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Near real-time flood forecasting, warning and management system based on satellite radar images, hydrological and hydraulic models and in-situ data

Deliverables

We expect to improve flood forecast accuracy with better real time information of soil moisture. Which helps in better flood mitigation, flood warning and reservoir operation. At the moment the first test applications have been made with the hydrological forecasting system. Project results are expected to be usable commercially in water power production and reduction of flood damage. Socially forecasts supports land use planning but also recreation possibilities can be taken into account better at heavily regulated lakes. Dissemination of project results happens through free distribution leaflets (already published one), shared project brochures and given presentations. The developed method can be used in other river-catchments for some extent.
The FloodMan system prototype, including harmonization of flood products, automatic storage from different production systems, distributed production storage and web visualizing: Norut IT has developed the FloodMan system prototype, which makes it possible for the users to utilize flood products (e.g. EO satellite algorithms, hydrological and hydraulic models and in-situ measurements). The project partners operate the system during the project lifetime, and it will be used during the demonstration of the project results. But the system (or at least parts of the system) will also be useful for the partners after the completion of the project. At the end of the project, end users (like SYKE) can use the FloodMan system to generate products used in hydrological models, while research institutes (like Norut IT) can use the system in their daily research activities. Norut IT intends to exploit the FloodMan system (or at least parts of it) in future applications and for commercial applications. The main components developed are: Harmonization of flood products: The project has defined some common attributes for all data sets. In order for the user to save a flood product in the Production storage, the common attributes do always need to be filled in. Automatic storage from different production systems: It offers support for automatic storage of processed flood products into the Production storage. A simple web-based insertion tool will also be developed for use by partners that do not have automatic production or is not able to use the automatic storage tool. Distributed Production storage including databases and catalogue service: The Catalog and Storage Service is based on GIN (Geographic Information Network) software developed at Norut IT. The GIN software is the result of R&D work at Norut IT during the last couple of years. It has now reached a sufficient level of maturity and stability to be useful for other research projects like FloodMan. At the same time experience gained in Floodman will influence further evolution of GIN. In addition a GIN-based Web Map Service (WMS) will be developed. Web visualizing: The results will be made available as flood products for visualising in a web-based viewer. Using the web viewer the user can browse/search for flood products, and then view the resulting products as simple images (e.g. shape files) or view the attribute values of the product. The hydrological models and in-situ measurements are also saved and viewed in the same way as the other geographical results in the FloodMan system. A simple WMS client will be developed, that make selected flood products available to public users as simple images (shape files) integrated in a map.
The Synthetic Aperture Radar (SAR) detected flood product is a map showing what areas are flooded at a specific time. The flood extent is based on a SAR water area image combined with water masks showing the normal water contour. From the output of the SAR water area is a water mask, showing the water covered area in the image. The classified image is merged with a Web Map Server (WMS) background map. The map image is reached on line WMS.
We expect to improve flood forecast accuracy with better real time information of flooding. Which helps in better flood mitigation, flood warning and reservoir operation. At the moment the first test applications has been made with the hydrological forecasting system. Project results are expected to be usable commercially in water power production and reduction of flood damage. Socially forecasts increases public awareness, supports land use planning but also recreation possibilities can be taken into account better at heavily regulated lakes. Dissemination of project results happens through free distribution leaflets (already published one), shared project brochures and given presentations. The developed method can be used in other river-catchments for some extent.
We have developed a method for mapping flood extent from space born Synthetic Aperture Radar (SAR) data. The method is a fully automatic texture based maximum likelihood method. In order to design a near-real time system, we aim at an algorithm that is robust, independent of user input and with low computational demand. The method works well on open surface water. Windy and icy conditions weaken the accuracy. Likewise, the method must be initiated differently in order to correctly classify inundated forests. The method has been implemented in the production line for the FloodMan prototype. The method has been applied to ERS, RADARSAT and ENVISAT ASAR images, covering all FloodMan test areas. The proposed method is texture based, and uses the local data range, mean and variance as texture features. These texture features are computed from geocoded intensity images, and are logarithmically transformed before utilised in the detector. A simple maximum likelihood (ML) classifier that assumes Gaussian class probabilities is used to discriminate dry land from surface water. The ML classifier is trained from a pre-classification based on thresholding the log-mean image, where the threshold is computed automatically from data. Thus, we do not have to provide training data which accuracy is crucial to the performance of most supervised classifiers. For the supervised surface water detector presented in, training data had to be provided for each scene and incidence angle. In terms of misclassified pixels, the performance of the proposed method at 23º incidence angle is comparable to the performance of thresholding at 45º, for still open water bodies. Further, the proposed method is robust with respect to changes in topography and slight changes in vegetation types. Thus, we have a well performing, location independent method that is completely independent of user input.
The Soil Moisture Content (SMC), besides representing a water resource for plants, is a very important parameter of the hydrological cycle, since it is able to influence the runoff during precipitative events. In particular, the amount of water in the first 10 cm of soil, as boundary between the atmosphere and the land surface, is able to influence the mechanisms driving the runoff, the evapotranspiration, the surface heat fluxes and the biogeochemical cycles. Therefore the moisture of the first layer of soil plays an important role in flood prediction as an initial condition of the watershed system and as a soil state variable that controls the evapotranspiration fluxes. SMC is an essential input variable required by the hydrological models. With the algorithm, which is based on most recent techniques of statistical approaches and the employ of Neural Networks, we expect to obtain in near real time the moisture values of the first centimetres layer from the SAR (ENVISAT or ERS) C-band acquisitions. Respect to the traditional ground based techniques, this method allows observing large areas in a very short time and measures integrated values instead of several sparse samples. Algorithm will be tested on the images of the test sites selected for the project and can be used in other river-catchments for some extent.
More accurate water level predictions and flood maps should be available after the project on the internet. Maps and internet 'portal' supports land use planning and flood mitigation and increases public awareness. The web-based flood portal should, in near future, contain also other flood related information. At the moment we have collated flood related information and produced some accurate flood maps from the test area to support SAR-algorithm development for flood extent analysis. In near future SAR-algorithm accuracy must be analysed and cost-benefit analysis should be carried out. Project results are expected to be usable commercially in water power production and in flood mitigation. Socially maps are increasing public safety as well as supports land use planning. Dissemination happens through free distribution leaflets (already published one), shared project brochures and given presentations. The developed method can be used in other river-catchments as well. If results are tried and tested then customer trainings are possible and collaboration with project partners continues after the project. We all share also dissemination information among partners and support each others. It is also likely that our customers are interested on this and therefore would help us with the dissemination.

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