Combining Measurements and Models to Understand the Effects of Changing Dissolved Organic Carbon Loads on Lake Carbon Cycles: Model-Data Fusion Analysis of a Whole-Lake Experiment

English Abstract:

Aquatic carbon cycles are globally significant and will likely be altered by ongoing environmental changes, yet describing carbon cycle processes and predicting future stocks and fluxes is challenging. Aquatic scientists have approached this challenge using “traditional” data and experiments, new high-frequency data streams from automated sensors, and process and simulation models, but have rarely integrated data and models into cohesive analyses. We used model-data fusion techniques to integrate data and a carbon process model in order to understand the results of a whole-ecosystem manipulation in which we increased the load of organic carbon to one basin of an experimental lake, while maintaining the other basin as a control. This experiment mimicked ongoing changes in dissolved organic carbon (DOC) observed in many regions, and resulted in a doubling of the DOC concentration and a similar increase in water color in the experimental basin. Increased water color reduced the depth of the mixed layer, with complex effects on average light climate. Both gross primary production and respiration increased as a result of the manipulation; the increase in respiration was larger, and so net ecosystem production declined. Our analyses reveal some of the mechanisms by which changing DOC loads may alter aquatic carbon cycles, and demonstrate the usefulness of model-data fusion techniques in the aquatic sciences.