Tixeront, J.: Prévision des apports des cours d'eau, Publ. Assoc. Int. Hydrol. Sci., 63, 118–126, 1964.
Troch, P. A., Martinez, G. F., Pauwels, V. R., Durcik, M., Sivapalan, M., Harman, C., and Huxman, T.: Climate and vegetation water use efficiency at catchment scales, Hydrol. Process., 23, 2409, https://doi.org/10.1002/hyp.7358, 2009.
van Oorschot, F., van der Ent, R. J., Hrachowitz, M., and Alessandri, A.: Climate-controlled root zone parameters show potential to improve water flux simulations by land surface models, Earth Syst. Dynam., 12, 725–743, https://doi.org/10.5194/esd-12-725-2021, 2021.
van Oorschot, F., van der Ent, R. J., Alessandri, A., and Hrachowitz, M.: Influence of irrigation on root zone storage capacity estimation, Hydrol. Earth Syst. Sci., 28, 2313–2328, https://doi.org/10.5194/hess-28-2313-2024, 2024.
van Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B.: Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications, Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, 2024.
Vertessy, R. A., Watson, F. G., and Sharon, K. O.: Factors determining relations between stand age and catchment water balance in mountain ash forests, Forest. Ecol. Manage., 143, 13–26, 2001.
Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., and Gupta, H. V.: Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis, Hydrol. Process., 17, 455–476, 2003.
Wang, S., Hrachowitz, M., Schoups, G., and Stumpp, C.: Stable water isotopes and tritium tracers tell the same tale: no evidence for underestimation of catchment transit times inferred by stable isotopes in StorAge Selection (SAS)-function models, Hydrol. Earth Syst. Sci., 27, 3083–3114, https://doi.org/10.5194/hess-27-3083-2023, 2023.
Wang, S., Hrachowitz, M., and Schoups, G.: Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany, Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, 2024.
Wang, W., Zou, S., Shao, Q., Xing, W., Chen, X., Jiao, X., and Yu, Z.: The analytical derivation of multiple elasticities of runoff to climate change and catchment characteristics alteration, J. Hydrol., 541, 1042–1056, 2016.
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.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., and Pavelsky, T. M.: MERIT Hydro: a high-resolution global hydrography map based on latest topography dataset, Water Resour. Res., 55, 5053–5073, 2019.
Yuan, W., Zheng, Y., Piao, S., Ciais, P., Lombardozzi, D., Wang, Y., and Yang, S.: Increased atmospheric vapor pressure deficit reduces global vegetation growth, Sci. Adv., 5, eaax1396, https://doi.org/10.1126/sciadv.aax1396, 2019.
Zhang, L., Hickel, K., Dawes, W. R., Chiew, F. H., Western, A. W., and Briggs, P. R.: A rational function approach for estimating mean annual evapotranspiration, Water Resour. Res., 40, W02502, https://doi.org/10.1029/2003WR002710, 2004.
Short summary
This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
This study explores the impact of climatic variability on root zone water storage capacities...
