Blazkova, S. and Beven, K.
(2000)
*Prediction limits of flood frequency curves on catchments within the Elbe River Basin.*
PIK Report, 65 (2).
pp. 570-578.
ISSN 1436-0179

## Abstract

There is a large uncertainty in high return period flood frequency estimates even on gauged catchments with long series of annual maxima. On catchments with a short series or on ungauged catchments the situation is much worse. One way that might improve prediction is the use of models to simulate continuous long series of data. The Generalized Likelihood Uncertainty Estimation (GLUE) methodology (BEVEN, 1993) makes it possible to estimate the uncertainty as computed prediction limits. GLUE does not assume that an optimum set of model parameters exists. Many different parameter sets will generally produce acceptable simulations while the ranking of the parameter sets according to some criterion of goodness of fit (likelihood) would be different on various portions of the data for the same catchment. The great advantage of the GLUE procedure is that various kinds of data can be used for computation of likelihood of the simulations, i.e. to decide if a simulation (a set of parameters) is behavioural or not. For the flood frequency curve it could be quantiles of observed flood frequency curve for short return periods (1 to 10 years), the flood frequency curve found by regionalization for ungauged catchments, the flow duration curve, the frequency curve of snow water equivalent. It can also be measured rainfall runoff data (as shown in CAMERON et al., 1999) or results of the mapping of saturated areas. On a large catchment likelihood measures can be computed for subcatchments where data for conditioning are available. Goodness of fit on different kinds of data can be combined to give a resultant likelihood computed e.g. using Bayes equation or fuzzy combination rules. The contribution shows the use of various kinds of conditioning data on the examples of catchments within the Elbe River Basin. The interpretation and use of the prediction bounds in a Decision Support System is also discussed.

Item Type: | Journal Article |
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Journal or Publication Title: | PIK Report |

Uncontrolled Keywords: | /dk/atira/pure/subjectarea/asjc/1900 |

Subjects: | |

ID Code: | 129649 |

Deposited By: | ep_importer_pure |

Deposited On: | 11 Dec 2018 15:18 |

Refereed?: | Yes |

Published?: | Published |

Last Modified: | 01 Jan 2020 11:43 |

URI: | https://eprints.lancs.ac.uk/id/eprint/129649 |

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