Articles | Volume 24, issue 2
https://doi.org/10.5194/nhess-24-411-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-24-411-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Climate change impacts on regional fire weather in heterogeneous landscapes of central Europe
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland
Andrea Böhnisch
Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany
Ralf Ludwig
Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany
Manuela I. Brunner
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland
Related authors
No articles found.
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.
Benjamin Poschlod, Laura Sailer, Alexander Sasse, Anastasia Vogelbacher, and Ralf Ludwig
EGUsphere, https://doi.org/10.5194/egusphere-2025-2483, https://doi.org/10.5194/egusphere-2025-2483, 2025
Short summary
Short summary
Europe was hit by severe droughts in recent years resulting in extreme low flow conditions in rivers. Here, we investigate future climate change effects on river droughts in Bavaria. We find increasing severity for the low peak discharge and low flow duration in a warmer climate. This is caused by hotter and drier summers, where the joint occurrence of heat and drought intensifies. Further, we show that conditions in the year before the drought gain more importance in a warmer climate.
Emma Ford, Manuela I. Brunner, Hannah Christensen, and Louise Slater
EGUsphere, https://doi.org/10.5194/egusphere-2025-1493, https://doi.org/10.5194/egusphere-2025-1493, 2025
Short summary
Short summary
This study aims to improve prediction and understanding of extreme flood events in UK near-natural catchments. 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.
Bailey J. Anderson, Eduardo Muñoz-Castro, Lena M. Tallaksen, Alessia Matano, Jonas Götte, Rachael Armitage, Eugene Magee, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-1391, https://doi.org/10.5194/egusphere-2025-1391, 2025
Short summary
Short summary
When flood happen during, or shortly after, droughts, the impacts of can be magnified. In hydrological research, defining these events can be challenging. Here we have tried to address some of the challenges defining these events using real-world examples. We show how different methodological approaches differ in their results, make suggestions on when to use which approach, and outline some pitfalls of which researchers should be aware.
Eduardo Muñoz-Castro, Bailey J. Anderson, Paul C. Astagneau, Daniel L. Swain, Pablo A. Mendoza, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-781, https://doi.org/10.5194/egusphere-2025-781, 2025
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 more difficult to capture in semi-arid high-mountain catchments than in humid low-elevation catchments.
Paul C. Astagneau, Raul R. Wood, Mathieu Vrac, Sven Kotlarski, Pradeebane Vaittinada Ayar, Bastien François, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-3966, https://doi.org/10.5194/egusphere-2024-3966, 2025
Short summary
Short summary
To study floods and droughts 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.
Joren Janzing, Niko Wanders, Marit van Tiel, Barry van Jaarsveld, Dirk Nikolaus Karger, and Manuela Irene Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-3072, https://doi.org/10.5194/egusphere-2024-3072, 2024
Short summary
Short summary
Process representation in hyper-resolution large-scale hydrological models (LHM) 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.
Carolin Boos, Sophie Reinermann, Raul Wood, Ralf Ludwig, Anne Schucknecht, David Kraus, and Ralf Kiese
EGUsphere, https://doi.org/10.5194/egusphere-2024-2864, https://doi.org/10.5194/egusphere-2024-2864, 2024
Preprint archived
Short summary
Short summary
We applied a biogeochemical model on grasslands in the pre-Alpine Ammer region in Germany and analyzed the influence of soil and climate on annual yields. In drought affected years, total yields were decreased by 4 %. Overall, yields decrease with rising elevation, but less so in drier and hotter years, whereas soil organic carbon has a positive impact on yields, especially in drier years. Our findings imply, that adapted management in the region allows to mitigate yield losses from drought.
Raul R. Wood, Joren Janzing, Amber van Hamel, Jonas Götte, Dominik L. Schumacher, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2905, https://doi.org/10.5194/egusphere-2024-2905, 2024
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 from 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.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
Short summary
Short summary
Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
David Gampe, Clemens Schwingshackl, Andrea Böhnisch, Magdalena Mittermeier, Marit Sandstad, and Raul R. Wood
Earth Syst. Dynam., 15, 589–605, https://doi.org/10.5194/esd-15-589-2024, https://doi.org/10.5194/esd-15-589-2024, 2024
Short summary
Short summary
Using a special suite of climate simulations, we determine if and when climate change is detectable and translate this to the global warming prevalent in the corresponding year. Our results show that, at 1.5°C warming, >85 % of the global population (>95 % at 2.0° warming) is already exposed to nighttime temperatures altered by climate change beyond natural variability. Furthermore, even incremental changes in global warming levels result in increased human exposure to emerged climate signals.
