Articles | Volume 10, issue 9
Geosci. Model Dev., 10, 3411–3423, 2017
Geosci. Model Dev., 10, 3411–3423, 2017

Development and technical paper 18 Sep 2017

Development and technical paper | 18 Sep 2017

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

Adrien Gaudard1, Robert Schwefel2, Love Råman Vinnå2, Martin Schmid1, Alfred Wüest1,2, and Damien Bouffard1,2 Adrien Gaudard et al.
  • 1Eawag, Swiss Federal Institute of Aquatic Science and Technology, Surface Waters, Research and Management, Seestrasse 79, 6047 Kastanienbaum, Switzerland
  • 2École Polytechnique Fédérale de Lausanne, Physics of Aquatic Systems Laboratory, Margaretha Kamprad Chair, EPFL-ENAC-IIE-APHYS, 1015 Lausanne, Switzerland

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

Short summary
The study of lakes often uses numerical models to reproduce the processes occurring in nature as accurately as possible. Due to the complexity of natural systems, all numerical models need to leave aside or simplify many of the relevant processes. In this work, we improve the modelling of the impact of wind on the internal currents in deep lakes. This improves the reproduction of deep mixing, which influences the concentrations of oxygen and nutrients, with biological and chemical consequences.