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The Bayesian total error analysis (BATEA) methodology directly addresses both input and output errors in hydrological modeling, requiring the modeler to make explicit, rather than implicit, assumptions about the likely extent of data uncertainty. This study considers a BATEA assessment of two North American catchments: (1) French Broad River and (2) Potomac basins. It assesses the performance of the conceptual Variable Infiltration Capacity (VIC) model with and without accounting for input (precipitation) uncertainty. The results show the considerable effects of precipitation errors on the predicted hydrographs (especially the prediction limits) and on the calibrated parameters. In addition, the performance of BATEA in the presence of severe model errors is analyzed. While BATEA allows a very direct treatment of input uncertainty and yields some limited insight into model errors, it requires the specification of valid error models, which are currently poorly understood and require further work. Moreover, it leads to computationally challenging highly dimensional problems. For some types of models, including the VIC implemented using robust numerical methods, the computational cost of BATEA can be reduced using Newton-type methods.

Item Type:	Refereed Article
Keywords:	uncertainty hydrological modelling BATEA rainfall
Research Division:	Engineering
Research Group:	Environmental engineering
Research Field:	Air pollution modelling and control
Objective Division:	Environmental Management
Objective Group:	Other environmental management
Objective Field:	Other environmental management not elsewhere classified
UTAS Author:	Franks, SW (Professor Stewart Franks)
ID Code:	86413
Year Published:	2006
Web of Science® Times Cited:	44
Deposited By:	Engineering
Deposited On:	2013-09-14
Last Modified:	2013-10-14
Downloads:	0