Bayesian multiresponse calibration of TOPMODEL: Application to the Haute-Mentue catchment, Switzerland

This paper introduces a general framework that evaluates a numerical Bayesian multiresponse calibration approach based on a Gibbs within Metropolis searching algorithm and a statistical likelihood function. The methodology has been applied with two versions of TOPMODEL on the Haute‐Mentue experiment...

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Bibliographic Details
Published in:Water resources research 2010-08, Vol.46 (8), p.1-n/a
Main Authors: Balin Talamba, Daniela, Parent, Eric, Musy, André
Format: Article
Language:English
Subjects:
additional information
Algorithms
Bayesian multiresponse calibration
Calibration
Experimental basins
Freshwater
Gibbs within Metropolis algorithm
Groundwater
Hydrology
Hypotheses
internal variables
Life Sciences
Methods
Optimization techniques
Parameter estimation
Runoff
Saturated soils
Saturation deficit
Silica
Simulation
TOPMODEL
Uncertainty
Watersheds
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Summary:This paper introduces a general framework that evaluates a numerical Bayesian multiresponse calibration approach based on a Gibbs within Metropolis searching algorithm and a statistical likelihood function. The methodology has been applied with two versions of TOPMODEL on the Haute‐Mentue experimental basin in Switzerland. The approach computes the following: the parameter's uncertainty, the parametric uncertainty of the output responses stemming from parameter uncertainty, and the predictive uncertainty of the output responses stemming from an error term including, indiscriminately in a lumped way, model structure and input and output errors. Two case studies are presented: The first one applies this methodology with the classical TOPMODEL to assess the role of two‐response calibration (observed discharge and soil saturation deficits) on model parameters and output uncertainty. The second one uses a three‐response calibration (observed discharge, silica, and calcium stream water concentrations) with a modified version of TOPMODEL to study the uncertainty of the parameters and of the simulated responses. Despite its limitations, the present multiresponse Bayesian approach proved a valuable tool in uncertainty analyses, and it contributed to a better understanding of the role of the internal variables and the value of additional information for enhancing model structure robustness and for checking the performance of conceptual models.
ISSN:0043-1397
1944-7973
DOI:10.1029/2007WR006449