Articles | Volume 21, issue 2
https://doi.org/10.5194/nhess-21-643-2021
© Author(s) 2021. 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-21-643-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Feb 2021
Research article |
| 16 Feb 2021
| 16 Feb 2021
Are OpenStreetMap building data useful for flood vulnerability modelling?
Marco Cerri
Section Hydrology, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Max Steinhausen
Section Hydrology, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Geography Department, Humboldt-Universität zu Berlin, 12489 Berlin, Germany
Heidi Kreibich
Section Hydrology, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Kai Schröter
CORRESPONDING AUTHOR
Section Hydrology, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Related authors
No articles found.
Golbarg Goshtasbpour, Jannick Alpers, Kai Schröter, and Hannes Müller-Thomy
EGUsphere, https://doi.org/10.5194/egusphere-2026-186, https://doi.org/10.5194/egusphere-2026-186, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
It is important to know how often an extreme precipitation amount can occur. This frequency can be derived via analysis of observed time series. With precipitation traditionally observed at single points (the rain gauges), the derived frequencies are not valid for e.g., catchments. Valid areal extreme values can be achieved by a subsequent reduction in space. This study analyses the impact of seasons, durations, frequencies and topography on reduction factors for extreme values (5 min to 1 day).
Ravikumar Guntu, Guilherme Samprogna Mohor, Annegret H. Thieken, Meike Müller, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 26, 163–186, https://doi.org/10.5194/nhess-26-163-2026, https://doi.org/10.5194/nhess-26-163-2026, 2026
Short summary
Short summary
The 2021 flood in Germany caused severe damage to companies, with over half reporting losses above € 100 000. Using probabilistic models, we identify key factors driving direct damage and business interruption. Water depth, flow velocity and company exposure were key factors, but preparedness played a crucial role. Companies that took good precaution recovered faster. Our findings stress the value of early warnings and risk communication to reduce damage from unprecedented flood events.
Apoorva Singh, Ravikumar Guntu, Nivedita Sairam, Kasra Rafiezadeh Shahi, Anna Buch, Melanie Fischer, Chandrika Thulaseedharan Dhanya, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 26, 103–118, https://doi.org/10.5194/nhess-26-103-2026, https://doi.org/10.5194/nhess-26-103-2026, 2026
Short summary
Short summary
We develop novel probabilistic models to estimate flash flood losses of companies and households in Germany. Using multiple flash flood events, we identify key drivers of flash floods loss. FLEMO flash model reveals that for companies, the effectiveness of emergency measures is crucial in mitigating losses. In contrast, household benefit more from knowledge about emergency action, suggesting adaptation strategies can effectively reduce flash flood losses.
Aaron Buhrmann, Cecilia I. Nievas, Nivedita Sairam, James E. Daniell, Heidi Kreibich, and Seth Bryant
EGUsphere, https://doi.org/10.5194/egusphere-2025-5172, https://doi.org/10.5194/egusphere-2025-5172, 2025
Short summary
Short summary
Our research lays the groundwork for the next generation of disaster risk modelling by improving how building-level value and use are estimated across Germany. By testing multiple data sources and methods, we identify a transparent, adaptable approach that enhances forecasts of damage and recovery—helping protect lives, property, and communities.
Alina Bill-Weilandt, Nivedita Sairam, Dennis Wagenaar, Kasra Rafiezadeh Shahi, Heidi Kreibich, Perrine Hamel, and David Lallemant
EGUsphere, https://doi.org/10.5194/egusphere-2025-3706, https://doi.org/10.5194/egusphere-2025-3706, 2025
Short summary
Short summary
Flooding is a major cause of agricultural loss globally. We introduce a framework for developing and evaluating flood damage models for crops. The study presents the most comprehensive review of such models for rice to date and offers practical guidance on model selection and expected errors when transferring models across regions. We provide models and lookup tables that can be used in flood risk assessments in rice-producing regions.
Kai Schröter, Pia-Johanna Schweizer, Benedikt Gräler, Lydia Cumiskey, Sukaina Bharwani, Janne Parviainen, Chahan M. Kropf, Viktor Wattin Håkansson, Martin Drews, Tracy Irvine, Clarissa Dondi, Heiko Apel, Jana Löhrlein, Stefan Hochrainer-Stigler, Stefano Bagli, Levente Huszti, Christopher Genillard, Silvia Unguendoli, Fred Hattermann, and Max Steinhausen
Nat. Hazards Earth Syst. Sci., 25, 3055–3073, https://doi.org/10.5194/nhess-25-3055-2025, https://doi.org/10.5194/nhess-25-3055-2025, 2025
Short summary
Short summary
With the increasing negative impacts of extreme weather events globally, it is crucial to align efforts to manage disasters with measures to adapt to climate change. We identify challenges in systems and organizations working together. We suggest that collaboration across various fields is essential and propose an approach to improve collaboration, including a framework for better stakeholder engagement and an open-source data system that helps gather and connect important information.
