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Calibrated parameter values are within published and experimentally derived ranges.
This conventional approach to probabilistic modelling has significant shortcomings as the extent of generalising the use of the calibrated parameter values is uncertain.
In addition, new tools are presented here to identify where unique calibrated parameter sets are required for different observational data (e.g., should the calibrated parameter set differ between alongshore locations at a site) and investigate the convergence of GLUE estimated optimum parameter values over increasing numbers of Monte Carlo samples.
We recommend moving away from the use of a firn model with one calibrated parameter set to infer atmospheric histories, and instead suggest using multiple parameter sets, preferably with multiple representations of uncertain processes, to assist in quantification of the uncertainties.
We then relate the calibrated parameter values of these models to physical and climatological characteristics of the watershed using multivariate linear regression analysis, and evaluate the regionalization potential of our proposed models against those of the literature.
In this technical note, we discuss a specific class of regionalization methods, those based on spatial proximity, which transfers hydrological information (typically calibrated parameter sets) from neighbor gauged stations to the target ungauged station.
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Besides, the validity of calibrated parameters was tested in the hammer mill.
Finally, using the SCEM-UA optimisation algorithm, we calibrated parameters that affect seepage onto these time series.
Findings from the case study show that the calibrated parameters are usually different from the initially assumed values.
The results demonstrate that the model based on the calibrated parameters is capable of offering reasonable and robust predictions.
The calibrated parameters appeared to have very low uncertainty while allowing a good modelisation of the observed water content variations.
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