ADVANCING UNDERSTANDING OF LAKE WATER QUALITY THROUGH TIME WITH MODULAR COMPOSITIONAL LEARNING

Water quality is integral to the ecosystem services of lakes and is an emergent property of complex spatial and temporal dynamics, which makes predicting changes in lake water quality challenging. Modular Compositional Learning (MCL) is a framework within the Ecology Knowledge-Guided Machine Learning (Eco-KGML) paradigm that allows ecosystem phenomena, such as water quality, to be simulated by coupled modules that are either process-based models or machine learning models. Lake metabolism, an important metric of water quality, is a prime candidate for modeling with MCL. Metabolism can be broken down into component modules representing atmospheric gas exchange, net primary production, ecosystem respiration, and transport of organic matter within the water column. We have created modules within a MCL framework to represent these four processes and linked them with an existing MCL model of lake thermal structure to model dynamics of dissolved oxygen, dissolved organic carbon, and particulate organic carbon along the water column at hourly intervals over the course of 5 years within Lake Mendota (Wisconsin, USA). By swapping out process-based versions of each module with machine learning versions, we can determine where and when there are ecosystem processes integral to observed water quality that the processed-based models are not currently capturing. Consequently, MCL offers novel opportunities for us to revise our understanding of the drivers of water quality to finer spatial and temporal scales.

Primary Presenter: Bennett McAfee, University of Wisconsin–Madison (bennettjmcafee@gmail.com)

Authors:

Bennett McAfee, University of Wisconsin–Madison  (bmcafee@wisc.edu)

Robert Ladwig, Aarhus University  (rladwig@ecos.au.dk)

Abhilash Neog, Virginia Tech  (abhilash22@vt.edu)

Cayelan Carey, Virginia Tech  (cayelan@vt.edu)

Anuj Karpatne, Virginia Tech  (karpatne@vt.edu)

Arka Daw, Oak Ridge National Laboratory  (darka@vt.edu)

Paul Hanson, University of Wisconsin–Madison  (pchanson@wisc.edu)