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Study region: Inner Mongolia Autonomous Region, North China Study focus: Sensible heat flux (H) quantifies the intensity of water evapotranspiration, making the factors influencing H pivotal to water consumption. North China's semi-arid grasslands are characterized by widespread grazing and strong wind conditions. Grazing strategy modifies surface air heat transfer resistance via vegetation and consequently alters H. Nevertheless, the majority of studies overlook the impact of the observed variables' time scale on H estimation. This paper employs a ''big-leaf'' model to estimate H, incorporating both grazing management strategy and data time scale, validated using the Bowen ratio, and attempted to analyze the factors that affect H. New hydrological insights for the region: Aerodynamic characteristics estimated from daily data typically exceed estimates derived from hourly data. The influence of surface radiative temperature and air temperature on H outweighs that of aerodynamic resistance, whereas aerodynamic resistance accounts for the disparity in estimating H between daily and hourly data. In grazing-prohibited grasslands, the daily H values estimated by the Big Leaf model closely match those obtained from the Bowen ratio. When estimating the hourly H value for grazing prohibited and grazing grasslands, the increase was 25.95 w/m2 and 52.27 w/m2 compared to the daily scale, respectively. For the estimation of water and heat fluxes across different regions, the temporal scale of input data is a pivotal factor.

Agricultural water resources consumption and soil salinity interact with the distribution of radiation energy in arid salinized farmland. Understanding the temporal changes in energy distribution is necessary to enhance water resource use efficiency. Based on two years of monitoring the energy fluxes in a salinized sunflower field, it was found that field energy fluxes were dependent on available energy, and the temporal dynamics of energy distribution was strongly influenced by crop growth. As expected, soil moisture and meteorological conditions were the main factors limiting latent heat (LE) fluxes in the relatively dry year, with leaf area index showing a more significant positive correlation with LE under abundant soil moisture. Furthermore, path analysis revealed that soil moisture affected energy distribution at budding and flowering stages through the negative regulation of soil surface heat fluxes and the positive regulation of LE. The accumulation of soil salt decreased LE (with Pearson correlation coefficient of −0.59 and a total effect of −0.55), and positive regulated sensible heat (with Pearson correlation coefficient of 0.27 and a total effect of 0.31). Additionally, approximately 40% of actual evapotranspiration was contributed by groundwater, potentially influencing energy fluxes. Moreover, a negative correlation was observed between surface albedo and salinity, which might be another pathway influencing energy distribution. Our findings are important to understand energy distribution and water consumption during the crop growth period in salinized field in arid area.

Deforestation plays an essential role in land cover changes in tropical area. It resulted in changing surface biophysical characteristics such as albedo and surface roughness altering the climate, heat fluxes as well as CO2 fluxes. Thus, this study aims to analyse the biogeophysical and biogeochemical processes in forest, shrub, grass, and soybean. Also, the study analyses the coupling strength in the terrestrial segment. This analysis is conducted using the Community Land Model Version 5 (CLM5). The results showed that land-use changes alter surface heat fluxes. Forest changing into shrub, grass, and soybean decreased latent heat fluxes and increased sensible heat fluxes. Land-use changes also alter carbon uptake, although it did not necessarily reduce the net primary production (NPP) and efficiency. Forest changing into soybean will increase NPP, while its changes to grass and shrub will decrease NPP. Among all land covers, soybean had higher carbon use efficiency (CUE) due to the availability of water and soil nutrient supply. In comparison, the forest had lower CUE due to its high respiration to maintain its biomass. Coupling strength between soil moisture and latent heat fluxes in all land covers was positive, but the R-squared was relatively low. This result indicated that there was another factor impacting surface energy balance partition such as vegetation cover. [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.

