Simulating Lake Responses to Climate Change with a Mechanistic Water Quality Model

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3/1/2011 - 2/28/2013

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  • Katherine McMahon, UW-Madison

Lakes are integrative sentinels of climate change. Southern Wisconsin lakes already struggle with excessive nutrient loadings and resulting eutrophication. Anticipated changes in Wisconsin’s climate such as altered precipitation patterns and a longer growing season will further stress lakes with poor water quality. In the proposed work, we will use a coupled hydrodynamic-mechanistic ecosystem process model that captures complex non-linear relationships among linked physical, chemical, and biological components of lakes to predict the response of eutrophic Wisconsin lakes to a changing climate. Our study site is Lake Mendota, an archetypal eutrophied north temperate lake with significant value to the City of Madison, Dane County, and the State of Wisconsin. We will use the model DYRESM-CA EDYM that has been previously calibrated using observed data to simulate lake response to climate change scenarios for the years 2008, 2009, and 2010. Scenarios will be characterized by differences in the timing of ice breakup, spring warming, and storm timing/frequency/intensity. Response metrics will include number of days with chlorophyll-a exceeding 5 μg/L, peak algal biomass, timing of cyanobacterial blooms, internal phosphorus loading, timing of hypolimnetic hypoxia, and ecosystem metabolism. Scenarios that produce particularly extreme changes in response metrics relative to the baseline will be selected for further investigation. The characteristics of these scenarios (e.g. storm frequency or spring warming rate) will then be “titrated” to better infer response thresholds. The overarching goal of this work is to improve our ability to use mechanistic water quality models to simulate complex ecosystem dynamics in the face of changing climate. We seek to develop predictive models that can be used to empower water quality managers to make regulatory decisions about nutrient loadings and to forecast potentially toxic cyanobacterial bloom formation, in an altered climate.

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