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.
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
Hydrol. Earth Syst. Sci., 27, 2479–2497, https://doi.org/10.5194/hess-27-2479-2023, https://doi.org/10.5194/hess-27-2479-2023, 2023
Short summary
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.
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.
Elizaveta Felsche and Ralf Ludwig
Nat. Hazards Earth Syst. Sci., 21, 3679–3691, https://doi.org/10.5194/nhess-21-3679-2021, https://doi.org/10.5194/nhess-21-3679-2021, 2021
Short summary
Short summary
This study applies artificial neural networks to predict drought occurrence in Munich and Lisbon, with a lead time of 1 month. An analysis of the variables that have the highest impact on the prediction is performed. The study shows that the North Atlantic Oscillation index and air pressure 1 month before the event have the highest importance for the prediction. Moreover, it shows that seasonality strongly influences the goodness of prediction for the Lisbon domain.
Manuela I. Brunner, Eric Gilleland, and Andrew W. Wood
Earth Syst. Dynam., 12, 621–634, https://doi.org/10.5194/esd-12-621-2021, https://doi.org/10.5194/esd-12-621-2021, 2021
Short summary
Short summary
Compound hot and dry events can lead to severe impacts whose severity may depend on their timescale and spatial extent. Here, we show that the spatial extent and timescale of compound hot–dry events are strongly related, spatial compound event extents are largest at
sub-seasonal timescales, and short events are driven more by high temperatures, while longer events are more driven by low precipitation. Future climate impact studies should therefore be performed at different timescales.
Nicola Maher, Sebastian Milinski, and Ralf Ludwig
Earth Syst. Dynam., 12, 401–418, https://doi.org/10.5194/esd-12-401-2021, https://doi.org/10.5194/esd-12-401-2021, 2021
Benjamin Poschlod, Ralf Ludwig, and Jana Sillmann
Earth Syst. Sci. Data, 13, 983–1003, https://doi.org/10.5194/essd-13-983-2021, https://doi.org/10.5194/essd-13-983-2021, 2021
Short summary
Short summary
This study provides a homogeneous data set of 10-year rainfall return levels based on 50 simulations of the Canadian Regional Climate Model v5 (CRCM5). In order to evaluate its quality, the return levels are compared to those of observation-based rainfall of 16 European countries from 32 different sources. The CRCM5 is able to capture the general spatial pattern of observed extreme precipitation, and also the intensity is reproduced in 77 % of the area for rainfall durations of 3 h and longer.
Manuela I. Brunner, Lieke A. Melsen, Andrew W. Wood, Oldrich Rakovec, Naoki Mizukami, Wouter J. M. Knoben, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 105–119, https://doi.org/10.5194/hess-25-105-2021, https://doi.org/10.5194/hess-25-105-2021, 2021
Short summary
Short summary
Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable model ideally reproduces both local flood characteristics and regional aspects of flooding. In this paper we investigate how such characteristics are represented by hydrologic models. Our results show that both the modeling of local and regional flood characteristics are challenging, especially under changing climate conditions.
Fabian von Trentini, Emma E. Aalbers, Erich M. Fischer, and Ralf Ludwig
Earth Syst. Dynam., 11, 1013–1031, https://doi.org/10.5194/esd-11-1013-2020, https://doi.org/10.5194/esd-11-1013-2020, 2020
Short summary
Short summary
We compare the inter-annual variability of three single-model initial-condition large ensembles (SMILEs), downscaled with three regional climate models over Europe for seasonal temperature and precipitation, the number of heatwaves, and maximum length of dry periods. They all show good consistency with observational data. The magnitude of variability and the future development are similar in many cases. In general, variability increases for summer indicators and decreases for winter indicators.
