H082-09
Combining coupled hydrological-hydraulic modeling of river networksand sparse multisatellite observations to define stage-fall-discharge laws
Combining coupled hydrological-hydraulic modeling of river networksand sparse multisatellite observations to define stage-fall-discharge laws
Thursday, 10 December 2020: 04:24
Virtual
Abstract:
Current and forthcoming altimetry satellites provide increasing observability of river surface variabilities hence
an increasing "hydraulic visibility" [1]. However, due to non observable river bathymetry and friction but also
because of the sampling and quality of remotely sensed data, discharge estimation remains a challenging problem.
The idea here is to use an integrated hydrodynamic model of a river network as a spatio-temporal and physical
extrapolator of multisource data and flow physics.
In order to perform operational discharge estimates from altimetry data, this contribution proposes a metho-
dology to estimate Stage-Fall-Discharge (SFD) laws from the combination of river network models and sparse
multisatellite data. The hydrodynamic model consists in a 1D SWE calibrated on altimetry data
[2], coupled to [4] the MGB regional hydrological model. A general SFD law is used and
its parameters are estimated from the hydrodynamic model outputs, including multiscale water surface slope, and
altimetric data using a MCMC algorthim following [3].
The method is applied on the Negro-Branco river network (Amazon basin) and representing a
challenging case including multichannel reaches, confluences and backwater affected reaches. The multisatellite
dataset contains altimetric data from Jason3 and Sentinel3A and 3B and covers the time period february 2016 to
may 2019. The added value of using water surface slope simulated by a hydrodynamic model to estimate rating
curve parameters is investigated along with the link to "hydraulic geometry" coefficients and their spatial variations.
The extrapolation of discharge using S3B data is sucessfully tested.
The method should be applicable to any river basin, using altimetry data and is designed
to benefit from dynamic water masks observation when available.
an increasing "hydraulic visibility" [1]. However, due to non observable river bathymetry and friction but also
because of the sampling and quality of remotely sensed data, discharge estimation remains a challenging problem.
The idea here is to use an integrated hydrodynamic model of a river network as a spatio-temporal and physical
extrapolator of multisource data and flow physics.
In order to perform operational discharge estimates from altimetry data, this contribution proposes a metho-
dology to estimate Stage-Fall-Discharge (SFD) laws from the combination of river network models and sparse
multisatellite data. The hydrodynamic model consists in a 1D SWE calibrated on altimetry data
[2], coupled to [4] the MGB regional hydrological model. A general SFD law is used and
its parameters are estimated from the hydrodynamic model outputs, including multiscale water surface slope, and
altimetric data using a MCMC algorthim following [3].
The method is applied on the Negro-Branco river network (Amazon basin) and representing a
challenging case including multichannel reaches, confluences and backwater affected reaches. The multisatellite
dataset contains altimetric data from Jason3 and Sentinel3A and 3B and covers the time period february 2016 to
may 2019. The added value of using water surface slope simulated by a hydrodynamic model to estimate rating
curve parameters is investigated along with the link to "hydraulic geometry" coefficients and their spatial variations.
The extrapolation of discharge using S3B data is sucessfully tested.
The method should be applicable to any river basin, using altimetry data and is designed
to benefit from dynamic water masks observation when available.
Ref [1] P.-A. Garambois, S. Calmant, H. Roux, A. Paris, J. Monnier, P. Finaud-Guyot, A. Montazem, and J. San-
tos da Silva. Hydro. Proc., 2017.
[2] K. Larnier, J. Monnier, P.-A. Garambois, and J. Verley. IPSE, 2020.
[3] A. Paris, R. Dias de Paiva, J. Santos da Silva, D. Medeiros Moreira, S. Calmant, P.-A. Garambois, W. Colli-
schonn, M.-P. Bonnet, and F. Seyler. WRR 2016.
[4] L. Pujol, P.-A. Garambois, P. Finaud-Guyot, J. Monnier, K. Larnier, R. Mosé, S. Biancamaria, H. Yésou,
D. Moreira, A. Paris, and S. Calmant. JoH 2020.