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
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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.
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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.
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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.
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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
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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.
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We evaluated a multi-site stochastic regional weather generator (nsRWG) for its ability to capture the cross-scale extremity of high 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 spatio-temporal scales. Results show nsRWG simulates well the extremity patterns of HPEs, though it overestimates short-duration, small-extent events.
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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.
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Nat. Hazards Earth Syst. Sci., 20, 967–979, https://doi.org/10.5194/nhess-20-967-2020, https://doi.org/10.5194/nhess-20-967-2020, 2020
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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.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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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
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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
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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
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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
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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
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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.
Xiaoxiang Guan, Dung Viet Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-143, https://doi.org/10.5194/nhess-2024-143, 2024
Revised manuscript under review for NHESS
Short summary
Short summary
We evaluated a multi-site stochastic regional weather generator (nsRWG) for its ability to capture the cross-scale extremity of high 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 spatio-temporal scales. Results show nsRWG simulates well the extremity patterns of HPEs, though it overestimates short-duration, small-extent events.
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
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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
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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.
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. Discuss., https://doi.org/10.5194/nhess-2024-97, https://doi.org/10.5194/nhess-2024-97, 2024
Revised manuscript accepted for NHESS
Short summary
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The July 2021 flood in Central Europe was one of the deadliest floods in Europe in the past 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 was only slightly different. The presented methodology of spatial counterfactuals generates plausible unprecedented events and helps better prepare for future extreme 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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Zhihua He, Katy Unger-Shayesteh, Sergiy Vorogushyn, Stephan M. Weise, Doris Duethmann, Olga Kalashnikova, Abror Gafurov, and Bruno Merz
Hydrol. Earth Syst. Sci., 24, 3289–3309, https://doi.org/10.5194/hess-24-3289-2020, https://doi.org/10.5194/hess-24-3289-2020, 2020
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Quantifying the seasonal contributions of the runoff components, including groundwater, snowmelt, glacier melt, and rainfall, to streamflow is highly necessary for understanding the dynamics of water resources in glacierized basins given the vulnerability of snow- and glacier-dominated environments to the current climate warming. Our study provides the first comparison of two end-member mixing approaches for hydrograph separation in glacierized basins.
Benjamin Winter, Klaus Schneeberger, Kristian Förster, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 20, 1689–1703, https://doi.org/10.5194/nhess-20-1689-2020, https://doi.org/10.5194/nhess-20-1689-2020, 2020
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In this paper two different methods to generate spatially coherent flood events for probabilistic flood risk modelling are compared: on the one hand, a semi-conditional multi-variate dependence model applied to discharge observations and, on the other hand, a continuous hydrological modelling of synthetic meteorological fields generated by a multi-site weather generator. The results of the two approaches are compared in terms of simulated spatial patterns and overall flood risk estimates.
Flavio Lehner, Clara Deser, Nicola Maher, Jochem Marotzke, Erich M. Fischer, Lukas Brunner, Reto Knutti, and Ed Hawkins
Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, https://doi.org/10.5194/esd-11-491-2020, 2020
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Projections of climate change are uncertain because climate models are imperfect, future greenhouse gases emissions are unknown and climate is to some extent chaotic. To partition and understand these sources of uncertainty and make the best use of climate projections, large ensembles with multiple climate models are needed. Such ensembles now exist in a public data archive. We provide several novel applications focused on global and regional temperature and precipitation projections.
Ankit Agarwal, Norbert Marwan, Rathinasamy Maheswaran, Ugur Ozturk, Jürgen Kurths, and Bruno Merz
Hydrol. Earth Syst. Sci., 24, 2235–2251, https://doi.org/10.5194/hess-24-2235-2020, https://doi.org/10.5194/hess-24-2235-2020, 2020
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In the climate/hydrology network, each node represents a geographical location of climatological data, and links between nodes are set up based on their interaction or similar variability. Here, using network theory, we first generate a node-ranking measure and then prioritize the rain gauges to identify influential and expandable stations across Germany. To show the applicability of the proposed approach, we also compared the results with existing traditional and contemporary network measures.
Ayse Duha Metin, Nguyen Viet Dung, Kai Schröter, Sergiy Vorogushyn, Björn Guse, Heidi Kreibich, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 20, 967–979, https://doi.org/10.5194/nhess-20-967-2020, https://doi.org/10.5194/nhess-20-967-2020, 2020
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For effective risk management, flood risk should be properly assessed. Traditionally, risk is assessed by making the assumption of invariant flow or loss probabilities (the chance that a given discharge or loss is exceeded) within the river catchment during a single flood event. However, in reality, flooding is more severe in some regions than others. This study indicates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.
Björn Guse, Bruno Merz, Luzie Wietzke, Sophie Ullrich, Alberto Viglione, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 24, 1633–1648, https://doi.org/10.5194/hess-24-1633-2020, https://doi.org/10.5194/hess-24-1633-2020, 2020
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Floods are influenced by river network processes, among others. Flood characteristics of tributaries may affect flood severity downstream of confluences. The impact of flood wave superposition is investigated with regard to magnitude and temporal matching of flood peaks. Our study in Germany and Austria shows that flood wave superposition is not the major driver of flood severity. However, there is the potential for large floods at some confluences in cases of temporal matching of flood peaks.
Jürgen Kurths, Ankit Agarwal, Roopam Shukla, Norbert Marwan, Maheswaran Rathinasamy, Levke Caesar, Raghavan Krishnan, and Bruno Merz
Nonlin. Processes Geophys., 26, 251–266, https://doi.org/10.5194/npg-26-251-2019, https://doi.org/10.5194/npg-26-251-2019, 2019
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We examined the spatial diversity of Indian rainfall teleconnection at different timescales, first by identifying homogeneous communities and later by computing non-linear linkages between the identified communities (spatial regions) and dominant climatic patterns, represented by climatic indices such as El Nino–Southern Oscillation, Indian Ocean Dipole, North Atlantic Oscillation, Pacific Decadal Oscillation and Atlantic Multi-Decadal Oscillation.
Dirk Diederen, Ye Liu, Ben Gouldby, Ferdinand Diermanse, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 19, 1041–1053, https://doi.org/10.5194/nhess-19-1041-2019, https://doi.org/10.5194/nhess-19-1041-2019, 2019
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Floods affect many communities and cause a large amount of damage worldwide.
