Predicting Daily Net Radiation Using Minimum Climatological Data
Predicting Daily Net Radiation Using Minimum Climatological Data
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DOI：
10.1061/(asce)0733-9437(2003)129:4(256
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发表时间：
2003-07
期刊：
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通讯作者：
S. Irmak;A. Irmak;James W. Jones;T. Howell;J. Jacobs;R. Allen;G. Hoogenboom
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文献类型：
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作者：
S. Irmak;A. Irmak;James W. Jones;T. Howell;J. Jacobs;R. Allen;G. Hoogenboom
净辐射(IR\dN\N)是计算参考蒸散量的关键变量，也是许多其他物理和生物过程的驱动力。粮食及农业组织灌溉和排水文件第56号[FAO56(Allen等人报告)》中概述的程序。(1998)]用于日预测已得到广泛应用。然而，当考虑到美国和世界各地缺乏详细的气候数据时，似乎需要一种方法，能够以较少的输入和计算来预测每日\IR\dN\N。本研究的目的是开发两个替代公式，以减少FAO56-IR\dN程序的输入和计算强度，以预测日Rn，并评估这些公式在美国东南部湿润地区和美国两个干旱地区的性能。建立了两个方程。第一个公式[基于测量的RS(\IR\ds-M\N)]需要测量的最高和最低气温(\IT\dmax\N和\IT\dmin\N)、测量的太阳辐射(\IR\ds\N)以及从地球到太阳的反向相对距离(\ID\dR\N)。第二个公式[基于预测-IR\DS\N(\IR\DS-P\N)]需要\IT\Dmax\N、\IT\Dmin\N、平均相对湿度(RH\Dean\N)，并预测\IR\DS\N。这两个公式在不同的地点进行了性能评估，包括潮湿和干旱，以及沿海和内陆地区(佛罗里达州盖恩斯维尔；佛罗里达州迈阿密；佛罗里达州坦帕；佐治亚州蒂夫顿；佐治亚州沃特金斯维尔；阿拉巴马州莫比尔；犹他州洛根；以及德克萨斯州布什兰)。在美国。在所有地点和所评估的所有年份，由IR-DS-M\N方程预测的日Rn值与由FAO56-IR\dN得到的值非常一致。日Rn预报的标准误差(SEP)一般较小，在0.35~0.73MJ·m-2·d·U-1·N之间，沿海地区SEP值较低。决定系数很高，从盖恩斯维尔的0.96到迈阿密和坦帕的0.99。当每天的预测在三天内平均时，也得到了类似的结果，SEP值大约低了30%。Ir-ds-M-N方程和FAO56-Rn方程的预测值与实测值的比较表明，在大多数情况下，Ir-ds-M-N方程的预测值与FAO56-Ir\dN方程的预测值相当或更好。与盖恩斯维尔、沃特金斯维尔、洛根和灌木丛地区的实测\IR\dN\N相比，Rs-P方程的性能相当好，并提供了与FAO56-\IR\dN\N程序相似或更好的每日\IR\dN\N预报。仅使用所有地点的Tmax、Tmin和RH数据，IR-DS-P\N方程就能够解释至少79%的变异性。结果表明，这两个方程对日IR\dN的预测都是简单、可靠和实用的。Rs-P方程的显著优点是，在没有实际测量的\IR\dN的情况下，它能以合理的精度预测日\IR\dN。对于工程师、农学家、气候学家和其他人来说，这是一项重大的改进和贡献，因为他们使用的是国家气象局的气候数据集，这些数据集定期只记录\IT\Dmax\N和\IT\Dmin\N。
Net radiation (\IR\dn\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 \IR\dn\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 \IR\dn\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 FAO56-\IR\dn\N 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 (\IR\Ds-M\N)] requires measured maximum and minimum air temperatures (\IT\dmax\N and \IT\Dmin\N), measured solar radiation (\IR\ds\N), and inverse relative distance from Earth to sun (\Id\dr\N). The second equation [predicted-\IR\ds\N-based (\IR\Ds-P\N)] requires \IT\Dmax\N, \IT\Dmin\N, mean relative humidity (RH\Dmean\N), and predicted \IR\ds\N. 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 \IR\Ds-M\N equation were in close agreement with those obtained from the FAO56-\IR\dn\N 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\U-2\N d\U-1\N 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 \IR\Ds-M\N equation and FAO56-Rn predictions with the measured \IR\dn\N values showed that the \IR\Ds-M\N equations’ predictions were as good or better than the FAO56-\IR\dn\N in most cases. The performance of the Rs-P equation was quite good when compared with the measured \IR\dn\N in Gainesville, Watkinsville, Logan, and Bushland locations and provided similar or better daily \IR\dn\N predictions than the FAO56-\IR\dn\N procedures. The \IR\Ds-P\N equation was able to explain at least 79% of the variability in \IR\dn\N 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 \IR\dn\N. The significant advantage of the Rs-P equation is that it can be used to predict daily \IR\dn\N with a reasonable precision when measured \IR\ds\N 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 \IT\Dmax\N and \IT\Dmin\N on a regular basis.