Integrating spatially explicit sensitivity and uncertainty analysis in a multi-criteria decision analysis-based groundwater potential zone model
Stephen Fildes, David Bruce, Ian Clark, Tom Raimondo, Robert G. Keane, Okke Batelaan
Abstract
This study presents a spatially explicit sensitivity and uncertainty analysis approach to a GIS-based multi-criteria groundwater potential zone model. The study addressed a deficiency in the way groundwater potential mapping results are typically presented using discrete class outputs without assessment of their certainty with respect to variations in criteria weighting, one of the main contributors to output uncertainty in GIS-based multi-criteria decision analysis studies. We argue, moderating groundwater potential mapping results with localised uncertainty levels will help to refine and prioritise groundwater exploration efforts. The approach also enables a better understanding of the underlying factors influencing uncertainty in model outputs, which can help to inform the calibration of input parameters to improve model performance. Although the procedures presented in this study have been applied to other types of multi-criteria evaluations, its integration in GIS-based groundwater potential modelling has received little attention. We provide a case study focused on a fractured rock environment surrounding the township of Hawker in South Australia where new groundwater resources are sought. Small incremental weight changes were applied one-at-a-time and automated as a task in ArcGIS Pro, built using the ArcPy Python module that interacts with spatial tools allowing geographical analysis. The approach is applicable to both continuous and discrete class-based mapping outputs and enabled a deeper understanding of model output behaviour with respect to criteria weighting alternatives. The case study findings demonstrate the potential value of the approach in mitigating uncertainty and improving confidence in locating sites with high groundwater potential.