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| Titel |
A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model |
| VerfasserIn |
F. Hossain, E. N. Anagnostou, K.-H. Lee |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 11, no. 4 ; Nr. 11, no. 4 (2004-09-24), S.427-440 |
| Datensatznummer |
250009323
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| Publikation (Nr.) |
 copernicus.org/npg-11-427-2004.pdf |
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| Zusammenfassung |
| This study presents a simple and efficient scheme for Bayesian estimation of
uncertainty in soil moisture simulation by a Land Surface Model (LSM). The
scheme is assessed within a Monte Carlo (MC) simulation framework based on
the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. A
primary limitation of using the GLUE method is the prohibitive computational
burden imposed by uniform random sampling of the model's parameter
distributions. Sampling is improved in the proposed scheme by stochastic
modeling of the parameters' response surface that recognizes the non-linear
deterministic behavior between soil moisture and land surface parameters.
Uncertainty in soil moisture simulation (model output) is approximated
through a Hermite polynomial chaos expansion of normal random variables that
represent the model's parameter (model input) uncertainty. The unknown
coefficients of the polynomial are calculated using limited number of model
simulation runs. The calibrated polynomial is then used as a fast-running
proxy to the slower-running LSM to predict the degree of representativeness
of a randomly sampled model parameter set. An evaluation of the scheme's
efficiency in sampling is made through comparison with the fully random MC
sampling (the norm for GLUE) and the nearest-neighborhood sampling
technique. The scheme was able to reduce computational burden of random MC
sampling for GLUE in the ranges of 10%-70%. The scheme was also
found to be about 10% more efficient than the nearest-neighborhood
sampling method in predicting a sampled parameter set's degree of
representativeness. The GLUE based on the proposed sampling scheme did not
alter the essential features of the uncertainty structure in soil moisture
simulation. The scheme can potentially make GLUE uncertainty estimation for
any LSM more efficient as it does not impose any additional structural or
distributional assumptions. |
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