Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-4035-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-4035-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
06 Sep 2022
Data description paper |
| 06 Sep 2022
| 06 Sep 2022
A 500-year annual runoff reconstruction for 14 selected European catchments
Sadaf Nasreen
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Suchdol, Prague 16500, Czech Republic
Markéta Součková
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Suchdol, Prague 16500, Czech Republic
Mijael Rodrigo Vargas Godoy
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Suchdol, Prague 16500, Czech Republic
Ujjwal Singh
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Suchdol, Prague 16500, Czech Republic
Yannis Markonis
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Suchdol, Prague 16500, Czech Republic
Rohini Kumar
UFZ-Helmholtz Centre for Environmental Research, 04318 Leipzig, Germany
Oldrich Rakovec
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Suchdol, Prague 16500, Czech Republic
UFZ-Helmholtz Centre for Environmental Research, 04318 Leipzig, Germany
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Suchdol, Prague 16500, Czech Republic
T. G. Masaryk Water Research Institute, p.r.i., Prague 16000, Czech Republic
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Tam V. Nguyen, Rohini Kumar, José L. J. Ledesma, Pia Ebeling, Jan Fleckenstein, and Andreas Musolff
EGUsphere, https://doi.org/10.5194/egusphere-2025-6246, https://doi.org/10.5194/egusphere-2025-6246, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Lumped and landscape-explicit dissolved organic carbon (DOC) models are commonly calibrated using stream DOC concentrations, while internal DOC dynamics in different model compartments are not given enough attention. Our study shows that stream DOC alone is insufficient to constrain DOC dynamics. Applying models calibrated in this way under changing boundary conditions may therefore lead to unrealistic results.
Pia Ebeling, Alexander Hubig, Alexander Wachholz, Ulrike Scharfenberger, Sarah Haug, Tam Nguyen, Fanny Sarrazin, Masooma Batool, Andreas Musolff, and Rohini Kumar
Earth Syst. Sci. Data, 18, 691–712, https://doi.org/10.5194/essd-18-691-2026, https://doi.org/10.5194/essd-18-691-2026, 2026
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The updated river water quality data set for Germany offers longer records, new variables such as water temperature and oxygen, and time series of pollution sources, and it adds more stations with both water quality and flow data. These improvements provide clearer insights into how stream water quality changes over time and how human activities affect aquatic ecosystems.
Robert Reinecke, Tanjila Akhter, Annemarie Bäthge, Ricarda Dietrich, Sebastian Gnann, Simon N. Gosling, Danielle Grogan, Andreas Hartmann, Stefan Kollet, Rohini Kumar, Richard Lammers, Sida Liu, Yan Liu, Nils Moosdorf, Bibi Naz, Sara Nazari, Chibuike Orazulike, Yadu Pokhrel, Jacob Schewe, Mikhail Smilovic, Maryna Strokal, Wim Thiery, Yoshihide Wada, Shan Zuidema, and Inge de Graaf
Geosci. Model Dev., 19, 523–542, https://doi.org/10.5194/gmd-19-523-2026, https://doi.org/10.5194/gmd-19-523-2026, 2026
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Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP (The Inter-Sectoral Impact Model Intercomparison Project) groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.
Simon P. Heselschwerdt, Thorsten Wagener, Lan Wang-Erlandsson, Anna M. Ukkola, Yannis Markonis, Yuting Yang, and Peter Greve
EGUsphere, https://doi.org/10.5194/egusphere-2025-5896, https://doi.org/10.5194/egusphere-2025-5896, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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Precipitation on land is split into different pathways, contributing to runoff (blue water) or to plant water use (green water). Climate change alters this balance and shapes how future precipitation is divided. We use global climate models to study these changes and their drivers. We find that more extreme five-day precipitation is the main driver and routes more future precipitation into blue water, even where average precipitation decreases, with consequences for water and land management.
Malve Heinz, Annelie Holzkämper, Rohini Kumar, Sélène Ledain, Pascal Horton, and Bettina Schaefli
EGUsphere, https://doi.org/10.5194/egusphere-2025-5447, https://doi.org/10.5194/egusphere-2025-5447, 2025
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Droughts increasingly threaten agriculture. Improving soils to store more water, for example by increasing soil organic carbon, can help. We simulated this in a Swiss catchment and found that more soil carbon slightly increased soil water storage and evapotranspiration, modestly reduced floods, and shortened periods with very little streamflow. However in warmer, drier areas, these periods with little streamflow could sometimes last longer.
Rajani Kumar Pradhan, Yannis Markonis, Francesco Marra, Efthymios I. Nikolopoulos, Simon Michael Papalexiou, and Vincenzo Levizzani
Hydrol. Earth Syst. Sci., 29, 4929–4949, https://doi.org/10.5194/hess-29-4929-2025, https://doi.org/10.5194/hess-29-4929-2025, 2025
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This study compared global satellite and reanalysis precipitation datasets 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.
