Articles | Volume 30, issue 10
https://doi.org/10.5194/hess-30-3041-2026
© 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/hess-30-3041-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The potential of green infrastructure in urban pluvial flood mitigation – a scenario-based modelling study in Berlin
Sophia Dobkowitz
CORRESPONDING AUTHOR
Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
Leon Frederik De Vos
Chair of Hydraulic Engineering, Technical University of Munich (TUM), Arcisstraße 21, 80333 München, Germany
Deva Charan Jarajapu
Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
Sarah Lindenlaub
Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
Guilherme Samprogna Mohor
Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
Omar Seleem
Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
Axel Bronstert
Institute of Environmental Science and Geography, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
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Various floods hit Germany recently. While there was a river flood with some dike breaches in 2013, flooding in 2016 resulted directly from heavy rainfall, causing overflowing drainage systems in urban areas and destructive flash floods in steep catchments. Based on survey data, we analysed how residents coped with these different floods. We observed significantly different flood impacts, warnings, behaviour and recovery, offering entry points for tailored risk communication and support.
Guilherme S. Mohor, Annegret H. Thieken, and Oliver Korup
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We explored differences in the damaging process across different flood types, regions within Germany, and six flood events through a numerical model in which the groups can learn from each other. Differences were found mostly across flood types, indicating the importance of identifying them, but there is great overlap across regions and flood events, indicating either that socioeconomic or temporal information was not well represented or that they are in fact less different within our cases.
Cited articles
ALKIS: Amtliches Liegenschaftskatasterinformationssystem (ALKIS), Senatsverwaltung für Stadtentwicklung, Bauen und Wohnen, Berlin, https://fbinter.stadt-berlin.de/fb/index.jsp (last access: 1 October 2024), 2024. a
ATKIS: Digital Terrain Model, Senatsverwaltung für Stadtentwicklung, Bauen und Wohnen, Berlin, https://www.berlin.de/sen/sbw/stadtdaten/geoinformation/landesvermessung/geotopographie-atkis/dgm-digitale-gelaendemodelle/ (last access: 2024-10-01, 2024. a
Autodesk: InfoWorks ICM, Autodesk Inc., https://www.autodesk.com/products/infoworks-icm/overview (last access: 15 October 2025), 2023. a
Berghäuser, L., Schoppa, L., Ulrich, J., Dillenardt, L., Jurado, O. E., Passow, C., Mohor, G. S., Seleem, O., Petrow, T., and Thieken, A.: Starkregen in Berlin, Tech. rep., Institut für Umweltwissenschaften und Geographie, 2021. a
Bhaskar, A. S., Hogan, D. M., Nimmo, J. R., and Perkins, K. S.: Groundwater recharge amidst focused stormwater infiltration, Hydrol. Process., 32, 2058–2068, 2018. a
Bürger, G., Pfister, A., and Bronstert, A.: Temperature-driven rise in extreme sub-hourly rainfall, J. Climate, 32, 7597–7609, 2019. a
Bürger, G., Pfister, A., and Bronstert, A.: Zunehmende Starkregenintensitäten als Folge der Klimaerwärmung: Datenanalyse und Zukunftsprojektion, Hydrologie und Wasserbewirtschaftung: HyWa = Hydrology and water resources management, Germany, Hrsg.: Fachverwaltungen des Bundes und der Länder, 65, 262–271, 2021. a
