Articles | Volume 25, issue 9
https://doi.org/10.5194/nhess-25-3075-2025
© 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-25-3075-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Sep 2025
Research article |
| 05 Sep 2025
| 05 Sep 2025
The ability of a stochastic regional weather generator to reproduce heavy-precipitation events across scales
Xiaoxiang Guan
CORRESPONDING AUTHOR
GFZ Helmholtz Centre for Geosciences, Section Hydrology, Potsdam 14473, Germany
Viet Dung Nguyen
GFZ Helmholtz Centre for Geosciences, Section Hydrology, Potsdam 14473, Germany
Paul Voit
Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam 14476, Germany
Bruno Merz
GFZ Helmholtz Centre for Geosciences, Section Hydrology, Potsdam 14473, Germany
Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam 14476, Germany
Maik Heistermann
Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam 14476, Germany
Sergiy Vorogushyn
GFZ Helmholtz Centre for Geosciences, Section Hydrology, Potsdam 14473, Germany
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Katharina Lengfeld, Paul Voit, Frank Kaspar, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 1227–1232, https://doi.org/10.5194/nhess-23-1227-2023, https://doi.org/10.5194/nhess-23-1227-2023, 2023
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Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
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Heiko Apel, Sergiy Vorogushyn, and Bruno Merz
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Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 22, 2791–2805, https://doi.org/10.5194/nhess-22-2791-2022, https://doi.org/10.5194/nhess-22-2791-2022, 2022
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Maik Heistermann, Heye Bogena, Till Francke, Andreas Güntner, Jannis Jakobi, Daniel Rasche, Martin Schrön, Veronika Döpper, Benjamin Fersch, Jannis Groh, Amol Patil, Thomas Pütz, Marvin Reich, Steffen Zacharias, Carmen Zengerle, and Sascha Oswald
Earth Syst. Sci. Data, 14, 2501–2519, https://doi.org/10.5194/essd-14-2501-2022, https://doi.org/10.5194/essd-14-2501-2022, 2022
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Geosci. Instrum. Method. Data Syst., 11, 75–92, https://doi.org/10.5194/gi-11-75-2022, https://doi.org/10.5194/gi-11-75-2022, 2022
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Maik Heistermann, Till Francke, Martin Schrön, and Sascha E. Oswald
Hydrol. Earth Syst. Sci., 25, 4807–4824, https://doi.org/10.5194/hess-25-4807-2021, https://doi.org/10.5194/hess-25-4807-2021, 2021
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Abhirup Banerjee, Bedartha Goswami, Yoshito Hirata, Deniz Eroglu, Bruno Merz, Jürgen Kurths, and Norbert Marwan
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Miriam Bertola, Alberto Viglione, Sergiy Vorogushyn, David Lun, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 1347–1364, https://doi.org/10.5194/hess-25-1347-2021, https://doi.org/10.5194/hess-25-1347-2021, 2021
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We estimate the contribution of extreme precipitation, antecedent soil moisture and snowmelt to changes in small and large floods across Europe.
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Gustavo Andrei Speckhann, Heidi Kreibich, and Bruno Merz
Earth Syst. Sci. Data, 13, 731–740, https://doi.org/10.5194/essd-13-731-2021, https://doi.org/10.5194/essd-13-731-2021, 2021
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Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
Cited articles
Apel, H., Martínez Trepat, O., Hung, N. N., Chinh, D. T., Merz, B., and Dung, N. V.: Combined fluvial and pluvial urban flood hazard analysis: concept development and application to Can Tho city, Mekong Delta, Vietnam, Nat. Hazards Earth Syst. Sci., 16, 941–961, https://doi.org/10.5194/nhess-16-941-2016, 2016.
Apel, H., Vorogushyn, S., and Merz, B.: Brief communication: Impact forecasting could substantially improve the emergency management of deadly floods: case study July 2021 floods in Germany, Nat. Hazards Earth Syst. Sci., 22, 3005–3014, https://doi.org/10.5194/nhess-22-3005-2022, 2022.