Kasra Rafiezadeh Shahi, Nivedita Sairam, Lukas Schoppa, Le Thanh Sang, Do Ly Hoai Tan, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 25, 2845–2861, https://doi.org/10.5194/nhess-25-2845-2025, https://doi.org/10.5194/nhess-25-2845-2025, 2025
Short summary
Short summary
Ho Chi Minh City (HCMC) faces severe flood risks from climatic and socio-economic changes, requiring effective adaptation solutions. Flood loss estimation is crucial, but advanced probabilistic models accounting for key drivers and uncertainty are lacking. This study presents a probabilistic flood loss model with a feature selection paradigm for HCMC’s residential sector. Experiments using new survey data from flood-affected households demonstrate the model's superior performance.
Anna Buch, Dominik Paprotny, Kasra Rafiezadeh Shahi, Heidi Kreibich, and Nivedita Sairam
Nat. Hazards Earth Syst. Sci., 25, 2437–2453, https://doi.org/10.5194/nhess-25-2437-2025, https://doi.org/10.5194/nhess-25-2437-2025, 2025
Short summary
Short summary
Many households in Vietnam depend on revenue from micro-businesses (shop houses). However, losses caused by regular flooding are not modelled. Business turnover, building age, and water depth were found to be the main drivers of flood losses of micro-businesses. We built and validated probabilistic models (non-parametric Bayesian networks) that estimate flood losses of micro-businesses. The results help with flood risk management and adaption decision making for micro-businesses.
Shahin Khosh Bin Ghomash, Heiko Apel, Kai Schröter, and Max Steinhausen
Nat. Hazards Earth Syst. Sci., 25, 1737–1749, https://doi.org/10.5194/nhess-25-1737-2025, https://doi.org/10.5194/nhess-25-1737-2025, 2025
Short summary
Short summary
This work introduces RIM2D (Rapid Inundation Model 2D), a hydrodynamic model for precise and rapid flood predictions that is ideal for early warning systems. We demonstrate RIM2D's ability to deliver detailed and localized flood forecasts using the June 2023 flood in Braunschweig, Germany, as a case study. This research highlights the readiness of RIM2D and the required hardware for integration into operational flood warning and impact-based forecasting systems.
André Felipe Rocha Silva, Julian Cardoso Eleutério, Heiko Apel, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 25, 1501–1520, https://doi.org/10.5194/nhess-25-1501-2025, https://doi.org/10.5194/nhess-25-1501-2025, 2025
Short summary
Short summary
This work uses agent-based modelling to evaluate the impact of flood warning and evacuation systems on human losses during the 2021 Ahr Valley flood in Germany. While the first flood warning with evacuation instructions is identified as timely, its lack of detail and effectiveness resulted in low public risk awareness. Better dissemination of warnings and improved risk perception and preparedness among the population could reduce casualties by up to 80 %.
Maurice W. M. L. Kalthof, Jens de Bruijn, Hans de Moel, Heidi Kreibich, and Jeroen C. J. H. Aerts
Nat. Hazards Earth Syst. Sci., 25, 1013–1035, https://doi.org/10.5194/nhess-25-1013-2025, https://doi.org/10.5194/nhess-25-1013-2025, 2025
Short summary
Short summary
Our study explores how farmers in India's Bhima basin respond to consecutive droughts. We simulated farmers' individual choices – like changing crops or digging wells – and their effects on profits, yields, and water resources. Results show these adaptations, while improving incomes, ultimately increase drought vulnerability and damage. Such insights emphasize the need for alternative adaptations and highlight the value of socio-hydrological models in shaping policies to lessen drought impacts.
Nadja Veigel, Heidi Kreibich, Jens A. de Bruijn, Jeroen C. J. H. Aerts, and Andrea Cominola
Nat. Hazards Earth Syst. Sci., 25, 879–891, https://doi.org/10.5194/nhess-25-879-2025, https://doi.org/10.5194/nhess-25-879-2025, 2025
Short summary
Short summary
This study explores how social media, specifically Twitter (X), can help us understand public reactions to floods in Germany from 2014 to 2021. Using large language models, we extract topics and patterns of behavior from flood-related tweets. The findings offer insights to improve communication and disaster management. Topics related to low-impact flooding contain descriptive hazard-related content, while the focus shifts to catastrophic impacts and responsibilities during high-impact events.
Belinda Rhein and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 25, 581–589, https://doi.org/10.5194/nhess-25-581-2025, https://doi.org/10.5194/nhess-25-581-2025, 2025
Short summary
Short summary
In July 2021, flooding killed 190 people in Germany, 134 of them in the Ahr valley, making it the deadliest flood in recent German history. The flash flood was extreme in terms of water levels, flow velocities and flood extent, and early warning and evacuation were inadequate. Many died on the ground floor or in the street, with older and impaired individuals especially vulnerable. Clear warnings should urge people to seek safety rather than save belongings, and timely evacuations are essential.