Caretta, M. A., Mukherji, A., Arfanuzzaman, M., Betts, R. A., Gelfan, A., Hirabayashi, Y., Lissner, T. K., Gunn, E. L., Liu, J., Morgan, R., Mwanga, S., and Supratid, S.: Water, in: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, 2022. a
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco, California, USA, pp. 785–794, https://doi.org/10.1145/2939672.2939785, 2016. a
Crameri, F.: Scientific colour maps, Zenodo, 10, 5281, https://doi.org/10.5281/zenodo.1243862, 2018. a
Crameri, F., Shephard, G. E., and Heron, P. J.: The misuse of colour in science communication, Nat. Commun., 11, 5444, https://doi.org/10.1038/s41467-020-19160-7, 2020. a
Cristiano, E., Annis, A., Apollonio, C., Pumo, D., Urru, S., Viola, F., Deidda, R., Pelorosso, R., Petroselli, A., Tauro, F., Grimaldif, S., Francipane, A., Alongi, F., Noto, L. V., Hoes, O., Klapwijkh, F., Schmitth, B., and Nardi, F.: Multilayer blue-green roofs as nature-based solutions for water and thermal insulation management, Hydrol. Res., 53, 1129–1149, 2022. a
De Vos, L. F., Rüther, N., Mahajan, K., Dallmeier, A., and Broich, K.: Establishing Improved Modeling Practices of Segment-Tailored Boundary Conditions for Pluvial Urban Floods, Water, 16, 2448, https://doi.org/10.3390/w16172448, 2024. a
Dobkowitz, S., Bronstert, A., and Heistermann, M.: Water retention by green infrastructure to mitigate urban flooding: a meta-analysis, Urban Water J., 22, 1–16, https://doi.org/10.1080/1573062X.2025.2472325, 2025. a, b, c, d
DWA: DWA-Regelwerk, Merkblatt DWA-M 165-1, Deutsche Vereinigung fur Wasserwirtschaft, Abwasser und Abfall e. V. (DWA), 2021. a
DWD: Heavy rain catalogue of the German National Meteorological Service, KOSTRA-DWD, version 2020, Deutscher Wetterdienst (DWD), https://www.dwd.de/DE/leistungen/kostra_dwd_rasterwerte/kostra_dwd_rasterwerte.html (last access: 1 October 2025), 2020a. a
DWD: KOSTRA-DWD, version 2020, tables for download, Datenverarbeitung Altnöder, https://www.openko.de/kostra-dwd-2020-tabellen-kostenlos-herunterladen/ (last access: 1 February 2025), 2020b. a
DWD: Vieljährige Mittelwerte, Deutscher Wetterdienst (DWD), https://www.dwd.de/DE/leistungen/klimadatendeutschland/vielj_mittelwerte.html (last access: 9 September 2025), 2023. a
Fletcher, T. D., Andrieu, H., and Hamel, P.: Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art, Adv. Water Resour., 51, 261–279, 2013. a
Fletcher, T. D., Shuster, W., Hunt, W. F., Ashley, R., Butler, D., Arthur, S., Trowsdale, S., Barraud, S., Semadeni-Davies, A., Bertrand-Krajewski, J.-L., Mikkelsen, P. S., Rivard, G., Uhl, M., Dagenais, D., and Viklander, M.: SUDS, LID, BMPs, WSUD and more–The evolution and application of terminology surrounding urban drainage, Urban Water J., 12, 525–542, 2015. a
Fu, X., Liu, J., Shao, W., Mei, C., Wang, D., and Yan, W.: Evaluation of permeable brick pavement on the reduction of stormwater runoff using a coupled hydrological model, Water, 12, 2821, https://doi.org/10.3390/w12102821, 2020. a, b
GDV: Naturgefahren. Starkregenbilanz 2002 bis 2021: Bundesweit 12,6 Milliarden Euro Schäden, Gesamtverband der Versicherer (GDV), https://www.gdv.de/gdv/medien/medieninformationen/
starkregenbilanz-2002-bis-2021-bundesweit-12-6-milliarden-euro-schaeden-137444 (last access: 1 September 2025), 2023. a
GFZ German Research Centre for Geosciences: HOWAS 21, Helmholtz Centre Potsdam [data set], https://doi.org/10.1594/GFZ.SDDB.HOWAS21, 2020. a
Gerl, T., Kreibich, H., Franco, G., Marechal, D., and Schröter, K.: A Review of Flood Loss Models as Basis for Harmonization and Benchmarking, PLOS ONE, 11, e0159791, https://doi.org/10.1371/journal.pone.0159791, 2016. a
