Articles | Volume 26, issue 5
https://doi.org/10.5194/nhess-26-2189-2026
© Author(s) 2026. 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-26-2189-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 May 2026
Research article |
| 11 May 2026
| 11 May 2026
Considering rainfall events from a neighborhood improves local flood frequency analysis
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Felix Fauer
Institute for Meteorology, Freie Universität Berlin, Berlin, Germany
Maik Heistermann
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
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what ifscenarios, simulate historical rainfall in different locations to estimate flood levels. Our new study refines this by deriving more-plausible local scenarios, using the June 2024 Bavaria flood as a case study. This method could improve preparedness for future floods.
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Maik Heistermann, Till Francke, Martin Schrön, and Sascha E. Oswald
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Cosmic-ray neutron sensing (CRNS) is a non-invasive technique used to obtain estimates of soil water content (SWC) at a horizontal footprint of around 150 m and a vertical penetration depth of up to 30 cm. However, typical CRNS applications require the local calibration of a function which converts neutron counts to SWC. As an alternative, we propose a generalized function as a way to avoid the use of local reference measurements of SWC and hence a major source of uncertainty.
Gerd Bürger and Maik Heistermann
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Our subject is a new catalogue of radar-based heavy rainfall events (CatRaRE) over Germany and how it relates to the concurrent atmospheric circulation. We classify reanalyzed daily atmospheric fields of convective indices according to CatRaRE, using conventional statistical and more recent machine learning algorithms, and apply them to present and future atmospheres. Increasing trends are projected for CatRaRE-type probabilities, from reanalyzed as well as from simulated atmospheric fields.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
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Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
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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Estimating the severity of a rainfall event based on the damage caused is easy but highly depends on the affected region. A less biased measure for the extremeness of an event is its rarity combined with its spatial extent. In this brief communication, we investigate the sensitivity of such measures to the underlying dataset and highlight the importance of considering multiple spatial and temporal scales using the devastating rainfall event in July 2021 in central Europe as an example.
Omar Seleem, Georgy Ayzel, Axel Bronstert, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 809–822, https://doi.org/10.5194/nhess-23-809-2023, https://doi.org/10.5194/nhess-23-809-2023, 2023
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Data-driven models are becoming more of a surrogate that overcomes the limitations of the computationally expensive 2D hydrodynamic models to map urban flood hazards. However, the model's ability to generalize outside the training domain is still a major challenge. We evaluate the performance of random forest and convolutional neural networks to predict urban floodwater depth and investigate their transferability outside the training domain.
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
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
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In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
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
Short summary
Short summary
To better understand how the frequency and intensity of heavy precipitation events (HPEs) will change with changing climate and to adapt disaster risk management accordingly, we have to quantify the extremeness of HPEs in a reliable way. We introduce the xWEI (cross-scale WEI) and show that this index can reveal important characteristics of HPEs that would otherwise remain hidden. We conclude that the xWEI could be a valuable instrument in both disaster risk management and research.
Michael Dietze, Rainer Bell, Ugur Ozturk, Kristen L. Cook, Christoff Andermann, Alexander R. Beer, Bodo Damm, Ana Lucia, Felix S. Fauer, Katrin M. Nissen, Tobias Sieg, and Annegret H. Thieken
Nat. Hazards Earth Syst. Sci., 22, 1845–1856, https://doi.org/10.5194/nhess-22-1845-2022, https://doi.org/10.5194/nhess-22-1845-2022, 2022
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The flood that hit Europe in July 2021, specifically the Eifel, Germany, was more than a lot of fast-flowing water. The heavy rain that fell during the 3 d before also caused the slope to fail, recruited tree trunks that clogged bridges, and routed debris across the landscape. Especially in the upper parts of the catchments the flood was able to gain momentum. Here, we discuss how different landscape elements interacted and highlight the challenges of holistic future flood anticipation.
