Articles | Volume 10, issue 2
https://doi.org/10.5194/ascmo-10-195-2024
© 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/ascmo-10-195-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
26 Nov 2024
| 26 Nov 2024
A non-stationary climate-informed weather generator for assessing future flood risks
GFZ German Research Centre for Geosciences, Section Hydrology, 14473 Potsdam, Germany
Sergiy Vorogushyn
GFZ German Research Centre for Geosciences, Section Hydrology, 14473 Potsdam, Germany
Katrin Nissen
Institute of Meteorology, Free University of Berlin, Berlin, Germany
Lukas Brunner
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
now at: Research Unit Sustainability and Climate Risk, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, 20144 Hamburg, Germany
Bruno Merz
GFZ German Research Centre for Geosciences, Section Hydrology, 14473 Potsdam, Germany
Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
Related authors
Xiaoxiang Guan, Viet Dung Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 25, 3075–3086, https://doi.org/10.5194/nhess-25-3075-2025, https://doi.org/10.5194/nhess-25-3075-2025, 2025
Short summary
Short summary
We evaluated a multi-site stochastic regional weather generator (nsRWG) for its ability to capture the cross-scale extremity of heavy-precipitation events (HPEs) in Germany. We generated 100 realizations of 72 years of daily synthetic precipitation data. The performance was assessed using WEI and xWEI indices, which measure event extremity across spatiotemporal scales. The results show that nsRWG simulates the extremity patterns of HPEs well, although it overestimates short-duration small-extent events.
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.
Xiaoxiang Guan, Baoying Shan, Viet Dung Nguyen, and Bruno Merz
EGUsphere, https://doi.org/10.5194/egusphere-2025-1509, https://doi.org/10.5194/egusphere-2025-1509, 2025
Short summary
Short summary
Understanding and predicting extreme floods is crucial for reducing disaster risks, yet existing models struggle with unprecedented events. We tested multiple modeling approaches across 400+ river catchments in Central Europe and found that deep learning models outperform traditional methods but still underestimate extreme floods. Our findings suggest that combining data-driven models with physical knowledge can improve flood predictions, helping communities better prepare for future extremes.
Shahin Khosh Bin Ghomash, Patricio Yeste, Heiko Apel, and Viet Dung Nguyen
Nat. Hazards Earth Syst. Sci., 25, 975–990, https://doi.org/10.5194/nhess-25-975-2025, https://doi.org/10.5194/nhess-25-975-2025, 2025
Short summary
Short summary
Hydrodynamic models are vital for predicting floods, like those in Germany's Ahr region in July 2021. We refine the RIM2D model for the Ahr region, analyzing the impact of various factors using Monte Carlo simulations. Accurate parameter assignment is crucial, with channel roughness and resolution playing key roles. Coarser resolutions are suitable for flood extent predictions, aiding early-warning systems. Our work provides guidelines for optimizing hydrodynamic models in the Ahr region.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 29, 447–463, https://doi.org/10.5194/hess-29-447-2025, https://doi.org/10.5194/hess-29-447-2025, 2025
Short summary
Short summary
Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small catchments compared to large catchments, and spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show effects. The results can improve estimations of probabilities of extreme floods.
Viet Dung Nguyen, Jeroen Aerts, Max Tesselaar, Wouter Botzen, Heidi Kreibich, Lorenzo Alfieri, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 24, 2923–2937, https://doi.org/10.5194/nhess-24-2923-2024, https://doi.org/10.5194/nhess-24-2923-2024, 2024
Short summary
Short summary
Our study explored how seasonal flood forecasts could enhance insurance premium accuracy. Insurers traditionally rely on historical data, yet climate fluctuations influence flood risk. We employed a method that predicts seasonal floods to adjust premiums accordingly. Our findings showed significant year-to-year variations in flood risk and premiums, underscoring the importance of adaptability. Despite limitations, this research aids insurers in preparing for evolving risks.
Hung Nghia Nguyen, Quan Quan Le, Dung Viet Nguyen, Tan Hong Cao, Toan Quang To, Hai Do Dac, Melissa Wood, and Ivan D. Haigh
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-107, https://doi.org/10.5194/nhess-2024-107, 2024
Revised manuscript under review for NHESS
Short summary
Short summary
The paper focuses on inundation process in a highest climate vulnerability area of the Mekong Delta, main drivers and future impacts, this is importance alert to decision makers and stakeholder for investment of infrastructure, adaptation approaches and mitigating impacts.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
Short summary
Short summary
In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Xiaoxiang Guan, Viet Dung Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 25, 3075–3086, https://doi.org/10.5194/nhess-25-3075-2025, https://doi.org/10.5194/nhess-25-3075-2025, 2025
Short summary
Short summary
We evaluated a multi-site stochastic regional weather generator (nsRWG) for its ability to capture the cross-scale extremity of heavy-precipitation events (HPEs) in Germany. We generated 100 realizations of 72 years of daily synthetic precipitation data. The performance was assessed using WEI and xWEI indices, which measure event extremity across spatiotemporal scales. The results show that nsRWG simulates the extremity patterns of HPEs well, although it overestimates short-duration small-extent events.
Eva Holtanová, Jan Koláček, and Lukas Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3360, https://doi.org/10.5194/egusphere-2025-3360, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
Our global analysis assess temperature annual cycle and its changes using an innovative statistical approach. We reveal, e.g., slight temperature decreases during the historical period in some parts of the year. Future projections show different rates of warming between seasons, resulting in changes in the amplitude. The diagnostics introduced here can be tailored for different purposes and applied to other climatic variables, without making any prior assumptions about the annual cycle shape.
