Uncertainty estimation of hydrological models using bayesian inference methods
Intensive investigations of hydrologic model calibration during the last two decades have resulted in a reasonably good understanding of the issues involved in the process of estimating the numerous parameters employed by these codes. Nevertheless, these classical "batch" calibration...
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| Language: | en_US |
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The University of Arizona.
1999
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| Online Access: | http://hdl.handle.net/10150/626808 http://arizona.openrepository.com/arizona/handle/10150/626808 |
| Summary: | Intensive investigations of hydrologic model calibration during the last two decades
have resulted in a reasonably good understanding of the issues involved in the process of
estimating the numerous parameters employed by these codes. Nevertheless, these
classical "batch" calibration approaches require substantial amounts of data to be stable,
and the subsequent model forecasts do not usually represent the various imbedded
uncertainties. Especially in the light of thousands of uncalibrated catchments in need of
model simulations for streamflow predictions, a parameter estimation approach is
required that is able to simultaneously perform model calibration and prediction without
neglecting the substantial uncertainties in the computed forecasts. This thesis introduces
the Bayesian Recursive Estimation scheme (BaRE), a method derived from Bayesian
probability computation and adapted for the use in "on-line" hydrologic model
calibration. The results of preliminary case studies are presented to illustrate the
practicality of this simple and efficient approach. |
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