Bardossy, A. and Plate, E. J.: Space-time model for daily rainfall using atmospheric circulation patterns, Water Resour. Res., 28, 1247–1259, https://doi.org/10.1029/91WR02589, 1992.
Beniston, M. and Stephenson, D. B.: Extreme climatic events and their evolution under changing climatic conditions, Global Planet. Change, 44, 1–9, https://doi.org/10.1016/j.gloplacha.2004.06.001, 2004.
Benoit, L. and Mariethoz, G.: Generating synthetic rainfall with geostatistical simulations, WIREs Water, 4, e1199, https://doi.org/10.1002/wat2.1199, 2017.
Breinl, K., Turkington, T., and Stowasser, M.: Stochastic generation of multi-site daily precipitation for applications in risk management, J. Hydrol., 498, 23–35, https://doi.org/10.1016/j.jhydrol.2013.06.015, 2013.
Caldas-Alvarez, A., Augenstein, M., Ayzel, G., Barfus, K., Cherian, R., Dillenardt, L., Fauer, F., Feldmann, H., Heistermann, M., Karwat, A., Kaspar, F., Kreibich, H., Lucio-Eceiza, E. E., Meredith, E. P., Mohr, S., Niermann, D., Pfahl, S., Ruff, F., Rust, H. W., Schoppa, L., Schwitalla, T., Steidl, S., Thieken, A. H., Tradowsky, J. S., Wulfmeyer, V., and Quaas, J.: Meteorological, impact and climate perspectives of the 29 June 2017 heavy precipitation event in the Berlin metropolitan area, Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, 2022
Chen, J. and Brissette, F. P.: Stochastic generation of daily precipitation amounts: review and evaluation of different models, Clim. Res., 59, 189–145, https://doi.org/10.3354/cr01214, 2014.
Christensen, J. H. and Christensen, O. B.: Severe summertime flooding in Europe, Nature, 421, 805–806, https://doi.org/10.1038/421805a, 2003.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018.
ECAD: European Climate Assessment and Dataset project: E-OBS gridded precipitation and temperature data, https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php#datafiles (last access: 23 June 2025), Copernicus Climate Data Store [data set], 2025.
Ehmele, F., Kautz, L.-A., Feldmann, H., He, Y., Kadlec, M., Kelemen, F. D., Lentink, H. S., Ludwig, P., Manful, D., and Pinto, J. G.: Adaptation and application of the large LAERTES-EU regional climate model ensemble for modeling hydrological extremes: a pilot study for the Rhine basin, Nat. Hazards Earth Syst. Sci., 22, 677–692, https://doi.org/10.5194/nhess-22-677-2022, 2022.
Falter, D., Schröter, K., Dung, N. V., Vorogushyn, S., Kreibich, H., Hundecha, Y., Apel, H., and Merz, B.: Spatially coherent flood risk assessment based on long-term continuous simulation with a coupled model chain, J. Hydrol., 524, 182–193, https://doi.org/10.1016/j.jhydrol.2015.02.021, 2015.
Fatichi, S., Ivanov, V. Y., and Caporali, E.: Simulation of future climate scenarios with a weather generator, Adv. Water Resour., 34, 448–467, https://doi.org/10.1016/j.advwatres.2010.12.013, 2011.
Fauer, F. S., Ulrich, J., Jurado, O. E., and Rust, H. W.: Flexible and consistent quantile estimation for intensity–duration–frequency curves, Hydrol. Earth Syst. Sci., 25, 6479–6494, https://doi.org/10.5194/hess-25-6479-2021, 2021.
Fowler, H. J. and Kilsby, C. G.: A regional frequency analysis of United Kingdom extreme rainfall from 1961 to 2000, Int. J. Climatol., 23, 1313–1334, https://doi.org/10.1002/joc.943, 2003.
