Contents | Journal of Irrigation and Drainage Engineering 129, 4

Net radiation $(R_{n})$ is a key variable for computing reference evapotranspiration and is a driving force in many other physical and biological processes. The procedures outlined in the Food and Agriculture Organization Irrigation and Drainage Paper No. 56 [FAO56 (reported by Allen et al. in 1998)] for predicting daily $R_{n}$ have been widely used. However, when the paucity of detailed climatological data in the United States and around the world is considered, it appears that there is a need for methods that can predict daily $R_{n}$ with fewer input and computation. The objective of this study was to develop two alternative equations to reduce the input and computation intensity of the $FAO 56 - R_{n}$ procedures to predict daily $R_{n}$ and evaluate the performance of these equations in the humid regions of the southeast and two arid regions in the United States. Two equations were developed. The first equation [measured- $R_{s}$ -based $(R_{s - M})]$ requires measured maximum and minimum air temperatures $(T_{\max}$ and $T_{\min}),$ measured solar radiation $(R_{s}),$ and inverse relative distance from Earth to sun $(d_{r}) .$ The second equation [predicted- $R_{s}$ -based $(R_{s - P})]$ requires $T_{\max},$ $T_{\min},$ mean relative humidity $({RH}_{mean}),$ and predicted $R_{s} .$ The performance of both equations was evaluated in different locations including humid and arid, and coastal and inland regions (Gainesville, Fla.; Miami, Fla.; Tampa, Fla.; Tifton, Ga.; Watkinsville, Ga.; Mobile, Ala.; Logan, Utah; and Bushland, Tex.) in the United States. The daily $R_{n}$ values predicted by the $R_{s - M}$ equation were in close agreement with those obtained from the FAO56- $R_{n}$ in all locations and for all years evaluated. In general, the standard error of daily $R_{n}$ predictions (SEP) were relatively small, ranging from 0.35 to 0.73 MJ m⁻² d⁻¹ with coastal regions having lower SEP values. The coefficients of determination were high, ranging from 0.96 for Gainesville to 0.99 for Miami and Tampa. Similar results, with approximately 30% lower SEP values, were obtained when daily predictions were averaged over a three-day period. Comparisons of $R_{s - M}$ equation and $FAO 56 - R_{n}$ predictions with the measured $R_{n}$ values showed that the $R_{s - M}$ equations’ predictions were as good or better than the $FAO 56 - R_{n}$ in most cases. The performance of the $R_{s - P}$ equation was quite good when compared with the measured $R_{n}$ in Gainesville, Watkinsville, Logan, and Bushland locations and provided similar or better daily $R_{n}$ predictions than the $FAO 56 - R_{n}$ procedures. The $R_{s - P}$ equation was able to explain at least 79% of the variability in $R_{n}$ predictions using only $T_{\max},$ $T_{\min},$ and RH data for all locations. It was concluded that both proposed equations are simple, reliable, and practical to predict daily $R_{n} .$ The significant advantage of the $R_{s - P}$ equation is that it can be used to predict daily $R_{n}$ with a reasonable precision when measured $R_{s}$ is not available. This is a significant improvement and contribution for engineers, agronomists, climatologists, and others when working with National Weather Service climatological datasets that only record $T_{\max}$ and $T_{\min}$ on a regular basis.