Uncertainty Assessment in Hydrologic Modeling: ...

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XVI International Conference on Computational Methods in Water Resources (CMWR-XVI) Ingeniørhuset

Uncertainty Assessment in Hydrologic Modeling: Comparison of GLUE and Markov Chain Monte Carlo methods
Author:Roberta-Serena Blasone <rsb@er.dtu.dk> (Institute of Environment & Resources, Technical University of Denmark)
Jasper A. Vrugt <vrugt@lanl.gov> (Earth and Environmental Sciences Division, Los Alamos National Laboratory)
Henrik Madsen <hem@dhi.dk> (DHI Water & Environment)
Dan Rosbjerg <dr@er.dtu.dk> (Institute of Environment & Resources, Technical University of Denmark)
Presenter:Roberta-Serena Blasone <rsb@er.dtu.dk> (Institute of Environment & Resources, Technical University of Denmark)
Date: 2006-06-18     Track: Special Sessions     Session: Data assimilation in water resources modelling
DOI:10.4122/1.1000000370

In recent years, predictive uncertainty analysis in hydrologic modeling has become an active area of research. Many different methods have been proposed to obtain meaningful confidence intervals on the model predictions. These methods rely on different assumptions on how input, output, parameter and model structural error are made explicit, and therefore generate different output prediction uncertainty ranges. One of the most commonly used methods to quantify output uncertainty is the Generalized Likelihood Uncertainty Estimation technique, GLUE advocated by Beven and coworkers (1992). Despite its features of generality and ease of implementation, the main drawbacks of GLUE are the high level of subjectivity in determining the threshold cutoff value and likelihood measure, and the high computational cost of its implementation. Recently, Vrugt et al. (2005) proposed the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm for computationally efficient estimation of the high probability density region (HPDF) of the parameter space. Contrary to GLUE, the SCEM-UA method is a Markov Chain Monte Carlo sampler that periodically updates the proposal distribution of the parameters to converge to the HDPF in the parameter space. In this study we compare the efficiency and effectiveness of GLUE and SCEM-UA for parameter uncertainty assessment in hydrologic modeling. Both methods are applied to a set of increasingly complex conceptual watershed models. The results demonstrate that the adaptive nature of the SCEM-UA method significantly reduces the computational burden to obtain a behavioral sample set of points from the HPDF of the parameter space.