Since we choose to live in natural flood plains and are unable to prevent all floods, a system of insurance and reinsurance was set up.
For these institutes to not fail, estimates are required of the frequency of large-scale flood events.
We explore a new method to obtain a large catalogue of synthetic, spatially coherent, large-scale river discharge events, using a recent (gridded) European discharge data set.
Eva Steirou, Lars Gerlitz, Heiko Apel, Xun Sun, and Bruno Merz
Hydrol. Earth Syst. Sci., 23, 1305–1322, https://doi.org/10.5194/hess-23-1305-2019, https://doi.org/10.5194/hess-23-1305-2019, 2019
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We investigate whether flood probabilities in Europe vary for different large-scale atmospheric circulation conditions. Maximum seasonal river flows from 600 gauges in Europe and five synchronous atmospheric circulation indices are analyzed. We find that a high percentage of stations is influenced by at least one of the climate indices, especially during winter. These results can be useful for preparedness and damage planning by (re-)insurance companies.
Ayse Duha Metin, Nguyen Viet Dung, Kai Schröter, Björn Guse, Heiko Apel, Heidi Kreibich, Sergiy Vorogushyn, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 18, 3089–3108, https://doi.org/10.5194/nhess-18-3089-2018, https://doi.org/10.5194/nhess-18-3089-2018, 2018
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We present a comprehensive sensitivity analysis considering changes along the complete flood risk chain to understand how changes in different drivers affect flood risk. Results show that changes in dike systems or in vulnerability may outweigh changes in often investigated components, such as climate change. Although the specific results are conditional on the case study and assumptions, they highlight the need for a broader consideration of potential drivers of change in a comprehensive way.
Nguyen Van Khanh Triet, Nguyen Viet Dung, Bruno Merz, and Heiko Apel
Nat. Hazards Earth Syst. Sci., 18, 2859–2876, https://doi.org/10.5194/nhess-18-2859-2018, https://doi.org/10.5194/nhess-18-2859-2018, 2018
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In this study we provide an estimation of flood damages and risks to rice cultivation in the Mekong Delta. The derived modelling concept explicitly takes plant phenomenology and timing of floods in a probabilistic modelling framework into account. This results in spatially explicit flood risk maps to rice cultivation, quantified as expected annual damage. Furthermore, the changes in flood risk of two land-use scenarios were estimated and discussed.
Giuliano Di Baldassarre, Heidi Kreibich, Sergiy Vorogushyn, Jeroen Aerts, Karsten Arnbjerg-Nielsen, Marlies Barendrecht, Paul Bates, Marco Borga, Wouter Botzen, Philip Bubeck, Bruna De Marchi, Carmen Llasat, Maurizio Mazzoleni, Daniela Molinari, Elena Mondino, Johanna Mård, Olga Petrucci, Anna Scolobig, Alberto Viglione, and Philip J. Ward
Hydrol. Earth Syst. Sci., 22, 5629–5637, https://doi.org/10.5194/hess-22-5629-2018, https://doi.org/10.5194/hess-22-5629-2018, 2018
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One common approach to cope with floods is the implementation of structural flood protection measures, such as levees. Numerous scholars have problematized this approach and shown that increasing levels of flood protection can generate a false sense of security and attract more people to the risky areas. We briefly review the literature on this topic and then propose a research agenda to explore the unintended consequences of structural flood protection.
Sergey Tyagunov, Sergiy Vorogushyn, Cristina Muñoz Jimenez, Stefano Parolai, and Kevin Fleming
Nat. Hazards Earth Syst. Sci., 18, 2345–2354, https://doi.org/10.5194/nhess-18-2345-2018, https://doi.org/10.5194/nhess-18-2345-2018, 2018
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A methodological framework for the multi-hazard (earthquake and flood) failure analysis of fluvial dikes due to liquefaction is presented. Failure probability of the earthen structures is presented in the form of a fragility surface as a function of both seismic and hydraulic load. It is emphasized that the potential interactions between the two hazards should not be ignored in risk analyses and decision-making.
Marlies Holkje Barendrecht, Alberto Viglione, Heidi Kreibich, Sergiy Vorogushyn, Bruno Merz, and Günter Blöschl
Proc. IAHS, 379, 193–198, https://doi.org/10.5194/piahs-379-193-2018, https://doi.org/10.5194/piahs-379-193-2018, 2018
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The aim of this paper is to assess whether a Socio-Hydrological model can be calibrated to data artificially generated from it. This is not trivial because the model is highly nonlinear and it is not clear what amount of data would be needed for calibration. We demonstrate that, using Bayesian inference, the parameters of the model can be estimated quite accurately from relatively few data, which could be available in real case studies.
Heiko Apel, Zharkinay Abdykerimova, Marina Agalhanova, Azamat Baimaganbetov, Nadejda Gavrilenko, Lars Gerlitz, Olga Kalashnikova, Katy Unger-Shayesteh, Sergiy Vorogushyn, and Abror Gafurov
Hydrol. Earth Syst. Sci., 22, 2225–2254, https://doi.org/10.5194/hess-22-2225-2018, https://doi.org/10.5194/hess-22-2225-2018, 2018
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Central Asia crucially depends on water resources supplied by snow melt in the mountains during summer. To support water resources management we propose a generic tool for statistical forecasts of seasonal discharge based on multiple linear regressions. The predictors are observed precipitation and temperature, snow coverage, and discharge. The automatically derived models for 13 different catchments provided very skilful forecasts in April, and acceptable forecasts in January.
Nguyen Le Duy, Ingo Heidbüchel, Hanno Meyer, Bruno Merz, and Heiko Apel
Hydrol. Earth Syst. Sci., 22, 1239–1262, https://doi.org/10.5194/hess-22-1239-2018, https://doi.org/10.5194/hess-22-1239-2018, 2018
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This study analyzes the influence of local and regional meteorological factors on the isotopic composition of precipitation. The impact of the different factors on the isotopic condition was quantified by multiple linear regression of all factor combinations combined with relative importance analysis. The proposed approach might open a pathway for the improved reconstruction of paleoclimates based on isotopic records.
