数字农科院2.0

Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China

文献类型： 外文期刊

作者： Feng, Yu;Jia, Yue;Cui, Ningbo;Zhao, Lu;Li, Chen;Feng, Yu;Jia, Yue;Cui, Ningbo;Zhao, Lu;Li, Chen;Feng, Yu;Gong, Daozhi

作者机构：

关键词： Reference evapotranspiration;Hargreaves model;Temperature data;Bayesian theory;Sichuan basin

期刊名称： AGRICULTURAL WATER MANAGEMENT

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Accurate estimation of reference evapotranspiration is vital to hydrological and ecological processes. The FAO-56 Penman-Monteith (PM) model has the higher accuracy for ET0 estimation, but it requires many meteorological inputs that are not commonly available. Therefore an ideal method is needed requiring as minimal as possible input data variables without affecting the accuracy of estimation. The temperature based models are especially interesting due to its input data, air temperature, can be monitored easily and is one of the commonly available climatic inputs. Among the temperature-based models, the Hargreaves (HG) model requiring maximum and minimum air temperature as the inputs is considered as one of the simplest and accurate ET0 methods, but this method needed a local calibration. The present study calibrated the HG model using Bayesian theory at 19 meteorological stations in Sichuan basin of southwest China. Meteorological data during 1961-1990 were used for the calibration and data during 1991-2014 were used for the validation. The results confirmed that the locally calibrated HG model (with average RRMSE, MAE and NS of 0.284, 0.433 mm/d and 0.783) performed better than the original HG model (with average RRMSE, MAE and NS of 0.567, 0.959 mm/d and 0.134). Both of the calibrated and original HG models overestimated ET0 at daily, monthly and annual timescale, but the calibrated HG model provided closer average values with PM ET0, which could confirm the good performances of the calibrated HG model. Therefore, the calibrated HG model could be highly recommended for estimating ET0 when only temperature data are available. (C) 2016 Elsevier B.V. All rights reserved.

分类号： S2

  • 相关文献

[1]Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Feng, Yu,Peng, Yong,Cui, Ningbo,Feng, Yu,Peng, Yong,Cui, Ningbo,Gong, Daozhi,Zhang, Kuandi.

[2]Effects of land use patterns on soil aggregate stability in Sichuan Basin, China. Zhang, Zhen,Wei, Chaofu,Xie, Deti,Gao, Ming,Zeng, Xibai. 2008

[3]Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Feng, Yu,Cui, Ningbo,Zhang, Qingwen,Zhao, Lu,Feng, Yu,Cui, Ningbo,Zhang, Qingwen,Zhao, Lu,Feng, Yu,Gong, Daozhi.

[4]Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. Feng, Yu,Gong, Daozhi,Cui, Ningbo,Zhao, Lu,Cui, Ningbo,Zhao, Lu,Cui, Ningbo,Hu, Xiaotao.

[5]Spatiotemporal variation of reference evapotranspiration during 1954-2013 in Southwest China. Feng, Yu,Gong, Daozhi,Cui, Ningbo,Zhao, Lu,Cui, Ningbo,Zhao, Lu,Zhang, Kuandi.

[6]Clustering analysis of regional reference evapotranspiration and its components based on climatic variables across northeast China, 1961-2010. Liu, Yuan,Liu, Buchun,Yang, Xiaojuan,Bai, Wei,Liu, Yuan,Liu, Buchun,Yang, Xiaojuan,Bai, Wei,Liu, Yuan,Liu, Buchun,Yang, Xiaojuan,Bai, Wei. 2016

[7]Spatiotemporal Variation Of Reference Evapotranspiration D.uring 1954-2013 In Southwest C hina. Feng, Y, Cui, NB, Zhao, L, Gong, DZ, Zhang, KD. 2017

[8]Modeling Reference Evapotranspiration Using Extreme L.earning Machine And Generalized R egression Neural Network Only With Temperature Data. Feng, Y, Peng, Y, Cui, NB, Gong, DZ, Zhang, KD. 2017

[9]Evaluation Of Random Forests And G.eneralized Regression Neural Networks F or Daily Reference Evapotranspiration Modelling. Feng, Y, Cui, NB, Gong, DZ, Zhang, QW, Zhao, L. 2017

[10]Calibration Of Hargreaves Model For R.eference Evapotranspiration Estimation In S ichuan Basin Of Southwest China. Feng, Y, Jia, Y, Cui, NB, Zhao, L, Li, C, Gong, DZ. 2017

[11]Genetic Algorithm-Optimized Extreme Learning Machine Model for Estimating Daily Reference Evapotranspiration in Southwest China. Quanshan Liu,Zongjun Wu,Ningbo Cui,Wenjiang Zhang,Yaosheng Wang,Xiaotao Hu,Daozhi Gong,Shunsheng Zheng. 2022

[12]Spatiotemporal changes and driving factors of reference evapotranspiration and crop evapotranspiration for cotton production in China from 1960 to 2019. Yuexia Su,Jian Wang,Junhong Li,Lizhi Wang,Kunfeng Wang,Ao Li,Lei Gao,Zhanbiao Wang. 2023

[13]Improvement Of Makkink Model For R.eference Evapotranspiration Estimation Using T emperature Data In Northwest China. Zhang, QW, Cui, NB, Feng, Y, Gong, DZ, Hu, XT. 2018

[14]Improved Hargreaves Model Based On Multiple Intelligent Optimization Algorithms To Estimate Reference Crop Evapotranspiration In Humid Areas Of Southwest China. Wu, ZJ, Cui, NB, Zhu, B, Zhao, L, Wang, XK, Hu, XT, Wang, YS, Zhu, SD. 2021

作者其他论文 更多>>