Articles | Volume 28, issue 1
https://doi.org/10.5194/hess-28-1-2024
© 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-28-1-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Water cycle changes in Czechia: a multi-source water budget perspective
Mijael Rodrigo Vargas Godoy
CORRESPONDING AUTHOR
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czechia
Yannis Markonis
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czechia
Oldrich Rakovec
UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czechia
Michal Jenicek
Department of Physical Geography and Geoecology, Charles University, Prague, Czechia
Riya Dutta
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czechia
Department of Environmental Science and Engineering, IIT (ISM) Dhanbad, Dhanbad-826004, India
Rajani Kumar Pradhan
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czechia
Zuzana Bešťáková
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czechia
Jan Kyselý
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czechia
Institute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czechia
Roman Juras
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czechia
Simon Michael Papalexiou
Department of Civil Engineering, University of Calgary, Calgary, Canada
Global Institute for Water Security, University of Saskatchewan, Saskatoon, Canada
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czechia
Martin Hanel
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czechia
Related authors
Vishal Thakur, Yannis Markonis, Rohini Kumar, Johanna Ruth Thomson, Mijael Rodrigo Vargas Godoy, Martin Hanel, and Oldrich Rakovec
Hydrol. Earth Syst. Sci., 29, 4395–4416, https://doi.org/10.5194/hess-29-4395-2025, https://doi.org/10.5194/hess-29-4395-2025, 2025
Short summary
Short summary
Understanding the changes in water movement in earth is crucial for everyone. To quantify this water movement there are several techniques. We examined how different methods of estimating evaporation impact predictions of various types of water movement across Europe. We found that, while these methods generally agree on whether changes are increasing or decreasing, they differ in magnitude. This means selecting the right evaporation method is crucial for accurate predictions of water movement.
Sadaf Nasreen, Markéta Součková, Mijael Rodrigo Vargas Godoy, Ujjwal Singh, Yannis Markonis, Rohini Kumar, Oldrich Rakovec, and Martin Hanel
Earth Syst. Sci. Data, 14, 4035–4056, https://doi.org/10.5194/essd-14-4035-2022, https://doi.org/10.5194/essd-14-4035-2022, 2022
Short summary
Short summary
This article presents a 500-year reconstructed annual runoff dataset for several European catchments. Several data-driven and hydrological models were used to derive the runoff series using reconstructed precipitation and temperature and a set of proxy data. The simulated runoff was validated using independent observed runoff data and documentary evidence. The validation revealed a good fit between the observed and reconstructed series for 14 catchments, which are available for further analysis.
Vishal Thakur, Yannis Markonis, Rohini Kumar, Johanna Ruth Thomson, Mijael Rodrigo Vargas Godoy, Martin Hanel, and Oldrich Rakovec
Hydrol. Earth Syst. Sci., 29, 4395–4416, https://doi.org/10.5194/hess-29-4395-2025, https://doi.org/10.5194/hess-29-4395-2025, 2025
Short summary
Short summary
Understanding the changes in water movement in earth is crucial for everyone. To quantify this water movement there are several techniques. We examined how different methods of estimating evaporation impact predictions of various types of water movement across Europe. We found that, while these methods generally agree on whether changes are increasing or decreasing, they differ in magnitude. This means selecting the right evaporation method is crucial for accurate predictions of water movement.
Ondrej Hotovy, Ondrej Nedelcev, Jan Seibert, and Michal Jenicek
Hydrol. Earth Syst. Sci., 29, 4199–4217, https://doi.org/10.5194/hess-29-4199-2025, https://doi.org/10.5194/hess-29-4199-2025, 2025
Short summary
Short summary
Rain falling on snow (RoS) can accelerate snowmelt and affect runoff, potentially causing severe flooding. We assessed the regional and seasonal variations in the occurrence of RoS in mountainous catchments in central Europe under different perturbations of future climate. Our results showed that RoS changes driven by climate change vary greatly among regions, across elevations, and within the cold season. However, most projections suggested a decrease in RoS events and RoS-driven runoff.
Jan Řehoř, Rudolf Brázdil, Oldřich Rakovec, Martin Hanel, Milan Fischer, Rohini Kumar, Jan Balek, Markéta Poděbradská, Vojtěch Moravec, Luis Samaniego, Yannis Markonis, and Miroslav Trnka
Hydrol. Earth Syst. Sci., 29, 3341–3358, https://doi.org/10.5194/hess-29-3341-2025, https://doi.org/10.5194/hess-29-3341-2025, 2025
Short summary
Short summary
We present a robust method for identification and classification of global land drought events (GLDEs) based on soil moisture. Two models were used to calculate soil moisture and delimit soil drought over global land from 1980–2022, with clusters of 775 and 630 GLDEs. Using four spatiotemporal and three motion-related characteristics, we categorized GLDEs into seven severity and seven dynamic categories. The frequency of GLDEs has generally increased in recent decades.
Ondřej Nedělčev, Michael Matějka, Kamil Láska, Zbyněk Engel, Jan Kavan, and Michal Jenicek
The Cryosphere, 19, 2457–2473, https://doi.org/10.5194/tc-19-2457-2025, https://doi.org/10.5194/tc-19-2457-2025, 2025
Short summary
Short summary
The annual variability of runoff has not been analysed in the maritime Antarctic. Thus, we simulated and analysed rain, snow and glacier contributions to runoff related to climate variability in a small catchment over 11 years. The majority of the runoff came from snowmelt. Inter-annual variability in total runoff was associated with large variability in glacier runoff. Between October and May, 92 % of the runoff occurred, with significant runoff events outside the usual measurement season.