Hossein Abbasizadeh, Petr Maca, Martin Hanel, Mads Troldborg, and Amir AghaKouchak
Hydrol. Earth Syst. Sci., 29, 4761–4790, https://doi.org/10.5194/hess-29-4761-2025, https://doi.org/10.5194/hess-29-4761-2025, 2025
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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 the accuracy drop between the training and testing phases, improving the model's interpretability, and mitigating overfitting issues, especially with small training samples.
Katoria Lekarkar, Oldrich Rakovec, Rohini Kumar, Stefaan Dondeyne, and Ann van Griensven
EGUsphere, https://doi.org/10.5194/egusphere-2025-4526, https://doi.org/10.5194/egusphere-2025-4526, 2025
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Belgium has faced intense droughts in recent years, causing major losses across sectors. To assess their rarity, we used a hydrological model to reconstruct fifty years of soil moisture in the country. We show that 2011–2020 experienced the most severe droughts since 1971, with nearly 30 % of the decade under drought. We also show that rainfall-based indicators underestimate soil moisture droughts, so including soil moisture monitoring can give decision-makers a clearer picture of drought risks.
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
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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.
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
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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.
Pia Ebeling, Andreas Musolff, Rohini Kumar, Andreas Hartmann, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 29, 2925–2950, https://doi.org/10.5194/hess-29-2925-2025, https://doi.org/10.5194/hess-29-2925-2025, 2025
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Groundwater is a crucial resource at risk due to droughts. To understand drought effects on groundwater levels in Germany, we grouped 6626 wells into six regional and two national patterns. Weather explained half of the level variations with varied response times. Shallow groundwater responds fast and is more vulnerable to short droughts (a few months). Dampened deep heads buffer short droughts but suffer from long droughts and recoveries. Two nationwide trend patterns were linked to human water use.
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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.
Masooma Batool, Fanny J. Sarrazin, and Rohini Kumar
Earth Syst. Sci. Data, 17, 881–916, https://doi.org/10.5194/essd-17-881-2025, https://doi.org/10.5194/essd-17-881-2025, 2025
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Our paper presents a reconstruction and analysis of the gridded P surplus in European landscapes from 1850 to 2019 at a 5 arcmin resolution. By utilizing 48 different estimates, we account for uncertainties in major components of the P surplus. Our findings highlight substantial historical changes, with the total P surplus in the EU 27 tripling over 170 years. Our dataset enables flexible aggregation at various spatial scales, providing critical insights for land and water management strategies.
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
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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
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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.
Fanny J. Sarrazin, Sabine Attinger, and Rohini Kumar
Earth Syst. Sci. Data, 16, 4673–4708, https://doi.org/10.5194/essd-16-4673-2024, https://doi.org/10.5194/essd-16-4673-2024, 2024
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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
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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.
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
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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.
Mijael Rodrigo Vargas Godoy, Yannis Markonis, Oldrich Rakovec, Michal Jenicek, Riya Dutta, Rajani Kumar Pradhan, Zuzana Bešťáková, Jan Kyselý, Roman Juras, Simon Michael Papalexiou, and Martin Hanel
Hydrol. Earth Syst. Sci., 28, 1–19, https://doi.org/10.5194/hess-28-1-2024, https://doi.org/10.5194/hess-28-1-2024, 2024
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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.
Arianna Borriero, Rohini Kumar, Tam V. Nguyen, Jan H. Fleckenstein, and Stefanie R. Lutz
Hydrol. Earth Syst. Sci., 27, 2989–3004, https://doi.org/10.5194/hess-27-2989-2023, https://doi.org/10.5194/hess-27-2989-2023, 2023
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We analyzed the uncertainty of the water transit time distribution (TTD) arising from model input (interpolated tracer data) and structure (StorAge Selection, SAS, functions). We found that uncertainty was mainly associated with temporal interpolation, choice of SAS function, nonspatial interpolation, and low-flow conditions. It is important to characterize the specific uncertainty sources and their combined effects on TTD, as this has relevant implications for both water quantity and quality.
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
Carolin Winter, Tam V. Nguyen, Andreas Musolff, Stefanie R. Lutz, Michael Rode, Rohini Kumar, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 27, 303–318, https://doi.org/10.5194/hess-27-303-2023, https://doi.org/10.5194/hess-27-303-2023, 2023
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The increasing frequency of severe and prolonged droughts threatens our freshwater resources. While we understand drought impacts on water quantity, its effects on water quality remain largely unknown. Here, we studied the impact of the unprecedented 2018–2019 drought in Central Europe on nitrate export in a heterogeneous mesoscale catchment in Germany. We show that severe drought can reduce a catchment's capacity to retain nitrogen, intensifying the internal pollution and export of nitrate.
