Articles | Volume 25, issue 8
https://doi.org/10.5194/nhess-25-2885-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-2885-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
27 Aug 2025
Research article | Highlight paper |
| 27 Aug 2025
| 27 Aug 2025
Impact-based temporal clustering of multiple meteorological hazard types in southwestern Germany
Katharina Küpfer
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research Troposphere Research (IMKTRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Center for Disaster Management and Risk Reduction Technology (CEDIM), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Alexandre Tuel
Institute of Geography and Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
now at: Galeio, Paris, France
Michael Kunz
Institute of Meteorology and Climate Research Troposphere Research (IMKTRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Center for Disaster Management and Risk Reduction Technology (CEDIM), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Cited articles
Abdi, H.: Encyclopedia of Research Design, Coefficient of Variation, SAGE Publications, Inc., Thousand Oaks, California, United States, 169–171, https://doi.org/10.4135/9781412961288, 2010. a
Banerjee, C., Bevere, L., Gabers, H., Grollimund, B., Lechner, R., and Weigel, A.: sigma 01/2024: Natural catastrophes in 2023, Tech. rep., Swiss Re Management Ltd, Swiss Re Institute, https://www.swissre.com/institute/research/sigma-research/sigma-2024-01.html (last access: 25 June 2024), 2024. a, b
Banfi, F. and De Michele, C.: Temporal Clustering of Precipitation Driving Landslides Over the Italian Territory, Earths Future, 12, e2023EF003885, https://doi.org/10.1029/2023EF003885, 2024. a
Barras, H., Martius, O., Nisi, L., Schroeer, K., Hering, A., and Germann, U.: Multi-day hail clusters and isolated hail days in Switzerland – large-scale flow conditions and precursors, Weather Clim. Dynam., 2, 1167–1185, https://doi.org/10.5194/wcd-2-1167-2021, 2021. a
Bloomfield, H. C., Hillier, J., Griffin, A., Kay, A., Shaffrey, L. C., Pianosi, F., James, R., Kumar, D., Champion, A., and Bates, P.: Co-occurring wintertime flooding and extreme wind over Europe, from daily to seasonal timescales, Wea. Clim. Extrem., 39, 100550, https://doi.org/10.1016/j.wace.2023.100550, 2023. a, b
Bloomfield, H. C., Bates, P., Shaffrey, L. C., Hillier, J., Champion, A., Cotterill, D., Pope, J. O., and Kumar, D.: Synoptic conditions conducive for compound wind-flood events in Great Britain in present and future climates, Environ. Res. Lett., 19, 024019, https://doi.org/10.1088/1748-9326/ad1cb7, 2024. a, b
Brabson, B. B. and Palutikof, J. P.: Tests of the generalized pareto distribution for predicting extreme wind speeds, J. Appl. Meteorol. Climatol., 39, 1627–1640, https://doi.org/10.1175/1520-0450(2000)039<1627:TOTGPD>2.0.CO;2, 2000. a
Brommundt, J. and Bárdossy, A.: Spatial correlation of radar and gauge precipitation data in high temporal resolution, Adv. Geosci., 10, 103–109, https://doi.org/10.5194/adgeo-10-103-2007, 2007. 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, c
Cannon, D. J., Kirshbaum, D. J., and Gray, S. L.: Under what conditions does embedded convection enhance orographic precipitation?, Q. J. Roy. Meteor. Soc., 138, 391–406, https://doi.org/10.1002/qj.926, 2012. a
Claassen, J. N., Ward, P. J., Daniell, J., Koks, E. E., Tiggeloven, T., and de Ruiter, M. C.: A new method to compile global multi-hazard event sets, Sci. Rep., 13, 13808, https://doi.org/10.1038/s41598-023-40400-5, 2023. a
Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Verlag, Berlin, Germany, https://doi.org/10.1007/978-1-4471-3675-0, 2001. a
Deutscher Wetterdienst (DWD): Pressemitteilung: Deutschlandwetter im Sommer 2013, Deutscher Wetterdienst, https://www.dwd.de/DE/presse/pressemitteilungen/DE/2013/20130829_DeutschlandwetterimSommer.html?nn=583156 (last access: 3 September 2024), 2013. a
