|
|
|
| Estimation model of evapotranspiration(ET0) of different reference crops in Jiangsu area |
| WANG Ting1,2, LIU Chunwei1*, ZHANG Pei3, WANG Ranghui1,QIU Rangjian1, ZHOU Limin1, WANG Meng1 |
| 1. Jiangsu Key Laboratory of Agricultural Meteorology/School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, China; 2. Xinjiang Climate Center, Urumqi, Xinjiang 830002, China; 3. Jiangsu Climate Center, Nanjing, Jiangsu 210008, China |
|
|
|
|
Abstract To study the applicability of ET0 estimation methods for different reference crops in Jiangsu area, this study collected meteorological data from January 1957 to December 2019 in Xuzhou site, Gaoyou site, and Kunshan site, Jiangsu Province, and used 12 different models to estimate the refe-rence crop evapotranspiration(ET0)at each site. Among the estimation models, Priestly-Taylor, Hansen, Jensen-Haise and Makkink are models based on radiation, MC-Cloud, 1985 Hargreaves and Thornthwaite are based on temperature, Copais, Valiantzas 1 and Valiantzas 2 are integrated me-thods, SVM and XGBoost are machine learning models. The calculated values of 12 models for estimating ET0 were compared with the Penman-Monteith model(PM). The results showed 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 ra-diation 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 ET0 when the meteorological data in Jiangsu area are imperfect.
|
|
Received: 31 March 2021
|
|
|
|
|
|
|
