韩信,张宝忠,魏征,李益农,陈鹤.考虑气象因子不确定性的参考作物蒸散量预报方法[J].中国水利水电科学研究院学报,2021,19(1):33-44
考虑气象因子不确定性的参考作物蒸散量预报方法
Prediction method of reference crop evapotranspiration considering the uncertainty of meteorological factors
投稿时间:2020-08-12  
DOI:10.13244/j.cnki.jiwhr.20200167
中文关键词:  参考作物蒸散量  气象因子  不确定性  贝叶斯预报系统  径向基神经网络
英文关键词:reference evapotranspiration  meteorological factors  uncertainty  Bayesian forecasting system  radial basis function neural network
基金项目:国家自然科学基金(51822907);中国水利水电科学研究院“五大人才”计划(ID0145B742017)
作者单位E-mail
韩信 中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室, 北京 100038
中国农业大学 水利与土木工程学院, 北京 100083 
 
张宝忠 中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室, 北京 100038 zhangbz@iwhr.com 
魏征 中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室, 北京 100038  
李益农 中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室, 北京 100038  
陈鹤 中国水利水电科学研究院 流域水循环模拟与调控国家重点实验室, 北京 100038  
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中文摘要:
      参考作物蒸散量(ET0)的准确预测预报对于制定作物灌溉制度与实时灌溉调度具有重要意义,然而气象因子的不确定性极大的影响着ET0的预测精度。因此本研究采用马尔科夫蒙特卡罗模拟与自适应采样算法相结合的方法(AM-MCMC)对气象因子的不确定性进行修正,以气象站实测ET0作为标准值,利用径向基神经网络(RBF)模型建立气象因子与ET0的映射关系,建立基于气象因子不确定性的ET0不确定性预测模型(CU-RBF),并以华北平原农田下垫面为例进行验证。结果表明,与传统的RBF确定性预报结果相比,CU-RBF预测结果的各精度评价效果均有所提高,纳什系数提高了10%,均方根误差和平均绝对误差分别降低了16.94%、17.05%,且单独修正平均风速的CU-RBFWs预测模型效果比分别单独修正最高温度、平均相对湿度的预测模型效果好。考虑气象因子不确定性开展ET0的预报研究,减小了预测值与实际值的误差,可为农田下垫面的未来水分管理提供科学依据。
英文摘要:
      The accurate prediction of reference crop evapotranspiration (ET0) is of great significance for the formulation of crop irrigation schedule and real-time irrigation scheduling. However,the uncertainty of meteorological factors greatly affects the prediction accuracy of ET0. Therefore,in this study,Markov Monte Carlo simulation combined with adaptive sampling algorithm (AM-MCMC) was used to modify the uncertainty of meteorological factors. ET0 measured by meteorological station was taken as standard value,and radial basis function neural network (RBF) model was used to describe the mapping relationship between meteorological factors and ET0. Then ET0 uncertainty prediction model (CU-RBF) considering the uncertainty of meteorological factors was established. The farmland-underlying surface of North China Plain was taken as an example to verify the model. The results show that,compared with the traditional RBF deterministic prediction results, the accuracy evaluation effects of CU-RBF prediction results are improved, the NSE is increased by 10%,the RMSE and MAE are reduced by 16.94% and 17.05% respectively. Moreover,the prediction model with correction of average Ws is more effective than that with correction of Tmax and RH. Considering the uncertainty of meteorological factors,the prediction of ET0 is more consistent with the actual growth status of the crop,which provides a scientific basis for improved water management during crop production.
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