Collaborative Research: MRA: Advancing process understanding of lake water quality to macrosystem scales with knowledge-guided machine learning

Despite the growing influence of human activities on lakes, there is remarkably sparse information on lake water quality at continental scales. Moreover, we have only a nascent understanding of how broadscale changes in key drivers, such as climate and land use, control water quality at continental scales. Thus, it is a challenge to understand how ecological knowledge, based on a few relatively well-studied lakes, applies to the continental U.S., where data are limited for 1000s of lakes.

Because of the large number of lakes and the complexity of the water quality problem, machine learning may prove useful. However, recent advances in machine learning that have shown great success in commercial applications have yet to be fully applied to problems in natural systems, such as lake water quality, in part because of lower data volumes. In addition, a fundamental goal of basic ecological research is mechanistic understanding of the way the world works, a goal missing in many machine learning approaches.

This project develops ecology-knowledge guided machine learning (Eco-KGML) as a framework for leveraging the power of both ecological understanding and machine learning in modeling lake water quality across the U.S. Eco-KGML improves the accuracy of water quality predictions and advances the discovery of new knowledge about water quality processes. To broaden the impacts of this work, the project supports participation of women and underrepresented minorities in STEM (science, technology, engineering, and math) through a training program consisting of cohorts of undergraduate students, recruited from historically-excluded groups, who work on Eco-KGML research projects each summer.

This program provides authentic research experiences that evolve into individual research projects during the academic year and engage students in cross-disciplinary, cross-institutional, collaborative science in a supportive environment. This project also improves STEM education through production and dissemination of an interactive software module that introduces students to Eco-KGML concepts.

The broader impact of this project extends beyond the participating universities through collaborations with U.S. federal agency partners and collaborators from the National Ecological Observatory Network (NEON) that inform, and feed back to, agency and NEON priorities.

This project develops ecology-knowledge guided machine learning (Eco-KGML) as a conceptual framework for modeling lake and reservoir water quality (WQ) dynamics at macrosystem scales. Eco-KGML uses hybrid combinations of dynamical process-based models and ML models to scale WQ processes from well-studied lakes to macrosystem-scales across the U.S with the help of geographically extensive WQ data.

This project focuses on the specific WQ metrics of water clarity, phytoplankton biomass, and hypolimnetic anoxia, in addressing the questions: What are the dominant processes governing water quality and how do they vary across space and time? How do climate, land use, and ecosystem memory interact to affect water quality dynamics from local to macrosystem-scales?

What are the broad spatial and long-term patterns of change in lake water quality? In addressing these questions, a new line of research is enabled in Eco-KGML models for lake WQ, which are not only aimed at improving predictive performance of WQ variables but can also lead to discovery of new knowledge about WQ processes at a range of spatio-temporal scales.

Novel research in estimating process parameters of a lake, given its WQ observations, in a computationally efficient and generalizable manner is explored using ML methods. The ML-based models for lake WQ enable the discovery of new relationships among WQ variables at every lake, along with extracting relevant time lags. Through novel research in modular compositional learning (MCL), Eco-KGML models are developed to identify which WQ processes are dominant at a given lake and how they interact to influence overall WQ dynamics.

Moreover, the Eco-KGML models learn and distinguish processes specific to a single lake from those that generalize across types of lakes according to its ecological characteristics. This flexible and comprehensive use of both scientific knowledge and data enable the study of scale-dependent relationships between lakes and their drivers while providing more robust predictions for lakes across multiple temporal and spatial scales.

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.