Knoche, F., Schumacher, F., Zamzow, M., Sohrt, J., Rehfeld-Klein, M., Matzinger, A., Johne, U., Meier, I., Rouault, P., Pawlowsky-Reusing, and E Schütz, P.: Strategic planning of blue-green infrastructure to reduce surface water pollution from combined sewer overflows, in: Extended abstracts, 16th International Conference on Urban Drainage, Delft, The Netherlands, https://kompetenz-wasser.de/media/pages/forschung/publikationen/, 2024. a, b, c, d
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World map of the Köppen–Geiger climate classification updated, Meteorol. Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a
LAWA: LAWA Starkregenportal, Bund-/Länder-Arbeitsgemeinschaft Wasser (LAWA), https://lawa-starkregenportal.okeanos.ai/ (last access: 9 October 2025), 2025. a
Locatelli, L., Guerrero, M., Russo, B., Martínez-Gomariz, E., Sunyer, D., and Martínez, M.: Socio-economic assessment of green infrastructure for climate change adaptation in the context of urban drainage planning, Sustainability, 12, 3792, https://doi.org/10.3390/su12093792, 2020. a
LUBW: Anhänge 1 a, b, c zum Leitfaden Kommunales Starkregenrisikomanagement in Baden-Württemberg, Landesanstalt für Umwelt Baden-Württemberg (LUBW), 2020. a
Mishra, S. K. and Singh, V. P.: Soil conservation service curve number (SCS-CN) methodology, vol. 42, Springer Science & Business Media, https://doi.org/10.1007/978-94-017-0147-1, 2003. a
Mohor, G. S., Thieken, A. H., and Korup, O.: Residential flood loss estimated from Bayesian multilevel models, Nat. Hazards Earth Syst. Sci., 21, 1599–1614, https://doi.org/10.5194/nhess-21-1599-2021, 2021. a
Monberg, R. J., Howe, A. G., Ravn, H. P., and Jensen, M. B.: Exploring structural habitat heterogeneity in sustainable urban drainage systems (SUDS) for urban biodiversity support, Urban Ecosyst., 21, 1159–1170, 2018. a
Montalvo, C., Reyes-Silva, J., Sañudo, E., Cea, L., and Puertas, J.: Urban pluvial flood modelling in the absence of sewer drainage network data: A physics-based approach, J. Hydrol., 634, 131043, https://doi.org/10.1016/j.jhydrol.2024.131043, 2024. a
OpenStreetMap contributors: OpenStreetMap, OpenStreetMap Wiki, https://www.openstreetmap.org (last access: 1 December 2024), 2024. a
Ross, C. W., Prihodko, L., Anchang, J. Y., Kumar, S. S., Ji, W., and Hanan, N. P.: Global Hydrologic Soil Groups (HYSOGs250m) for Curve Number-Based Runoff Modeling. ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1566, 2018. a
Owolabi, T. A., Mohandes, S. R., and Zayed, T.: Investigating the impact of sewer overflow on the environment: A comprehensive literature review paper, J. Environ. Manage., 301, 113810, https://doi.org/10.1016/j.jenvman.2021.113810, 2022. a, b, c
Paprotny, D., Kreibich, H., Morales-Nápoles, O., Wagenaar, D., Castellarin, A., Carisi, F., Bertin, X., Merz, B., and Schröter, K.: A probabilistic approach to estimating residential losses from different flood types, Nat. Hazards, 105, 2569–2601, 2021. a
Peng, Z., Jinyan, K., Wenbin, P., Xin, Z., and Yuanbin, C.: Effects of Low-Impact Development on Urban Rainfall Runoff under Different Rainfall Characteristics, Pol. J. Environ. Stud., 28, 771–783, https://doi.org/10.15244/pjoes/85348, 2019. a, b, c
Riechel, M.: Auswirkungen von Mischwassereinleitungen auf die Berliner Stadtspree. Project acronym: SAM-CSO. Kompetenzzentrum Wasser Berlin gGmbH, Berlin, Germany, 2009. a
Riechel, M., Matzinger, A., Pawlowsky-Reusing, E., Sonnenberg, H., Uldack, M., Heinzmann, B., Caradot, N., von Seggern, D., and Rouault, P.: Impacts of combined sewer overflows on a large urban river–Understanding the effect of different management strategies, Water Res., 105, 264–273, 2016. a
