Probabilistic depth–damage curves for assessment of flood-induced building losses

Abstract

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.

New insights into US flood vulnerability revealed from flood insurance big data

“…Despite this growing recognition that there is weak correlation between depth and damage, the use of monotonic depth-damage functions in risk analyses remains widespread. This runs contrary to literature which suggests the treatment of depth-damage functions should be probabilistic [7][8][9][21][22][23][24] . We thus employ the National Flood Insurance Program's (NFIP) database of over 2M historic flood claims to provide empirical insights into building vulnerability in the US, where we find low agreement between NFIP damage observations and commonly applied depthdamage approaches.…”

“…Many researchers and practitioners use these relationships off-the-shelf, assuming they are well-calibrated and universally applicable. In fact, a wealth of literature notes the substantial scatter and underappreciated uncertainty in depth-damage estimates [5][6][7][8] . As Freni et al 9 summarized, uncertainty derived from depth-damage curves is the main bottleneck in estimating flood damage for a wide variety of applications ranging from climate change studies to cost-benefit calculations justifying massive infrastructure projects.…”

Unravelling the Importance of Uncertainties in Global-Scale Coastal Flood Risk Assessments under Sea Level Rise

“…The depth-damage curve is parameterized with a parameter specifying the flood depth that destroys 50% of the asset value. Two assumptions for its value are made, namely the value of 1 m used by [3], and 1.5 m defined based on the analysis of two databases [34,35]. See further details in Appendix D.…”

Minimal Building Flood Fragility and Loss Function Portfolio for Resilience Analysis at the Community Level

“…Recently, there were some attempts to develop numerical flood fragilities for specific building classes including masonry buildings [63], and wood frame buildings [64] along with other empirical flood fragilities based on collected field data from [65,66]. Additionally, some researchers tried to enhance the current stage-damage functions [31,67] and propagate uncertainties in both the empirical [68] and synthetic stage-damage functions [19]. However, those probabilistic methods were not general enough to develop a portfolio of flood fragility functions that could be used at the community-level.…”