A generic physical vulnerability model for floods: review and concept for data-scarce regions

Abstract

Abstract. The use of different methods for physical flood vulnerability assessment has evolved over time, from traditional single-parameter stage–damage curves to multi-parameter approaches such as multivariate or indicator-based models. However, despite the extensive implementation of these models in flood risk assessment globally, a considerable gap remains in their applicability to data-scarce regions. Considering that these regions are mostly areas with a limited capacity to cope with disasters, there is an essential need for assessing the physical vulnerability of the built environment and contributing to an improvement of flood risk reduction. To close this gap, we propose linking approaches with reduced data requirements, such as vulnerability indicators (integrating major damage drivers) and damage grades (integrating frequently observed damage patterns). First, we present a review of current studies of physical vulnerability indicators and flood damage models comprised of stage–damage curves and the multivariate methods that have been applied to predict damage grades. Second, we propose a new conceptual framework for assessing the physical vulnerability of buildings exposed to flood hazards that has been specifically tailored for use in data-scarce regions. This framework is operationalized in three steps: (i) developing a vulnerability index, (ii) identifying regional damage grades, and (iii) linking resulting index classes with damage patterns, utilizing a synthetic “what-if” analysis. The new framework is a first step for enhancing flood damage prediction to support risk reduction in data-scarce regions. It addresses selected gaps in the literature by extending the application of the vulnerability index for damage grade prediction through the use of a synthetic multi-parameter approach. The framework can be adapted to different data-scarce regions and allows for integrating possible modifications to damage drivers and damage grades.

Physical vulnerability assessment to flash floods using an indicator‐based methodology based on building properties and flow parameters

“…The validation process can be used for analysing past flood consequences, allowing for subsequent calibration through the damage that may occur in the future (Papathoma‐Köhle, Cristofari, et al, 2019), although conditioned by the lack of damage data, as in this study area. In most studies, the indicators are weighted empirically without validation regarding their selection and weighting (Malgwi et al, 2020; Papathoma‐Köhle, 2016). Despite the uncertainty affecting the results, indicator‐based methodologies are a simple, flexible tool applicable by several users, including decision makers and those responsible for spatial planning and management (Balica, Douben, & Wright, 2009; Barroca, Bernardara, Mouchel, & Hubert, 2006).…”

“…As the characteristics of the exposed elements are frequently unconsidered (Fuchs, 2009; Papathoma‐Köhle, 2016), indicator‐based methodologies have been gaining importance in vulnerability assessments, particularly regarding the buildings' physical vulnerability in data‐scarce regions (Fuchs, Keiler, Ortlepp, Schinke, & Papathoma‐Köhle, 2019; Malgwi, Fuchs, & Keiler, 2020; Papathoma‐Köhle, Cristofari, et al, 2019; Papathoma‐Köhle, Schlögl, & Fuchs, 2019). These are common in the socioeconomic context, but have only recently been explored for physical vulnerability (e.g., Guillard‐Gonçalves, Zêzere, Pereira, & Garcia, 2016; Kappes et al, 2012; Müller, Reiter, & Weiland, 2011; Papathoma‐Köhle, 2016; Silva & Pereira, 2014; Stephenson & D'Ayala, 2014).…”

Nonlinear Simulation and Vulnerability Analysis of Masonry Structures Impacted by Flash Floods

“…Including all failure modes will lead to unnecessarily complicated descriptions. Consequently, damage states should capture predominantly damage patterns of the masonry structure to serve as a compromise between comprehensiveness and simplicity [48]. Besides, the working scale of the indices should also be considered, as there is a distinction between damage indices that refer to components, buildings, or even building clusters [47].…”

Improving flood damage assessments in data-scarce areas by retrieval of building characteristics through UAV image segmentation and machine learning – a case study of the 2019 floods in southern Malawi

“…Nonetheless, the authors state that a generalization of the procedure needs to be studied further. Another example of using indicator-based approaches regarding physical vulnerability, specifically tailored for data-scarce regions, is given by Malgwi et al (2020). In this study, a conceptual framework is proposed that combines vulnerability indexes and regional damage grades (frequently observed damage patterns) by utilizing a synthetic "what-if" assessment by experts.…”