基于思维进化算法优化的东北地区参考作物蒸散量估算  

Estimation of Reference Crop Evapotranspiration in Northeast China Based on Mind Evolutionary Algorithm Optimization

作　　者：刘琦 董娟 韩晓阳[2,4] 乔江波 鱼洋 袁银颍 朱元骏[1,2,3,4] LIU Qi;DONG Juan;HAN Xiao-yang;QIAO Jiang-bo;YU Yang;YUAN Yin-ying;ZHU Yuan-jun(The Research Center of Soil and Water Conservation and Ecological Environment,Chinese Academy of Sciences and Ministry of Education,Yangling 712100,Shaanxi Province,China;Institute of Soil and Water Conservation,Chinese Academy of Sciences and Ministry of Water Resources,Yangling 712100,Shaanxi Province,China;University of Chinese Academy of Sciences,Beijing 100049,China;College of Soil and Water Conservation Science and Engineering,Northwest A&F University,Yangling 712100,Shaanxi Province,China)

机构地区：[1]中国科学院教育部水土保持与生态环境研究中心,陕西杨凌712100 [2]中国科学院水利部水土保持研究所,陕西杨凌712100 [3]中国科学院大学,北京100049 [4]西北农林科技大学水土保持科学与工程学院,陕西杨凌712100

出　　处：《节水灌溉》2024年第11期69-78,共10页Water Saving Irrigation

基　　金：国家自然科学基金项目(42377316)。

摘　　要：准确估算参考作物蒸散发(Reference crop evapotranspiration,ET_(0))对于农业水资源管理至关重要。东北地区是我国最重要的粮食产区,但该区域纬度相对较高、气温相对较低,ET_(0)影响因素多、估算的不确定性高。研究选取东北地区20个代表性气象站点1961-2019年气象数据,采用Mann-Kendall非参数趋势检验及反距离加权插值法模拟东北地区ET_(0)时空变化特征,并利用思维进化算法(Mind Evolutionary Algorithm,MEA)优化模型参数,以FAO-56 Penman-Monteith公式计算结果为标准值,比较9种不同输入因子的模型精度,结果表明:(1)1961-2019年东北地区ET_(0)的年平均值在567.81~1080.66 mm之间,东北地区北部的年均ET_(0)值呈上升趋势,中部平原及南部沿海呈下降趋势;(2)通过对东北地区20个站点使用不同类型模型计算ET_(0)的评估,优化前精度表现:辐射型模型>湿度型模型>温度型模型。其中Mak模型在东北地区的计算精度最高,相应的R^(2)、NSE、RMSE、和MAE中位数值分别为0.801、0.786、0.570 mm/d和0.331 mm/d;(3)MEA算法优化后,对9种经验模型的R^(2)、NSE、RMSE和MAE提升幅度分别为14.43~47.15%、14.84~50.47%、5.42~46.79%、7.47~39.86%。优化后的Mak模型相应的R^(2)、NSE、RMSE、和MAE中位数值分别为0.910、0.907、0.510 mm/d、0.291 mm/d。因此,在气象资料缺乏情景下,Mak模型可作为东北地区ET_(0)计算的最优模型,并且MEA算法优化能够高效提高模型计算精度,实现了准确性和效率之间更优化的平衡。Accurate estimation of reference crop evapotranspiration(ET_(0))is crucial for effective agricultural water resource management.The Northeast region of China,a vital grain-producing area,presents unique challenges due to its relatively high latitude and lower temperatures,which contribute to numerous factors affecting ET_(0) and high estimation uncertainty.This study uses the Mind Evolutionary Algorithm(MEA)to optimize the ET_(0) model and compares the accuracy of nine different input factors to identify the best ET_(0) calculation model for the region.Meteorological data from 20 weather stations spanning from 1961 to 2019 were applied.The Mann-Kendall trend test and inverse distance weighting interpolation were used to analyze the spatiotemporal variations of ET_(0).The MEA was then applied to optimize the model parameters,and the results were compared with the FAO-56 Penman-Monteith formula.Between 1961 and 2019,the results showed that the annual average ET_(0) in the Northeast region ranged from 567.81 to 1080.66 mm.The northern part of the region showed an increasing trend in ET_(0),while the central plains and southern coastal areas exhibited a decreasing trend.Before optimization,the accuracy ranking of different models for ET_(0) calculation at the 20 stations was as follows:radiation-based models>humidity-based models>temperature-based models.The Mak model demonstrated the highest level of accuracy,with median values of R^(2),NSE,RMSE,and MAE at 0.801,0.786,0.570 mm/d,and 0.331 mm/d,respectively.In addition,after optimization using the MEA,the improvements in R^(2),NSE,RMSE,and MAE for the nine empirical models ranged from 14.43%to 47.15%,14.84%to 50.47%,5.42%to 46.79%,and 7.47%to 39.86%,respectively.The optimized Mak model showed median values of R^(2),NSE,RMSE,and MAE at 0.910,0.907,0.510 mm/d,and 0.291 mm/d,respectively.Therefore,in scenarios with limited meteorological data,the Mak model can be considered the optimal choice for ET_(0) calculation in the Northeast region.The MEA optimization improves t

关 键 词：东北地区 参考作物蒸散量 思维进化算法 时空特征 经验模型 

分 类 号：S274.3[农业科学—农业水土工程]