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

Abstract

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.

Multiscale Learnable Physical Modeling and Data Assimilation Framework: Application to High-Resolution Regionalized Hydrological Simulation of Flash Floods

“…Future work aims to enhance the MLPM-PR framework by incorporating (i) LSTM networks to learn multi-frequential temporal dependencies from various physical data and better inform model components, (ii) the mathematical properties and response of universal differential equations sets for flexible hydrological modeling in time and space, and (iii) coupling with differentiable river network hydraulic modeling to perform information feedback by assimilation of hydraulic observables (Pujol et al, 2022), such as the unprecedented hydraulic visibility (Garambois et al, 2017) brought by SWOT (Surface Water and Ocean Topography) and multi-satellite data (e.g., with VDA in Pujol et al (2020); Malou et al (2021)), enabling the efficient fusion of machine learning with process-based modeling to advance the discovery of scale-relevant hydrological laws.…”

Hydrometeorological Extreme Events in Africa: The Role of Satellite Observations for Monitoring Pluvial and Fluvial Flood Risk

“…Even though WSE could directly be assimilated, it is preferred to assimilate discharge to overcome biases or geometry issues. Recent studies (Pujol et al 2022;Malou et al 2021, among others) in South American basins where many in situ gauges could be used for validation, proved that satellite altimetry assimilation can improve discharge predictions and, consequently, floods and droughts predictions.…”