Improving Variational Estimation of Surface Turbulent Fluxes Through Characterizing the Effect of Vegetation Dynamics on the Bulk Heat Transfer

Friday, 18 December 2015: 16:00
3022 (Moscone West)
Abedehalsadat Abdolghafoorian1, Leila Farhadi1 and Sayed M. Bateni2, (1)George Washington University, Washington, DC, United States, (2)University of Hawaii at Manoa, Honolulu, HI, United States
Estimation of turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into a variational data assimilation (VDA) framework has been the subject of numerous studies. The VDA approaches are focused on the estimation of two key parameters that regulate the partitioning of available energy between sensible and latent heat fluxes. These two unknown parameters are neutral bulk heat transfer coefficient (CHN) (that scales the sum of the turbulent heat fluxes) and evaporative fraction (EF) (that scales partitioning between the turbulent heat fluxes). CHN mainly depends on the roughness of the surface and varies on the time scales of changing vegetation phenology. The existing VDA methods assumed that the variations in vegetation phenology over the period of one month are negligible and took CHN as a monthly constant parameter. However, during the growing season, bare soil may turn into a fully vegetated surface within a few weeks. Thus, assuming a constant CHN value may result in a significant amount of error in the estimation of surface fluxes, especially in regions with a high temporal variation in vegetation cover. In this study, we advance the VDA approach by taking CHN as a function of leaf area index (LAI), which allows us to characterize the dynamic effect of vegetation phenology on CHN. The performance of the new VDA model is tested over four field sites, namely Brookings, Audubon, and Bondville in the US and Daman in China. The results show that the new model outperforms the previous one and decreases the root-mean-square-error (RMSE) in sensible and latent heat flux estimates across the four sites on average by 31% and 19% respectively.