Which Wetland? National Dataset Helps Reduce Flood Risk

Article Citation:  Bousquin, J. and K. Hychka. 2019. A geospatial assessment of flood vulnerability reduction by freshwater wetlands—a benefit indicators approach. Frontiers in Environmental Science, (7) 54: 1-14 https://doi.org/10.3389/fenvs.2019.00054

In the past few years, several areas of the United States have seen major flooding. In addition to intense hurricanes such as Harvey and Irma, many areas of the country have experienced damage from heavy rains. Just this past spring, nearly 200 rivers in the Midwest and along the Mississippi River flooded at the same time– impacting hundreds of communities and causing billions in estimated damage.

Figure 1. Aerial view of damage after Hurricane Harvey in 2017. Source South Carolina National Guard on https://www.flickr.com/photos/scguard/36564448210/

Flood risk is not a problem that is expected to decrease in the future; however, some areas may have a solution already nearby.

Wetlands, or water-covered marshy areas, are a vital ecosystem that can help protect against flooding. Think of freshwater wetlands like sponges: they are very good at sucking up surface water and slowing releasing it back out into the environment. A typical wetland can even store up to one million gallons of water. In contrast, paved surfaces like parking lots are not good at preventing flooding– runoff from rain accumulates quickly in urban environments. Having wetlands near urban areas can help reduce flooding impacts on both the environment and the people who live there because the wetlands can capture the runoff. 

Communities across the U.S. in flood-prone areas may already have wetlands that could be protected, or restored to help with potential flooding. However, with limited time, resources, and funds how do communities know where they should focus their efforts?

Building a National Dataset

Researchers created a national dataset to help communities understand what wetland areas are most important when it comes to reducing flood risk. Using datasets that describe population and land type, the research team sought to answer two questions about conserving and restoring wetlands: 1) how many people will benefit through reduced risk for future flooding, and flood damage? and 2) how much will those people benefit, or how large will the risk reduction be? 

To answer these questions, researchers created a new national dataset and then zeroed in on Harris County, Texas as an example of how this dataset could be applied at the local level. Harris County contains Houston, Texas – an area that was impacted heavily by Hurricane Harvey in 2017. Researchers created the dataset by overlaying population, flood-risk, and land type data. The team examined “catchments” or the area that drains into a stream and calculated how the  area of every catchment containing freshwater wetlands. This data was compared to the number of people who live in the area to understand how many people would benefit if wetlands were protected or restored nearby. The result was a dataset that can help decision makers understand where protection or restoration will get them the most bang for their buck.

Figure 2. A visual representation of how the study used population, flood-risk, and catchment data to create a national dataset. Source modified from Bousquin and Hychka, 2019 at https://doi.org/10.3389/fenvs.2019.00054

 

Figure 3. The percent of each catchment that contains freshwater wetlands throughout the contiguous United States. Source Bousquin and Hychka, 2019 at https://doi.org/10.3389/fenvs.2019.00054
Figure 4. Level of population in flood-prone areas using two different datasets from A) FEMA and B) the USEPA throughout the contiguous United States. Source Bousquin and Hychka, 2019 at https://doi.org/10.3389/fenvs.2019.00054

Researchers also ran simulations of different wetland restoration scenarios to understand how restoring each wetland would benefit the population in that area.  

As Easy as ABC

The majority of catchments that have a high percentage of wetland coverage are in the Great Lakes, along the Mississippi, and near the East Coast. Also, more people were at risk for flooding in the eastern half of the United States – areas that often see more intense storms like hurricanes. 

At the local level, researchers were able to assign priorities to each wetland area in Harris County and give specific recommendations to which wetlands should be the highest priority to be protected, and which ones should be restored.

Figure 5. A map of a sub-region of Texas with a close-up of Harris County. Catchment areas are highlighted by different colors to note which areas should have their wetlands either restored or protected. Priority of each is noted with A being the highest priority and C being the lowest. Source Bousquin and Hychka, 2019 at https://doi.org/10.3389/fenvs.2019.00054
Put the Money Where the Wetlands Are

Harris County recently approved 2.5 billion dollars for flood-risk reduction projects. Information like this dataset provides local decision-makers with the ability to see where their dollars can have an effect and benefit the most people based on population data in that area. 

By leveraging a wetland’s sponge like ability to absorb runoff, communities can reduce flood-risk. Restoring and protecting wetlands identified by this analysis could help millions of people in danger of flooding across the country be more prepared for future storms. 

 

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Brittany Maule

Brittany Maule

I earned my Master's in Biology from Ball State University in 2017, studying how everyday human products like the compounds in bug spray and Tylenol affect the organisms that live in our streams and rivers. I'm interested in how human pollutants play a role in our aquatic ecosystems, especially since we use them for so many important functions! Currently, I work at Green Seal - a nonprofit that strives to make all sorts of products safer for human health and the environment. When I'm not working on my science communication stuff, I can be found hiking or curled up with a book and warm mug of tea.

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