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
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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
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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
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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.
Pia Ebeling, Rohini Kumar, Stefanie R. Lutz, Tam Nguyen, Fanny Sarrazin, Michael Weber, Olaf Büttner, Sabine Attinger, and Andreas Musolff
Earth Syst. Sci. Data, 14, 3715–3741, https://doi.org/10.5194/essd-14-3715-2022, https://doi.org/10.5194/essd-14-3715-2022, 2022
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Environmental data are critical for understanding and managing ecosystems, including the mitigation of water quality degradation. To increase data availability, we present the first large-sample water quality data set (QUADICA) of riverine macronutrient concentrations combined with water quantity, meteorological, and nutrient forcing data as well as catchment attributes. QUADICA covers 1386 German catchments to facilitate large-sample data-driven and modeling water quality assessments.
Robert Schweppe, Stephan Thober, Sebastian Müller, Matthias Kelbling, Rohini Kumar, Sabine Attinger, and Luis Samaniego
Geosci. Model Dev., 15, 859–882, https://doi.org/10.5194/gmd-15-859-2022, https://doi.org/10.5194/gmd-15-859-2022, 2022
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The recently released multiscale parameter regionalization (MPR) tool enables
environmental modelers to efficiently use extensive datasets for model setups.
It flexibly ingests the datasets using user-defined data–parameter relationships
and rescales parameter fields to given model resolutions. Modern
land surface models especially benefit from MPR through increased transparency and
flexibility in modeling decisions. Thus, MPR empowers more sound and robust
simulations of the Earth system.
Joni Dehaspe, Fanny Sarrazin, Rohini Kumar, Jan H. Fleckenstein, and Andreas Musolff
Hydrol. Earth Syst. Sci., 25, 6437–6463, https://doi.org/10.5194/hess-25-6437-2021, https://doi.org/10.5194/hess-25-6437-2021, 2021
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Increased nitrate concentrations in surface waters can compromise river ecosystem health. As riverine nitrate uptake is hard to measure, we explore how low-frequency nitrate concentration and discharge observations (that are widely available) can help to identify (in)efficient uptake in river networks. We find that channel geometry and water velocity rather than the biological uptake capacity dominate the nitrate-discharge pattern at the outlet. The former can be used to predict uptake.
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
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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
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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.
Cited articles
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moorea, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., Zheng, X., and Google brain:
Tensorflow: A system for large-scale machine learning, in: 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16, 2–4 November 2016, Savannah, GA, USA, pp. 265–283, 2016. a, b
Armstrong, M. S., Kiem, A. S., and Vance, T. R.:
Comparing instrumental, palaeoclimate, and projected rainfall data: Implications for water resources management and hydrological modelling, Journal of Hydrology: Regional Studies, 31, 100728, https://doi.org/10.1016/j.ejrh.2020.100728, 2020. a
Arnold, T. B.:
kerasR: R interface to the keras deep learning library, Journal of Open Source Software, 2, 296, https://doi.org/10.21105/joss.00296, 2017. a, b
Ayzel, G., Kurochkina, L., and Zhuravlev, S.:
The influence of regional hydrometric data incorporation on the accuracy of gridded reconstruction of monthly runoff, Hydrol. Sci. J., 0, https://doi.org/10.1080/02626667.2020.1762886, 1–12, 2020. a
Boch, R. and Spötl, C.:
Reconstructing palaeoprecipitation from an active cave flowstone, J. Quaternary Sci., 26, 675–687, 2011. a
