Articles | Volume 27, issue 13
https://doi.org/10.5194/hess-27-2479-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-2479-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
10 Jul 2023
Review article |
| 10 Jul 2023
| 10 Jul 2023
Floods and droughts: a multivariate perspective
Manuela Irene Brunner
CORRESPONDING AUTHOR
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland
Invited contribution by Manuela Irene Brunner, recipient of the Division Outstanding Early Career Scientist Award 2022.
Related authors
Jens Oprel, Tobias Jonas, Andreas S. Stordal, Felix N. Matt, Gaute Lappegard, Manuela I. Brunner, and Jan Magnusson
EGUsphere, https://doi.org/10.5194/egusphere-2026-5026, https://doi.org/10.5194/egusphere-2026-5026, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Snow models can be improved by adjusting snowfall inputs to match high-quality snow depth observations from airborne lidar scans. Existing methods cannot reliably adjust these inputs, especially in regions where melt occurred before the observations. By accounting for such losses, our new loss-compensated method provides a reliable way to integrate snow observations into models. Furthermore, this approach can also improve results in years without observations, using data from previous years.
Jonas Götte, Paul C. Astagneau, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 30, 6235–6248, https://doi.org/10.5194/hess-30-6235-2026, https://doi.org/10.5194/hess-30-6235-2026, 2026
Short summary
Short summary
While the effect of water bodies on flood peaks at different time resolutions has been demonstrated in the past, it remains unclear how they affect the ratio between daily and hourly peaks. Our results show that (1) hourly flows are dampened much more strongly than daily flows, which leads to similar daily and hourly flood peaks downstream of reservoirs; and (2) the attenuation effect is particularly pronounced in catchments that are heavily influenced by water bodies.
Joren Janzing and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2026-3487, https://doi.org/10.5194/egusphere-2026-3487, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Large streamflow droughts seem to be related to spatially more homogeneous drivers than smaller droughts. Mechanisms causing this difference are not well understood. We bridge the scale gap between large and small droughts using observations and a drought typology. We find that large droughts are characterized by one or two drought types and each type has its own spatial footprint. Common drought types in a river do not always lead to large droughts, which can be important for water managers.
Jan P. Bohl, Raul R. Wood, Corinna Frank, Paul C. Astagneau, Jonas Peters, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 30, 4667–4698, https://doi.org/10.5194/hess-30-4667-2026, https://doi.org/10.5194/hess-30-4667-2026, 2026
Short summary
Short summary
To assess climate impacts on streamflow, we need models that can predict streamflow under future conditions. This study compares three model types: data-driven (LSTM - long short-term memory), conceptual (HBV - Hydrologiska Byråns Vattenbalansavdelning), and hybrid (LSTM-HBV). LSTMs perform best overall, but HBV and hybrid models generalize better to warmer climates. Hybrid models are a promising tool for climate impact assessments, combining LSTMs accuracy with better generalizability of traditional models. In snowy regions, all models struggle to generalize.
Rashid Akbary, Eleonora Dallan, Paul C. Astagneau, Raul R. Wood, Francesco Marra, Manuela I. Brunner, and Marco Borga
Hydrol. Earth Syst. Sci., 30, 4117–4139, https://doi.org/10.5194/hess-30-4117-2026, https://doi.org/10.5194/hess-30-4117-2026, 2026
Short summary
Short summary
Heavy short rain can trigger flash floods and debris flows. In this study we evaluated how well climate models reproduce these events in Switzerland. We compared finer and coarser resolution models with high-quality hourly precipitation observations across small to large areas. The finer models better captured where short, intense precipitation occurs, but their errors changed with area size. Flood risk studies should therefore account for these scale-related errors.
Emma Ford, Manuela I. Brunner, Hannah Christensen, and Louise Slater
Hydrol. Earth Syst. Sci., 30, 2135–2160, https://doi.org/10.5194/hess-30-2135-2026, https://doi.org/10.5194/hess-30-2135-2026, 2026
Short summary
Short summary
This study aims to improve prediction and understanding of extreme flood events in near-natural catchments across the United Kingdom. We develop a machine learning framework to assess the contribution of different features to flood magnitude estimation. We find weather patterns are weak predictors and stress the importance of evaluating model performance across and within catchments.
Sadaf Nasreen, Oldrich Rakovec, Rohini Kumar, Manuela I. Brunner, Ujjwal Singh, Petr Maca, Yannis Markonis, and Martin Hanel
EGUsphere, https://doi.org/10.5194/egusphere-2026-973, https://doi.org/10.5194/egusphere-2026-973, 2026
Short summary
Short summary
European droughts threaten water and agriculture, but how distinct drought processes will change under warming is uncertain. We examine seven mechanisms across Europe using 1971 to 2000 observations and 2070 to 2099 projections. Changes are regional: the Mediterranean shifts to longer, more severe droughts, while Northern and Western Central Europe often improve. Temperature-driven mechanisms, especially rain to snow transitions, respond most, guiding targeted adaptation.
Eduardo Muñoz-Castro, Bailey J. Anderson, Paul C. Astagneau, Daniel L. Swain, Pablo A. Mendoza, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 30, 825–848, https://doi.org/10.5194/hess-30-825-2026, https://doi.org/10.5194/hess-30-825-2026, 2026
Short summary
Short summary
Flood impacts can be enhanced when they occur after droughts, yet the effectiveness of hydrological models in simulating these events remains unclear. Here, we calibrated four conceptual hydrological models across 63 catchments in Chile and Switzerland to assess their ability to detect streamflow extremes and their transitions. We show that drought-to-flood transitions are generally poorly captured, especially in semi-arid high-mountain catchments than in humid low-elevation ones.
Joren Janzing, Niko Wanders, Marit van Tiel, Barry van Jaarsveld, Dirk N. Karger, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 29, 7041–7071, https://doi.org/10.5194/hess-29-7041-2025, https://doi.org/10.5194/hess-29-7041-2025, 2025
Short summary
Short summary
Process representation in hyper-resolution large-scale hydrological models (LHMs) limits model performance, particularly in mountain regions. Here, we update mountain process representation in an LHM and compare different meteorological forcing products. Structural and parametric changes in snow, glacier, and soil processes improve discharge simulations, while meteorological forcing remains a major control on model performance. Our work can guide future development of LHMs.
Bailey J. Anderson, Eduardo Muñoz-Castro, Lena M. Tallaksen, Alessia Matano, Jonas Götte, Rachael Armitage, Eugene Magee, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 29, 6069–6092, https://doi.org/10.5194/hess-29-6069-2025, https://doi.org/10.5194/hess-29-6069-2025, 2025
Short summary
Short summary
When floods happen during or shortly after droughts, the impacts of each of the events can be magnified. In hydrological research, defining these events represents a challenging and important task in the process of understanding where and why they occur. We have used real-word examples to address some of these challenges and show different approaches influence outcomes. We make suggestions on when to use which approach and outline some pitfalls of which researchers should be aware.
Paul C. Astagneau, Raul R. Wood, Mathieu Vrac, Sven Kotlarski, Pradeebane Vaittinada Ayar, Bastien François, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 29, 5695–5718, https://doi.org/10.5194/hess-29-5695-2025, https://doi.org/10.5194/hess-29-5695-2025, 2025
Short summary
Short summary
To study floods and droughts that are likely to change in the future, we use climate projections from climate models. However, we first need to adjust the systematic biases of these projections at the catchment scale before using them in hydrological models. Our study compares statistical methods that can adjust these biases but specifically for climate projections that enable a quantification of internal climate variability. We provide recommendations on the most appropriate methods.
Raul R. Wood, Joren Janzing, Amber van Hamel, Jonas Götte, Dominik L. Schumacher, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 29, 4153–4178, https://doi.org/10.5194/hess-29-4153-2025, https://doi.org/10.5194/hess-29-4153-2025, 2025
Short summary
Short summary
Continuous and high-quality meteorological datasets are crucial to study extreme hydro-climatic events. We here conduct a comprehensive spatio-temporal evaluation of precipitation and temperature for four climate reanalysis datasets, focusing on mean and extreme metrics, variability, trends, and the representation of droughts and floods over Switzerland. Our analysis shows that all datasets have some merit when limitations are considered, and that one dataset performs better than the others.
Amber van Hamel, Peter Molnar, Joren Janzing, and Manuela Irene Brunner
Hydrol. Earth Syst. Sci., 29, 2975–2995, https://doi.org/10.5194/hess-29-2975-2025, https://doi.org/10.5194/hess-29-2975-2025, 2025
Short summary
Short summary
Suspended sediment is a natural component of rivers, but extreme suspended sediment concentrations (SSCs) can have negative impacts on water use and aquatic ecosystems. We identify the main factors influencing the spatial and temporal variability of annual SSC regimes and extreme SSC events. Our analysis shows that different processes are more important for annual SSC regimes than for extreme events and that compound events driven by glacial melt and high-intensity rainfall led to the highest SSCs.