Bruno Merz, Günter Blöschl, Robert Jüpner, Heidi Kreibich, Kai Schröter, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 24, 4015–4030, https://doi.org/10.5194/nhess-24-4015-2024, https://doi.org/10.5194/nhess-24-4015-2024, 2024
Short summary
Short summary
Flood risk assessments help us decide how to reduce the risk of flooding. Since these assessments are based on probabilities, it is hard to check their accuracy by comparing them to past data. We suggest a new way to validate these assessments, making sure they are practical for real-life decisions. This approach looks at both the technical details and the real-world situations where decisions are made. We demonstrate its practicality by applying it to flood emergency planning.
Dominik Paprotny, Belinda Rhein, Michalis I. Vousdoukas, Paweł Terefenko, Francesco Dottori, Simon Treu, Jakub Śledziowski, Luc Feyen, and Heidi Kreibich
Hydrol. Earth Syst. Sci., 28, 3983–4010, https://doi.org/10.5194/hess-28-3983-2024, https://doi.org/10.5194/hess-28-3983-2024, 2024
Short summary
Short summary
Long-term trends in flood losses are regulated by multiple factors, including climate variation, population and economic growth, land-use transitions, reservoir construction, and flood risk reduction measures. Here, we reconstruct the factual circumstances in which almost 15 000 potential riverine, coastal and compound floods in Europe occurred between 1950 and 2020. About 10 % of those events are reported to have caused significant socioeconomic impacts.
Viet Dung Nguyen, Jeroen Aerts, Max Tesselaar, Wouter Botzen, Heidi Kreibich, Lorenzo Alfieri, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 24, 2923–2937, https://doi.org/10.5194/nhess-24-2923-2024, https://doi.org/10.5194/nhess-24-2923-2024, 2024
Short summary
Short summary
Our study explored how seasonal flood forecasts could enhance insurance premium accuracy. Insurers traditionally rely on historical data, yet climate fluctuations influence flood risk. We employed a method that predicts seasonal floods to adjust premiums accordingly. Our findings showed significant year-to-year variations in flood risk and premiums, underscoring the importance of adaptability. Despite limitations, this research aids insurers in preparing for evolving risks.
Niklas Ebers, Kai Schröter, and Hannes Müller-Thomy
Nat. Hazards Earth Syst. Sci., 24, 2025–2043, https://doi.org/10.5194/nhess-24-2025-2024, https://doi.org/10.5194/nhess-24-2025-2024, 2024
Short summary
Short summary
Future changes in sub-daily rainfall extreme values are essential in various hydrological fields, but climate scenarios typically offer only daily resolution. One solution is rainfall generation. With a temperature-dependent rainfall generator climate scenario data were disaggregated to 5 min rainfall time series for 45 locations across Germany. The analysis of the future 5 min rainfall time series showed an increase in the rainfall extremes values for rainfall durations of 5 min and 1 h.
Seth Bryant, Heidi Kreibich, and Bruno Merz
Proc. IAHS, 386, 181–187, https://doi.org/10.5194/piahs-386-181-2024, https://doi.org/10.5194/piahs-386-181-2024, 2024
Short summary
Short summary
Our study found that simplifying data in flood risk models can introduce errors. We tested 344 damage functions and found errors up to 40 % of the total asset value. This means large-scale flood risk assessments may have significant errors due to the modelling approach. Our research highlights the need for more attention to data aggregation in flood risk models.
Seth Bryant, Guy Schumann, Heiko Apel, Heidi Kreibich, and Bruno Merz
Hydrol. Earth Syst. Sci., 28, 575–588, https://doi.org/10.5194/hess-28-575-2024, https://doi.org/10.5194/hess-28-575-2024, 2024
Short summary
Short summary
A new algorithm has been developed to quickly produce high-resolution flood maps. It is faster and more accurate than current methods and is available as open-source scripts. This can help communities better prepare for and mitigate flood damages without expensive modelling.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Thulasi Vishwanath Harish, Nivedita Sairam, Liang Emlyn Yang, Matthias Garschagen, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 23, 1125–1138, https://doi.org/10.5194/nhess-23-1125-2023, https://doi.org/10.5194/nhess-23-1125-2023, 2023
Short summary
Short summary
Coastal Asian cities are becoming more vulnerable to flooding. In this study we analyse the data collected from flood-prone houses in Ho Chi Minh City to identify what motivates the households to adopt flood precautionary measures. The results revealed that educating the households about the available flood precautionary measures and communicating the flood protection measures taken by the government encourage the households to adopt measures without having to experience multiple flood events.
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
Short summary
Short summary
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.