Arim Yoon, Cathy Hohenegger, Jiawei Bao, and Lukas Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3221, https://doi.org/10.5194/egusphere-2025-3221, 2025
Short summary
Short summary
We studied how removing the Amazon rainforest impacts extreme weather by using an advanced global model that resolves convection. Our results show deforestation significantly intensifies short but severe rainfall, leading to more frequent droughts and flooding. Temperatures rise sharply, creating dangerous heat conditions harmful to human health and productivity. Wind speeds drastically increase. These findings provide a stark warning of the effects of continuing deforestation of the Amazon.
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.
Subham Mukherjee, Kei Namba, Katrin M. Nissen, Ehsan Razipoor, Stefan Heiland, and Brigitta Schütt
EGUsphere, https://doi.org/10.5194/egusphere-2025-469, https://doi.org/10.5194/egusphere-2025-469, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Berlin’s parks are vital for recreation, biodiversity, and climate resilience, yet they face growing challenges from socio-economic inequalities and climate change. Our review examines how factors like gentrification and extreme weather impact access to and sustainability of these parks. By analysing over 200 studies, we highlight the need for inclusive policies, community engagement, and climate-adaptive park designs to ensure that Berlin’s parks remain accessible, resilient, and socially just.
Pedro Henrique Lima Alencar, Saskia Arndt, Kei Namba, Márk Somogyvári, Frederik Bart, Fabio Brill, Juan Dueñas, Peter Feindt, Daniel Johnson, Nariman Mahmoodi, Christoph Merz, Subham Mukherjee, Katrin Nissen, Eva Nora Paton, Tobias Sauter, Dörthe Tetzlaff, Franziska Tügel, Thomas Vogelpohl, Stenka Valentinova Vulova, Behnam Zamani, and Hui Hui Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-428, https://doi.org/10.5194/egusphere-2025-428, 2025
Short summary
Short summary
As climate change escalates, the Berlin-Brandenburg region faces new challenges. Climate change-induced extreme events are expected to cause new conflicts to emerge and aggravate existing ones. To guide future research, we co-develop a list of key questions on climate and water challenges in the region. Our findings highlight the need for new research approaches. We expect this list to provide a roadmap for actionable knowledge production to address climate and water challenges in the region.
Xiaoxiang Guan, Baoying Shan, Viet Dung Nguyen, and Bruno Merz
EGUsphere, https://doi.org/10.5194/egusphere-2025-1509, https://doi.org/10.5194/egusphere-2025-1509, 2025
Short summary
Short summary
Understanding and predicting extreme floods is crucial for reducing disaster risks, yet existing models struggle with unprecedented events. We tested multiple modeling approaches across 400+ river catchments in Central Europe and found that deep learning models outperform traditional methods but still underestimate extreme floods. Our findings suggest that combining data-driven models with physical knowledge can improve flood predictions, helping communities better prepare for future extremes.
Tim Busker, Daniela Rodriguez Castro, Sergiy Vorogushyn, Jaap Kwadijk, Davide Zoccatelli, Rafaella Loureiro, Heather J. Murdock, Laurent Pfister, Benjamin Dewals, Kymo Slager, Annegret H. Thieken, Jan Verkade, Patrick Willems, and Jeroen C. J. H. Aerts
EGUsphere, https://doi.org/10.5194/egusphere-2025-828, https://doi.org/10.5194/egusphere-2025-828, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
In July 2021, the Netherlands, Luxembourg, Germany, and Belgium were hit by an extreme flood event with over 200 fatalities. Our study provides, for the first time, critical insights into the operational flood early-warning systems in this entire region. Based on 13 expert interviews, we conclude that the systems strongly improved in all countries. Interviewees stressed the need for operational impact-based forecasts, but emphasized that its operational implementation is challenging.
Franziska Tügel, Katrin M. Nissen, Lennart Steffen, Yangwei Zhang, Uwe Ulbrich, and Reinhard Hinkelmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-445, https://doi.org/10.5194/egusphere-2025-445, 2025
Short summary
Short summary
This study examines how extreme rainfall in Berlin, Germany, may intensify due to global warming and how that could worsen flooding in a selected part of the city. We assess the role of the drainage system, infiltration from unsealed surfaces, and a potential adaptation scenario with all roofs as retention roofs in reducing flooding under extreme rainfall. Combining climate and hydrodynamic simulations, we provide insights into future challenges and possible solutions for urban flood management.
Shahin Khosh Bin Ghomash, Patricio Yeste, Heiko Apel, and Viet Dung Nguyen
Nat. Hazards Earth Syst. Sci., 25, 975–990, https://doi.org/10.5194/nhess-25-975-2025, https://doi.org/10.5194/nhess-25-975-2025, 2025
Short summary
Short summary
Hydrodynamic models are vital for predicting floods, like those in Germany's Ahr region in July 2021. We refine the RIM2D model for the Ahr region, analyzing the impact of various factors using Monte Carlo simulations. Accurate parameter assignment is crucial, with channel roughness and resolution playing key roles. Coarser resolutions are suitable for flood extent predictions, aiding early-warning systems. Our work provides guidelines for optimizing hydrodynamic models in the Ahr region.
