Title: Combining generalized likelihood uncertainty estimation (GLUE) and Bayesian model averaging (BMA) to evaluate conceptual model and parameter uncertainty in groundwater modelling
Authors: Rojas Mujica, Rodrigo
Feyen, Luc
Dassargues, Alain #
Issue Date: 2007
Publisher: European Geosciences Union
Host Document: Abstract + poster presented at the European Geosciences Union (EGU) Meeting, 15-20 Apr. Austria vol:9 issue:06533
Conference: European Geosciences Union (EGU) Meeting location:Austria date:15-20 April 2007
Abstract: Uncertainty in groundwater models is mainly caused by a lack of knowledge to fully
describe the input parameters and by simplifications of complex system configurations
in simple conceptual models. Methods to deal with the uncertainty derived from the
input parameters are well documented in the literature, whereas the uncertainty derived
from the conceptual model has received less formal attention. Normally, once a
conceptual model is successfully calibrated it is rarely questioned and, generally, it is
used in order to obtain predictions of variables of interest. Nevertheless, it is known
that the calibration of groundwater models is mathematically non-unique and that different
conceptual models combined with different parameter sets may produce similar
results. In addition to this, uncertainty estimations based on a single conceptual model
are prone to statistical bias and underestimation of predictive uncertainty (Neuman,
This study proposes a methodology to account for the conceptual model and parameter
uncertainty through the combination of the Generalised Likelihood Uncertainty
Estimation (GLUE) methodology (Beven and Binley, 1992) and Bayesian Model Averaging
(BMA) (Draper, 1995; Hoeting et al., 1999). For a suit of alternative models,
parameter uncertainty is derived using the GLUE methodology. Subsequently, the predictive
distributions obtained with the different conceptual models are combined following
BMA to obtain a multi-model prediction. Hereby, the weights of the individual
models are the estimated posterior model likelihoods, which represent the ability of
each of the models in simulating the observed groundwater levels. The methodology is
illustrated using a synthetic 3-dimensional steady-state groundwater model with spatially
varying hydraulic conductivity. Results show that the proposed methodology is
a flexible tool to obtain posterior model likelihoods updated in a BMA scheme. The
BMA prediction results are a good compromise between all the alternative conceptual
models and encompasses, in most cases, the 95% confidence intervals obtained with
the models individually. This confirms that the estimation of predictive uncertainty
based on one conceptual model is prone to bias and underestimation.
Publication status: published
KU Leuven publication type: IMa
Appears in Collections:Department of Earth and Environmental Sciences
Division of Geology
# (joint) last author

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