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
Abstract. This contribution presents a novel multi-dimensional (multi-D) hydraulic-hydrological numerical model with variational data assimilation capabilities. It allows multi-scale modeling over large domains, combining in situ observations with high-resolution hydro-meteorology and satellite data. The multi-D hydraulic model relies on the 2D shallow water equations solved with a 1D2D adapted single finite volume solver. 1Dlike reaches are built through meshing methods that cause the 2D solver to degenerate into 1D. They are connected to 2D portions that act as local zooms, for modeling complex flow zones such as floodplains and confluences, via 1Dlike-2D interfaces. An existing parsimonious hydrological model, GR4H, is implemented and coupled to the hydraulic model. The forward-inverse multi-D computational model is successfully validated on academic and real cases of increasing complexity, including using the second order scheme version. Assimilating multiple observations of flow signatures leads to accurate inferences of multi-variate and spatially distributed parameters among bathymetry-friction, upstream/lateral hydrographs, and hydrological model parameters. This notably demonstrates the possibility for information feedback towards upstream hydrological catchments, that is backward hydrology. A 1Dlike model of part of the Garonne river is built and accurately reproduces flow lines and propagations of a 2D reference model. A multi-D model of the complex Adour basin network, inflowed by the semi-distributed hydrological model, is built. High resolution flow simulations are obtained on a large domain, including fine zooms on floodplains, with a relatively low computational cost since the network contains mostly 1Dlike reaches. The current work constitutes an upgrade of the Dassflow computational platform. The adjoint of the whole tool chain is obtained by automatic code differentiation.
Abstract. This contribution presents a novel multi-dimensional (multi-D) hydraulic–hydrological numerical model with variational data assimilation capabilities. It allows multi-scale modeling over large domains, combining in situ observations with high-resolution hydrometeorology and satellite data. The multi-D hydraulic model relies on the 2D shallow-water equations solved with a 1D–2D adapted single finite-volume solver. One-dimensional-like reaches are built through meshing methods that cause the 2D solver to degenerate into 1D. They are connected to 2D portions that act as local zooms, for modeling complex flow zones such as floodplains and confluences, via 1D-like–2D interfaces. An existing parsimonious hydrological model, GR4H, is implemented and coupled to the hydraulic model. The forward-inverse multi-D computational model is successfully validated on virtual and real cases of increasing complexity, including using the second-order scheme version. Assimilating multiple observations of flow signatures leads to accurate inferences of multi-variate and spatially distributed parameters among bathymetry friction, upstream and lateral hydrographs and hydrological model parameters. This notably demonstrates the possibility for information feedback towards upstream hydrological catchments, that is, backward hydrology. A 1D-like model of part of the Garonne River is built and accurately reproduces flow lines and propagations of a 2D reference model. A multi-D model of the complex Adour basin network, with inflow from the semi-distributed hydrological model, is built. High-resolution flow simulations are obtained on a large domain, including fine zooms on floodplains, with a relatively low computational cost since the network contains mostly 1D-like reaches. The current work constitutes an upgrade of the DassFlow computational platform. The adjoint of the whole tool chain is obtained by automatic code differentiation.
<p>With the upcoming SWOT satellite mission, which should provide spatially dense river surface elevations, widths and slopes observations globally, comes the need to pertinently use such data into hydrodynamic models, from the reach to hydrographic network scales. Based on the HiVDI (Hierarchical Variational Discharge Inversion) modeling strategy ([1,2], DassFlow software<sup>1</sup>), this work tackles the forward and inverse modeling capabilities of distributed channel parameters and inflows (in the 1D Saint-Venant model) from multisatellite observations of river surface. Several synthetic cases are designed to study fluvial and torrential flows signatures and assess the inference capabilities of model parameters (inflows, bathymetry, friction) given different observation patterns. Accurate inferences of both inflows and distributed channel parameters (bathymetry-friction) is achievable even with a minimum spatial observability between inflows. A sensitivity analysis of the inferences to prior hydraulic parameter values and to regularization parameters is performed. Next a real case is studied: 871km 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 observations and dense in situ GPS flow lines, (ii) average river top widths from optical imagery, (iii) upstream and lateral flows from the MGB large-scale hydrological model [3]. The calibrated effective hydraulic model closely fits satellite altimetry observations of WS signatures and contains real-like spatial variabilities and flood wave propagations (frequential features analyzed with identifiability maps [2]). Synthetic SWOT observations are generated from the simulated flowlines and the identifiability of model parameters (579 bathymetry points, 17 friction patches and 22 upstream and lateral hydrographs) is tested using the HiVDI computational inverse method and given hydraulically coherent prior guesses and regularization parameter values. Inferences of channel parameters carried out on this fine hydraulic model applied at large scale give satisfying results considering the challenging inverse problems solved globally in space and time, even with noisy SWOT data. Inferences of spatially distributed temporal parameters (lateral inflows) give satisfying results as well, with even small scale hydrograph variations being infered accurately.