TY  - JOUR
A1  - Costa, Alexandre Cunha
A1  - Bronstert, Axel
A1  - Kneis, David
T1  - Probabilistic flood forecasting for a mountainous headwater catchment using a nonparametric stochastic dynamic approach
T2  - Hydrological sciences journal = Journal des sciences hydrologiques
N2  - 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 m(3)/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.
KW  - streamflow probabilistic forecasting
KW  - time series analysis
KW  - stochastic dynamical systems
KW  - parametric and nonparametric comparison
Y1  - 2012
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/36338
SN  - 0262-6667
VL  - 57
IS  - 1
SP  - 10
EP  - 25
PB  - Routledge, Taylor & Francis Group
CY  - Abingdon
ER  -