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Sensitivity analysis of the rice model WARM in Europe: exploring the effects of different locations, climates and methods of analysis on model sensitivity to crop parameters

机译:欧洲水稻模型WARM的敏感性分析:探索不同地点,气候和分析方法对模型对作物参数敏感性的影响

摘要

An application of Morris and Sobol' sensitivity analysis methods to the rice model WARM is presented to assess model sensitivity to its parameters. The output considered is aboveground biomass at maturity, simulated at five European rice districts (France, Greece, Italy, Portugal, Spain) for years characterized by low, intermediate, and high continentality. The total-effect index of Sobol' and Morrisindices (mean µ and standard deviation of the ratios output changes/parameter variations) were used as sensitivity measures. Radiation use efficiency (RUE), optimum temperature (Topt), and leaf area index at emergence (LAIini) were generally ranked as first, second and third most relevant parameters. Exceptions were observed, depending on the sensitivity method (e.g. LAIini resulted not relevant by the Morris method), or site-continentality pattern (e.g. with intermediate continentality in Spain, LAIini and Topt were second andthird ranked; with low continentality in Portugal, RUE was outranked by Topt). Low s values associated with the most relevant parameters indicated limited parameter interactions. The importance of sensitivity analyses by exploring location × climate combinations is discussed as pre-requisite to evaluate either novel crop modelling approaches or the application of known modelling solutions to conditions not exploredpreviously. The need of developing tools for sensitivity analysis within the modelling environment is also emphasized.
机译:提出了Morris和Sobol敏感性分析方法在水稻模型WARM中的应用,以评估模型对其参数的敏感性。所考虑的输出是成熟时的地上生物量,在五个欧洲稻区(法国,希腊,意大利,葡萄牙,西班牙)模拟了多年,其特征为低,中和高大陆性。将Sobol'和Morrisindices的总效果指数(平均μ和输出变化/参数变化之比的标准偏差)用作敏感度度量。辐射利用效率(RUE),最佳温度(Topt)和出苗时叶面积指数(LAIini)通常被列为第一,第二和第三最相关的参数。观察到例外,具体取决于灵敏度方法(例如,Lorini结果与莫里斯方法无关),或场地连续性模式(例如,在西班牙为中大陆性,LAIini和Topt分别排在第二和第三;在葡萄牙为低大陆性,RUE为由Topt排名)。与最相关的参数关联的s值低表示有限的参数交互作用。通过探索位置×气候组合来进行敏感性分析的重要性被讨论为评估新颖的作物建模方法或将已知的建模解决方案应用于以前未曾探索过的条件的前提。还强调了在建模环境中开发用于敏感性分析的工具的需求。

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