Dietz, H., Fischer, S., and Gierschek, C.: Wohngebäudeversicherung, VVW GmbH, ISBN 9783862987795, 2015. a
GDV: Hochwasser in Süddeutschland: Schäden um die zwei Milliarden Euro erwartet, Gesamtverband der Deutschen Versicherungswirtschaft e.V., https://www.gdv.de/gdv/medien/medieninformationen/
hochwasser-suedeutschland-schadenschaetzung-versicherer-178816 (last access: 28 June 2024), 2024. a
Gliksman, D., Averbeck, P., Becker, N., Gardiner, B., Goldberg, V., Grieger, J., Handorf, D., Haustein, K., Karwat, A., Knutzen, F., Lentink, H. S., Lorenz, R., Niermann, D., Pinto, J. G., Queck, R., Ziemann, A., and Franzke, C. L. E.: Review article: A European perspective on wind and storm damage – from the meteorological background to index-based approaches to assess impacts, Nat. Hazards Earth Syst. Sci., 23, 2171–2201, https://doi.org/10.5194/nhess-23-2171-2023, 2023. a
Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I., and Wernli, H.: Balancing Europe’s wind-power output through spatial deployment informed by weather regimes, Nat. Clim. Change, 7, 557–562, https://doi.org/10.1038/nclimate3338, 2017. a
Hillier, J., Bloomfield, H., Manning, C., Shaffrey, L., Bates, P., and Kumar, D.: Increasingly seasonal jet stream drives stormy episodes with joint wind-flood risk in Great Britain, EarthArXiv [preprint], https://doi.org/10.31223/X5V989, 2024. a
Hillier, J. K., Macdonald, N., Leckebusch, G. C., and Stavrinides, A.: Interactions between apparently “primary” weather-driven hazards and their cost, Environ. Res. Lett., 10, 104003, https://doi.org/10.1088/1748-9326/10/10/104003, 2015. a, b, c, d
Karremann, M. K., Pinto, J. G., von Bomhard, P. J., and Klawa, M.: On the clustering of winter storm loss events over Germany, Nat. Hazards Earth Syst. Sci., 14, 2041–2052, https://doi.org/10.5194/nhess-14-2041-2014, 2014. a, b
Karwat, A., Franzke, C. L. E., Pinto, J. G., Lee, S.-S., and Blender, R.: Northern Hemisphere Extratropical Cyclone Clustering in ERA5 Reanalysis and the CESM2 Large Ensemble, J. Climate, 37, 1347–1365, https://doi.org/10.1175/JCLI-D-23-0160.1, 2024. a
Kautz, L.-A., Martius, O., Pfahl, S., Pinto, J. G., Ramos, A. M., Sousa, P. M., and Woollings, T.: Atmospheric blocking and weather extremes over the Euro-Atlantic sector – a review, Weather Clim. Dynam., 3, 305–336, https://doi.org/10.5194/wcd-3-305-2022, 2022. a
Kopp, J., Rivoire, P., Ali, S. M., Barton, Y., and Martius, O.: A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods, Hydrol. Earth Syst. Sci., 25, 5153–5174, https://doi.org/10.5194/hess-25-5153-2021, 2021. a, b
Kreibich, H., Bubeck, P., Kunz, M., Mahlke, H., Parolai, S., Khazai, B., Daniell, J., Lakes, T., and Schröter, K.: A review of multiple natural hazards and risks in Germany, Nat. Hazards, 74, 1–26, https://doi.org/10.1007/s11069-014-1265-6, 2014. a, b, c
Kron, W., Löw, P., and Kundzewicz, Z. W.: Changes in risk of extreme weather events in Europe, Environ. Sci. Policy, 100, 74–83, https://doi.org/10.1016/j.envsci.2019.06.007, 2019. a, b, c
Kunz, M.: Simulation von Starkniederschlägen mit langer Andauer über Mittelgebirgen, PhD thesis, Institut für Meteorologie und Klimaforschung (IMK), Universität Karlsruhe (TH), https://doi.org/10.5445/IR/1012003, 2003. a
Kunz, M.: The skill of convective parameters and indices to predict isolated and severe thunderstorms, Nat. Hazards Earth Syst. Sci., 7, 327–342, https://doi.org/10.5194/nhess-7-327-2007, 2007. a
Kunz, M.: Characteristics of large-scale orographic precipitation in a linear perspective, J. Hydrometeorol., 12, 27–44, https://doi.org/10.1175/2010JHM1231.1, 2011. a
Kunz, M., Blahak, U., Handwerker, J., Schmidberger, M., Punge, H. J., Mohr, S., Fluck, E., and Bedka, K. M.: The severe hailstorm in southwest Germany on 28 July 2013: characteristics, impacts and meteorological conditions, Q. J. Roy. Meteor. Soc., 144, 231–250, https://doi.org/10.1002/qj.3197, 2018. a, b