Brunella Bonaccorso, Carmelo Cammalleri, Athanasios Loukas, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 22, 1857–1862, https://doi.org/10.5194/nhess-22-1857-2022, https://doi.org/10.5194/nhess-22-1857-2022, 2022
Animesh K. Gain, Yves Bühler, Pascal Haegeli, Daniela Molinari, Mario Parise, David J. Peres, Joaquim G. Pinto, Kai Schröter, Ricardo M. Trigo, María Carmen Llasat, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 22, 985–993, https://doi.org/10.5194/nhess-22-985-2022, https://doi.org/10.5194/nhess-22-985-2022, 2022
Short summary
Short summary
To mark the 20th anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences, we highlight 11 key publications covering major subject areas of NHESS that stood out within the past 20 years.
Annegret H. Thieken, Guilherme Samprogna Mohor, Heidi Kreibich, and Meike Müller
Nat. Hazards Earth Syst. Sci., 22, 165–185, https://doi.org/10.5194/nhess-22-165-2022, https://doi.org/10.5194/nhess-22-165-2022, 2022
Short summary
Short summary
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.
Valeria Cigala, Giulia Roder, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 22, 85–96, https://doi.org/10.5194/nhess-22-85-2022, https://doi.org/10.5194/nhess-22-85-2022, 2022
Short summary
Short summary
Non-male scientists constitute a minority in the geoscience professional environment, and they are underrepresented in disaster risk reduction planning. So far the international agenda has failed to effectively promote gender inclusion in disaster policy, preventing non-male scientists from career development and recognition. Here we share the thoughts, experiences, and priorities of women and non-binary scientists as a starting point to expand the discourse and promote intersectional research.
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
Short summary
Short summary
Dams are an important element of water resources management. Data about dams are crucial for practitioners, scientists, and policymakers. We present the most comprehensive open-access dam inventory for Germany to date. The inventory combines multiple sources of information. It comprises 530 dams with information on name, location, river, start year of construction and operation, crest length, dam height, lake area, lake volume, purpose, dam structure, and building characteristics.
Cited articles
Alfieri, L., Feyen, L., Salamon, P., Thielen, J., Bianchi, A., Dottori, F., and Burek, P.: Modelling the socio-economic impact of river floods in Europe, Nat. Hazards Earth Syst. Sci., 16, 1401–1411, https://doi.org/10.5194/nhess-16-1401-2016, 2016. a
Amadio, M., Scorzini, A. R., Carisi, F., Essenfelder, A. H., Domeneghetti, A., Mysiak, J., and Castellarin, A.: Testing empirical and synthetic flood damage models: the case of Italy, Nat. Hazards Earth Syst. Sci., 19, 661–678, https://doi.org/10.5194/nhess-19-661-2019, 2019. a
Amirebrahimi, S., Rajabifard, A., Mendis, P., and Ngo, T.: A framework for a
microscale flood damage assessment and visualization for a building using
BIM–GIS integration, Int. J. Digit. Earth, 9,
363–386, https://doi.org/10.1080/17538947.2015.1034201, 2016. a
Apel, H., Aronica, G. T., Kreibich, H., and Thieken, A.: Flood risk
analyses–how detailed do we need to be?, Nat. Hazards, 49, 79–98,
https://doi.org/10.1007/s11069-008-9277-8, 2009. a, b
Barrington-Leigh, C. and Millard-Ball, A.: The world’s user-generated road
map is more than 80 % complete, Plos One, 12, 1–20,
https://doi.org/10.1371/journal.pone.0180698, 2017. a, b
Basu, S., Kumbier, K., Brown, J. B., and Yu, B.: Iterative random forests to
discover predictive and stable high-order interactions, P.