Sergiy Vorogushyn, Li Han, Heiko Apel, Viet Dung Nguyen, Björn Guse, Xiaoxiang Guan, Oldrich Rakovec, Husain Najafi, Luis Samaniego, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 25, 2007–2029, https://doi.org/10.5194/nhess-25-2007-2025, https://doi.org/10.5194/nhess-25-2007-2025, 2025
Short summary
Short summary
The July 2021 flood in central Europe was one of the deadliest floods in Europe in the recent decades and the most expensive flood in Germany. In this paper, we show that the hydrological impact of this event in the Ahr valley could have been even worse if the rainfall footprint trajectory had been only slightly different. The presented methodology of spatial counterfactuals generates plausible unprecedented events and helps to better prepare for future extreme floods.
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
Short summary
Short summary
Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
Short summary
Short summary
This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Shailendra Pratap, Yannis Markonis, and Cécile Blanchet
Clim. Past Discuss., https://doi.org/10.5194/cp-2024-68, https://doi.org/10.5194/cp-2024-68, 2024
Preprint withdrawn
Short summary
Short summary
Our study investigates the influence of oceanic changes on regional hydroclimate (precipitation and temperature) patterns, in Europe and North America during the Medieval Climate Anomaly period. Our findings suggest that centennial-scale variations in terrestrial thermodynamics, sea surface temperatures, and shifts in the Intertropical Convergence Zone likely played a role in shaping regional hydroclimate patterns. Our outcomes will offer insights into how hydroclimate may evolve in the future.
Hossein Abbasizadeh, Petr Maca, Martin Hanel, Mads Troldborg, and Amir AghaKouchak
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-297, https://doi.org/10.5194/hess-2024-297, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Here, we represented catchments as networks of variables connected by cause-and-effect relationships. By comparing the performance of statistical and machine learning methods with and without incorporating causal information to predict runoff properties, we showed that causal information can enhance models' robustness by reducing accuracy drop between training and testing phases, improving the model's interpretability, and mitigating overfitting issues, especially with small training samples.
Beijing Fang, Emanuele Bevacqua, Oldrich Rakovec, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, https://doi.org/10.5194/hess-28-3755-2024, 2024
Short summary
Short summary
We use grid-based runoff from a hydrological model to identify large spatiotemporally connected flood events in Europe, assess extent trends over the last 70 years, and attribute the trends to different drivers. Our findings reveal a general increase in flood extent, with regional variations driven by diverse factors. The study not only enables a thorough examination of flood events across multiple basins but also highlights the potential challenges arising from changing flood extents.
Rajani Kumar Pradhan, Yannis Markonis, Francesco Marra, Efthymios I. Nikolopoulos, Simon Michael Papalexiou, and Vincenzo Levizzani
EGUsphere, https://doi.org/10.5194/egusphere-2024-1626, https://doi.org/10.5194/egusphere-2024-1626, 2024
Short summary
Short summary
This study compared global satellite and one reanalysis precipitation dataset to assess diurnal variability. We found that all datasets capture key diurnal precipitation patterns, with maximum precipitation in the afternoon over land and early morning over the ocean. However, there are differences in the exact timing and amount of precipitation. This suggests that it is better to use a combination of datasets for potential applications rather than relying on a single dataset.
Kingsley Nnaemeka Ogbu, Oldrich Rakovec, Luis Samaniego, Gloria Chinwendu Okafor, Bernhard Tischbein, and Hadush Meresa
Proc. IAHS, 385, 211–218, https://doi.org/10.5194/piahs-385-211-2024, https://doi.org/10.5194/piahs-385-211-2024, 2024
Short summary
Short summary
In this study, the MPR-mHM technique was applied in four data-scarce basins in Nigeria. Remotely sensed rainfall datasets were used as model forcings to evaluate the mHM capability in reproducing observed stream discharge under single and multivariable model calibration frameworks. Overall, model calibration performances displayed satisfactory outputs as evident in the Kling-Gupta Efficiency (KGE) scores across most basins.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
Short summary
Short summary
Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Petr Kavka, Jiří Cajthaml, Adam Tejkl, and Martin Hanel
Abstr. Int. Cartogr. Assoc., 6, 120, https://doi.org/10.5194/ica-abs-6-120-2023, https://doi.org/10.5194/ica-abs-6-120-2023, 2023
Markéta Součková, Roman Juras, Kryštof Dytrt, Vojtěch Moravec, Johanna Ruth Blöcher, and Martin Hanel
Nat. Hazards Earth Syst. Sci., 22, 3501–3525, https://doi.org/10.5194/nhess-22-3501-2022, https://doi.org/10.5194/nhess-22-3501-2022, 2022
Short summary
Short summary
Avalanches are natural hazards that threaten people and infrastructure. With climate change, avalanche activity is changing. We analysed the change in frequency and size of avalanches in the Krkonoše Mountains, Czechia, and detected important variables with machine learning tools from 1979–2020. Wet avalanches in February and March have increased, and slab avalanches have decreased and become smaller. The identified variables and their threshold levels may help in avalanche decision-making.
