HR: 11:30h
AN: H32C-05    [Abstracts]
TI: Application of a Multi-Scheme Ensemble Prediction System and an Ensemble Classification Method to Streamflow Forecasting
AU: * Pahlow, M
EM: markus.pahlow@rub.de
AF: Ruhr-University Bochum, Universitaetsstr. 150, Bochum, 44801, Germany
AU: Moehrlen, C
EM: com@weprog.com
AF: WEPROG, Aahaven 5, Ebberup, 5631, Denmark
AU: Joergensen, J
EM: juj@weprog.com
AF: WEPROG, Aahaven 5, Ebberup, 5631, Denmark
AU: Hundecha, Y
EM: yeshewatesfa.hundecha@rub.de
AF: Ruhr-University Bochum, Universitaetsstr. 150, Bochum, 44801, Germany
AB: Europe has experienced a number of unusually long-lasting and intense rainfall events in the last decade, resulting in severe floods in most European countries. Ensemble forecasts emerged as a valuable resource to provide decision makers in case of emergency with adequate information to protect downstream areas. However, forecasts should not only provide a best guess of the state of the stream network, but also an estimate of the range of possible outcomes. Ensemble forecast techniques are a suitable tool to obtain the required information. Furthermore a wide range of uncertainty that may impact hydrological forecasts can be accounted for using an ensemble of forecasts. The forecasting system used in this study is based on a multi-scheme ensemble prediction method and forecasts the meteorological uncertainty on synoptic scales as well as the resulting forecast error in weather derived products. Statistical methods are used to directly transform raw weather output to derived products and thereby utilize the statistical capabilities of each ensemble forecast. The forecasting system MS-EPS (Multi-Scheme Ensemble Prediction System) used in this study is a limited area ensemble prediction system using 75 different numerical weather prediction (NWP) model parameterisations. These individual ‘schemes' each differ in their formulation of the fast meteorological processes. The MS-EPS forecasts are used as input for a hydrological model (HBV) to generate an ensemble of streamflow forecasts. Determining the most probable forecast from an ensemble of forecasts requires suitable statistical tools. They must enable a forecaster to interpret the model output, to condense the information and to provide the desired product. For this purpose, a probabilistic multi-trend filter (pmt-filter) for statistical post-processing of the hydrological ensemble forecasts is used in this study. An application of the forecasting system is shown for a watershed located in the eastern part of Germany for the severe flooding of the river Elbe in August of 2002.
DE: 1821 Floods
DE: 1847 Modeling
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting