Articles | Volume 385
https://doi.org/10.5194/piahs-385-65-2024
© 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/piahs-385-65-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Post-conference publication
| 
18 Apr 2024
Post-conference publication |
| 18 Apr 2024
| 18 Apr 2024
Regional multi-objective calibration for distributed hydrological modelling: a decision tree based approach
Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, Italy
Alberto Viglione
Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, Italy
Jost von Hardenberg
Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, Italy
Larisa Tarasova
Department Catchment Hydrology, Helmholtz Centre for Environmental Research, Halle (Saale), Germany
Stefano Basso
Department Catchment Hydrology, Helmholtz Centre for Environmental Research, Halle (Saale), Germany
Norwegian Institute for Water Research (NIVA), Oslo, Norway
Ralf Merz
Department Catchment Hydrology, Helmholtz Centre for Environmental Research, Halle (Saale), Germany
Juraj Parajka
Centre for Water Resource Systems, TU Wien, Vienna, Austria
Institute of Hydraulic Engineering and Water Resources Management, TU Wien, Vienna, Austria
Centre for Water Resource Systems, TU Wien, Vienna, Austria
Institute of Hydraulic Engineering and Water Resources Management, TU Wien, Vienna, Austria
Department of Hydraulic Engineering, Tsinghua University, Beijing, China
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Jürgen Komma, Peter Valent, Miriam Bertola, Juraj Parajka, Klaus Haslinger, Benedikt Bica, Georg Pistotnik, Korbinian Breinl, Gabriele Müller, Lovrenc Pavlin, Bianca Kahl, Achim Naderer, and Günter Blöschl
Hydrol. Earth Syst. Sci., 30, 4075–4093, https://doi.org/10.5194/hess-30-4075-2026, https://doi.org/10.5194/hess-30-4075-2026, 2026
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In September 2024 an exceptional flood hit the Austrian Danube Basin, the largest in parts of Lower Austria since records began. Using weather and river data, we compare this event with historic floods from 1899, 2002 and 2013. The 2024 flood was driven by a slow-moving rainstorm and saturated soils, causing record flows at smaller rivers. The study shows a shift toward more localized, intense floods, highlighting the need for adaptive flood-risk management in a warming climate.
Asma Khalil, Rui Tong, Zahra Majid, Borbála Széles, Miriam Bertola, and Juraj Parajka
EGUsphere, https://doi.org/10.5194/egusphere-2026-2880, https://doi.org/10.5194/egusphere-2026-2880, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Study signifies how the use of limited streamflow records with satellite data shows promising approach to improve hydrological predictions in data-sparse regions. Results show combining satellite observations with even a small number of targeted river flow measurements can support reliable hydrologic modeling in data limited catchments. Further, this approach can enhance research dynamics in regions with no or limited observed data availability by using consistent datasets of satellite products.
Luigi Cafiero, Miriam Bertola, Peter Valent, Francesco Laio, Günter Blöschl, and Alberto Viglione
EGUsphere, https://doi.org/10.5194/egusphere-2026-2808, https://doi.org/10.5194/egusphere-2026-2808, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Floods are expected to become more frequent and intense under climate change, but their future evolution remains uncertain. We developed a process-based framework to separate key flood drivers in Austria. Using an event-based rainfall–runoff model with climate projections, we found that increasing precipitation intensity is the main driver of future flood changes, while drier soils tend to reduce moderate floods.
Matteo Nurisso, Jost von Hardenberg, Marco Cadau, Silvia Caprioli, Paolo Ghinassi, Supriyo Ghosh, Nikolay Koldunov, Natalia Nazarova, Maqsood Mubarak Rajput, Emanuele Tovazzi, and Paolo Davini
EGUsphere, https://doi.org/10.5194/egusphere-2026-1115, https://doi.org/10.5194/egusphere-2026-1115, 2026
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We present a new open-source software called AQUA designed to handle and evaluate the vast amounts of data produced by high-resolution climate simulations. As climate models become more detailed, their output can become too large and complex for traditional analysis methods. We introduce a new framework for data access, processing, and comparison that is fast, reliable, and easy to automate. Our results show that it can efficiently manage very large datasets and support real-time quality checks.