Alessia Matanó, Raed Hamed, Manuela I. Brunner, Marlies H. Barendrecht, and Anne F. Van Loon
Hydrol. Earth Syst. Sci., 29, 2749–2764, https://doi.org/10.5194/hess-29-2749-2025, https://doi.org/10.5194/hess-29-2749-2025, 2025
Short summary
Short summary
Persistent droughts change how rivers respond to rainfall. Our study of over 5000 catchments worldwide found that hydrological and soil moisture droughts decrease river-flow response to rain, especially in arid regions, while vegetation decline slightly increases it. Snow-covered areas are more resilient due to stored water buffering changes. Droughts can also cause long-lasting changes, with short and intense droughts reducing river response to rainfall and prolonged droughts increasing it.
Bailey J. Anderson, Manuela I. Brunner, Louise J. Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 28, 1567–1583, https://doi.org/10.5194/hess-28-1567-2024, https://doi.org/10.5194/hess-28-1567-2024, 2024
Short summary
Short summary
Elasticityrefers to how much the amount of water in a river changes with precipitation. We usually calculate this using average streamflow values; however, the amount of water within rivers is also dependent on stored water sources. Here, we look at how elasticity varies across the streamflow distribution and show that not only do low and high streamflows respond differently to precipitation change, but also these differences vary with water storage availability.
Julia Miller, Andrea Böhnisch, Ralf Ludwig, and Manuela I. Brunner
Nat. Hazards Earth Syst. Sci., 24, 411–428, https://doi.org/10.5194/nhess-24-411-2024, https://doi.org/10.5194/nhess-24-411-2024, 2024
Short summary
Short summary
We assess the impacts of climate change on fire danger for 1980–2099 in different landscapes of central Europe, using the Canadian Forest Fire Weather Index (FWI) as a fire danger indicator. We find that today's 100-year FWI event will occur every 30 years by 2050 and every 10 years by 2099. High fire danger (FWI > 21.3) becomes the mean condition by 2099 under an RCP8.5 scenario. This study highlights the potential for severe fire events in central Europe from a meteorological perspective.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
Short summary
Short summary
CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Manuela Irene Brunner and Philippe Naveau
Hydrol. Earth Syst. Sci., 27, 673–687, https://doi.org/10.5194/hess-27-673-2023, https://doi.org/10.5194/hess-27-673-2023, 2023
Short summary
Short summary
Reservoir regulation affects various streamflow characteristics. Still, information on when water is stored in and released from reservoirs is hardly available. We develop a statistical model to reconstruct reservoir operation signals from observed streamflow time series. By applying this approach to 74 catchments in the Alps, we find that reservoir management varies by catchment elevation and that seasonal redistribution from summer to winter is strongest in high-elevation catchments.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
Short summary
Short summary
Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
Manuela I. Brunner and Louise J. Slater
Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, https://doi.org/10.5194/hess-26-469-2022, 2022
Short summary
Short summary
Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.
Álvaro Ossandón, Manuela I. Brunner, Balaji Rajagopalan, and William Kleiber
Hydrol. Earth Syst. Sci., 26, 149–166, https://doi.org/10.5194/hess-26-149-2022, https://doi.org/10.5194/hess-26-149-2022, 2022
Short summary
Short summary
Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.
Jens Oprel, Tobias Jonas, Andreas S. Stordal, Felix N. Matt, Gaute Lappegard, Manuela I. Brunner, and Jan Magnusson
EGUsphere, https://doi.org/10.5194/egusphere-2026-5026, https://doi.org/10.5194/egusphere-2026-5026, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Snow models can be improved by adjusting snowfall inputs to match high-quality snow depth observations from airborne lidar scans. Existing methods cannot reliably adjust these inputs, especially in regions where melt occurred before the observations. By accounting for such losses, our new loss-compensated method provides a reliable way to integrate snow observations into models. Furthermore, this approach can also improve results in years without observations, using data from previous years.
Jonas Götte, Paul C. Astagneau, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 30, 6235–6248, https://doi.org/10.5194/hess-30-6235-2026, https://doi.org/10.5194/hess-30-6235-2026, 2026
Short summary
Short summary
While the effect of water bodies on flood peaks at different time resolutions has been demonstrated in the past, it remains unclear how they affect the ratio between daily and hourly peaks. Our results show that (1) hourly flows are dampened much more strongly than daily flows, which leads to similar daily and hourly flood peaks downstream of reservoirs; and (2) the attenuation effect is particularly pronounced in catchments that are heavily influenced by water bodies.
Joren Janzing and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2026-3487, https://doi.org/10.5194/egusphere-2026-3487, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Large streamflow droughts seem to be related to spatially more homogeneous drivers than smaller droughts. Mechanisms causing this difference are not well understood. We bridge the scale gap between large and small droughts using observations and a drought typology. We find that large droughts are characterized by one or two drought types and each type has its own spatial footprint. Common drought types in a river do not always lead to large droughts, which can be important for water managers.
Jan P. Bohl, Raul R. Wood, Corinna Frank, Paul C. Astagneau, Jonas Peters, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 30, 4667–4698, https://doi.org/10.5194/hess-30-4667-2026, https://doi.org/10.5194/hess-30-4667-2026, 2026
Short summary
Short summary
To assess climate impacts on streamflow, we need models that can predict streamflow under future conditions. This study compares three model types: data-driven (LSTM - long short-term memory), conceptual (HBV - Hydrologiska Byråns Vattenbalansavdelning), and hybrid (LSTM-HBV). LSTMs perform best overall, but HBV and hybrid models generalize better to warmer climates. Hybrid models are a promising tool for climate impact assessments, combining LSTMs accuracy with better generalizability of traditional models. In snowy regions, all models struggle to generalize.
Rashid Akbary, Eleonora Dallan, Paul C. Astagneau, Raul R. Wood, Francesco Marra, Manuela I. Brunner, and Marco Borga
Hydrol. Earth Syst. Sci., 30, 4117–4139, https://doi.org/10.5194/hess-30-4117-2026, https://doi.org/10.5194/hess-30-4117-2026, 2026
Short summary
Short summary
Heavy short rain can trigger flash floods and debris flows. In this study we evaluated how well climate models reproduce these events in Switzerland. We compared finer and coarser resolution models with high-quality hourly precipitation observations across small to large areas. The finer models better captured where short, intense precipitation occurs, but their errors changed with area size. Flood risk studies should therefore account for these scale-related errors.
Emma Ford, Manuela I. Brunner, Hannah Christensen, and Louise Slater
Hydrol. Earth Syst. Sci., 30, 2135–2160, https://doi.org/10.5194/hess-30-2135-2026, https://doi.org/10.5194/hess-30-2135-2026, 2026
Short summary
Short summary
This study aims to improve prediction and understanding of extreme flood events in near-natural catchments across the United Kingdom. We develop a machine learning framework to assess the contribution of different features to flood magnitude estimation. We find weather patterns are weak predictors and stress the importance of evaluating model performance across and within catchments.
Sadaf Nasreen, Oldrich Rakovec, Rohini Kumar, Manuela I. Brunner, Ujjwal Singh, Petr Maca, Yannis Markonis, and Martin Hanel
EGUsphere, https://doi.org/10.5194/egusphere-2026-973, https://doi.org/10.5194/egusphere-2026-973, 2026
Short summary
Short summary
European droughts threaten water and agriculture, but how distinct drought processes will change under warming is uncertain. We examine seven mechanisms across Europe using 1971 to 2000 observations and 2070 to 2099 projections. Changes are regional: the Mediterranean shifts to longer, more severe droughts, while Northern and Western Central Europe often improve. Temperature-driven mechanisms, especially rain to snow transitions, respond most, guiding targeted adaptation.
Eduardo Muñoz-Castro, Bailey J. Anderson, Paul C. Astagneau, Daniel L. Swain, Pablo A. Mendoza, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 30, 825–848, https://doi.org/10.5194/hess-30-825-2026, https://doi.org/10.5194/hess-30-825-2026, 2026
Short summary
Short summary
Flood impacts can be enhanced when they occur after droughts, yet the effectiveness of hydrological models in simulating these events remains unclear. Here, we calibrated four conceptual hydrological models across 63 catchments in Chile and Switzerland to assess their ability to detect streamflow extremes and their transitions. We show that drought-to-flood transitions are generally poorly captured, especially in semi-arid high-mountain catchments than in humid low-elevation ones.
Joren Janzing, Niko Wanders, Marit van Tiel, Barry van Jaarsveld, Dirk N. Karger, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 29, 7041–7071, https://doi.org/10.5194/hess-29-7041-2025, https://doi.org/10.5194/hess-29-7041-2025, 2025
Short summary
Short summary
Process representation in hyper-resolution large-scale hydrological models (LHMs) limits model performance, particularly in mountain regions. Here, we update mountain process representation in an LHM and compare different meteorological forcing products. Structural and parametric changes in snow, glacier, and soil processes improve discharge simulations, while meteorological forcing remains a major control on model performance. Our work can guide future development of LHMs.
Bailey J. Anderson, Eduardo Muñoz-Castro, Lena M. Tallaksen, Alessia Matano, Jonas Götte, Rachael Armitage, Eugene Magee, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 29, 6069–6092, https://doi.org/10.5194/hess-29-6069-2025, https://doi.org/10.5194/hess-29-6069-2025, 2025
Short summary
Short summary
When floods happen during or shortly after droughts, the impacts of each of the events can be magnified. In hydrological research, defining these events represents a challenging and important task in the process of understanding where and why they occur. We have used real-word examples to address some of these challenges and show different approaches influence outcomes. We make suggestions on when to use which approach and outline some pitfalls of which researchers should be aware.
