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Bayesian estimation of uncertainty in land surface-atmosphere flux predictions

Franks, Stewart W. and Beven, Keith J. (1997) Bayesian estimation of uncertainty in land surface-atmosphere flux predictions. Journal of Geophysical Research: Atmospheres, 102 (D20). pp. 23991-23999. ISSN 0747-7309

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Abstract

This study addresses the assessment of uncertainty associated with predictions of land surface-atmosphere fluxes using Bayesian Monte Carlo simulation within the generalized likelihood uncertainty estimation (GLUE) methodology. Even a simple soil vegetation-atmosphere transfer (SVAT) scheme is shown to lead to multiple acceptable parameterizations when calibration data are limited to timescales of typical intensive field campaigns. The GLUE methodology assigns a likelihood weight to each acceptable simulation. As more data become available, these likelihood weights may be updated by using Bayes equation. Application of the GLUE methodology can be shown to reveal deficiencies in model structure and the benefit of additional calibration data. The method is demonstrated with data sets taken from FIFE sites in Kansas, and ABRACOS data from the Amazon. Estimates of uncertainty are propagated for each data set revealing significant predictive uncertainty. The value of additional periods of data is then evaluated through comparing updated uncertainty estimates with previous estimates using the Shannon entropy measure.

Item Type: Article
Journal or Publication Title: Journal of Geophysical Research: Atmospheres
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Departments: Faculty of Arts & Social Sciences > Politics & International Relations (Merged into PPR 2010-08-01)
Faculty of Science and Technology > Lancaster Environment Centre
ID Code: 21917
Deposited By: ep_ss_importer
Deposited On: 11 Feb 2009 14:40
Refereed?: Yes
Published?: Published
Last Modified: 17 Sep 2013 08:18
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/21917

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