Zixi Li, Yan Li, Rui Tong, Peizhe Cheng, Fuqiang Tian, and Yao Zhuang
EGUsphere, https://doi.org/10.5194/egusphere-2026-2193, https://doi.org/10.5194/egusphere-2026-2193, 2026
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Satellite soil moisture is too coarse and ground measurements are too sparse to describe field conditions. Drone microwave sensing helps fill this gap, but mixed signals from vegetation and surface variability reduce accuracy. We combine drone microwave, optical, and thermal data in a Bayesian framework to improve soil moisture estimates and quantify uncertainty. Field tests in China show higher accuracy, lower bias, and highlight small-scale heterogeneity as a key source of uncertainty.
Francisco J. Doblas-Reyes, Jenni Kontkanen, Irina Sandu, Mario Acosta, Mohammed Hussam Al Turjmam, Ivan Alsina-Ferrer, Miguel Andrés-Martínez, Costanza Anerdi, Leo Arriola, Marvin Axness, Marc Batlle Martín, Peter Bauer, Tobias Becker, Daniel Beltrán, Sebastian Beyer, Hendryk Bockelmann, Pierre-Antoine Bretonnière, Sebastien Cabaniols, Silvia Caprioli, Miguel Castrillo, Aparna Chandrasekar, Suvarchal Cheedela, Victor Correal, Emanuele Danovaro, Paolo Davini, Jussi Enkovaara, Claudia Frauen, Barbara Früh, Aina Gaya Àvila, Paolo Ghinassi, Rohit Ghosh, Supriyo Ghosh, Iker González, Katherine Grayson, Matthew Griffith, Ioan Hadade, Christopher Haine, Carl Hartick, Utz-Uwe Haus, Shane Hearne, Heikki Järvinen, Bernat Jiménez, Amal John, Marlin Juchem, Thomas Jung, Jessica Kegel, Matthias Kelbling, Kai Keller, Bruno Kinoshita, Theresa Kiszler, Daniel Klocke, Lukas Kluft, Nikolay Koldunov, Tobias Kölling, Joonas Kolstela, Luis Kornblueh, Sergey Kosukhin, Aleksander Lacima-Nadolnik, Jeisson Javier Leal Rojas, Jonni Lehtiranta, Tuomas Lunttila, Anna Luoma, Pekka Manninen, Alexey Medvedev, Sebastian Milinski, Ali Mohammed, Sebastian Müller, Devaraju Naryanappa, Natalia Nazarova, Sami Niemelä, Bimochan Niraula, Henrik Nortamo, Aleksi Nummelin, Matteo Nurisso, Pablo Ortega, Stella Paronuzzi, Xabier Pedruzo-Bagazgoitia, Charles Pelletier, Carlos Peña, Suraj Polade, Himansu Kesari Pradhan, Rommel Quintanilla, Tiago Quintino, Thomas Rackow, Jouni Räisänen, Maqsood Mubarak Rajput, René Redler, Balthasar Reuter, Nuno Rocha Monteiro, Francesc Roura-Adserias, Silva Ruppert, Susan Sayed, Reiner Schnur, Tanvi Sharma, Dmitry Sidorenko, Outi Sievi-Korte, Albert Soret, Christian Steger, Bjorn Stevens, Jan Streffing, Jaleena Sunny, Luiggi Tenorio, Stephan Thober, Ulf Tigerstedt, Oriol Tinto, Juha Tonttila, Heikki Tuomenvirta, Lauri Tuppi, Ginka Van Thielen, Emanuele Vitali, Jost von Hardenberg, Ingo Wagner, Nils Wedi, Jan Wehner, Sven Willner, Xavier Yepes-Arbós, Florian Ziemen, and Janos Zimmermann
Geosci. Model Dev., 19, 2821–2848, https://doi.org/10.5194/gmd-19-2821-2026, https://doi.org/10.5194/gmd-19-2821-2026, 2026
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The Climate Change Adaptation Digital Twin (Climate DT) pioneers the operationalisation of global climate projections. It produces global simulations with local granularity for adaptation decision-making. Applications are embedded to generate tailored indicators. A unified workflow orchestrates all components in several supercomputers. Data management ensures consistency and streaming enables real-time use. It is a complementary innovation to initiatives like CMIP, CORDEX, and climate services.