Lukas Brunner and Andrea K. Steiner
Atmos. Meas. Tech., 10, 4727–4745, https://doi.org/10.5194/amt-10-4727-2017, https://doi.org/10.5194/amt-10-4727-2017, 2017
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Atmospheric blocking is a weather pattern where a stable high pressure system blocks the westerly flow at mid-latitudes. We provide, for the first time, a global perspective on blocking and related impacts, based on satellite observations from GPS radio occultation for 2006–2016. We find strong direct and remote effects on the vertical atmospheric structure revealing significant temperature and humidity anomalies up to 15 km. The observations will help for a better insight into blocking impacts.
Ankit Agarwal, Norbert Marwan, Maheswaran Rathinasamy, Bruno Merz, and Jürgen Kurths
Nonlin. Processes Geophys., 24, 599–611, https://doi.org/10.5194/npg-24-599-2017, https://doi.org/10.5194/npg-24-599-2017, 2017
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Extreme events such as floods and droughts result from synchronization of different natural processes working at multiple timescales. Investigation on an observation timescale will not reveal the inherent underlying dynamics triggering these events. This paper develops a new method based on wavelets and event synchronization to unravel the hidden dynamics responsible for such sudden events. This method is tested with synthetic and real-world cases and the results are promising.
Martin Hoelzle, Erlan Azisov, Martina Barandun, Matthias Huss, Daniel Farinotti, Abror Gafurov, Wilfried Hagg, Ruslan Kenzhebaev, Marlene Kronenberg, Horst Machguth, Alexandr Merkushkin, Bolot Moldobekov, Maxim Petrov, Tomas Saks, Nadine Salzmann, Tilo Schöne, Yuri Tarasov, Ryskul Usubaliev, Sergiy Vorogushyn, Andrey Yakovlev, and Michael Zemp
Geosci. Instrum. Method. Data Syst., 6, 397–418, https://doi.org/10.5194/gi-6-397-2017, https://doi.org/10.5194/gi-6-397-2017, 2017
Nguyen Van Khanh Triet, Nguyen Viet Dung, Hideto Fujii, Matti Kummu, Bruno Merz, and Heiko Apel
Hydrol. Earth Syst. Sci., 21, 3991–4010, https://doi.org/10.5194/hess-21-3991-2017, https://doi.org/10.5194/hess-21-3991-2017, 2017
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In this study we provide a numerical quantification of changes in flood hazard in the Vietnamese Mekong Delta as a result of dyke development. Other important drivers to the alteration of delta flood hazard are also investigated, e.g. tidal level. The findings of our study are substantial valuable for the decision makers in Vietnam to develop holistic and harmonized floods and flood-related issues management plan for the whole delta.
Katrin M. Nissen and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 17, 1177–1190, https://doi.org/10.5194/nhess-17-1177-2017, https://doi.org/10.5194/nhess-17-1177-2017, 2017
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The effect of climate change on potentially infrastructure damaging heavy precipitation events in Europe is investigated. A novel technique records not only event frequency but also event size, duration and severity as these parameters determine the potential consequences of the event. Over most of Europe the frequency and size of heavy precipitation events is predicted to increase. Moreover, the most severe events are predicted for future periods.
Mathias Seibert, Bruno Merz, and Heiko Apel
Hydrol. Earth Syst. Sci., 21, 1611–1629, https://doi.org/10.5194/hess-21-1611-2017, https://doi.org/10.5194/hess-21-1611-2017, 2017
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Seasonal early warning is vital for drought management in arid regions like the Limpopo Basin in southern Africa. This study shows that skilled seasonal forecasts can be achieved with statistical methods built upon driving factors for drought occurrence. These are the hydrological factors for current streamflow and meteorological drivers represented by anomalies in sea surface temperatures of the surrounding oceans, which combine to form unique combinations in the drought forecast models.
Lars Gerlitz, Sergiy Vorogushyn, Heiko Apel, Abror Gafurov, Katy Unger-Shayesteh, and Bruno Merz
Hydrol. Earth Syst. Sci., 20, 4605–4623, https://doi.org/10.5194/hess-20-4605-2016, https://doi.org/10.5194/hess-20-4605-2016, 2016
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Most statistically based seasonal precipitation forecast models utilize a small set of well-known climate indices as potential predictor variables. However, for many target regions, these indices do not lead to sufficient results and customized predictors are required for an accurate prediction.
This study presents a statistically based routine, which automatically identifies suitable predictors from globally gridded SST and climate variables by means of an extensive data mining procedure.
Aline Murawski, Gerd Bürger, Sergiy Vorogushyn, and Bruno Merz
Hydrol. Earth Syst. Sci., 20, 4283–4306, https://doi.org/10.5194/hess-20-4283-2016, https://doi.org/10.5194/hess-20-4283-2016, 2016
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To understand past flood changes in the Rhine catchment and the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. Here the link between patterns and local climate is tested, and the skill of GCMs in reproducing these patterns is evaluated.
Heidi Kreibich, Kai Schröter, and Bruno Merz
Proc. IAHS, 373, 179–182, https://doi.org/10.5194/piahs-373-179-2016, https://doi.org/10.5194/piahs-373-179-2016, 2016
Heiko Apel, Oriol Martínez Trepat, Nguyen Nghia Hung, Do Thi Chinh, Bruno Merz, and Nguyen Viet Dung
Nat. Hazards Earth Syst. Sci., 16, 941–961, https://doi.org/10.5194/nhess-16-941-2016, https://doi.org/10.5194/nhess-16-941-2016, 2016
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Many urban areas experience both fluvial and pluvial floods, thus this study aims to analyse fluvial and pluvial flood hazards as well as combined pluvial and fluvial flood hazards. This combined fluvial–pluvial flood hazard analysis is performed in a tropical environment for Can Tho city in the Mekong Delta. The final results are probabilistic hazard maps, showing the maximum inundation caused by floods of different magnitudes along with an uncertainty estimation.
Lukas Brunner, Andrea K. Steiner, Barbara Scherllin-Pirscher, and Martin W. Jury
Atmos. Chem. Phys., 16, 4593–4604, https://doi.org/10.5194/acp-16-4593-2016, https://doi.org/10.5194/acp-16-4593-2016, 2016
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Atmospheric blocking refers to persistent high-pressure systems which block the climatological flow at midlatitudes. We explore blocking with observations from GPS radio occultation (RO), a satellite-based remote-sensing system. Using two example cases, we find that RO data robustly capture blocking, highlighting the potential of RO observations to complement models and reanalysis as a basis for blocking research.
