In an urban setting, land uses such as industrial, residential, and transportation are associated with different stormwater contaminants, including toxicants (fossil fuels, plastics, pesticides, metals), bacteria, and nutrients. In addition to addressing contaminants, stormwater solutions benefit nearby communities by reducing human exposure to toxicants, improving air quality, reducing urban heat islands, and providing access to nature. The extent to which those benefits are realized depends on the surrounding environment – what the land is used for, what type of stormwater solution is put in place, and other local contexts such as community identity and economic or social vulnerabilities. To simultaneously achieve preferred outcomes for people, wildlife, and the environment, decision-making should be guided by these social, cultural, economic, and ecological factors but standard decision-making tools and models often overlook these important local contexts. Although quantitative tools – such as cost-benefit analyses and ecosystem models – may be preferable, they are slow to develop and require extensive data that is not available in many places. Therefore, decision support tools appropriate for complex, coupled human and natural systems remain underutilized. The challenge is identifying a tool that can bring together the complex web of environmental and human factors, which is pivotal for advancing more equitable stormwater projects and realizing the full suite of possible benefits.
What research tool was utilized or developed?
A team of researchers at the University of Washington and The Nature Conservancy identified a less time- and data-intensive tool called a qualitative network model (QNM), which can evaluate the full suite of possible benefits from stormwater solutions for Puget Sound. At its core, this tool is a conceptual model in which diverse components of the natural-human urban stormwater system are connected into a web (or network) by how they interact with one another via positive or negative relationships. These relationships are constructed using existing information from literature or expert judgment in its absence. Therefore, QNMs are well suited to systems where data isn’t available or when the relationships between system components are not well understood.
