The exposure component

The WRI uses four modular components - exposure, susceptibility, coping capacity, and adaptive capacity (see Figure 2.4). Exposure is described as elements (for example, people and infrastructure) present in hazard zones (UNISDR 2009). The WRI uses humans as its measure of exposure, calculating exposure by creating an average annual number of individuals exposed to

hazard events, which include earthquakes, cyclones, drought, and flooding. Additionally, there is an increasing awareness that susceptibility to disasters comes not just from exposure to natural hazards, but also to other factors such as population growth and climate change (Huppert and Sparks, 2006). One of the strengths of the WRI exposure component is that is can accommodate all hazards, contingent on the calculation of a spatially referenced exposure surface. To explore the idea of including hazards that are both potential and outside of the scope of typical hazard risk assessments the WRI includes sea-level rise as an additional component of its exposure calculation.

3.4.1 Calculating exposure

The overall exposure score is the aggregate of exposure to each of the five hazards on an annual basis, by US state and by South Carolina county. Exposure is calculated by creating an exposure surface and then adding the population located within these risk zones. The population data used for this research was 2012 US population estimates found in the United States’ Census Bureau’s American Community Survey (ACS).

In the WRI model (Figure 3.3), exposure scores for cyclones,

earthquakes, and flooding were given full weight, while drought and sea level rise were multiplied by .5, giving them half weight. Drought is a slow onset hazard that has great spatial extent. As such, it tends to expose large amounts of the population in areas that it affects, exerting undue influence on the exposure component as well as on the WRI as a whole. There is also some uncertainty in the measurement of drought exposure (Peduzzi et al., 2009). Exposure to sea- level rise, while also slow onset, has a lower spatial extent than drought.

However, as the computation of sea level rise lacks a probabilistic component, it is not possible to calculate annual exposure for this hazard (ADW 2012). For this reason as well as the uncertainty involved in projecting future risk to a hazard, sea level rise also received a weight of half in the WRI exposure component. Following the WRI method, these same weights were used for the USDRI.

3.4.2. Data

Data on all of the hazards but sea-level rise comes from the United Nations Environment Programme / Global Resource Information Database’s (UNEP/GRID) Project for Risk Evaluation, Vulnerability, Information and Early Warning Global Risk Data Platform (PREVIEW). PREVIEW is a web-based geographic information system that provides over 60 types of data on exposure and risk for nine different hazards, including four used in the WRI (Giulani and Peduzzi 2011). PREVIEW data, discussed in more detail later in this chapter as individual hazards are discussed, incorporates population exposed to hazards as

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Figure 3.3: Makeup of the WRI and USDRI exposure components

well as hazard frequency and spatial extent. Thus it represents a probabilistic method of calculating exposure (Birkmann 2011).

The one hazard that PREVIEW does not cover is exposure to sea-level rise. The WRI calculates sea level rise exposure using population data from the UNEP Global Environmental Outlook Data Portal and sea level rise data from the University of Kansas’ Center for Remote Sensing of Ice Sheets (CReSIS).

Although the combination of these two datasets allows for an estimate of population exposed to sea-level rise, it is not feasible to include a frequency component for this hazard. Thus, it is weighted differently in the WRI exposure calculation. Additionally, there is considerable error found in geo-referencing the UNEP and CReSIS data; doing so tends to result in underestimation of exposure, especially for more sparsely populated areas (Birkmann, 2011).

To overcome this error, as well as to incorporate more recent data, the USDRI utilizes sea-level rise data from the Surging Seas sea level rise dataset, run by Climate Central. Surging Seas combines population data from the 2010 US Census as well as a tidal model to quantify human and structural exposure relative to mean high tide levels. By using mean high tide as a benchmark, Surging Seas attempts to account for the underestimation of sea level rise impact found in works that use only elevation as a guide (Strauss et al. 2012). At the time of this writing, Surging Seas data is only available for the 48 contiguous United States. Thus sea-level rise data for Alaska and Hawaii were calculated using the method detailed in the WRI. Statistical comparison of the Surging

Seas and CReSIS sea level rise data using a paired samples t-test revealed that there was no significant difference in the means of the two datasets (sig. = 439).

3.4.3 Procedures

For all of the hazards except for sea-level rise, rasterized physical

exposure data was obtained from the PREVIEW data portal (Table 3.1). These rasters were then clipped, using ARCMap software, with a state map of the United States as well as a county-level map of South Carolina. To determine

exposure for each individual hazard, the raster values within each state were summed. For sea level rise, data were obtained directly from Climate Central for each of the US states and South Carolina counties found in the study, with the exception of Alaska and Hawaii. For these two states, rasters of UNEP

population and CReSIS sea level data (1 meter increase) were clipped, and then the number of people found in areas where the population and sea-level rise rasters intersected was used as the exposure surface.

Table 3.1: Variables in the exposure component

Exposure Variable (N=5) Source Supporting Literature Physical exposure to

cyclones PREVEW Global Risk Data Platform Giulani and Peduzzi (2011) Physical exposure to

earthquakes

PREVEW Global Risk Data Platform Giulani and Peduzzi (2011)

Physical exposure to

floods PREVEW Global Risk Data Platform Giulani and Peduzzi (2011) Physical exposure to

drought PREVEW Global Risk Data Platform Giulani and Peduzzi (2011) Physical exposure to

sea-level rise Surging Seas Data Portal (48 contiguous states) CReSIS (Alaska and Hawaii)

The final exposure value is the sum of the weighted populations at risk divided by the total population in the enumeration unit (state and/or county). It is expressed as a percentage, and represents the number of people in a

geographic area exposed to all in the model on an annual basis.