U. Dayan, K. Nissen, and U. Ulbrich
Nat. Hazards Earth Syst. Sci., 15, 2525–2544, https://doi.org/10.5194/nhess-15-2525-2015, https://doi.org/10.5194/nhess-15-2525-2015, 2015
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This review discusses published studies analyzing the atmospheric conditions that induce extreme precipitation over the eastern and western Mediterranean regions. It presents a systematic description of the interlacing role of several atmospheric processes of different scales - local, meso, and synoptic - that enable the development of torrential rains.
J. Hall, B. Arheimer, G. T. Aronica, A. Bilibashi, M. Boháč, O. Bonacci, M. Borga, P. Burlando, A. Castellarin, G. B. Chirico, P. Claps, K. Fiala, L. Gaál, L. Gorbachova, A. Gül, J. Hannaford, A. Kiss, T. Kjeldsen, S. Kohnová, J. J. Koskela, N. Macdonald, M. Mavrova-Guirguinova, O. Ledvinka, L. Mediero, B. Merz, R. Merz, P. Molnar, A. Montanari, M. Osuch, J. Parajka, R. A. P. Perdigão, I. Radevski, B. Renard, M. Rogger, J. L. Salinas, E. Sauquet, M. Šraj, J. Szolgay, A. Viglione, E. Volpi, D. Wilson, K. Zaimi, and G. Blöschl
Proc. IAHS, 370, 89–95, https://doi.org/10.5194/piahs-370-89-2015, https://doi.org/10.5194/piahs-370-89-2015, 2015
A. Gafurov, S. Vorogushyn, D. Farinotti, D. Duethmann, A. Merkushkin, and B. Merz
The Cryosphere, 9, 451–463, https://doi.org/10.5194/tc-9-451-2015, https://doi.org/10.5194/tc-9-451-2015, 2015
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Spatially distributed snow-cover data are available only for the recent past from remote sensing. Sometimes we need snow-cover data over a longer period for climate impact analysis for the calibration/validation of hydrological models. In this study we present a methodology to reconstruct snow cover in the past using available long-term in situ data and recently available remote sensing snow-cover data. The results show about 85% accuracy although only a limited number of stations (7) were used.
K. Schröter, M. Kunz, F. Elmer, B. Mühr, and B. Merz
Hydrol. Earth Syst. Sci., 19, 309–327, https://doi.org/10.5194/hess-19-309-2015, https://doi.org/10.5194/hess-19-309-2015, 2015
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Extreme antecedent precipitation, increased initial hydraulic load in the river network and strong but not extraordinary event precipitation were key drivers for the flood in June 2013 in Germany. Our results are based on extreme value statistics and aggregated severity indices which we evaluated for a set of 74 historic large-scale floods. This flood database and the methodological framework enable the rapid assessment of future floods using precipitation and discharge observations.
N. V. Manh, N. V. Dung, N. N. Hung, B. Merz, and H. Apel
Hydrol. Earth Syst. Sci., 18, 3033–3053, https://doi.org/10.5194/hess-18-3033-2014, https://doi.org/10.5194/hess-18-3033-2014, 2014
J. Hall, B. Arheimer, M. Borga, R. Brázdil, P. Claps, A. Kiss, T. R. Kjeldsen, J. Kriaučiūnienė, Z. W. Kundzewicz, M. Lang, M. C. Llasat, N. Macdonald, N. McIntyre, L. Mediero, B. Merz, R. Merz, P. Molnar, A. Montanari, C. Neuhold, J. Parajka, R. A. P. Perdigão, L. Plavcová, M. Rogger, J. L. Salinas, E. Sauquet, C. Schär, J. Szolgay, A. Viglione, and G. Blöschl
Hydrol. Earth Syst. Sci., 18, 2735–2772, https://doi.org/10.5194/hess-18-2735-2014, https://doi.org/10.5194/hess-18-2735-2014, 2014
B. Merz, J. Aerts, K. Arnbjerg-Nielsen, M. Baldi, A. Becker, A. Bichet, G. Blöschl, L. M. Bouwer, A. Brauer, F. Cioffi, J. M. Delgado, M. Gocht, F. Guzzetti, S. Harrigan, K. Hirschboeck, C. Kilsby, W. Kron, H.-H. Kwon, U. Lall, R. Merz, K. Nissen, P. Salvatti, T. Swierczynski, U. Ulbrich, A. Viglione, P. J. Ward, M. Weiler, B. Wilhelm, and M. Nied
Nat. Hazards Earth Syst. Sci., 14, 1921–1942, https://doi.org/10.5194/nhess-14-1921-2014, https://doi.org/10.5194/nhess-14-1921-2014, 2014
J. M. Delgado, B. Merz, and H. Apel
Nat. Hazards Earth Syst. Sci., 14, 1579–1589, https://doi.org/10.5194/nhess-14-1579-2014, https://doi.org/10.5194/nhess-14-1579-2014, 2014
S. Uhlemann, A. H. Thieken, and B. Merz
Nat. Hazards Earth Syst. Sci., 14, 189–208, https://doi.org/10.5194/nhess-14-189-2014, https://doi.org/10.5194/nhess-14-189-2014, 2014
S. Vorogushyn and B. Merz
Hydrol. Earth Syst. Sci., 17, 3871–3884, https://doi.org/10.5194/hess-17-3871-2013, https://doi.org/10.5194/hess-17-3871-2013, 2013
A. Domeneghetti, S. Vorogushyn, A. Castellarin, B. Merz, and A. Brath
Hydrol. Earth Syst. Sci., 17, 3127–3140, https://doi.org/10.5194/hess-17-3127-2013, https://doi.org/10.5194/hess-17-3127-2013, 2013
N. V. Manh, B. Merz, and H. Apel
Hydrol. Earth Syst. Sci., 17, 3039–3057, https://doi.org/10.5194/hess-17-3039-2013, https://doi.org/10.5194/hess-17-3039-2013, 2013
D. Duethmann, J. Zimmer, A. Gafurov, A. Güntner, D. Kriegel, B. Merz, and S. Vorogushyn
Hydrol. Earth Syst. Sci., 17, 2415–2434, https://doi.org/10.5194/hess-17-2415-2013, https://doi.org/10.5194/hess-17-2415-2013, 2013
M. Nied, Y. Hundecha, and B. Merz
Hydrol. Earth Syst. Sci., 17, 1401–1414, https://doi.org/10.5194/hess-17-1401-2013, https://doi.org/10.5194/hess-17-1401-2013, 2013
S. Uhlemann, R. Bertelmann, and B. Merz
Hydrol. Earth Syst. Sci., 17, 895–911, https://doi.org/10.5194/hess-17-895-2013, https://doi.org/10.5194/hess-17-895-2013, 2013
N. V. Dung, B. Merz, A. Bárdossy, and H. Apel
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-1-275-2013, https://doi.org/10.5194/nhessd-1-275-2013, 2013
Revised manuscript not accepted
B. Merz, H. Kreibich, and U. Lall
Nat. Hazards Earth Syst. Sci., 13, 53–64, https://doi.org/10.5194/nhess-13-53-2013, https://doi.org/10.5194/nhess-13-53-2013, 2013
Related subject area
Statistics
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Reducing reliability bias in assessments of extreme weather risk using calibrating priors
A robust approach to Gaussian process implementation
Spatiotemporal functional permutation tests for comparing observed climate behavior to climate model projections
Parametric model for post-processing visibility ensemble forecasts
Spatiotemporal methods for estimating subsurface ocean thermal response to tropical cyclones
Applying different methods to model dry and wet spells at daily scale in a large range of rainfall regimes across Europe
Comparison of climate time series – Part 5: Multivariate annual cycles
Regridding uncertainty for statistical downscaling of solar radiation
Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change
Statistical modeling of the space–time relation between wind and significant wave height
