HYDRASENS: integrating radar remote sensing, hydrologic and hydraulic modelling for surface water management
(2012) , 113 pages
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- Abstract
- An important issue in water management is to adequately respond to extreme events. Floods often cause large disasters, which may include casualties, loss of properties, damage and economic losses. One way to respond to such disasters is to anticipate to its occurrence, such that people can be warned in time, enabling them to secure valuable properties and evacuate in time. Therefore, hydrologic models and/or hydraulic models can be used to predict discharge volumes, flood extents and heights. However, as models are prone to errors, external data may be used to improve the model output and increase the prediction accuracy. One source of external data is remotely sensed information. In the HYDRASENS project, new strategies were explored to integrate radar remote sensing, hydrologic, and hydraulic modelling for water management purposes through data assimilation. The project also aims at demonstrating the applicability of advanced data assimilation schemes for a set of water management problems. As soil moisture determines the partitioning of precipitation into infiltration and direct runoff, the retrieval of this variable from remote sensing received major attention within this project. At the micro-catchment scale, this is achieved using a combination of advanced hydro-geophysical techniques, focusing on Ground Penetrating Radar (GPR), which is optimized and integrated for fast and high resolution soil moisture measurements. At the catchment scale soil moisture is derived from radar remote sensing through a new retrieval scheme which omits the use of detailed measurements of surface roughness. Alternatively, possibility theory is explored for the retrieval of soil moisture from Synthetic Aperture Radar (SAR) data in order to assess the uncertainty in the soil moisture retrievals. Further, it was validated whether time series of radar images can be used to obtain soil moisture information. Besides soil moisture, another important variable for water management is the extent of floods. Remote sensing of floods was generally thought to be unfeasible within urbanized areas and beneath a vegetation cover. Through fusing of SAR data and high accuracy digital elevation models a solution for these problems was found. Furthermore, the uncertainty of the flood maps due to speckle in the radar image was studied. In order to better understand the relationship between ground- and satellite-based radar data, the link between nearby (GPR) and satellite-based radar (SAR) remote sensing was partly explored. Scaling properties of soil moisture were studied and the soil moisture values derived from both these sensor types at different observation scales were examined and compared. The remote sensing and modelling aspects of the proposal are then integrated in a number of hydrologic modelling and data assimilation studies. At the micro-catchment scale, the impact of the small-scale variability of GPR-derived soil moisture on hydrologically modelled runoff was quantified. At the catchment scale, soil moisture information, obtained through the newly developed retrieval technique, the possibilistic retrieval algorithm and change detection techniques, are incorporated in the hydrologic and hydraulic model through adequate data assimilation algorithms, aiming at improved predictions of soil moisture fields and discharge. Methodologies to assimilate flood extents and inundation depths into hydraulic models have been developed. Finally, a coupled hydrologic/hydraulic model was developed and different data assimilation systems were implemented for this coupled model. Finally, a number of scenarios relevant to water management were identified and were analysed based on state-of-the-art methodologies developed within this project.
- Affiliations
Laboratory of Hydrology and Water Management Ghent University, Belgium Department of Mathematical Modelling, Statistics and Bioinformatics Ghent University, Belgium Centre de recherche public Gabriel Lippmann Belvaux, Luxembourg
Citations
Verhoest, N., Vanclooster, M., De Baets, B., Pauwels, V., & Hoffmann, L. (2012). HYDRASENS: integrating radar remote sensing, hydrologic and hydraulic modelling for surface water management. https://hdl.handle.net/2078.5/159613