Ganguli, P. and Merz, B.: Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe, Scientific Reports, 9, 1–14, https://doi.org/10.1038/s41598-019-49822-6, 2019.
Gao, C., Guan, X. J., Booij, M. J., Meng, Y., and Xu, Y. P.: A new framework for a multi-site stochastic daily rainfall model: Coupling a univariate Markov chain model with a multi-site rainfall event model, J. Hydrol., 598, 126478, https://doi.org/10.1016/j.jhydrol.2021.126478, 2021.
Gauch, M., Klotz, D., Kratzert, F., Nearing, S., Hochreiter, S., and Lin, J.: A Machine Learner's Guide to Streamflow Prediction, in: Workshop on AI for Earth Sciences 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada, 12 December 2020, https://cs.uwaterloo.ca/~jimmylin/publications/ 2020.
Gvoždíková, B., Müller, M., and Kašpar, M.: Spatial patterns and time distribution of central European extreme precipitation events between 1961 and 2013, Int. J. Climatol., 39, 3282–3297, https://doi.org/10.1002/joc.6019, 2019.
Haberlandt, U., Hundecha, Y., Pahlow, M., and Schumann, A. H.: Rainfall generators for application in flood studies, in: Flood Risk Assessment and Management, edited by: Schumann, A. H., Springer, Netherlands, 117–147, https://doi.org/10.1007/978-90-481-9917-4_7, 2011.
Harris, C. N. P., Quinn, A. D., and Bridgeman, J.: The use of probabilistic weather generator information for climate change adaptation in the UK water sector, Meteorol. Appl., 21, 129–140, https://doi.org/10.1002/met.1335, 2014.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Hu, G. and Franzke, C. L. E.: Evaluation of Daily Precipitation Extremes in Reanalysis and Gridded Observation-Based Data Sets Over Germany, Geophys. Res. Lett., 47, e2020GL089624, https://doi.org/10.1029/2020GL089624, 2020.
Jeferson de Medeiros, F., Prestrelo de Oliveira, C. and Avila-Diaz, A. Evaluation of extreme precipitation climate indices and their projected changes for Brazil: From CMIP3 to CMIP6. Weather and Climate Extremes, 38, 100511, https://doi.org/10.1016/j.wace.2022.100511, 2022.
Kiem, A. S., Kuczera, G., Kozarovski, P., Zhang, L., and Willgoose, G.: Stochastic generation of future hydroclimate using temperature as a climate change covariate, Water Resour. Res., 57, 2020WR027331, https://doi.org/10.1029/2020WR027331, 2021.
Koutsoyiannis, D., Kozonis, D., and Manetas, A.: A mathematical framework for studying rainfall intensity-duration-frequency relationships, J. Hydrol., 206, 118–135, https://doi.org/10.1016/S0022-1694(98)00097-3, 1998.
Kreibich, H., Petrow, T., Thieken, A. H., Müller, M., and Merz, B.: Consequences of the extreme flood event of August 2002 in the city of Dresden, Germany, in: Sustainable Water Management Solutions for Large Cities, edited by: Savic, D. A., Mariño, M. A., Savenije, H. H. G., and Bertoni, J. C., IAHS Publication, 293, 164–173, 2005.
Kreibich, H., Di Baldassarre, G., Vorogushyn, S., Aerts, J. C. J. H., Apel, H., Aronica, G. T., Arnbjerg-Nielsen, K., Bouwer, L.M., Bubeck, P., Caloiero, T., Chinh, D.T., Cortès, M., Gain, A.K., Giampá, V., Kuhlicke, C., Kundzewicz, Z. W., Llasat, M. C., Mård, J., Matczak, P., Mazzoleni, M., Molinari, D., Dung, N. V., Petrucci, O., Schröter, K., Slager, K., Thieken, A. H., Ward, P. J., and Merz, B.: Adaptation to flood risk: Results of international paired flood event studies, Earth's Future, 5, 953–965, https://doi.org/10.1002/2017ef000606, 2017.