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
Short summary
Short summary
The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Elena Xoplaki, Florian Ellsäßer, Jens Grieger, Katrin M. Nissen, Joaquim G. Pinto, Markus Augenstein, Ting-Chen Chen, Hendrik Feldmann, Petra Friederichs, Daniel Gliksman, Laura Goulier, Karsten Haustein, Jens Heinke, Lisa Jach, Florian Knutzen, Stefan Kollet, Jürg Luterbacher, Niklas Luther, Susanna Mohr, Christoph Mudersbach, Christoph Müller, Efi Rousi, Felix Simon, Laura Suarez-Gutierrez, Svenja Szemkus, Sara M. Vallejo-Bernal, Odysseas Vlachopoulos, and Frederik Wolf
Nat. Hazards Earth Syst. Sci., 25, 541–564, https://doi.org/10.5194/nhess-25-541-2025, https://doi.org/10.5194/nhess-25-541-2025, 2025
Short summary
Short summary
Europe frequently experiences compound events, with major impacts. We investigate these events’ interactions, characteristics, and changes over time, focusing on socio-economic impacts in Germany and central Europe. Highlighting 2018’s extreme events, this study reveals impacts on water, agriculture, and forests and stresses the need for impact-focused definitions and better future risk quantification to support adaptation planning.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 29, 447–463, https://doi.org/10.5194/hess-29-447-2025, https://doi.org/10.5194/hess-29-447-2025, 2025
Short summary
Short summary
Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small catchments compared to large catchments, and spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show effects. The results can improve estimations of probabilities of extreme floods.
Nicola Maher, Adam S. Phillips, Clara Deser, Robert C. Jnglin Wills, Flavio Lehner, John Fasullo, Julie M. Caron, Lukas Brunner, and Urs Beyerle
EGUsphere, https://doi.org/10.5194/egusphere-2024-3684, https://doi.org/10.5194/egusphere-2024-3684, 2024
Short summary
Short summary
We present a new multi-model large ensemble archive (MMLEAv2) and introduce the newly updated Climate Variability Diagnostics Package version 6 (CVDPv6), which is designed specifically for use with large ensembles. For highly variable quantities, we demonstrate that a model might evaluate poorly or favourably compared to the single realisation of the world that the observations represent, highlighting the need for large ensembles for model evaluation.
Bruno Merz, Günter Blöschl, Robert Jüpner, Heidi Kreibich, Kai Schröter, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 24, 4015–4030, https://doi.org/10.5194/nhess-24-4015-2024, https://doi.org/10.5194/nhess-24-4015-2024, 2024
Short summary
Short summary
Flood risk assessments help us decide how to reduce the risk of flooding. Since these assessments are based on probabilities, it is hard to check their accuracy by comparing them to past data. We suggest a new way to validate these assessments, making sure they are practical for real-life decisions. This approach looks at both the technical details and the real-world situations where decisions are made. We demonstrate its practicality by applying it to flood emergency planning.
Viet Dung Nguyen, Jeroen Aerts, Max Tesselaar, Wouter Botzen, Heidi Kreibich, Lorenzo Alfieri, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 24, 2923–2937, https://doi.org/10.5194/nhess-24-2923-2024, https://doi.org/10.5194/nhess-24-2923-2024, 2024
Short summary
Short summary
Our study explored how seasonal flood forecasts could enhance insurance premium accuracy. Insurers traditionally rely on historical data, yet climate fluctuations influence flood risk. We employed a method that predicts seasonal floods to adjust premiums accordingly. Our findings showed significant year-to-year variations in flood risk and premiums, underscoring the importance of adaptability. Despite limitations, this research aids insurers in preparing for evolving risks.
Hung Nghia Nguyen, Quan Quan Le, Dung Viet Nguyen, Tan Hong Cao, Toan Quang To, Hai Do Dac, Melissa Wood, and Ivan D. Haigh
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-107, https://doi.org/10.5194/nhess-2024-107, 2024
Revised manuscript under review for NHESS
Short summary
Short summary
The paper focuses on inundation process in a highest climate vulnerability area of the Mekong Delta, main drivers and future impacts, this is importance alert to decision makers and stakeholder for investment of infrastructure, adaptation approaches and mitigating impacts.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
Short summary
Short summary
Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Seth Bryant, Heidi Kreibich, and Bruno Merz
Proc. IAHS, 386, 181–187, https://doi.org/10.5194/piahs-386-181-2024, https://doi.org/10.5194/piahs-386-181-2024, 2024
Short summary
Short summary
Our study found that simplifying data in flood risk models can introduce errors. We tested 344 damage functions and found errors up to 40 % of the total asset value. This means large-scale flood risk assessments may have significant errors due to the modelling approach. Our research highlights the need for more attention to data aggregation in flood risk models.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
Short summary
Short summary
In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Seth Bryant, Guy Schumann, Heiko Apel, Heidi Kreibich, and Bruno Merz
Hydrol. Earth Syst. Sci., 28, 575–588, https://doi.org/10.5194/hess-28-575-2024, https://doi.org/10.5194/hess-28-575-2024, 2024
Short summary
Short summary
A new algorithm has been developed to quickly produce high-resolution flood maps. It is faster and more accurate than current methods and is available as open-source scripts. This can help communities better prepare for and mitigate flood damages without expensive modelling.
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
Short summary
Short summary
Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Katrin M. Nissen, Martina Wilde, Thomas M. Kreuzer, Annika Wohlers, Bodo Damm, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 23, 2737–2748, https://doi.org/10.5194/nhess-23-2737-2023, https://doi.org/10.5194/nhess-23-2737-2023, 2023
Short summary
Short summary
The effect of climate change on rockfall probability in the German low mountain regions is investigated in observations and in 23 different climate scenario simulations. Under a pessimistic greenhouse gas scenario, the simulations suggest a decrease in rockfall probability. This reduction is mainly caused by a decrease in the number of freeze–thaw cycles due to higher atmospheric temperatures.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Tamzin E. Palmer, Carol F. McSweeney, Ben B. B. Booth, Matthew D. K. Priestley, Paolo Davini, Lukas Brunner, Leonard Borchert, and Matthew B. Menary
Earth Syst. Dynam., 14, 457–483, https://doi.org/10.5194/esd-14-457-2023, https://doi.org/10.5194/esd-14-457-2023, 2023
Short summary
Short summary
We carry out an assessment of an ensemble of general climate models (CMIP6) based on the ability of the models to represent the key physical processes that are important for representing European climate. Filtering the models with the assessment leads to more models with less global warming being removed, and this shifts the lower part of the projected temperature range towards greater warming. This is in contrast to the affect of weighting the ensemble using global temperature trends.
