Equifinality of formal (DREAM) and informal (GLUE) bayesian approaches in hydrologic modeling?

Journal Article · Mon Dec 31 23:00:00 EST 2007 · Stochastic Environmental Research and Risk Assessment

DOI:https://doi.org/10.1007/s00477-008-0284-9· OSTI ID:960772

Vrugt, Jasper A ^[1]; Robinson, Bruce A ^[1]; Gupta, Hoshin V ^[2]

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1. Los Alamos National Laboratory
2. NON LANL

In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchments.

• Research Organization: Los Alamos National Laboratory (LANL)
• Sponsoring Organization: DOE
• DOE Contract Number: AC52-06NA25396
• OSTI ID: 960772
• Report Number(s): LA-UR-08-07136; LA-UR-08-7136
• Journal Information: Stochastic Environmental Research and Risk Assessment, Journal Name: Stochastic Environmental Research and Risk Assessment Journal Issue: 7 Vol. 23; ISSN 1436-3240
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
ALGORITHMS
COMPARATIVE EVALUATIONS
COMPUTER CODES
COMPUTERIZED SIMULATION
DETERMINISTIC ESTIMATION
HYDROLOGY
MARKOV PROCESS
MONTE CARLO METHOD
WATERSHEDS