CAREER: Towards Watersheds as Water Treatment Plants through Advances in Distributed Sensing and Control

University Of Texas At Austin, Austin TX

Investigators

Abstract

This Faculty Early Career Development (CAREER) award supports research that will explore the potential for real-time control of hydraulic infrastructure to reduce nutrient pollution in river networks. Nutrient pollution is one of the most costly and widespread environmental problems facing surface water systems worldwide. Nitrogen and phosphorus from agricultural and urban runoff fuel the growth of algae in rivers, lakes, and coastal waters. These algal blooms deplete oxygen levels and release toxins that are hazardous to human health. This project will investigate how real-time control of hydraulic infrastructure like dams can treat nutrient pollution by emulating processes from wastewater treatment plants at the watershed scale. This new approach has the potential to substantially reduce nutrient pollution impacts while obviating the need for expensive infrastructure expansion projects. These research efforts will be complemented with an educational plan designed to promote active learning of surface water quality concepts through the development of a new computer simulation game targeted at students of all levels. The research activities will be well integrated into teaching and outreach plans, to further disseminate the impacts through active and interactive learning opportunities to broaden participation in science, technology, engineering, and mathematics (STEM). This project will reveal new fundamental knowledge about the effects of hydraulic controls on nutrient dynamics using a combination of computational modeling and real-world experimental assessments. First, a new computational water quality model will be developed to simulate nutrient fate and transport in river networks accounting for unsteady hydraulics, multispecies reaction kinetics, and hyporheic exchange. Next, new methods for water quality data assimilation will be investigated to provide real-time estimates of water quality required for effective control. Third, optimal reservoir operation strategies for nutrient and algae removal will be explored using a robust model-predictive control approach and evaluated at the river network scale. Modeling, estimation, and control of nutrient dynamics will be validated against measured data using both a laboratory-scale testbed and system-scale case studies focused on real-world watersheds of different sizes. This research will characterize and quantify the potential for active control of reservoirs to mitigate nutrient pollution and algal blooms at the watershed scale. To facilitate broad educational outreach, the results of this research will be integrated into a new ‘sandbox’ computer game that will simulate nutrient-ecosystem interactions in river networks. By encouraging students to steward aquatic life in their own virtual ecosystems, this simulation game will organically teach the mechanics of eutrophication and the interventions needed to prevent it. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.