Friedrich Boeing, Oldrich Rakovec, Rohini Kumar, Luis Samaniego, Martin Schrön, Anke Hildebrandt, Corinna Rebmann, Stephan Thober, Sebastian Müller, Steffen Zacharias, Heye Bogena, Katrin Schneider, Ralf Kiese, Sabine Attinger, and Andreas Marx
Hydrol. Earth Syst. Sci., 26, 5137–5161, https://doi.org/10.5194/hess-26-5137-2022, https://doi.org/10.5194/hess-26-5137-2022, 2022
Short summary
Short summary
In this paper, we deliver an evaluation of the second generation operational German drought monitor (https://www.ufz.de/duerremonitor) with a state-of-the-art compilation of observed soil moisture data from 40 locations and four different measurement methods in Germany. We show that the expressed stakeholder needs for higher resolution drought information at the one-kilometer scale can be met and that the agreement of simulated and observed soil moisture dynamics can be moderately improved.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
Short summary
Short summary
Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Sadaf Nasreen, Markéta Součková, Mijael Rodrigo Vargas Godoy, Ujjwal Singh, Yannis Markonis, Rohini Kumar, Oldrich Rakovec, and Martin Hanel
Earth Syst. Sci. Data, 14, 4035–4056, https://doi.org/10.5194/essd-14-4035-2022, https://doi.org/10.5194/essd-14-4035-2022, 2022
Short summary
Short summary
This article presents a 500-year reconstructed annual runoff dataset for several European catchments. Several data-driven and hydrological models were used to derive the runoff series using reconstructed precipitation and temperature and a set of proxy data. The simulated runoff was validated using independent observed runoff data and documentary evidence. The validation revealed a good fit between the observed and reconstructed series for 14 catchments, which are available for further analysis.
Guoqiang Tang, Martyn P. Clark, Simon Michael Papalexiou, Andrew J. Newman, Andrew W. Wood, Dominique Brunet, and Paul H. Whitfield
Earth Syst. Sci. Data, 13, 3337–3362, https://doi.org/10.5194/essd-13-3337-2021, https://doi.org/10.5194/essd-13-3337-2021, 2021
Short summary
Short summary
Probabilistic estimates are useful to quantify the uncertainties in meteorological datasets. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1° spatial resolution from 1979 to 2018. It is expected to be useful for hydrological and meteorological applications in North America.
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, https://doi.org/10.5194/gmd-14-3843-2021, 2021
Short summary
Short summary
We analyse water storage compartments, water flows, and human water use sectors included in 16 global water models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b. We develop a standard writing style for the model equations. We conclude that even though hydrologic processes are often based on similar equations, in the end these equations have been adjusted, or the models have used different values for specific parameters or specific variables.
Erwin Rottler, Axel Bronstert, Gerd Bürger, and Oldrich Rakovec
Hydrol. Earth Syst. Sci., 25, 2353–2371, https://doi.org/10.5194/hess-25-2353-2021, https://doi.org/10.5194/hess-25-2353-2021, 2021
Short summary
Short summary
The mesoscale hydrological model (mHM) forced with an ensemble of climate projection scenarios was used to assess potential future changes in flood seasonality in the Rhine River basin. Results indicate that future changes in flood characteristics are controlled by increases in precipitation sums and diminishing snowpacks. The decreases in snowmelt can counterbalance increasing precipitation, resulting in only small and transient changes in streamflow maxima.
Manuela I. Brunner, Lieke A. Melsen, Andrew W. Wood, Oldrich Rakovec, Naoki Mizukami, Wouter J. M. Knoben, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 105–119, https://doi.org/10.5194/hess-25-105-2021, https://doi.org/10.5194/hess-25-105-2021, 2021
Short summary
Short summary
Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable model ideally reproduces both local flood characteristics and regional aspects of flooding. In this paper we investigate how such characteristics are represented by hydrologic models. Our results show that both the modeling of local and regional flood characteristics are challenging, especially under changing climate conditions.
Guoqiang Tang, Martyn P. Clark, Andrew J. Newman, Andrew W. Wood, Simon Michael Papalexiou, Vincent Vionnet, and Paul H. Whitfield
Earth Syst. Sci. Data, 12, 2381–2409, https://doi.org/10.5194/essd-12-2381-2020, https://doi.org/10.5194/essd-12-2381-2020, 2020
Short summary
Short summary
Station observations are critical for hydrological and meteorological studies, but they often contain missing values and have short measurement periods. This study developed a serially complete dataset for North America (SCDNA) from 1979 to 2018 for 27 276 precipitation and temperature stations. SCDNA is built on multiple data sources and infilling/reconstruction strategies to achieve high-quality estimates which can be used for a variety of applications.