Bonacina, L.:
The European drought of 1921, Nature, 112, 488–489, 1923. a
Brázdil, R. and Dobrovolný, P.:
Historical climate in Central Europe during the last 500 years, The Polish Climate in the European Context: An Historical Overview, Springer, Dordrecht, the Netherlands, p. 41, 2009. a
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, b, c, d
Brázdil, R., Kiss, A., Luterbacher, J., Nash, D. J., and Řezníčková, L.:
Documentary data and the study of past droughts: a global state of the art, Clim. Past, 14, 1915–1960, https://doi.org/10.5194/cp-14-1915-2018, 2018. a, b
Brázdil, R., Demarée, G. R., Kiss, A., Dobrovolný, P., Chromá, K., Trnka, M., Dolák, L., Řezníčková, L., Zahradníček, P., Limanowka, D., and Jourdain, S.:
The extreme drought of 1842 in Europe as described by both documentary data and instrumental measurements, Clim. Past, 15, 1861–1884, https://doi.org/10.5194/cp-15-1861-2019, 2019. a
Büntgen, U., Frank, D. C., Nievergelt, D., and Esper, J.:
Summer temperature variations in the European Alps, AD 755–2004, J. Climate, 19, 5606–5623, 2006. a
Büntgen, U., Franke, J., Frank, D., Wilson, R., González-Rouco, F., and Esper, J.:
Assessing the spatial signature of European climate reconstructions, Clim. Res., 41, 125–130, 2010. a
Caillouet, L., Vidal, J.-P., Sauquet, E., Devers, A., and Graff, B.:
Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871, Hydrol. Earth Syst. Sci., 21, 2923–2951, https://doi.org/10.5194/hess-21-2923-2017, 2017. a
Casas-Gómez, P., Sánchez-Salguero, R., Ribera, P., and Linares, J. C.:
Contrasting Signals of the Westerly Index and North Atlantic Oscillation over the Drought Sensitivity of Tree-Ring Chronologies from the Mediterranean Basin, Atmosphere, 11, 644, https://doi.org/10.3390/atmos11060644, 2020. a
Casty, C., Wanner, H., Luterbacher, J., Esper, J., and Böhm, R.:
Temperature and precipitation variability in the European Alps since 1500, Int. J. Climatol., 25, 1855–1880, 2005. a
Chen, X., Huang, J., Han, Z., Gao, H., Liu, M., Li, Z., Liu, X., Li, Q., Qi, H., and Huang, Y.:
The importance of short lag-time in the runoff forecasting model based on long short-term memory, J. Hydrol., 589, 125359, https://doi.org/10.1016/j.jhydrol.2020.125359, 2020. a
Contreras, P., Orellana-Alvear, J., Muñoz, P., Bendix, J., and Célleri, R.:
Influence of Random Forest Hyperparameterization on Short-Term Runoff Forecasting in an Andean Mountain Catchment, Atmosphere, 12, 238, https://doi.org/10.3390/atmos12020238, 2021. a
Cook, E. R., Seager, R., Kushnir, Y., Briffa, K. R., Büntgen, U., Frank, D., Krusic, P. J., Tegel, W., van der Schrier, G., Andreu-Hayles, L., Baillie, M., Baittinger, C., Bleicher, N., Bonde, N., Brown, D., Carrer, M., Cooper, R., Čufar, K., Dittmar, C., Esper, J., Griggs, C., Gunnarson, B., Günther, B., Gutierrez, E., Haneca, K., Helama, S., Herzig, F., Heussner, K. U., Hofmann, J., Janda, P., Kontic, R., Köse, N., Kyncl, T., Levanič, T., Linderholm, H., Manning, S., Melvin, T. M., Miles, D., Neuwirth, B., Nicolussi, K., Nola, P., Panayotov, M., Popa, I., Rothe, A., Seftigen, K., Seim, A., Svarva, H., Svoboda, M., Thun, T., Timonen, M., Touchan, R., Trotsiuk, V., Trouet, V., Walder, F., Ważny, T., Wilson, R., and Zang, C.: Old World megadroughts and pluvials during the Common Era, Science Advances, 1, e1500561, https://doi.org/10.1126/sciadv.1500561, 2015. a, b, c, d, e, f, g, h, i, j, k
Coron, L., Thirel, G., Delaigue, O., Perrin, C., and Andréassian, V.:
The suite of lumped GR hydrological models in an R package, Environ. Modell. Softw., 94, 166–171, 2017. a
Dobrovolný, P., Moberg, A., Brázdil, R., Pfister, C., Glaser, R., Wilson, R., van Engelen, A., Limanówka, D., Kiss, A., Halíčková, M., Macková, J., Riemann, D., Luterbacher, J., and Böhm, R.: Monthly, seasonal and annual temperature reconstructions for Central Europe derived from documentary evidence and instrumental records since AD 1500, Climatic Change, 101, 69–107, 2010. a, b, c, d, e
