Vulnerability, Exposure, and Impacts
Lecture
Mon., Jan. 22
Structural fragility curve
\[ \Pr(\textrm{failure}) = f(\textrm{hazard}, \theta) \]
Figure 1: Parameterized fragilities as a function of surge and wave height for MSSS concrete bridges in South Carolina (a). (b): fragility curves for bridges subjected to tsunami hazard for low (solid lines) and moderate (dashed lines) flow rates (Gidaris et al., 2017).
Flood depth-damage curve
Figure 2: Boxplots show Wing et al. (2020) analysis of insurance claims. Lines show USACE depth-damage curves.
Seawall cost-benefit analysis
For a given storm, the total damages can be estimated by summing the damages for each property. \[ \textrm{Total damages} = \sum_{i \in \, \textrm{exposure}} \textrm{Hazard}_i \times \textrm{Vulnerability}_i \]
Figure 3: Proposed “Ike Dike”
Trends in exposure
Figure 4: Global asset exposure to river and coastal flooding using the population and land-use methods (Jongman et al., 2012).
Example: North Carolina
Figure 5: Spatial distribution of the properties within our database that were built during the (a) 1800–1900, (b) 1900–1950, (c) 1950–2000 and (d) 2000–2018 periods (Tedesco et al., 2020).
Changing vulnerability
Interventions such as floodproofing can shift the vulnerability curve
Figure 6: Floodproofing in Houston, TX (Houston Public Media)
For example: Trapezoidal EAD
Running regional flood models is computationally expensive. Often, a model may have been run for a few different nominal return levels. For example, we might have flood depths at each grid for the nominal 10, 25, 50, 100, 250, and 500 year floods.
