Concerns regarding the impact of climate change, food price volatility, and weather uncertainty have motivated users of simulation models to consider uncertainty in their simulations. One way to do this is to integrate uncertainty components in the model equations, thus turning the model into a problem of numerical integration. Most of these problems do not have analytical solutions, and researchers, therefore, apply numerical approximation methods. This article presents a novel approach to conducting an uncertainty analysis as an alternative to the computationally burdensome Monte Carlo-based (MC) methods. The developed method is based on the degree three Gaussian quadrature (GQ) formulae and is tested using three large-scale simulation models. While the standard single GQ method often produces low-quality approximations, the results of this study demonstrate that the proposed approach reduces the approximation errors by a factor of nine using only 3.4% of the computational effort required by the MC-based methods in the most computationally demanding model.

对气候变化，粮食价格波动和天气不确定性的影响的担忧促使模拟模型的用户考虑其模拟中的不确定性。一种方法是将不确定性分量整合到模型方程中，从而使模型成为数值积分问题。这些问题大多数都没有解析解，因此研究人员应用了数值逼近方法。本文提出了一种进行不确定性分析的新颖方法，以替代计算量大的基于Monte Carlo的（MC）方法。所开发的方法基于三阶高斯正交（GQ）公式，并使用三个大型仿真模型进行了测试。虽然标准的单一GQ方法通常会产生低质量的近似值，