Riechel, M., Matzinger, A., Pallasch, M., Joswig, K., Pawlowsky-Reusing, E., Hinkelmann, R., and Rouault, P.: Sustainable urban drainage systems in established city developments: Modelling the potential for CSO reduction and river impact mitigation, J. Environ. Manage., 274, 111207, 2020. a
Rossman, L. A.: Storm water management model user's manual, version 5.1, National Risk Management Research Laboratory, Office of Research and Development, US Environmental Protection Agency, 2010. a
Samprogna Mohor, G., Lindenlaub, S., and Thieken, A.: Fast and operational building damage estimation tool for urban pluvial flooding, in: EGU General Assembly 2025, pp. EGU25–10095, Vienna, Austria, 27 April–2 May 2025, https://doi.org/10.5194/egusphere-egu25-10095, 2025. a, b
Schlea, D., Martin, J. F., Ward, A. D., Brown, L. C., and Suter, S. A.: Performance and water table responses of retrofit rain gardens, J. Hydrol. Eng., 19, 05014002, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000797, 2014. a, b
Staccione, A., Essenfelder, A. H., Bagli, S., and Mysiak, J.: Connected urban green spaces for pluvial flood risk reduction in the Metropolitan area of Milan, Sustain. Cities Soc., 104, 105288, https://doi.org/10.1016/j.scs.2024.105288, 2024. a
Steffen, L. and Hinkelmann, R.: hms++: Open-source shallow water flow model with focus on investigating computational performance, SoftwareX, 22, 101397, https://doi.org/10.1016/j.softx.2023.101397, 2023. a
Thieken, A., Kreibich, H., Müller, M., and Lamond, J.: Data collection for a better understanding of what causes flood damage – experiences with telephone surveys, chap. 7, in: Flood damage survey and assessment: new insights from research and practice, edited by: Molinari, D., Menoni, S., and Ballio, F., AGU, Wiley, pp. 95–106, https://doi.org/10.1002/9781119217930.ch7, 2017. a
Thieken, A. H., Müller, M., Kreibich, H., and Merz, B.: Flood damage and influencing factors: New insights from the August 2002 flood in Germany, Water Resour. Res., 41, W12430, https://doi.org/10.1029/2005WR004177, 2005. a
Tügel, F., Nissen, K. M., Steffen, L., Zhang, Y., Ulbrich, U., and Hinkelmann, R.: Extreme precipitation and flooding in Berlin under climate change and effects of selected grey and blue-green measures, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-445, 2025. a, b, c, d, e, f, g, h, i, j
Watrin, V. d. R., Blanco, C. J. C., and Gonçalves, E. D.: Thermal and hydrological performance of extensive green roofs in Amazon climate, Brazil, in: Proceedings of the Institution of Civil Engineers-Engineering Sustainability, vol. 173, Thomas Telford Ltd, pp. 125–134, https://doi.org/10.1680/jensu.18.00060, 2019. a
Weyrauch, P., Matzinger, A., Pawlowsky-Reusing, E., Plume, S., von Seggern, D., Heinzmann, B., Schroeder, K., and Rouault, P.: Contribution of combined sewer overflows to trace contaminant loads in urban streams, Water Res., 44, 4451–4462, 2010. a
Zhang, L., Ye, Z., and Shibata, S.: Assessment of rain garden effects for the management of urban storm runoff in Japan, Sustainability, 12, 9982, https://doi.org/10.3390/su12239982, 2020. a, b
Short summary
Surface sealing makes cities vulnerable to flooding caused by heavy rain. Green infrastructure, such as green roofs, can reduce flooding. This modelling study investigates the potential of green infrastructure to reduce runoff, flood water depth and building damage. Bioretention systems turned out to be the most space efficient compared to green roofs and pervious pavement. For larger rain events, more green infrastructure implementation is needed to achieve relevant flood mitigation.
Surface sealing makes cities vulnerable to flooding caused by heavy rain. Green infrastructure,...