Position paper: Sensitivity analysis of spatially distributed environmental models- a pragmatic framework for the exploration of uncertainty sources. (December 2020) - Koo, Hyeongmo; Iwanaga, Takuya; Croke, Barry F.W.; Jakeman, Anthony J.; Yang, Jing; Wang, Hsiao-Hsuan; Sun, Xifu; Lü, Guonian; Li, Xin; Yue, Tianxiang; Yuan, Wenping; Liu, Xintao; et al.
Abstract: 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. Highlights: A pragmatic framework of sensitivity analyses is provided for spatially distributed environmental models. The framework prescribes sequential steps in which important considerations are highlighted. The framework benefits users of sensitivity analyses in environmental modeling and GIS communities.