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.

营养负荷和气候变化是湖泊初级生产力的重要驱动力。了解和预测响应当地和全球强迫的初级生产（PP）的未来变化是发展可持续湖泊管理的主要挑战。这项研究的目的是了解和表征在瑞士日内瓦湖连续两年（2012年和2013年）之间观测到的PP率和养分浓度存在巨大差异的机制。为此，我们应用了一维（1D）物理-生物地球化学模型系统。水生生物地球化学模型框架（FABM）接口用于将通用海洋湍流模型（GOTM）与生物地球化学模型，即生态区域海洋模型（ERGOM）耦合。我们通过调整物理参数来校准GOTM，与观察到的温度曲线。实施了一种模型校准方法，以最大程度地减少模型数据的失配并优化与浮游植物生长动力学有关的生物学参数。

根据我们的结果，模拟的表面混合层深度更深，2012年冬季从湖中散失的热量和水柱中的湍动能比2013年要高得多，这表明湖中由冷却驱动的深度混合在2012年。我们发现两年间上层结石内部的磷负荷存在显着差异，2012年的估算值高于2013年的水平。ERGOM预测，浮游植物的营养限制较弱，2012年的增长率较高。显然，深层混合事件导致营养物质（特别是溶解的无机磷酸盐）大量流向生产面层，并在生产季节后期形成大量藻华。相反，2013年的养分周转率较低，因此PP较低。