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]

• Optical-microwave scintillometers (OMS) are compared to eddy covariance systems (EC). • Surface hydrothermal conditions and different principles cause divergent measurements. • Irrigation, precipitation, and advection affect the divergent measurements. • OMS does not need to capture all-scale eddies and can improve energy balance closure. The eddy-covariance (EC) method has been widely used to measure sensible (H) and latent (LE) heat fluxes, however, it has limited spatial representativeness (hundreds of meters' scale). Optical-microwave scintillometers (OMS) can directly measure kilometer-scale H and LE, which can be used to validate satellite remote sensing products and model simulations. Therefore, quantitative comparisons and analyses between H and LE measured by OMS and EC are important. In this study, the H and LE values measured by OMS and EC were compared and analyzed over two different surfaces: alpine meadow (homogeneous underlying surface) and irrigated cropland (heterogeneous underlying surface). Results showed that the H (LE) difference between OMS and EC measurements was approximately 17% (18%) and 27% (37%) over the homogeneous and heterogeneous underlying surfaces during the vegetation growing season, respectively. Furthermore, the difference in measured LE from OMS and EC (Δ L E O M S − E C ) is caused by the combination of the difference in land surface hydrothermal conditions (LSHCs) and the different principles of OMS and EC measurements. On the one hand, Δ L E O M S − E C varied with the changing LSHCs within the instruments' source areas over the heterogeneous surface, and the value could be decreased by irrigation and precipitation events. On the other hand, OMS averages large-scale measurements over both space and time and does not need to capture all-scale eddies (e.g., large-scale eddies), so the occurrence of advection increases Δ L E O M S − E C . Finally, compared with that of EC system, the energy balance closure ratio of OMS system was similar over the homogeneous underlying surface but significantly increased by 26% over the heterogeneous underlying surface. These results indicate that OMS has a potential advantage in measuring sensible and latent heat fluxes, particularly over heterogeneous underlying surfaces. [ABSTRACT FROM AUTHOR]

Abstract: Indian economy is largely depending upon the agricultural productivity and thus influences the trade among the SAARC countries. High-resolution and good-quality regional weather forecasts are necessary for planners, resource managers, insurers and national agro-advisory services. In this study, high resolution updated land-surface state in terms of vegetation fraction (VF) from operational vegetation index products of Indian geostationary satellite (INSAT 3A) sensor (CCD) was utilized in numerical weather prediction (NWP) model (e.g. WRF) to investigate its impact on short-range weather forecast over the control run. Results showed that the updated vegetation fraction from INSAT 3A CCD improved the low-level 24h temperature (∼18%) and moisture (∼10%) forecast in comparison to control run. The 24h rainfall forecast was also improved (more than 5%) over central and southern India with the use of updated vegetation fraction compared to control experiment. INSAT 3A VF based experiment also showed a net improvement of 27% in surface sensible heat fluxes from WRF in comparison to control experiment when both were compared with area-averaged measurements from Large Aperture Scintillometer (LAS). This triggers the need of more and more use of realistic and updated land surface states through satellite remote sensing data as well as in situ micrometeorological measurements to improve the forecast quality, skill and consistency. [Copyright &y& Elsevier]

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]

Abstract: In this paper, we attempt to fit sensible heat fluxes obtained by the profile method between 1–2 and 1–4m height into the autoregressive integrated moving average (ARIMA) family. In both the profiles, fluxes are found to fit into the first-order autoregression [AR (1)] model, which is the continuous analog of the first-order Markov chain. The ARIMA (1, 0, 0) model was able to simulate the heat flux value for 1h in advance for both the profiles (1–2 and 1–4m height) generating a prediction error of 18.28% and 4.95%, respectively. [Copyright &y& Elsevier]

