Ensemble-based algorithm for error reduction in hydraulics in the context of flood forecasting

Sébastien Barthélémy; Sophie Ricci; Etienne Le Pape; Mélanie Rochoux; Olivier Thual; Nicole Goutal; Johan Habert; Andrea Piacentini; Gabriel Jonville; Fabrice Zaoui; Philippe Gouin

doi:10.1051/e3sconf/20160718022

All issues

Volume 7 (2016)

E3S Web Conf., 7 (2016) 18022

References

Open Access

Issue		E3S Web Conf. Volume 7, 2016 3^rd European Conference on Flood Risk Management (FLOODrisk 2016)


Article Number		18022
Number of page(s)		9
Section		Forecasting and warning
DOI		https://doi.org/10.1051/e3sconf/20160718022
Published online		20 October 2016

Ricci S., Piacentini A., Thual O., Le Pape E. and Jonville G., (2011). Correction of upstream flow and hydraulics state with a data assimilation in the context of flood forecasting. Hydrol. Earth Syst. Sci 15, 1–21. [Google Scholar]
Habert J., Ricci S., Thual O., Le Pape E., Piacentini A., Goutal N., Jonville G., Rochoux M., (2016). Reduction of the uncertainties in the water level-discharge relation of a 1D hydraulic model in the context of operational flood forecasting. Journal of Hydrology, 532, 52–64. [CrossRef] [Google Scholar]
Barthélémy S., Ph D (2015) Assimilation de données ensembliste et couplage de modèles hydrauliques 1D-2D pour la prévision des crues en temps réel. Application au réseau hydraulique Adour maritime, Université de Toulouse, France. [Google Scholar]
Goutal N. and Maurel F., (2002). A finite volume solver for 1D shallow water equations applied to an actual river. Int. J. Numer. Meth. Fluids, 38 (2), 1–19. [Google Scholar]
Beven K., Freer J., 2001. A dynamic topmodel. Hydrol. Process., 15, 1993–2011. [Google Scholar]
Boyaval S. (2012). A fast Monte-Carlo method with a Reduced Basis of Control Variates applied to Uncertainty Propagation and Bayesian Estimation. CMAME, 241–244. [Google Scholar]
Wiener P. (1938). The Homogeneous Chaos. American Journal of Mathematics. Vol. 60(4), 897–936. [Google Scholar]
Ghanem R. and Spanos P. (1991). Stochastic finite elements, A spectral approach, Dover. [Google Scholar]
Evensen G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 10143–10162. [Google Scholar]
Burgers G., Jan Van Leeuwen P. and Evensen G. (1998). Analysis Scheme in the Ensemble Kalman Filter. Monthly Weather Review, 126(6), 1719–1724. [CrossRef] [Google Scholar]
Anderson J. L. and Anderson S. L. (1999). A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts, Monthly Weather Review, 127, 2741–2758. [Google Scholar]
Anderson J. L. (2007). An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus, Series A, Dynamic Meteorology and Oceanography, 59, 210–224. [CrossRef] [Google Scholar]
Desroziers G., Berre L., Chaonik B. and Poli P. (2005). Diagnosis of observation, background and analysiserror statistics in observation space. Quarterly Journal of the Royal Meteorological Society, 131(2005), 3385–3396. [CrossRef] [Google Scholar]
Buis S., Piacentini A. and Déclat D. (2006). PALM: a computational framework for assembling highperformance computing applications. Concurrency and Computation: Practice and experience, 18(2), 231–245. [CrossRef] [Google Scholar]
Li H., Kalnay E. and Miyoshi T. (2009). Simultaneous estimation of covariance inflation and observation errors within an Ensemble Kalman Filter. Quarterly Journal of the Royal Meteorological Society, 135(February), 523–533. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] Ricci S., Piacentini A., Thual O., Le Pape E. and Jonville G., (2011). Correction of upstream flow and hydraulics state with a data assimilation in the context of flood forecasting. Hydrol. Earth Syst. Sci 15, 1–21. [Google Scholar]

[2] Habert J., Ricci S., Thual O., Le Pape E., Piacentini A., Goutal N., Jonville G., Rochoux M., (2016). Reduction of the uncertainties in the water level-discharge relation of a 1D hydraulic model in the context of operational flood forecasting. Journal of Hydrology, 532, 52–64. [CrossRef] [Google Scholar]

[3] Barthélémy S., Ph D (2015) Assimilation de données ensembliste et couplage de modèles hydrauliques 1D-2D pour la prévision des crues en temps réel. Application au réseau hydraulique Adour maritime, Université de Toulouse, France. [Google Scholar]

[4] Goutal N. and Maurel F., (2002). A finite volume solver for 1D shallow water equations applied to an actual river. Int. J. Numer. Meth. Fluids, 38 (2), 1–19. [Google Scholar]

[5] Beven K., Freer J., 2001. A dynamic topmodel. Hydrol. Process., 15, 1993–2011. [Google Scholar]

[6] Boyaval S. (2012). A fast Monte-Carlo method with a Reduced Basis of Control Variates applied to Uncertainty Propagation and Bayesian Estimation. CMAME, 241–244. [Google Scholar]

[7] Wiener P. (1938). The Homogeneous Chaos. American Journal of Mathematics. Vol. 60(4), 897–936. [Google Scholar]

[8] Ghanem R. and Spanos P. (1991). Stochastic finite elements, A spectral approach, Dover. [Google Scholar]

[9] Evensen G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 10143–10162. [Google Scholar]

[10] Burgers G., Jan Van Leeuwen P. and Evensen G. (1998). Analysis Scheme in the Ensemble Kalman Filter. Monthly Weather Review, 126(6), 1719–1724. [CrossRef] [Google Scholar]

[11] Anderson J. L. and Anderson S. L. (1999). A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts, Monthly Weather Review, 127, 2741–2758. [Google Scholar]

[12] Anderson J. L. (2007). An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus, Series A, Dynamic Meteorology and Oceanography, 59, 210–224. [CrossRef] [Google Scholar]

[13] Desroziers G., Berre L., Chaonik B. and Poli P. (2005). Diagnosis of observation, background and analysiserror statistics in observation space. Quarterly Journal of the Royal Meteorological Society, 131(2005), 3385–3396. [CrossRef] [Google Scholar]

[14] Buis S., Piacentini A. and Déclat D. (2006). PALM: a computational framework for assembling highperformance computing applications. Concurrency and Computation: Practice and experience, 18(2), 231–245. [CrossRef] [Google Scholar]

[15] Li H., Kalnay E. and Miyoshi T. (2009). Simultaneous estimation of covariance inflation and observation errors within an Ensemble Kalman Filter. Quarterly Journal of the Royal Meteorological Society, 135(February), 523–533. [Google Scholar]