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Assimilating SAR-derived water level data into a hydraulic model: a case study

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Abstract
Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction in the uncertainty at the analysis step. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data.
Keywords
STATISTICS, PARAMETERS, CALIBRATION, ENSEMBLE KALMAN FILTER, SEQUENTIAL DATA ASSIMILATION, PARTICLE FILTER, MONTE-CARLO, UNCERTAINTY, FLOOD

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Chicago
Giustarini, Laura, Patrick Matgen, Renaud Hostache, Mara Montanari, Douglas Antonio Plaza Guingla, Valentijn Pauwels, Gabriëlle De Lannoy, et al. 2011. “Assimilating SAR-derived Water Level Data into a Hydraulic Model: a Case Study.” Hydrology and Earth System Sciences 15 (7): 2349–2365.
APA
Giustarini, L., Matgen, P., Hostache, R., Montanari, M., Plaza Guingla, D. A., Pauwels, V., De Lannoy, G., et al. (2011). Assimilating SAR-derived water level data into a hydraulic model: a case study. HYDROLOGY AND EARTH SYSTEM SCIENCES, 15(7), 2349–2365.
Vancouver
1.
Giustarini L, Matgen P, Hostache R, Montanari M, Plaza Guingla DA, Pauwels V, et al. Assimilating SAR-derived water level data into a hydraulic model: a case study. HYDROLOGY AND EARTH SYSTEM SCIENCES. 2011;15(7):2349–65.
MLA
Giustarini, Laura, Patrick Matgen, Renaud Hostache, et al. “Assimilating SAR-derived Water Level Data into a Hydraulic Model: a Case Study.” HYDROLOGY AND EARTH SYSTEM SCIENCES 15.7 (2011): 2349–2365. Print.
@article{1860806,
  abstract     = {Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction in the uncertainty at the analysis step. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data.},
  author       = {Giustarini, Laura and Matgen, Patrick and Hostache, Renaud and Montanari, Mara and Plaza Guingla, Douglas Antonio and Pauwels, Valentijn and De Lannoy, Gabri{\"e}lle and De Keyser, Robain and Pfister, L and Hoffmann, L and Savenije, HHG},
  issn         = {1027-5606},
  journal      = {HYDROLOGY AND EARTH SYSTEM SCIENCES},
  keyword      = {STATISTICS,PARAMETERS,CALIBRATION,ENSEMBLE KALMAN FILTER,SEQUENTIAL DATA ASSIMILATION,PARTICLE FILTER,MONTE-CARLO,UNCERTAINTY,FLOOD},
  language     = {eng},
  number       = {7},
  pages        = {2349--2365},
  title        = {Assimilating SAR-derived water level data into a hydraulic model: a case study},
  url          = {http://dx.doi.org/10.5194/hess-15-2349-2011},
  volume       = {15},
  year         = {2011},
}

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