Cited articles
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015, Scientific Data, 5, 170191, https://doi.org/10.1038/sdata.2017.191, 2018. a, b, c, d
Aires, F.: Combining datasets of satellite-retrieved products. Part I: Methodology and water budget closure, J. Hydrometeorol., 15, 1677–1691, 2014. a
Allan, R. P.: Regime dependent changes in global precipitation, Clim. Dynam., 39, 827–840, 2012. a
Bandhauer, M., Isotta, F., Lakatos, M., Lussana, C., Båserud, L., Izsák, B., Szentes, O., Tveito, O. E., and Frei, C.: Evaluation of daily precipitation analyses in E-OBS (v19.0e) and ERA5 by comparison to regional high-resolution datasets in European regions, Int. J. Climatol., 42, 727–747, https://doi.org/10.1002/joc.7269, 2022. a, b
Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., van Dijk, A. I. J. M., Huffman, G. J., Adler, R. F., and Wood, E. F.: Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS, Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, 2019. a
Berghuijs, W. R., Woods, R. A., and Hrachowitz, M.: A precipitation shift from snow towards rain leads to a decrease in streamflow, Nat. Clim. Change, 4, 583–586, https://doi.org/10.1038/nclimate2246, 2014. a
Bešťáková, Z., Strnad, F., Vargas Godoy, M. R., Singh, U., Markonis, Y., Hanel, M., Máca, P., and Kyselý, J.: Changes of the aridity index in Europe from 1950 to 2019, Theor. Appl. Climatol., 151, 587–601, https://doi.org/10.1007/s00704-022-04266-3, 2022. a, b
Boé, J. and Terray, L.: Uncertainties in summer evapotranspiration changes over Europe and implications for regional climate change, Geophys. Res. Lett., 35, L05702, https://doi.org/10.1029/2007GL032417, 2008. a
Brázdil, R., Trnka, M., Dobrovolný, P., Chromá, K., Hlavinka, P., and Žalud, Z.: Variability of droughts in the Czech Republic, 1881–2006, Theor. Appl. Climatol., 97, 297–315, https://doi.org/10.1007/s00704-008-0065-x, 2009. a, b
Brázdil, R., Dobrovolný, P., Trnka, M., Kotyza, O., Řezníčková, L., Valášek, H., Zahradníček, P., and Štěpánek, P.: Droughts in the Czech Lands, 1090–2012 AD, Clim. Past, 9, 1985–2002, https://doi.org/10.5194/cp-9-1985-2013, 2013. a
Brázdil, R., Trnka, M., Mikšovský, J., Řezníčková, L., and Dobrovolný, P.: Spring-summer droughts in the Czech Land in 1805–2012 and their forcings, Int. J. Climatol., 35, 1405–1421, https://doi.org/10.1002/joc.4065, 2015. a
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018. a, b, c
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., and Bauer, d. P.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011. a
Dierauer, J. R., Whitfield, P. H., and Allen, D. M.: Climate Controls on Runoff and Low Flows in Mountain Catchments of Western North America, Water Resour. Res., 54, 7495–7510, https://doi.org/10.1029/2018WR023087, 2018. a
Dubrovsky, M., Svoboda, M. D., Trnka, M., Hayes, M. J., Wilhite, D. A., Zalud, Z., and Hlavinka, P.: Application of relative drought indices in assessing climate-change impacts on drought conditions in Czechia, Theor. Appl. Climatol., 96, 155–171, https://doi.org/10.1007/s00704-008-0020-x, 2009. a
Fallah, A., O, S., and Orth, R.: Climate-dependent propagation of precipitation uncertainty into the water cycle, Hydrol. Earth Syst. Sci., 24, 3725–3735, https://doi.org/10.5194/hess-24-3725-2020, 2020. a, b
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315, https://doi.org/10.1002/joc.5086, 2017. a
Hanel, M., Rakovec, O., Markonis, Y., Máca, P., Samaniego, L., Kyselý, J., and Kumar, R.: Revisiting the recent European droughts from a long-term perspective, Sci. Rep., 8, 9499, https://doi.org/10.1038/s41598-018-27464-4, 2018. a, b
Hargreaves, G. H. and Samani, Z. A.: Estimating Potential Evapotranspiration, J. Irr. Drain. Div.-ASCE, 108, 225–230, https://doi.org/10.1061/JRCEA4.0001390, 1982. a
Hari, V., Rakovec, O., Markonis, Y., Hanel, M., and Kumar, R.: Increased future occurrences of the exceptional 2018–2019 Central European drought under global warming, Sci. Rep., 10, 12207, https://doi.org/10.1038/s41598-020-68872-9, 2020. a
Hassler, B. and Lauer, A.: Comparison of Reanalysis and Observational Precipitation Datasets Including ERA5 and WFDE5, Atmosphere, 12, 1462, https://doi.org/10.3390/atmos12111462, 2021. a
Held, I. M. and Soden, B. J.: Robust Responses of the Hydrological Cycle to Global Warming, J. Climate, 19, 5686–5699, https://doi.org/10.1175/JCLI3990.1, 2006. a
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A.: Very high resolution interpolated climate surfaces for global land areas, Int. J. Climatol., 25, 1965–1978, https://doi.org/10.1002/joc.1276, 2005. a
