Improving surface heat flux estimation for a large lake through model optimization and two‐point calibration: The case of Lake Geneva

Abstract

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.

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

“…This result is consistent with the difference in the time variation of the heat content in the EG and PG areas computed from the SurHF (Figure b) and confirms that the heat content in the eastern Grand Lac is higher than that in the western Grand Lac . Furthermore, it shows that the SurHF ( Q N ) for Lake Geneva is the dominant source of heat input into the lake and that other processes such as advective heat flux play only a minor role, as was demonstrated previously (Rahaghi et al, ). Water mass movement in the lake is not sufficient to eliminate these horizontal gradients.…”

“…The average spatial pattern of the net SurHF for 2009 to 2014 (Figure ) shows a range of >40 Wm ‐2 and has a mean spatial standard deviation of ~13 Wm ‐2 . This variability emphasizes that a single‐point analysis can lead to sizable errors in the estimation of the SurHF and hence the heat budget of a large water body (Rahaghi et al, ). A small negative mean spatiotemporal heat flux of −1 Wm ‐2 (indicated in Figure ) is found for the entire period considered, that is, the lake slightly cooled during the period from 2009 to 2014, if we assume that SurHF dominates the lake's energy budget.…”

A modeling approach to identify the effective forcing exerted by wind on a prealpine lake surrounded by a complex topography

“…[], among others, applied this approach to the large Canadian and U.S. lakes with surface areas in the order of 20,000 km 2 , where the complex interaction between lake and mountains is not present and larger‐scale (∼10 km grid) atmospheric models are able to capture the spatial variations of the wind stress. Accordingly, one of the novelties of the proposed approach is that it widens to poorly monitored prealpine lakes the possibility to investigate 3‐D phenomena that require a detailed representation of the wind as input, such as the direct circulation [ Strub and Powell , ; Lemmin and D'Adamo , ], the transversal gradients of surface temperature [ Pan et al ., ], the differential deepening of the mixed layer [ Imberger and Parker , ], the mean residence time and the interbasin exchange rates [ Rueda et al ., ], the actual surface heat fluxes [ Venäläinen et al ., ; Irani Rahaghi et al ., ], the excitation of the higher horizontal [ Lemmin et al ., ; Valerio et al ., ] and vertical [ Boehrer , ] modes, and the identification of upwelling structures [ Appt et al ., ].…”

Surface Water Temperature Heterogeneity at Subpixel Satellite Scales and Its Effect on the Surface Cooling Estimates of a Large Lake: Airborne Remote Sensing Results From Lake Geneva

“…Following studies at other sites (e.g., Davies et al, ; Woolway et al, ), we applied a constant water surface emissivity of 0.97. Based on satellite LSWT data and meteorological parameters from a numerical model calibrated at two locations on Lake Geneva, the Monin‐Obukhov similarity theory (Monin & Obukhov, ) was found to provide the best estimates of turbulent heat fluxes over this lake (Rahaghi et al, ). We applied this calibrated model to the subpixel‐scale short‐term data presented here (section ).…”

“…A set of coupled equations (equations 3e, Table ) had to be solved iteratively to obtain the drag, humidity, and temperature bulk transfer coefficients, that is, C d , C e , and C h , respectively, in order to calculate the turbulent surface heat fluxes, Q ev and Q co . The details of these equations, their solution procedure, and their calibration can be found elsewhere (Rahaghi et al, ; Woolway et al, ; Zeng et al, ).…”