EFFICIENT AND USER-FRIENDLY FLASH FLOOD FORECASTING WITH UNCERTAINTY FOR FAST RESPONDING CATCHMENTS Gerd H. Schmitz, Johannes Cullmann, Wilfried Görner, Andy Philipp, Ronny Peters Institute of Hydrology and Meteorology, Dresden University of Technology.
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EFFICIENT AND USER-FRIENDLY FLASH FLOOD FORECASTING WITH UNCERTAINTYFOR FAST RESPONDING CATCHMENTS
Gerd H. Schmitz, Johannes Cullmann, Wilfried Görner, Andy Philipp, Ronny Peters
Institute of Hydrology and Meteorology, Dresden University of Technology
The curse of flash flood prone catchments: A serious treat to society and a challenge for early warning systems
Problems of flash flood forecasting in fast responding catchments
Numerical models involvemuch computational effort
However: Real time consideration ofprobabilities requires Monte Carlo analysis
Required: Robust and fast but nonetheless accurate models
Development of a reliablehydrological/hydraulic model for the considered catchment as a preparatory step
Replacing the models by adequate Artificial Neural Networks
Watershed specific methodology includingquantification of the uncertainty of the forecast
Unreliability of precipitation forecast
However: Precipitation forecast is assumed to be correct
Required: Quantification of the forecast uncertainty (model+data)
Real time updating of the forecast uncertainty!
Motivation: Weißeritz Flash Flood 2002
Left: Dresden Central Train Station flooded by Weißeritz River
Right: Devastated Buildings along the Weißeritz River near the City of Freital
Methodology PAI-OFF – Based on Artificial Neural Networks (ANN)
Taking advantage of the speed and accuracy of Artificial Neural Networks (ANN) opens new perspectives in hydrological forecasting:
The final goal is to enable users to asses the propagation of forecasting uncertainty online!
PAI-OFF offers this possibility on the basis of a synthesis of ANN and physically based process models of the considered catchmnent.
To this end observed data is insufficient for training ANN based systems. The solution proposed here is to train the ANN with a data base that is generated by means of reliable hydrological/hydraulic modeling as shown in the scheme to the right.
Benefits: Precautionary measures for high risk flood areas
PAI-OFF output hydrographs forKriebstein gauge / catchment of theFreiberger Mule
Forecast of a floodwave with 24, 38 and 48hours forecasting time
The black, green and red bars indicate thestarting time of the forecast
The computational time for every forecastconstitutes about half a second.
PAI-OFF ensemble forecast
80 realisations of the second rainfall peak of the
2002 flood event span the quantiles of the
G. H. Schmitz, J. Cullmann, W. Görner. F. Lennartz, W. Dröge (2005): PAI-OFF - Eine neue Strategie zur Hochwasservorhersage in schnellreagierenden Einzugsgebieten. H&W H5, 226-234.
J. Cullmann, G.H. Schmitz and W. Görner. 2006, PAI-OFF: a new strategy for online flood forecasting in mountainous catchments. IAHS Red Book Series 303.
J. Cullmann (2007): Online flood forecasting in fast responding catchments on the basis of a synthesis of artificial neural networks and process models. Dissertation TU Dresden .
J. Cullmann, R. Peters, V. Mishra (2006): Flow analysis with WaSiM-ETH – Model parameter sensitivity at different scales. Advances in Geosciences 9, 73-77.
G. H. Schmitz, J. Cullmann, R. Peters, W. Görner, F. Lennartz (2006): PAI-OFF: A new way to online flood forecasting in flash flood prone catchments. In review at Water Resources Research.