Lenderink, G. and Fowler, H. J.: Understanding rainfall extremes, Nat. Clim. Change, 7, 391–393, https://doi.org/10.1038/nclimate3305, 2017.
Lengfeld, K., Winterrath, T., Junghänel, T., Hafer, M., and Becker, A.: Characteristic spatial extent of hourly and daily precipitation events in Germany derived from 16 years of radar data, Meteorol. Z., 28, 363–378, https://doi.org/10.1127/metz/2019/0964, 2019.
Lengfeld, K., Walawender, E., Winterrath, T., and Becker, A.: CatRaRE: A Catalogue of radar-based heavy rainfall events in Germany derived from 20 years of data, Meteorol. Z., 30, 469–487, https://doi.org/10.1127/metz/2021/1088, 2021.
Makkonen, L.: Plotting Positions in Extreme Value Analysis, J. Appl. Meteorol. Clim., 45, 334–340, https://doi.org/10.1175/JAM2349.1, 2006.
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themel, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I.: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003, https://doi.org/10.1029/2009RG000314, 2010.
Matte, D., Christensen, J. H., and Ozturk, T.: Spatial extent of precipitation events: when big is getting bigger, Clim. Dynam., 58, 1861–1875, https://doi.org/10.1007/s00382-021-05998-0, 2022.
Mehrotra, R. and Sharma, A.: Development and Application of a Multisite Rainfall Stochastic Downscaling Framework for Climate Change Impact Assessment, Water Resour. Res., 46, W07526, https://doi.org/10.1029/2009WR008423, 2010.
Merz, B., Blöschl, G., Vorogushyn, S., Dottori, F., Aerts, J. C. J. H., Bates, P., Bertola, M., Kemter, M., Kreibich, H., Lall, U., and Macdonald, E.: Causes, impacts and patterns of disastrous river floods, Nature Reviews Earth & Environment, 2, 592–609, https://doi.org/10.1038/s43017-021-00195-3, 2021.
Merz, B., Nguyen, V. D., Guse, B., Han, L., Guan, X., Rakovec, O., Samaniego, L., Ahrens, B., and Vorogushyn, S.: Spatial counterfactuals to explore disastrous flooding, Environ. Res. Lett., 19, 044022, https://doi.org/10.1088/1748-9326/ad22b9, 2024.
Minářová, J., Müller, M., Clappier, A., and Kašpar, M.: Comparison of extreme precipitation characteristics between the Ore Mountains and the Vosges Mountains (Europe), Theor. Appl. Climatol., 133, 1249–1268, https://doi.org/10.1007/s00704-017-2247-x, 2018.
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.
Montanari, A., Merz, B., and Blöschl, G.: HESS Opinions: The sword of Damocles of the impossible flood, Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, 2024.
Müller, M. and Kaspar, M.: Event-adjusted evaluation of weather and climate extremes, Nat. Hazards Earth Syst. Sci., 14, 473–483, https://doi.org/10.5194/nhess-14-473-2014, 2014.
Najibi, N., Perez, A. J., Arnold, W., Schwarz, A., Maendly, R., and Steinschneider, S.: (2024). A statewide, weather-regime based stochastic weather generator for process-based bottom-up climate risk assessments in California – Part II: Thermodynamic and dynamic climate change scenarios, Climate Services, 34, 100485, https://doi.org/10.1016/j.cliser.2024.100485, 2024.
NatCatSERVICE: Global Natural Catastrophe and Socioeconomic Impact Database, Munich Re, https://www.munichre.com/en/reinsurance/business/non-life/natcatservice/index.html(last access: 28 Aug 2025), 2023
Naveau, P., Huser, R., Ribereau, P. and Hannart, A.: Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection, Water Resour. Res., 52, 2753–2769, https://doi.org/10.1002/2015WR018552, 2016.
