EAGER: The implications of interacting land use legacies and drought cycles for lake district carbon cycling

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Data from the past can usefully inform the future when frameworks such as models direct which data to use and how to combine them. Predicting the consequences of global change is particularly complex, yet of pressing concern. This project combines long-term data collected from lakes throughout Wisconsin with a new modeling framework to predict the amount of carbon and other nutrients stored in lake sediments, and thus the extent to which lakes contribute to greenhouse gas emissions and future climate. Results will improve predictions of existing climate models by incorporating the role of lakes, which are dominant landscape features. The research team is integrated vertically (faculty, postdoctoral researcher, graduate, undergraduate students) allowing opportunities to provide and receive mentoring in the scientific process at all levels. Project results will be incorporated into three courses at the University of Notre Dame and will engage science teachers from a high need intermediate school in South Bend, Indiana, through the development of curricular modules.The project focuses on lake ecosystems and two of their climate regulating services, carbon burial and greenhouse gas emission, recognizing regional contributions as emergent properties of burial and emission processes at local sites. Its goal is to develop models as tools to leverage long-term data on regional meteorology and land use in order to predict regional lake carbon burial and greenhouse gas emissions. Model-data assimilation will be used to address two questions: how do decadal drought cycles modify lake biogeochemistry, and how does agricultural intensification and its legacies alter lake responses to drought? An existing, spatially-explicit, landscape-driven biogeochemistry model will be augmented with sub-models to identify emergent effects of interactions between hydrology and land use that drive long-term material loads to lakes, include temperature-dependent biogeochemical rates, account for material exchange between lake sediments and the water column, and run Monte Carlo simulations of the model to propagate uncertainties in lake morphometry and biogeochemical rates. These modeling efforts will be followed by assimilation of a thirty-year database quantifying hydrology, water chemistry, and primary production across multiple cycles of drought and across spatial gradients including forest and agricultural land use. After testing specific questions with the results from model-data assimilation, the researchers will combine retrospective and experimental modeling to predict lake carbon biogeochemistry over the next 20 years. The research will contribute new insights into lake contributions to regional elemental cycling and will more generally explore how temporal variability and legacies interact to alter ecosystem processes.