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Parameter estimation in rainfall-runoff models is affected by uncertainties in the measured input/output data (typically, rainfall and runoff, respectively), as well as model error. Despite advances in data collection and model construction, we expect input uncertainty to be particularly significant (because of the high spatial and temporal variability of precipitation) and to remain considerable in the foreseeable future. Ignoring this uncertainty compromises hydrological modeling, potentially yielding biased and misleading results. This paper develops a Bayesian total error analysis methodology for hydrological models that allows (indeed, requires) the modeler to directly and transparently incorporate, test, and refine existing understanding of all sources of data uncertainty in a specific application, including both rainfall and runoff uncertainties. The methodology employs additional (latent) variables to filter out the input corruption given the model hypothesis and the observed data. In this study, the input uncertainty is assumed to be multiplicative Gaussian and independent for each storm, but the general framework allows alternative uncertainty models. Several ways of incorporating vague prior knowledge of input corruption are discussed, contrasting Gaussian and inverse gamma assumptions; the latter method avoids degeneracies in the objective function. Although the general methodology is computationally intensive because of the additional latent variables, a range of modern numerical methods, particularly Monte Carlo analysis combined with fast Newton-type optimization methods and Hessian-based covariance analysis, can be employed to obtain practical solutions.

Item Type:	Refereed Article
Keywords:	uncertainty hydrological modelling BATEA
Research Division:	Engineering
Research Group:	Environmental Engineering
Research Field:	Environmental Engineering Modelling
Objective Division:	Environment
Objective Group:	Land and Water Management
Objective Field:	Land and Water Management of environments not elsewhere classified
UTAS Author:	Franks, SW (Professor Stewart Franks)
ID Code:	86409
Year Published:	2006
Web of Science® Times Cited:	415
Deposited By:	Engineering
Deposited On:	2013-09-14
Last Modified:	2013-10-14
Downloads:	6 View Download Statistics