@article {29245,
	title = {Using calibration period ensembles of climate and flow data to optimize the characterization of hydrologic model prediction uncertainty.},
	journal = {Geophysical Research Abstracts},
	volume = {21},
	year = {2019},
	abstract = {<p>
	Current modelling practice to account for calibration period climate uncertainty or flow measurement uncertainty relies upon relatively simple stochastic measurement error models that are often calibrated in conjunction with hydrologic model parameters. Continued advances in uncertainty-based model calibration to account for data uncertainty, either through formal Bayesian inference, Approximate Bayesian Computation (ABC), or informal approaches like the limits of acceptability approach in GLUE, require that such calibration approaches can be applied using more complex stochastic measurement error models that are developed independent of hydrologic model application. Two such recent examples are 1) the Newman et al.(2015) gridded ensemble historical precipitation and temperature data set for the continental United States and parts of Canada and the 2) the hydraulics-based Bayesian rating curve\&nbsp;{\textellipsis}
</p>
},
	author = {H. Liu and B. Tolson}
}