Emile-Geay, J., McKay, N. P., Kaufman, D. S., Von Gunten, L., Wang, J., Anchukaitis, K. J., Abram, N. J., Addison, J. A., Curran, M. A., Evans, M. N., Henley, B. J., Hao, Z., Martrat, B., McGregor, H. V., Neukom, R., Pederson, G. T., Stenni, B., Thirumalai, K., Werner, J. P., Xu, C., Divine, D. V., Dixon, B. C., Gergis, J., Mundo, I. A., Nakatsuka, T., Phipps, S. J., Routson, C. C., Steig, E. J., Tierney, J. E., Tyler, J. J., Allen, K. J., Bertler, N. A. N., Björklund, J., Chase, B. M., Chen, M.-T., Cook, E., de Jong, R., DeLong, K. L., Dixon, D. A., Ekaykin, A. A., Ersek, V., Filipsson, H. L., Francus, P., Freund, M. B., Frezzotti, M., Gaire, N. P., Gajewski, K., Ge, Q., Goosse, H., Gornostaeva, A., Grosjean, M., Horiuchi, K., Hormes, A., Husum, K., Isaksson, E., Kandasamy, S., Kawamura, K., Kilbourne, K. H., Koç, N., Leduc, G., Linderholm, H. W., Lorrey, A. M., Mikhalenko, V., Mortyn, P. G., Motoyama, H., Moy, A. D., Mulvaney, R., Munz, P. M., Nash, D. J., Oerter, H., Opel, T., Orsi, A. J., Ovchinnikov, D. V., Porter, T. J., Roop, H. A., Saenger, C., Sano, M., Sauchyn, D., Saunders, K. M., Seidenkrantz, M.-S., Severi, M., Shao, X., Sicre, M.-A., Sigl, M., Sinclair, K., St. George, S., St. Jacques, J.-M., Thamban, M., Kuwar Thapa, U., Thomas, E. R., Turney, C., Uemura, R., Viau, A. E., Vladimirova, D. O., Wahl, E. R., White, J. W. C., Yu, Z., Zinke, J., and PAGES2k Consortium: A global multiproxy database for temperature reconstructions of the Common Era, Scientific Data, 4, 170088, https://doi.org/10.1038/sdata.2017.88, 2017. a
Fathi, M. M., Awadallah, A. G., Abdelbaki, A. M., and Haggag, M.:
A new Budyko framework extension using time series SARIMAX model, J. Hydrol., 570, 827–838, 2019. a
Fekete, B. M., Vörösmarty, C. J., and Grabs, W.:
Global, composite runoff fields based on observed river discharge and simulated water balances, Tech. Rep. 22, Global Runoff Data Centre, Koblenz, Germany, 1999. a
Foresee, F. D. and Hagan, M. T.:
Gauss-Newton approximation to Bayesian learning, in: Proceedings of international conference on neural networks (ICNN'97), vol. 3, pp. 1930–1935, IEEE, Houston, TX, USA, 1997. a
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.:
GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019. a, b, c
Ghiggi, G., Humphrey, V., Seneviratne, S., and Gudmundsson, L.:
G-RUN ENSEMBLE: A Multi-Forcing Observation-Based Global Runoff Reanalysis, Water Resour. Res., 57, e2020WR028787, https://doi.org/10.1029/2020WR028787, 2021. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.:
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.:
Updated high-resolution grids of monthly climatic observations–the CRU TS3.10 Dataset, Int. J. Climatol., 34, 623–642, 2014. a
Haslinger, K. and Blöschl, G.:
Space-time patterns of meteorological drought events in the European Greater Alpine Region over the past 210 years, Water Resour. Res., 53, 9807–9823, 2017. a
Hochreiter, S. and Schmidhuber, J.:
Long short-term memory, Neural Comput., 9, 1735–1780, 1997. a
Hu, C., Wu, Q., Li, H., Jian, S., Li, N., and Lou, Z.:
Deep learning with a long short-term memory networks approach for rainfall-runoff simulation, Water, 10, 1543, https://doi.org/10.3390/w10111543, 2018. a, b
Im, S., Kim, H., Kim, C., and Jang, C.:
Assessing the impacts of land use changes on watershed hydrology using MIKE SHE, Environ. Geol., 57, 231, https://doi.org/10.1007/s00254-008-1303-3, 2009. a
Ionita, M., Tallaksen, L. M., Kingston, D. G., Stagge, J. H., Laaha, G., Van Lanen, H. A. J., Scholz, P., Chelcea, S. M., and Haslinger, K.:
The European 2015 drought from a climatological perspective, Hydrol. Earth Syst. Sci., 21, 1397–1419, https://doi.org/10.5194/hess-21-1397-2017, 2017. a
Jeong, J., Barichivich, J., Peylin, P., Haverd, V., McGrath, M. J., Vuichard, N., Evans, M. N., Babst, F., and Luyssaert, S.:
Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for global land-surface models, Geosci. Model Dev., 14, 5891–5913, https://doi.org/10.5194/gmd-14-5891-2021, 2021. a
Ji, Y., Dong, H.-T., Xing, Z.-X., Sun, M.-X., Fu, Q., and Liu, D.:
Application of the decomposition-prediction-reconstruction framework to medium-and long-term runoff forecasting, Water Supply, 21, 696–709, 2021. a
Kingma, D. P. and Ba, J.:
Adam: A method for stochastic optimization, arXiv [preprint], arXiv:1412.6980, 2014. a
