Estimation of multiple inflows and effective channel by assimilation of multi-satellite hydraulic signatures: The ungauged anabranching Negro river

Abstract

With the upcoming SWOT satellite mission, which should provide spatially dense river surface elevation, width and slope observations globally, comes the opportunity to assimilate such data into hydrodynamic models, from the reach scale to the hydrographic network scale. Based on the HiVDI (Hierarchical Variational Discharge Inversion) modeling strategy (Larnier et al. [1]), this study tackles the forward and inverse modeling capabilities of distributed channel parameters and multiple inflows (in the 1D Saint-Venant model) from multisatellite observations of river surface. It is shown on synthetic cases that the estimation of both inflows and distributed channel parameters (bathymetry-friction) is achievable with a minimum spatial observability between inflows as long as their hydraulic signature is sampled. Next, a real case is studied: 871 km of the Negro river (Amazon basin) including complex multichannel reaches, 21 tributaries and backwater controls from major confluences. An effective modeling approach is proposed using (i) WS elevations from ENVISAT data and dense in situ GPS flow lines (Moreira [2]), (ii) average river top widths from optical imagery (Pekel et al. [3]), (iii) upstream and lateral flows from the MGB large-scale hydrological model (Paiva et al. [4]). The calibrated effective hydraulic model closely fits satellite altimetry observations and presents real like spatial variabilities; flood wave propagation and water surface observation frequential features are analyzed with identifiability maps following Brisset et al. [5]. Synthetic SWOT observations are generated from the simulated flowlines and allow to infer model parameters (436 effective bathymetry points, 17 friction

Hybrid Neural Network – Variational Data Assimilation algorithm to infer river discharges from SWOT-like data

“…Applications of HiVDI algorithm have been instructive in different and complex contexts, see e.g. Tuozzolo et al (2019); Garambois et al (2020); Pujol et al (2020). Comparison of this algorithm results can be found in Frasson et al (Submitted).…”

Multi-dimensional hydrological-hydraulic model with variational data assimilation for river networks and floodplains

“…Simple inference tests are carried on the confluence case from Subsection 3.2.1, following Pujol et al (2020), in a twin experiment setup. A control vector c = (Q 1 (t) , Q 2 (t)) is considered, where Q 1 and Q 2 are sinusoidal inflow hydrographs injected at the upstream cell of the two upstream reaches.…”

Amazon Hydrology From Space: Scientific Advances and Future Challenges

“…Many studies have been discussing the utility of the mission to better estimate hydraulic variables in the Amazon, from reach (lower Madeira River; Brêda et al., 2019) to the basin scale (Emery et al., 2020; Wongchuig et al., 2020). New frameworks for the incorporation of satellite altimetry water levels will set up the development of the next generation of hydraulic models for the Amazon, aiming at better representing local processes as water surface heterogeneities that occur due to hydraulic controls as channel width reductions (Garambois et al., 2017; Montazem et al., 2019; Pujol et al., 2020).…”