Hänsel, S., Ustrnul, Z., Łupikasza, E., and Skalak, P.: Assessing seasonal drought variations and trends over Central Europe, Adv. Water Resour., 127, 53–75, https://doi.org/10.1016/j.advwatres.2019.03.005, 2019. a
Jaagus, J., Aasa, A., Aniskevich, S., Boincean, B., Bojariu, R., Briede, A., Danilovich, I., Castro, F. D., Dumitrescu, A., Labuda, M., Labudová, L., Lõhmus, K., Melnik, V., Mõisja, K., Pongracz, R., Potopová, V., Řezníčková, L., Rimkus, E., Semenova, I., Stonevičius, E., Štěpánek, P., Trnka, M., Vicente-Serrano, S. M., Wibig, J., and Zahradníček, P.: Long-term changes in drought indices in eastern and central Europe, Int. J. Climatol., 42, 225–249, https://doi.org/10.1002/joc.7241, 2022. a
Janowiak, J. E. and Xie, P.: CAMS–OPI: A Global Satellite–Rain Gauge Merged Product for Real-Time Precipitation Monitoring Applications, J. Climate, 12, 3335–3342, https://doi.org/10.1175/1520-0442(1999)012<3335:COAGSR>2.0.CO;2, 1999. a
Jenicek, M. and Ledvinka, O.: Importance of snowmelt contribution to seasonal runoff and summer low flows in Czechia, Hydrol. Earth Syst. Sci., 24, 3475–3491, https://doi.org/10.5194/hess-24-3475-2020, 2020. a
Jenicek, M., Hnilica, J., Nedelcev, O., and Sipek, V.: Future changes in snowpack will impact seasonal runoff and low flows in Czechia, J. Hydrol., 37, 100899, https://doi.org/10.1016/j.ejrh.2021.100899, 2021. a
Kašpar, M., Bližňák, V., Hulec, F., and Müller, M.: High-resolution spatial analysis of the variability in the subdaily rainfall time structure, Atmos. Res., 248, 105202, https://doi.org/10.1016/j.atmosres.2020.105202, 2021. a
Kašpárek, L. and Kožín, R.: Changes in precipitation and runoff in river basins in the Czech Republic during the period of intense warming, Vodohospodářské technicko-ekonomické informace, Výzkumný ústav vodohospodářský T. G. Masaryka, veřejná výzkumná instituce, 64, 12–27, 2022. a
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 Reanalysis: General Specifications and Basic Characteristics, J. Meteorol. Soc. Jpn Ser. II, 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015. a
Kumar, R., Samaniego, L., and Attinger, S.: Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour. Res., 49, 360–379, https://doi.org/10.1029/2012WR012195, 2013. a, b
Kyselý, J. and Beranová, R.: Climate-change effects on extreme precipitation in central Europe: uncertainties of scenarios based on regional climate models, Theor. Appl. Climatol., 95, 361–374, https://doi.org/10.1007/s00704-008-0014-8, 2009. a, b
Kyselý, J., Gaál, L., Beranová, R., and Plavcová, E.: Climate change scenarios of precipitation extremes in Central Europe from ENSEMBLES regional climate models, Theor. Appl. Climatol., 104, 529–542, https://doi.org/10.1007/s00704-010-0362-z, 2011. a
Lavers, D. A., Simmons, A., Vamborg, F., and Rodwell, M. J.: An evaluation of ERA5 precipitation for climate monitoring, Q. J. Roy. Meteor. Soc., 148, 3152–3165, https://doi.org/10.1002/qj.4351, 2022. a
Lhotka, O., Trnka, M., Kyselý, J., Markonis, Y., Balek, J., and Možný, M.: Atmospheric Circulation as a Factor Contributing to Increasing Drought Severity in Central Europe, J. Geophys. Res.-Amtos., 125, e2019JD032269, https://doi.org/10.1029/2019JD032269, 2020. a
Lorenz, C. and Kunstmann, H.: The Hydrological Cycle in Three State-of-the-Art Reanalyses: Intercomparison and Performance Analysis, J. Hydrometeorol., 13, 1397–1420, https://doi.org/10.1175/JHM-D-11-088.1, 2012. a
Markonis, Y., Kumar, R., Hanel, M., Rakovec, O., Máca, P., and AghaKouchak, A.: The rise of compound warm-season droughts in Europe, Sci. Adv., 7, eabb9668, https://doi.org/10.1126/sciadv.abb9668, 2021. a, b
Moazamnia, M., Hassanzadeh, Y., Nadiri, A. A., Khatibi, R., and Sadeghfam, S.: Formulating a strategy to combine artificial intelligence models using Bayesian model averaging to study a distressed aquifer with sparse data availability, J. Hydrol., 571, 765–781, https://doi.org/10.1016/j.jhydrol.2019.02.011, 2019. a
Moravec, V., Markonis, Y., Rakovec, O., Svoboda, M., Trnka, M., Kumar, R., and Hanel, M.: Europe under multi-year droughts: how severe was the 2014–2018 drought period?, Environ. Res. Lett., 16, 034062, https://doi.org/10.1088/1748-9326/abe828, 2021. a
Mozny, M., Trnka, M., Vlach, V., Vizina, A., Potopova, V., Zahradnicek, P., Stepanek, P., Hajkova, L., Staponites, L., and Zalud, Z.: Past (1971–2018) and future (2021–2100) pan evaporation rates in the Czech Republic, J. Hydrol., 590, 125390, https://doi.org/10.1016/j.jhydrol.2020.125390, 2020. a