Kolachian, R. and Saghafian, B.:
Hydrological drought class early warning using support vector machines and rough sets, Environ. Earth Sci., 80, 1–15, 2021. a
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.:
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018. a
Kress, A., Saurer, M., Siegwolf, R. T., Frank, D. C., Esper, J., and Bugmann, H.:
A 350 year drought reconstruction from Alpine tree ring stable isotopes, Global Biogeochem. Cy., 24, 1–16, https://doi.org/10.1029/2009GB003613, 2010. a
Krysanova, V., Vetter, T., and Hattermann, F.:
Detection of change in drought frequency in the Elbe basin: comparison of three methods, Hydrol. Sci. J., 53, 519–537, 2008. a
Kuhn, M.:
Caret: classification and regression training, Astrophysics Source Code Library, https://ui.adsabs.harvard.edu/abs/2015ascl.soft05003K (last access: 21 December 2021), pp. ascl–1505, 2015. a
Kwak, J., Lee, J., Jung, J., and Kim, H. S.:
Case Study: Reconstruction of Runoff Series of Hydrological Stations in the Nakdong River, Korea, Water, 12, 3461, https://doi.org/10.3390/w12123461, 2020. a, b
Laaha, G., Gauster, T., Tallaksen, L. M., Vidal, J.-P., Stahl, K., Prudhomme, C., Heudorfer, B., Vlnas, R., Ionita, M., Van Lanen, H. A. J., Adler, M.-J., Caillouet, L., Delus, C., Fendekova, M., Gailliez, S., Hannaford, J., Kingston, D., Van Loon, A. F., Mediero, L., Osuch, M., Romanowicz, R., Sauquet, E., Stagge, J. H., and Wong, W. K.:
The European 2015 drought from a hydrological perspective, Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017, 2017. a
Leggewie, C. and Mauelshagen, F.:
Climate change and cultural transition in Europe, Brill, Leiden, the Netherlands, 2018. a
Li, Y., Wei, J., Wang, D., Li, B., Huang, H., Xu, B., and Xu, Y.:
A Medium and Long-Term Runoff Forecast Method Based on Massive Meteorological Data and Machine Learning Algorithms, Water, 13, 1308, https://doi.org/10.3390/w13091308, 2021. a
Ljungqvist, F. C., Piermattei, A., Seim, A., Krusic, P. J., Büntgen, U., He, M., Kirdyanov, A. V., Luterbacher, J., Schneider, L., Seftigen, K., Stahle, D. W., Villalba, R., Yang, B., and Esper, J.: Ranking of tree-ring based hydroclimate reconstructions of the past millennium, Quaternary Sci. Rev., 230, 106074, https://doi.org/10.1016/j.quascirev.2019.106074, 2020. a
Luoto, T. P. and Nevalainen, L.:
Quantifying climate changes of the Common Era for Finland, Clim. Dynam., 49, 2557–2567, 2017. a
MacKay, D. J.:
A practical Bayesian framework for backpropagation networks, Neural Comput., 4, 448–472, 1992. a
Manabe, S.:
Climate and the ocean circulation: I. The atmospheric circulation and the hydrology of the earth's surface, Mon. Weather Rev., 97, 739–774, 1969. a
Markonis, Y. and Koutsoyiannis, D.:
Scale-dependence of persistence in precipitation records, Nat. Clim. Change, 6, 399–401, 2016. a
Markonis, Y., Hanel, M., Máca, P., Kyselỳ, J., and Cook, E.:
Persistent multi-scale fluctuations shift European hydroclimate to its millennial boundaries, Nat. Commun., 9, 1–12, 2018. a
Martínez-Sifuentes, A. R., Villanueva-Díaz, J., and Estrada-Ávalos, J.:
Runoff reconstruction and climatic influence with tree rings, in the Mayo river basin, Sonora, Mexico, iForest, 13, 98, https://doi.org/10.3832/ifor3190-013, 2020. a
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.:
An overview of the global historical climatology network-daily database, J. Atmos. Ocean. Tech., 29, 897–910, 2012. a
Middelkoop, H., Daamen, K., Gellens, D., Grabs, W., Kwadijk, J. C., Lang, H., Parmet, B. W., Schädler, B., Schulla, J., and Wilke, K.:
Impact of climate change on hydrological regimes and water resources management in the Rhine basin, Climatic Change, 49, 105–128, 2001. a
Moberg, A., Mohammad, R., and Mauritsen, T.:
Analysis of the Moberg et al. (2005) hemispheric temperature reconstruction, Clim. Dynam., 31, 957–971, 2008. a
Moravec, V., Markonis, Y., Rakovec, O., Kumar, R., and Hanel, M.:
A 250-year European drought inventory derived from ensemble hydrologic modeling, Geophys. Res. Lett., 46, 5909–5917, 2019. a
