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 JWARP  Vol.12 No.3 , March 2020
The Impact of a Heterogeneous Surface on Spatiotemporal Uncertainties of Sensible Heat, Latent Heat, and CO2 Flux Measured over the Secondary Forest
Abstract: The turbulent fluxes, such as sensible and latent heat fluxes and CO2 flux, are globally observed over various terrestrial areas in order to understand the interaction between biosphere and atmosphere. Although the turbulent flux observations are generally performed on a horizontally homogeneous surface, the spatial distribution of the soil moisture is not homogeneous even on cultivated land with homogeneous vegetation, indicating that the development of each plant would be different and that the plant physiology, such as photosynthesis and growth, would be heterogeneous. In this study, to clarify the impact of a heterogeneous surface on spatiotemporal uncertainty of turbulent fluxes, a simultaneous flux observation experiment was conducted at different heights (20 m and 30 m) above the ground surface in a secondary seasonal tropical forest located in the Tak Province, Thailand. We defined ε as the spatial uncertainty of the turbulent flow flux, as proposed by Kim et al. (2011b) [1], and observed that ε of CO2 flux was high, whereas ε of sensible and latent heat fluxes were low. This is likely to be caused by spatial uncertainty such as a heterogeneous surface. The CO2 environment was heterogeneous; however, sensible and latent heat environments were homogeneous because the source area received insolation uniformly. Therefore, the analytical results for the CO2 flux presented a different pattern from those exhibited by the analytical results of the latent and sensible heat fluxes.
Cite this paper: Sakai, N. , Komori, D. , Kon, M. and Kim, W. (2020) The Impact of a Heterogeneous Surface on Spatiotemporal Uncertainties of Sensible Heat, Latent Heat, and CO2 Flux Measured over the Secondary Forest. Journal of Water Resource and Protection, 12, 171-182. doi: 10.4236/jwarp.2020.123011.
References

[1]   Kim, W., Komori, D. and Cho, J. (2011) The Characteristic of Fractional Uncertainty on Eddy Covariance Measurement. Journal of Agricultural Meteorology, 67, 163-171.
https://doi.org/10.2480/agrmet.67.3.10

[2]   Kolmogorov, A.N. (1941) Local Structure of Turbulence in an Incompressible Viscous Fluid at Very Large Reynolds Numbers. Doklady Akademiia Nauk, 30, 301-305.

[3]   Obukhov, A.M. (1946) Turbulentnost’ v temperaturnoj-neodnorodnoj atmosfere (Turbulence in an Atmosphere with a Non-Uniform Temperature). Trudy Instituta, 95-115.

[4]   Monin, A.S. and Obukhov, A.M. (1954) Basic Laws of Turbulent Mixing in the Surface Layer of the Atmosphere. Trudy Geofiz, Instituta Akademii Nauk, 24, 163-187.

[5]   Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw, K., Pilegaard, K., Schmid, H., Valentini, R., Verma, S., Vesala, T., Wilson, K. and Wofsy, S. (2001) FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities.Bulletin of the American Meteorological Society, 82, 2415-2434.
https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2

[6]   Saegusa, N., Yamamoto, S., Hirata, R., Ohtani, Y., Ide, R., Asanuma, J., Gamo, M., Hirano, T., Kondo, H., Kosugi, Y., Li, S., Nakai, Y., Takagi, K., Tani, M. and Wang, H. (2008) Temporal and Spatial Variations in the Seasonal Patterns of CO2 Flux in Boreal, Temperature, and Tropical Forests in East Asia. Agricultural and Forest Meteorology, 148, 700-713.
https://doi.org/10.1016/j.agrformet.2007.12.006

[7]   Fisher, J., Tu, K. and Baldocchi, D. (2008) Global Estimates of the Land-Atmosphere Water Flux Based on Monthly AVHRR and ISLSCP-II Data, Validated at 16 FLUXNET Sites. Remote Sensing of Environment, 112, 901-919.
https://doi.org/10.1016/j.rse.2007.06.025

