Model-based data analysis of the effect of winter mixing on primary production in a lake under reoligotrophication


Nutrient loading, in combination with climate change are important drivers of primary productivity in lakes. Understanding and forecasting future changes in primary production (PP) in response to local and global forcing are major challenges for developing sustainable lake management. The objective of this study is to understand and characterize the mechanisms underlying the large differences in observed PP rates and nutrient concentrations between two consecutive years (2012 and 2013) in Lake Geneva, Switzerland. For this purpose, we apply a one-dimensional (1D) physical–biogeochemical model system. The Framework of Aquatic Biogeochemical models (FABM) interface is used to couple the General Ocean Turbulence Model (GOTM) with a biogeochemical model, the Ecological Regional Ocean Model (ERGOM). We calibrated GOTM, by adjusting physical parameters, with the observed temperature profiles. A model calibration method is implemented to minimize model-data misfits and to optimize the biological parameters related to phytoplankton growth dynamics. According to our results, the simulated surface mixed layer depth is deeper and heat loss from the lake and turbulent kinetic energy in the water column are much higher in winter 2012 than that in 2013, pointing to a cooling-driven, deep mixing in the lake in 2012. We found significant differences in internal phosphorus loads in the epilimnion between the two years, with estimates for 2012 being higher than those for 2013. ERGOM predicts weak nutrient limitation on phytoplankton and higher growth rates in 2012. Apparently, the deep mixing event led to high turnover of nutrients (particularly dissolved inorganic phosphate) to the productive surface layers, and a massive algal bloom developed later in the productive season. In contrary, the turnover of nutrients in 2013 was weak and consequently the PP was low. Our findings demonstrate the utility of a coupled physical–biological model framework for the investigation of the meteorological and physical controls of PP dynamics in aquatic systems.
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