Murphy, C., Broderick, C., Burt, T. P., Curley, M., Duffy, C., Hall, J., Harrigan, S., Matthews, T. K. R., Macdonald, N., McCarthy, G., McCarthy, M. P., Mullan, D., Noone, S., Osborn, T. J., Ryan, C., Sweeney, J., Thorne, P. W., Walsh, S., and Wilby, R. L.:
A 305-year continuous monthly rainfall series for the island of Ireland (1711–2016), Clim. Past, 14, 413–440, https://doi.org/10.5194/cp-14-413-2018, 2018. a
Nash, J. E. and Sutcliffe, J. V.:
River flow forecasting through conceptual models part I–A discussion of principles, J. Hydrol., 10, 282–290, 1970. a
Nicault, A., Alleaume, S., Brewer, S., Carrer, M., Nola, P., and Guiot, J.:
Mediterranean drought fluctuation during the last 500 years based on tree-ring data, Clim. Dynam., 31, 227–245, 2008. a
Okut, H.:
Bayesian regularized neural networks for small n big p data, in: Artificial neural networks-models and applications, in: Artificial Neural Networks, edited by: Rosa, J. L. G., IntechOpen, 21–23, https://doi.org/10.5772/63256, 2016. a, b
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. 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
Pfister, C., Brázdil, R., Glaser, R., Barriendos, M., Camuffo, D., Deutsch, M., Dobrovolný, P., Enzi, S., Guidoboni, E., Kotyza, O., Militzer, S., Rácz, L., and Rodrigo, F. S.: Documentary evidence on climate in sixteenth-century Europe, Climatic Change, 43, 55–110, 1999. a
Pfister, C., Weingartner, R., and Luterbacher, J.:
Hydrological winter droughts over the last 450 years in the Upper Rhine basin: a methodological approach, Hydrol. Sci. J., 51, 966–985, 2006. a
Proctor, C., Baker, A., Barnes, W., and Gilmour, M.:
A thousand year speleothem proxy record of North Atlantic climate from Scotland, Clim. Dynam., 16, 815–820, 2000. a
Quayle, R. G., Peterson, T. C., Basist, A. N., and Godfrey, C. S.:
An operational near-real-time global temperature index, Geophys. Res. Lett., 26, 333–335, 1999. a
Reinecke, R., Müller Schmied, H., Trautmann, T., Andersen, L. S., Burek, P., Flörke, M., Gosling, S. N., Grillakis, M., Hanasaki, N., Koutroulis, A., Pokhrel, Y., Thiery, W., Wada, Y., Yusuke, S., and Döll, P.:
Uncertainty of simulated groundwater recharge at different global warming levels: a global-scale multi-model ensemble study, Hydrol. Earth Syst. Sci., 25, 787–810, https://doi.org/10.5194/hess-25-787-2021, 2021. a
Rivera, J. A., Araneo, D. C., and Penalba, O. C.:
Threshold level approach for streamflow drought analysis in the Central Andes of Argentina: a climatological assessment, Hydrol. Sci. J., 62, 1949–1964, 2017. a
Sadaf, N., Součková, M., Godoy, M. R. V., Singh, U., Markonis, Y., Kumar, R., Rakovec, O., and Hanel, M.:
Supporting data for A 500-year runoff reconstruction for European catchments, figshare [data set], https://doi.org/10.6084/m9.figshare.15178107, 2021. a, b
Seiller, G., Anctil, F., and Perrin, C.:
Multimodel evaluation of twenty lumped hydrological models under contrasted climate conditions, Hydrol. Earth Syst. Sci., 16, 1171–1189, https://doi.org/10.5194/hess-16-1171-2012, 2012. a
Smith, K. A., Barker, L. J., Tanguy, M., Parry, S., Harrigan, S., Legg, T. P., Prudhomme, C., and Hannaford, J.:
A multi-objective ensemble approach to hydrological modelling in the UK: an application to historic drought reconstruction, Hydrol. Earth Syst. Sci., 23, 3247–3268, https://doi.org/10.5194/hess-23-3247-2019, 2019. a
Su, W., Tao, J., Wang, J., and Ding, C.:
Current research status of large river systems: a cross-continental comparison, Environ. Sci. Pollut. R., 27, 39413–39426, 2020. a
Sun, J., Liu, Y., Wang, Y., Bao, G., and Sun, B.:
Tree-ring based runoff reconstruction of the upper Fenhe River basin, North China, since 1799 AD, Quatern. Int., 283, 117–124, 2013. a
Sung, J. H. and Chung, E.-S.:
Development of streamflow drought severity–duration–frequency curves using the threshold level method, Hydrol. Earth Syst. Sci., 18, 3341–3351, https://doi.org/10.5194/hess-18-3341-2014, 2014. a
Swierczynski, T., Brauer, A., Lauterbach, S., Martín-Puertas, C., Dulski, P., von Grafenstein, U., and Rohr, C.:
A 1600 yr seasonally resolved record of decadal-scale flood variability from the Austrian Pre-Alps, Geology, 40, 1047–1050, 2012. a
