Application of the generalized likelihood uncertainty estimation (GLUE) approach for assessing uncertainty in hydrological models: a review

Abstract

The generalized likelihood uncertainty estimation (GLUE) technique is an innovative uncertainty method that is often employed with environmental simulation models. Over the past years, hydrological literature has seen a large increase in the number of papers dealing with uncertainty. There are now a lot of citations to their original paper which illustrates GLUE tremendous impact. GLUE's popularity can be attributed to its simplicity and its applicability to nonlinear systems, including those for which a unique calibration is not apparent. The GLUE was introduced for use in uncertainty analysis of watershed models has now been extended well beyond rainfall-runoff watershed models. Given the widespread adoption of GLUE analyses for a broad range or problems, it is appropriate that the validity of the approach be examined with care. In this article, we present an overview of the application of GLUE for assessing uncertainty distribution in hydrological models particularly surface and subsurface hydrology and briefly describe algorithms for sampling of the prior parameter in hydrologic simulation models.

Parameter uncertainty analysis for an operational hydrological model using residual based and limits of acceptability approaches

“…CC BY 4.0 License. distributed computer systems as well as its general strategy in dealing with equifinality in model calibration make it an appealing framework (Blasone et al, 2008;Shen et al, 2012;Mirzaei et al, 2015).…”

Groundwater Simulations and Uncertainty Analysis Using MODFLOW and Geostatistical Approach with Conditioning Multi-Aquifer Spatial Covariance

“…When the values of the NSE increased from 0.6 to 0.7, the number of non-behavioral parameter sets increased slightly, except for Aquifer 3 wherein the semivariogram presented a pure nugget which is evidence of a highly heterogeneous physical formation at Aquifer 3. The use of GLUE with our proposed simulation approach in assessing and conditioning groundwater modeling results and uncertainties is suitable, however, GLUE approaches have been criticized and evaluated for not being formally Bayesian [27,[31][32][33]46].…”

“…This determination refers to "equifinality" for model uncertainty analysis [30]. It is noted that most relevant hydrological and groundwater studies have tended to use GLUE for their uncertainty analysis, such as Chu et al [29] and Mirzaei et al [31] in hydrological modeling; Hassan et al [27], Jackson et al [32], and Marchant et al [33] in groundwater modeling; and Wang et al [34] and Huang et al [28] in wetland modeling.…”

Uncertainty analysis for extreme flood events in a semi-arid region

“…Engineering systems, such as wastewater treatment plants, soil remediation systems, water purification systems, flood control systems, are subject to uncertainty but decisions on their planning, design, operation, and management are often made without accounting for it (Sohrabi et al 2003). There are comprehensive taxonomies of uncertainty in the literature which discuss different types and sources of uncertainty (Haimes 1998;van Asselt and Rotmans 2002;Chen and Chau 2006;Mirzaei et al 2015;Goodarzi et al 2012;Salarpour et al 2013). For the purpose of this paper, it is important to recognize two basic kinds of uncertainty that are fundamentally different from each other: natural and epistemic uncertainty.…”