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A probabilistic assessment of climate change and related impacts should consider a large range of potential future climate scenarios. State-of-the-art climate models, especially coupled atmosphere-ocean general circulation models and Regional Climate Model ...
Bayesian inference of posterior parameter distributions has become widely used in hydrological modeling to estimate the associated modeling uncertainty. The classical underlying statistical model assumes a Gaussian modeling error with zero mean and a given ...
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 version ...