</p><div> <p>This study brings insights in:</p> </div><ol><li> <p>the hydraulic visibility of multiple inflows hydrographs signature at large scale with SWOT;</p> </li> <li> <p>the simultaneous identifiability of spatially distributed channel parameters and inflows by assimilation of satellite altimetry data;</p> </li> <li> <p>the need to further taylor and scale hydrodynamic models and assimilation methods to improve potential information feedbacks to hydrological modules in integrated chains.</p> </li> </ol><div> <p><strong>References:</strong></p> </div><p>[1] Larnier, Monnier, Garambois, Verley. (2019) River discharge and bathymetry estimations from SWOT altimetry measurements.</p><p>[2] Brisset, Monnier, Garambois, Roux. (2018) On the assimilation of altimetric data in 1d Saint-Venant river flow models. AWR, doi: 10.1016/j.advwatres.2018.06.004.</p><p>[3] Paiva, Buarque, Collischonn, et al. Large-scale hydrologic and hydrodynamic modeling of the amazon river basin. WRR, doi: 10.1002/wrcr.20067.</p><p>&#160;</p><p>&#160;</p>
<div>In a context of climate change and potential intensification of the hydrological cycle, improving representation of water fluxes within river basins is of paramount importance&#160; for hydrological sciences and operational forecasts. To leverage multi-sourced observations (in situ, satellite, drones) of the critical zone, innovative approaches integrating hydraulic-hydrological modeling and multi-variate assimilation methods are needed. They should enable ingesting spatially distributed forcings, physiographic descriptors, hydrodynamic signatures from multi-source observables, and tackle calibration/correction problems in integrated models. Water surface observables are valuable to constrain hydraulic models of river reaches ([1] and references therein) and complex flow zones (confluences, floodplains), forced by spatially distributed inflows ([2], [3]). Since hydraulic large scale modeling can be computationally costly, a combination of effective 1D representations and 2D zooms, completed by hydrological modules, may be useful. This contribution presents the development of a complete multi-dimensional hydraulic-hydrological toolchain, based on the 2D hydraulic model and variational data assimilation platform DassFlow [4,5]. A new method for multi-dimensional hydraulic modeling, relying on a single 2D 2nd order solver applied to 1Dlike-2D meshes, is presented. Inferences of large composite control vectors, including hydrological and hydraulic controls, are carried out on academic and real cases in twin experiments. Accurate results are achieved given sufficient observability of parameters signatures, including information feedback from the river network to upstream hydrological catchments models.</div><div>[1] Larnier et al. "River discharge and bathymetry estimation from SWOT altimetry measurements", Inverse Problems in Science and Engineering 29, 6 (2021), pp. 759-789.</div><div>[2] Pujol et al. Estimation of multiple inflows and effective channel by assimilation of multi-satellite hydraulic signatures: The ungauged anabranching Negro river. Journal of Hydrology, 591:125331, 2020.</div><div>[3] Malou et al. (2021). Generation and analysis of stage-fall-discharge laws from coupled hydrological-hydraulic river network model integrating sparse multi-satellite data. Journal of Hydrology, 603, 126993.</div><div>[4] Data assimilation for free surface flows. Technical report, Mathematics Institute of Toulouse-INSA group-CS corp. CNES-CNRS, 2019.</div><div>[5] Monnier et al. Inverse algorithms for 2D shallow 893 water equations in presence of wet dry fronts. application to flood plain dynamics. Advances in Water Resources, 894 97:11&#8211;24, 2016.</div>
In a context of climate change and potential intensification of the hydrological cycle, improving representation of water fluxes within river basins is of paramount importance both for hydrological sciences and operational forecasts. New integrated approaches are required for exploring synergies between spatially distributed flow models and datasets, combining in situ observations with high-resolution hydro-meteorology and satellite data. To take advantage of this unprecedented observations of the critical zone, innovative approaches integrating hydraulic-hydrological modeling and multivariate assimilation methods are needed. They should enable ingesting spatially distributed forcings, physiographic descriptors hydrodynamic signatures from remotely-sensed and in situ observables, and tackle calibration problems in integrated hydraulic-hydrological models. Crucially, the pertinence of the information assimilation relies on model-data coherence: water surface observables are valuable to constrain hydraulic models of river reaches (Larnier et al. (2020) and references therein) and complex river network portions, forced by spatially distributed inflows (Pujol et al. (2020), Malou et al. (under redaction)). Since hydraulic modeling at the scale of a river basin can be computationally costly, a combination of effective 1D and 2D representations, complemented by hydrological modules, may be useful. Complex river-floodplain interaction zones may be modeled in 2D zooms, while 1D approaches can fit simpler reaches. This contribution presents the development of a complete hydraulichydrological toolchain based on the 2D hydraulic model and variational data assimilation platform DassFlow 2 . A 1D effective modeling approach based on a 2D shallow water model is tested. Then, the implementation of hydrological modules within the DassFlow VDA framework is presented.
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