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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This paper presents a dense network of cosmic-ray neutron sensing (CRNS) to measure spatio-temporal soil moisture patterns during a 2-month campaign in the Wüstebach headwater catchment in Germany. Stationary, mobile, and airborne CRNS technology monitored the root-zone water dynamics as well as spatial heterogeneity in the 0.4 km2 area. The 15 CRNS stations were supported by a hydrogravimeter, biomass sampling, and a wireless soil sensor network to facilitate holistic hydrological analysis.
Till Francke, Maik Heistermann, Markus Köhli, Christian Budach, Martin Schrön, and Sascha E. Oswald
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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Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools like soil moisture, snow, or vegetation. This study presents a directional shielding approach, aiming to measure in specific directions only. The results show that non-directional neutron transport blurs the signal of the targeted direction. For typical instruments, this does not allow acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates is feasible.
Felix S. Fauer, Jana Ulrich, Oscar E. Jurado, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6479–6494, https://doi.org/10.5194/hess-25-6479-2021, https://doi.org/10.5194/hess-25-6479-2021, 2021
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Extreme rainfall events are modeled in this study for different timescales. A new parameterization of the dependence between extreme values and their timescale enables our model to estimate extremes on very short (1 min) and long (5 d) timescales simultaneously. We compare different approaches of modeling this dependence and find that our new model improves performance for timescales between 2 h and 2 d without affecting model performance on other timescales.
Jana Ulrich, Felix S. Fauer, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6133–6149, https://doi.org/10.5194/hess-25-6133-2021, https://doi.org/10.5194/hess-25-6133-2021, 2021
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The characteristics of extreme precipitation on different timescales as well as in different seasons are relevant information, e.g., for designing hydrological structures or managing water supplies. Therefore, our aim is to describe these characteristics simultaneously within one model. We find similar characteristics for short extreme precipitation at all considered stations in Germany but pronounced regional differences with respect to the seasonality of long-lasting extreme events.
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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Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil moisture in footprints extending over hectometres in the horizontal and decimetres in the vertical. This study, however, demonstrates the potential of CRNS to obtain spatio-temporal patterns of soil moisture beyond isolated footprints. To that end, we analyse data from a unique observational campaign that featured a dense network of more than 20 neutron detectors in an area of just 1 km2.
Cited articles
Abbasian, M., Wright, D. B., Notaro, M., Vavrus, S., and Vimont, D. J.: Flood frequency sampling error: insights from regional analysis, stochastic storm transposition, and physics-based modeling, J. Hydrol., 133802, https://doi.org/10.1016/j.jhydrol.2025.133802, 2025. a, b
Anusha, G. S. and Maheswaran, R.: Quantitative assessment of automated threshold selection methods for Generalized Pareto Distribution for modelling precipitation extremes in the Indian subcontinent, J. Hydrol., 134166, https://doi.org/10.1016/j.jhydrol.2025.134166, 2025. a