Paul C. Astagneau, Raul R. Wood, Mathieu Vrac, Sven Kotlarski, Pradeebane Vaittinada Ayar, Bastien François, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 29, 5695–5718, https://doi.org/10.5194/hess-29-5695-2025, https://doi.org/10.5194/hess-29-5695-2025, 2025
Short summary
Short summary
To study floods and droughts that are likely to change in the future, we use climate projections from climate models. However, we first need to adjust the systematic biases of these projections at the catchment scale before using them in hydrological models. Our study compares statistical methods that can adjust these biases but specifically for climate projections that enable a quantification of internal climate variability. We provide recommendations on the most appropriate methods.
Raul R. Wood, Joren Janzing, Amber van Hamel, Jonas Götte, Dominik L. Schumacher, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 29, 4153–4178, https://doi.org/10.5194/hess-29-4153-2025, https://doi.org/10.5194/hess-29-4153-2025, 2025
Short summary
Short summary
Continuous and high-quality meteorological datasets are crucial to study extreme hydro-climatic events. We here conduct a comprehensive spatio-temporal evaluation of precipitation and temperature for four climate reanalysis datasets, focusing on mean and extreme metrics, variability, trends, and the representation of droughts and floods over Switzerland. Our analysis shows that all datasets have some merit when limitations are considered, and that one dataset performs better than the others.
Amber van Hamel, Peter Molnar, Joren Janzing, and Manuela Irene Brunner
Hydrol. Earth Syst. Sci., 29, 2975–2995, https://doi.org/10.5194/hess-29-2975-2025, https://doi.org/10.5194/hess-29-2975-2025, 2025
Short summary
Short summary
Suspended sediment is a natural component of rivers, but extreme suspended sediment concentrations (SSCs) can have negative impacts on water use and aquatic ecosystems. We identify the main factors influencing the spatial and temporal variability of annual SSC regimes and extreme SSC events. Our analysis shows that different processes are more important for annual SSC regimes than for extreme events and that compound events driven by glacial melt and high-intensity rainfall led to the highest SSCs.
Alessia Matanó, Raed Hamed, Manuela I. Brunner, Marlies H. Barendrecht, and Anne F. Van Loon
Hydrol. Earth Syst. Sci., 29, 2749–2764, https://doi.org/10.5194/hess-29-2749-2025, https://doi.org/10.5194/hess-29-2749-2025, 2025
Short summary
Short summary
Persistent droughts change how rivers respond to rainfall. Our study of over 5000 catchments worldwide found that hydrological and soil moisture droughts decrease river-flow response to rain, especially in arid regions, while vegetation decline slightly increases it. Snow-covered areas are more resilient due to stored water buffering changes. Droughts can also cause long-lasting changes, with short and intense droughts reducing river response to rainfall and prolonged droughts increasing it.
Bailey J. Anderson, Manuela I. Brunner, Louise J. Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 28, 1567–1583, https://doi.org/10.5194/hess-28-1567-2024, https://doi.org/10.5194/hess-28-1567-2024, 2024
Short summary
Short summary
Elasticityrefers to how much the amount of water in a river changes with precipitation. We usually calculate this using average streamflow values; however, the amount of water within rivers is also dependent on stored water sources. Here, we look at how elasticity varies across the streamflow distribution and show that not only do low and high streamflows respond differently to precipitation change, but also these differences vary with water storage availability.
Julia Miller, Andrea Böhnisch, Ralf Ludwig, and Manuela I. Brunner
Nat. Hazards Earth Syst. Sci., 24, 411–428, https://doi.org/10.5194/nhess-24-411-2024, https://doi.org/10.5194/nhess-24-411-2024, 2024
Short summary
Short summary
We assess the impacts of climate change on fire danger for 1980–2099 in different landscapes of central Europe, using the Canadian Forest Fire Weather Index (FWI) as a fire danger indicator. We find that today's 100-year FWI event will occur every 30 years by 2050 and every 10 years by 2099. High fire danger (FWI > 21.3) becomes the mean condition by 2099 under an RCP8.5 scenario. This study highlights the potential for severe fire events in central Europe from a meteorological perspective.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
Short summary
Short summary
CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Manuela Irene Brunner and Philippe Naveau
Hydrol. Earth Syst. Sci., 27, 673–687, https://doi.org/10.5194/hess-27-673-2023, https://doi.org/10.5194/hess-27-673-2023, 2023
Short summary
Short summary
Reservoir regulation affects various streamflow characteristics. Still, information on when water is stored in and released from reservoirs is hardly available. We develop a statistical model to reconstruct reservoir operation signals from observed streamflow time series. By applying this approach to 74 catchments in the Alps, we find that reservoir management varies by catchment elevation and that seasonal redistribution from summer to winter is strongest in high-elevation catchments.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
Short summary
Short summary
Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
Manuela I. Brunner and Louise J. Slater
Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, https://doi.org/10.5194/hess-26-469-2022, 2022
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Short summary
Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.
Álvaro Ossandón, Manuela I. Brunner, Balaji Rajagopalan, and William Kleiber
Hydrol. Earth Syst. Sci., 26, 149–166, https://doi.org/10.5194/hess-26-149-2022, https://doi.org/10.5194/hess-26-149-2022, 2022
Short summary
Short summary
Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.
Cited articles
Abatan, A. A., Gutowski, W. J., Ammann, C. M., Kaatz, L., Brown, B. G., Buja,
L., Bullock, R., Fowler, T., Gilleland, E., and Gotway, J. H.: Multiyear
droughts and pluvials over the upper Colorado River basin and associated
circulations, J. Hydrometeorol., 18, 799–818, https://doi.org/10.1175/JHM-D-16-0125.1, 2017. a
Alvarez-Garreton, C., Pablo Boisier, J., Garreaud, R., Seibert, J., and Vis,
M.: Progressive water deficits during multiyear droughts in basins with long
hydrological memory in Chile, Hydrol. Earth Syst. Sci., 25, 429–446, https://doi.org/10.5194/hess-25-429-2021, 2021. a
Andreadis, K. M., Clark, E. A., Wood, A. W., Hamlet, A. F., and Lettenmaier,
D. P.: Twentieth-Century drought in the conterminous United States, J. Hydrometeorol., 6, 985–1001, 2005. a
Ansari, R. and Grossi, G.: Spatio-temporal evolution of wet–dry event
features and their transition across the Upper Jhelum Basin (UJB) in South Asia, Nat. Hazards Earth Syst. Sci., 22, 287–302,
https://doi.org/10.5194/nhess-22-287-2022, 2022. a
Bales, R. C., Goulden, M. L., Hunsaker, C. T., Conklin, M. H., Hartsough,
P. C., O'Geen, A. T., Hopmans, J. W., and Safeeq, M.: Mechanisms controlling
the impact of multi-year drought on mountain hydrology, Sci. Rep., 8, 1–8, https://doi.org/10.1038/s41598-017-19007-0, 2018. a
Bárdossy, A.: Copula-based geostatistical models for groundwater quality
parameters, Water Resour. Res., 42, 1–12, https://doi.org/10.1029/2005WR004754, 2006. a
Barker, L. J., Hannaford, J., Parry, S., Smith, K. A., Tanguy, M., and
Prudhomme, C.: Historic hydrological droughts 1891–2015: Systematic
characterisation for a diverse set of catchments across the UK, Hydrol. Earth Syst. Sci., 23, 4583–4602, https://doi.org/10.5194/hess-23-4583-2019, 2019. a
Becker, A., Junghänel, T., Hafer, M., Köcher, A., Rustemeier, E., Weigl, E.,
and Wittich, K.-P.: Erste hydro-klimatologische Einordnung der Starkregen
und Dauerregen in Deutschland zum Ende eines sehr nassen Juli 2017,
Tech. rep., Deutscher Wetterdienst, https://docplayer.org/52950805-Erste-hydro-klimatologische-einordnung-der
(last access: 15 January 2023), 2017. a
Bedford, T. and Cooke, R. M.: Vines–a new graphical model for dependent random variables, Ann. Stat., 30, 1031–1068, https://doi.org/10.1214/aos/1031689016, 2002. a, b
Ben Aissia, M.-A., Chebana, F., Ouarda, T., Roy, L., Bruneau, P., and Barbet,
M.: Dependence evolution of hydrological characteristics, applied to floods
in a climate change context in Quebec, J. Hydrol., 519, 148–163,
https://doi.org/10.1016/j.jhydrol.2014.06.042, 2014. a
Bender, J., Wahl, T., and Jensen, J.: Multivariate design in the presence of
non-stationarity, J. Hydrol., 514, 123–130, https://doi.org/10.1016/j.jhydrol.2014.04.017, 2014. a
Berghuijs, W. R., Allen, S. T., Harrigan, S., and Kirchner, J. W.: Growing
spatial scales of synchronous river flooding in Europe, Geophys. Res. Lett., 46, 1423–1428, https://doi.org/10.1029/2018GL081883, 2019. a, b, c, d
Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M., and Vrac, M.:
Multivariate statistical modelling of compound events via pair-copula
constructions: Analysis of floods in Ravenna (Italy), Hydrol. Earth Syst. Sci., 21, 2701–2723, https://doi.org/10.5194/hess-21-2701-2017, 2017.
