Modelling flood losses to microbusinesses in Ho Chi Minh City, Vietnam

• Verfasst von: Buch, Anna [VerfasserIn]
• Verfasst von: Paprotny, Dominik [VerfasserIn]
• Verfasst von: Shahi, Kasra Rafiezadeh [VerfasserIn]
• Verfasst von: Kreibich, Heidi [VerfasserIn]
• Verfasst von: Sairam, Nivedita [VerfasserIn]
• Titel: Modelling flood losses to microbusinesses in Ho Chi Minh City, Vietnam
• Verf.angabe: Anna Buch, Dominik Paprotny, Kasra Rafiezadeh Shahi, Heidi Kreibich, Nivedita Sairam
• E-Jahr: 2024
• Jahr: 19 August 2024
• Umfang: 24 S.
• Fussnoten: Gesehen am 20.01.2025
• Titel Quelle: Enthalten in: EGUsphere
• Ort Quelle: Göttingen : Copernicus GmbH, 2022
• Jahr Quelle: 2024
• Band/Heft Quelle: (2024), Artikel-ID 2340, Seite 1-24
• DOI: doi:10.5194/egusphere-2024-2340
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1915134218

Abstract

Abstract. Microbusinesses are important sources of livelihood for low- and middle-income households. In Ho Chi Minh City (HCMC), Vietnam, many microbusinesses are set up in the ground floor of residential houses susceptible to urban floods. Increasing flood risk in HCMC threatens the financial resources of microbusinesses by damaging business contents and causing business interruption. Since flood loss estimations are rarely conducted at object-level resolution and are often focused on households or large companies, the losses suffered by microbusinesses are often overlooked. This study aims to derive the drivers of flood losses in microbusinesses by applying a Conditional Random Forest to survey data (content losses: n=317; business interruption losses: n=361) collected from microbusinesses in HCMC. The variability of content losses and business interruption were adequately explained by the revenues of the businesses from monthly sales, age of the building where the business is established and water depth in the building during the flood event. Based on the identified drivers, probabilistic loss models (non-parametric Bayesian Networks) were developed using a combination of data-driven and expert-based model formulation. The models estimated the flood losses for HCMC’s microbusinesses with a mean absolute error of 3.8 % for content losses and 18.7 % for business interruption losses. The Bayesian Network model for business interruption performed with a similar predictive performance when it was regionally transferred and applied to comparable survey data from another Vietnamese city, Can Tho. The flood loss models introduced in this study make it possible to derive flood risk metrics specific to microbusinesses to support adaptation decision making and risk transfer mechanisms.