Kunz, M., Wandel, J., Fluck, E., Baumstark, S., Mohr, S., and Schemm, S.: Ambient conditions prevailing during hail events in central Europe, Nat. Hazards Earth Syst. Sci., 20, 1867–1887, https://doi.org/10.5194/nhess-20-1867-2020, 2020. a
Kunz, M., Karremann, M. K., and Mohr, S.: Klimawandel in Deutschland, Auswirkungen des Klimawandels auf Starkniederschläge, Gewitter und Schneefall, Springer, Berlin, Heidelberg, 73–84, https://doi.org/10.1007/978-3-662-66696-8_7, 2023. a
Küpfer, K.: Code for Küpfer et al. 2025 (NHESS): Impact-based temporal clustering of multiple meteorological hazard types in southwestern Germany, version 1.0, RADAR [code], https://doi.org/10.35097/bkbwu1c6cbrqjsq6, 2025. a
Lee, R., White, C. J., Adnan, M. S. G., Douglas, J., Mahecha, M. D., O'Loughlin, F. E., Patelli, E., Ramos, A. M., Roberts, M. J., Martius, O., Tubaldi, E., van den Hurk, B., Ward, P. J., and Zscheischler, J.: Reclassifying historical disasters: From single to multi-hazards, Sci. Total Environ., 912, 169120, https://doi.org/10.1016/j.scitotenv.2023.169120, 2024. a
Mailier, P. J., Stephenson, D. B., Ferro, C. A., and Hodges, K. I.: Serial clustering of extratropical cyclones, Mon. Weather Rev., 134, 2224–2240, https://doi.org//10.1175/MWR3160.1, 2006. a, b, c, d
Martius, O., Pfahl, S., and Chevalier, C.: A global quantification of compound precipitation and wind extremes, Geophys. Res. Lett., 43, 7709–7717, https://doi.org/10.1002/2016GL070017, 2016. a, b
Merz, B., Kreibich, H., Schwarze, R., and Thieken, A.: Review article “Assessment of economic flood damage”, Nat. Hazards Earth Syst. Sci., 10, 1697–1724, https://doi.org/10.5194/nhess-10-1697-2010, 2010. a
Mitchell-Wallace, K., Jones, M., Hillier, J., and Foote, M.: Natural catastrophe risk management and modelling: A practitioner's guide, John Wiley & Sons, https://doi.org/10.1002/9781118906057, 2017. a, b, c
Mohr, S., Kunz, M., and Geyer, B.: Hail potential in Europe based on a regional climate model hindcast, Geophys. Res. Lett., 42, 10904–10912, https://doi.org/10.1002/2015GL067118, 2015. a
Mohr, S., Kunz, M., Richter, A., and Ruck, B.: Statistical characteristics of convective wind gusts in Germany, Nat. Hazards Earth Syst. Sci., 17, 957–969, https://doi.org/10.5194/nhess-17-957-2017, 2017. a, b, c, d
Mohr, S., Ehret, U., Kunz, M., Ludwig, P., Caldas-Alvarez, A., Daniell, J. E., Ehmele, F., Feldmann, H., Franca, M. J., Gattke, C., Hundhausen, M., Knippertz, P., Küpfer, K., Mühr, B., Pinto, J. G., Quinting, J., Schäfer, A. M., Scheibel, M., Seidel, F., and Wisotzky, C.: A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 1: Event description and analysis, Nat. Hazards Earth Syst. Sci., 23, 525–551, https://doi.org/10.5194/nhess-23-525-2023, 2023. a, b, c
Mühr, B., Eisenstein, L., Pinto, J. G., Knippertz, P., Mohr, S., and Kunz, M.: CEDIM Forensic Disaster Analysis Group (FDA): Winter storm series: Ylenia, Zeynep, Antonia (int: Dudley, Eunice, Franklin) – February 2022 (NW & Central Europe), Tech. rep., Center for Disaster Management and Risk Reduction Technology (CEDIM), https://doi.org/10.5445/IR/1000143470, 2022. a
Pinto, J. G., Bellenbaum, N., Karremann, M. K., and Della-Marta, P. M.: Serial clustering of extratropical cyclones over the North Atlantic and Europe under recent and future climate conditions, J. Geophys. Res., 118, 12476–12485, https://doi.org/10.1002/2013JD020564, 2013. a
Pinto, J. G., Ulbrich, S., Economou, T., Stephenson, D. B., Karremann, M. K., and Shaffrey, L. C.: Robustness of serial clustering of extratropical cyclones to the choice of tracking method, Tellus A, 68, 32204, https://doi.org/10.3402/tellusa.v68.32204, 2016. a
Polt, K. D., Ward, P. J., Ruiter, M. d., Bogdanovich, E., Reichstein, M., Frank, D., and Orth, R.: Quantifying impact-relevant heatwave durations, Environ. Res. Lett., 18, 104005, https://doi.org/10.1088/1748-9326/acf05e, 2023. a
Priestley, M. D. K., Dacre, H. F., Shaffrey, L. C., Hodges, K. I., and Pinto, J. G.: The role of serial European windstorm clustering for extreme seasonal losses as determined from multi-centennial simulations of high-resolution global climate model data, Nat. Hazards Earth Syst. Sci., 18, 2991–3006, https://doi.org/10.5194/nhess-18-2991-2018, 2018. a
Puskeiler, M., Kunz, M., and Schmidberger, M.: Hail statistics for Germany derived from single-polarization radar data, Atmos. Res., 178–179, 459–470, https://doi.org/10.1016/j.atmosres.2016.04.014, 2016. a
Rauthe, M., Steiner, H., Riediger, U., Mazurkiewicz, A., and Gratzki, A.: A Central European precipitation climatology – Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS), Metorol. Z., 22, 235–256, https://doi.org/10.1127/0941-2948/2013/0436, 2013. a
Raymond, C., Horton, R. M., Zscheischler, J., Martius, O., AghaKouchak, A., Balch, J., Bowen, S. G., Camargo, S. J., Hess, J., Kornhuber, K., Oppenheimer, M., Ruane, A. C., Wahl, T., and White, K.: Understanding and managing connected extreme events, Nat. Clim. Change, 10, 611–621, https://doi.org/10.1038/s41558-020-0790-4, 2020. a
Ripley, B. D.: Spatial statistics, Wiley Series in Probability and Statistics, John Wiley & Sons, Hoboken, NJ, USA, https://doi.org/10.1002/0471725218, 1981. a
Rösch, T. and Treffinger, P.: Cluster Analysis of Distribution Grids in Baden-Württemberg, Energies, 12, 4016, https://doi.org/10.3390/en12204016, 2019. a
Ruiter, M. C. d. and Loon, A. F. v.: The challenges of dynamic vulnerability and how to assess it, iScience, 25, 104720, https://doi.org/10.1016/j.isci.2022.104720, 2022. a
Ruiter, M. C. d., Couasnon, A., Homberg, M. J. C. v. d., Daniell, J. E., Gill, J. C., and Ward, P. J.: Why we can no longer ignore consecutive disasters, Earths Future, 8, e2019EF001425, https://doi.org/10.1029/2019EF001425, 2020. a, b
Schäfer, A., Mühr, B., Daniell, J., Ehret, U., Ehmele, F., Küpfer, K., Brand, J., Wisotzky, C., Skapski, J., Rentz, L., Mohr, S., and Kunz, M.: Hochwasser Mitteleuropa, Juli 2021 (Deutschland), Tech. Rep. 1, Center for Disaster Management and Risk Reduction Technology (CEDIM), https://doi.org/10.5445/IR/1000135730, 2021. a
Statistisches Landesamt Baden-Württemberg: Pressemitteilung 28/2022, Statistisches Landesamt Baden-Württemberg, https://www.statistik-bw.de/Presse/Pressemitteilungen/2022028 (last access: 17 December 2024), 2022. a
Stucki, M. and Egli, T.: Synthesebericht Elementarschutzregister Hagel: Untersuchungen zur Hagelgefahr und zum Widerstand der Gebäudehülle, Tech. rep., Präventionsstiftung der kantonalen Gebäudeversicherungen, Bern, 2007. a
Swiss Re: Weihnachten vor 20 Jahren: Die Stürme Lothar und Martin richten verheerende Schäden in ganz Europa an, Swiss Re, https://www.swissre.com/risk-knowledge/mitigating-climate-risk/winter-storms-in-europe/weihnachten-vor-20-jahren-die-sturme-lothar-martin.html (last access: 19 December 2024), 2019. a
Taszarek, M., Allen, J. T., Púčik, T., Hoogewind, K. A., and Brooks, H. E.: Severe Convective Storms across Europe and the United States. Part II: ERA5 Environments Associated with Lightning, Large Hail, Severe Wind, and Tornadoes, J. Climate, 33, 10263–10286, https://doi.org/10.1175/JCLI-D-20-0346.1, 2020. a
Thieken, A. H., Bessel, T., Kienzler, S., Kreibich, H., Müller, M., Pisi, S., and Schröter, K.: The flood of June 2013 in Germany: how much do we know about its impacts?, Nat. Hazards Earth Syst. Sci., 16, 1519–1540, https://doi.org/10.5194/nhess-16-1519-2016, 2016. a
