The most common and internationally accepted method of assessing building damage due to flooding is through the application of a depth-damage curve (DDC). A DDC relates the percent damage or estimated economic loss to a buildings' structural integrity and/or contents directly to a given water level (depth). The DDC generally represents an average structure within a given building category, e.g. one-storey single-family residence. Given the great variability across any given structural category, the variation in building materials, construction quality across communities and the singular focus on depth for estimation of losses, it is important to communicate the uncertainty and potential variability of the expected losses in any assessment. In this paper, probabilistic depth-damage curves (PDDCs) are developed based on synthetically derived DDCs from communities in southern Ontario. The generated PDDCs are based on assumed loss thresholds for minor and major loss levels, as spent in Canadian dollars. The economic loss estimates obtained in this way and their likelihood of being exceeded at any given flood depth express more transparently the potential building losses. An applied example of this method is included for both aggregate and building-by-building loss estimation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.