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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/npg-2020-32
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2020-32
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  24 Aug 2020

24 Aug 2020

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This preprint is currently under review for the journal NPG.

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

Kevin Larnier1,2,3 and Jerome Monnier1,2 Kevin Larnier and Jerome Monnier
  • 1Institut de Mathématiques de Toulouse (IMT), France
  • 2INSA Toulouse, France
  • 3CS corporation, Business Unit Espace, Toulouse, France

Abstract. A new algorithm to estimate river discharges from altimetry measurements only is designed. A first estimation is obtained by an artificial neural network trained from the altimetry large scale water surface measurements plus drainage area information. The combination of this purely data-based estimation and a dedicated algebraic flow model provides a first physically-consistent estimation. The latter is next employed as the first guess of an advanced variational data assimilation formulation. The final estimation is highly accurate for rivers presenting features within the learning partition; for rivers far outside the learning partition, the space-time variations of discharge remain accurately approximated however the global estimation presents a potential bias. Indeed, it is shown that if the estimation is based on the hydrodynamics models only, the inverse problem may be well-defined but up to a bias only (the bias scales the global estimation). This bias is removed thanks to the ANN but for rivers in the learning partition only. For rivers outside the learning partition, any mean value (eg. annual, seasonal) enables to remove the bias. Finally, the present hybrid and hierarchical inversion strategy seems to provide much more accurate estimations compared to the state-of-the-art for the considered 29 heterogeneous river portions.

Kevin Larnier and Jerome Monnier

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Kevin Larnier and Jerome Monnier

Kevin Larnier and Jerome Monnier

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Latest update: 29 Oct 2020
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Short summary
A hybrid A.I. algorithm (data-driven and physically-informed) to estimate river discharges observed by the altimetry satellite SWOT (launch 2021).
A hybrid A.I. algorithm (data-driven and physically-informed) to estimate river discharges...
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