An energy balance approach to determine regional evapotranspiration based on planetary boundary layer similarity theory and regularly recorded data
An energy balance approach to determine regional evapotranspiration based on planetary boundary layer similarity theory and regularly recorded data
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基于行星边界层相似理论和定期记录数据确定区域蒸散量的能量平衡方法
DOI：
10.1029/wr023i011p02050
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发表时间：
1987
期刊：
影响因子：
--
通讯作者：
J. Hatfield
中科院分区：
文献类型：
--
作者：
S. Abdulmumin;L. Myrup;J. Hatfield
开发了一个能量平衡程序，仅使用定期记录的太阳辐射和探空仪数据来确定区域蒸散量。能量平衡基本方程的主要输入项是地面净辐射和感热通量。净辐射由经验和半经验辐射平衡方程和感热通量项确定，感热通量项来自于由行星边界层相似理论概念导出的空气动力学质量传递方程。该程序给出了更好的结果，一个小的（100公顷）农业流域比一个可比的程序的基础上纯粹的空气动力学传质的考虑，特别是在1和2天的时间。对于一个更大的（1000公顷）水文流域的模型达到了可接受的精度，每月估计区域蒸散量。在这个时间尺度上，与观测值的比较得出的斜率为0.97至1.14，相关系数为0.90至0.91。对于每月的比较，纯空气动力学模型几乎达到了相同的精度。这些结果是使用用于校准模型的相同数据集获得的。当使用独立数据时，月估计值的准确性下降（斜率0.75，相关性0.40）。对于每两个月一次的估计，准确性提高（斜率0.92，相关性0.73）。
An energy balance procedure was developed to determine regional evapotranspiration using only regularly recorded solar radiation and rawinsonde data. The major input terms in the basic energy balance equation are daily surface net radiation and sensible heat flux. Net radiation was determined by empirical and semiempirical radiation balance equations and the sensible heat flux term from an aerodynamic mass transfer equation derived from concepts of planetary boundary layer similarity theory. The procedure gave better results for a small (100 ha) agricultural watersheds than a comparable procedure based on purely aerodynamic mass transfer considerations, especially on 1- and 2-day periods. For a larger (1000 ha) hydrologic watershed the model achieved acceptable accuracy for monthly estimates of regional evapotranspiration. On this time scale, comparison with observation yielded slopes from 0.97 to 1.14 with correlations between 0.90 and 0.91. For the monthly comparison, the purely aerodynamical model achieved virtually the same accuracy. These results were obtained using the same data set that was used to calibrate the model. When independent data were used, the accuracy for the monthly estimate degraded (slope 0.75, correlation 0.40). For bimonthly estimates, accuracy improved (slope 0.92, correlation 0.73).