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Comment on “Bayesian recursive parameter estimation for hydrologic models” by M. Thiemann, M. Trosset, H. Gupta, and S. Sorooshian

Comment on “Bayesian recursive parameter estimation for hydrologic models” by M. Thiemann, M.... We believe that the paper by [2001] (hereinafter referred to as TTGS), while formally correct within the limitations of the underlying assumptions of their analysis, does not lead to sensible results in respect of the uncertainties normally associated with the parameters of hydrological models. Moreover, we feel that where these assumptions are reasonable, there may be more effective ways of achieving the same ends. In effect, the BARE methodology and the GLUE approach that they contrast it with, represent two extreme positions in a spectrum of possible Bayesian approaches to the problem. For instance, the results presented in the paper suggest that, in the BARE algorithm as implemented by TTGS , all of the error is treated as if it were “measurement error” and the estimated model parameters are obtained without any appreciable uncertainty. In other words, the estimated model appears, from the TTGS results, to be an almost deterministic, or true, representation of the system. In GLUE, on the other hand, the sources of error are treated implicitly in weighting the predictions of multiple (non‐error free) models, without strong assumptions about a measurement error model (which might indeed vary from model realization to model realization). However, both http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Water Resources Research Wiley

Comment on “Bayesian recursive parameter estimation for hydrologic models” by M. Thiemann, M. Trosset, H. Gupta, and S. Sorooshian

Water Resources Research , Volume 39 (5) – May 1, 2003

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References (30)

Publisher
Wiley
Copyright
Copyright © 2003 by the American Geophysical Union.
ISSN
0043-1397
eISSN
1944-7973
DOI
10.1029/2001WR001183
Publisher site
See Article on Publisher Site

Abstract

We believe that the paper by [2001] (hereinafter referred to as TTGS), while formally correct within the limitations of the underlying assumptions of their analysis, does not lead to sensible results in respect of the uncertainties normally associated with the parameters of hydrological models. Moreover, we feel that where these assumptions are reasonable, there may be more effective ways of achieving the same ends. In effect, the BARE methodology and the GLUE approach that they contrast it with, represent two extreme positions in a spectrum of possible Bayesian approaches to the problem. For instance, the results presented in the paper suggest that, in the BARE algorithm as implemented by TTGS , all of the error is treated as if it were “measurement error” and the estimated model parameters are obtained without any appreciable uncertainty. In other words, the estimated model appears, from the TTGS results, to be an almost deterministic, or true, representation of the system. In GLUE, on the other hand, the sources of error are treated implicitly in weighting the predictions of multiple (non‐error free) models, without strong assumptions about a measurement error model (which might indeed vary from model realization to model realization). However, both

Journal

Water Resources ResearchWiley

Published: May 1, 2003

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