Fabian Willibald, Sven Kotlarski, Adrienne Grêt-Regamey, and Ralf Ludwig
The Cryosphere, 14, 2909–2924, https://doi.org/10.5194/tc-14-2909-2020, https://doi.org/10.5194/tc-14-2909-2020, 2020
Short summary
Short summary
Climate change will significantly reduce snow cover, but the extent remains disputed. We use regional climate model data as a driver for a snow model to investigate the impacts of climate change and climate variability on snow. We show that natural climate variability is a dominant source of uncertainty in future snow trends. We show that anthropogenic climate change will change the interannual variability of snow. Those factors will increase the vulnerabilities of snow-dependent economies.
Cited articles
Aalbers, E. E., Lenderink, G., van Meijgaard, E., and van den Hurk, B. J. J. M.: Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?, Clim. Dynam., 50, 4745–4766, https://doi.org/10.1007/s00382-017-3901-9, 2018. a
Abatzoglou, J. T., Juang, C. S., Williams, A. P., Kolden, C. A., and Westerling, A. L.: Increasing Synchronous Fire Danger in Forests of the Western United States, Geophys. Res. Lett., 48, e2020GL091377, https://doi.org/10.1029/2020GL091377, 2021. a
Bakke, S. J., Wanders, N., van der Wiel, K., and Tallaksen, L. M.: A data-driven model for Fennoscandian wildfire danger, Nat. Hazards Earth Syst. Sci., 23, 65–89, https://doi.org/10.5194/nhess-23-65-2023, 2023. a
Barbero, R., Abatzoglou, J. T., Larkin, N. K., Kolden, C. A., and Stocks, B.: Climate change presents increased potential for very large fires in the contiguous United States, Int. J. Wildland Fire, 24, 892–899, https://doi.org/10.1071/WF15083, 2015. a, b
Barbero, R., Abatzoglou, J. T., Pimont, F., Ruffault, J., and Curt, T.: Attributing Increases in Fire Weather to Anthropogenic Climate Change Over France, Front. Earth Sci., 8, 892–899, https://doi.org/10.3389/feart.2020.00104, 2020. a, b
Bayrisches Landesamt für Umwelt: Naturräumliche Gliederung Bayerns, https://www.lfu.bayern.de/natur/naturraeume/index.htm (last access: 2 February 2024), 2024. a
Bowman, D. M. J. S., Kolden, C. A., Abatzoglou, J. T., Johnston, F. H., van der Werf, G. R., and Flannigan, M.: Vegetation fires in the Anthropocene, Nature Reviews Earth and Environment, 1, 500–515, https://doi.org/10.1038/s43017-020-0085-3, 2020. a, b, c
Bradshaw, L. S., Deeming, J. E., Burgan, R. E., and Cohen, J. D.: The 1978 National Fire-Danger Rating System: technical documentation, Tech. rep., https://doi.org/10.2737/INT-GTR-169, 1984. a
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, 1–11, https://doi.org/10.1038/s43247-021-00248-x, 2021. a, b
Brönnimann, S., Rajczak, J., Fischer, E. M., Raible, C. C., Rohrer, M., and Schär, C.: Changing seasonality of moderate and extreme precipitation events in the Alps, Nat. Hazards Earth Syst. Sci., 18, 2047–2056, https://doi.org/10.5194/nhess-18-2047-2018, 2018. a
Böhnisch, A., Ludwig, R., and Leduc, M.: Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for central Europe, Earth Syst. Dynam., 11, 617–640, https://doi.org/10.5194/esd-11-617-2020, 2020. a
Böhnisch, A., Mittermeier, M., Leduc, M., and Ludwig, R.: Hot Spots and Climate Trends of Meteorological Droughts in Europe – Assessing the Percent of Normal Index in a Single-Model Initial-Condition Large Ensemble, Front. Water, 3, 107, https://doi.org/10.3389/frwa.2021.716621, 2021. a, b, c, d
Böhnisch, A., Felsche, E., and Ludwig, R.: European heatwave tracks: using causal discovery to detect recurring pathways in a single-regional climate model large ensemble, Environ. Res. Lett., 18, 014038, https://doi.org/10.1088/1748-9326/aca9e3, 2023. a
