基于全局敏感性分析的WOFOST模型参数优化

Optimization of WOFOST Model Parameters Based on Global Sensitivity Analysis

敏感性分析是模型参数优化的基础,同时也是作物生长模型本地化过程的重要环节。以中国水利水电科学研究院大兴试验基地2015-2016的夏玉米为研究对象,运用全局敏感性分析方法 EFAST对WOFOST模型的49个作物参数进行分析,在此基础上运用PEST软件对产量、干物质量、叶面积指数的敏感参数进行优化。结果表明:(1)对产量(WSO)敏感的参数主要有二氧化碳(CO2)同化率在12℃下的矫正因子、最大光合速率在30℃下的校正因子、开花到成熟之间的积温等;(2)对干物质量(TAGP)敏感的参数主要有CO2同化率在12℃下的矫正因子、在35℃时叶面积的生命周期、生育期为0时的比叶面积等;(3)对叶面积指数(LAI)敏感的参数主要有根的同化物转换效率、生育期为0时的比叶面积、CO2同化率在12℃下的矫正因子等;(4)参数优化后,WOFOST模型对夏玉米的模拟效果较好,相对误差RE小于5%,一致性指数d均大于0.9,标准均方根误差nRMSE均小于20%。优化后的参数对于北京地区的夏玉米模拟效果良好。

Sensitivity analysis lays the foundation for parameter optimization,and it was also an important part of the localization and regionalization process of the crop growth model.The study focused on experimental data of summer maize from 2015 - 2016 at Daxing Experimental Base.The EFAST method was used to perform global sensitivity analysis on the 49 varieties of the WOFOST model parameters.Then PEST software was used to optimize the sensitivity parameters of yield,amount of dry matter,and leaf area index.We found that: ① The most sensitivity parameters for yield were mainly the reduction factor of gross assimilation rate at 12 ℃( TMNFTB12) ,the reduction factor of AMAX at 30 ℃ ( TMPFTB30) ,thermal time from anthesis to maturity ( TSUM2) ; ②The most sensitivity parameters for amount of dry matter were mainly the reduction factor of gross assimilation rate at 12 ℃( TMNFTB12) ,life span of leaves growing at 35 ℃( SPAN) ,specific leaf area at development stage of 0( SLATB0) ; ③The most sensitivity parameters for leaf area index were mainly conversion efficiency of assimilation into root ( CVR) 、specific leaf area at development stage of 0 ( SLATB0) ,the reduction factor of gross assimilation rate at 12 ℃ ( TMNFTB12; ④WOFOST simulation was much more improved after optimizing parameters.The relative error was less than 5%,the index of agreement was higher than 0.9,the normalized root mean square error was less than 20%.The result reflects that the parameters optimized have good adaptability in Daxing,Beijing.