Net Surface Heat Flux (SurHF) was estimated from 2008 to 2014 for Lake Geneva (Switzerland/France), using long-term temperature depth profiles at two locations, hourly maps of reanalysis meteorological data from a numerical weather model and lake surface water temperatures from calibrated satellite imagery. Existing formulas for different heat flux components were combined into 54 different total SurHF models. The coefficients in these models were calibrated based on SurHF optimization. Four calibration factors characterizing the incoming long-wave radiation, sensible, and latent heat fluxes were further investigated for the six best performing models. The combination of the modified parameterization of the Brutsaert equation for incoming atmospheric radiation and of similarity theory-based bulk parameterization algorithms for latent and sensible surface heat fluxes provided the most accurate SurHF estimates. When optimized for one lake temperature profile location, SurHF models failed to predict the temperature profile at the other location due to the spatial variability of meteorological parameters between the two locations. Consequently, the optimal SurHF models were calibrated using two profile locations. The results emphasize that even relatively small changes in calibration factors, particularly in the atmospheric emissivity, significantly modify the estimated long-term heat content. The lack of calibration can produce changes in the calculated heat content that are much higher than the observed annual climate change-induced trend. The calibration improved parameterization of bulk transfer coefficients, mainly under low wind regimes.

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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.

The dynamics of spatial heterogeneity of lake surface water temperature (LSWT) at subpixel satellite scale O(1 m) and its effect on the surface cooling estimation at typical satellite pixel areas O(1 km 2 ) were investigated using an airborne platform. The measurements provide maps that revealed spatial LSWT variability with unprecedented detail. The cold season data did not show significant LSWT heterogeneity and hence no surface cooling spatial variability. However, based on three selected daytime subpixel-scale maps, LSWT patterns showed a variability of >2°C in the spring and >3.5°C in the summer, corresponding to a spatial surface cooling range of >20 and >40 W/m 2 , respectively. Due to the nonlinear relationship between turbulent surface heat fluxes and LSWT, negatively skewed LSWT distributions resulted in negatively skewed surface cooling patterns under very stable or predominantly unstable atmospheric boundary layer (ABL) conditions and positively skewed surface cooling patterns under predominantly stable ABL conditions. Implementing a mean spatial filter, the effect of area-averaged LSWT on the surface cooling estimation up to a typical satellite pixel was assessed. The effect of the averaging filter size on the mean spatial surface cooling values was negligible, except for predominantly stable ABL conditions. In that situation, a reduction of~3.5 W/m 2 was obtained when moving from high O(1 m) to low O(1 km) pixel resolution.Plain Language Summary Lake surface water temperature (LSWT) is one of the main parameters required for estimating surface cooling at the air-water interface and is also essential for understanding other processes (such as ecosystem dynamics, climate change, and numerical weather prediction) in lakes. Usually, surface cooling is determined from in situ point measurements or satellite images. Satellite thermal images resolve surface areas with a typical pixel resolution of O(1 km). Therefore, satellite data can depict large-scale thermal patterns. But can LSWT spatial variability be significant at subpixel satellite resolution over a large lake? What is the effect of such variability on area-averaged surface cooling estimates? To address these questions, a measurement system, including a balloon-launched airborne platform for thermography and a catamaran for in situ measurements along predefined tracks, was used for LSWT mapping and calibration. Surface cooling patterns were then estimated using a calibrated bulk model. Results showed insignificant LSWT heterogeneity and hence no surface cooling spatial variability during the cold seasons. However, a notable spatial variability of >2°C and > 3.5°C was found in spring and summer, respectively. The effect of LSWT heterogeneity on surface cooling variability was significant, in particular, when air-water temperature differences were close to 0.

Water inherent optical properties (IOPs) contain integrative information on the optical constituents of surface waters. In lakes, IOP measurements have not been traditionally collected. This study describes how high-frequency IOP profiles can be used to document short-term physical and biogeochemical processes that ultimately influence the long-term trajectory of lake ecosystems. Between October 2018 and May 2020, we collected 1373 high-resolution hyperspectral IOP profiles in the uppermost 50 m of the large mesotrophic Lake Geneva (Switzerland−France), using an autonomous profiler. A data set of this size and content does not exist for any other lake. Results showed seasonal variations in the IOPs, following the expected dynamic of phytoplankton. We found systematic diel patterns in the IOPs. Phases of these diel cycles were consistent year-round, and amplitudes correlated to the diurnal variations of dissolved oxygen, clarifying the link between IOPs and phytoplankton metabolism. Diel amplitudes were largest in spring and summer under low wind condition. Wind-driven changes in thermal stratification impacted the dynamic of the IOPs, illustrating the potential of high-frequency profiles of water optical properties to increase our understanding of carbon cycling in lake ecosystems.