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This paper presents an improvement of a one-dimensional lake hydrodynamic model (Simstrat) to characterize the vertical thermal\udstructure of deep lakes. Using physically based arguments, we refine the transfer of wind energy to basin-scale internal waves\ud(BSIWs). We consider the properties of the basin, the characteristics\udof the wind time series and the stability of the\udwater column to filter and thereby optimize the magnitude of wind energy transferred to BSIWs. We show that this filtering\udprocedure can significantly improve the accuracy of modelled temperatures, especially in the deep water of lakes such as Lake\udGeneva, for which the root mean square error between observed and simulated temperatures was reduced by up to 40 %. The\udmodification, tested on four different lakes, increases model accuracy and contributes to a significantly better reproduction of\udseasonal deep convective mixing, a fundamental parameter for biogeochemical processes such as oxygen depletion. It also improves\udmodelling over long time series for the purpose of climate change studies

Optimizing the parameterization of deep mixing and internal seiches in one-dimensional hydrodynamic models: a case study with Simstrat v1.3