Turbulence data collected over a total of 25 days during two summers are used to describe processes responsible for the nonstationarity of turbulent sensible heat fluxes at Summit, Greenland. A stationarity test shows that about 40% of the data are classified as nonstationary. Three main factors are explored to account for the large fraction of nonstationary runs: (1) intermittency of turbulence in stable conditions, (2) changes in net all-wave radiation in response to cloud forcing, and (3) diurnal trends in stability. A classification procedure that accounts for the intermittent nature of turbulence shows that during stable, nonstationary conditions 50% of the total sensible heat flux is realized in 22% of the sampling time. Intermittency often occurs at Summit during periods characterized by weak and irregular horizontal winds in combination with strong stability. Rapid changes in net all-wave radiation in response to cloud forcing results in nonstationarity during unstable conditions. Between 0930–1130 and 1900–1930 UTC turbulent heat fluxes are not only small in magnitude but also typically change sign, with nonstationarity during these periods often as high as 65%. These results should help resolve some of the present uncertainties in obtaining reliable fluxes at this site, in particular under stable atmospheric conditions. [ABSTRACT FROM AUTHOR]

This paper examines the interannual variability of latent (LE) and sensible (H) heat fluxes over the North Indian Ocean for the summer season (June, July, August), that show positive linear trends during 1958–2017. Using Air-Sea Flux (OAFlux) from NCEP/NCAR reanalysis-I, the variability and trends in LE and H heat fluxes are compared with changes in the position and pressure of the South Asia Low (SAL). Investigation of interactions between the air-sea heat fluxes and SAL pressure center through correlations analysis and with analysis of composites of humidity gradient, temperature gradient and vector wind fields is carried out. The results show that the SAL plays an important role in the variability of air-sea heat fluxes over North Indian Ocean. There is a negative correlation between SAL pressure and its latitude position. A change of the SAL from a north location to a south location induces changes in North Indian Ocean surface winds, humidity and temperature. These, combined with sea surface warming trends, produce trends in summer latent and sensible heat fluxes. • Latent and sensible heat fluxes over north Indian Ocean show positive linear trends during summer rainy season. • Correlations between detrended monthly mean heat fluxes and detrended South Asia low (SAL) indices have been calculated. • The correlations show that the latitudinal movement of SAL play a significant role in the variability of heat fluxes. • Composite analysis of various meteorological fields for different states of SAL have been performed. • The composite analysis indicate a strong connection between trends in SAL and trends in the Indian Ocean heat fluxes. [ABSTRACT FROM AUTHOR]

Both sensible and latent heat fluxes are important components in the land surface energy budget, and accurately estimating them on kilometer-scale are important for understanding and modeling local hydro meteorological process. Scintillometry could provide kilometer-scale averaged turbulent heat fluxes, and has been used as ground-based remote sensing for estimating surface energy and water budget. This research compared heat fluxes obtained using an optical-microwave scintillometer (OMS) and an eddy covariance system (EC) over a typical temperate deciduous forest in hilly area, northern China. The observation period mainly represents growing season, from April to October 2018. We estimated the sensible and latent heat fluxes using both the two-wavelength method and the bichromatic correlation method, and the EC measurements were used as a benchmark. The results show the sensible heat fluxes from OMS showed 2% higher than EC, whilst the latent heat fluxes from OMS showed around 10% higher than EC. Changes in hydrological conditions or footprints have no significant effects on the measured turbulent fluxes from OMS. We also founded that there are slightly differences between fluxes derived by the two-wavelength method and the bichromatic correlation method due to assumed r Tq value. Based on these results, we conclude that OMS can be a reliable approach to measure kilometer-scale surface turbulent heat fluxes over the hilly area, and its ability to estimate kilometer-area sensible and latent heat fluxes looks promising. • This is a unique dataset of the two-wavelength method in calculated latent and sensible heat fluxes, which are lacking in the literature. • The microwave scintillometer (RPG-MWSC-160, Radiometer Physics GmbH, Germany) is a new commercialized microwave scintillometer device, and there is rare literature on its performance over a typical temperate deciduous forest in hilly area. • The result can provides much needed information about the performance of the OMS system and, more generally, two-wavelength scintillometry. [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]