Cannon, A. J.: Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables, Clim. Dynam., 50, 31–49, https://doi.org/10.1007/s00382-017-3580-6, 2018. a, b
ClimEx: Data Access, https://www.climex-project.org/data-access/ (last access: 5 February 2024), 2024. a
CLMS: Corine Land Cover 2018, Version 2020-20u1, Tech. rep., Corine Land Management Service (CLMS) [data set], https://doi.org/10.2909/71c95a07-e296-44fc-b22b-415f42acfdf0, 2021. a, b
Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, Springer, London, ISBN 978-1-84996-874-4 978-1-4471-3675-0, https://doi.org/10.1007/978-1-4471-3675-0, 2001. a, b
Conedera, M., Krebs, P., Valese, E., Cocca, G., Schunk, C., Menzel, A., Vacik, H., Cane, D., Japelj, A., Muri, B., Ricotta, C., Oliveri, S., and Pezzatti, G. B.: Characterizing Alpine pyrogeography from fire statistics, Appl. Geogr., 98, 87–99, https://doi.org/10.1016/j.apgeog.2018.07.011, 2018. a, b
Copernicus Climate Change Service, Climate Data Store: Fire danger indices historical data from the Copernicus Emergency Management Service, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.0e89c522, 2019. a
Deser, C., Knutti, R., Solomon, S., and Phillips, A. S.: Communication of the role of natural variability in future North American climate, Nat. Clim. Change, 2, 775–779, https://doi.org/10.1038/nclimate1562, 2012. a, b
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, b, c, d, e
Di Giuseppe, F., Pappenberger, F., Wetterhall, F., Krzeminski, B., Camia, A., Libertá, G., and San Miguel, J.: The Potential Predictability of Fire Danger Provided by Numerical Weather Prediction, J. Appl. Meteorol. Clim., 55, 2469–2491, https://doi.org/10.1175/JAMC-D-15-0297.1, 2016. a, b, c, d, e, f
European Environment Agency: European Digital Elevation Model (EU-DEM), https://www.eea.europa.eu/en/datahub/datahubitem-view/d08852bc-7b5f-4835-a776-08362e2fbf4b?activeAccordion=735550 (last access: 1 February 2024), 2016. a
Fargeon, H., Pimont, F., Martin-StPaul, N., De Caceres, M., Ruffault, J., Barbero, R., and Dupuy, J.-L.: Projections of fire danger under climate change over France: where do the greatest uncertainties lie?, Clim. Change, 160, 479–493, https://doi.org/10.1007/s10584-019-02629-w, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Felsche, E., Böhnisch, A., and Ludwig, R.: Inter-seasonal connection of typical European heatwave patterns to soil moisture, npj Clim.d Atmos. Sci., 6, 1–11, https://doi.org/10.1038/s41612-023-00330-5, 2023. a
Fischer, E. M., Beyerle, U., and Knutti, R.: Robust spatially aggregated projections of climate extremes, Nat. Clim. Change, 3, 1033–1038, https://doi.org/10.1038/nclimate2051, 2013. a
Fyfe, J. C., Derksen, C., Mudryk, L., Flato, G. M., Santer, B. D., Swart, N. C., Molotch, N. P., Zhang, X., Wan, H., Arora, V. K., Scinocca, J., and Jiao, Y.: Large near-term projected snowpack loss over the western United States, Nat. Commun., 8, 14996, https://doi.org/10.1038/ncomms14996, 2017. a, b, c, d, e
Giannaros, T. M., Papavasileiou, G., Lagouvardos, K., Kotroni, V., Dafis, S., Karagiannidis, A., and Dragozi, E.: Meteorological Analysis of the 2021 Extreme Wildfires in Greece: Lessons Learned and Implications for Early Warning of the Potential for Pyroconvection, Atmosphere, 13, 475, https://doi.org/10.3390/atmos13030475, 2022. a, b
Gillett, N. P., Cannon, A. J., Malinina, E., Schnorbus, M., Anslow, F., Sun, Q., Kirchmeier-Young, M., Zwiers, F., Seiler, C., Zhang, X., Flato, G., Wan, H., Li, G., and Castellan, A.: Human influence on the 2021 British Columbia floods, Weather Climate Extremes, 36, 100441, https://doi.org/10.1016/j.wace.2022.100441, 2022. a
Hawkins, E.: Warming Stripes, Tech. rep., https://www.climate-lab-book.ac.uk/2018/warming-stripes/ (last access: 1 February 2024), 2018. a
