EFFICIENT AND USER-FRIENDLY FLASH FLOOD FORECASTING WITH UNCERTAINTY
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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

  • Forecasting the probability of critical flood discharge and water levelstogether with more meaningful information for the public as e.g.inundated areas and probable flooding at well known landmarks

  • The scope of the forecast narrows with decreasing forecasting time!

  • Continuous information on the development of flood magnitude and probability similar to radio traffic service

  • Development and publication of flood maps with probabilities of inundation of endangered areas

  • Approximate forecast periods together with the expected ranges of uncertainty and water level-dependent emergency plans for all critical locations

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

Predicted flow

REFERENCES:

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.


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