 |
| Titel |
Bayesian estimation of parameters in a regional hydrological model |
| VerfasserIn |
K. Engeland, L. Gottschalk |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 6, no. 5 ; Nr. 6, no. 5, S.883-898 |
| Datensatznummer |
250003772
|
| Publikation (Nr.) |
 copernicus.org/hess-6-883-2002.pdf |
|
|
|
|
|
| Zusammenfassung |
| This study evaluates the
applicability of the distributed, process-oriented Ecomag model for prediction
of daily streamflow in ungauged basins. The Ecomag model is applied as a
regional model to nine catchments in the NOPEX area, using Bayesian statistics
to estimate the posterior distribution of the model parameters conditioned on
the observed streamflow. The distribution is calculated by Markov Chain Monte
Carlo (MCMC) analysis. The Bayesian method requires formulation of a likelihood
function for the parameters and three alternative formulations are used. The
first is a subjectively chosen objective function that describes the goodness of
fit between the simulated and observed streamflow, as defined in the GLUE
framework. The second and third formulations are more statistically correct
likelihood models that describe the simulation errors. The full statistical
likelihood model describes the simulation errors as an AR(1) process, whereas
the simple model excludes the auto-regressive part. The statistical parameters
depend on the catchments and the hydrological processes and the statistical and
the hydrological parameters are estimated simultaneously. The results show that
the simple likelihood model gives the most robust parameter estimates. The
simulation error may be explained to a large extent by the catchment
characteristics and climatic conditions, so it is possible to transfer knowledge
about them to ungauged catchments. The statistical models for the simulation
errors indicate that structural errors in the model are more important than
parameter uncertainties.
Keywords: regional hydrological model, model
uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis |
| |
|
| Teil von |
|
|
|
|
|
|
|
|