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. a
Barredo, J. I.: Major flood disasters in Europe: 1950–2005, Nat. Hazards, 42, 125–148, https://doi.org/10.1007/s11069-006-9065-2, 2007. a
Bentzien, S. and Friederichs, P.: Decomposition and graphical portrayal of the quantile score, Q. J. Roy. Meteor. Soc., 140, 1924–1934, https://doi.org/10.1002/qj.2284, 2014. a, b
Borga, M., Boscolo, P., Zanon, F., and Sangati, M.: Hydrometeorological analysis of the 29 August 2003 flash flood in the Eastern Italian Alps, J. Hydrometeorol., 8, 1049–1067, https://doi.org/10.1175/JHM593.1, 2007. a
Boughton, W.: A review of the USDA SCS curve number method, Aust. J. Soil Res., 27, 511–523, https://doi.org/10.1071/SR9890511, 1989. a
Bürger, G. and Heistermann, M.: Shallow and deep learning of extreme rainfall events from convective atmospheres, Nat. Hazards Earth Syst. Sci., 23, 3065–3077, https://doi.org/10.5194/nhess-23-3065-2023, 2023. a
Bürger, G. and Heistermann, M.: Present and future trends of extreme short-term rainfall events in Germany, by downscaling convective environments of ERA5 and a CMIP6 ensemble, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3584, 2025. a
Cooley, D.: Return periods and return levels under climate change, in: Extremes in a Changing Climate: Detection, Analysis and Uncertainty, Springer, https://doi.org/10.1007/978-94-007-4479-0_4, 97–114, 2012. a
CRED/UCLouvain: EM-DAT International Disaster Database, http://www.emdat.be (last access: 25 January 2024), 2023. a
Miami Conservancy District and Morgan, A. E.: Exhibits to Accompany Report of the Chief Engineer, Arthur E. Morgan: Submitting a Plan for the Protection of the District from Flood Damage, Miami Conservancy District, ISBN-10 1018383476, 1916. a
Emmanuel, I., Payrastre, O., Andrieu, H., and Zuber, F.: A method for assessing the influence of rainfall spatial variability on hydrograph modeling. First case study in the Cevennes Region, southern France, J. Hydrol., 555, 314–322, https://doi.org/10.1016/j.jhydrol.2017.10.011, 2017. a
European Commission, Directorate-General for Environment: A compilation of reporting sheets adopted by water directors common implementation strategy for the Water Framework Directive (2000/60/EC). Guidance document No 29, https://circabc.europa.eu/sd/a/acbcd98a-9540-480e-a876-420b7de64eba/Floods%2520Reporting%2520guidance%2520-%2520final_with%2520revised%2520paragraph%25204.2.3.pdf, (last access: 27 June 2024), 2013. a
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. a
Fauer, F. S. and Rust, H. W.: Non-stationary large-scale statistics of precipitation extremes in central Europe, Stoch. Env. Res. Risk. A., 37, 4417–4429, https://doi.org/10.1007/s00477-023-02515-z, 2023. a
Fisher, R. A. and Tippett, L. H. C.: Limiting forms of the frequency distribution of the largest or smallest member of a sample, Math. Proc. Camb. Philos. Soc., 24, 180–190, https://doi.org/10.1017/S0305004100015681, 1928. a
Fontaine, T. A. and Potter, K. W.: Estimating probabilities of extreme rainfalls, J. Hydraul. Eng., 115, 1562–1575, https://doi.org/10.1061/(ASCE)0733-9429(1989)115:11(1562), 1989. a
Fuller, W. E.: Flood flows, T. Am. Soc. Civ. Eng., 77, 564–617, 1914. a
Gaume, E., Livet, M., Desbordes, M., and Villeneuve, J.-P.: Hydrological analysis of the river Aude, France, flash flood on 12 and 13 November 1999, J. Hydrol., 286, 135–154, https://doi.org/10.1016/j.jhydrol.2003.09.015, 2004. a
Gaume, E., Gaál, L., Viglione, A., Szolgay, J., Kohnová, S., and Blöschl, G.: Bayesian MCMC approach to regional flood frequency analyses involving extraordinary flood events at ungauged sites, J. Hydrol., 394, 101–117, 2010. a
Gnedenko, B. V.: Sur La Distribution Limite Du Terme Maximum D'Une Serie Aleatoire, Ann. Math., 44, 423–453, 1943. a
