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Generalized likelihood uncertainty estimation (GLUE) is one of the widely-used methods for quantifying uncertainty in flood inundation mapping. However, the subjective nature of its application involving the definition of the likelihood measure and the criteria for defining acceptable versus unacceptable models can lead to different results in quantifying uncertainty bounds. The objective of this paper is to perform a sensitivity analysis of the effect of the choice of likelihood measures and cut-off thresholds used in selecting behavioral and non-behavioral models in the GLUE methodology. By using a dataset for a reach along the White River in Seymour, Indiana, multiple prior distributions, likelihood measures and cut-off thresholds are used to investigate the role of subjective decisions in applying the GLUE methodology for uncertainty quantification related to topography, streamflow and Manning’s n. Results from this study show that a normal pdf produces a narrower uncertainty bound compared to a uniform pdf for an uncertain variable. Similarly, a likelihood measure based on water surface elevations is found to be less affected compared to other likelihood measures that are based on flood inundation area and width. Although the findings from this study are limited due to the use of a single test case, this paper provides a framework that can be utilized to gain a better understanding of the uncertainty while applying the GLUE methodology in flood inundation mapping

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Sensitivity of Subjective Decisions in the GLUE Methodology for Quantifying the Uncertainty in the Flood Inundation Map for Seymour Reach in Indiana, USA