潜在蒸散发量估算方法在河南省的适用性分析

利用FAO56-PM法计算潜在蒸散发时气象资料往往不易满足。针对该问题,本文研究了辐射法、温度法和基于温度及辐射资料的RBF神经网络预测模型。以FAO56-PM法计算值为标准,比较分析了Priestley-Taylor法、Hargreaves法、Mc Cloud法以及Makkink法在河南省五个典型地区(安阳、新乡、郑州、驻马店、信阳)的适用效果。并以新乡地区为例评价了校正参数后各估算方法和基于温度及辐射资料的RBF神经网络预测模型的适用性。结果表明,Makkink法在五个典型地区估算的潜在蒸散发量误差较小,其余方法误差较大。校正参数后,各估算方法在新乡地区的估算结果均得到明显改进,具有较好的地区适用性。基于温度及辐射资料的RBF神经网络预测模型具有较高的预测精度,可应用于潜在蒸散发量的估算和预测。

Applicability of Potential Evapotranspiration Methods in Henan Province

FAO56-PM is the standard method for estimating potential evapotranspiration. However, the meteorological data required by the FAO56-PM method are not always available at a given station. This paper evaluated four existing methods and developed a temperature and radiation data-based RBF neural network model. We compared the performance of two temperature-based methods (Hargreaves method and Mc Cloud method) and two radiation-based methods (Priestley-Taylor method and Makkink method) with the FAO56-PM in five typical areas (Anyang, Xinxiang, Zhengzhou, Zhumadian, Xinyang) in Henan Province, China. The results of uncalibrated methods show that the Makkink method performs well while larger biases occur for the other methods. Calibration methods were performed for the Xinxiang data, the results show that lower error of all the methods compared to the uncalibrated methods. Besides, the temperature and radiation data-based RBF neural network model in Xinxiang is of high precision of prediction, and it can be used for the prediction of evapotranspiration.