Heiko Apel, Sergiy Vorogushyn, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 22, 3005–3014, https://doi.org/10.5194/nhess-22-3005-2022, https://doi.org/10.5194/nhess-22-3005-2022, 2022
Short summary
Short summary
The paper presents a fast 2D hydraulic simulation model for flood propagation that enables operational forecasts of spatially distributed inundation depths, flood extent, flow velocities, and other flood impacts. The detailed spatial forecast of floods and flood impacts is a large step forward from the currently operational forecasts of discharges at selected gauges, thus enabling a more targeted flood management and early warning.
Katrin M. Nissen, Stefan Rupp, Thomas M. Kreuzer, Björn Guse, Bodo Damm, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 22, 2117–2130, https://doi.org/10.5194/nhess-22-2117-2022, https://doi.org/10.5194/nhess-22-2117-2022, 2022
Short summary
Short summary
A statistical model is introduced which quantifies the influence of individual potential triggering factors and their interactions on rockfall probability in central Europe. The most important factor is daily precipitation, which is most effective if sub-surface moisture levels are high. Freeze–thaw cycles in the preceding days can further increase the rockfall hazard. The model can be applied to climate simulations in order to investigate the effect of climate change on rockfall probability.
Michael Dietze, Rainer Bell, Ugur Ozturk, Kristen L. Cook, Christoff Andermann, Alexander R. Beer, Bodo Damm, Ana Lucia, Felix S. Fauer, Katrin M. Nissen, Tobias Sieg, and Annegret H. Thieken
Nat. Hazards Earth Syst. Sci., 22, 1845–1856, https://doi.org/10.5194/nhess-22-1845-2022, https://doi.org/10.5194/nhess-22-1845-2022, 2022
Short summary
Short summary
The flood that hit Europe in July 2021, specifically the Eifel, Germany, was more than a lot of fast-flowing water. The heavy rain that fell during the 3 d before also caused the slope to fail, recruited tree trunks that clogged bridges, and routed debris across the landscape. Especially in the upper parts of the catchments the flood was able to gain momentum. Here, we discuss how different landscape elements interacted and highlight the challenges of holistic future flood anticipation.
Abhirup Banerjee, Bedartha Goswami, Yoshito Hirata, Deniz Eroglu, Bruno Merz, Jürgen Kurths, and Norbert Marwan
Nonlin. Processes Geophys., 28, 213–229, https://doi.org/10.5194/npg-28-213-2021, https://doi.org/10.5194/npg-28-213-2021, 2021
Miriam Bertola, Alberto Viglione, Sergiy Vorogushyn, David Lun, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 1347–1364, https://doi.org/10.5194/hess-25-1347-2021, https://doi.org/10.5194/hess-25-1347-2021, 2021
Short summary
Short summary
We estimate the contribution of extreme precipitation, antecedent soil moisture and snowmelt to changes in small and large floods across Europe.
In northwestern and eastern Europe, changes in small and large floods are driven mainly by one single driver (i.e. extreme precipitation and snowmelt, respectively). In southern Europe both antecedent soil moisture and extreme precipitation significantly contribute to flood changes, and their relative importance depends on flood magnitude.
Gustavo Andrei Speckhann, Heidi Kreibich, and Bruno Merz
Earth Syst. Sci. Data, 13, 731–740, https://doi.org/10.5194/essd-13-731-2021, https://doi.org/10.5194/essd-13-731-2021, 2021
Short summary
Short summary
Dams are an important element of water resources management. Data about dams are crucial for practitioners, scientists, and policymakers. We present the most comprehensive open-access dam inventory for Germany to date. The inventory combines multiple sources of information. It comprises 530 dams with information on name, location, river, start year of construction and operation, crest length, dam height, lake area, lake volume, purpose, dam structure, and building characteristics.
Lukas Brunner, Angeline G. Pendergrass, Flavio Lehner, Anna L. Merrifield, Ruth Lorenz, and Reto Knutti
Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, https://doi.org/10.5194/esd-11-995-2020, 2020
Short summary
Short summary
In this study, we weight climate models by their performance with respect to simulating aspects of historical climate and their degree of interdependence. Our method is found to increase projection skill and to correct for structurally similar models. The weighted end-of-century mean warming (2081–2100 relative to 1995–2014) is 3.7 °C with a likely (66 %) range of 3.1 to 4.6 °C for the strong climate change scenario SSP5-8.5; this is a reduction of 0.4 °C compared with the unweighted mean.
Anna Louise Merrifield, Lukas Brunner, Ruth Lorenz, Iselin Medhaug, and Reto Knutti
Earth Syst. Dynam., 11, 807–834, https://doi.org/10.5194/esd-11-807-2020, https://doi.org/10.5194/esd-11-807-2020, 2020
Short summary
Short summary
Justifiable uncertainty estimates of future change in northern European winter and Mediterranean summer temperature can be obtained by weighting a multi-model ensemble comprised of projections from different climate models and multiple projections from the same climate model. Weights reduce the influence of model biases and handle dependence by identifying a projection's model of origin from historical characteristics; contributions from the same model are scaled by the number of members.
