高邮灌区参考作物腾发量预报模型研究OACSTPCD
Predictive models for reference evapotranspiration in Gaoyou irrigation district
[目的]探索高邮灌区的参考作物腾发量(ET0)预报方法,提升灌溉预报精度.[方法]基于高邮灌区2003-2017年实测气象数据及2016-2017年气温预报数据,以FAO-56 Penman-Monteith(PM)计算的ET0为基准,将气温预报数据代入率定后的Blaney-Criddle(BC)、Hargreaves-Samani(HS)、McCloud(MC)和简化的PM(PMT)模型,比较不同模型的ET0预报精度.[结果]基于上述4种模型进行ET0预报时,1~7 d预见期的平均均方根误差分别为1.07、1.00、1.16、0.99 mm/d,绝对误差平均值分别为0.85、0.74、0.94、0.75 mm/d,相关系数平均值分别为0.79、0.81、0.76、0.81.[结论]HS和PMT模型的预报精度最好,优于BC和MC模型,MC模型的预报精度最差.建议采用率定后的HS和PMT模型对高邮灌区ET0进行预报.
[Objective]Gaoyou irrigation district is located at the low reaches of the Yangtze River in northern Jiangsu province,China.To improve its irrigation management,we compared four models for predicting the reference evapotranspiration(ET0)in the district.[Method]The analysis was based on meteorological data measured from 2003 to 2017 and the temperature forecasted from 2016 to 2017 in the district,from which we calculated the ET0 using the Penman-Monteith(PM)formula recommended by FAO-56.Using these calculated ET0,we forecasted its change using the Blaney-Criddle(BC),Hargreaves-Samani(HS),McCloud(MC)and reduced PM(PMT)model,respectively.[Result]For forecasting ET0 1 to 7 days in advance,the average root mean square error of the BC,HS,MC and PMP model was 1.07,1.00,1.16,0.99 mm/d,respectively;their associated average mean absolute error was 0.85,0.74,0.94,0.75 mm/d,respectively;their associated average correlation coefficient with measured data was 0.79,0.81,0.76,0.81,respectively.Overall,the results of HS and PMT model are comparable and both models are superior to other models for forecasting the ET0 up to 7 days in advance.[Conclusion]Among the four models we compared,the HS and PMT models are more accurate for predicting ET0 change 1-7 days in advance for the Gaoyou irrigation district.
刘梦;仇锦先;张秝湲;王洁;丁奠元;刘博
扬州市勘测设计研究院有限公司,江苏 扬州 225002扬州大学 水利科学与工程学院,江苏 扬州 225009江苏省水资源服务中心,南京 210029扬州市水利局,江苏 扬州 225009
农业科学
参考作物腾发量气温预报灌溉
reference evapotranspirationtemperature forecastirrigation
《灌溉排水学报》 2024 (004)
28-33,49 / 7
国家自然科学基金(52079119);江苏省水利厅科技合作项目(JSZC-320000-HYGS-C2023-00472);江苏省高等学校基础科学研究面上项目(21KJB210021)
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