a
Blöschl, G., Kiss, A., Viglione, A., Barriendos, M., and Böhm, O.: Current European flood-rich period exceptional compared with past 500 years,
Nature, 583, 560–566, https://doi.org/10.1038/s41586-020-2478-3, 2020. a
Blum, A. G., Ferraro, P. J., Archfield, S. A., and Ryberg, K. R.: Causal effect of impervious cover on annual flood magnitude for the United States, Geophys. Res. Lett., 47, e2019GL086480, https://doi.org/10.1029/2019GL086480, 2020. a
Brönnimann, S., Stucki, P., Franke, J., Valler, V., Brugnara, Y., Hand, R., Slivinski, L. C., Compo, G. P., Sardeshmukh, P. D., Lang, M., and Schaefli, B.: Influence of warming and atmospheric circulation changes on multidecadal European flood variability, Clim. Past, 18, 919–933,
https://doi.org/10.5194/cp-18-919-2022, 2022. a
Brunner, M. I.: Reservoir regulation affects droughts and floods at local and
regional scales, Environ. Res. Lett., 16, 124016, https://doi.org/10.1088/1748-9326/ac36f6, 2021. a, b
Brunner, M. I. and Dougherty, E. M.: Varying importance of storm types and
antecedent conditions for local and regional floods, Water Resour. Res., 58, e2022WR033249, https://doi.org/10.1029/2022WR033249, 2022. a, b
Brunner, M. I. and Fischer, S.: Snow-influenced floods are more strongly
connected in space than purely rainfall-driven floods, Environ. Res. Lett., 17, 104038, https://doi.org/10.1088/1748-9326/ac948f, 2022. a, b
Brunner, M. I. and Gilleland, E.: Stochastic simulation of streamflow and
spatial extremes: a continuous, wavelet-based approach, Hydrol. Earth Syst. Sci., 24, 3967–3982, https://doi.org/10.5194/hess-24-3967-2020, 2020. a
Brunner, M. I. and Gilleland, E.: Complex high- and low-flow networks differ in their spatial correlation characteristics, drivers, and changes, Water
Resour. Res., 57, e2021WR030049, https://doi.org/10.1029/2021WR030049, 2021. a, b, c
Brunner, M. I. and Sikorska, A. E.: Dependence of flood peaks and volumes in
modeled runoff time series: effect of data disaggregation and distribution,
J. Hydrol., 572, 620–629, https://doi.org/10.1016/j.jhydrol.2019.03.024, 2018. a
Brunner, M. I. and Slater, L. J.: Extreme floods in Europe: going beyond
observations using reforecast ensemble pooling, Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, 2022. a
Brunner, M. I. and Stahl, K.: Temporal hydrological drought clustering varies
with climate and land-surface processes, Environ. Res. Lett., 18, 034011, https://doi.org/10.1088/1748-9326/acb8ca, 2023. a, b
Brunner, M. I. and Tallaksen, L. M.: Proneness of European catchments to
multiyear streamflow droughts, Water Resour. Res., 55, 8881–8894,
https://doi.org/10.1029/2019WR025903, 2019. a, b, c, d
Brunner, M. I., Seibert, J., and Favre, A.-C.: Bivariate return periods and
their importance for flood peak and volume estimation, Wire's Water, 3, 819–833, https://doi.org/10.1002/wat2.1173, 2016. a, b, c
Brunner, M. I., Viviroli, D., Sikorska, A. E., Vannier, O., Favre, A.-C., and
Seibert, J.: Flood type specific construction of synthetic design hydrographs, Water Resour. Res., 53, 1–17, https://doi.org/10.1002/2016WR019535, 2017. a, b
Brunner, M. I., Furrer, R., Sikorska, A. E., Viviroli, D., Seibert, J., and
Favre, A.-C.: Synthetic design hydrographs for ungauged catchments: A
comparison of regionalization methods, Stoch. Environ. Res. Risk A., 32, 1993–2023, https://doi.org/10.1007/s00477-018-1523-3, 2018. a
Brunner, M. I., Bárdossy, A., and Furrer, R.: Technical note: Stochastic
simulation of streamflow time series using phase randomization, Hydrol. Earth Syst. Sci., 23, 3175–3187, https://doi.org/10.5194/hess-23-3175-2019, 2019a. a
Brunner, M. I., Furrer, R., and Favre, A.-C.: Modeling the spatial dependence
of floods using the Fisher copula, Hydrol. Earth Syst. Sci., 23, 107–124, https://doi.org/10.5194/hess-23-107-2019, 2019b. a, b
Brunner, M. I., Hingray, B., Zappa, M., and Favre, A. C.: Future trends in the interdependence between flood peaks and volumes: Hydro-climatological
drivers and uncertainty, Water Resour. Res., 55, 1–15, https://doi.org/10.1029/2019WR024701, 2019c. a, b
Brunner, M. I., Liechti, K., and Zappa, M.: Extremeness of recent drought
events in Switzerland: Dependence on variable and return period choice, Nat. Hazards and Earth Syst. Sci., 19, 2311–2323, https://doi.org/10.5194/nhess-19-2311-2019, 2019d. a, b
Brunner, M. I., Gilleland, E., Wood, A., Swain, D. L., and Clark, M.: Spatial
dependence of floods shaped by spatiotemporal variations in meteorological
and land-surface processes, Geophys. Res. Lett., 47, e2020GL088000, https://doi.org/10.1029/2020GL088000, 2020a. a, b, c
Brunner, M. I., Papalexiou, S., Clark, M. P., and Gilleland, E.: How probable
is widespread flooding in the United States?, Water Resour. Res., 56, e2020WR028096, https://doi.org/10.1029/2020WR028096, 2020b. a, b, c
Brunner, M. I., Melsen, L. A., Wood, A. W., Rakovec, O., Mizukami, N., Knoben, W. J. M., and Clark, M. P.: Flood spatial coherence, triggers and performance in hydrological simulations: large-sample evaluation of four
streamflow-calibrated models, Hydrol. Earth Syst. Sci., 25, 105–119, https://doi.org/10.5194/hess-25-105-2021, 2021a. a
Brunner, M. I., Swain, D. L., Gilleland, E., and Wood, A.: Increasing
importance of temperature as a driver of streamflow drought spatial extent,
Environ. Res. Lett., 16, 024038, https://doi.org/10.1088/1748-9326/abd2f0, 2021b. a, b, c, d
Brunner, M. I., Swain, D. L., Wood, R. R., Willkofer, F., Done, J. M.,
Gilleland, E., and Ludwig, R.: An extremeness threshold determines the
regional response of floods to changes in rainfall extremes, Commun. Earth Environ., 2, 173, https://doi.org/10.1038/s43247-021-00248-x, 2021c. a
Bunde, A., Büntgen, U., Ludescher, J., Luterbacher, J., and Von Storch, H.: Is there memory in precipitation?, Nat. Clim. Change, 3, 174–175,
https://doi.org/10.1038/nclimate1830, 2013. a
Chen, L. and Ford, T. W.: Future changes in the transitions of monthly-to-seasonal precipitation extremes over the Midwest in Coupled
Model Intercomparison Project Phase 6 models, Int. J. Climatol., 43, 255–274, https://doi.org/10.1002/joc.7756, 2023. a, b, c
Coles, S.: An introduction to statistical modeling of extreme values, Springer, London, https://doi.org/10.1007/978-1-4471-3675-0, 2001. a, b, c
Coles, S., Heffernan, J., and Tawn, J.: Dependence measures for extreme value
analyses, Extremes, 2, 339–365, https://doi.org/10.1023/A:1009963131610, 1999. a
Cowling, A., Hall, P., and Phillips, M. J.: Bootstrap confidence regions for
the intensity of a Poisson point process, J. Am. Stat. Assoc., 91, 1516–1524, 1996. a
Davison, A. C., Padoan, S. A., and Ribatet, M.: Statistical modeling of spatial extremes, Stat. Sci., 27, 161–186, https://doi.org/10.1214/11-STS376, 2012. a, b
De Luca, P., Messori, G., Wilby, R. L., Mazzoleni, M., and Di Baldassarre, G.: Concurrent wet and dry hydrological extremes at the global scale, Earth