Cited articles
Ailliot, P., Allard, D., Monbet, V., and Naveau, P.: Stochastic weather generators: an overview of weather type models, Journal de La Société Française de Statistique, 156, 101–113, 2015.
Akaike, H.: Information Theory as an Extension of the Maximum Likelihood Principle, in: Second International Symposium on Information Theory, edited by: Petrov, B. N. and Csaki, F., 267–281 pp., Akademiai Kiado, Budapest, 1973.
Bárdossy, A. and Plate, E. J.: Space-time model for daily rainfall using atmospheric circulation patterns, Water Resour. Res., 28, 1247–1259, 1992.
Baxevani, A. and Lennartsson, J.: A spatiotemporal precipitation generator based on a censored latent Gaussian field, Water Resour. Res., 51, 4338–4358, https://doi.org/10.1002/2014WR016455, 2015.
Beck, C. and Philipp, A.: Evaluation and comparison of circulation type classifications for the European domain, Phys. Chem. Earth, 35, 374–387, https://doi.org/10.1016/j.pce.2010.01.001, 2010.
Bennett, B., Thyer, M., Leonard, M., Lambert, M., and Bates, B.: A comprehensive and systematic evaluation framework for a parsimonious daily rainfall field model, J. Hydrol., 556, 1123–1138, https://doi.org/10.1016/J.JHYDROL.2016.12.043, 2018.
Berg, P., Moseley, C., and Haerter, J. O.: Strong increase in convective precipitation in response to higher temperatures, Nat. Geosci., 6, 181–185, https://doi.org/10.1038/ngeo1731, 2013.
Blázkova, S. and Beven, K. J.: Flood frequency prediction for data limited catchments in the Czech Republic using a stochastic rainfall model and TOPMODEL, J. Hydrol., 195, 256–278, 1997.
Breinl, K., Turkington, T., and Stowasser, M.: Stochastic generation of multi-site daily precipitation for applications in risk management, J. Hydrol., 498, 23–35, https://doi.org/10.1016/j.jhydrol.2013.06.015, 2013.
Brunner, L., Pendergrass, A. G., Lehner, F., Merrifield, A. L., Lorenz, R., and Knutti, R.: Reduced global warming from CMIP6 projections when weighting models by performance and independence, Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, 2020.
Burnham, K. P. and Anderson, D. R.: Multimodel Inference: Understanding AIC and BIC in Model Selection, Sociological Methods and Research, 33, 261–304, https://doi.org/10.1177/0049124104268644, 2004.
Cahynová, M. and Huth, R.: Atmospheric circulation influence on climatic trends in Europe: An analysis of circulation type classifications from the COST733 catalogue, Int. J. Climatol., 36, 2743–2760, https://doi.org/10.1002/joc.4003, 2016.
Calinski, T. and Harabasz, J.: A dendrite method for cluster analysis, Commun. Stat. A Theor., 3, 1–27, 1974.
Cannon, A. J.: A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology, Hydrol. Process., 24, 673–685, https://doi.org/10.1002/hyp.7506, 2010.
Cannon, A. J.: Reductions in daily continental-scale atmospheric circulation biases between generations of global climate models: CMIP5 to CMIP6, Environ. Res. Lett., 15, 064006, https://doi.org/10.1088/1748-9326/ab7e4f, 2020.
Coppola, E., Nogherotto, R., Ciarlo', J. M., Giorgi, F., van Meijgaard, E., Kadygrov, N., Iles, C., Corre, L., Sandstad, M., Somot, S., Nabat, P., Vautard, R., Levavasseur, G., Schwingshackl, C., Sillmann, J., Kjellström, E., Nikulin, G., Aalbers, E., Lenderink, G., Christensen, O. B., Boberg, F., Sørland, S. L., Demory, M. E., Bülow, K., Teichmann, C., Warrach-Sagi, K., and Wulfmeyer, V.: Assessment of the European Climate Projections as Simulated by the Large EURO-CORDEX Regional and Global Climate Model Ensemble, J. Geophys. Res.-Atmos., 126, e2019JD032356, https://doi.org/10.1029/2019JD032356, 2021a.
Coppola, E., Raffaele, F., Giorgi, F., Giuliani, G., Xuejie, G., Ciarlo, J. M., Rae Sines, T., Torres-Alavez, J. A., Das, S., di Sante, F., Pichelli, E., Glazer, R., Müller, S. K., Abba Omar, S., Ashfaq, M., Bukovsky, M., Im, E.-S., Jacob, D., Teichmann, C., Remedio, A., Remke, T., Kriegsmann, A., Bülow, K., Weber, T., Buntemeyer, L., Siecke, K., and Rechid, D.: Climate hazard indices projections based on CORDEX-CORE, CMIP5 and CMIP6 ensemble, Clim. Dynam., 57, 1293–1383, https://doi.org/10.1007/s00382-021-05640-z, 2021b.
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.
Duan, Q., Sorooshian, S., and Gupta, V.: Effective and Efficient Global Optimization for Conceptual Rainfall-Runoff Models, Water Resour. Res., 28, 1015–1031, https://doi.org/10.1029/91WR02985, 1992.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Eyring, V., Bock, L., Lauer, A., Righi, M., Schlund, M., Andela, B., Arnone, E., Bellprat, O., Brötz, B., Caron, L.-P., Carvalhais, N., Cionni, I., Cortesi, N., Crezee, B., Davin, E. L., Davini, P., Debeire, K., de Mora, L., Deser, C., Docquier, D., Earnshaw, P., Ehbrecht, C., Gier, B. K., Gonzalez-Reviriego, N., Goodman, P., Hagemann, S., Hardiman, S., Hassler, B., Hunter, A., Kadow, C., Kindermann, S., Koirala, S., Koldunov, N., Lejeune, Q., Lembo, V., Lovato, T., Lucarini, V., Massonnet, F., Müller, B., Pandde, A., Pérez-Zanón, N., Phillips, A., Predoi, V., Russell, J., Sellar, A., Serva, F., Stacke, T., Swaminathan, R., Torralba, V., Vegas-Regidor, J., von Hardenberg, J., Weigel, K., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP, Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, 2020.