Nguyen, V. D., Merz, B., Hundecha, Y., Haberlandt, U., and Vorogushyn, S.: Comprehensive evaluation of an improved large-scale multi-site weather generator for Germany, Int. J. Climatol., 41, 4933–4956, https://doi.org/10.1002/joc.7107, 2021.
Nguyen, V. D., Vorogushyn, S., Nissen, K., Brunner, L., and Merz, B.: A non-stationary climate-informed weather generator for assessing future flood risks, Adv. Stat. Clim. Meteorol. Oceanogr., 10, 195–216, https://doi.org/10.5194/ascmo-10-195-2024, 2024.
Orlanski, I.: A Rational Subdivision of Scales for Atmospheric Processes, B. Am. Meteorol. Soc., 56, 527–530, 1975.
Philipp, A., Della-Marta, P. M., Jacobeit, J., Fereday, D. R., Jones, P. D., Moberg, A., and Wanner, H.: Long-Term Variability of Daily North Atlantic–European Pressure Patterns since 1850 Classified by Simulated Annealing Clustering, J. Climate, 20, 4065–4095, https://doi.org/10.1175/jcli4175.1, 2007.
Qin, X. and Lu, Y.: Study of Climate Change Impact on Flood Frequencies: A Combined Weather Generator and Hydrological Modeling Approach, J. Hydrometeorol., 15, 1205–1219, https://doi.org/10.1175/JHM-D-13-0126.1, 2014.
Ramos, A. M., Trigo, R. M., and Liberato, M. L. R.: Ranking of multi-day extreme precipitation events over the Iberian Peninsula, Int. J. Climatol., 37, 607–620, https://doi.org/10.1002/joc.4726, 2017.
Sairam, N., Brill, F., Sieg, T., Farrag, M., Kellermann, P., Nguyen, V. D., Lüdtke, S., Merz, B., Schröter, K., Vorogushyn, S., and Kreibich, H.: Process-Based Flood Risk Assessment for Germany, Earth's Future, 9, e2021EF002259, https://doi.org/10.1029/2021EF002259, 2021.
Serinaldi, F. and Kilsby, C. G.: Simulating daily rainfall fields over large areas for collective risk estimation, J. Hydrol., 512, 285–302, https://doi.org/10.1016/j.jhydrol.2014.02.043, 2014.
Steinschneider, S. and Brown, C.: A semiparametric multivariate, multisite weather generator with low-frequency variability for use in climate risk assessments, Water Resour. Res., 49, 7205–7220, https://doi.org/10.1002/wrcr.20528, 2013.
Swiss Re Institute: sigma: Natural catastrophes in 2023: Gearing up for today's and tomorrow's weather risks (No 1/2024), Swiss Re Ltd., Zurich, Switzerland, https://www.swissre.com/dam/jcr:c9385357-6b86-486a-9ad8-78679037c10e/2024-03-sigma1-natural-catastrophes.pdf, (last access: 28 August 2025), 2024.
Szönyi, M., Roezer, V., Deubelli, T., Ulrich, J., MacClune, K., Laurien, F., and Norton, R.: PERC Flood event review “Bernd” https://floodresilience.net/zurich-flood-resilience-alliance/ (last access: 28 Aug 2025), 2022.
Thieken, A. H., Samprogna Mohor, G., Kreibich, H., and Müller, M.: Compound inland flood events: different pathways, different impacts and different coping options, Nat. Hazards Earth Syst. Sci., 22, 165–185, https://doi.org/10.5194/nhess-22-165-2022, 2022.
Tseng, S. C., Chen, C. J., and Senarath, S. U. S.: Evaluation of multi-site precipitation generators across scales, Int. J. Climatol., 40, 4638–4656, https://doi.org/10.1002/joc.6480, 2020.
Ullrich, S. L., Hegnauer, M., Nguyen, D. V., Merz, B., Kwadijk, J., and Vorogushyn, S.: Comparative evaluation of two types of stochastic weather generators for synthetic precipitation in the Rhine basin, J. Hydrol., 601, 126544, https://doi.org/10.1016/j.jhydrol.2021.126544, 2021.