Trouet, V., Diaz, H., Wahl, E., Viau, A., Graham, R., Graham, N., and Cook, E.:
A 1500-year reconstruction of annual mean temperature for temperate North America on decadal-to-multidecadal time scales, Environ. Res. Lett., 8, 024008, 2013. a
Tshimanga, R., Hughes, D., and Kapangaziwiri, E.:
Initial calibration of a semi-distributed rainfall runoff model for the Congo River basin, Phys. Chem. Earth Pt. A/B/C, 36, 761–774, 2011. a
Uehlinger, U. F., Wantzen, K. M., Leuven, R. S., and Arndt, H.: The Rhine river basin, in: Rivers of Europe, edited by: Tockner, K., Academic Press, London, ISBN 978-0-12-369449-2, 2009. a
van der Schrier, G., Allan, R. P., Ossó, A., Sousa, P. M., Van de Vyver, H., Van Schaeybroeck, B., Coscarelli, R., Pasqua, A. A., Petrucci, O., Curley, M., Mietus, M., Filipiak, J., Štěpánek, P., Zahradníček, P., Brázdil, R., Řezníčková, L., van den Besselaar, E. J. M., Trigo, R., and Aguilar, E.:
The 1921 European drought: impacts, reconstruction and drivers, Clim. Past, 17, 2201–2221, https://doi.org/10.5194/cp-17-2201-2021, 2021. a
Van Houdt, G., Mosquera, C., and Nápoles, G.:
A review on the long short-term memory model, Artif. Intell. Rev., 53, 5929–5955, 2020. a
Vansteenberge, S., Verheyden, S., Cheng, H., Edwards, R. L., Keppens, E., and Claeys, P.:
Paleoclimate in continental northwestern Europe during the Eemian and early Weichselian (125–97 ka): insights from a Belgian speleothem, Clim. Past, 12, 1445–1458, https://doi.org/10.5194/cp-12-1445-2016, 2016. a
Wang, W., Gelder, P. H. V., and Vrijling, J.:
Comparing Bayesian regularization and cross-validated early-stopping for streamflow forecasting with ANN models, IAHS Publications-Series of Proceedings and Reports, 311, 216–221, 2007. a
Werbos, P. J.:
Backpropagation through time: what it does and how to do it, Proceedings of the IEEE, 78, 1550–1560, 1990. a
Wetter, O. and Pfister, C.:
An underestimated record breaking event – why summer 1540 was likely warmer than 2003, Clim. Past, 9, 41–56, https://doi.org/10.5194/cp-9-41-2013, 2013. a, b, c
Wilhelm, B., Arnaud, F., Sabatier, P., Crouzet, C., Brisset, E., Chaumillon, E., Disnar, J.-R., Guiter, F., Malet, E., Reyss, J.-L., Tachikawa, K., Bard, E., and Delannoy, J.-J.: 1400 years of extreme precipitation patterns over the Mediterranean French Alps and possible forcing mechanisms, Quaternary Res., 78, 1–12, 2012. a
Wilson, R. J., Luckman, B. H., and Esper, J.:
A 500 year dendroclimatic reconstruction of spring–summer precipitation from the lower Bavarian Forest region, Germany, Int. J. Climatol., 25, 611–630, 2005. a
Xiang, Z., Yan, J., and Demir, I.:
A rainfall-runoff model with LSTM-based sequence-to-sequence learning, Water Resour. Res., 56, e2019WR025326, https://doi.org/10.1029/2019WR025326, 2020. a
Xoplaki, E., Luterbacher, J., Paeth, H., Dietrich, D., Steiner, N., Grosjean, M., and Wanner, H.:
European spring and autumn temperature variability and change of extremes over the last half millennium, Geophys. Res. Lett., 32, L15713, https://doi.org/10.1029/2005GL023424, 2005. a
Ye, L., Jabbar, S. F., Abdul Zahra, M. M., and Tan, M. L.:
Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem, Complexity, 2021, 6631564, https://doi.org/10.1155/2021/6631564, 2021. a, b
Yevjevich, V. M.:
Objective approach to definitions and investigations of continental hydrologic droughts, An, Hydrology papers, Colorado State University. Libraries, 23, 1967. a
Zappa, M. and Kan, C.:
Extreme heat and runoff extremes in the Swiss Alps, Nat. Hazards Earth Syst. Sci., 7, 375–389, https://doi.org/10.5194/nhess-7-375-2007, 2007. a
Zhang, X., Liang, F., Yu, B., and Zong, Z.:
Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting, J. Hydrol., 409, 696–709, 2011. a
Zuo, G., Luo, J., Wang, N., Lian, Y., and He, X.:
Two-stage variational mode decomposition and support vector regression for streamflow forecasting, Hydrol. Earth Syst. Sci., 24, 5491–5518, https://doi.org/10.5194/hess-24-5491-2020, 2020. a
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.
This article presents a 500-year reconstructed annual runoff dataset for several European...
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