Falter, D., Schröter, K., Nguyen, V. D., Vorogushyn, S., Kreibich, H., Hundecha, Y., Apel, H., and Merz, B.: Spatially coherent flood risk assessment based on long-term continuous simulation with a coupled model chain, J. Hydrol., 524, 182–193, https://doi.org/10.1016/j.jhydrol.2015.02.021, 2015.
Falter, D., Nguyen, V. D., Vorogushyn, S., Schröter, K., Hundecha, Y., Kreibich, H., Apel, H., Theisselmann, F., and Merz, B.: Continuous, large-scale simulation model for flood risk assessments: Proof-of-concept, J. Flood Risk Manage., 9, 1–96, https://doi.org/10.1111/jfr3.12105, 2014.
Farnham, D. J., Doss-Gollin, J., and Lall, U.: Regional extreme precipitation events: robust inference from redibly simulated GCM variables, Water Resour. Res., 54, 3809–3824, https://doi.org/10.1002/2017WR021318, 2018.
Fernandez-Granja, J. A., Casanueva, A., Bedia, J., and Fernandez, J.: Improved atmospheric circulation over Europe by the new generation of CMIP6 earth system models, Clim. Dynam., 56, 3527–3540, https://doi.org/10.1007/s00382-021-05652-9, 2021.
Fleig, A. K., Tallaksen, L. M., James, P., Hisdal, H., and Stahl, K.: Attribution of European precipitation and temperature trends to changes in synoptic circulation, Hydrol. Earth Syst. Sci., 19, 3093–3107, https://doi.org/10.5194/hess-19-3093-2015, 2015.
Fowler, H. J., Kilsby, C. G., O'Connell, P. E., and Burton, A.: A weather-type conditioned multi-site stochastic rainfall model for the generation of scenarios of climatic variability and change, J. Hydrol., 308, 50–66, https://doi.org/10.1016/j.jhydrol.2004.10.021, 2005.
Fowler, H. J., Ekström, M., Blenkinsop, S., and Smith, A. P.: Estimating change in extreme European precipitation using a multimodel ensemble, J. Geophys. Res., 112, D18104, doi:10.1029/2007JD008619, 2007.
Grimaldi, S., Petroselli, A., and Serinaldi, F.: A continuous simulation model for design-hydrograph estimation in small and ungauged watersheds, Hydrol. Sci. J., 57, 1035–1051, https://doi.org/10.1080/02626667.2012.702214, 2012.
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012.
Haberlandt, U. and Radtke, I.: Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows, Hydrol. Earth Syst. Sci., 18, 353–365, https://doi.org/10.5194/hess-18-353-2014, 2014.
Haberlandt, U., Hundecha, Y., Pahlow, M., and Schumann, A.: Rainfall Generators for Application in Flood Studies, in: Flood Risk Assessment and Management, edited by: Schumann, A. H., 117–147, Springer, Dordrecht, https://doi.org/10.1007/978-90-481-9917-4_7, 2011.
Haberlandt, U., Belli, A., and Bárdossy, A.: Statistical downscaling of precipitation using a stochastic rainfall model conditioned on circulation patterns – an evaluation of assumptions, Int. J. Climatol., 35, 417–432, https://doi.org/10.1002/joc.3989, 2015.
Haerter, J. O., Berg, P., and Hagemann, S.: Heavy rain intensity distributions on varying time scales and at different temperatures, J. Geophys. Res, 115, 17102, https://doi.org/10.1029/2009JD013384, 2010.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, M., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A.k Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janiskova, M. Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Higham, N. J.: Computing the nearest correlation matrix – a problem from finance, IMA Journal of Numerical Analysis, 22, 329–343, https://doi.org/10.1093/imanum/22.3.329, 2002.
Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., Bao, J., Bastin, S., Behravesh, M., Bergemann, M., Biercamp, J., Bockelmann, H., Brokopf, R., Brüggemann, N., Casaroli, L., Chegini, F., Datseris, G., Esch, M., George, G., Giorgetta, M., Gutjahr, O., Haak, H., Hanke, M., Ilyina, T., Jahns, T., Jungclaus, J., Kern, M., Klocke, D., Kluft, L., Kölling, T., Kornblueh, L., Kosukhin, S., Kroll, C., Lee, J., Mauritsen, T., Mehlmann, C., Mieslinger, T., Naumann, A. K., Paccini, L., Peinado, A., Praturi, D. S., Putrasahan, D., Rast, S., Riddick, T., Roeber, N., Schmidt, H., Schulzweida, U., Schütte, F., Segura, H., Shevchenko, R., Singh, V., Specht, M., Stephan, C. C., von Storch, J.-S., Vogel, R., Wengel, C., Winkler, M., Ziemen, F., Marotzke, J., and Stevens, B.: ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales, Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, 2023.
Hundecha, Y. and Merz, B.: Exploring the relationship between changes in climate and floods using a model-based analysis, Water Resour. Res., 48, W04512, https://doi.org/10.1029/2011WR010527, 2012.
Hundecha, Y., Pahlow, M., and Schumann, A.: Modeling of daily precipitation at multiple locations using a mixture of distributions to characterize the extremes, Water Resour. Res., 45, W12412, https://doi.org/10.1029/2008WR007453, 2009.
