An interdisciplinary team of researchers including ESSIC Scientist Michael Gerst has developed a suite of tools to estimate the total economic value of improving water quality in urban streams.
As urban populations continue to grow, waterways are under threat. Economic development near urban streams can cause sediment erosion and surface runoff, stressors that have resulted in “urban stream syndrome.” Streams suffering from urban stream syndrome experience channel erosion, a decline in wildlife, and increased pollutant loads – all threats to a stream’s ecosystem services.
“Figuring out how to value improvements to a stream requires sophisticated computational tools,” said Gerst, an associate research professor with ESSIC/CISESS motivated by helping stakeholders identify problems and solutions at the intersection of the environment, technology, and society. “However, there is often a disconnect between the tools scientists and agencies have to assess solutions and the things that local communities see and experience, such as clarity of stream water. Our work provides a set of tools to bridge that gap.”
The researchers developed an integrated ecological framework that can be used to monetize the economic benefits of addressing urban stream syndrome. This framework translates measurable indicators of stream water quality into ecological endpoints.
The framework builds off of a water quality model that predicts six water quality indicators – biotic index, fecal coliform, specific conductance, total nitrogen, total phosphorus, and turbidity – under alternative policy interventions. These indicators are used to measure quality levels for three ecological endpoints: stream ecosystem condition, human health risk, and annual number of murky water days. Finally, the researchers sent out a survey asking how much households were willing to pay for improvements in stream water quality.
This interdisciplinary approach allows the framework to consider urban stream stressors, conditions, human uses, and preferences, translating changes in measurable water quality indicators into monetary benefit estimates.
To illustrate the method, Gerst and his team applied the framework to example policy scenarios in an urban county of North Carolina. They found that in this region, residents are willing to pay roughly $127 per household and $54 million in total for water quality improvements. These improvements would also increase stream bank tree cover by 25% and decrease runoff from impervious surfaces, such as streets and parking lots.
“The framework’s modular approach represents a promising approach for predicting water quality improvements in other growing metro areas, “ said Gerst.