江苏地区不同参考作物蒸发蒸腾量估算模型  

Estimation model of evapotranspiration ( ET 0)of different reference crops in Jiangsu area

作　　者：王婷[1,2] 刘春伟 张佩[3] 王让会 邱让建[1] 周丽敏[1] 王蒙[1] WANG Ting;LIU Chunwei;ZHANG Pei;WANG Ranghui;QIU Rangjian;ZHOU Limin;WANG Meng(Jiangsu Key Laboratory of Agricultural Meteorology/School of Applied Meteorology,Nanjing University of Information Science&Technology,Nanjing,Jiangsu 210044,China;Xinjiang Climate Center,Urumqi,Xinjiang 830002,China;Jiangsu Climate Center,Nanjing,Jiangsu 210008,China)

机构地区：[1]江苏省农业气象重点实验室/南京信息工程大学应用气象学院,江苏南京210044 [2]新疆维吾尔自治区气候中心,新疆乌鲁木齐830002 [3]江苏省气候中心,江苏南京210008

出　　处：《排灌机械工程学报》2023年第1期70-79,共10页Journal of Drainage and Irrigation Machinery Engineering

基　　金：国家自然科学基金资助项目(51309132)。

摘　　要：为了研究不同参考作物蒸发蒸腾量ET 0估算方法在江苏地区的适用性,收集了江苏省徐州市、高邮市和昆山市1957年1月至2019年12月的气象数据,采用12种不同模型估算了各站点的ET 0,其中模型Priestly-Taylor,Hansen,Jensen-Haise,Makkink是基于辐射数据的模型;MC-Cloud,1985 Hargreaves,Thornthwaite是基于温度数据的;Copais,Valiantzas 1和Valiantzas 2是综合法模型;XGBoost和SVM是机器学习模型.12种ET 0的估算模型计算值分别与Penman-Monteith模型(PM)计算值进行比较,结果表明:各站点的综合评价指数GPI最高的为机器学习模型中的SVM模型;在输入参数相同的情况下,机器学习模型模拟精度优于综合法和温度法以及辐射法中的Pristley-Taylor和Makkink模型;机器学习模型随着输入参数减少,模拟精度依次降低.研究结果可以为江苏地区气象数据不完善时估算ET 0提供科学依据.To study the applicability of ET 0 estimation methods for different reference crops in Jiangsu area,meteorological data was collected from January 1957 to December 2019 in Xuzhou site,Gaoyou site,and Kunshan site,Jiangsu Province were collected,and 12 different models were used to estimate the reference crop evapotranspiration(ET 0)at each site were used.Among the estimation models,Priestly-Taylor,Hansen,Jensen-Haise and Makkink were modeled based on radiation.MC-Cloud,1985 Hargreaves and Thornthwaite were based on temperature.Copais,Valiantzas 1 and Valiantzas 2 were integrated methods.SVM and XGBoost were machine learning models.The calculated values of 12 models for estimating ET 0 were compared with the Penman-Monteith model(PM).The results show that the SVM model has the highest GPI(comprehensive evaluation index)value of the three sites.With the same input parameters,the simulation accuracy of the machine learning model is better than that of Priestley-Taylor and Makkink models in the synthesis method,the temperature method,and the radiation method.As the input parameters of machine learning model decrease,the simulation accuracy of the machine learning model decreases in turn.The above research results can provide a scientific basis for estimating ET 0 when the meteorological data in Jiangsu area are imperfect.

关 键 词：参考作物蒸发蒸腾量 估算模型 PENMAN-MONTEITH 机器学习 江苏 

分 类 号：S274.3[农业科学—农业水土工程] S164.1[农业科学—农业工程]