Understanding lake dynamics is crucial to provide scientifically credible information for ecosystem management. In this context, three-dimensional hydrodynamic models are a key information source to assess critical but often subtle changes in lake dynamics occurring at all spatio-temporal scales. However, those models require time-consuming calibrations, often carried out by trial-and-error. Through a new coupling of open source software, we present here a flexible and computationally inexpensive automated calibration framework. The method, tailored to the calibration data available to the user, aims at (i) reducing the time spent on calibration, and (ii) making three-dimensional lake modelling accessible to a broader range of users. It is demonstrated for two different lakes (Lake Geneva and Greifensee) with an extensive multi-variable observational dataset. Models mean absolute errors are reduced by up to ~50% over the baseline. Guidelines on heat and momentum transfer parameters are given with their dependence on the observational setup.

了解湖泊动态对于为生态系统管理提供科学可靠的信息至关重要。在这种情况下，三维水动力模型是评估在所有时空尺度上发生的关键但通常微妙的湖泊动力学变化的关键信息来源。但是，这些模型需要耗时的校准，通常是通过反复试验进行的。通过开源软件的新结合，我们在这里提出了一种灵活且计算成本低廉的自动校准框架。针对用户可用的校准数据量身定制的方法旨在（i）减少花费在校准上的时间，以及（ii）使更广泛的用户可以访问三维湖泊模型。在两个不同的湖泊（日内瓦湖和格赖芬塞湖）上通过广泛的多变量观测数据集进行了证明。模型意味着绝对误差比基准降低了约50％。给出了热和动量传递参数的指南，这些指南取决于观测设置。