A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions
A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions
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DOI：
10.1016/j.ecolmodel.2009.12.003
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发表时间：
2010-03-24
影响因子：
3.1
通讯作者：
Acutis, M.
中科院分区：
文献类型：
--
作者：
Confalonieri, R.;Bregaglio, S.;Acutis, M.
生物物理模型的评估通常是通过估计测量数据和模拟数据之间的一致性来进行的，更少的情况下，是通过使用其他方面的指数来进行的，例如模型复杂性和过度参数化。尽管模型的稳健性很重要，尤其是对于大面积的应用，但目前还没有关于其量化的建议。在本文中，我们想就这一问题展开讨论，提出了一种基于模型误差与探测条件比率的可变性来量化稳健性的第一种方法。我们使用模型效率(EF)来量化模型预测中的误差，并使用基于累积降雨量和参考蒸散量的归一化农业气象指数(SAM)来表征应用条件。用EF和SAM的总体标准差来量化它们的变异性。该指标在估计气象变量和作物状态变量的模型中进行了测试。根据模型的特点以及模拟过程的类型和数量，讨论了稳健性指标(I-R)所提供的值。在相同的模型类型下，I-R随模拟过程的数量和过度参数化度的增加而增加。I-R与两个最常用的模型误差指数(RRMSE、EF)之间没有相关性。这支持将其纳入综合系统进行模式评估。(C)2009爱思唯尔B.V.保留所有权利。
The evaluation of biophysical models is usually carried out by estimating the agreement between measured and simulated data and, more rarely, by using indices for other aspects, like model complexity and overparameterization. In spite of the importance of model robustness, especially for large area applications, no proposals for its quantification are available. In this paper, we would like to open a discussion on this issue, proposing a first approach for a quantification of robustness based on the variability of model error to variability of explored conditions ratio. We used modelling efficiency (EF) for quantifying error in model predictions and a normalized agrometeorological index (SAM) based on cumulated rainfall and reference evapotranspiration to characterize the conditions of application. Population standard deviations of EF and SAM were used to quantify their variability. The indicator was tested for models estimating meteorological variables and crop state variables. The values provided by the robustness indicator (I-R) were discussed according to the models' features and to the typology and number of processes simulated. I-R increased with the number of processes simulated and, within the same typology of model, with the degree of overparameterization. No correlation were found between I-R and two of the most used indices of model error (RRMSE, EF). This supports its inclusion in integrated systems for model evaluation. (C) 2009 Elsevier B.V. All rights reserved.