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Evaporation from the soil surface (E) can be a significant source of water loss in arid areas. In sparsely vegetated systems, E is expected to be a function of soil, climate, irrigation regime, precipitation patterns, and plant canopy development and will therefore change dynamically at both daily and seasonal time scales. The objectives of this research were to quantify E in an isolated, drip-irrigated vineyard in an arid environment and to simulate below canopy E using the HYDRUS (2-D/3-D) model. Specific focus was on variations of E both temporally and spatially across the inter-row. Continuous above canopy measurements, made in a commercial vineyard, included evapotranspiration, solar radiation, air temperature and humidity, and wind speed and direction. Short-term intensive measurements below the canopy included actual and potential E and solar radiation along transects between adjacent vine-rows. Potential and actual E below the canopy were highly variable, both diurnally and with distance from the vine-row, as a result of shading and distinct wetted areas typical to drip irrigation. While the magnitude of actual E was mostly determined by soil water content, diurnal patterns depended strongly on position relative to the vine-row due to variable shading patterns. HYDRUS (2-D/3-D) successfully simulated the magnitude, diurnal patterns, and spatial distribution of E, including expected deviations as a result of variability in soil saturated hydraulic conductivity. © 2014. American Geophysical Union.

Evaporation from the soil surface (E) can be a significant source of water loss in arid areas. In sparsely vegetated systems, E is expected to be a function of soil, climate, irrigation regime, precipitation patterns, and plant canopy development and will therefore change dynamically at both daily and seasonal time scales. The objectives of this research were to quantify E in an isolated, drip-irrigated vineyard in an arid environment and to simulate below canopy E using the HYDRUS (2-D/3-D) model. Specific focus was on variations of E both temporally and spatially across the inter-row. Continuous above canopy measurements, made in a commercial vineyard, included evapotranspiration, solar radiation, air temperature and humidity, and wind speed and direction. Short-term intensive measurements below the canopy included actual and potential E and solar radiation along transects between adjacent vine-rows. Potential and actual E below the canopy were highly variable, both diurnally and with distance from the vine-row, as a result of shading and distinct wetted areas typical to drip irrigation. While the magnitude of actual E was mostly determined by soil water content, diurnal patterns depended strongly on position relative to the vine-row due to variable shading patterns. HYDRUS (2-D/3-D) successfully simulated the magnitude, diurnal patterns, and spatial distribution of E, including expected deviations as a result of variability in soil saturated hydraulic conductivity.

While numerous algorithms exist for predicting incident atmospheric long-wave radiation under clear (Lclr) and cloudy skies, few comparisons have been published to assess the accuracy of the different algorithms. Virtually no comparisons have been made for both clear and cloudy skies across multiple sites. This study evaluates the accuracy of 13 algorithms for predicting incident long-wave radiation under clear skies, ten cloud correction algorithms, and four algorithms for all-sky conditions using data from 21 sites across North America and China. Data from five research sites were combined with publicly available data from nine sites in the AmeriFlux network for initial evaluation and optimization of cloud cover estimates; seven additional AmeriFlux sites were used as an independent test of the algorithms. Clear-sky algorithms that excelled in predicting Lclr were the Dilley, Prata, and Angström algorithms. Root mean square deviation (RMSD) between predicted and measured 30-minute or hourly Lclr averaged approximately 23 W m-2 for these three algorithms across all sites, while RMSD of daily estimates was as low as 14 W m-2. Cloud-correction algorithms of Kimball, Unsworth, and Crawford described the data best when combined with the Dilley clear-sky algorithm. Average RMSD across all sites for these three cloud corrections was approximately 24 to 25 W m -2 for 30-minute or hourly estimates and approximately 15 to 16 W m-2 for daily estimates. The Kimball and Unsworth cloud corrections require an estimate of cloud cover, while the Crawford algorithm corrects for cloud cover directly from measured solar radiation. Optimum limits in the clearness index, defined as the ratio of observed solar radiation to theoretical terrestrial solar radiation, for complete cloud cover and clear skies were suggested for the Kimball and Unsworth algorithms. Application of the optimized algorithms to seven independent sites yielded similar results. On the basis of the results, the recom