Syst. Dynam., 11, 251–266, https://doi.org/10.5194/esd-11-251-2020, 2020. a, b
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate
change projections: The role of internal variability, Clim. Dynam., 38,
527–546, https://doi.org/10.1007/s00382-010-0977-x, 2012. a
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio,
P. N., Fiore, A., Frankignoul, C., Fyfe, J. C., Horton, D. E., Kay, J. E.,
Knutti, R., Lovenduski, N. S., Marotzke, J., McKinnon, K. A., Minobe, S.,
Randerson, J., Screen, J. A., Simpson, I. R., and Ting, M.: Insights from
earth system model initial-condition large ensembles and future prospects,
Nat. Clim. Change, 10, 277–286, https://doi.org/10.1038/s41558-020-0731-2, 2020. a
Di Baldassarre, G., Martinez, F., Kalantari, Z., and Viglione, A.: Drought and flood in the Anthropocene: Feedback mechanisms in reservoir operation,
Earth Syst. Dynam., 8, 225–233, https://doi.org/10.5194/esd-8-225-2017, 2017. a
Diederen, D., Liu, Y., Gouldby, B., Diermanse, F., and Vorogushyn, S.:
Stochastic generation of spatially coherent river discharge peaks for
continental event-based flood risk assessment, Nat. Hazards Earth Syst. Sci., 19, 1041–1053, https://doi.org/10.5194/nhess-19-1041-2019, 2019. a
Diffenbaugh, N. S., Swain, D. L., Touma, D., and Lubchenco, J.: Anthropogenic
warming has increased drought risk in California, P. Natl. Acad. Sci. USA, 112, 3931–3936, https://doi.org/10.1073/pnas.1422385112, 2015. a
Dixon, P. M.: Ripley's K Function, in: Wiley StatsRef: Statistics Reference Online, edited by: Balakrishnan, N., Colton, T., Everitt, B., Piegorsch, W., Ruggeri, F., and Teugels, J. L., Wiley, https://doi.org/10.1002/9781118445112.stat07751, 2013. a
Edwards, A. L.: An introduction to linear regression and correlation, in: 1st Edn., W. H. Freeman, San Francisco, ISBN 10:0716705613, 1976. a
Espinoza, J. C., Ronchail, J., Guyot, J. L., Junquas, C., Drapeau, G.,
Martinez, J. M., Santini, W., Vauchel, P., Lavado, W., Ordoñez, J., and
Espinoza, R.: From drought to flooding: Understanding the abrupt 2010-11
hydrological annual cycle in the Amazonas River and tributaries,
Environ. Res. Lett., 7, 024008, https://doi.org/10.1088/1748-9326/7/2/024008, 2012. a
Favre, A.-C., Quessy, J.-F., and Toupin, M.-H.: The new family of Fisher
copulas to model upper tail dependence and radial asymmetry: properties and
application to high-dimensional rainfall data, Environmetrics, 29, 1–17,
https://doi.org/10.1002/env.2494, 2018. a
Folland, C. K., Hannaford, J., Bloomfield, J. P., Kendon, M., Svensson, C.,
Marchant, B. P., Prior, J., and Wallace, E.: Multi-annual droughts in the
English Lowlands: A review of their characteristics and climate drivers
in the winter half-year, Hydrol. Earth Syst. Sci., 19, 2353–2375,
https://doi.org/10.5194/hess-19-2353-2015, 2015. a
Gaál, L., Szolgay, J., Kohnová, S., Hlavčová, K., Parajka, J., Viglione,
A., Merz, R., and Blöschl, G.: Dependence between flood peaks and volumes: a
case study on climate and hydrological controls, Hydrolog. Sci. J., 60, 968–984, https://doi.org/10.1080/02626667.2014.951361, 2015. a, b, c
Ganguli, P. and Ganguly, A. R.: Space-time trends in U.S. meteorological
droughts, J. Hydrol.: Reg. Stud., 8, 235–259, https://doi.org/10.1016/j.ejrh.2016.09.004, 2016. a
Genest, C., Rémillard, B., and Beaudoin, D.: Goodness-of-fit tests for
copulas: A review and a power study, Insurance: Mathematics and Economics,
44, 199–213, https://doi.org/10.1016/j.insmatheco.2007.10.005, 2009. a
Ghizzoni, T., Roth, G., and Rudari, R.: Multivariate skew-t approach to the
design of accumulation risk scenarios for the flooding hazard, Adv. Water Resour., 33, 1243–1255, https://doi.org/10.1016/j.advwatres.2010.08.003, 2010. a
Ghizzoni, T., Roth, G., and Rudari, R.: Multisite flooding hazard assessment in the Upper Mississippi River, J. Hydrol., 412–413, 101–113,
https://doi.org/10.1016/j.jhydrol.2011.06.004, 2012. a
Goodrich, G. B.: Multidecadal climate variability and drought in the United
States, Geogr. Compass, 1, 713–738, https://doi.org/10.1111/j.1749-8198.2007.00035.x, 2007. a
Gräler, B.: Modelling skewed spatial random fields through the spatial vine copula, Spat. Stat., 10, 87–102, https://doi.org/10.1016/j.spasta.2014.01.001, 2014. a, b, c
Gräler, B., van den Berg, M. J., Vandenberghe, S., Petroselli, A., Grimaldi, S., Baets, B. D., and Verhoest, N. E. C.: Multivariate return periods in hydrology: a critical and practical review on synthetic design hydrograph estimation, Hydrol. Earth Syst. Sci., 17, 1281–1296,
https://doi.org/10.5194/hess-17-1281-2013, 2013. a, b, c, d, e
Gu, X., Zhang, Q., Singh, V. P., Chen, Y. D., and Shi, P.: Temporal clustering of floods and impacts of climate indices in the Tarim River basin, China, Global Planet. Change, 147, 12–24,
https://doi.org/10.1016/j.gloplacha.2016.10.011, 2016. a, b, c
Gumbel, E. J.: The return period of flood flows, Ann. Math. Stat., 12, 163–190, https://doi.org/10.1214/aoms/1177731747, 1941. a
Hamill, T. M., Whitaker, J. S., and Mullen, S. L.: Reforecasts: An important dataset for improving weather predictions, B. Am. Meteorol. Soc., 87, 33–46, https://doi.org/10.1175/BAMS-87-1-33, 2006. a
Hannaford, J., Lloyd-Hughes, B., Keef, C., Parry, S., and Prudhomme, C.:
Examining the large-scale spatial coherence of European drought using regional indicators of precipitation and streamflow deficit, Hydrol.
Process., 25, 1146–1162, https://doi.org/10.1002/hyp.7725, 2010. a
He, X. and Sheffield, J.: Lagged compound occurrence of droughts and pluvials
globally over the past seven decades, Geophys. Res. Lett., 47, e2020GL087924, https://doi.org/10.1029/2020gl087924, 2020. a, b, c
Heffernan, J. E. and Tawn, J.: A conditional approach to modelling multivariate extreme values, J. Roy. Stat. Soc. Ser. B, 66, 497–546,
https://doi.org/10.1111/j.1467-9868.2004.02050.x, 2004. a, b, c
Henriques, A. G. and Santos, M. J. J.: Regional drought distribution model,
Phys. Chem. Earth Pt. B, 24, 19–22, https://doi.org/10.1016/S1464-1909(98)00005-7, 1999. a
Herman, J. D., Reed, P. M., Zeff, H. B., Characklis, G. W., and Lamontagne, J.: Synthetic drought scenario generation to support bottom-up water supply
vulnerability assessments, J. Water Resour. Pl. Manage., 142, 1–13, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000701, 2016. a
Hisdal, H. and Tallaksen, L. M.: Estimation of regional meteorological and
hydrological drought characteristics: A case study for Denmark, J. Hydrol., 281, 230–247, https://doi.org/10.1016/S0022-1694(03)00233-6, 2003. a
Hosking, J. R. M.: Modeling persistence in hydrological time series using
fractional differencing, Water Resour. Res., 20, 1898–1908,
https://doi.org/10.1029/WR020i012p01898, 1984. a
Hurst, H. E.: The problem of long-term storage in reservoirs, Int.
