The Importance of Systematic Spatial Variability in the Surface Heat Flux of a Large Lake: A Multiannual Analysis for Lake Geneva

Abstract

The spatiotemporal surface heat flux (SurHF) distribution over Lake Geneva, the largest lake in Western Europe, was estimated for a 7-year period (2008)(2009)(2010)(2011)(2012)(2013)(2014). Data sources included hourly maps of over-the-lake assimilated meteorological data from a validated numerical weather model and lake surface water temperature (LSWT) from satellite imagery. A set of bulk algorithms, previously optimized and calibrated at two locations in Lake Geneva, was used. Results indicate a systematic long-term average spatial range of >40 Wm -2 between different parts of the lake and little year-to-year variability. This variability is mainly due to topographically induced wind sheltering over parts of the lake, which in turn produces spatial variability in the sensible and latent heat fluxes. These results are supported by a systematic spatial heat content variability obtained from long-term temperature profile measurements in the lake. During spring, a lower SurHF spatial range was evident. Unlike other seasons, the spring spatial variability of air-water temperature differences and, to a lesser extent, the global radiation variability resulting from sheltering by the mountainous topography were the main drivers of the SurHF spatial variability. Analysis of the atmospheric thermal boundary layer showed stable conditions from March to early June and unstable conditions for the rest of the year. This regime change can explain the low SurHF spatial variability observed during spring. The results emphasize that spatial variability in meteorological and LSWT patterns, and consequently in the spatiotemporal SurHF data, should be considered when assessing the time evolution of the heat budget of large lakes. Plain Language SummaryHeat exchange at the air-water interface is the main driver affecting the heat content of a lake. Usually, the surface heat flux (SurHF) is determined from a single-location analysis. However, the spatial variability of the lake surface water temperature (LSWT) and meteorological parameters can be notable. Can such variability induce significant SurHF variability? What are the major factors controlling the spatial variability of SurHF? To address these questions, the spatiotemporal SurHF of Lake Geneva, the largest lake in Western Europe, was estimated for a 7-year period using satellite LSWT patterns and hourly maps of meteorological data. The results indicated that, compared to the mean value, the average spatial SurHF range can be significant. Heat content calculations based on long-term temperature profile measurements in different parts of the lake confirm this. Ignoring the spatial variability of SurHF can lead to sizable errors in the estimation of the heat budget of a large lake. The SurHF spatial variation is mainly due to wind sheltering over parts of the lake except for spring, when the LSWT spatial contrast is the dominant factor. Such a seasonal regime change can be explained by the atmospheric boundary layer dynamics over the lake.

Re-discovering Robert E. Horton's Lake Evaporation Formulae: New Directions for Evaporation Physics

“…Over large lakes, temperature varies with bathymetry due to variable rates of vertical mixing in large lakes -however, this variability only depends on lake bathymetry which can be treated as a static parameter, and heat exchange can be modeled or observed with better accuracy in larger lakes from space observations (as noted before). On the other hand, in a study by Rahaghi et al (2019), it was shown that radiation at the surface of a large Swiss lake (Lake Geneva) varied on the order greater than 40 Wm -2 in different parts of the same lake, which is quite a significant error for a large lake and was attributed to shading effect by clouds, a dynamic error. In terrestrial hydrology, where radiation budget is calculated from temperature (e.g.…”

Effects of natural surfactants on the spatial variability of surface water temperature under intermittent light winds on Lake Geneva

“…Those ocean measurements were carried out under upward heat fluxes and thus unstable ABL conditions, whereas our measurements were taken under stable conditions (ζ = 0.93) and net surface warming (Fig. 5a), which is typical for the spring period over Lake Geneva (Rahaghi et al, 2019b). The difference between our measurements and those carried out in the ocean illustrate two mechanisms through which slicks can affect the surface water temperature: In the ocean studies, the reduction of temperature in slicks was associated with the surfactants' ability to suppress The air-side friction velocity is calculated following Zeng et al (1998), accounting for atmospheric boundary layer stability and measurement height;…”

“…Air-water exchange dynamics are different under stable and unstable Atmospheric Boundary layer (ABL) (Kara et al, 2005;Mahrt and Hristov, 2017). Although near surface dynamics under stable ABL conditions have been studied in lakes during events (e.g., Yusup and Liu, 2016;Rahaghi et al, 2019b), their potential differences in slick and non-slick areas under stable ABL and light wind conditions during DWL formation have not yet been reported. However, these differences can strongly affect air-water exchange.…”

“…In Lake Geneva, it was shown that winds are light (<4 m s -1 ) 80 -90% of the time during the stratification period (May to October; Lemmin and D'Adamo, 1996). Stable ABL conditions have been observed to last for several months in spring and early summer (Rahaghi et al, 2019b;Lemmin, 2020), thus indicating that stable ABL, low wind conditions are important in the annual air-water exchange cycle.…”

Rediscovering Robert E. Horton's lake evaporation formulae: new directions for evaporation physics

“…However, this variability only depends on lake bathymetry which can be treated as a static parameter, and heat exchange can be modeled or observed with better accuracy in larger lakes from space observations (as noted before). On the other hand, in a study by Rahaghi et al (2019), it was shown that radiation at the surface of a large Swiss lake (Lake Geneva) varied of the order of greater than 40 W m −2 in different parts of the same lake, which is quite a significant error for a large lake, and was attributed to shading effect by clouds, which is a dynamic error. In terrestrial hydrology, where the radiation budget is calculated from temperature (e.g., Bohn et al, 2013), Horton's method has a particular advantage.…”