Muelchi, R., Rössler, O., Schwanbeck, J., Weingartner, R., and Martius, O.: River runoff in Switzerland in a changing climate – runoff regime changes and their time of emergence, Hydrol. Earth Syst. Sci., 25, 3071–3086, https://doi.org/10.5194/hess-25-3071-2021, 2021. a
Munier, S. and Aires, F.: A new global method of satellite dataset merging and quality characterization constrained by the terrestrial water budget, Remote Sens. Environ., 205, 119–130, 2018. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a, b, c
Nedelcev, O. and Jenicek, M.: Trends in seasonal snowpack and their relation to climate variables in mountain catchments in Czechia, Hydrolog. Sci. J., 66, 2340–2356, https://doi.org/10.1080/02626667.2021.1990298, 2021. a, b
Pan, M. and Wood, E. F.: Data assimilation for estimating the terrestrial water budget using a constrained ensemble Kalman filter, J. Hydrometeorol., 7, 534–547, 2006. a
Pan, M., Sahoo, A. K., Troy, T. J., Vinukollu, R. K., Sheffield, J., and Wood, E. F.: Multisource estimation of long-term terrestrial water budget for major global river basins, J. Climate, 25, 3191–3206, 2012. a
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., Ammann, C., Arain, M. A., Ardö, J., Arkebauer, T., Arndt, S. K., Arriga, N., Aubinet, M., Aurela, M., Baldocchi, D., Barr, A., Beamesderfer, E., Marchesini, L. B., Bergeron, O., Beringer, J., Bernhofer, C., Berveiller, D., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Boike, J., Bolstad, P. V., Bonal, D., Bonnefond, J.-M., Bowling, D. R., Bracho, R., Brodeur, J., Brümmer, C., Buchmann, N., Burban, B., Burns, S. P., Buysse, P., Cale, P., Cavagna, M., Cellier, P., Chen, S., Chini, I., Christensen, T. R., Cleverly, J., Collalti, A., Consalvo, C., Cook, B. D., Cook, D., Coursolle, C., Cremonese, E., Curtis, P. S., D’Andrea, E., da Rocha, H., Dai, X., Davis, K. J., Cinti, B. D., Grandcourt, A. d., Ligne, A. D., De Oliveira, R. C., Delpierre, N., Desai, A. R., Di Bella, C. M., Tommasi, P. d., Dolman, H., Domingo, F., Dong, G., Dore, S., Duce, P., Dufrêne, E., Dunn, A., Dušek, J., Eamus, D., Eichelmann, U., ElKhidir, H. A. M., Eugster, W., Ewenz, C. M., Ewers, B., Famulari, D., Fares, S., Feigenwinter, I., Feitz, A., Fensholt, R., Filippa, G., Fischer, M., Frank, J., Galvagno, M., Gharun, M., Gianelle, D., Gielen, B., Gioli, B., Gitelson, A., Goded, I., Goeckede, M., Goldstein, A. H., Gough, C. M., Goulden, M. L., Graf, A., Griebel, A., Gruening, C., Grünwald, T., Hammerle, A., Han, S., Han, X., Hansen, B. U., Hanson, C., Hatakka, J., He, Y., Hehn, M., Heinesch, B., Hinko-Najera, N., Hörtnagl, L., Hutley, L., Ibrom, A., Ikawa, H., Jackowicz-Korczynski, M., Janouš, D., Jans, W., Jassal, R., Jiang, S., Kato, T., Khomik, M., Klatt, J., Knohl, A., Knox, S., Kobayashi, H., Koerber, G., Kolle, O., Kosugi, Y., Kotani, A., Kowalski, A., Kruijt, B., Kurbatova, J., Kutsch, W. L., Kwon, H., Launiainen, S., Laurila, T., Law, B., Leuning, R., Li, Y., Liddell, M., Limousin, J.-M., Lion, M., Liska, A. J., Lohila, A., López-Ballesteros, A., López-Blanco, E., Loubet, B., Loustau, D., Lucas-Moffat, A., Lüers, J., Ma, S., Macfarlane, C., Magliulo, V., Maier, R., Mammarella, I., Manca, G., Marcolla, B., Margolis, H. A., Marras, S., Massman, W., Mastepanov, M., Matamala, R., Matthes, J. H., Mazzenga, F., McCaughey, H., McHugh, I., McMillan, A. M. S., Merbold, L., Meyer, W., Meyers, T., Miller, S. D., Minerbi, S., Moderow, U., Monson, R. K., Montagnani, L., Moore, C. E., Moors, E., Moreaux, V., Moureaux, C., Munger, J. W., Nakai, T., Neirynck, J., Nesic, Z., Nicolini, G., Noormets, A., Northwood, M., Nosetto, M., Nouvellon, Y., Novick, K., Oechel, W., Olesen, J. E., Ourcival, J.-M., Papuga, S. A., Parmentier, F.-J., Paul-Limoges, E., Pavelka, M., Peichl, M., Pendall, E., Phillips, R. P., Pilegaard, K., Pirk, N., Posse, G., Powell, T., Prasse, H., Prober, S. M., Rambal, S., Rannik, U., Raz-Yaseef, N., Rebmann, C., Reed, D., Dios, V. R. d., Restrepo-Coupe, N., Reverter, B. R., Roland, M., Sabbatini, S., Sachs, T., Saleska, S. R., Sánchez-Cañete, E. P., Sanchez-Mejia, Z. M., Schmid, H. P., Schmidt, M., Schneider, K., Schrader, F., Schroder, I., Scott, R. L., Sedlák, P., Serrano-Ortíz, P., Shao, C., Shi, P., Shironya, I., Siebicke, L., Šigut, L., Silberstein, R., Sirca, C., Spano, D., Steinbrecher, R., Stevens, R. M., Sturtevant, C., Suyker, A., Tagesson, T., Takanashi, S., Tang, Y., Tapper, N., Thom, J., Tomassucci, M., Tuovinen, J.-P., Urbanski, S., Valentini, R., van der Molen, M., van Gorsel, E., van Huissteden, K., Varlagin, A., Verfaillie, J., Vesala, T., Vincke, C., Vitale, D., Vygodskaya, N., Walker, J. P., Walter-Shea, E., Wang, H., Weber, R., Westermann, S., Wille, C., Wofsy, S., Wohlfahrt, G., Wolf, S., Woodgate, W., Li, Y., Zampedri, R., Zhang, J., Zhou, G., Zona, D., Agarwal, D., Biraud, S., Torn, M., and Papale, D.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Scientific Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020. a