[8]   Schmid, H.P. and Lloyd, C.R. (1999) Spatial Representativeness and the Location Bias of Flux Footprints over Inhomogeneous Areas. Agricultural and Forest Meteorology, 93, 195-209.
https://doi.org/10.1016/S0168-1923(98)00119-1

[9]   Foken, T. (2006) 50 Years of the Monin-Obukhov Similarity Theory. Boundary-Layer Meteorology, 119, 431-447.
https://doi.org/10.1007/s10546-006-9048-6

[10]   Oren, R., Hsieh, C., Stoy, P., Albertson, J., Mccarthy, H., Harrell, P. and Katul, G. (2006) Estimating the Uncertainty in Annual Net Ecosystem Carbon Exchange: Spatial Variation in Turbulent Fluxes and Sampling Errors in Eddy-Covariance Measurements. Global Change Biology, 12, 883-896.
https://doi.org/10.1111/j.1365-2486.2006.01131.x

[11]   Wesely, M.L. and Hart, R.L. (1985) Variability of Short Term Eddy-Correlation Estimates of Mass Exchange. U.S. Environmental Protection Agency, Washington DC, 591-612.
https://doi.org/10.1007/978-94-009-5305-5_35

[12]   Abernethy, R.B., Benedict, R.P. and Dowdell, R.B. (1985) ASME Measurement Uncertainty. Journal of Fluids Engineering, 107, 161-164.
https://doi.org/10.1115/1.3242450

[13]   Vickers, D. and Mahrt, L. (1997) Quality Control and Flux Sampling Problems for Tower and Aircraft Data. Journal of Atmospheric and Ocean Technology, 14, 512-526.
https://doi.org/10.1175/1520-0426(1997)014<0512:QCAFSP>2.0.CO;2

[14]   Bevington, P.R. and Robinson, D.K. (2003) Data Reduction and Error Analysis for the Physical Science. McGraw-Hill, New York.

[15]   Finkelstein, P. and Sims, P. (2001) Sampling Error in Eddy Correlation Flux Measurements. Journal of Geophysical Research: Atmospheres, 106, 3503-3509.
https://doi.org/10.1029/2000JD900731

[16]   Kim, W., Cho, J., Komori, D., Aoki, M., Yokozawa, M., Kanae, S. and Oki, T. (2011) Tolerance of Eddy Covariance Flux Measurement. Hydrological Research Letters, 5, 73-77.
https://doi.org/10.3178/hrl.5.73

[17]   Kim, W., Miyata, A., Ashraf, A., Maruyama, A., Chidthaison, A., Jaikaeo, C., Komori, D., Ikoma, E., Sakurai, G., Seoh, H., Son, I., Cho, J., Kim, J., Ono, K., Nusit, K., Moon, K., Mano, M., Yokozawa, M., Baten, M.A., Sanwangsri, M., Toda, M., Chaun, N., Polsan, P., Yonemura, S., Kim, S., Miyazaki, S., Kanae, S., Phonkasi, S., Kammales, S., Takimoto, T., Nakai, T., Iizumi, T., Surapipith, V., Sonklin, W., Lee, Y., Inoue, Y., Kim, Y. and Oki, T. (2015) Flux Pro as a Realtime monitoring and Surveying System for Eddy Covariance Flux Measurement. Journal of Agricultural Meteorology, 71, 32-50.
https://doi.org/10.2480/agrmet.D-14-00034

[18]   Kim, W., Komori, D., Cho, J., Kanae, S. and Oki, T. (2014) Long-Term Analysis of Evapotranspiration over a Diverse Land Use Area in Northern Thailand. Hydrological Research Letters, 8, 45-50.
https://doi.org/10.3178/hrl.8.45

[19]   Dyer, A. and Hicks, B. (1970) Flux-Gradient Relationships in the Constant Flux Layer. Quarterly Journal of the Royal Meteorological Society, 96, 715-721.
https://doi.org/10.1002/qj.49709641012

[20]   Monsi, M. and Saeki, T. (1953) über den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung für die Stoffproduktion. Japanese Journal of Botany, 14, 22-52.

 
 
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