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首页> 外文期刊>Agricultural and Forest Meteorology >A Bayesian framework for model calibration, comparison and analysis: application to four models for the biogeochemistry of a Norway spruce forest.
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A Bayesian framework for model calibration, comparison and analysis: application to four models for the biogeochemistry of a Norway spruce forest.

机译：贝叶斯模型校准，比较和分析框架：应用于挪威云杉林生物地球化学的四个模型。

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摘要

Four different parameter-rich process-based models of forest biogeochemistry were analysed in a Bayesian framework consisting of three operations: (1) Model calibration, (2) Model comparison, (3) Analysis of model-data mismatch. Data were available for four output variables common to the models: soil water content and emissions of N2O, NO and CO2. All datasets consisted of time series of daily measurements. Monthly averages and quantiles of the annual frequency distributions of daily emission rates were calculated for comparison with equivalent model outputs. This use of the data at model-appropriate temporal scale, together with the choice of heavy-tailed likelihood functions that accounted for data uncertainty through random and systematic errors, helped prevent asymptotic collapse of the parameter distributions in the calibration. Model behaviour and how it was affected by calibration was analysed by quantifying the normalised RMSE and r2 for the different output variables, and by decomposition of the MSE into contributions from bias, phase shift and variance error. The simplest model, BASFOR, seemed to underestimate the temporal variance of nitrogenous emissions even after calibration. The model of intermediate complexity, DAYCENT, simulated the time series well but with large phase shift. COUP and MoBiLE-DNDC were able to remove most bias through calibration. The Bayesian framework was shown to be effective in improving the parameterisation of the models, quantifying the uncertainties in parameters and outputs, and evaluating the different models. The analysis showed that there remain patterns in the data - in particular infrequent events of very high nitrogenous emission rate - that are unexplained by any of the selected forest models and that this is unlikely to be due to incorrect model parameterisation.
机译：在由三个操作组成的贝叶斯框架中分析了四个不同的基于参数的基于过程的森林生物地球化学模型：（1）模型校准，（2）模型比较，（3）模型数据不匹配分析。该模型共有四个输出变量的数据：土壤含水量和N 2 O，NO和CO 2 的排放。所有数据集均由每日测量的时间序列组成。计算每日排放率年度频率分布的月平均值和分位数，以与等效模型输出进行比较。数据在模型适当的时间尺度上的使用以及重尾似然函数的选择，这些函数通过随机和系统误差解决了数据的不确定性，有助于防止校准过程中参数分布的渐进性崩溃。通过对不同输出变量的归一化RMSE和 r 2 进行量化，并将MSE分解为偏差的贡献，分析了模型行为及其受校准的影响相移和方差误差。最简单的模型BASFOR即使在校准后也似乎低估了氮排放的时间变化。中间复杂度模型DAYCENT很好地模拟了时间序列，但相移较大。 COUP和MoBiLE-DNDC能够通过校准消除大多数偏差。贝叶斯框架被证明在改善模型的参数化，量化参数和输出的不确定性以及评估不同模型方面是有效的。分析表明，数据中仍然存在模式，尤其是氮排放速率非常高的罕见事件，任何选定的森林模型都无法解释这种模式，这不太可能是由于模型参数设置错误所致。

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