Hawkins, E. and Sutton, R.: The Potential to Narrow Uncertainty in Regional Climate Predictions, B. Am. Meteorol. Soc., 90, 1095–1108, https://doi.org/10.1175/2009BAMS2607.1, 2009. a
Hoffman, K. M., Christianson, A. C., Gray, R. W., and Daniels, L.: Western Canada's new wildfire reality needs a new approach to fire management, Environ. Res. Lett., 17, 061001, https://doi.org/10.1088/1748-9326/ac7345, 2022. a, b
IPCC: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, USA, https://doi.org/doi:10.1017/9781009157896.001, 2021. a, b
Kirchmeier-Young, M. C., Zwiers, F. W., Gillett, N. P., and Cannon, A. J.: Attributing extreme fire risk in Western Canada to human emissions, Climatic Change, 144, 365–379, https://doi.org/10.1007/s10584-017-2030-0, 2017. a
Leduc, M., Mailhot, A., Frigon, A., Martel, J.-L., Ludwig, R., Brietzke, G. B., Giguère, M., Brissette, F., Turcotte, R., Braun, M., and Scinocca, J.: The ClimEx Project: A 50-Member Ensemble of Climate Change Projections at 12-km Resolution over Europe and Northeastern North America with the Canadian Regional Climate Model (CRCM5), J. Appl. Meteorol. Clim., 58, 663–693, https://doi.org/10.1175/JAMC-D-18-0021.1, 2019. a, b, c, d, e
Martynov, A., Laprise, R., Sushama, L., Winger, K., Šeparović, L., and Dugas, B.: Reanalysis-driven climate simulation over CORDEX North America domain using the Canadian Regional Climate Model, version 5: model performance evaluation, Clim. Dynam., 41, 2973–3005, https://doi.org/10.1007/s00382-013-1778-9, 2013. a
McArthur, A. G.: Fire behaviour in eucalypt forests. Forestry and Timber Bureau Leaflet 107, https://vgls.sdp.sirsidynix.net.au/client/search/asset/1299701/0 (last access: 5 February 2024), 1967. a
Miller, J., Böhnisch, A., Brunner, M., and Ludwig, R.: Fire Weather Index for Hydrological Bavaria from 1980–2099 derived from the 50 member CRCM5-LE, EnviDat [data set], https://doi.org/10.16904/envidat.466, 2023. a
Mittermeier, M., Braun, M., Hofstätter, M., Wang, Y., and Ludwig, R.: Detecting Climate Change Effects on Vb Cyclones in a 50-Member Single-Model Ensemble Using Machine Learning, Geophys. Res. Lett.s, 46, 14653–14661, https://doi.org/10.1029/2019GL084969, 2019. a, b
Mpelasoka, F. S. and Chiew, F. H. S.: Influence of Rainfall Scenario Construction Methods on Runoff Projections, J. Hydrometeorol., 10, 1168–1183, https://doi.org/10.1175/2009JHM1045.1, 2009. a, b
Pausas, J. G. and Paula, S.: Fuel shapes the fire–climate relationship: evidence from Mediterranean ecosystems, Global Ecol. Biogeogr., 21, 1074–1082, https://doi.org/10.1111/j.1466-8238.2012.00769.x, 2012. a
Piani, C., Haerter, J. O., and Coppola, E.: Statistical bias correction for daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187–192, https://doi.org/10.1007/s00704-009-0134-9, 2010. a
Poschlod, B., Willkofer, F., and Ludwig, R.: Impact of Climate Change on the Hydrological Regimes in Bavaria, Water, 12, 1599, https://doi.org/10.3390/w12061599, 2020. a, b, c
Ruffault, J., Moron, V., Trigo, R. M., and Curt, T.: Daily synoptic conditions associated with large fire occurrence in Mediterranean France: evidence for a wind-driven fire regime, Int. J. Climatol., 37, 524–533, https://doi.org/10.1002/joc.4680, 2017. a
Ruffault, J., Curt, T., Moron, V., Trigo, R. M., Mouillot, F., Koutsias, N., Pimont, F., Martin-StPaul, N., Barbero, R., Dupuy, J.-L., Russo, A., and Belhadj-Khedher, C.: Increased likelihood of head-induced large wildfires in the Mediterrranean, Sci. Rep., 10, 13790, https://doi.org/10.1038/s41598-020-70069-z, 2020. a, b, c, d