Grimaldi, S., Petroselli, A., Alonso, G., and Nardi, F.: Flow time estimation with spatially variable hillslope velocity in ungauged basins, Adv. Water Resour., 33, 1216–1223, https://doi.org/10.1016/j.advwatres.2010.06.003, 2010. a
Gumbel, E. J.: Statistics of Extremes, Columbia University Press, https://doi.org/10.7312/gumb92958, 1958. a
Guse, B., Hofherr, Th., and Merz, B.: Introducing empirical and probabilistic regional envelope curves into a mixed bounded distribution function, Hydrol. Earth Syst. Sci., 14, 2465–2478, https://doi.org/10.5194/hess-14-2465-2010, 2010. a
Halbert, K., Nguyen, C. C., Payrastre, O., and Gaume, E.: Reducing uncertainty in flood frequency analyses: a comparison of local and regional approaches involving information on extreme historical floods, J. Hydrol., 541, 90–98, https://doi.org/10.1016/j.jhydrol.2016.01.017, 2016. a
Hansen, E. M.: Probable maximum precipitation for design floods in the United States, J. Hydrol., 96, 267–278, https://doi.org/10.1016/0022-1694(87)90158-2, 1987. a
Klemes, V.: Probability of extreme hydrometeorological events-a different approach, IAHS-AISH P., 213, pp. 167–176, 1993. a
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. a
Llasat, M. C., Llasat-Botija, M., Prat, M. A., Porcú, F., Price, C., Mugnai, A., Lagouvardos, K., Kotroni, V., Katsanos, D., Michaelides, S., Yair, Y., Savvidou, K., and Nicolaides, K.: High-impact floods and flash floods in Mediterranean countries: the FLASH preliminary database, Adv. Geosci., 23, 47–55, https://doi.org/10.5194/adgeo-23-47-2010, 2010. a
Maidment, D., Olivera, F., Calver, A., Eatherall, A., and Fraczek, W.: Unit hydrograph derived from a spatially distributed velocity field, Hydrol. Process., 10, 831–844, https://doi.org/10.1002/(SICI)1099-1085(199606)10:6<831::AID-HYP374>3.0.CO;2-N, 1996. a
Marchi, L., Borga, M., Preciso, E., and Gaume, E.: Characterisation of selected extreme flash floods in Europe and implications for flood risk management, J. Hydrol., 394, 118–133, https://doi.org/10.1016/j.jhydrol.2010.07.017, 2010. a
Merz, B., Basso, S., Fischer, S., Lun, D., Blöschl, G., Merz, R., Guse, B., Viglione, A., Vorogushyn, S., Macdonald, E., Wietzke, L., and Schumann, A.: Understanding heavy tails of flood peak distributions, Water Resour. Res., 58, e2021WR030506, https://doi.org/10.1029/2021WR030506, 2022. a
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., https://doi.org/10.1088/1748-9326/ad22b9, 2024. a
Merz, R. and Blöschl, G.: Flood frequency hydrology: 1. Temporal, spatial, and causal expansion of information, Water Resour. Res., 44, https://doi.org/10.1029/2007WR006744, 2008. a
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Stationarity is dead: whither water management?, Science, 319, 573–574, https://doi.org/10.1126/science.1151915, 2008. a
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. a
Morrison, J. E. and Smith, J. A.: Stochastic modeling of flood peaks using the generalized extreme value distribution, Water Resour. Res., 38, 41–1, 2002. a
Nguyen, C. C., Gaume, E., and Payrastre, O.: Regional flood frequency analyses involving extraordinary flood events at ungauged sites: further developments and validations, J. Hydrol., 508, 385–396, https://doi.org/10.1016/j.jhydrol.2013.09.058, 2014. a
Petrucci, O., Aceto, L., Bianchi, C., Bigot, V., Brázdil, R., Pereira, S., Kahraman, A., Kılıç, Ö., Kotroni, V., Llasat, M. C., Llasat-Botija, M., Papagiannaki, K., Pasqua, A. A., Řehoř, J., Geli, J. R., Salvati, P., Vinet, F., and Zêzere, J. L.: Flood fatalities in Europe, 1980–2018: variability, features, and lessons to learn, Water-Sui., 11, 1682, https://doi.org/10.3390/w11081682, 2019. a
Seibert, S. P., Auerswald, K., Seibert, S. P., and Auerswald, K.: Abflussentstehung–wie aus Niederschlag Abfluss wird, Hochwasserminderung im ländlichen Raum: Ein Handbuch zur quantitativen Planung, https://doi.org/10.1007/978-3-662-61033-6_4, 61–93, 2020. a