Natl. Acad. Sci. USA, 115, 1943–1948, https://doi.org/10.1073/pnas.1711236115,
2018. a
Bivand, R., Keitt, T., and Rowlingson, B.: rgdal: Bindings for the “Geospatial” Data Abstraction Library, available at:
https://CRAN.R-project.org/package=rgdal (last access 4 March 2020), r package version
1.4–8, 2019. a
Blanco-Vogt, A. and Schanze, J.: Assessment of the physical flood susceptibility of buildings on a large scale – conceptual and methodological frameworks, Nat. Hazards Earth Syst. Sci., 14, 2105–2117, https://doi.org/10.5194/nhess-14-2105-2014, 2014. a
Blöschl, G., Nester, T., Komma, J., Parajka, J., and Perdigão, R. A. P.: The June 2013 flood in the Upper Danube Basin, and comparisons with the 2002, 1954 and 1899 floods, Hydrol. Earth Syst. Sci., 17, 5197–5212, https://doi.org/10.5194/hess-17-5197-2013, 2013. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a, b, c, d
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A.: Classification and Regression Trees, Taylor & Francis Ltd, Boca Raton, FL, USA, 1984. a
Bucklin, D. and Basille, M.: rpostgis: linking R with a PostGIS spatial
database, The R Journal, 10, 251–268, available at:
https://journal.r-project.org/archive/2018/RJ-2018-025/index.html (last access: 4 March 2020),
2018. a
Bui, Q.-T., Nguyen, Q.-H., Nguyen, X. L., Pham, V. D., Nguyen, H. D., and Pham, V.-M.: Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping, J. Hydrol., 581, 124379, https://doi.org/10.1016/j.jhydrol.2019.124379, 2020. a
Cammerer, H., Thieken, A. H., and Lammel, J.: Adaptability and transferability of flood loss functions in residential areas, Nat. Hazards Earth Syst. Sci., 13, 3063–3081, https://doi.org/10.5194/nhess-13-3063-2013, 2013. a, b
Carisi, F., Schröter, K., Domeneghetti, A., Kreibich, H., and Castellarin, A.: Development and assessment of uni- and multivariable flood loss models for Emilia-Romagna (Italy), Nat. Hazards Earth Syst. Sci., 18, 2057–2079, https://doi.org/10.5194/nhess-18-2057-2018, 2018. a, b
Changnon, S. A.: Shifting economic impacts from weather extremes in the
United States: A result of societal changes, not global warming,
Nat. Hazards, 29, 273–290, 2003. a
Chinh, D. T., Gain, A., Dung, N., Haase, D., and Kreibich, H.: Multi-Variate Analyses of Flood Loss in Can Tho City, Mekong Delta, Water-Sui., 8, 6,https://doi.org/10.3390/w8010006, 2015. a
Conradt, T., Roers, M., Schröter, K., Elmer, F., Hoffmann, P., Koch, H.,
Hattermann, F., and Wechsung, F.: Comparison of the extreme floods of 2002
and 2013 in the German part of the Elbe River basin and their runoff
simulation by SWIM-live, Hydrol. Wasserbewirts., 57,
241–245, https://doi.org/10.5675/HyWa_2013,5_4, 2013. a
Conway, J., Eddelbuettel, D., Nishiyama, T., Prayaga, S. K., and Tiffin, N.:
RPostgreSQL: R Interface to the “PostgreSQL” Database System, available at: https://cran.r-project.org/web/packages/RPostgreSQL/index.html (last access: 4 March 2020), r package
version 0.6-2, 2017. a
de Moel, H., Jongman, B., Kreibich, H., Merz, B., Penning-Rowsell, E. and Ward, P. J.: Flood risk assessments at different spatial scales, Mitig Adapt Strateg Glob Change, 20, 865–890, https://doi.org/10.1007/s11027-015-9654-z, 2015. a, b
Dietz, H.: Wohngebäudeversicherung Kommentar, VVW Verlag
Versicherungswirtschaft GmbH, Karlsruhe, 2 Edn., 1999. a
Dottori, F., Figueiredo, R., Martina, M. L. V., Molinari, D., and Scorzini, A. R.: INSYDE: a synthetic, probabilistic flood damage model based on explicit cost analysis, Nat. Hazards Earth Syst. Sci., 16, 2577–2591, https://doi.org/10.5194/nhess-16-2577-2016, 2016. a, b
Elmer, F., Thieken, A. H., Pech, I., and Kreibich, H.: Influence of flood frequency on residential building losses, Nat. Hazards Earth Syst. Sci., 10, 2145–2159, https://doi.org/10.5194/nhess-10-2145-2010, 2010. a
Felder, G., Gómez-Navarro, J., Zischg, A., Raible, C., Röthlisberger, V.,
Bozhinova, D., Martius, O., and Weingartner, R.: From global circulation to
local flood loss: Coupling models across the scales, Sci. Total
Environ., 635, 1225–1239, https://doi.org/10.1016/j.scitotenv.2018.04.170, 2018. a
Figueiredo, R. and Martina, M.: Using open building data in the development of exposure data sets for catastrophe risk modelling, Nat. Hazards Earth Syst. Sci., 16, 417–429, https://doi.org/10.5194/nhess-16-417-2016, 2016. a
Figueiredo, R., Schröter, K., Weiss-Motz, A., Martina, M. L. V., and Kreibich, H.: Multi-model ensembles for assessment of flood losses and associated uncertainty, Nat. Hazards Earth Syst. Sci., 18, 1297–1314, https://doi.org/10.5194/nhess-18-1297-2018, 2018. a, b
Genuer, R., Poggi, J. ., and Tuleau-Malot, C.: Variable selection using random forests, Pattern Recogn. Lett., 31, 2225–2236, 2010. a
GFZ German Research Centre for
Geosciences: HOWAS 21, Helmholtz
Centre Potsdam, 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, b, c
Gneiting, T. and Raftery, A.: Strictly Proper Scoring Rules,
Prediction, and Estimation, J. Am. Stat.
