中文摘要：

考虑到水面蒸发气象影响因子之间普遍存在显著相关,若应用所有气象因子建立水面蒸发量计算模型则存在信息重叠问题,而主成分分析方法可以解决该问题。烟台地区水面蒸发气象影响因子的主成分分析表明,前3个主成分累计贡献率已达88.32%,故提取3个主成分已能满足要求。第1主成分代表空气冷热状况;第2主成分代表空气动力状况;第3主成分代表太阳辐射。利用3个主成分建立三元一次回归方程,并与应用所有气象影响因子建立的多元线性回归方程相比较,结果显示,主成分分析方法建立的回归方程的偏回归系数均通过t检验,达到极显著水平,多元线性回归方程,虽拟合效果稍优于主成分分析方法,但偏回归系数b3未通过t检验,系数显著性水平不如主成分分析法。图3,表7,参8。

英文摘要：

Considering that significant correlation exists among weather factors influencing water surface evaporation, if using all the weather factors to found water surface evaporation model, there will be an information overlapping. Principle component analysis method could solve this kind of problem. The result of principle component analysis of weather factors influencing water surface evaporation in Yantai region shows that, the first three principle＇s accumulative contribution rate has reached 88.32 %, it could meet the demand. The first principle component stands for air hot and cold status; the second one stands for air impetus status; and the third one stands for solar radiation. Applying this three principle components to build regression equation and comparing with multiple linear regression equation which using all the weather factors as variables, it is found that： all the partial regression coefficients pass t test with significant correlation. Multiple linear regression equation, the fitting is a bit better than the principle component analysis method, but partial regression coefficient b3 doesn＇t pass t test. The coefficient significance level is not as high as principle component analysis method.