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Bayesian analysis of input uncertainty in hydrological modeling: 2. Application
journal contribution
posted on 2023-05-17, 19:29 authored by Kavetski, D, Kuczera, G, Franks, SWThe 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.
History
Publication title
Water Resources ResearchVolume
42Article number
W03408Number
W03408Pagination
1-10ISSN
1944-7973Department/School
School of EngineeringPublisher
AGUPlace of publication
USARights statement
Copyright 2006 by the American Geophysical Union.Repository Status
- Restricted