San-Miguel-Ayanz, J., Costa, H., de Rigo, D., Libertà, G., Vivancos, T. A., Tracy Durrant, D. N., Löffler, P., and Moore, P.: Basic criteria to assess wildfire risk at the Pan-European level, Tech. rep., https://ec.europa.eu/jrc/en/publication/basic-criteria-assess-wildfire-risk-pan-european-level (last access: 12 December 2021), 2018. a
Separović, L., Alexandru, A., Laprise, R., Martynov, A., Sushama, L., Winger, K., Tete, K., and Valin, M.: Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model, Clim. Dynam., 41, 3167–3201, https://doi.org/10.1007/s00382-013-1737-5, 2013. a
Turco, M., Rosa-Cánovas, J. J., Bedia, J., Jerez, S., Montávez, J. P., Llasat, M. C., and Provenzale, A.: Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models, Nat. Commun., 9, 3821, https://doi.org/10.1038/s41467-018-06358-z, 2018. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Meth., 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Vitolo, C., Di Giuseppe, F., Krzeminski, B., and San-Miguel-Ayanz, J.: A 1980-2018 global fire danger re-analysis dataset for the Canadian Fire Weather Indices, Sci. Data, 6, 190032, https://doi.org/10.1038/sdata.2019.32, 2019. a, b
Vitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Libertá, G., and Krzeminski, B.: ERA5-based global meteorological wildfire danger maps, Sci. Data, 7, 216, https://doi.org/10.1038/s41597-020-0554-z, 2020. a, b, c, d
von Trentini, F., Leduc, M., and Ludwig, R.: Assessing natural variability in RCM signals: comparison of a multi model EURO-CORDEX ensemble with a 50-member single model large ensemble, Clim. Dynam., 53, 1963–1979, https://doi.org/10.1007/s00382-019-04755-8, 2019. a, b, c
von Trentini, F., Aalbers, E. E., Fischer, E. M., and Ludwig, R.: Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe, Earth Syst. Dynam., 11, 1013–1031, https://doi.org/10.5194/esd-11-1013-2020, 2020. a
Vrac, M.: Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction, Hydrol. Earth Syst. Sci., 22, 3175–3196, https://doi.org/10.5194/hess-22-3175-2018, 2018. a
Wang, X., Wotton, B. M., Cantin, A. S., Parisien, M.-A., Anderson, K., Moore, B., and Flannigan, M. D.: cffdrs: an R package for the Canadian Forest Fire Danger Rating System, Ecol. Process., 6, 5, https://doi.org/10.1186/s13717-017-0070-z, 2017. a, b, c
Wastl, C., Schunk, C., Leuchner, M., Pezzatti, G. B., and Menzel, A.: Recent climate change: Long-term trends in meteorological forest fire danger in the Alps, Agr. Forest Meteorol., 162-163, 1–13, https://doi.org/10.1016/j.agrformet.2012.04.001, 2012. a, b
Willkofer, F., Wood, R. R., von Trentini, F., 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, b
Wood, R. R.: Role of mean and variability change in changes in European annual and seasonal extreme precipitation events, Earth Syst. Dynam., 14, 797–816, https://doi.org/10.5194/esd-14-797-2023, 2023. a, b, c
Wood, R. R. and Ludwig, R.: Analyzing Internal Variability and Forced Response of Subdaily and Daily Extreme Precipitation Over Europe, Geophys. Res. Lett. 47, e2020GL089300, https://doi.org/10.1029/2020GL089300, 2020. a
Wotton, B. M.: Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications, Environ. Ecol. Stat., 16, 107–131, https://doi.org/10.1007/s10651-007-0084-2, 2009. a
Yang, W., Gardelin, M., Olsson, J., and Bosshard, T.: Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden, Nat. Hazards Earth Syst. Sci., 15, 2037–2057, https://doi.org/10.5194/nhess-15-2037-2015, 2015. a, b, c
Zscheischler, J., Fischer, E. M., and Lange, S.: The effect of univariate bias adjustment on multivariate hazard estimates, Earth Syst. Dynam., 10, 31–43, https://doi.org/10.5194/esd-10-31-2019, 2019. a
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.
We assess the impacts of climate change on fire danger for 1980–2099 in different landscapes of...
Altmetrics
Final-revised paper
Preprint