Integrating Radar Remote Sensing, Hydrologic and Hydraulic Modelling for Surface Water Management - HYDRASENS

Coordinating Institution: CRP Gabriel Lippmann
Contracting Partner(s): University of Ghent (B) , University of Louvain (B)
Other Partner(s): University of Bristol (UK)
From: 01/12/2006
To: 30/06/2012
Budget: 216,185.00€
Contact(s): Matgen Patrick

Summary

The HYDRASENS project aims at studying the integration of radar remote sensing into coupled hydrologic and hydraulic models for surface water management. During this 5-year project, five teams from Belgium and Luxembourg closely collaborate on different issues concerning the retrieval of soil moisture and flood extents from radar imagery, scaling of soil moisture from Ground Penetrating Radar (GPR) measurements and radar data, and the assimilation of these data into hydrologic and hydraulic models. During the third year, the majority of field work planned within the project has been performed. CLM2.0, a hydrologic model was calibrated for the Alzette catchment and coupled to a 1-D hydraulic model. In order to assimilate data in this coupled model, a particle filter was implemented.

The data assimilation technique developed in this study is based on the Particle Filter (PF), an ensemble filtering method that has its origin in Bayesian estimation. Unlike the widely used Ensemble Kalman Filter (EnKF) which simplifies the recursive estimation by assuming a Gaussian distribution for state variables, the PF relaxes the need for restrictive assumptions regarding the forms of the probability density functions; that is, PF can easily manage the propagation of a non-Gaussian distribution through nonlinear hydrologic and hydrodynamic models. The PF-based data assimilation scheme was applied on the coupled hydrologic-hydraulic model.

Our study demonstrates that the information contained in radar flood images can lead to improved flood inundation modelling. The experiments conducted with synthetically generated observations, integrated with a 1D hydrodynamic model, showed that the PF permits to recover water depth from a corrupted hydrodynamic model by assimilating synthetic observations that are realistic in terms of accuracy for remote sensing-derived water levels. By selecting the most likely model runs, the PF closes the overall water balance. It thus allows inferring input data (i.e. whole-catchment precipitation) and model parameters that gave the most likely simulations given the observations. The approach may indeed be viewed as a way to diagnose the functioning of hydrologic systems.

Our results lead to the conclusion that with currently available remote sensing-derived water level products the proposed assimilation scheme shows the highest utility in ungauged catchments requiring the use of coupled hydrologic-hydraulic models. The same methodology will be applied to assimilate remote sensing-derived soil moisture estimates into the hydrologic-hydraulic model.

Programme:

  • STEREO II, Recherche en Observation de la Terre

Foreign Funding Agency:

  • Service Fédéral Public de programmation Scientifique (Belgium)

Refereed Scientific Publications:

  • Montanari M., Hostache R., Matgen P., Schumann G., Pfister L., Hoffmann L., Calibration and sequential updating of a coupled hydrologic-hydraulic model using remote sensing-derived water stages, Hydrol. Earth Syst. Sci., 13, 367-380, 2009.
  • Hostache R., Matgen P., Schumann G., Puech C., Hoffmann L., Pfister L., Water level estimation and reduction of hydraulic model calibration uncertainties using satellite SAR images of floods, IEEE T. Geosci. Remote, 47, 431-441, 2009. Schumann G., Bates P.D., Horritt M.S., Matgen P., and Pappenberger F., Progress in integration of remote sensing derived flood extent and stage data and hydraulic models, Review of Geophysics, 47, RG4001, 2009. Neal J.,
  • Schumann G., Bates P.D., Buytaert W., Matgen P., and Pappenberger F., A data assimilation approach to discharge from space, Hydrological Processes, 23, 3641-3649, 2009.
Other Publications:
  • Heitz S., Matgen P., Lievens H., Verhoest N. E. C., Hissler C., Pfister L., Hoffmann L., Evaluation des capteurs micro-ondes actifs et passifs pour la caractérisation de l’humidité du sol, Proceedings de la 193ème session du Comité Scientifique et Techniquede la Société Hydrotechnique de France, Toulouse, France, March 31 – April 1 2009.
Project Website:

Figure: TerraSAR-X image : 17 March 2008: Flood Event of the Alzette River. In yellow the GPS survey that was performed on the same day.