Assoc. Sci. Hydrol. Bull., 1, 13–27, https://doi.org/10.1080/02626665609493644, 1956. a
Ibebuchi, C. C.: Patterns of atmospheric circulation in Western Europe
linked to heavy rainfall in Germany: preliminary analysis into the 2021
heavy rainfall episode, Theor. Appl. Climatol., 148, 269–283,
https://doi.org/10.1007/s00704-022-03945-5, 2022. a
Ionita, M. and Nagavciuc, V.: Changes in drought features at the European
level over the last 120 years, Nat. Hazards Earth Syst. Sci., 21, 1685–1701, https://doi.org/10.5194/nhess-21-1685-2021, 2021. a
Ionita, M., Lohmann, G., Rimbu, N., Chelcea, S., and Dima, M.: Interannual to
decadal summer drought variability over Europe and its relationship to
global sea surface temperature, Clim. Dynam., 38, 363–377,
https://doi.org/10.1007/s00382-011-1028-y, 2012. a
Joe, H.: Dependence modeling with copulas, CRC Press, Taylor & Francis Group, Boca Raton, https://doi.org/10.1201/b17116, 2015. a, b
Keef, C., Svensson, C., and Tawn, J. A.: Spatial dependence in extreme river
flows and precipitation for Great Britain, J. Hydrol., 378, 240–252, https://doi.org/10.1016/j.jhydrol.2009.09.026, 2009. a, b, c
Keef, C., Tawn, J. A., and Lamb, R.: Estimating the probability of widespread
flood events, Environmetrics, 24, 13–21, https://doi.org/10.1002/env.2190, 2013. a, b, c
Kemter, M., Merz, B., Marwan, N., Vorogushyn, S., and Blöschl, G.: Joint
trends in flood magnitudes and spatial extents across Europe, Geophys. Res. Lett., 47, 1–8, https://doi.org/10.1029/2020GL087464, 2020. a
Kendall, M. G.: A new measure of rank correlation, Biometrika, 30, 81,
https://doi.org/10.2307/2332226, 1937. a
Kiran, K. G. and Srinivas, V. V.: Multivariate regional frequency analysis
using conditional extreme values approach, Water Resour. Res., 58, e2021WR031095, https://doi.org/10.1029/2021WR031095, 2022. a
Kiriliouk, A., Rootzén, H., Segers, J., and Wadsworth, J. L.: Peaks over
thresholds modeling with multivariate generalized Pareto distributions,
Technometrics, 61, 123–135, https://doi.org/10.1080/00401706.2018.1462738, 2019. a
Kreienkamp, F., Philip, S. Y., Tradowsky, J. S., Kew, S. F., Lorenz, P.,
Arrighi, J., Belleflamme, A., Bettmann, T., Caluwaerts, S., Chan, S. C.,
Ciavarella, A., De Cruz, L., de Vries, H., Demuth, N., Ferrone, A., Fischer,
E. M., Fowler, H. J., Goergen, K., Heinrich, D., Henrichs, Y., Lenderink, G.,
Kaspar, F., Nilson, E., L Otto, F. E., Ragone, F., Seneviratne, S. I., Singh, R. K., Skålevåg, A., Termonia, P., Thalheimer, L., van Aalst, M., Van den Bergh, J., Van de Vyver, H., Vannitsem, S., van Oldenborgh, G. J., Van Schaeybroeck, B., Vautard, R., Vonk, D., and Wanders, N.: Rapid attribution of heavy rainfall events leading to the severe flooding in Western Europe during July 2021, Tech. rep., world weather attribution,
https://www.worldweatherattribution.org/wp-content/uploads/Scientific-report-Western-Europe-floods-2021-attribution.pdf
(last access: 15 January 2023), 2021. a, b
Kussul, N., Shelestov, A., and Skakun, S.: Grid system for flood extent
extraction from satellite images, Earth Sci. Inform., 1, 105–117, https://doi.org/10.1007/s12145-008-0014-3, 2008. a, b
Laio, F.: Cramer-von Mises and Anderson-Darling goodness of fit tests for extreme value distributions with unknown parameters, Water Resour. Res., 40, W09308, https://doi.org/10.1029/2004WR003204, 2004. a
Lall, U. and Sharma, A.: A nearest neighbor bootstrap for resampling hydrologic time series, Water Resour. Res., 32, 679–693, https://doi.org/10.1029/95WR02966, 1996. a
Lancaster, G., Iatsenko, D., Pidde, A., Ticcinelli, V., and Stefanovska, A.:
Surrogate data for hypothesis testing of physical systems, Phys. Rep., 748, 1–60, https://doi.org/10.1016/j.physrep.2018.06.001, 2018. a
Le, P. D., Leonard, M., and Westra, S.: Modeling spatial dependence of rainfall extremes across multiple durations, Water Resour. Res., 54, 2233–2248, https://doi.org/10.1002/2017WR022231, 2018. a
Lee, D. and Joe, H.: Multivariate extreme value copulas with factor and tree
dependence structures, Extremes, 21, 147–176, https://doi.org/10.1007/s10687-017-0298-0, 2017. a
Lee, T., Modarres, R., and Ouarda, T. B.: Data-based analysis of bivariate
copula tail dependence for drought duration and severity, Hydrol. Process., 27, 1454–1463, https://doi.org/10.1002/hyp.9233, 2013. a
Liu, J. and Zhang, Y.: Multi-temporal clustering of continental floods and
associated atmospheric circulations, J. Hydrol., 555, 744–759,
https://doi.org/10.1016/j.jhydrol.2017.10.072, 2017. a, b
Liu, J., Jia, J., Yang, Y., Tang, M., Xue, Y., and Lu, H.: Risk assessment for drought-flood abrupt alternation in the Pearl River Basin, China, IOP Conf. Ser.: Mater. Sci. Eng., 452, 022029, https://doi.org/10.1088/1757-899X/452/2/022029, 2018. a
Lun, D., Fischer, S., Viglione, A., and Blöschl, G.: Detecting flood-rich and
flood-poor periods in annual peak discharges across Europe, Water Resour.
Res., 56, e2019WR026575, https://doi.org/10.1029/2019WR026575, 2020. a, b, c
Mandelbrot, B. B.: Une classe de processus stochastiques homothetiques a soi:
Application a la loi climatologique de H. E. Hurst, Comptes rendus de
l'Académie des sciences, 260, 3274–3276, 1965. a
Mandelbrot, B. B.: A fast fractional Gaussian noise generator, Water Resour. Res., 7, 543–553, 1971. a
Marsh, T. J., Parry, S., Kendon, M. C., and Hannaford, J.: The 2010–12 drought and subsequent extensive flooding, Centre for Ecology & Hydrology, 54 pp., 2013. a
McCabe, G. J. and Wolock, D. M.: Extensive droughts in the conterminous
United States during multiple centuries, Earth Interact., 26, 84–93,
https://doi.org/10.1175/EI-D-21-0021.1, 2022. a
Mediero, L., Jiménez-Alvarez, A., and Garrote, L.: Design flood hydrographs from the relationship between flood peak and volume, Hydrol. Earth Syst. Sci., 14, 2495–2505, https://doi.org/10.5194/hess-14-2495-2010, 2010. a
Mediero, L., Kjeldsen, T. R., Macdonald, N., Kohnova, S., Merz, B., Vorogushyn, S., Wilson, D., Alburquerque, T., Blöschl, G., Bogdanowicz, E., Castellarin, A., Hall, J., Kobold, M., Kriauciuniene, J., Lang, M., Madsen, H., Onuşluel Gül, G., Perdigão, R. A. P., Roald, L. A., Salinas, J. L.,
Toumazis, A. D., Veijalainen, N., and Orarinsson, O.: Identification of
coherent flood regions across Europe by using the longest streamflow
records, J. Hydrol., 528, 341–360, https://doi.org/10.1016/j.jhydrol.2015.06.016, 2015. a, b, c
Mejia, J. M., Rodriguez‐Iturbe, I., and Dawdy, D. R.: Streamflow simulation:
2. The broken line process as a potential model for hydrologic simulation,
Water Resour. Res., 8, 931–941, https://doi.org/10.1029/WR008i004p00931, 1972. a
Merz, B., Nguyen, V. D., and Vorogushyn, S.: Temporal clustering of floods in
Germany: Do flood-rich and flood-poor periods exist?, J. Hydrol., 541, 824–838, https://doi.org/10.1016/j.jhydrol.2016.07.041, 2016. a, b, c, d
Metin, A. D., Dung, N. V., Schröter, K., Vorogushyn, S., Guse, B., Kreibich, H., and Merz, B.: The role of spatial dependence for large-scale flood risk estimation, Nat. Hazards Earth Syst. Sci., 20, 967–979, https://doi.org/10.5194/nhess-20-967-2020, 2020. a
Meylan, P., Favre, A.-C., and Musy, A.: Predictive hydrology. A frequency
analysis approach, Science Publishers, St. Helier, Jersey, British Channel
Islands, ISBN 9781578087471, 2012. a
Moon, H., Gudmundsson, L., and Seneviratne, S. I.: Drought persistence errors
in global climate models, J. Geophys. Res.-Atmos., 123, 3483–3496, https://doi.org/10.1002/2017JD027577, 2018. a
Moravec, V., Markonis, Y., Rakovec, O., Svoboda, M., Trnka, M., Kumar, R., and Hanel, M.: Europe under multi-year droughts: How severe was the 2014–2018 drought period?, Environ. Res. Lett., 16, 034062, https://doi.org/10.1088/1748-9326/abe828, 2021. a
Moreira, E. E., Martins, D. S., and Pereira, L. S.: Assessing drought cycles in SPI time series using a Fourier analysis, Nat. Hazards Earth Syst. Sci., 15, 571–585, https://doi.org/10.5194/nhess-15-571-2015, 2015. a
Mosley, L. M.: Drought impacts on the water quality of freshwater systems;
review and integration, Earth-Sci. Rev., 140, 203–214,
https://doi.org/10.1016/j.earscirev.2014.11.010, 2015. a
