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The sensible heat is an important component in surface energy partitioning over the land surface. This paper compared the sensible heat fluxes measured by a large aperture scintillometer system (LAS) and an eddy covariance system (EC) over a rice paddy with a patch of mulberry seedlings in the east China coastal region during the period from 13 September–11 October 2015. During the observation period, easterlies and northerlies prevailed, and 96% easterlies and northerlies had a speed of 0–6 m s-1. The sensible heat fluxes measured by the two systems reflected that the value of HLAS generally was inclined to be larger than HEC with the average difference of 20.30 W m-2, and the uncertainty for two instruments was less than 17 W m-2. Analysis of the average footprint resulted that the mulberry seedling field always had a higher contribution to LAS than that to EC, which could be the reason that HLAS was always larger than HEC. During the days when the contributions of the mulberry seedling field to the two systems were close to each other, the sensible heat flux measurements of the two instruments were similar. The case analysis on typical sunny days showed that there would be larger sensible heat fluxes over the mulberry seedling field than in the rice paddy field especially under larger net radiation conditions. [ABSTRACT FROM AUTHOR]

The paper summarizes the preliminary results from the analysis of data collected during the 2008 and 2009 vegetative seasons in a northern Italy vineyard (Vitis vinifera L., Barbera variety) and the simulations carried out in the same period with the land surface model UTOPIA. The aim of the work is to study the influence of the meteorological factors on the plant conditions. We collected a set of standard and advanced meteorological, physiological and physical data and we investigated the performance of UTOPIA in describing the different components of the energy and hydrological processes (in particular the evapotranspiration), with a special focus on the vegetation and soil. The comparison between observed data and UTOPIA simulations showed satisfactory results for the soil variables (RRMSE ranging between 15% and 40%, and correlation coefficients of 0.9). Net radiation and sensible heat fluxes RRMSE (30% and 63% respectively) suggest that both the calibration of the vegetation parameters (including the influence of the grass among vine rows) and the availability of more specific measurements are very important. [ABSTRACT FROM AUTHOR]

As two competitive pathways of surface energy partitioning, latent (LE) and sensible (H) heat fluxes are expected to be strongly influenced by climate change and wide spread of global greening in recent several decades. Quantifying the surface energy fluxes (i.e., LE and H) variations and controlling factors is still a challenge because of the discrepancy in existing models, parameterizations, as well as driven datasets. In this study, we assessed the ability of random forest (RF, a machine learning method) and further predicted the global surface energy fluxes (i.e., LE and H) by combining FLUXNET observations, climate reanalysis and satellite-based leaf area index (LAI). The results show that the surface energy fluxes variations can be highly explained by the established RF models. The coefficient of determination (R2) ranges from 0.66 to 0.89 for the LE, and from 0.53 to 0.90 for the H across 10 plant functional types (PFTs), respectively. Meanwhile, the root mean square error (RMSE) ranges from 12.20 W/m2 to 21.94 W/m2 for the LE and from 12.05 W/m2 to 22.34 W/m2 for the H at a monthly scale, respectively. The important influencing factors in building RF models are divergent with respect to LE and H, but the solar radiation is common to both LE and H and to all 10 PFTs in this study. We also found a contrasting trend of LE and H: a positive trend in LE and a negative trend in H during 1982–2016 and these contrasting trends are dominated by the elevated CO2 concentration level. Our study suggested an important role of the CO2 concentration in determining surface energy partitioning which is needed to be considered in future studies. [ABSTRACT FROM AUTHOR]