Excellence in Research: Understanding the interactions of terrestrial and freshwater systems for developing a data-driven decision-making framework to manage Algal Blooms

Human sources of nutrients, such as point discharges from wastewater treatment plants, and nutrient rich runoff from agriculture and urban areas, are driving factors of harmful algal blooms. Conservation practices, also called best management practices and sometimes green infrastructure, are strategies to reduce runoff and nutrient loads from urban watersheds.

Best management practices have the potential to control harmful algal blooms in freshwater lakes by reducing nutrient exports in upstream urban watersheds. However, there is a lack of understanding of the relationships among the implementation of best management practices, landscape export of nutrients, and the occurrences of harmful algal blooms. The overarching goal of this research is to understand the hydrological, meteorological, biological, physical, and geochemical processes affecting the landscape export of nutrients and to evaluate the effects of best management practices on the occurrence of harmful algal blooms.

This project aims to create a decision support tool for determining the most cost-effective best management practices for urban watersheds to reduce nutrient escape and subsequently control harmful algal blooms. The project will involve key partners, community members, and stakeholders throughout the planning process to ensure broad utility of the resulting strategic plan.

The project will also provide educational activities for graduate and undergraduate students from historically underrepresented groups, with the goal of cultivating diversity for the next generation of professionals. The outcome of this project will be incorporated into the educational activities coordinated by the PI to educate the K-12 educators in Florida on sustainable water management with the goal of developing a curriculum that can be used by teachers from under-resourced middle schools in rural areas of Florida.

The research objectives of this proposal are: Objective 1 - provide a better understanding of the key hydrological, meteorological, biological, physical, and geochemical processes affecting landscape nutrient export in freshwater lakes; Objective 2 - predict occurrences of harmful algal blooms based on learned relationships between nutrient fluxes and nutrient pollution from a terrestrial system using physics-informed machine learning; and Objective 3 - design an innovative data-driven decision-making framework for evaluating the effectiveness of conservation practices in managing harmful algal blooms in downstream water bodies. This research targets shedding light on the interactive hydrological, meteorological, biological, physical, and geochemical processes affecting the transport and flux of nutrients from terrestrial systems to freshwater lakes, by applying physics-informed machine learning and developing data-driven decision-making frameworks.

The project will formulate water quality protection strategies for terrestrial systems that promote sustainability of freshwater ecosystems. The project will bridge disciplinary research on hydrology, data science, and mathematical optimization. The research team will use a system-level approach driven through convergent collaboration with the goal of providing a comprehensive understanding of integrated terrestrial freshwater systems.

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.