Hutson, A. D.: A semi-parametric quantile function estimator for use in bootstrap estimation procedures, Stat. Comput., 12, 331–338, https://doi.org/10.1023/A:1020783911574, 2002.
IPCC: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Lee, H., and Romero, J., IPCC, Geneva, Switzerland, 184 pp., https://doi.org/10.59327/IPCC/AR6-9789291691647, 2023.
Johnson, F. and Sharma, A.: Measurement of GCM skill in predicting variables relevant for hydroclimatological assessments, J. Climate, 22, 4373–4382, https://doi.org/10.1175/2009JCLI2681.1, 2009.
Jong, B.-T., Delworth, T. L., Cooke, W. F., Tseng, K.-C., and Murakami, H.: Increases in extreme precipitation over the Northeast United States using high-resolution climate model simulations. npj Climate and Atmospheric Science, 6, 18, https://doi.org/10.1038/s41612-023-00347-w, 2023.
Kiem, A. S., Kuczera, G., Kozarovski, P., Zhang, L., and Willgoose, G.: Stochastic generation of future hydroclimate using temperature as a climate change covariate, Water Resour. Res., 57, 2020WR027331, https://doi.org/10.1029/2020WR027331, 2021.
Kim, H., Kim, S., Shin, H., and Heo, J. H.: Appropriate model selection methods for nonstationary generalized extreme value models, J. Hydrol., 547, 557–574, https://doi.org/10.1016/j.jhydrol.2017.02.005, 2017.
Kleiber, W., Katz, R. W., and Rajagopalan, B.: Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes, Water Resour. Res., 48, W01523, https://doi.org/10.1029/2011WR011105, 2012.
Knist, S., Goergen, K., and Simmer, C.: Evaluation and projected changes of precipitation statistics in convection-permitting WRF climate simulations over Central Europe, Clim. Dynam., 55, 325–341, https://doi.org/10.1007/s00382-018-4147-x, 2020.
Liu, Y., Wright, D. B., and Lorenz, D. J.: A nonstationary stochastic rainfall generator conditioned on global climate models for design flood analyses in the Mississippi and other large river basins, Water Resour. Res., 60, e2023WR036826, https://doi.org/10.1029/2023WR036826, 2024.
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themel, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I.: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003, https://doi.org/10.1029/2009RG000314, 2010.
Marra, F., Koukoula, M., Canale, A., and Peleg, N.: Predicting extreme sub-hourly precipitation intensification based on temperature shifts, Hydrol. Earth Syst. Sci., 28, 375–389, https://doi.org/10.5194/hess-28-375-2024, 2024.
Merrifield, A. L., Brunner, L., Lorenz, R., Humphrey, V., and Knutti, R.: Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications, Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, 2023.
Metin, A. D., Dung, N. V., Schröter, K., Guse, B., Apel, H., Kreibich, H., Vorogushyn, S., and Merz, B.: How do changes along the risk chain affect flood risk?, Nat. Hazards Earth Syst. Sci., 18, 3089–3108, https://doi.org/10.5194/nhess-18-3089-2018, 2018.
Murawski, A., Bürger, G., Vorogushyn, S., and Merz, B.: Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin, Hydrol. Earth Syst. Sci., 20, 4283–4306, https://doi.org/10.5194/hess-20-4283-2016, 2016.
Murawski, A., Vorogushyn, S., Bürger, G., Gerlitz, L., and Merz, B.: Do Changing Weather Types Explain Observed Climatic Trends in the Rhine Basin? An Analysis of Within- and Between-Type Changes, J. Geophys. Res.-Atmos., 123, 1562–1584, https://doi.org/10.1002/2017JD026654, 2018.
Najibi, N., Perez, A. J., Arnold, W., Schwarz, A., Maendly, R., and Steinschneider, S.: A statewide, weather-regime based stochastic weather generator for process-based bottom-up climate risk assessments in California – Part I: Model evaluation, Climate Services, 34, 100489, https://doi.org/10.1016/j.cliser.2024.100489, 2024a.
Najibi, N., Perez, A. J., Arnold, W., Schwarz, A., Maendly, R., and Steinschneider, S.: A statewide, weather-regime based stochastic weather generator for process-based bottom-up climate risk assessments in California – Part II: Thermodynamic and dynamic climate change scenarios, Climate Services, 34, 100485, https://doi.org/10.1016/j.cliser.2024.100485, 2024b.
Naveau, P., Huser, R., Ribereau, P., and Hannart, A.: Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection, Water Resour. Res., 52, 2753–2769, https://doi.org/10.1002/2015WR018552, 2016.
Nguyen, V. D., Merz, B., Hundecha, Y., Haberlandt, U., and Vorogushyn, S.: Comprehensive evaluation of an improved large-scale multi-site weather generator for Germany, Int. J. Climatol., 41, 4933–4956, https://doi.org/10.1002/joc.7107, 2021.
Nguyen, V. D., Vorogushyn, S., Nissen, K., Brunner, L., and Merz, B.: A long-term consistent synthetic weather data for historical and future periods in Germany, GFZ Data Services [data set], https://doi.org/10.5880/GFZ.4.4.2024.003, 2024.
Nied, M., Pardowitz, T., Nissen, K., Ulbrich, U., Hundecha, Y., and Merz, B.: On the relationship between hydro-meteorological patterns and flood types, J. Hydrol., 519, 3249–3262, https://doi.org/10.1016/j.jhydrol.2014.09.089, 2014.
