 |
| Titel |
Ensemble prediction of floods – catchment non-linearity and forecast probabilities |
| VerfasserIn |
J. Komma, C. Reszler, G. Blöschl, T. Haiden |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1561-8633
|
| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 7, no. 4 ; Nr. 7, no. 4 (2007-07-12), S.431-444 |
| Datensatznummer |
250004653
|
| Publikation (Nr.) |
 copernicus.org/nhess-7-431-2007.pdf |
|
|
|
|
|
| Zusammenfassung |
| Quantifying the uncertainty of flood forecasts by ensemble methods is
becoming increasingly important for operational purposes. The aim of this
paper is to examine how the ensemble distribution of precipitation forecasts
propagates in the catchment system, and to interpret the flood forecast
probabilities relative to the forecast errors. We use the 622 km2 Kamp
catchment in Austria as an example where a comprehensive data set, including
a 500 yr and a 1000 yr flood, is available. A spatially-distributed
continuous rainfall-runoff model is used along with ensemble and
deterministic precipitation forecasts that combine rain gauge data, radar
data and the forecast fields of the ALADIN and ECMWF numerical weather
prediction models. The analyses indicate that, for long lead times, the
variability of the precipitation ensemble is amplified as it propagates
through the catchment system as a result of non-linear catchment response.
In contrast, for lead times shorter than the catchment lag time (e.g. 12 h and
less), the variability of the precipitation ensemble is decreased
as the forecasts are mainly controlled by observed upstream runoff and
observed precipitation. Assuming that all ensemble members are equally
likely, the statistical analyses for five flood events at the Kamp showed
that the ensemble spread of the flood forecasts is always narrower than the
distribution of the forecast errors. This is because the ensemble forecasts
focus on the uncertainty in forecast precipitation as the dominant source of
uncertainty, and other sources of uncertainty are not accounted for.
However, a number of analyses, including Relative Operating Characteristic
diagrams, indicate that the ensemble spread is a useful indicator to assess
potential forecast errors for lead times larger than 12 h. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|