Assoc., 102, 359–378, https://doi.org/10.1198/016214506000001437, 2007. a, b
Goodchild, M. F.: Citizens as sensors: the world of volunteered geography,
Geojournal, 69, 211–221, https://doi.org/10.1007/s10708-007-9111-y, 2007. a
Gregorutti, B., Michel, B., and Saint-Pierre, P.: Correlation and variable
importance in random forests, Stat. Comput., 27, 659–678,
https://doi.org/10.1007/s11222-016-9646-1, 2017. a
Hasanzadeh Nafari, R., Ngo, T., and Lehman, W.: Calibration and validation of FLFArs – a new flood loss function for Australian residential structures, Nat. Hazards Earth Syst. Sci., 16, 15–27, https://doi.org/10.5194/nhess-16-15-2016, 2016. a
Hecht, R., Kunze, C., and Hahmann, S.: Measuring Completeness of Building
Footprints in OpenStreetMap over Space and Time, ISPRS Int.
J. Geogr. Inf., 2, 1066–1091, https://doi.org/10.3390/ijgi2041066, 2013. a, b
Hijmans, R. J.: raster: Geographic Data Analysis and Modeling,
available at: https://CRAN.R-project.org/package=raster (last access: 4 March 2020), r package version
3.0-7, 2019. a
Hoeppe, P.: Trends in weather related disasters – Consequences for insurers and society, Weather Climate Extremes, 11, 70–79,
https://doi.org/10.1016/j.wace.2015.10.002, 2016. a
Huang, B. and Boutros, P.: The parameter sensitivity of random forests, BMC
Bioinformatics, 17, 331, https://doi.org/10.1186/s12859-016-1228-x, 2016. a, b
Irwin, A.: No PhDs needed: how citizen science is transforming research,
Nature, 562, 480, https://doi.org/10.1038/d41586-018-07106-5, 2018. a
Jongman, B.: Effective adaptation to rising flood risk, Nat. Commun., 9, 1986, https://doi.org/10.1038/s41467-018-04396-1, 2018. a
Jongman, B., Kreibich, H., Apel, H., Barredo, J. I., Bates, P. D., Feyen, L., Gericke, A., Neal, J., Aerts, J. C. J. H., and Ward, P. J.: Comparative flood damage model assessment: towards a European approach, Nat. Hazards Earth Syst. Sci., 12, 3733–3752, https://doi.org/10.5194/nhess-12-3733-2012, 2012. a, b
Jung, M.: LecoS — A python plugin for automated landscape ecology
analysis, Ecol. Inf., 31, 18–21,
https://doi.org/10.1016/j.ecoinf.2015.11.006, 2016. a
Kienzler, S., Pech, I., Kreibich, H., Müller, M., and Thieken, A. H.: After the extreme flood in 2002: changes in preparedness, response and recovery of flood-affected residents in Germany between 2005 and 2011, Nat. Hazards Earth Syst. Sci., 15, 505–526, https://doi.org/10.5194/nhess-15-505-2015, 2015. a
Kreibich, H. and Thieken, A.: Coping with floods in the city of Dresden,
Germany, Nat. Haz., 51, 423–436, https://doi.org/10.1007/s11069-007-9200-8,
2009. a
Kron, W.: Flood Risk = Hazard ⋅ Values ⋅ Vulnerability, Water
Int., 30, 58–68, https://doi.org/10.1080/02508060508691837, 2005. a
Lang, S. and Tiede, D.: vLATE Extension für ArcGIS – vektorbasiertes Tool zur quantitativen Landschaftsstrukturanalyse, ESRI European User Conference 2003 Innsbruck, CDROM, (1986), 1–10, 2003. a
Liaw, A. and Wiener, M.: Classification and Regression by randomForest, R News, 2, 18–22, https://cran.r-project.org/doc/Rnews/Rnews_2002-3.pdf (last access: 3 February 2021), 2002. a
Lugeri, N., Kundzewicz, Z., Genovese, E., Hochrainer, S., and Radziejewski, M.: River flood risk and adaptation in Europe – assessment of the present status, Mitigation and Adaptation Strategies for Global Change, 15, 621–639, https://doi.org/10.1007/s11027-009-9211-8, 2010. a
Lüdtke, S., Schröter, K., Steinhausen, M., Weise, L., Figueiredo, R., and
Kreibich, H.: A Consistent Approach for Probabilistic Residential
Flood Loss Modeling in Europe, Water Resour. Res., 55,
10616–10635, https://doi.org/10.1029/2019WR026213, 2019. a, b
Merz, B., Kreibich, H., Thieken, A., and Schmidtke, R.: Estimation uncertainty of direct monetary flood damage to buildings, Nat. Hazards Earth Syst. Sci., 4, 153–163, https://doi.org/10.5194/nhess-4-153-2004, 2004. a
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
Merz, B., Elmer, F., Kunz, M., Mühr, B., Schroeter, K., and Uhlemann-Elmer,
S.: The extreme flood in June 2013 in Germany, Houille Blanche, 1,
5–10, https://doi.org/10.1051/lhb/2014001, 2014. a