Mudelsee, M., Börngen, M., Tetzlaff, G., and Grünewald, U.: No upward trends in the occurrence of extreme floods in central Europe, Nature, 425,
166–169, https://doi.org/10.1038/nature01928, 2003. a
Neal, J., Keef, C., Bates, P., Beven, K., and Leedal, D.: Probabilistic flood
risk mapping including spatial dependence, Hydrol. Process., 27, 1349–1363, https://doi.org/10.1002/hyp.9572, 2013. a, b, c
Nelsen, R. B.: An introduction to copulas, Springer Science & Business Media, New York, https://doi.org/10.1007/0-387-28678-0, 2006. a, b
Noone, S., Broderick, C., Duffy, C., Matthews, T., Wilby, R. L., and Murphy,
C.: A 250-year drought catalogue for the island of Ireland (1765–2015),
Int. J. Climatol., 37, 239–254, https://doi.org/10.1002/joc.4999, 2017. a
Noorisameleh, Z., Gough, W. A., Monirul, . M., and Mirza, Q.: Persistence and
spatial-temporal variability of drought severity in Iran, Environ. Sci. Pollut. Res., 28, 48808–48822, https://doi.org/10.1007/s11356-021-14100-4, 2021. a
Oesting, M. and Stein, A.: Spatial modeling of drought events using max-stable processes, Stoch. Environ. Res. Risk A., 32, 63–81,
https://doi.org/10.1007/s00477-017-1406-z, 2018. a
Papalexiou, S. M.: Unified theory for stochastic modelling of hydroclimatic
processes: Preserving marginal distributions, correlation structures, and
intermittency, Adv. Water Resour., 115, 234–252, https://doi.org/10.1016/j.advwatres.2018.02.013, 2018. a
Parry, S., Hannaford, J., Lloyd-Hughes, B., and Prudhomme, C.: Multi-year
droughts in Europe: analysis of development and causes, Hydrol. Res., 43, 689–706, https://doi.org/10.2166/nh.2012.024, 2012. a
Parry, S., Wilby, L. R., Prudhomme, C., and Wood, J. P.: A systematic assessment of drought termination in the United Kingdom, Hydrol. Earth Syst. Sci., 20, 4265–4281, https://doi.org/10.5194/hess-20-4265-2016, 2016. a
Perez Arango, J. D., Lintner, B. R., Carvalho, L. M., and Lyon, B.: Spatial
extents of tropical droughts during El Niño in current and future
climate in observations, reanalysis, and CMIP5 models, Geophys. Res. Lett., 48, e2021GL093701, https://doi.org/10.1029/2021GL093701, 2021. a
Prichard, D. and Theiler, J.: Generating surrogate data for time series with
several simultaneously measured variables, Phys. Rev. Lett., 73, 951–954, 1994. a
Pulley, S., Foster, I., and Antunes, P.: The dynamics of sediment-associated
contaminants over a transition from drought to multiple flood events in a
lowland UK catchment, Hydrol. Process., 30, 704–719, https://doi.org/10.1002/hyp.10616, 2016. a
Quessy, J. F., Rivest, L. P., and Toupin, M. H.: On the family of multivariate chi-square copulas, J. Multivar. Anal., 152, 40–60,
https://doi.org/10.1016/j.jmva.2016.07.007, 2016. a
Radziejewski, M., Bardossy, A., and Kundzewicz, Z.: Detection of change in
river flow using phase randomization, Hydrolog. Sci. J., 45, 547–558, https://doi.org/10.1080/02626660009492356, 2000. a
Rahimi, L., Deidda, C., and De Michele, C.: Origin and variability of
statistical dependencies between peak, volume, and duration of rainfall-driven flood events, Sci. Rep., 11, 5182, https://doi.org/10.1038/s41598-021-84664-1, 2021. a
Rakovec, O., Samaniego, L., Hari, V., Markonis, Y., Moravec, V., Thober, S.,
Hanel, M., and Kumar, R.: The 2018–2020 multi-year drought sets a new
benchmark in Europe, Earth's Future, 10, e2021EF002394, https://doi.org/10.1029/2021EF002394, 2022. a
Renard, B. and Lang, M.: Use of a Gaussian copula for multivariate extreme
value analysis: Some case studies in hydrology, Adv. Water Resour., 30, 897–912, 2007. a
Ripley, B. D.: Spatial statistics, John Wiley & Sons, Inc., Hoboken, NJ, USA, https://doi.org/10.1002/0471725218, 1981. a
Rootzén, H. and Tajvidi, N.: Multivariate generalized Pareto distributions, Bernoulli, 12, 917–930, https://doi.org/10.3150/bj/1161614952, 2006. a, b
Rootzén, H., Segers, J., and Wadsworth, J. L.: Multivariate generalized
Pareto distributions: Parametrizations, representations, and properties,
J. Multivar. Anal., 165, 117–131, https://doi.org/10.1016/j.jmva.2017.12.003, 2018. a, b
Rossi, G., Benedini, M., Tsakiris, G., and Giakoumakis, S.: On regional drought estimation and analysis, Water Resour. Manage., 6, 249–277,
https://doi.org/10.1007/BF00872280, 1992. a
Rudd, A. C., Kay, A. L., and Bell, V. A.: National-scale analysis of future
river flow and soil moisture droughts: potential changes in drought
characteristics, Climatic Change, 156, 323–340,
https://doi.org/10.1007/s10584-019-02528-0, 2019. a, b
Rupp, D. E., Chegwidden, O. S., Nijssen, B., and Clark, M. P.: Changing river
network synchrony modulates projected increases in high flows, Water Resour. Res., 57, e2020WR028713, https://doi.org/10.1029/2020WR028713, 2021. a
Salas, J. D. and Lee, T.: Nonparametric simulation of single-site seasonal
streamflows, J. Hydrol. Eng., 15, 284–296, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000189, 2010. a
Salvadori, G.: Bivariate return periods via 2-copulas, Stat. Methodol., 1, 129–144, 2004. a
Salvadori, G. and De Michele, C.: Multivariate multiparameter extreme value
models and return periods: A copula approach, Water Resour. Res., 46, W10501, https://doi.org/10.1029/2009WR009040, 2010. a
Salvadori, G. and Michele, C. D.: Multivariate real-time assessment of droughts via copula-based multi-site hazard trajectories and fans, J.
Hydrol., 526, 101–115, 2015. a
Salvadori, G., DeMichele, C., and Durante, F.: On the return period and design in a multivariate framework, Hydrol. Earth Syst. Sci., 15, 3293–3305, https://doi.org/10.5194/hess-15-3293-2011, 2011. a, b
Salvadori, G., Tomasicchio, G. R., and D'Alessandro, F.: Practical guidelines
for multivariate analysis and design in coastal and off-shore engineering,
Coast. Eng., 88, 1–14, https://doi.org/10.1016/j.coastaleng.2014.01.011, 2014. a
Sánchez-Pinillos, M., D’Orangeville, L., Boulanger, Y., Comeau, P., Wang,
J., Taylor, A. R., and Kneeshaw, D.: Sequential droughts: A silent trigger
of boreal forest mortality, Global Change Biol., 28, 542–556,
https://doi.org/10.1111/gcb.15913, 2022. a
Schreiber, T. and Schmitz, A.: Surrogate time series, Physica D, 142, 346–382, https://doi.org/10.1016/S0167-2789(00)00043-9, 2000. a
Schulte, M. and Schumann, A. H.: Extensive spatio-temporal assessment of flood events by application of pair-copulas, Proc. IAHS, 370, 177–181, https://doi.org/10.5194/piahs-370-177-2015, 2015. a
Segers, J.: Max-stable models for multivariate extremes, arXiv [preprint],
https://doi.org/10.48550/arXiv.1204.0332, 2012. a, b
Serinaldi, F.: Dismissing return periods!, Stoch. Environ. Res. Risk A., 29, 1179–1189, https://doi.org/10.1007/s00477-014-0916-1, 2015. a
Serinaldi, F.: Can we tell more than we can know? The limits of bivariate
drought analyses in the United States, Stoch. Environ. Res. Risk A., 30, 1691–1704, https://doi.org/10.1007/s00477-015-1124-3, 2016. a
Serinaldi, F. and Grimaldi, S.: Synthetic design hydrographs based on
distribution functions with finite support, J. Hydrol. Eng., 16, 434–446, https://doi.org/10.1061/(asce)he.1943-5584.0000339, 2011. a, b
Serinaldi, F. and Kilsby, C. G.: A blueprint for full collective flood risk
estimation: demonstration for European river flooding, Risk Anal., 37,
1958–1976, https://doi.org/10.1111/risa.12747, 2017. a
Sharma, A., Tarboton, D. G., and Lall, U.: Streamflow simulation: a
nonparametric approach, Water Resour. Res., 33, 291–308, 1997. a
Sharma, S. and Mujumdar, P.: Increasing frequency and spatial extent of
concurrent meteorological droughts and heatwaves in India, Sci. Rep., 7, 1–9, https://doi.org/10.1038/s41598-017-15896-3, 2017. a
Sheffield, J., Andreadis, K. M., Wood, E. F., and Lettenmaier, D. P.: Global
and continental drought in the second half of the twentieth century:
Severity-area-duration analysis and temporal variability of large-scale
events, J. Climate, 22, 1962–1981, https://doi.org/10.1175/2008JCLI2722.1, 2009. a
Shi, W., Huang, S., Liu, D., Huang, Q., Han, Z., Leng, G., Wang, H., Liang, H., Li, P., and Wei, X.: Drought-flood abrupt alternation dynamics and their
