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 ...
This paper addresses two major challenges in the field of climate change impact analysis on water resources systems: i) incorporation of a large range of potential climate change scenarios and ii) quantification of related modelling uncertainties. The deve ...
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 ...
Urban land-use planning and management are in constant mutation throughout the world. With sustainability as a goal, the use of participative GIS is becoming more and more in demand. Given the willingness of local authorities to test participatory models a ...
The present study analyzes the uncertainty induced by the use of different state-of-the-art climate models on the prediction of climate change impacts on the runoff regimes of 11 mountainous catchments in the Swiss Alps having current glaciation rates betw ...
The classical single-objective model calibration and validation approach using different time periods for each of them is known not to be sufficient to judge whether the model predictions are consistent or to detect model structural deficiencies. It is how ...