Modeling general circulation model bias via a combination of localized regression and quantile mapping methods
Evaluation of simulated responses to climate forcings: a flexible statistical framework using confirmatory factor analysis and structural equation modelling – Part 1: Theory
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Comparing climate time series – Part 2: A multivariate test
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Novel measures for summarizing high-resolution forecast performance
Copula approach for simulated damages caused by landfalling US hurricanes
Nonstationary extreme value analysis for event attribution combining climate models and observations
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Spatial trend analysis of gridded temperature data at varying spatial scales
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Influence of initial ocean conditions on temperature and precipitation in a coupled climate model's solution
NWP-based lightning prediction using flexible count data regression
An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging
Probabilistic evaluation of competing climate models
Assessing NARCCAP climate model effects using spatial confidence regions
Generalised block bootstrap and its use in meteorology
Estimating trends in the global mean temperature record
Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression
Analysis of variability of tropical Pacific sea surface temperatures
Evaluating NARCCAP model performance for frequencies of severe-storm environments
Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions
A comparison of two methods for detecting abrupt changes in the variance of climatic time series
Calibrating regionally downscaled precipitation over Norway through quantile-based approaches
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Autoregressive spatially varying coefficients model for predicting daily PM2.5 using VIIRS satellite AOT
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Joint inference of misaligned irregular time series with application to Greenland ice core data
Simulation of future climate under changing temporal covariance structures
Abdolnasser Sadeghkhani and Ali Sadeghkhani
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 73–87, https://doi.org/10.5194/ascmo-11-73-2025, https://doi.org/10.5194/ascmo-11-73-2025, 2025
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This paper presents a novel study on boxplot-valued data in climatological applications. Our methodologies are applied to the Berkeley Earth Surface Temperature Study. We validate our approaches through comprehensive simulations, comparing Bayesian and frequentist estimators for efficiency and accuracy. The results provide robust insights into climatic trends, particularly summer average temperatures across European countries.
Romain Pic, Clément Dombry, Philippe Naveau, and Maxime Taillardat
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 23–58, https://doi.org/10.5194/ascmo-11-23-2025, https://doi.org/10.5194/ascmo-11-23-2025, 2025
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Correctly forecasting weather is crucial for decision-making in various fields. Standard multivariate verification tools have limitations, and a single tool cannot fully characterize predictive performance. We formalize a framework based on aggregation and transformation to build interpretable verification tools. These tools target specific features of forecasts, improving predictive performance characterization and bridging the gap between theoretical and physics-based tools.
Stephen Jewson, Trevor Sweeting, and Lynne Jewson
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 1–22, https://doi.org/10.5194/ascmo-11-1-2025, https://doi.org/10.5194/ascmo-11-1-2025, 2025
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We investigate how to make statistical predictions of extreme weather such that events predicted to occur with a probability of 1 % will occur 1 % of the time. We apply the methods we describe to a standard extreme weather attribution example from the recent climate literature. We find that the methods we describe imply that extremes are roughly twice as likely as when estimated using maximum likelihood. We have developed a software package to make it easy to apply these methods.
Juliette Mukangango, Amanda Muyskens, and Benjamin W. Priest
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 143–158, https://doi.org/10.5194/ascmo-10-143-2024, https://doi.org/10.5194/ascmo-10-143-2024, 2024
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In this study, we investigated the performance of Gaussian process regression (GP) models in handling outlier-affected spatial datasets. Our findings emphasized that models with the proposed methods provided accurate predictions and reliable uncertainty quantification, showcasing resilience against outliers. Overall, our study contributes to advancing the understanding of GP regression in spatial contexts and offers practical solutions to enhance its applicability in outlier-rich environments.
Joshua P. French, Piotr S. Kokoszka, and Seth McGinnis
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 123–141, https://doi.org/10.5194/ascmo-10-123-2024, https://doi.org/10.5194/ascmo-10-123-2024, 2024
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Future climate behavior is typically modeled using computer-based simulations, which are generated for both historical and future time periods. The trustworthiness of these models can be assessed by determining whether the simulated historical climate matches what was observed. We provide a tool that allows researchers to identify major differences between observed climate and climate model predictions, which will hopefully lead to further model refinements.
Ágnes Baran and Sándor Baran
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 105–122, https://doi.org/10.5194/ascmo-10-105-2024, https://doi.org/10.5194/ascmo-10-105-2024, 2024
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The paper proposes a novel parametric model for statistical post-processing of visibility ensemble forecasts; investigates various approaches to parameter estimation; and, using two case studies, provides a detailed comparison with the existing state-of-the-art forecasts. The introduced approach consistently outperforms both the raw ensemble forecasts and the reference parametric post-processing method.