Ulrich, J., Jurado, O. E., Peter, M., Scheibel, M., and Rust, H. W.: Estimating IDF Curves Consistently over Durations with Spatial Covariates, Water, 12, 3119, https://doi.org/10.3390/w12113119, 2020.
Ulrich, J., Ritschel, C., Mack, L., Jurado, O. E., Fauer, F. S., Detring, C., and Joedicke, S.: IDF: Estimation and Plotting of IDF Curves, R package version 2.1.2, CRAN [code], https://CRAN.R-project.org/package=IDF (last access: 22 July 2022), 2021.
Voit, P.: xWEI-Quantifying-the-extremeness-of-precipitation-across-scales - Code Repository, Version v.1.0.1, Zenodo [code], https://doi.org/10.5281/zenodo.6556463, 2022.
Voit, P. and Heistermann, M.: A new index to quantify the extremeness of precipitation across scales, Nat. Hazards Earth Syst. Sci., 22, 2791–2805, https://doi.org/10.5194/nhess-22-2791-2022, 2022.
Voit, P. and Heistermann, M.: A downward-counterfactual analysis of flash floods in Germany, Nat. Hazards Earth Syst. Sci., 24, 2147–2164, https://doi.org/10.5194/nhess-24-2147-2024, 2024.
Vorogushyn, S., Han, L., Apel, H., Nguyen, V. D., Guse, B., Guan, X., Rakovec, O., Najafi, H., Samaniego, L., and Merz, B.: It could have been much worse: spatial counterfactuals of the July 2021 flood in the Ahr Valley, Germany, Nat. Hazards Earth Syst. Sci., 25, 2007–2029, https://doi.org/10.5194/nhess-25-2007-2025, 2025.
Wilks, D. S.: Multisite generalization of a daily stochastic precipitation generation model, J. Hydrol., 210, 178–191, https://doi.org/10.1016/S0022-1694(98)00186-3, 1998.
Winter, B., Schneeberger, K., Dung, N.V., Huttenlau, M., Achleitner, S., Stötter, J., Merz, B., and Vorogushyn, S.: A continuous modelling approach for design flood estimation on sub-daily time scale, Hydrolog. Sci. J., 64, 539–554, https://doi.org/10.1080/02626667.2019.1593419, 2019.
Woo, G.: Downward Counterfactual Search for Extreme Events, Frontiers in Earth Science, 7, 340, https://doi.org/10.3389/feart.2019.00340, 2019.
Yang, L., Franzke, C. L. E., and Duan, W.: Evaluation and projections of extreme precipitation using a spatial extremes framework, Int. J. Climatol., 43, 3453–3475, https://doi.org/10.1002/joc.8038, 2023.
Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson, T. C., Trewin, B., and Zwiers, F. W.: Indices for monitoring changes in extremes based on daily temperature and precipitation data, WIREs Climate Change, 2, 851–870, https://doi.org/10.1002/wcc.147, 2011.
Zhou, L., Meng, Y., Lu, C., Yin, S., and Ren, D.: A frequency-domain nonstationary multi-site rainfall generator for use in hydrological impact assessment, J. Hydrol., 585, 124770, https://doi.org/10.1016/j.jhydrol.2020.124770, 2020.
Short summary
We evaluated a multi-site stochastic regional weather generator (nsRWG) for its ability to capture the cross-scale extremity of heavy-precipitation events (HPEs) in Germany. We generated 100 realizations of 72 years of daily synthetic precipitation data. The performance was assessed using WEI and xWEI indices, which measure event extremity across spatiotemporal scales. The results show that nsRWG simulates the extremity patterns of HPEs well, although it overestimates short-duration small-extent events.
We evaluated a multi-site stochastic regional weather generator (nsRWG) for its ability to...
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