A few references

References for DassFlow

References introducing the approaches, methods, algorithms (and not those focusing on new applications, new flows, databases)

  • Know-hows on VDA & fundamentals of DassFlow’s algorithms: basics of inverse problems, optimal control, gradient-based methods, gradient computations, adjoints (equations, codes), codes assessements, covariances operators, regularizations terms, link with BLUE - Kalman filters etc :

    J. Monnier, “Variationnal Data Assimilation”, Open Online Course, INSA - University of Toulouse. consult the course.
  • Discharge estimation from altimetry data: HiVDI algorithm (based on 1D and 0.5D flow models). River discharge estimations, bathymetry estimations, identifiability maps (1D SW model = Saint-Venant’s equations):

    K. Larnier, J. Monnier. “Learning river features from altimetry”.
    K. Larnier, J. Monnier, P.-A. Garambois, J. Verley. “Estimation of river discharges from altimetry”. Inv. Pb Sc. Eng.(IPSE), 2020.
    P. Brisset, J. Monnier, P.-A. Garambois, H. Roux. “On the assimilation of altimetry data in 1D Saint-Venant river models”. Adv. Water Ress. (AWR) Vol. 19, 2018.

    See also a poster for HiVDI and an application .

  • The complete hydraulic 2d/1d -hydrology chain few advanced applications

    L. Pujol, P.-A. Garambois, J. Monnier, K. Larnier et al., submitted, 2022.
  • A few advanced applications (including complex flows analysis)

    L. Pujol, P.-A. Garambois, P. Finaud-Guyot, J. Monnier, K. Larnier, R. Mose, S. Biancamaria, H. Yesou, D. Moreira, A. Paris, S. Calmant. “Estimation of Multiple Inflows and Effective Channel by Assimilation of Multi-satellite Hydraulic Signatures: The Ungauged Anabranching Negro River”. J. of Hydrology (JoH), 2020.
    P.-A. Garambois, K. Larnier, J. Monnier, P. Finaud-Guyot, J. Verley, A. Montazem, S. Calmant. “Variational inference of effective channel and ungauged anabranching river discharge from multi-satellite water heights of different spatial sparsity”. J. of Hydrology (JoH), 2019.
    Tuozzolo, S., Lind, G., Overstreet, B., Mangano, J., Fonstad, M., Hagemann, M., Frasson, R., Larnier, K., Garambois, P.-A., Monnier, J., Durand, M. “Estimating river discharge with swath altimetry: A proof of concept using AirSWOT observations”. Geophys. Res. Lett. (GRL), 2019.
  • 2D flows & VDA inversions

    • Flood plain & FV schemes (including 2nd order VF schemes, wet-dry front dynamics, variational sensitivities):

      J. Monnier, F. Couderc, D. Dartus, K. Larnier, R. Madec, J.-P. Vila. “Inverse computational algorithms for 2D shallow water equations in presence of wet dry fronts. Application to flood plain dynamics”. Adv. Water Res. 2016.
    • Assimilation of a flood plain image into 2D shallow water model:

      R. Hostache, X. Lai, J. Monnier, C. Puech. J. Hydrology (JoH) (2010).
      X. Lai, J. Monnier J. Hydrology (2009) and M. Honnorat, X. Lai, J. Monnier, FX Le Dimet, Computational Methods for Water Ressources (2006) -Pearl river, unpublished elsewhere-.
    • Assimilation of Lagrangian surface data:

      M. Honnorat, J. Monnier, FX Le Dimet, Comput. Visu. Sc. (2009).
      M. Honnorat, J. Monnier, N. Riviere, E. Huot, FX Le Dimet, Comput. Visu. Sc. (2010).
  • Bed topography estimations beneath glaciers flows (mix of physical-based models and statistical-learning methods):

    J. Monnier, J. Zhu. “Estimations of bed topography elevation inland East Antarctica”.
    J. Monnier, J. Zhu. “Inference of the bottom topography in anisothermal mildly-sheared shallow ice flows”. Comput. Meth. Applied Meth. Eng.". CMAME 2019.

    Code version developped in Python.

  • Coupling 1D-2D SW models:

    J. Marin, J. Monnier, Math. Comput. Simul. (2009), E. Fernandez-Nieto, J. Marin, J. Monnier, Comput. Fluid. (2010).
    I. Gejadze, J. Monnier, Comp. Meth. Appl. Mech. Engnr. (2007).
  • Save memory and tune your gradient accuracy if using algorithmic - automatic differentiation:

    N. Martin, J. Monnier, The Cryosphere (2013) and N.Martin’s PhD thesis 2013.
  • Inference of rheometry & conditions at bottom in power-law fluid flows (low inertial flows e.g. glacier, lava flows):

    N. Martin, J. Monnier, Europ. J. Mech. B/Fluids (2014).
  • Variational sensitivities for Stokes power-law flows and advanced four fields FE schemes:

    N. Martin, J. Monnier, SIAM JSC (2013) and N.Martin’s PhD thesis 2013.

References for SMASH

  • Bayesian-like parameter estimation algorithm:
    [Huynh et al. in prep] following [Chelil et al. 2022].
  • Variational Data Assimilation (VDA):
    [Jay-Allemand et al. 2020]
  • Hybrid Variational Data Assimilation, Parameter regionalization (HVDA-PR):
    [Huynh et al. 2023a].
  • Bayesian-Guided Multivariate Regression and Bayesian estimation tools that can be used alone or with HD algorithms:
    [Huynh et al. 2023b]
  • Rainfall moments and Hydrological signatures computable at multiple scales ; signatures usable in optimization:
    [Huynh et al. 2022].
  • LSTM algorithms applicable to model inputs-outputs:
    [Huynh et al. in prep] after [Hashemi et al. 2021].