Bulk Transfer Coefficients Estimated From Eddy-Covariance Measurements Over Lakes and Reservoirs

Peer reviewe

Bulk Transfer Coefficients Estimated From Eddy‐Covariance Measurements Over Lakes and Reservoirs

AbstractThe drag coefficient, Stanton number and Dalton number are of particular importance for estimating the surface turbulent fluxes of momentum, heat and water vapor using bulk parameterization. Although these bulk transfer coefficients have been extensively studied over the past several decades in marine and large‐lake environments, there are no studies analyzing their variability for smaller lakes. Here, we evaluated these coefficients through directly measured surface fluxes using the eddy‐covariance technique over more than 30 lakes and reservoirs of different sizes and depths. Our analysis showed that the transfer coefficients (adjusted to neutral atmospheric stability) were generally within the range reported in previous studies for large lakes and oceans. All transfer coefficients exhibit a substantial increase at low wind speeds (<3 m s−1), which was found to be associated with the presence of gusts and capillary waves (except Dalton number). Stanton number was found to be on average a factor of 1.3 higher than Dalton number, likely affecting the Bowen ratio method. At high wind speeds, the transfer coefficients remained relatively constant at values of 1.6·10−3, 1.4·10−3, 1.0·10−3, respectively. We found that the variability of the transfer coefficients among the lakes could be associated with lake surface area. In flux parameterizations at lake surfaces, it is recommended to consider variations in the drag coefficient and Stanton number due to wind gustiness and capillary wave roughness while Dalton number could be considered as constant at all wind speeds.Plain Language Summary: In our study, we investigate the bulk transfer coefficients, which are of particular importance for estimation the turbulent fluxes of momentum, heat and water vapor in the atmospheric surface layer, above lakes and reservoirs. The incorrect representation of the surface fluxes above inland waters can potentially lead to errors in weather and climate prediction models. For the first time we made this synthesis using a compiled data set consisting of existing eddy‐covariance flux measurements over 23 lakes and 8 reservoirs. Our results revealed substantial increase of the transfer coefficients at low wind speeds, which is often not taken into account in models. The observed increase in the drag coefficient (momentum transfer coefficient) and Stanton number (heat transfer coefficient) could be associated with the presence of wind gusts and capillary waves. In flux parameterizations at lake surface, it is recommended to consider them for accurate flux representation. Although the bulk transfer coefficients were relatively constant at high wind speeds, we found that the Stanton number systematically exceeds the Dalton number (water vapor transfer coefficient), despite the fact they are typically considered to be equal. This difference may affect the Bowen ratio method and result in biased estimates of lake evaporation.Key Points: Bulk transfer coefficients exhibit a substantial increase at low wind speed The increase is explained by wind gustiness and capillary wave roughness At higher wind speed, drag coefficient and Stanton number decrease with lake surface area SHESF, Sao Francisco Hydroelectric CompanyDOE Ameriflux Network Management ProjectNSF North Temperate Lakes LTERU.S. Department of Energy Office of ScienceJapan Society for the Promotion of Science KAKENHISwedish Research CouncilÚNKP‐21‐3 New National Excellence Program of the Ministry for Innovation and Technology, HungaryRussian Science Foundation http://dx.doi.org/10.13039/501100006769Helmholtz Young Investigators GrantHelmholtz Association of German Research CentersAustrian Academy of SciencesAutonome Provinz Bozen‐SüdtirolDeutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Russian Ministry of Science and Higher EducationNational Research, Development and Innovation OfficeICOS‐Finland, University of Helsinkihttps://doi.org/10.5281/zenodo.659782