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Evaluation of crop model prediction and uncertainty using Bayesian parameter estimation and Bayesian model averaging
Agricultural and Forest Meteorology ( IF 6.2 ) Pub Date : 2021-10-26 , DOI: 10.1016/j.agrformet.2021.108686
Yujing Gao 1, 2 , Daniel Wallach 1, 3 , Toshihiro Hasegawa 4 , Liang Tang 5 , Ruoyang Zhang 6 , Senthold Asseng 1 , Tamer Kahveci 7 , Leilei Liu 5 , Jianqiang He 8 , Gerrit Hoogenboom 1, 2
Affiliation  

A recent trend in crop modeling has been the use of multi-model ensembles (MMEs) for impact assessment, especially as it relates to climate change. Studies have shown that, compared to individual models, the mean or median of a MME is a better predictor that is more accurate in making predictions and capable of providing model uncertainty information. In previous studies that used MMEs, each individual model was assigned an equal weight by simply averaging the predictions over all the models. Here we adopted a different method of creating MMEs, namely using Bayesian model averaging (BMA), which assigns different weights to individual models according to their performance of reproducing historical data. The main purpose of this study is to illustrate how BMA can be applied to crop models, and to compare its performance with simple model averaging. In addition, we also illustrate a full chain of analysis for crop model ensembles, which includes parameter estimation, ensemble creation and evaluation, uncertainty quantification and evaluation. The approaches were implemented with simulations of rice phenology using a small, three-member model ensemble. The results showed that the ensemble model e-BMA had nearly the same prediction accuracy as the best single model and that it predicts quite a bit better than the simply averaging ensemble named e-equal here. Squared bias was found to be the largest contributor to overall prediction uncertainty, both for the individual models and the ensemble models, and thus should be the first priority for model improvement. The estimated prediction uncertainties were larger than the variance of the observations. In the future, BMA should be tested more widely.



中文翻译:

使用贝叶斯参数估计和贝叶斯模型平均评估作物模型预测和不确定性

作物建模的最新趋势是使用多模型集合 (MME) 进行影响评估,尤其是与气候变化相关的影响评估。研究表明,与单个模型相比,MME 的均值或中位数是更好的预测指标,预测更准确,能够提供模型不确定性信息。在之前使用 MME 的研究中,通过简单地对所有模型的预测进行平均,为每个单独的模型分配了相同的权重。在这里,我们采用了一种不同的创建 MME 的方法,即使用贝叶斯模型平均 (BMA),它根据单个模型再现历史数据的性能为它们分配不同的权重。本研究的主要目的是说明 BMA 如何应用于作物模型,并将其性能与简单模型平均进行比较。此外,我们还说明了作物模型集成的完整分析链,包括参数估计、集成创建和评估、不确定性量化和评估。这些方法是通过使用小型三成员模型集合模拟水稻物候学来实现的。结果表明,集成模型e-BMA具有与最佳单一模型几乎相同的预测精度,并且它的预测比此处称为e-equal的简单平均集合好得多。发现平方偏差是整体预测不确定性的最大贡献者,无论是对于单个模型还是集合模型,因此应该是模型改进的首要任务。估计的预测不确定性大于观测的方差。未来,BMA 应该得到更广泛的测试。

更新日期:2021-10-27
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