Pellet, V., Aires, F., Munier, S., Fernández Prieto, D., Jordá, G., Dorigo, W. A., Polcher, J., and Brocca, L.: Integrating multiple satellite observations into a coherent dataset to monitor the full water cycle – application to the Mediterranean region, Hydrol. Earth Syst. Sci., 23, 465–491, https://doi.org/10.5194/hess-23-465-2019, 2019. a
Peterson, T. C. and Vose, R. S.: An overview of the Global Historical Climatology Network temperature database, B. Am. Meteorol. Soc., 78, 2837–2850, 1997. a
Potopová, V., Štěpánek, P., Možný, M., Türkott, L., and Soukup, J.: Performance of the standardised precipitation evapotranspiration index at various lags for agricultural drought risk assessment in the Czech Republic, Agr. Forest Meteorol., 202, 26–38, https://doi.org/10.1016/j.agrformet.2014.11.022, 2015. a
Povey, A. C. and Grainger, R. G.: Known and unknown unknowns: uncertainty estimation in satellite remote sensing, Atmos. Meas. Tech., 8, 4699–4718, https://doi.org/10.5194/amt-8-4699-2015, 2015. a
Rakovec, O., Kumar, R., Attinger, S., and Samaniego, L.: Improving the realism of hydrologic model functioning through multivariate parameter estimation, Water Resour. Res., 52, 7779–7792, https://doi.org/10.1002/2016WR019430, 2016a. a
Rakovec, O., Kumar, R., Mai, J., Cuntz, M., Thober, S., Zink, M., Attinger, S., Schäfer, D., Schrön, M., and Samaniego, L.: Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins, J. Hydrometeorol., 17, 287–307, https://doi.org/10.1175/JHM-D-15-0054.1, 2016b. a
Rakovec, O., Samaniego, L., Hari, V., Markonis, Y., Moravec, V., Thober, S., Hanel, M., and Kumar, R.: The 2018–2020 Multi-Year Drought Sets a New Benchmark in Europe, Earth's Future, 10, e2021EF002394, https://doi.org/10.1029/2021EF002394, 2022. a
Rivoire, P., Le Gall, P., Favre, A.-C., Naveau, P., and Martius, O.: High return level estimates of daily ERA-5 precipitation in Europe estimated using regionalized extreme value distributions, Weather and Climate Extremes, 38, 100500, https://doi.org/10.1016/j.wace.2022.100500, 2022. a
Rodell, M., Beaudoing, H. K., L'ecuyer, T. S., Olson, W. S., Famiglietti, J. S., Houser, P. R., Adler, R., Bosilovich, M. G., Clayson, C. A., Chambers, D., and Clark, E.: The observed state of the water cycle in the early twenty-first century, J. Climate, 28, 8289–8318, 2015. a
Roderick, M. L., Sun, F., Lim, W. H., and Farquhar, G. D.: A general framework for understanding the response of the water cycle to global warming over land and ocean, Hydrol. Earth Syst. Sci., 18, 1575–1589, https://doi.org/10.5194/hess-18-1575-2014, 2014. a
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and practice, vol. 2, World scientific, ISBN 978-981-02-2740-1, 2000. a
Sahoo, A. K., Pan, M., Troy, T. J., Vinukollu, R. K., Sheffield, J., and Wood, E. F.: Reconciling the global terrestrial water budget using satellite remote sensing, Remote Sens. Environ., 115, 1850–1865, 2011. a
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res, 46, W05523, https://doi.org/10.1029/2008WR007327, 2010. a, b, c
Samaniego, L., Thober, S., Wanders, N., Pan, M., Rakovec, O., Sheffield, J., Wood, E. F., Prudhomme, C., Rees, G., Houghton-Carr, H., Fry, M., Smith, K., Watts, G., Hisdal, H., Estrela, T., Buontempo, C., Marx, A., and Kumar, R.: Hydrological Forecasts and Projections for Improved Decision-Making in the Water Sector in Europe, B. Am. Meteorol. Soc., 100, 2451–2472, https://doi.org/10.1175/BAMS-D-17-0274.1, 2019. a
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., and Ziese, M.: GPCC full data reanalysis version 6.0 at 0.5: monthly land-surface precipitation from rain-gauges built on GTS-based and historic data, GPCC Data Rep., 10, https://doi.org/10.5676/DWD_GPCC/FD_M_V7_050, 2011. a
Schneider, U., Finger, P., Meyer-Christoffer, A., Rustemeier, E., Ziese, M., and Becker, A.: Evaluating the hydrological cycle over land using the newly-corrected precipitation climatology from the Global Precipitation Climatology Centre (GPCC), Atmosphere, 8, 52, https://doi.org/10.3390/atmos8030052, 2017. a
