Exposure is the degree to which people and the things they value could be affected or “touched” by coastal hazards.
Think about your favorite coastal location. What are the aspects of the natural and built (human developed) environment in that location that affect the likelihood that people or things they value will feel the impacts of a natural hazard there?
If the location is a house built on a beach on a barrier island such as the Outer Banks of North Carolina or South Padre Island in Texas, it is highly exposed to a potential coastal hazard, but it may be in a small community and the hazard (storm) may be low intensity. In this case, the exposure is not as high as, for example, if the location is New York City, and the hazard is a storm like Sandy. In this scenario, millions of people and things they value (including complex infrastructure and things of great cultural value) are exposed. Also, the hazard is high intensity, so this amplifies the exposure. This scenario is one of very high exposure.
The components one needs to consider when assessing exposure are as follows:
In Modules 4 and 5 you covered both storm and tsunami hazards and read about the intense Hurricanes Katrina, Sandy, Maria, Harvey, Dorian, and several others. It is well established that climate change is resulting in storms of increased intensity, although there is less evidence that the frequency is increasing. However, in some locations in the world tropical cyclones are a relatively common occurrence, so can be considered frequent. The pattern of tropical cyclone frequency is illustrated in the animation shown here.
Coastal communities are increasingly exposed to extreme weather events including categories 3, 4, and 5 hurricanes and storms that generate extreme rainfall events. For example, you need only look at the 2017 hurricane season and consider Hurricane Harvey’s impact on Houston and Hurricane Maria’s impact on Puerto Rico.
The physical features of the coastal landscape play a major role in determining exposure. For example, the low-lying coastline of southeastern Louisiana exposes the city of New Orleans and nearby communities to hurricane storm surges from the Gulf of Mexico. Complicating the effects of low elevation features such a narrow bays and inlets that can amplify storm surge. In Module 6 you looked at many case studies of coastal hazards and their impacts, including Super Typhoon Haiyan (Yolanda) that devastated Tacloban City in the Philippines in 2013. Tacloban is another city with a large, dense human population situated close to the ocean on land that sits at a very low elevation. But in addition, Tacloban is in San Pedro Bay. Watch the storm surge simulation of Typhoon Haiyan below to see the effect of magnification of surge height as it enters the confines of the bay.
Population density on Earth can be explored using this NASA Earth Observatory image [2]. Take a minute to explore this interactive Night Light image from 2016. Consider how this imagery illustrates the distribution of the human population on Earth. It certainly shows the large urban areas well. But one thing to keep in mind is that some locations will show as brighter at night not only due to a greater or denser population, but because of the economics of the area. A wealthier country will have a better and more reliable energy supply.
We have already considered examples, such as Typhoon Haiyan, and Superstorm Sandy, where very densely populated areas are impacted by severe coastal hazards.
If this world population map and that of global hurricane intensity shown above are compared, we can see that the Atlantic seaboard of the U.S., as well as the east coast of China, are examples of places with high population density coinciding with high exposure to intense cyclones. Superstorm Sandy was unusual in size and intensity for a late-season storm hitting the northeastern U.S., but based on the map of storm intensity, it was not a complete anomaly. Adding the fact that Sandy, with its incredibly large diameter, impacted so many of the densely populated cities of the eastern seaboard, this was a perfect example of high exposure of people and property. In Module 6 you read about the circumstances of Sandy and the fact that these population centers (including New York, Boston, Philadelphia, ….) are home to 50 million people with an economic output of $3.6 trillion/year. The area definitely has a high exposure to hurricanes as the climate warms.
Using the satellite image and OpenStreetMap [7], identify three cities (bright spots) that were beneath Sandy’s circulation (evident here as a swirl of clouds) when this picture was taken. To identify specific cities, use the above satellite image overlay to identify the names of states that contain these cities. Then pan and zoom on the interactive map of the eastern United States [7] to find the names of these cities.
The maps on the previous page show how the severity of Hurricane Sandy’s impacts on the East Coast of the United States was a product of regional differences in not only the intensity of its wind, surge, rain, and snow but also in the distribution of people and property. One of the main reasons that Hurricane Sandy was so destructive was that it made landfall in a densely populated and developed region, exposing many people and the things they value to damaging wind and water. However, this is only part of the story. As the following sections on sensitivity and adaptive capacity will explain, other characteristics of the people who lived in Sandy’s path – including their demographics and their capacity to plan, prepare, and rebuild – also played an important role in shaping their vulnerability.
Links
[1] http://coastal.er.usgs.gov/hurricanes/sandy/photo-comparisons/newjersey.php
[2] https://eoimages.gsfc.nasa.gov/images/imagerecords/90000/90008/earth_vir_2016_lrg.jpg
[3] https://www.unep.org/resources/report/unep-global-environment-alert-service
[4] http://dx.doi.org/10.7927/H4ST7MRB
[5] http://eoimages.gsfc.nasa.gov/images/imagerecords/79000/79545/sandy_vir_2012302_lrg.jpg
[6] http://openstreetmap.org
[7] https://www.openstreetmap.org/#map=5/37.300/-74.048