Articles | Volume 25, issue 7
https://doi.org/10.5194/nhess-25-2437-2025
https://doi.org/10.5194/nhess-25-2437-2025
Research article
 | 
22 Jul 2025
Research article |  | 22 Jul 2025

Modelling flood losses of micro-businesses in Ho Chi Minh City, Vietnam

Anna Buch, Dominik Paprotny, Kasra Rafiezadeh Shahi, Heidi Kreibich, and Nivedita Sairam

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Cited articles

Aerts, J. P. M., Uhlemann-Elmer, S., Eilander, D., and Ward, P. J.: Comparison of estimates of global flood models for flood hazard and exposed gross domestic product: a China case study, Nat. Hazards Earth Syst. Sci., 20, 3245–3260, https://doi.org/10.5194/nhess-20-3245-2020, 2020. 
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. 
Apel, H., Aronica, G. T., Kreibich, H., and Thieken, A. H.: 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. 
Bloch, R., Jha, A. K., and Lamond, J.: Cities and Flooding: A Guide to Integrated Urban Flood Risk Management for the 21st Century, The World Bank, https://doi.org/10.1596/978-0-8213-8866-2, 2012. 
Booij, M. J.: Flood Damage Assessment and Modelling in the Red River basin in Vietnam, International Workshop on Flood Controls Decision Support Systems (FLOCODS), in: International Workshop on Flood Controls Decision Support Systems (FLOCODS), Hanoi, Vietnam, 10–12 February 2004, 1–14, https://www.utwente.nl/en/et/cem/research/wem/people-attachments/booij/publications/44.pdf (last access: 20 January 2025), 2004. 
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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.
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