MAIN FIELDS OF EXPERTISE

• Applied and Computational Mathematics
• Hydraulics & Hydrology

SUMMARY

I. Gejadze is a research professor (Directeur des Recherche) in aplied and computational mathematics, currently working on river hydraulics and hydrology applications. He is a member of  the GHOSTE team. His activity is largely focused on development advanced data assimilation methods for continental water applications, particularly adapted for processing the remote sensing data. This subject had been inspired by the ongoing design of a new generation of satellites, capable of observing the continental water surfaces with a previously unprecedented level of precision and spatial coverage. The first mission called SWOT (Surface Water & Ocean Topography, joint project of NASA ans CNES) is scheduled for launch in 2021, with another ESA mission to follow. An anticipated practical outcome of the  GHOSTE research team is the global scale river discharge estimation system capable of processing the remote sensing data, including SWAT data and data from some other existing and past space missions. Another subjects include development of advanced calibration and forecasting algorithms for classical hydrology and water quality models.

SUPERVISION OF THESIS

• Thèse 1

M. Jay-Allemand, P. Javelle, I. Gejadze, P. Arnaud, P.-O. Malaterre, J.-A. Fine, D. Organde. On the potential of variational calibration for a fully distributed hydrological model: application to a Mediterranean catchment. Hydrology and Earth System Sciences (HESS), EGU, 2020, 24(11), pp.5519-5538, DOI: 10.5194/hess-24-5519-2020, hal-03085942, version 1.

I. Gejadze, P.-O. Malaterre, V. Shutyaev. On the use of derivatives in the polynomial chaos based global sensitivity and uncertainty analysis applied to the distributed parameter models. Journal of Computational Physics, Elsevier, 2019, 381, pp.218-245. DOI: 10.1016/j.jcp.2018.12.023, hal-02608782.

I. Gejadze, V. Shutyaev, F.-X. Le Dimet. Hessian-based covariance approximations in variational data assimilation. Russian Journal of Numerical Analysis and Mathematical Modelling, De Gruyter, 2018, 33 (1), pp.25-39. DOI: 10.1515/rnam-2018-0003, hal-02068763.

H. Oubanas, I. Gejadze, P.O. Malaterre, M. Durand, R. Wei, et al.. Discharge estimation in ungauged basins through variational data assimilation: the potential of the SWOT mission. Water Resources Research, American Geophysical Union, 2018, 54 (3), pp.2405-2423. DOI: 10.1002/2017WR021735, hal-02608723.

V. Shutyaev, I. Gejadze, A. Vidard, F.-X. Le Dimet. Optimal solution error quantification in variational data assimilation involving imperfect models. International Journal for Numerical Methods in Fluids, Wiley, 2017, 83 (3), pp.276-290. DOI: 10.1002/fld.4266, hal-01411666.

H. Oubanas, I. Gejadze, P.-O. Malaterre, F. Mercier. River discharge estimation using variational DA involving the full Saint-Venant model and synthetic SWOT-type observations. Journal of Hydrology, v. 559 (2017), pp. 638—647. DOI: 10.1016/j.jhydrol.2018.02.004.

I. Gejadze, H. Oubanas, V. Shutyaev. Implicit treatment of model error using inflated observation-error covariance. Quarterly Journal of the Royal Meteorological Society, Wiley, 2017, 143 (707), pp.2496-2508. DOI: 10.1002/qj.3102, hal-02607165.

I. Gejadze, P. Malaterre. Discharge estimation under uncertainty using variational methods with application to the full Saint-Venant hydraulic network model. International Journal for Numerical Methods in Fluids, Wiley, 2017, 83 (5), pp.405-430. DOI: 10.1002/fld.4273, hal-01720342.

I. Gejadze, P.O. Malaterre. Design of the control set in the framework of variational data assimilation. Journal of Computational Physics, Elsevier, 2016, 325, pp.358-379. DOI: 10.1016/j.jcp.2016.08.029, hal-01930661.

K.L. Brown, I. Gejadze, A. Ramage. A multilevel approach for computing the limited-memory Hessian and its inverse in variational data assimilation. SIAM Journal on Scientific Computing, Society for Industrial and Applied Mathematics, 2016, 38 (5), pp.A2934-A2963. DOI: 10.1137/15M1041407, hal-01502161.

Communications

M. Jay-Allemand, I. Gejadze, P. Javelle, D. Organde, J.A. Fine, et al. Data assimilation applied to a distributed rain-flow model for flash flood warnings. De la prévision des crues à la gestion de crise, Nov 2018, Avignon, France. pp.10. ⟨hal-02608802⟩

I.Gejadze I., Malaterre P.-O., Shutyaev V.P. On the use of derivatives in the PC-based global sensitivity and uncertainty analysis applied to high-dimensional models. Colloque National d’Assimilation de Données (CNA), Rennes, Sept. 26-28, 2018. (invited)

Gejadze I. Design of the control set in the framework of variational data assimilation. Jean-Kuntzmann laboratory Seminar Series, University of Grenoble Alpes, June 01, 2017. (invited)

Noémie Goutal, C. Goeury, R. Ata, S. Ricci, N. El Mocayd, et al.. Uncertainty quantification for river flow simulation applied to a real test case: the Garonne valley. SimHydro 2017, Jun 2017, Nice, France. pp.169-187, ⟨10.1007/978-981-10-7218-5_12⟩. ⟨hal-02609249⟩

Malaterre P.-O., Gejadze I., Oubanas H. Assimilation de données, observabilité et quantification d'incertitude appliquées à l'hydraulique à surface libre. Colloque National d’Assimilation de Données (CNA), Grenoble, Nov. 03 - Dec 02, 2016. (keynote lecture)

Rapports techniques

Malaterre P.-O., Oubanas H., Gejadze I, Billaud F. Mise au point et test d’une méthodologie pour calculer les débits à partir de mesures satellitaires altimétriques sur le Congo et l’Oubangui. Action CICOS D.6.12. Février 2019.