Krippen, ein Stadtteil von Bad Schandau, war im August 2002 zur Hälfte überflutet. Nach der Flut begann, unter wissenschaftlicher Begleitung durch das LfULG, ein Planungsprozess, der für das Dorf Krippen in konkrete Projekte zum vorbeugenden Hochwasserschutz mündete: Untersuchungen zur Wasserrückhaltung und Erosionsvermeidung auf landwirtschaftlichen Flächen oberhalb der Gemeinde Krippen (Modellierung des Oberflächenwasserabflusses mit Abfluss- und Erosionsprognosen); Überarbeitung des Hochwasseralarm- und Einsatzplans; Verminderung und Rückbau der Versiegelung von Flächen; Hochwasserschutz bei Neubau und die Sanierung von Gebäuden und Anlagen (Spiel- und Sportplatz, Dorfgemeinschaftshaus, Abwasserpumpwerk, Bau des Flutgrabens); Umweltbildungsmaßnahmen für Einwohner und Touristen (u. a. Hochwassertour - eine Wanderung zum Thema: Leben am Fluss); Förderung des sanften Tourismus (FahrradBus, Radtourismus, Terrainkurwege).

Der bisher noch wenig untersuchte Wellenaufgang im Übergangsbereich zwischen Brandung und Reflexion wurde experimentell untersucht. Darauf aufbauend wird ein geschlossener Berechnungsansatz für die Auflaufhöhe unregelmäßiger Wellen vorgeschlagen, der für brechende und nichtbrechende Wellen anwendbar ist.

Model-based reservoir management systems are indispensable for determining an optimal water resources management in river basins with multipurpose reservoirs. A recently developed management system will be described, using evolutionary algorithms to optimize both event-based and long-term operation of a reservoir system concerning multiple objectives with different units of measurement (Money, Dimensions, Ecology). The result are sets of so-called Pareto optimal solutions which represent the most useful compromises and can serve as a transparent information base for decision-making. In order to improve the ecological performance of multipurpose reservoirs, a dynamic operating scheme is implemented, which ensures that reservoir releases correspond to natural flow variability as far as possible. In addition water quality problems during the flood discharge and the release from selected layers of the water body will be discussed in brief.

A detailed and comprehensive description of the state of the art in the field of flood forecasting opens this work. Advantages and shortcomings of currently available methods are identified and discussed. Amongst others, one important aspect considers the most exigent weak point of today’s forecasting systems: The representation of all the fundamentally different event specific patterns of flood formation with one single set of model parameters. The study exemplarily proposes an alternative for overcoming this restriction by taking into account the different process characteristics of flood events via a dynamic parameterisation strategy. Other fundamental shortcomings in current approaches especially restrict the potential for real time flash flood forecasting, namely the considerable computational requirements together with the rather cumbersome operation of reliable physically based hydrologic models. The new PAI-OFF methodology (Process Modelling and Artificial Intelligence for Online Flood Forecasting) considers these problems and offers a way out of the general dilemma. It combines the reliability and predictive power of physically based, hydrologic models with the operational advantages of artificial intelligence. These operational advantages feature extremely low computation times, absolute robustness and straightforward operation. Such qualities easily allow for predicting flash floods in small catchments taking into account precipitation forecasts, whilst extremely basic computational requirements open the way for online Monte Carlo analysis of the forecast uncertainty. The study encompasses a detailed analysis of hydrological modeling and a problem specific artificial intelligence approach in the form of artificial neural networks, which build the PAI-OFF methodology. Herein, the synthesis of process modelling and artificial neural networks is achieved by a special training procedure. It optimizes the network according to the patterns of possible catchment reaction to rainstorms. This information is provided by means of a physically based catchment model, thus freeing the artificial neural network from its constriction to the range of observed data – the classical reason for unsatisfactory predictive power of netbased approaches. Instead, the PAI-OFF-net learns to portray the dominant process controls of flood formation in the considered catchment, allowing for a reliable predictive performance. The work ends with an exemplary forecasting of the 2002 flood in a 1700 km² East German watershed.

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