Predicting Daily Net Radiation Using Minimum Climatological Data


Net radiation (Rn) 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 Rn 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 Rn 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 FAO56−Rn procedures to predict daily Rn 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-Rs-based (Rs−M)] requires measured maximum and minimum air temperatures (Tmax and Tmin), measured solar radiation (Rs), and inverse relative distance from Earth to sun (dr). The second equation [predicted-Rs-based (Rs−P)] requires Tmax,Tmin, mean relative humidity (RHmean), and predicted Rs. 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 Rn values predicted by the Rs−M equation were in close agreement with those obtained from the FAO56-Rn in all locations and for all years evaluated. In general, the standard error of daily Rn 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 Rs−M equation and FAO56−Rn predictions with the measured Rn values showed that the Rs−M equations’ predictions were as good or better than the FAO56−Rn in most cases. The performance of the Rs−P equation was quite good when compared with the measured Rn in Gainesville, Watkinsville, Logan, and Bushland locations and provided similar or better daily Rn predictions than the FAO56−Rn procedures. The Rs−P equation was able to explain at least 79% of the variability in Rn predictions using only Tmax,Tmin, and RH data for all locations. It was concluded that both proposed equations are simple, reliable, and practical to predict daily Rn. The significant advantage of the Rs−P equation is that it can be used to predict daily Rn with a reasonable precision when measured Rs 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 Tmax and Tmin on a regular basis.


Earth Systems Research Center

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Journal of Irrigation and Drainage Engineering


American Society of Civil Engineers

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© 2003 American Society of Civil Engineers