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 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.

Derivation and Numerical Resolution of 2D Shallow Water Equations for Multi-Regime Flows of Herschel-Bulkley Fluids

“…COMSOL multiphysics employs a first order Discontinuous Galerkin scheme in space and Runge-Kutta in time. DassFlow employs demonstrated robust Finite Volume (FV) schemes (both first and second order), see [17,25,32] in the Newtonian case. For the present Herschel-Buckley case, the first order FV scheme has been enriched; details are presented in the appendix.…”

“…In this section, solution procedure for the Shallow Water equations using finite volume method of Godunov type is presented, following [17,25,35], see also [32]. 11) recalling that τ b expression reads…”

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

“…The present H2iVDI algorithm is implemented in the open-source computational software DassFlow [39]. Based on the recently released multi-dimensional 2D-1D software version [46], the estimation of discharge and bathymetry from satellite WS measurements (SWOT mission in particular) could be used to calibrate this complete surface flows numerical model which enable to simulate complex 1D networks, including braided portions [45] and local flood plains, see e.g. [46].…”

Adjoint-based spatially distributed calibration of a grid GR-based parsimonious hydrological model over 312 French catchments with SMASH platform

“…-Coupling to hydraulic models of the DassFlow variational data assimilation platform (Monnier et al, 2016) also recently wrapped in python, adapted to use satellite datasets either with 1D network model (Brisset et al, 2018 Pujol et al, 2020;Malou et al, 2021) or a multi-D model with fine 2D zooms (Pujol et al, 2022) with regularizations taylored for hydrological-hydraulic inverse problems.…”