Addison J. Hu, Mikael Kuusela, Ann B. Lee, Donata Giglio, and Kimberly M. Wood
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 69–93, https://doi.org/10.5194/ascmo-10-69-2024, https://doi.org/10.5194/ascmo-10-69-2024, 2024
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We introduce a new statistical framework to estimate the change in subsurface ocean temperature following the passage of a tropical cyclone (TC). Our approach combines tools handling seasonal variations and spatial dependence in the data, culminating in a three-dimensional characterization of the interaction between TCs and the ocean. Our work allows us to obtain new scientific insights, and we expect it to be generally applicable to studying the impact of TCs on other ocean phenomena.
Giorgio Baiamonte, Carmelo Agnese, Carmelo Cammalleri, Elvira Di Nardo, Stefano Ferraris, and Tommaso Martini
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 51–67, https://doi.org/10.5194/ascmo-10-51-2024, https://doi.org/10.5194/ascmo-10-51-2024, 2024
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In hydrology, the probability distributions are used to determine the probability of occurrence of rainfall events. In this study, two different methods for modeling rainfall time characteristics have been applied: a direct method and an indirect method that make it possible to relax the assumptions of the renewal process. The analysis was extended to two additional time variables that may be of great interest for practical hydrological applications: wet chains and dry chains.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 1–27, https://doi.org/10.5194/ascmo-10-1-2024, https://doi.org/10.5194/ascmo-10-1-2024, 2024
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This paper introduces a method to assess whether two data sets come from the same source. Current methods do not adequately consider spatial and temporal correlations and their annual cycles in a comprehensive test. This method addresses that gap, thereby providing a new and rigorous tool for evaluating the realism of climate simulations and measuring changes in variability over time.
Maggie D. Bailey, Douglas Nychka, Manajit Sengupta, Aron Habte, Yu Xie, and Soutir Bandyopadhyay
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 103–120, https://doi.org/10.5194/ascmo-9-103-2023, https://doi.org/10.5194/ascmo-9-103-2023, 2023
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To ensure photovoltaic (PV) plants last, we need to understand how climate change affects solar radiation. Climate models help predict future radiation for PV plants, but there is often uncertainty. We explore this uncertainty and its impact on building PV plants. We highlight the importance of considering uncertainties for accurate planning and management. A California case study shows a practical application for the solar industry.
Joel Zeder and Erich M. Fischer
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 83–102, https://doi.org/10.5194/ascmo-9-83-2023, https://doi.org/10.5194/ascmo-9-83-2023, 2023
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The intensities of recent heatwave events, such as the record-breaking heatwave in early June 2021 in the Pacific Northwest area, are substantially altered by climate change. We further quantify the contribution of the local weather situation and the land surface conditions with a statistical model suited for extreme data. Based on this method, we can answer
what ifquestions, such as estimating the change in the 2021 heatwave temperature if it happened in a world without climate change.
Said Obakrim, Pierre Ailliot, Valérie Monbet, and Nicolas Raillard
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 67–81, https://doi.org/10.5194/ascmo-9-67-2023, https://doi.org/10.5194/ascmo-9-67-2023, 2023
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Ocean wave climate has a significant impact on human activities, and its understanding is of socioeconomic and environmental importance. In this study, we propose a statistical model that predicts wave heights in a location in the Bay of Biscay. The proposed method allows us to understand the spatiotemporal relationship between wind and waves and predicts well both wind seas and swells.
Benjamin James Washington, Lynne Seymour, and Thomas L. Mote
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 1–28, https://doi.org/10.5194/ascmo-9-1-2023, https://doi.org/10.5194/ascmo-9-1-2023, 2023
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We develop new methodology to statistically model known bias in general atmospheric circulation models. We focus on Puerto Rico specifically because of other important ongoing and long-term ecological and environmental research taking place there. Our methods work even in the presence of Puerto Rico's broken climate record. With our methods, we find that climate change will not only favor a warmer and wetter climate in Puerto Rico, but also increase the frequency of extreme rainfall events.
Katarina Lashgari, Gudrun Brattström, Anders Moberg, and Rolf Sundberg
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 225–248, https://doi.org/10.5194/ascmo-8-225-2022, https://doi.org/10.5194/ascmo-8-225-2022, 2022
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This work theoretically motivates an extension of the statistical model used in so-called detection and attribution studies to structural equation modelling. The application of one of the models suggested is exemplified in a small numerical study, whose aim was to check the assumptions typically placed on ensembles of climate model simulations when constructing mean sequences. he result of this study indicated that some ensembles for some regions may not satisfy the assumptions in question.
Katarina Lashgari, Anders Moberg, and Gudrun Brattström
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 249–271, https://doi.org/10.5194/ascmo-8-249-2022, https://doi.org/10.5194/ascmo-8-249-2022, 2022
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The performance of a new statistical framework containing various structural equation modelling (SEM) models is evaluated in a pseudo-proxy experiment in comparison with the performance of statistical models used in many detection and attribution studies. Each statistical model was fitted to seven continental-scale regional temperature data sets. The results indicated the SEM specification is the most appropriate for describing the underlying latent structure of the simulated data analysed.
Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 205–224, https://doi.org/10.5194/ascmo-8-205-2022, https://doi.org/10.5194/ascmo-8-205-2022, 2022
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We study wind conditions and their potential future changes across the U.S. via a statistical conditional framework. We conclude that changes between historical and future wind directions are small, but wind speeds are generally weakened in the projected period, with some locations being intensified. Moreover, winter wind speeds are projected to decrease in the northwest, Colorado, and the northern Great Plains (GP), while summer wind speeds over the southern GP slightly increase in the future.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 73–85, https://doi.org/10.5194/ascmo-7-73-2021, https://doi.org/10.5194/ascmo-7-73-2021, 2021
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After a new climate model is constructed, a natural question is whether it generates realistic simulations. Here,
realisticdoes not mean that the detailed patterns on a particular day are correct, but rather that the statistics over many years are realistic. Past approaches to answering this question often neglect correlations in space and time. This paper proposes a method for answering this question that accounts for correlations in space and time.
