Appel, F.

Bach, H.

Badde, U.

Loew, A.

Ludwig, R.

Mauser, W.

Schulz, W.

English

MPG.PuRe

Data assimilation of remotely sensed information in operational flood forecast in Southwest Germany

Geophysical Research Abstracts, Vol. 7, 04724, 2005SRef-ID: 1607-7962/gra/EGU05-A-04724© European Geosciences Union 2005Data assimilation of remotely sensed information inoperational flood forecast in Southwest GermanyH. Bach (1), F. Appel (1), W. Schulz (2), U. Badde (2), R. Ludwig (3), A. Löw (3),W. Mauser (3)(1) VISTA Geowissenschaftliche Fernerkundung GmbH, Anton-Ferstl-Str.11, D-82234Wessling, Germany (bach@vista.geo.de / phone: ++49 8153 952203), (2)Hochwasservorhersagezentrale Baden-Württemberg, Landesanstalt für Umweltschutz,Karlsruhe, Germany, (3) Department of Earth and Environmental Sciences, LudwigMaximilians Universität München, GermanyFloods are the most affecting natural disasters in Europe. Flood damage can be re-duced if the runoff is predicted with sufficient accuracy and well enough in time sothat technical and precaution measures can be applied. Hydrological models are thetools to simulate runoff and allow flood forecast. Since models are only as good astheir input data, one possibility to improve flood forecast is to provide a better spatialcharacterisation of the watershed and to observe its change with time. Remote sens-ing methods allow the observation of some of these hydrological relevant watershedproperties.Therefore a project called “InFerno” has been originated to evaluate and demonstratethe capabilities of remote sensing with respect to operational flood forecasting. In-Ferno (Integration of remote sensing data in operational water balance and floodforecast modelling) is funded by the DLR and coordinated by the operational floodforecast centre of “Baden-Württemberg” (Southwest Germany), responsible for theforecast of the Upper Rhine and Neckar. The project setup is breaking new groundin several ways, i.e. by operationally assimilating remotely sensed data and retrievedinformation into existing information systems and hydrologic models. The major chal-lenges of the methodology are the implementation of algorithms developed for a mul-tisensoral synergy and the creation of robust, operationally applicable remote sensingproducts. On the other hand, the operational flood forecast must be adapted to makefull use of the new data sources.Thoroughly processed remote sensing data (optical and microwave) serve as sourceof information to deliver spatially distributed parameters of snow properties and soilmoisture. These highly dynamic land surface characteristics are crucial for the predic-tion and modeling of floods, since they largely determine the rapidly changing reten-tion and runoff producing capacities of a watershed. Especially in the starting phaseof a flood this information is valuable to determine the initial conditions of the model.The flood forecast centres integrate the remote sensing based maps on soil moistureand snow properties in the continuously operated water balance and flood forecastmodel LARSIM (Large Area Runoff Simulation Model). Results will be presented forthe assimilation of snow cover information derived from optical and microwave satel-lite data. The snow line determined from this synergetic approach is the most valuablerelated information for the model. Surface soil moisture information and its temporaldynamics retrieved from SAR are used for a parameterisation of the soil properties inthe hydrological model.

Data assimilation of remotely sensed information in operational flood forecast in Southwest Germany

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