Purpose of review

Forest models are tools for analysis and prediction of productivity and other services. Model outputs can only be useful if possible errors in inputs and model structure are recognized. However, errors cannot be quantified directly, making uncertainty inevitable. In this paper, we aim to clarify terminological confusion around the concepts of error and uncertainty and review current methods for addressing uncertainty in forest modelling.

Recent findings

Modellers increasingly recognize that all uncertainties—in data, model inputs and model structure—can be represented using probability distributions. This has stimulated the use of Bayesian methods for quantifying and reducing uncertainty and error in models of forests and other vegetation. The Achilles’ heel of Bayesian methods has always been their computational demand, but solutions are being found.

Summary

We conclude that future work will likely include (1) more use of Bayesian methods, (2) more use of hierarchical modelling, (3) replacement of model spin-up by Bayesian calibration, (4) more use of ensemble modelling and Bayesian model averaging, (5) new ways to account for model structural error in calibration, (6) better software for Bayesian calibration of complex models, (7) faster Markov chain Monte Carlo algorithms, (8) more use of model emulators, (9) novel uncertainty visualization techniques, (10) more use of graphical modelling and (11) more use of risk analysis.

中文翻译：

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量化和减少森林模型不确定性和误差的贝叶斯方法

审查目的

森林模型是用于分析和预测生产力及其他服务的工具。仅当识别出输入和模型结构中可能存在的错误时，模型输出才有用。但是，误差无法直接量化，因此不确定性不可避免。在本文中，我们旨在澄清围绕误差和不确定性概念的术语混淆，并回顾解决森林建模中不确定性的当前方法。

最近的发现

建模人员越来越认识到，可以使用概率分布来表示所有不确定性（数据，模型输入和模型结构中的不确定性）。这刺激了使用贝叶斯方法来量化和减少森林和其他植被模型中的不确定性和误差。贝叶斯方法的致命弱点一直是它们的计算需求，但是却找到了解决方案。

概要

我们得出结论，未来的工作将可能包括（1）更多使用贝叶斯方法，（2）更多使用分层建模，（3）用贝叶斯校准代替模型旋转，（4）更多使用集成建模和贝叶斯模型平均而言，（5）在校正中解决模型结构误差的新方法，（6）更好的用于复杂模型的贝叶斯校正的软件，（7）更快的马尔可夫链蒙特卡洛算法，（8）更多地使用模型仿真器，（9）新颖的不确定性可视化技术，（10）更多使用图形建模，（11）更多使用风险分析。