Trenczek, J., Lühr, O., Eiserbeck, L., Sandhövel, M., and Leuschner, V.: Übersicht vergangener Extremwetterschäden in Deutschland, Tech. rep., Prognos AG, https://www.prognos.com/sites/default/files/2022-07/Prognos_KlimawandelfolgenDeutschland_%C3%9Cbersicht%20vergangener%20Extremwettersch%C3%A4den_AP2_1.pdf (last access: 3 September 2024), 2022. 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, 2021b. a, b, c, d
Tuel, A. and Martius, O.: The influence of modes of climate variability on the sub-seasonal temporal clustering of extreme precipitation, iScience, 25, 103855, https://doi.org/10.1016/j.isci.2022.103855, 2022a. a
Tuel, A. and Martius, O.: Subseasonal temporal clustering of extreme precipitation in the northern hemisphere: Regionalization and physical drivers, J. Climate, 35, 3537–3555, https://doi.org/10.1175/JCLI-D-21-0562.1, 2022b. a
UNDRR: Report of the Open-ended Intergovernmental Expert Working Group on Indicators and Terminology relating to Disaster Risk Reduction, Tech. rep., United Nations Office for Disaster Risk Reduction (UNDRR), United Nations General Assembly, https://digitallibrary.un.org/record/852089 (last access: 3 September 2024), 2016. a
Villarini, G., Smith, J. A., Baeck, M. L., Vitolo, R., Stephenson, D. B., and Krajewski, W. F.: On the frequency of heavy rainfall for the Midwest of the United States, J. Hydrol., 400, 103–120, https://doi.org/10.1016/j.jhydrol.2011.01.027, 2011. a
Wang, Y., Tang, L., Chang, P.-L., and Tang, Y.-S.: Separation of convective and stratiform precipitation using polarimetric radar data with a support vector machine method, Atmos. Meas. Tech., 14, 185–197, https://doi.org/10.5194/amt-14-185-2021, 2021. a
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Academic Press, ISBN 9780127519661, https://doi.org/10.1016/C2017-0-03921-6, 2006. a
Xoplaki, E., Ellsäßer, F., Grieger, J., Nissen, K. M., Pinto, J. G., Augenstein, M., Chen, T.-C., Feldmann, H., Friederichs, P., Gliksman, D., Goulier, L., Haustein, K., Heinke, J., Jach, L., Knutzen, F., Kollet, S., Luterbacher, J., Luther, N., Mohr, S., Mudersbach, C., Müller, C., Rousi, E., Simon, F., Suarez-Gutierrez, L., Szemkus, S., Vallejo-Bernal, S. M., Vlachopoulos, O., and Wolf, F.: Compound events in Germany in 2018: drivers and case studies, Nat. Hazards Earth Syst. Sci., 25, 541–564, https://doi.org/10.5194/nhess-25-541-2025, 2025. a
Yang, Z. and Villarini, G.: Examining the capability of reanalyses in capturing the temporal clustering of heavy precipitation across Europe, Clim. Dynam., 53, 1845–1857, https://doi.org/10.1007/s00382-019-04742-z, 2019. a
Zscheischler, J., Westra, S., van den Hurk, B. J. J. M., Seneviratne, S. I., Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and Zhang, X.: Future climate risk from compound events, Nat. Clim. Change, 8, 469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018. a
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This study uses insurance loss data to examine how different types of extreme weather—such as floods, heavy rain, windstorms, and hail—occur together. It finds that multiple hazards often cluster seasonally, leading to higher losses than when events happen alone. The results highlight the need to assess multiple weather extremes jointly to better understand and manage risk.
This study uses insurance loss data to examine how different types of extreme weather—such as...
Short summary
Using loss data, we assess when and how single and multiple types of meteorological extremes (river floods and heavy rainfall events, windstorms and convective gusts, and hail) are related. We find that the combination of several types of hazards clusters robustly on a seasonal scale, whereas only some single hazard types occur in clusters. This can be associated with higher losses compared to isolated events. We argue for the relevance of jointly considering multiple types of hazards.
Using loss data, we assess when and how single and multiple types of meteorological extremes...
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