Papalexiou, S. M., Serinaldi, F., and Clark, M. P.: Large-Domain Multisite Precipitation Generation: Operational Blueprint and Demonstration for 1,000 Sites, Water Resour. Res., 59, e2022WR034094, https://doi.org/10.1029/2022WR034094, 2023.
Pfahl, S., O'Gorman, P. A., and Fischer, E. M.: Understanding the regional pattern of projected future changes in extreme precipitation, Nat. Clim. Change, 7, 423–428, https://doi.org/10.1038/NCLIMATE3287, 2017.
Philipp, A., Della-Marta, P. M., Jacobeit, J., Fereday, D. R., Jones, P. D., Moberg, A., and Wanner, H.: Long-term variability of daily North Atlantic-European pressure patterns since 1850 classified by simulated annealing clustering, J. Climate, 20, 4065–4095, https://doi.org/10.1175/JCLI4175.1, 2007.
Philipp, A., Beck, C., Huth, R., and Jacobeit, J.: Development and comparison of circulation type classifications using the COST 733 dataset and software, Int. J. Climatol., 36, 2673–2691, https://doi.org/10.1002/joc.3920, 2016.
Rahat, S. H., Steinschneider, S., Kucharski, J., Arnold, W., Olzewski, J., Walker, W., Maendly, R., Wasti, A., and Ray, P.: Characterizing hydrologic vulnerability under nonstationary climate and antecedent conditions using a process-informed stochastic weather generator, J. Water Resour. Plann. Manag., 148, 04022028, https://doi.org/10.1061/(ASCE)WR.1943-5452.0001557, 2022.
Ritzhaupt, N. and Maraun, D.: Consistency of Seasonal Mean and Extreme Precipitation Projections Over Europe Across a Range of Climate Model Ensembles, J. Geophys. Res.-Atmos., 128, e2022JD037845, https://doi.org/10.1029/2022JD037845, 2023.
Sairam, N., Brill, F., Sieg, T., Farrag, M., Kellermann, P., Nguyen, V. D., Lüdtke, S., Merz, B., Schröter, K., Vorogushyn, S., and Kreibich, H.: Process-based flood risk assessment for Germany, Earth's Future, 9, e2021EF002259, https://doi.org/10.1029/2021EF002259, 2021.
Serinaldi, F. and Kilsby, C. G.: Simulating daily rainfall fields over large areas for collective risk Estimation, J. Hydrol., 512, 285–302, https://doi.org/10.1016/j.jhydrol.2014.02.043, 2014.
Shepherd, T. G.: Atmospheric circulation as a source of uncertainty in climate change projections, Nat. Geosci., 7, 703–708, https://doi.org/10.1038/NGEO2253, 2014.
Steinschneider, S. and Brown, C.: A semiparametric multivariate, multisite weather generator with low-frequency variability for use in climate risk assessments, Water Resour. Res., 49, 7205–7220, https://doi.org/10.1002/wrcr.20528, 2013.
Steinschneider, S., Ray, P., Rahat, S. H., and Kucharski, J.: A weather-regime-based stochastic weather generator for climate vulnerability assessments of water systems in the western United States, Water Resour. Res., 55, 6923–6945, https://doi.org/10.1029/2018WR024446, 2019.
Torma, C., Giorgi, F., and Coppola, E.: Added value of regional climate modeling over areas characterized by complex terrain-precipitation over the Alps, J. Geophys. Res.-Atmos., 120, 3957–3972, https://doi.org/10.1002/2014JD022781, 2015.
Vaittinada Ayar, P., Blanchet, J., Paquet, E., and Penot, D.: Space-time simulation of precipitation based on weather pattern sub-sampling and meta-Gaussian model, J. Hydrol., 581, 124451, https://doi.org/10.1016/j.jhydrol.2019.124451, 2020.
Villarini, G., Smith, J. A., Serinaldi, F., Bales, J., Bates, P. D., and Krajewski, W. F.: Flood frequency analysis for nonstationary annual peak records in an urban drainage basin, Adv. Water Resour., 32, 1255–1266, https://doi.org/10.1016/j.advwatres.2009.05.003, 2009.
Wasko, C. and Sharma, A.: Continuous rainfall generation for a warmer climate using observed temperature sensitivities, J. Hydrol., 544, 575–590, https://doi.org/10.1016/J.JHYDROL.2016.12.002, 2017.
Wilks, D. S.: Adapting stochastic weather generation algorithms for climate change studies, Clim. Change, 22, 67–84, 1992.
Wilks, D. S.: Use of stochastic weather generators for precipitation downscaling, Wiley Interdisciplinary Reviews: Climate Change, 1, 898–907, https://doi.org/10.1002/wcc.85, 2010.
Wilks, D. S.: Stochastic weather generators for climate-change downscaling, part II: Multivariable and spatially coherent multisite downscaling. Wiley Interdisciplinary Reviews: Climate Change, 3, 267–278, https://doi.org/10.1002/wcc.167, 2012.
Winter, B., Schneeberger, K., Nguyen, V.D., Huttenlau, M., Achleitner, S., Stötter, J., Merz, B., and Vorogushyn, S.: A continuous modelling approach for design flood estimation on sub-daily time scale, Hydrol. Sci. J., 64, 539–554, https://doi.org/10.1080/02626667.2019.1593419, 2019.
Short summary
We present a novel stochastic weather generator conditioned on circulation patterns and regional temperature, accounting for dynamic and thermodynamic atmospheric changes. We extensively evaluate the model for the central European region. It statistically downscales precipitation for future periods, generating long, spatially and temporally consistent series. Results suggest an increase in extreme precipitation over the region, offering key benefits for hydrological impact studies.
We present a novel stochastic weather generator conditioned on circulation patterns and regional...