Thompson, V., Coumou, D., Beyerle, U., Ommer, J., Cloke, H. L., and Fischer, E.: Alternative rainfall storylines for the Western European July 2021 floods from ensemble boosting, Communications Earth and Environment, 6, 427, https://doi.org/10.1038/s43247-025-02386-y, 2025. a
U.S. Department of Agriculture-Soil Conservation Service: Estimation of Direct Runoff From Storm Rainfall, SCS National Engineering Handbook, Section 4, Hydrology. Chap. 10, https://lmpublicsearch.lm.doe.gov/SiteDocs/111673.pdf (last access: 4 May 2026), 1972. 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. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Voit, P.: A downward counterfactual analysis of flash floods in Germany – Code repository (v0.1), Zenodo [code], https://doi.org/10.5281/zenodo.10473424, last access: 15 August 2024. a, b
Voit, P. and Heistermann, M.: Brief communication: Stay local or go global? On the construction of plausible counterfactual scenarios to assess flash flood hazards, Nat. Hazards Earth Syst. Sci., 24, 4609–4615, https://doi.org/10.5194/nhess-24-4609-2024, 2024a. a, b
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. a
Winterrath, T., Rosenow, W., and Weigl, E.: On the DWD quantitative precipitation analysis and nowcasting system for real-time application in German flood risk management, weather radar and hydrology, IAHS-AISH P., 351, 323–329, 2012. a
Winterrath, T., Brendel, C., Hafer, M., Junghänel, T., Klameth, A., Lengfeld, K., Walawender, E., Weigl, E., and Becker, A.: Gauge-adjusted one-hour precipitation sum (RW):, RADKLIM Version 2017.002: Reprocessed gauge-adjusted radar data, one-hour precipitation sums (RW), Deutscher Wetterdienst (DWD)/German Weather Service [data set], https://doi.org//10.5676/DWD/RADKLIM_RW_V2017.002, 2018. a, b
WMO: Manual on estimation of probable maximum precipitation (PMP), https://library.wmo.int/viewer/35708/?offset=#page=1&viewer=picture&o=bookmarks&n=0&q=, (last access: 18 September 2024), 2009. a
Wright, D. B., Smith, J. A., and Baeck, M. L.: Flood frequency analysis using radar rainfall fields and stochastic storm transposition, Water Resour. Res., 50, 1592–1615, 2014. a
Wright, D. B., Mantilla, R., and Peters-Lidard, C. D.: A remote sensing-based tool for assessing rainfall-driven hazards, Environ. Modell. Softw., 90, 34–54, 2017. a
Wright, D. B., Yu, G., and England, J. F.: Six decades of rainfall and flood frequency analysis using stochastic storm transposition: review, progress, and prospects, J. Hydrol., 585, https://doi.org/10.1016/j.jhydrol.2020.124816, 2020. a
Zhou, Z., Smith, J. A., Wright, D. B., Baeck, M. L., Yang, L., and Liu, S.: Storm catalog-based analysis of rainfall heterogeneity and frequency in a complex terrain, Water Resour. Res., 55, 1871–1889, https://doi.org/10.1029/2018WR023567, 2019. a
Zhou, Z., Smith, J. A., Baeck, M. L., Wright, D. B., Smith, B. K., and Liu, S.: The impact of the spatiotemporal structure of rainfall on flood frequency over a small urban watershed: an approach coupling stochastic storm transposition and hydrologic modeling, Hydrol. Earth Syst. Sci., 25, 4701–4717, https://doi.org/10.5194/hess-25-4701-2021, 2021. a
Short summary
Reliable flood estimates are vital for effective flood risk management, yet short observation records often limit accuracy. We present a new approach that adds realistic "what if" flood scenarios from nearby similar catchments. Applied to thousands of small German basins, our method improves flood frequency estimates, reducing uncertainty and supporting more robust planning for extreme events.
Reliable flood estimates are vital for effective flood risk management, yet short observation...
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