Julie Bessac and Philippe Naveau
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 53–71, https://doi.org/10.5194/ascmo-7-53-2021, https://doi.org/10.5194/ascmo-7-53-2021, 2021
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We propose a new forecast evaluation scheme in the context of models that incorporate errors of the verification data. We rely on existing scoring rules and incorporate uncertainty and error of the verification data through a hidden variable and the conditional expectation of scores. By considering scores to be random variables, one can access the entire range of their distribution and illustrate that the commonly used mean score can be a misleading representative of the distribution.
Eric Gilleland
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 13–34, https://doi.org/10.5194/ascmo-7-13-2021, https://doi.org/10.5194/ascmo-7-13-2021, 2021
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Verifying high-resolution weather forecasts has become increasingly complicated,
and simple, easy-to-understand summary measures are a good alternative. Recent work has demonstrated some common pitfalls with many such summaries. Here, new summary measures are introduced that do not suffer from these drawbacks, while still providing meaningful information.
Thomas Patrick Leahy
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 1–11, https://doi.org/10.5194/ascmo-7-1-2021, https://doi.org/10.5194/ascmo-7-1-2021, 2021
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This study looked at estimating damages caused by hurricanes in the United States. It assessed the relationship between the maximum wind speed at landfall and the resulting damage caused. The study found that the complex processes that determine the size of the damages inflicted could be estimated using this simple relationship. This work could be used to examine how often extreme damage events are likely to occur and the impact of stronger hurricane winds on the US Atlantic and Gulf coasts.
Yoann Robin and Aurélien Ribes
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 205–221, https://doi.org/10.5194/ascmo-6-205-2020, https://doi.org/10.5194/ascmo-6-205-2020, 2020
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We have developed a new statistical method to describe how a severe weather event, such as a heat wave, may have been influenced by climate change. Our method incorporates both observations and data from various climate models to reflect climate model uncertainty. Our results show that both the probability and the intensity of the French July 2019 heatwave have increased significantly in response to human influence. We find that this heat wave might not have been possible without climate change.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 159–175, https://doi.org/10.5194/ascmo-6-159-2020, https://doi.org/10.5194/ascmo-6-159-2020, 2020
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Scientists often are confronted with the question of whether two time series are statistically distinguishable. This paper proposes a test for answering this question. The basic idea is to fit each time series to a time series model and then test whether the parameters in that model are equal. If a difference is detected, then new ways of visualizing those differences are proposed, including a clustering technique and a method based on optimal initial conditions.
Joshua North, Zofia Stanley, William Kleiber, Wiebke Deierling, Eric Gilleland, and Matthias Steiner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 79–90, https://doi.org/10.5194/ascmo-6-79-2020, https://doi.org/10.5194/ascmo-6-79-2020, 2020
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Very short-term forecasting, called nowcasting, is used to monitor storms that pose a significant threat to people and infrastructure. These threats could include lightning strikes, hail, heavy precipitation, strong winds, and possible tornados. This paper proposes a fast approach to nowcasting lightning threats using simple statistical methods. The proposed model results in fast nowcasts that are more accurate than a competitive, computationally expensive, approach.
Ola Haug, Thordis L. Thorarinsdottir, Sigrunn H. Sørbye, and Christian L. E. Franzke
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 1–12, https://doi.org/10.5194/ascmo-6-1-2020, https://doi.org/10.5194/ascmo-6-1-2020, 2020
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Trends in gridded temperature data are commonly assessed independently for each grid cell, ignoring spatial coherencies. This may severely affect the interpretation of the results. This article proposes a space–time model for temperatures that allows for joint assessments of the trend across locations. In a case study of summer season trends in Europe, it is found that the region with a significant trend under spatial coherency is vastly different from that under independent assessments.
Alexis Hannart
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 161–171, https://doi.org/10.5194/ascmo-5-161-2019, https://doi.org/10.5194/ascmo-5-161-2019, 2019
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In climate change attribution studies, one often seeks to maximize a signal-to-noise ratio, where the
signalis the anthropogenic response and the
noiseis climate variability. A solution commonly used in D&A studies thus far consists of projecting the signal on the subspace spanned by the leading eigenvectors of climate variability. Here I show that this approach is vastly suboptimal – in fact, it leads instead to maximizing the noise-to-signal ratio. I then describe an improved solution.
Moritz N. Lang, Georg J. Mayr, Reto Stauffer, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 115–132, https://doi.org/10.5194/ascmo-5-115-2019, https://doi.org/10.5194/ascmo-5-115-2019, 2019
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Accurate wind forecasts are of great importance for decision-making processes in today's society. This work presents a novel probabilistic post-processing method for wind vector forecasts employing a bivariate Gaussian response distribution. To capture a possible mismatch between the predicted and observed wind direction caused by location-specific properties, the approach incorporates a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction.
X. Joey Wang, John R. J. Thompson, W. John Braun, and Douglas G. Woolford
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 57–66, https://doi.org/10.5194/ascmo-5-57-2019, https://doi.org/10.5194/ascmo-5-57-2019, 2019
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This paper presents the analysis of data from small-scale laboratory experimental smouldering fires that were digitally video-recorded. The video images of these fires bear a resemblance to remotely sensed images of wildfires and provide an opportunity to fit and assess a spatial model for fire spread that attempts to account for uncertainty in fire growth. We found that the fitting method is feasible, and the spatial model provides a suitable mathematical for the fire spread process.
Robin Tokmakian and Peter Challenor
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 17–35, https://doi.org/10.5194/ascmo-5-17-2019, https://doi.org/10.5194/ascmo-5-17-2019, 2019
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As an example of how to robustly determine climate model uncertainty, the paper describes an experiment that perturbs the initial conditions for the ocean's temperature of a climate model. A total of 30 perturbed simulations are used (via an emulator) to estimate spatial uncertainties for temperature and precipitation fields. We also examined (using maximum covariance analysis) how ocean temperatures affect air temperatures and precipitation over land and the importance of feedback processes.
Thorsten Simon, Georg J. Mayr, Nikolaus Umlauf, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 1–16, https://doi.org/10.5194/ascmo-5-1-2019, https://doi.org/10.5194/ascmo-5-1-2019, 2019
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Lightning in Alpine regions is associated with events such as thunderstorms,
extreme precipitation, high wind gusts, flash floods, and debris flows.