Patrik Sleziak, Michal Danko, Martin Jančo, Ladislav Holko, and Juraj Parajka
EGUsphere, https://doi.org/10.5194/egusphere-2026-1120, https://doi.org/10.5194/egusphere-2026-1120, 2026
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We compare the Normalized Difference Snow Index from the VIIRS product with daily snow depth observations. The median snow cover mapping accuracy in Austria is 92.5 %. Snow cover classification is more accurate when optimal thresholds are estimated separately for individual months and elevation zones. At the catchment scale, thresholds are lower and mapping accuracy decreases in forested areas, particularly when the snow accumulation starts and at the end of the melting season.
Christian Czakay, Larisa Tarasova, and Bodo Ahrens
EGUsphere, https://doi.org/10.5194/egusphere-2025-3532, https://doi.org/10.5194/egusphere-2025-3532, 2025
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In this study, we simulated streamflow in German river catchments for climate projections using a deep learning model. Flood-generating processes were identified using explainable artificial intelligence. In the median, the models project mostly less rain-on-snow floods in Germany in the future and an overall lower importance of snowmelt. The average and strongest rain-on-snow floods will have a higher magnitude. The trends found for the individual climate models can vary considerably.
Emanuele Mombrini, Stefania Tamea, Alberto Viglione, and Roberto Revelli
Hydrol. Earth Syst. Sci., 29, 2255–2273, https://doi.org/10.5194/hess-29-2255-2025, https://doi.org/10.5194/hess-29-2255-2025, 2025
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In northwestern Italy, overall drought conditions appear to have worsened over the last 60 years due to both precipitation deficits and increased evapotranspiration caused by temperature increases. In addition to changes in drought conditions, changes in the characteristics of drought periods, both at a local and at a region-wide level, are found. Links between all the aforementioned changes and terrain characteristics are highlighted, finding generally worse conditions in lower-lying areas.
Hsing-Jui Wang, Ralf Merz, and Stefano Basso
Hydrol. Earth Syst. Sci., 29, 1525–1548, https://doi.org/10.5194/hess-29-1525-2025, https://doi.org/10.5194/hess-29-1525-2025, 2025
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Extreme floods are more common than expected. Knowing where these floods are likely to occur is key for risk management. Traditional methods struggle with limited data, causing uncertainty. We use common streamflow dynamics to indicate extreme flood propensity. Analyzing data from Atlantic Europe, northern Europe, and the US, we validate this novel approach and unravel intrinsic linkages between regional geographic patterns and extreme flood drivers.
Ralf Loritz, Alexander Dolich, Eduardo Acuña Espinoza, Pia Ebeling, Björn Guse, Jonas Götte, Sibylle K. Hassler, Corina Hauffe, Ingo Heidbüchel, Jens Kiesel, Mirko Mälicke, Hannes Müller-Thomy, Michael Stölzle, and Larisa Tarasova
Earth Syst. Sci. Data, 16, 5625–5642, https://doi.org/10.5194/essd-16-5625-2024, https://doi.org/10.5194/essd-16-5625-2024, 2024
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The CAMELS-DE dataset features data from 1582 streamflow gauges across Germany, with records spanning from 1951 to 2020. This comprehensive dataset, which includes time series of up to 70 years (median 46 years), enables advanced research on water flow and environmental trends and supports the development of hydrological models.
Günter Blöschl, Andreas Buttinger-Kreuzhuber, Daniel Cornel, Julia Eisl, Michael Hofer, Markus Hollaus, Zsolt Horváth, Jürgen Komma, Artem Konev, Juraj Parajka, Norbert Pfeifer, Andreas Reithofer, José Salinas, Peter Valent, Roman Výleta, Jürgen Waser, Michael H. Wimmer, and Heinz Stiefelmeyer
Nat. Hazards Earth Syst. Sci., 24, 2071–2091, https://doi.org/10.5194/nhess-24-2071-2024, https://doi.org/10.5194/nhess-24-2071-2024, 2024
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A methodology of regional flood hazard mapping is proposed, based on data in Austria, which combines automatic methods with manual interventions to maximise efficiency and to obtain estimation accuracy similar to that of local studies. Flood discharge records from 781 stations are used to estimate flood hazard patterns of a given return period at a resolution of 2 m over a total stream length of 38 000 km. The hazard maps are used for civil protection, risk awareness and insurance purposes.