potential driving forces in a changing environment, J. Hydrol., 597, 126179, https://doi.org/10.1016/j.jhydrol.2021.126179, 2021. a
Shiau, J. T.: Fitting drought duration and severity with two-dimensional
copulas, Water Resour. Manage., 20, 795–815, https://doi.org/10.1007/s11269-005-9008-9, 2006. a
Sklar, A.: Fonctions de répartition à n dimensions et leurs marges, Publ. Inst. Statist. Univ. Paris, 8, 229–231, 1959. a
Slater, L. J., Anderson, B., Buechel, M., Dadson, S., Han, S., Harrigan, S., Kelder, T., Kowal, K., Lees, T., Matthews, T., Murphy, C., and Wilby, R. L.: Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management, Hydrol. Earth Syst. Sci., 25, 3897–3935, https://doi.org/10.5194/hess-25-3897-2021, 2021. a
Spearman, C.: The proof and measurement of association between two things, Am. J. Psychol., 100, 441–471, 1904. a
Srinivas, V. V. and Srinivasan, K.: Hybrid matched-block bootstrap for
stochastic simulation of multiseason streamflows, J. Hydrol., 329, 1–15, https://doi.org/10.1016/j.jhydrol.2006.01.023, 2006. a
Srivastav, R. K. and Simonovic, S. P.: An analytical procedure for multi-site, multi-season streamflow generation using maximum entropy bootstrapping, Environ. Model. Softw., 59, 59–75, https://doi.org/10.1016/j.envsoft.2014.05.005, 2014. a
Stedinger, J. R. and Taylor, M. R.: Synthetic streamflow generation. 1. Model verification and validation, Water Resour. Res., 18, 909–918,
https://doi.org/10.1029/WR018i004p00909, 1982. a
Stephan, R., Erfurt, M., Terzi, S., Žun, M., Kristan, B., Haslinger, K., and Stahl, K.: An inventory of Alpine drought impact reports to explore past droughts in a mountain region, Nat. Hazards Earth Syst. Sci., 21, 2485–2501, https://doi.org/10.5194/nhess-21-2485-2021, 2021. a
Swain, D. L., Langenbrunner, B., Neelin, J. D., and Hall, A.: Increasing
precipitation volatility in twenty-first-century California, Nat. Clim. Change, 8, 427–433, https://doi.org/10.1038/s41558-018-0140-y, 2018. a, b
Szolgay, J., Gaál, L., Kohnová, S., Hlavčová, K., Výleta, R., Bacigál, T., and Blöschl, G.: A process-based analysis of the suitability of copula types for peak-volume flood relationships, Proc. IAHS, 370, 183–188, https://doi.org/10.5194/piahs-370-183-2015, 2015. a
Tanguy, M., Haslinger, K., Svensson, C., Parry, S., Barker, L. J., Hannaford,
J., and Prudhomme, C.: Regional differences in spatiotemporal drought
characteristics in Great Britain, Front. Environ. Sci., 9, 639649,
https://doi.org/10.3389/fenvs.2021.639649, 2021. a
Tatli, H.: Detecting persistence of meteorological drought via the Hurst
exponent, Meteorol. Appl., 22, 763–769, https://doi.org/10.1002/met.1519, 2015. a
Teutschbein, C., Quesada Montano, B., Todorović, A., and Grabs, T.: Streamflow droughts in Sweden: Spatiotemporal patterns emerging from six decades of observations, J. Hydrol.: Reg. Stud., 42, 101171,
https://doi.org/10.1016/j.ejrh.2022.101171, 2022. a, b
Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., and Farmer, J. D.:
Testing for nonlinearity in time series: the method of surrogate data,
Physica D, 58, 77–94, https://doi.org/10.1016/0167-2789(92)90102-S, 1992. a
Tong, S., Lai, Q., Zhang, J., Bao, Y., Lusi, A., Ma, Q., Li, X., and Zhang, F.: Spatiotemporal drought variability on the Mongolian Plateau from
1980–2014 based on the SPEI-PM, intensity analysis and Hurst exponent, Sci. Total Environ., 615, 1557–1565, https://doi.org/10.1016/j.scitotenv.2017.09.121, 2018. a
Touma, D., Michalak, A. M., Swain, D. L., and Diffenbaugh, N. S.:
Characterizing the spatial scales of extreme daily precipitation in the
United States, J. Climate, 31, 8023–8037, https://doi.org/10.1175/JCLI-D-18-0019.1, 2018. a
Tuel, A. and Martius, O.: A climatology of sub-seasonal temporal clustering of extreme precipitation in Switzerland and its links to extreme discharge,
Nat. Hazards Earth Syst. Sci., 21, 2949–2972,
https://doi.org/10.5194/nhess-21-2949-2021, 2021. a
Tuel, A., Schaefli, B., Zscheischler, J., and Martius, O.: On the links between sub-seasonal clustering of extreme precipitation and high discharge in Switzerland and Europe, Hydrol. Earth Syst. Sci., 26, 2649–2669, https://doi.org/10.5194/hess-26-2649-2022, 2022. a
van der Wiel, K., Wanders, N., Selten, F. M., and Bierkens, M. F. P.: Added
value of large ensemble simulations for assessing extreme river discharge in
a 2 ∘C warmer world, Geophys. Res. Lett., 46, 2093–2102,
https://doi.org/10.1029/2019GL081967, 2019. a
Van Loon, A. F., Tijdeman, E., Wanders, N., Van Lanen, H. A. J., Teuling,
A. J., and Uijlenhoet, R.: How climate seasonality modifies drought duration
and deficit, J. Geophys. Res.-Atmos., 119, 4640–4656, https://doi.org/10.1002/2013JD020383, 2014. a, b
Vidal, J. P., Martin, E., Franchistéguy, L., Habets, F., Soubeyroux, J. M., Blanchard, M., and Baillon, M.: Multilevel and multiscale drought reanalysis over France with the Safran-Isba-Modcou hydrometeorological suite,
Hydrol. Earth Syst. Sci., 14, 459–478, https://doi.org/10.5194/hess-14-459-2010, 2010. a
Villarini, G., Smith, J. A., Vitolo, R., and Stephenson, D. B.: On the temporal clustering of US floods and its relationship to climate teleconnection patterns, Int. J. Climatol., 33, 629–640, https://doi.org/10.1002/joc.3458, 2013. a, b, c
Vitolo, R., Stephenson, D. B., Cook, L. M., and Mitchell-Wallace, K.: Serial
clustering of intense European storms, Meteorol. Z., 18, 411–424, https://doi.org/10.1127/0941-2948/2009/0393, 2009. a
Wang, N., Lombardo, L., Tonini, M., and Cheng, W.: Space-time clustering of
flash floods in a changing climate (China, 1950–2015), Nat. Hazards Earth Syst. Sci., 21, 2109–2124, https://doi.org/10.5194/nhess-21-2109-2021, 2020. a
Ward, P. J., de Ruiter, M. C., Mård, J., Schröter, K., Van Loon, A., Veldkamp, T., von Uexkull, N., Wanders, N., AghaKouchak, A.,
Arnbjerg-Nielsen, K., Capewell, L., Carmen Llasat, M., Day, R., Dewals, B.,
Di Baldassarre, G., Huning, L. S., Kreibich, H., Mazzoleni, M., Savelli, E.,
Teutschbein, C., van den Berg, H., van der Heijden, A., Vincken, J. M.,
Waterloo, M. J., and Wens, M.: The need to integrate flood and drought
disaster risk reduction strategies, Water Secur., 11, 100070,
https://doi.org/10.1016/j.wasec.2020.100070, 2020. a, b
Willkofer, F., Wood, R. R., Trentini, F. V., Weismüller, J., Poschlod, B., and Ludwig, R.: A holistic modelling approach for the estimation of return levels of peak flows in Bavaria, Water, 12, 2349, https://doi.org/10.3390/w12092349, 2020. a
Yan, H. and Moradkhani, H.: A regional Bayesian hierarchical model for flood frequency analysis, Stoch. Environ. Res. Risk A., 29, 1019–1036, https://doi.org/10.1007/s00477-014-0975-3, 2015.
a
Yang, S., Wu, B., Zhang, R., and Zhou, S.: Relationship between an abrupt
drought-flood transition over mid-low reaches of the Yangtze River in
2011 and the intraseasonal oscillation over mid-high latitudes of East
Asia, Acta Meteorol. Sin., 27, 129–143, https://doi.org/10.1007/s13351-013-0201-0, 2013. a
Yue, Y., Liu, H. F., Mu, X. X., Qin, M. S., Wang, T. T., Wang, Q., and Yan,
Y. Q.: Spatial and temporal characteristics of drought and its correlation
with climate indices in Northeast China, PLoS ONE, 16, e0259774, https://doi.org/10.1371/journal.pone.0259774, 2021. a
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton,
R. M., Hurk, B. v. d., AghaKouchak, A., Jezequel, A., Mahecha, M. D., Maraun,
D., Ramos, A. M., Ridder, N. N., Thiery, W., and Vignotto, E.: A typology of
compound weather and climate events, Nat. Rev. Earth Environ., 1, 333–347, https://doi.org/10.1038/s43017-020-0060-z, 2020. a, b
Short summary
I discuss different types of multivariate hydrological extremes and their dependencies, including regional extremes affecting multiple locations, such as spatially connected flood events; consecutive extremes occurring in close temporal succession, such as successive droughts; extremes characterized by multiple characteristics, such as floods with jointly high peak discharge and flood volume; and transitions between different types of extremes, such as drought-to-flood transitions.
I discuss different types of multivariate hydrological extremes and their dependencies,...
