Computer models of environmental systems routinely inform decision making for water resource management. In this context, quantifying uncertainty in the important simulated outputs, and reducing uncertainty through assimilating historic system-state observations, is as important as the numerical model. However, implementing high-dimensional and stochastic workflows are challenging, often requiring that practitioners have theoretical and practical understanding of several advanced topics. Worse, implementing these important analyses can take substantial time and effort. This additional effort is often cited as justification for postponing, or even forgoing, these analyses.

Herein, we present scripting tools to facilitate the efficient and repeatable construction of high-dimensional, geostatistical-based PEST interfaces, including uncertainty analyses. As demonstrated, these tools can be applied with minimal effort to a model with varied temporal and spatial discretization. Ultimately, these tools can enable low-cost access to valuable decision-support analyses earlier and more frequently during the modeling workflow.

环境系统的计算机模型通常会为水资源管理决策提供依据。在这种情况下，量化重要模拟输出中的不确定性并通过吸收历史系统状态的观测值来减少不确定性与数值模型一样重要。但是，实施高维度和随机的工作流程具有挑战性，通常要求从业人员对几个高级主题有理论和实践的理解。更糟糕的是，实施这些重要的分析可能需要大量的时间和精力。人们通常将这种额外的努力称为推迟甚至放弃这些分析的理由。

在本文中，我们介绍了脚本工具，以帮助高效，可重复地构建基于地统计的高维PEST接口，包括不确定性分析。如图所示，这些工具可以轻松地应用于具有不同时空离散的模型。最终，这些工具可以使您在建模工作流程中更早，更频繁地以低成本访问有价值的决策支持分析。