Articles | Volume 21, issue 2
https://doi.org/10.5194/nhess-21-643-2021
https://doi.org/10.5194/nhess-21-643-2021
Research article
 | 
16 Feb 2021
Research article |  | 16 Feb 2021

Are OpenStreetMap building data useful for flood vulnerability modelling?

Marco Cerri, Max Steinhausen, Heidi Kreibich, and Kai Schröter

Related authors

Adaptive Behavior of Over a Million Individual Farmers Under Consecutive Droughts: A Large-Scale Agent-Based Modeling Analysis in the Bhima Basin, India
Maurice W. M. L. Kalthof, Jens de Bruijn, Hans de Moel, Heidi Kreibich, and Jeroen C. J. H. Aerts
EGUsphere, https://doi.org/10.5194/egusphere-2024-1588,https://doi.org/10.5194/egusphere-2024-1588, 2024
Short summary
Flood damage model bias caused by aggregation
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
Invited perspectives: Safeguarding the usability and credibility of flood hazard and risk assessments
Bruno Merz, Günter Blöschl, Robert Jüpner, Heidi Kreibich, Kai Schröter, and Sergiy Vorogushyn
EGUsphere, https://doi.org/10.5194/egusphere-2024-856,https://doi.org/10.5194/egusphere-2024-856, 2024
Short summary
Merging modelled and reported flood impacts in Europe in a combined flood event catalogue, 1950–2020
Dominik Paprotny, Belinda Rhein, Michalis I. Vousdoukas, Paweł Terefenko, Francesco Dottori, Simon Treu, Jakub Śledziowski, Luc Feyen, and Heidi Kreibich
EGUsphere, https://doi.org/10.5194/egusphere-2024-499,https://doi.org/10.5194/egusphere-2024-499, 2024
Short summary
Technical Note: Resolution enhancement of flood inundation grids
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

Related subject area

Risk Assessment, Mitigation and Adaptation Strategies, Socioeconomic and Management Aspects
Multisectoral analysis of drought impacts and management responses to the 2008–2015 record drought in the Colorado Basin, Texas
Stephen B. Ferencz, Ning Sun, Sean W. D. Turner, Brian A. Smith, and Jennie S. Rice
Nat. Hazards Earth Syst. Sci., 24, 1871–1896, https://doi.org/10.5194/nhess-24-1871-2024,https://doi.org/10.5194/nhess-24-1871-2024, 2024
Short summary
Simulating multi-hazard event sets for life cycle consequence analysis
Leandro Iannacone, Kenneth Otárola, Roberto Gentile, and Carmine Galasso
Nat. Hazards Earth Syst. Sci., 24, 1721–1740, https://doi.org/10.5194/nhess-24-1721-2024,https://doi.org/10.5194/nhess-24-1721-2024, 2024
Short summary
Analysis of the effects of urban micro-scale vulnerabilities on tsunami evacuation using an agent-based model – case study in the city of Iquique, Chile
Rodrigo Cienfuegos, Gonzalo Álvarez, Jorge León, Alejandro Urrutia, and Sebastián Castro
Nat. Hazards Earth Syst. Sci., 24, 1485–1500, https://doi.org/10.5194/nhess-24-1485-2024,https://doi.org/10.5194/nhess-24-1485-2024, 2024
Short summary
Factors of influence on flood risk perceptions related to Hurricane Dorian: an assessment of heuristics, time dynamics, and accuracy of risk perceptions
Laurine A. de Wolf, Peter J. Robinson, W. J. Wouter Botzen, Toon Haer, Jantsje M. Mol, and Jeffrey Czajkowski
Nat. Hazards Earth Syst. Sci., 24, 1303–1318, https://doi.org/10.5194/nhess-24-1303-2024,https://doi.org/10.5194/nhess-24-1303-2024, 2024
Short summary
Anticipating a risky future: long short-term memory (LSTM) models for spatiotemporal extrapolation of population data in areas prone to earthquakes and tsunamis in Lima, Peru
Christian Geiß, Jana Maier, Emily So, Elisabeth Schoepfer, Sven Harig, Juan Camilo Gómez Zapata, and Yue Zhu
Nat. Hazards Earth Syst. Sci., 24, 1051–1064, https://doi.org/10.5194/nhess-24-1051-2024,https://doi.org/10.5194/nhess-24-1051-2024, 2024
Short summary

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
Download
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.
Altmetrics
Final-revised paper
Preprint