Christina Franziska Radtke, Xiaoqiang Yang, Christin Müller, Jarno Rouhiainen, Ralf Merz, Stefanie R. Lutz, Paolo Benettin, Hong Wei, and Kay Knöller
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-109, https://doi.org/10.5194/hess-2024-109, 2024
Revised manuscript not accepted
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Most studies assume no difference between transit times of water and nitrate, because nitrate is transported by water. With an 8-year high-frequency dataset of isotopic signatures of both, water and nitrate, and a transit time model, we show the temporal varying difference of nitrate and water transit times. This finding is highly relevant for applied future research related to nutrient dynamics in landscapes under anthropogenic forcing and for managing impacts of nitrate on aquatic ecosystems.
Giulia Blandini, Francesco Avanzi, Simone Gabellani, Denise Ponziani, Hervé Stevenin, Sara Ratto, Luca Ferraris, and Alberto Viglione
The Cryosphere, 17, 5317–5333, https://doi.org/10.5194/tc-17-5317-2023, https://doi.org/10.5194/tc-17-5317-2023, 2023
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Automatic snow depth data are a valuable source of information for hydrologists, but they also tend to be noisy. To maximize the value of these measurements for real-world applications, we developed an automatic procedure to differentiate snow cover from grass or bare ground data, as well as to detect random errors. This procedure can enhance snow data quality, thus providing more reliable data for snow models.
Hsing-Jui Wang, Ralf Merz, Soohyun Yang, and Stefano Basso
Hydrol. Earth Syst. Sci., 27, 4369–4384, https://doi.org/10.5194/hess-27-4369-2023, https://doi.org/10.5194/hess-27-4369-2023, 2023
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Accurately assessing heavy-tailed flood behavior with limited data records is challenging and can lead to inaccurate hazard estimates. Our research introduces a new index that uses hydrograph recession to identify heavy-tailed flood behavior, compare severity, and produce reliable results with short data records. This index overcomes the limitations of current metrics, which lack physical meaning and require long records. It thus provides valuable insight into the flood hazard of river basins.
Stefano Della Fera, Federico Fabiano, Piera Raspollini, Marco Ridolfi, Ugo Cortesi, Flavio Barbara, and Jost von Hardenberg
Geosci. Model Dev., 16, 1379–1394, https://doi.org/10.5194/gmd-16-1379-2023, https://doi.org/10.5194/gmd-16-1379-2023, 2023
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The long-term comparison between observed and simulated outgoing longwave radiances represents a strict test to evaluate climate model performance. In this work, 9 years of synthetic spectrally resolved radiances, simulated online on the basis of the atmospheric fields predicted by the EC-Earth global climate model (v3.3.3) in clear-sky conditions, are compared to IASI spectral radiance climatology in order to detect model biases in temperature and humidity at different atmospheric levels.
Silvia Terzago, Giulio Bongiovanni, and Jost von Hardenberg
Hydrol. Earth Syst. Sci., 27, 519–542, https://doi.org/10.5194/hess-27-519-2023, https://doi.org/10.5194/hess-27-519-2023, 2023
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Reliable seasonal forecasts of the abundance of mountain snowpack over the winter/spring ahead provide valuable information for water management, hydropower production and ski tourism. We present a climate service prototype to generate multi-model ensemble seasonal forecasts of mountain snow depth, based on Copernicus seasonal forecast system meteorological data used to force the SNOWPACK model. The prototype shows skill at predicting snow depth below and above normal and extremely dry seasons.
Felipe A. Saavedra, Andreas Musolff, Jana von Freyberg, Ralf Merz, Stefano Basso, and Larisa Tarasova
Hydrol. Earth Syst. Sci., 26, 6227–6245, https://doi.org/10.5194/hess-26-6227-2022, https://doi.org/10.5194/hess-26-6227-2022, 2022
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Nitrate contamination of rivers from agricultural sources is a challenge for water quality management. During runoff events, different transport paths within the catchment might be activated, generating a variety of responses in nitrate concentration in stream water. Using nitrate samples from 184 German catchments and a runoff event classification, we show that hydrologic connectivity during runoff events is a key control of nitrate transport from catchments to streams in our study domain.
Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert Van Schaeybroeck, Llorenç Lledó, Nicolau Manubens, Emmanuel Roulin, M. Carmen Alvarez-Castro, Lauriane Batté, Pierre-Antoine Bretonnière, Susana Corti, Carlos Delgado-Torres, Marta Domínguez, Federico Fabiano, Ignazio Giuntoli, Jost von Hardenberg, Eroteida Sánchez-García, Verónica Torralba, and Deborah Verfaillie
Geosci. Model Dev., 15, 6115–6142, https://doi.org/10.5194/gmd-15-6115-2022, https://doi.org/10.5194/gmd-15-6115-2022, 2022
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CSTools (short for Climate Service Tools) is an R package that contains process-based methods for climate forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. In addition to describing the structure and methods in the package, we also present three use cases to illustrate the seasonal climate forecast post-processing for specific purposes.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Rui Tong, Juraj Parajka, Borbála Széles, Isabella Greimeister-Pfeil, Mariette Vreugdenhil, Jürgen Komma, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 26, 1779–1799, https://doi.org/10.5194/hess-26-1779-2022, https://doi.org/10.5194/hess-26-1779-2022, 2022
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The role and impact of using additional data (other than runoff) for the prediction of daily hydrographs in ungauged basins are not well understood. In this study, we assessed the model performance in terms of runoff, soil moisture, and snow cover predictions with the existing regionalization approaches. Results show that the best transfer methods are the similarity and the kriging approaches. The performance of the transfer methods differs between lowland and alpine catchments.
Elisa Brussolo, Elisa Palazzi, Jost von Hardenberg, Giulio Masetti, Gianna Vivaldo, Maurizio Previati, Davide Canone, Davide Gisolo, Ivan Bevilacqua, Antonello Provenzale, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 26, 407–427, https://doi.org/10.5194/hess-26-407-2022, https://doi.org/10.5194/hess-26-407-2022, 2022
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In this study, we evaluate the past, present and future quantity of groundwater potentially available for drinking purposes in the metropolitan area of Turin, north-western Italy. In order to effectively manage water resources, a knowledge of the water cycle components is necessary, including precipitation, evapotranspiration and subsurface reservoirs. All these components have been carefully evaluated in this paper, using observational datasets and modelling approaches.
David Lun, Alberto Viglione, Miriam Bertola, Jürgen Komma, Juraj Parajka, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 5535–5560, https://doi.org/10.5194/hess-25-5535-2021, https://doi.org/10.5194/hess-25-5535-2021, 2021
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We investigate statistical properties of observed flood series on a European scale. There are pronounced regional patterns, for instance: regions with strong Atlantic influence show less year-to-year variability in the magnitude of observed floods when compared with more arid regions of Europe. The hydrological controls on the patterns are quantified and discussed. On the European scale, climate seems to be the dominant driver for the observed patterns.
Twan van Noije, Tommi Bergman, Philippe Le Sager, Declan O'Donnell, Risto Makkonen, María Gonçalves-Ageitos, Ralf Döscher, Uwe Fladrich, Jost von Hardenberg, Jukka-Pekka Keskinen, Hannele Korhonen, Anton Laakso, Stelios Myriokefalitakis, Pirkka Ollinaho, Carlos Pérez García-Pando, Thomas Reerink, Roland Schrödner, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021, https://doi.org/10.5194/gmd-14-5637-2021, 2021
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This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in CMIP6. We give an overview of the model and describe in detail how it differs from its predecessor and the other EC-Earth3 configurations. The model's performance is characterized using coupled simulations conducted for CMIP6. The model has an effective equilibrium climate sensitivity of 3.9 °C and a transient climate response of 2.1 °C.
Paul C. Astagneau, Guillaume Thirel, Olivier Delaigue, Joseph H. A. Guillaume, Juraj Parajka, Claudia C. Brauer, Alberto Viglione, Wouter Buytaert, and Keith J. Beven
Hydrol. Earth Syst. Sci., 25, 3937–3973, https://doi.org/10.5194/hess-25-3937-2021, https://doi.org/10.5194/hess-25-3937-2021, 2021
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The R programming language has become an important tool for many applications in hydrology. In this study, we provide an analysis of some of the R tools providing hydrological models. In total, two aspects are uniformly investigated, namely the conceptualisation of the models and the practicality of their implementation for end-users. These comparisons aim at easing the choice of R tools for users and at improving their usability for hydrology modelling to support more transferable research.
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Short summary
The manuscript describes an application of PArameter Set Shuffling (PASS) approach in the Alpine region. A machine learning decision-tree algorithm is applied for the regional calibration of a conceptual semi-distributed hydrological model. Regional model efficiencies don't decrease significantly when moving in space from catchments used for the regional calibration (training) to catchments used for the procedure validation (test) and, in time, from the calibration to the verification period.
The manuscript describes an application of PArameter Set Shuffling (PASS) approach in the Alpine...
