Estimation of Minimum Canopy Resistance by EC Data and Its Application in the Interpolation of Latent Heat Flux

Accurate latent heat flux estimation is important for land-atmosphere exchange and water cycle research. The seasonal uncertainty of latent heat flux simulation by Penman-Monteith equation is caused by the minimum canopy resistance, which varies with various canopy conditions but is often set to a fixed value in present modeling studies. To solve the problem, the seasonal curve of the minimum canopy resistance is fitted based on an integral and piecewise fitting method which velies on multi-year measurements of EC (Eddy Covariance) flux. The Ningxiang flux station is taken as an example. Flux data from 2012-2015 are used to fit the seasonal curve of the minimum canopy resistance, and data from 2016 is used to verify the simulated results. It is found that the minimum canopy resistance has a seasonal variation, which is lower in the summer and higher in the winter. The modified simulation shows better results by applying seasonally varying minimum canopy resistance. Also the correlation coefficient, root mean square error and agreement of index are better than those using the original canopy resistance scheme. The modified scheme is then used to interpolate the missing data. Results indicate that the modified scheme is more stable than the traditional interpolating method, and the uncertainty of the input data can be determined by the differential equation. This research is helpful to keep flux data complete, and to provide scientific basis for the data application.