Evaluation of statistical gap fillings for continuous energy flux (evapotranspiration) measurements for two different land cover types

摘要

Over the past few decades, energy and water fluxes have been directly measured by a global flux network, which was established by regional and continental network sites based on an eddy covariance (EC) method. Although, the EC method possesses many advantages, its typical data coverage could not exceed 65 % due to various environmental factors including micrometeorological conditions and systematic malfunctions. In this study, four different methodologies were used to fill the gap in latent heat flux (LE) data. These methods were Food and Agriculture Organization Penman-Monteith (FAO_PM) equation, mean diurnal variation (MDV), Kalman filter, and dynamic linear regression (DLR). We used these methods to evaluate two flux towers at different land cover types located at Seolmacheon (SMC) and Cheongmicheon (CMC) in Korea. The LE estimated by four different approaches was a fairly close match to the observed LE, with the root mean square error ranging from 4.81 to 61.88 W m(-2) at SMC and from 0.89 to 60.27 W m(-2) at CMC. At both sites, the LE estimated by DLR showed the best result with the value of the coefficient of correlation (R), equal to 0.99. Cost-effectiveness analysis for evaluating four different gap-filling methods also confirmed that DLR showed the best cost effectiveness ratio (C/R). The Kalman filter showed the second highest C/R rank except in the winter season at SMC followed by MDV and FAO_PM. Energy closures with estimated LE led to further improved compare to the energy closure of the observed LE. The results showed that the estimated LE at CMC was a better fit with the observed LE than the estimated LE at SMC due to the more complicated topography and land cover at the SMC site. This caused more complex interactions between the surface and the atmosphere. The estimated LE with all approaches used in this study showed improvement in energy closure at both sites. The results of this study suggest that each method can be used as a gap-filling model for LE. However, it is important to consider the strengths and weaknesses of each method, the purpose of research, characteristics of the study site, study period and data availability.