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

被引:8
|
作者
Guseva, S. [1 ]
Armani, F. [2 ]
Desai, A. R. [3 ]
Dias, N. L. [4 ]
Friborg, T. [5 ]
Iwata, H. [6 ]
Jansen, J. [7 ,8 ]
Luko, G. [9 ]
Mammarella, I [10 ]
Repina, I [11 ,12 ]
Rutgersson, A. [13 ]
Sachs, T. [14 ]
Scholz, K. [15 ]
Spank, U. [16 ]
Stepanenko, V [12 ,17 ,18 ]
Torma, P. [9 ]
Vesala, T. [10 ,19 ]
Lorke, A. [1 ]
机构
[1] Univ Koblenz Landau, Inst Environm Sci, Landau, Germany
[2] Univ Fed Parana, Curitiba, Parana, Brazil
[3] Univ Wisconsin, Dept Atmospher & Ocean Sci, Madison, WI USA
[4] Univ Fed Parana, Dept Environm Engn, Curitiba, Parana, Brazil
[5] Dept Geosci & Nat Resource Management, Copenhagen, Denmark
[6] Shinshu Univ, Dept Environm Sci, Fac Sci, Matsumoto, Nagano, Japan
[7] Uppsala Univ, Dept Ecol & Genet Limnol, Uppsala, Sweden
[8] Univ Quebec Montreal, Grp Rech Interuniv Limnol, Dept Sci Biol, Montreal, PQ, Canada
[9] Budapest Univ Technol & Econ, Dept Hydraul & Water Resources Engn, Budapest, Hungary
[10] Univ Helsinki, Inst Atmospher & Earth Syst Res Phys, Fac Sci, Helsinki, Finland
[11] AM Obukhov Inst Atmospher Phys, Moscow, Russia
[12] Lomonosov Moscow State Univ, Res Comp Ctr, Moscow, Russia
[13] Uppsala Univ, Dept Earth Sci, Uppsala, Sweden
[14] GFZ German Res Ctr Geosci, Potsdam, Sweden
[15] Univ Innsbruck, Dept Ecol, Innsbruck, Austria
[16] Tech Univ Dresden, Inst Hydrol & Meteorol, Chair Meteorol, Fac Environm Sci, Tharandt, Germany
[17] Lomonosov Moscow State Univ, Fac Geog, Moscow, Russia
[18] Moscow Ctr Fundamental & Appl Math, Moscow, Russia
[19] Univ Helsinki, Inst Atmospher & Earth Syst Res Forest Sci, Fac Agr & Forestry, Helsinki, Finland
基金
俄罗斯科学基金会; 瑞典研究理事会; 日本学术振兴会;
关键词
bulk transfer coefficients; eddy-covariance; lakes; reservoirs; AIR-SEA FLUXES; DRAG COEFFICIENT; HEAT-FLUX; SENSIBLE HEAT; SURFACE; PARAMETERIZATION; TURBULENT; WATER; EXCHANGE; MODEL;
D O I
10.1029/2022JD037219
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The 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.
引用
收藏
页数:20
相关论文
共 50 条
  • [41] A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem
    Liu, S. M.
    Xu, Z. W.
    Wang, W. Z.
    Jia, Z. Z.
    Zhu, M. J.
    Bai, J.
    Wang, J. M.
    HYDROLOGY AND EARTH SYSTEM SCIENCES, 2011, 15 (04) : 1291 - 1306
  • [42] Surface energy balance of an extensive green roof as quantified by full year eddy-covariance measurements
    Heusinger, Jannik
    Weber, Stephan
    SCIENCE OF THE TOTAL ENVIRONMENT, 2017, 577 : 220 - 230
  • [43] Effects of Gentle Topography on Forest-Atmosphere Gas Exchanges and Implications for Eddy-Covariance Measurements
    Chen, Bicheng
    Chamecki, Marcelo
    Katul, Gabriel G.
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2020, 125 (11)
  • [44] Failure of Taylor's hypothesis in the atmospheric surface layer and its correction for eddy-covariance measurements
    Cheng, Yu
    Sayde, Chadi
    Li, Qi
    Basara, Jeffrey
    Selker, John
    Tanner, Evan
    Gentine, Pierre
    GEOPHYSICAL RESEARCH LETTERS, 2017, 44 (09) : 4287 - 4295
  • [45] Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction
    Fratini, G.
    McDermitt, D. K.
    Papale, D.
    BIOGEOSCIENCES, 2014, 11 (04) : 1037 - 1051
  • [46] Divergent environmental responses of long-term variations in evapotranspiration over four grassland ecosystems in China based on eddy-covariance measurements
    Zheng, Han
    Yu, Guirui
    Wang, Qiufeng
    Chen, Zhi
    Zhu, Xianjin
    Bao, Han
    Sun, Yuchen
    Niu, Panpan
    Li, Yingnian
    Shi, Peili
    Hao, Yanbin
    Zhang, Fawei
    Niu, Zhongen
    JOURNAL OF HYDROLOGY, 2023, 625
  • [47] Air-water gas exchange in lakes and reservoirs measured from a moving platform by underwater eddy covariance
    Berg, Peter
    Pace, Michael L.
    Buelo, Cal D.
    LIMNOLOGY AND OCEANOGRAPHY-METHODS, 2020, 18 (08): : 424 - 436
  • [48] Combining eddy-covariance and chamber measurements to determine the methane budget from a small, heterogeneous urban floodplain wetland park
    Morin, T. H.
    Bohrer, G.
    Stefanik, K. C.
    Rey-Sanchez, A. C.
    Matheny, A. M.
    Mitsch, W. J.
    AGRICULTURAL AND FOREST METEOROLOGY, 2017, 237 : 160 - 170
  • [49] Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015
    Fang, Junjun
    Fang, Jingchun
    Chen, Baozhang
    Zhang, Huifang
    Dilawar, Adil
    Guo, Man
    Liu, Shu'an
    SCIENTIFIC DATA, 2024, 11 (01)
  • [50] Effects of Measurement Height and Low-Pass-Filtering Corrections on Eddy-Covariance Flux Measurements Over a Forest Clearing with Complex Vegetation
    Oliver Reitz
    Alexander Graf
    Marius Schmidt
    Gunnar Ketzler
    Michael Leuchner
    Boundary-Layer Meteorology, 2022, 184 : 277 - 299