Parameter sensitivity analysis of crop growth models based on the extended Fourier Amplitude Sensitivity Test method
Parameter sensitivity analysis of crop growth models based on the extended Fourier Amplitude Sensitivity Test method
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DOI：
10.1016/j.envsoft.2013.06.007
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Jing Wang;Xin Li;L. Lu;F. Fang
中科院分区：
文献类型：
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作者：
Jing Wang;Xin Li;L. Lu;F. Fang
敏感性分析（SA）已成为理解、应用和开发模型的基本工具。然而，过去很少关注参数样本大小和参数变化范围对参数SA及其时间特性的影响。本文以气象观测数据为输入，统计数据为参数，对西北地区迎客绿洲2008年种植的玉米作物进行了模拟。此外，使用扩展傅里叶振幅灵敏度（EFAST）算法，对世界粮食研究（WOFOST）作物生长模型的47个作物参数进行SA。深入分析了参数样本大小和变化范围对参数SA的影响、SA的时间特性和多变量输出问题。结果表明，样本量对敏感性指标的收敛性影响很大。采用两类参数变化范围进行分析，结果表明两种参数空间的敏感参数存在明显差异。此外，以不同生长阶段的贮藏器官生物量为目标输出，讨论了参数敏感性的时间依赖性特征。结果表明，谷物生物量在整个发育阶段都存在多个敏感参数。另外，对12个敏感参数的分析证明，虽然某些参数对最终产量没有影响，但在某些生长阶段却起着关键作用，并且这些参数的重要性逐渐增加。最后，对不同状态变量输出进行敏感性分析，包括生物量、产量、叶面积指数和蒸腾系数。结果表明，不同变量过程的敏感参数是不同的。这项研究强调了考虑模型参数的多个特征以及模型在特定物候阶段的响应的重要性。
Sensitivity analysis (SA) has become a basic tool for the understanding, application and development of models. However, in the past, little attention has been paid to the effects of the parameter sample size and parameter variation range on the parameter SA and its temporal properties. In this paper, the corn crop planted in 2008 in the Yingke Oasis of northwest China is simulated based on meteorological observation data for the inputs and statistical data for the parameters. Furthermore, using the extended Fourier Amplitude Sensitivity (EFAST) algorithm, SA is performed on the 47 crop parameters of the WOrld FOod STudies (WOFOST) crop growth models. A deep analysis is conducted, including the effects of the parameter sample size and variation range on the parameter SA, the temporal properties and the multivariable output issues of SA. The results show that sample size highly affects the convergence of the sensitivity indices. Two types of parameter variation ranges are used for the analysis, and the results show that the sensitive parameters of the two parameter spaces are distinctly different. In addition, taking the storage organ biomasses at the different growth stages as the objective output, the time-dependent characteristics of the parameter sensitivity are discussed. The results show that several sensitive parameters exist in the grain biomass throughout the entire development stage. In addition, analyzing the twelve sensitive parameters has proven that although certain parameters have no effect on the final yield, they play key roles in certain growth stages, and the importance of these parameters gradually increases. Finally, the sensitivity analyses of different state variable outputs are performed, including the biomass, yield, leaf area index, and transpiration coefficient. The results suggest that the sensitive parameters of various variable processes differ. This study highlights the importance of considering multiple characteristics of the model parameters and the responses of the models in specific phenological stages.