Meyer, V., Becker, N., Markantonis, V., Schwarze, R., van den Bergh, J. C. J. M., Bouwer, L. M., Bubeck, P., Ciavola, P., Genovese, E., Green, C., Hallegatte, S., Kreibich, H., Lequeux, Q., Logar, I., Papyrakis, E., Pfurtscheller, C., Poussin, J., Przyluski, V., Thieken, A. H., and Viavattene, C.: Review article: Assessing the costs of natural hazards – state of the art and knowledge gaps, Nat. Hazards Earth Syst. Sci., 13, 1351–1373, https://doi.org/10.5194/nhess-13-1351-2013, 2013. a, b
Molinari, D., Scorzini, A. R., Arrighi, C., Carisi, F., Castelli, F., Domeneghetti, A., Gallazzi, A., Galliani, M., Grelot, F., Kellermann, P., Kreibich, H., Mohor, G. S., Mosimann, M., Natho, S., Richert, C., Schroeter, K., Thieken, A. H., Zischg, A. P., and Ballio, F.: Are flood damage models converging to “reality”? Lessons learnt from a blind test, Nat. Hazards Earth Syst. Sci., 20, 2997–3017, https://doi.org/10.5194/nhess-20-2997-2020, 2020. a, b
O'Brien, J.: gdalUtilities: Wrappers for “GDAL” Utilities Executables, available at: https://CRAN.R-project.org/package=gdalUtilities (last access: 4 March 2020), r package version 1.1.0, 2020. a
Paprotny, D., Kreibich, H., Morales-Nápoles, O., Terefenko, P., and Schröter, K.: Estimating exposure of residential assets to natural hazards in Europe using open data, Nat. Hazards Earth Syst. Sci., 20, 323–343, https://doi.org/10.5194/nhess-20-323-2020, 2020. a
Pebesma, E.: Simple Features for R: Standardized Support for Spatial Vector
Data, R J., 10, 439–446, https://doi.org/10.32614/RJ-2018-009, 2018. a
Penning-Rowsell, E. C. and Chatterton, J. B.: The benefits of flood alleviation: a manual of assessment techniques, Saxon House, Farnborough, Eng., 1977. a
Pittore, M., Wieland, M., and Fleming, K.: Perspectives on global dynamic
exposure modelling for geo-risk assessment, Nat. Hazards, 86, 7–30,
https://doi.org/10.1007/s11069-016-2437-3, 2017. a
Plapp, T. K.: Wahrnehmung von Risiken aus Naturkatastrophen: eine empirische Untersuchung in sechs gefährdeten Gebieten Süd- und Westdeutschlands – Risk perception of natural catastrophes: an empirical investigation in six endangers areas in South and West Germany: Karlsruher Reihe II – Band 2, edited by: Risikoforschung und Versicherungsmanagement, Karlsruhe, 2003 (in German). a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, available at:
https://www.R-project.org/ (last access: 3 February 2021), 2020. a
Rehan, B.: An innovative micro-scale approach for vulnerability and flood risk assessment with the application to property-level protection adoptions,
Nat. Hazards, 91, 1039–1057, https://doi.org/10.1007/s11069-018-3175-5, 2018. a
Rusnack, W.: Finds the minimum bounding box from a point cloud, available at:
https://github.com/BebeSparkelSparkel/MinimumBoundingBox (last access: 4 March 2020),
2017. a
Sairam, N., Schröter, K., Rözer, V., Merz, B., and Kreibich, H.: Hierarchical
Bayesian Approach for Modeling Spatiotemporal Variability in
Flood Damage Processes, Water Resour. Res., 55, 8223–8237,
https://doi.org/10.1029/2019WR025068, 2019. a, b
Schröter, K., Kreibich, H., Vogel, K., Riggelsen, C., Scherbaum, F., and Merz, B.: How useful are complex flood damage models?, Water Resour. Res.,
50, 3378–3395, https://doi.org/10.1002/2013WR014396, 2014. a, b, c, d
Schröter, K., Kunz, M., Elmer, F., Mühr, B., and Merz, B.: What made the June 2013 flood in Germany an exceptional event? A hydro-meteorological evaluation, Hydrol. Earth Syst. Sci., 19, 309–327, https://doi.org/10.5194/hess-19-309-2015, 2015. a, b, c
Schröter, K., Lüdtke, S., Vogel, K., Kreibich, H., and Merz, B.: Tracing the
value of data for flood loss modelling, E3S Web of Conferences, 3rd European Conference on Flood Risk Management (FLOODrisk 2016), 7, 05005,
https://doi.org/10.1051/e3sconf/20160705005, 2016. a, b, c
Sieg, T., Vogel, K., Merz, B., and Kreibich, H.: Tree-based flood damage
modeling of companies: Damage processes and model performance, Water
Resour. Res., 53, 6050–6068, https://doi.org/10.1002/2017WR020784, 2017. a
Sieg, T., Vogel, K., Merz, B., and Kreibich, H.: Seamless Estimation of
Hydrometeorological Risk Across Spatial Scales, Earths Future, 7, 574–581, https://doi.org/10.1029/2018EF001122, 2019. a
Smith, D.: Flood damage estimation - a review of urban stage-damage curves and loss functions, Water SA, 20, 231–238, 1994. a