We present a statistical approach to predict lightning counts based on
numerical weather predictions. Lightning counts are considered on a grid
with 18 km mesh size. Skilful prediction is obtained for a forecast horizon
of 5 days over complex terrain.
Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 53–63, https://doi.org/10.5194/ascmo-4-53-2018, https://doi.org/10.5194/ascmo-4-53-2018, 2018
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Millions of people worldwide are at a risk of coastal flooding, and this number will increase as the climate continues to change. This study analyzes how climate change affects future flood hazards. A new model that uses multiple climate variables for flood hazard is developed. For the case study of Norfolk, Virginia, the model predicts 23 cm higher flood levels relative to previous work. This work shows the importance of accounting for climate change in effectively managing coastal risks.
Amy Braverman, Snigdhansu Chatterjee, Megan Heyman, and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 93–105, https://doi.org/10.5194/ascmo-3-93-2017, https://doi.org/10.5194/ascmo-3-93-2017, 2017
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In this paper, we introduce a method for expressing the agreement between climate model output time series and time series of observational data as a probability value. Our metric is an estimate of the probability that one would obtain two time series as similar as the ones under consideration, if the climate model and the observed series actually shared the same underlying climate signal.
Joshua P. French, Seth McGinnis, and Armin Schwartzman
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 67–92, https://doi.org/10.5194/ascmo-3-67-2017, https://doi.org/10.5194/ascmo-3-67-2017, 2017
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We assess the mean temperature effect of global and regional climate model combinations for the North American Regional Climate Change Assessment Program using varying classes of linear regression models, including possible interaction effects. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We conclusively show that accounting for multiple comparisons is important for making proper inference.
László Varga and András Zempléni
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 55–66, https://doi.org/10.5194/ascmo-3-55-2017, https://doi.org/10.5194/ascmo-3-55-2017, 2017
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This paper proposes a new generalisation of the block bootstrap methodology, which allows for any positive real number as expected block size. We use this bootstrap for determining the p values of a homogeneity test for copulas. The methods are applied to a temperature data set - we have found some significant changes in the dependence structure between the standardised temperature values of pairs of observation points within the Carpathian Basin.
Andrew Poppick, Elisabeth J. Moyer, and Michael L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 33–53, https://doi.org/10.5194/ascmo-3-33-2017, https://doi.org/10.5194/ascmo-3-33-2017, 2017
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We show that ostensibly empirical methods of analyzing trends in the global mean temperature record, which appear to de-emphasize assumptions, can nevertheless produce misleading inferences about trends and associated uncertainty. We illustrate how a simple but physically motivated trend model can provide better-fitting and more broadly applicable results, and show the importance of adequately characterizing internal variability for estimating trend uncertainty.
John Tipton, Mevin Hooten, and Simon Goring
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 1–16, https://doi.org/10.5194/ascmo-3-1-2017, https://doi.org/10.5194/ascmo-3-1-2017, 2017
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We present a statistical framework for the reconstruction of historic temperature patterns from sparse, irregular data collected from observer stations. A common statistical technique for climate reconstruction uses modern era data as a set of temperature patterns that can be used to estimate the spatial temperature patterns. We present a framework for exploration of different assumptions about the sets of patterns used in the reconstruction while providing statistically rigorous estimates.
Georgina Davies and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 155–169, https://doi.org/10.5194/ascmo-2-155-2016, https://doi.org/10.5194/ascmo-2-155-2016, 2016
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Sea surface temperature (SST) is a key component of global climate models, particularly in the tropical Pacific Ocean where El Niño and La Nina events have worldwide implications. In our paper, we analyse monthly SSTs in the Niño 3.4 region and find a transformation that removes a spatial mean-variance dependence for each month. For 10 out of 12 months in the year, the transformed monthly time series gave more accurate or as accurate forecasts than those from the untransformed time series.
Eric Gilleland, Melissa Bukovsky, Christopher L. Williams, Seth McGinnis, Caspar M. Ammann, Barbara G. Brown, and Linda O. Mearns
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 137–153, https://doi.org/10.5194/ascmo-2-137-2016, https://doi.org/10.5194/ascmo-2-137-2016, 2016
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Several climate models are evaluated under current climate conditions to determine how well they are able to capture frequencies of severe-storm environments (conditions conducive for the formation of hail storms, tornadoes, etc.). They are found to underpredict the spatial extent of high-frequency areas (such as tornado alley), as well as underpredict the frequencies in the areas.
Whitney K. Huang, Michael L. Stein, David J. McInerney, Shanshan Sun, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, https://doi.org/10.5194/ascmo-2-79-2016, 2016
Sergei N. Rodionov
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 63–78, https://doi.org/10.5194/ascmo-2-63-2016, https://doi.org/10.5194/ascmo-2-63-2016, 2016
David Bolin, Arnoldo Frigessi, Peter Guttorp, Ola Haug, Elisabeth Orskaug, Ida Scheel, and Jonas Wallin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 39–47, https://doi.org/10.5194/ascmo-2-39-2016, https://doi.org/10.5194/ascmo-2-39-2016, 2016
Julie Bessac, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1–16, https://doi.org/10.5194/ascmo-2-1-2016, https://doi.org/10.5194/ascmo-2-1-2016, 2016
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Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes. We also discuss the relative advantages of hidden and observed regime-switching models.
E. M. Schliep, A. E. Gelfand, and D. M. Holland
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 59–74, https://doi.org/10.5194/ascmo-1-59-2015, https://doi.org/10.5194/ascmo-1-59-2015, 2015
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There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the US motivates the need for advanced statistical models to predict air quality metrics. We propose a statistical model that jointly models ground-monitoring station data and satellite-obtained data allowing for temporal and spatial misalignment, missingness, and spatially and temporally varying correlation to enhance prediction of particulate matter.
R. Philbin and M. Jun
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 29–44, https://doi.org/10.5194/ascmo-1-29-2015, https://doi.org/10.5194/ascmo-1-29-2015, 2015
T. K. Doan, J. Haslett, and A. C. Parnell
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 15–27, https://doi.org/10.5194/ascmo-1-15-2015, https://doi.org/10.5194/ascmo-1-15-2015, 2015
W. B. Leeds, E. J. Moyer, and M. L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 1–14, https://doi.org/10.5194/ascmo-1-1-2015, https://doi.org/10.5194/ascmo-1-1-2015, 2015
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...