While numerous algorithms exist for predicting incident atmospheric long-wave radiation under clear (Lclr) and cloudy skies, few comparisons have been published to assess the accuracy of the different algorithms. Virtually no comparisons have been made for both clear and cloudy skies across multiple sites. This study evaluates the accuracy of 13 algorithms for predicting incident long-wave radiation under clear skies, ten cloud correction algorithms, and four algorithms for all-sky conditions using data from 21 sites across North America and China. Data from five research sites were combined with publicly available data from nine sites in the AmeriFlux network for initial evaluation and optimization of cloud cover estimates; seven additional AmeriFlux sites were used as an independent test of the algorithms. Clear-sky algorithms that excelled in predicting Lclr were the Dilley, Prata, and Angström algorithms. Root mean square deviation (RMSD) between predicted and measured 30-minute or hourly Lclr averaged approximately 23 W m-2 for these three algorithms across all sites, while RMSD of daily estimates was as low as 14 W m-2. Cloud-correction algorithms of Kimball, Unsworth, and Crawford described the data best when combined with the Dilley clear-sky algorithm. Average RMSD across all sites for these three cloud corrections was approximately 24 to 25 W m -2 for 30-minute or hourly estimates and approximately 15 to 16 W m-2 for daily estimates. The Kimball and Unsworth cloud corrections require an estimate of cloud cover, while the Crawford algorithm corrects for cloud cover directly from measured solar radiation. Optimum limits in the clearness index, defined as the ratio of observed solar radiation to theoretical terrestrial solar radiation, for complete cloud cover and clear skies were suggested for the Kimball and Unsworth algorithms. Application of the optimized algorithms to seven independent sites yielded similar results. On the basis of the results, the recom

Radiation exchange at the surface plays a critical role in the surface energy balance, plant microclimate, and plant growth. The ability to simulate the surface energy balance and the microclimate within the plant canopy is contingent upon simulation of the surface radiation exchange. A validation and modification exercise of the Simultaneous Heat and Water (SHAW) model was conducted for simulating the surface short-wave and long-wave radiation exchange over and within wheat, maize and soya bean plant canopies using data collected at Yucheng in the North China Plain and near Ames, Iowa. Whereas model testing was limited to monocultures and mixed canopies of green and senesced leaves, methodologies were developed for simulating short-wave and long-wave radiation fluxes applicable to a multi-species, multi-layer plant canopy. Although the original SHAW model slightly underpredicted reflected solar radiation with a mean bias error (MBE) of -5 to -10 W m-2, one would conclude that the simulations were quite reasonable if within-canopy measurements were not available. However, within-canopy short-wave radiation was considerably underestimated (MBE of approximately -20 W m-2) by the original SHAW model. Additionally, leaf temperatures tended to be overpredicted (MBE = +0.76 °C) near the top of the canopy and underpredicted near the bottom (MBE = -1.12 °C). Modification to the SHAW model reduced MBE of above canopy reflected radiation to -1 to -6 W m-2 and within-canopy radiation simulations to approximately -6 W m-2; bias in leaf temperature was reduced to less than 0.4 °C. Model modifications resulted in essentially no change in simulated evapotranspiration for wheat, 4.5% lower for maize and 1% higher for soya bean. Alternative approaches for simulating canopy transmissivity to diffuse radiation were tested in the modified version and had a minor influence on simulated short-wave radiation, but made almost no difference in simulated long-wave radiation or evapotranspira

Radiation exchange at the surface plays a critical role in the surface energy balance, plant microclimate, and plant growth. The ability to simulate the surface energy balance and the microclimate within the plant canopy is contingent upon simulation of the surface radiation exchange. A validation and modification exercise of the Simultaneous Heat and Water (SHAW) model was conducted for simulating the surface short-wave and long-wave radiation exchange over and within wheat, maize and soya bean plant canopies using data collected at Yucheng in the North China Plain and near Ames, Iowa. Whereas model testing was limited to monocultures and mixed canopies of green and senesced leaves, methodologies were developed for simulating short-wave and long-wave radiation fluxes applicable to a multi-species, multi-layer plant canopy. Although the original SHAW model slightly underpredicted reflected solar radiation with a mean bias error (MBE) of -5 to -10 W m-2, one would conclude that the simulations were quite reasonable if within-canopy measurements were not available. However, within-canopy short-wave radiation was considerably underestimated (MBE of approximately -20 W m-2) by the original SHAW model. Additionally, leaf temperatures tended to be overpredicted (MBE = +0.76 °C) near the top of the canopy and underpredicted near the bottom (MBE = -1.12 °C). Modification to the SHAW model reduced MBE of above canopy reflected radiation to -1 to -6 W m-2 and within-canopy radiation simulations to approximately -6 W m-2; bias in leaf temperature was reduced to less than 0.4 °C. Model modifications resulted in essentially no change in simulated evapotranspiration for wheat, 4.5% lower for maize and 1% higher for soya bean. Alternative approaches for simulating canopy transmissivity to diffuse radiation were tested in the modified version and had a minor influence on simulated short-wave radiation, but made almost no difference in simulated long-wave radiation or evapotranspira