Teng, J.: Flood inundation modelling: A review of methods, recent advances
and uncertainty analysis, Environ. Model. Softw., 90, 201–216, 2017. a
Teske, D.: Geocoder Accuracy Ranking, in: Process Design for Natural
Scientists, Communications in Computer and Information Science,
Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-662-45006-2_13,
161–174, 2014. a
Thieken, A., 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, 1–16, https://doi.org/10.1029/2005WR004177,
2005. a, b, c
Thieken, A., Petrow, T., Kreibich, H., and Merz, B.: Insurability and
Mitigation of Flood Losses in Private Households in Germany, Risk Anal., 26, 383–395, https://doi.org/10.1111/j.1539-6924.2006.00741.x, 2006. a
Thieken, A., Kreibich, H., Müller, M., and Merz, B.: Coping with floods:
preparedness, response and recovery of flood-affected residents in Germany in 2002, Hydrolog. Sci. J., 52, 1016–1037,
https://doi.org/10.1623/hysj.52.5.1016, 2007. 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, b
Thieken, A., Kreibich, H., Müller, M., and Lamond, J.: Data collection for a
better understanding of what causes flood damage: experiences with telephone surveys: in Flood damage survey and assessment: new insights from research and practice, Geophys. Monogr., 228, 95–106, 2017. a
Ulbrich, U., Brücher, T., Fink, A., Leckebusch, G., Krüger, A., and Pinto,
J.: The central European floods of August 2002: Part 2 Synoptic
causes and considerations with respect to climatic change, Weather, 58,
434–442, https://doi.org/10.1256/wea.61.03B, 2003.
a
UNISDR: Sendai Framework for Disaster Risk Reduction 2015–2030, Tech. rep., United Nations International Strategy for DisasterReduction, available at: https://www.undrr.org/publication/sendai-framework-disaster-risk-reduction-2015-2030 (last access: 3 February 2021), 2015. a
Vogel, K., Weise, L., Schröter, K., and Thieken, A.: Identifying Driving
Factors in Flood-Damaging Processes Using Graphical Models,
Water Resour. Res., 54, 8864–8889, https://doi.org/10.1029/2018WR022858, 2018. a
Wagenaar, D., de Jong, J., and Bouwer, L. M.: Multi-variable flood damage modelling with limited data using supervised learning approaches, Nat. Hazards Earth Syst. Sci., 17, 1683–1696, https://doi.org/10.5194/nhess-17-1683-2017, 2017. a, b, c
Wagenaar, D., Lüdtke, S., Schröter, K., Bouwer, L., and Kreibich, H.:
Regional and Temporal Transferability of Multivariable Flood Damage Models, Water Resour. Res., 54, 3688–3703,
https://doi.org/10.1029/2017WR022233, 2018. a, b
Wang, Z., Lai, C., Chen, X., Yang, B., Zhao, S., and Bai, X.: Flood hazard risk assessment model based on random forest, J. Hydrol., 527,
1130–1141, https://doi.org/10.1016/j.jhydrol.2015.06.008, 2015. a
Wickham, H.: Reshaping Data with the reshape Package, J. Stat. Soft., 21, 1–20, https://www.jstatsoft.org/article/view/v021i12 (last access: 3 February 2021), 2007. a
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R.,
Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L.,
Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., Takahashi, K., Vaughan, D., Wilke, C., Woo, K., and Yutani, H.: Welcome to the tidyverse, J. Open Source Softw., 4, 1686, https://doi.org/10.21105/joss.01686, 2019. a
Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J., and Bouwman, A.: A framework for global river flood risk assessments, Hydrol. Earth Syst. Sci., 17, 1871–1892, https://doi.org/10.5194/hess-17-1871-2013, 2013. a
Zhai, G., Fukuzono, T., and Ikeda, S.: Modeling flood damage: Case of Tokai flood 2000, J. Am. Water Resour. As., 41, 77–92, 2005. a
Short summary
Effective flood management requires information about the potential consequences of flooding. We show how openly accessible data from OpenStreetMap can support the estimation of flood damage for residential buildings. Working with methods of machine learning, the building geometry is used to predict flood damage in combination with information about inundation depth. Our approach makes it easier to transfer models to regions where no detailed data of flood impacts have been observed yet.
Effective flood management requires information about the potential consequences of flooding. We...
Altmetrics
Final-revised paper
Preprint