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  Probabilistic flood forecasting for a mountainous headwater catchment using a nonparametric stochastic dynamic approach

Costa, A. C., Bronstert, A., Kneis, D. (2012): Probabilistic flood forecasting for a mountainous headwater catchment using a nonparametric stochastic dynamic approach. - Hydrological Sciences Journal - Journal des Sciences Hydrologiques, 57, 1, 10-25.
https://doi.org/10.1080/02626667.2011.637043

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Costa, Alexandre Cunha1, Autor
Bronstert, Axel1, Autor
Kneis, David1, Autor
Affiliations:
1RIMAX Publications, RIMAX, Deutsches GeoForschungsZentrum, Potsdam, ou_368066              

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Schlagwörter: OPAQUE, RIMAX
 Zusammenfassung: Hydrological models are commonly used to perform real-time runoff forecasting for flood warning. Their application requires catchment characteristics and precipitation series that are not always available. An alternative approach is nonparametric modelling based only on runoff series. However, the following questions arise: Can nonparametric models show reliable forecasting? Can they perform as reliably as hydrological models? We performed probabilistic forecasting one, two and three hours ahead for a runoff series, with the aim of ascribing a probability density function to predicted discharge using time series analysis based on stochastic dynamics theory. The derived dynamic terms were compared to a hydrological model, LARSIM. Our procedure was able to forecast within 95% confidence interval 1-, 2- and 3-h ahead discharge probability functions with about 1.40 m3/s of range and relative errors (%) in the range [–30; 30]. The LARSIM model and the best nonparametric approaches gave similar results, but the range of relative errors was larger for the nonparametric approaches.

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 Datum: 2012
 Publikationsstatus: Final veröffentlicht
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Titel: Hydrological Sciences Journal - Journal des Sciences Hydrologiques
Genre der Quelle: Zeitschrift, SCI, Scopus
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Seiten: - Band / Heft: 57 (1) Artikelnummer: - Start- / Endseite: 10 - 25 Identifikator: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals206