Climatic Characteristics and Modeling Evaluation of Pan Evapotranspiration over Henan Province, China

Miao Zhang, Bo Su, Majid Nazeer, Muhammad Bilal, Pengcheng Qi and Ge Han
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Miao Zhang: School of Environmental Science and Tourism, Nanyang Normal University, Wolong Road No. 1638, Nanyang 473061, China
Bo Su: School of Environmental Science and Tourism, Nanyang Normal University, Wolong Road No. 1638, Nanyang 473061, China
Majid Nazeer: Key Laboratory of Digital Land and Resources, East China University of Technology, Nanchang 330013, China
Muhammad Bilal: School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Pengcheng Qi: School of Environmental Science and Tourism, Nanyang Normal University, Wolong Road No. 1638, Nanyang 473061, China
Ge Han: School of Remote Sensing and Information Engineering, Wuhan University, Luoyu Road No. 129, Wuhan 430079, China

Land, 2020, vol. 9, issue 7, 1-14

Abstract: Pan evapotranspiration (E) is an important physical parameter in agricultural water resources research. Many climatic factors affect E, and one of the essential challenges is to model or predict E utilizing limited climatic parameters. In this study, the performance of four different artificial neural network (ANN) algorithms i.e., multiple hidden layer back propagation (MBP), generalized regression neural network (GRNN), probabilistic neural networks (PNN), and wavelet neural network (WNN) and one empirical model namely Stephens–Stewart (SS) were employed to predict monthly E. Long-term climatic data (i.e., 1961–2013) was used for the validation of the proposed model in the Henan province of China. It was found that different models had diverse prediction accuracies in various geographical locations, MBP model outperformed other models over almost all stations (maximum R 2 = 0.96), and the WNN model was the best over two sites, the accuracies of the five models ranked as MBP, WNN, GRNN, PNN, and SS. The performances of WNN and GRNN were almost the same, five-input ANN models provided better accuracy than the two-input (solar radiation (R o ) and air temperature (T)) SS empirical model (R 2 = 0.80). Similarly. the two-input ANN models (maximum R 2 = 0.83) also generally performed better than the two-input (R o and T) SS empirical model. The study could reveal that the above ANN models can be used to predict E successfully in hydrological modeling over Henan Province.

Keywords: pan evapotranspiration; climatic characteristics; artificial neural network; Henan province (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2020
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