David Andrew Barry, Damien Bouffard, Abolfazl Irani Rahaghi, Ulrich Lemmin
In many lakes, surface heat flux (SHF) is the most important component controlling the lake’s energy content. Accurate methods for the determination of SHF are valuable for water management, and for use in hydrological and meteorological models. Large lakes, not surprisingly, are subject to spatially and temporally varying meteorological conditions, and hence SHF. Here, we report on an investigation for estimating the SHF of a large European lake, Lake Geneva. We evaluated several bulk formulas to estimate Lake Geneva’s SHF based on different data sources. A total of 64 different surface heat flux models were realized using existing representations for different heat flux components. Data sources to run the models included meteorological data (from an operational numerical weather prediction model, COSMO-2) and lake surface water temperature (LSWT, from satellite imagery). Models were calibrated at two points in the lake for which regular depth profiles of temperature are available, and which enabled computation of the total heat content variation. The latter, computed for 03.2008-12.2013, was the metric used to rank the different models. The best calibrated model was then selected to calculate the spatial distribution of SHF. Analysis of the model results shows that evaporative and convective heat fluxes are the dominant terms controlling the spatial pattern of SHF. The former is significant in all seasons while the latter plays a role only in fall and winter. Meteorological observations illustrate that wind-sheltering, and to some extent relative humidity variability, are the main reasons for the observed large-scale spatial variability. In addition, both modeling and satellite observations indicate that, on average, the eastern part of the lake is warmer than the western part, with a greater temperature contrast in spring and summer than in fall and winter whereas the SHF spatial splitting is stronger in fall and winter. This is mainly due to negative heat flux values (net cooling) and stronger wind forcing, and consequently stronger mixing, in cold seasons.
2015