Skliris, N., Zika, J. D., Nurser, G., Josey, S. A., and Marsh, R.: Global water cycle amplifying at less than the Clausius-Clapeyron rate, Sci. Rep., 6, 1–9, 2016. a
Svoboda, V., Hanel, M., Máca, P., and Kyselý, J.: Projected changes of rainfall event characteristics for the Czech Republic, J. Hydrol. Hydromech., 64, 415–425, https://doi.org/10.1515/johh-2016-0036, 2016. a, b
Trenberth, K. E., Fasullo, J. T., and Mackaro, J.: Atmospheric moisture transports from ocean to land and global energy flows in reanalyses, J. Climate, 24, 4907–4924, 2011. a
Trnka, M., Balek, J., Štěpánek, P., Zahradníček, P., Možný, M., Eitzinger, J., Žalud, Z., Formayer, H., Turňa, M., Nejedlík, P., Semerádová, D., Hlavinka, P., and Brázdil, R.: Drought trends over part of Central Europe between 1961 and 2014, Clim. Res., 70, 143–160, https://doi.org/10.3354/cr01420, 2016. a
United Nations: World Population Prospects 2022: Summary of Results, Statistical Papers – United Nations (Ser. A), Population and Vital Statistics Report, United Nations, ISBN 978-92-1-001438-0, https://doi.org/10.18356/9789210014380, 2022. a
Uppala, S. M., Kållberg, P. W., Simmons, A. J., Andrae, U., Da Costa Bechtold, V., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A., Li, X., Balmaseda, A., Beljaars, A. C. M., Van De Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40 re-analysis, Q. J. Roy. Meteor. Soc., 131, 2961–3012, 2005. a
Vanella, D., Longo-Minnolo, G., Belfiore, O. R., Ramírez-Cuesta, J. M., Pappalardo, S., Consoli, S., D’Urso, G., Chirico, G. B., Coppola, A., Comegna, A., Toscano, A., Quarta, R., Provenzano, G., Ippolito, M., Castagna, A., and Gandolfi, C.: Comparing the use of ERA5 reanalysis dataset and ground-based agrometeorological data under different climates and topography in Italy, J. Hydrol., 42, 101182, https://doi.org/10.1016/j.ejrh.2022.101182, 2022. a
Vargas Godoy, M. R.: MiRoVaGo/ugc_cwc: v1.0.0 (v1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.10438307, 2023. a
Vargas Godoy, M. R., Markonis, Y., Hanel, M., Kyselý, J., and Papalexiou, S. M.: The Global Water Cycle Budget: A Chronological Review, Surv. Geophys., 42, 1075–1107, https://doi.org/10.1007/s10712-021-09652-6, 2021. a
Vecchi, G. A., Soden, B. J., Wittenberg, A. T., Held, I. M., Leetmaa, A., and Harrison, M. J.: Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing, Nature, 441, 73–76 , https://doi.org/10.1038/nature04744, 2006. a
Vicente-Serrano, S. M., Domínguez-Castro, F., Reig, F., Tomas-Burguera, M., Peña-Angulo, D., Latorre, B., Beguería, S., Rabanaque, I., Noguera, I., Lorenzo-Lacruz, J., and El Kenawy, A.: A global drought monitoring system and dataset based on ERA5 reanalysis: A focus on crop-growing regions, Geosci. Data J., 10, 505–518, https://doi.org/10.1002/gdj3.178, 2022. a
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. a
Xiao, M., Gao, M., Vogel, R. M., and Lettenmaier, D. P.: Runoff and Evapotranspiration Elasticities in the Western United States: Are They Consistent With Dooge's Complementary Relationship?, Water Resour. Res., 56, e2019WR026719, https://doi.org/10.1029/2019WR026719, 2020. a
Zaitchik, B. F., Rodell, M., Biasutti, M., and Seneviratne, S. I.: Wetting and drying trends under climate change, Nat. Water, 1, 502–513, https://doi.org/10.1038/s44221-023-00073-w, 2023. a
Zhang, Y., Pan, M., and Wood, E. F.: On creating global gridded terrestrial water budget estimates from satellite remote sensing, in: Remote Sensing and Water Resources, 59–78, Springer, https://doi.org/10.1007/978-3-319-32449-4_4, 2016. a
Zhao, L., Xia, J., Xu, C.-y., Wang, Z., Sobkowiak, L., and Long, C.: Evapotranspiration estimation methods in hydrological models, J. Geogr. Sci., 23, 359–369, https://doi.org/10.1007/s11442-013-1015-9, 2013. a
Short summary
The study introduces a novel benchmarking method based on the water cycle budget for hydroclimate data fusion. Using this method and multiple state-of-the-art datasets to assess the spatiotemporal patterns of water cycle changes in Czechia, we found that differences in water availability distribution are dominated by evapotranspiration. Furthermore, while the most significant temporal changes in Czechia occur during spring, the median spatial patterns stem from summer changes in the water cycle.
The study introduces a novel benchmarking method based on the water cycle budget for...