Sensitivity analysis (SA) has been used to evaluate the behavior and quality of environmental models by estimating the contributions of potential uncertainty sources to quantities of interest (QoI) in the model output. Although there is an increasing literature on applying SA in environmental modeling, a pragmatic and specific framework for spatially distributed environmental models (SD-EMs) is lacking and remains a challenge. This article reviews the SA literature for the purposes of providing a step-by-step pragmatic framework to guide SA, with an emphasis on addressing potential uncertainty sources related to spatial datasets and the consequent impact on model predictive uncertainty in SD-EMs. The framework includes: identifying potential uncertainty sources; selecting appropriate SA methods and QoI in prediction according to SA purposes and SD-EM properties; propagating perturbations of the selected potential uncertainty sources by considering the spatial structure; and verifying the SA measures based on post-processing. The proposed framework was applied to a SWAT (Soil and Water Assessment Tool) application to demonstrate the sensitivities of the selected QoI to spatial inputs, including both raster and vector datasets - for example, DEM and meteorological information - and SWAT (sub)model parameters. The framework should benefit SA users not only in environmental modeling areas but in other modeling domains such as those embraced by geographical information system communities.

灵敏度分析（SA）已用于通过估计潜在不确定性源对模型输出中感兴趣量（QoI）的贡献来评估环境模型的行为和质量。尽管有越来越多的文献报道将SA应用于环境建模，但是缺乏实用且特定的空间分布环境模型（SD-EM）框架，并且仍然是一个挑战。本文回顾了SA文献，目的是提供逐步的实用框架来指导SA，重点是解决与空间数据集有关的潜在不确定性源，以及由此对SD-EMs中模型预测不确定性的影响。该框架包括：确定潜在的不确定性来源；根据SA目的和SD-EM属性选择合适的SA方法和预测QoI；通过考虑空间结构传播所选潜在不确定性源的扰动；并根据后处理验证SA措施。拟议的框架已应用于SWAT（土壤和水评估工具）应用程序，以证明所选QoI对空间输入的敏感性，包括栅格和矢量数据集（例如DEM和气象信息）以及SWAT（子）模型参数。该框架不仅应使SA用户在环境建模领域中受益，而且还将使其他建模领域（如地理信息系统社区所拥护的领域）受益。并根据后处理验证SA措施。拟议的框架已应用于SWAT（土壤和水评估工具）应用程序，以证明所选QoI对空间输入的敏感性，包括栅格和矢量数据集（例如DEM和气象信息）以及SWAT（子）模型参数。该框架不仅应使SA用户在环境建模领域中受益，而且还将使其他建模领域（如地理信息系统社区所拥护的领域）受益。并根据后处理验证SA措施。拟议的框架已应用于SWAT（土壤和水评估工具）应用程序，以证明所选QoI对空间输入的敏感性，包括栅格和矢量数据集（例如DEM和气象信息）以及SWAT（子）模型参数。该框架不仅应使SA用户在环境建模领域中受益，而且还将使其他建模领域（如地理信息系统社区所拥护的领域）受益。DEM和气象信息-以及SWAT（子）模型参数。该框架不仅应使SA用户在环境建模领域中受益，而且还将使其他建模领域（如地理信息系统社区所拥护的领域）受益。DEM和气象信息-以及SWAT（子）模型参数。该框架不仅应使SA用户在环境建模领域中受益，而且还将使其他建模领域（如地理信息系统社区所拥护的领域）受益。