the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hybrid Neural Network – Variational Data Assimilation algorithm to infer river discharges from SWOT-like data
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.
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RC1: 'Review "Hybrid Neural Network - Variational Data Assimilation algorithm to infer river discharges from SWOT-like data"', Anonymous Referee #1, 03 Sep 2020
- AC1: 'Our point-to-point answers', Jerome Monnier, 25 Sep 2020
- AC3: 'Same point-to-point answers but in a clearer typing format', Jerome Monnier, 03 Oct 2020
- AC5: 'The revised version following your comments and our answers', Jerome Monnier, 03 Oct 2020
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SC1: 'Comments to the authors', Shunji Kotsuki, 22 Sep 2020
- AC2: 'Our point-to-point answers', Jerome Monnier, 25 Sep 2020
- AC4: 'Our point-to-point answers in a clearer typing format', Jerome Monnier, 03 Oct 2020
-
RC2: '[Comments to the Authors]', Anonymous Referee #2, 05 Nov 2020
- AC6: 'Our answers', Jerome Monnier, 10 Nov 2020
-
RC1: 'Review "Hybrid Neural Network - Variational Data Assimilation algorithm to infer river discharges from SWOT-like data"', Anonymous Referee #1, 03 Sep 2020
- AC1: 'Our point-to-point answers', Jerome Monnier, 25 Sep 2020
- AC3: 'Same point-to-point answers but in a clearer typing format', Jerome Monnier, 03 Oct 2020
- AC5: 'The revised version following your comments and our answers', Jerome Monnier, 03 Oct 2020
-
SC1: 'Comments to the authors', Shunji Kotsuki, 22 Sep 2020
- AC2: 'Our point-to-point answers', Jerome Monnier, 25 Sep 2020
- AC4: 'Our point-to-point answers in a clearer typing format', Jerome Monnier, 03 Oct 2020
-
RC2: '[Comments to the Authors]', Anonymous Referee #2, 05 Nov 2020
- AC6: 'Our answers', Jerome Monnier, 10 Nov 2020
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Cited
3 citations as recorded by crossref.
- Hybrid Neural Network - Variational Data Assimilation algorithm to infer river discharges from SWOT-like data K. LARNIER & J. MONNIER 10.1007/s10596-023-10225-2
- Estimation of multiple inflows and effective channel by assimilation of multi-satellite hydraulic signatures: The ungauged anabranching Negro river L. Pujol et al. 10.1016/j.jhydrol.2020.125331
- Covariance kernels investigation from diffusive wave equations for data assimilation in hydrology T. Malou & J. Monnier 10.1088/1361-6420/ac509d