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Inferences of discharge, bathymetry and flow models parametrizationSimulations at watershed scale with local zooms, flood plain dynamics
Below are presented some capabilities (more or less recent...) of DassFlow software (1D and 2D shallow flow models). Update. DassFlow is now part of DassHydro (Data Assimilation for Hydrology) plateform. The H2iVDI (Hybrid Hierarchical Variational Discharge Inference) algorithm dedicated to spatial hydrologyThe computations aim at estimating the river discharge from altimetry measurements of the water surface only (forthcoming SWOT mission, NASA-CNES). The same computations can be applied to numerous rivers worldwide, see Fig. below.
The test river presented below is the Garonne river right next to our offices. A portion between Toulouse and Malause (dataset provided by IMFT, D. Dartus, H. Roux et al.). Another illustration is presented on a portion of the Sacramento River (California, USA; dataset provided by M. Durand, Ohio State Univ.).
You can consult short notes on the SWOT blog too (not recent anymore...): On Variational Data Assimilation of SWOT-like data in 1D and 2D river flow models (april 2016). See also initial thoughs:
On variational sensitivities, data assimilation and inversion for small scale river flows (nov. 2013). DassFlow 1D dedicated to spatial hydrology (combined with H2iVDI algorithm)DassFlow 1D solves: a) the 1D SW model (Saint-Venant's equations) in variables (S,Q) using either an innovative low Froude finite volume schemes (1st or 2nd order) or the standard Priessmann's scheme; b) the diffusive wave equations (standard version and double scale version) using FE scheme.River geometry are described by cross-sections based on trapezium superimpositions (description consistent with altimetry measurements). The identified/control parameters are the upstream discharge (time series), the friction coefficient K (Manning-Strckler, potentially varying), the bed elevation (effective bathymetry). Potentially it is the downstream rating curve and the initial condition too. A test river next to Toulouse: Garonne riverThe considered Garonne river portion is between Toulouse and Maulause. The 1158 computational cross-sections derive from a linear interpolation between the measured cross-sections and LIDAR information in the flood plain (5 m accuracy). Datasets prepared by Fluid Mech. Institute (IMFT) Toulouse. ![]() ![]() Figure. Left: Cross-section locations in the 1D model (Garonne river). Right: cross-section example (1D model). Dataset prepared by IMFT. SWOT measurements SWOT-like observations are simulated using the SWOT simulator (co-developed at LEGOS by S. Biancamaria et al.). The SWOT like observations are given by SWOT-Band of 200 m (On Fig. they are averaged at 1km scale). These SWOT observations are not synchronous: each overpass provides 2 swaths, 60km width each. Each swath is splitted into 200m width bands. Each band corresponds to a single water elevation measurement (accuracy ~ +/- 30 cm). Estimation of the river bathymetry and the discharge1 day revisiting period case (CalVal satellite phase)
The Variationa Data Assimilation process implemented into DassFlow enables to identify the triplet (Qin(t); b(x), K(h)), that is the inflow discharge, an effective bathymetry b(x) and the corresponding varying friction coefficient K(h) (h denotes the water depth).
An other example with ~21 days revisiting period (nominal satellite phase) The considered river is the Sacramento river, California. The dataset has been provided by M. Durand et al. (Ohio State Univ., USA).
DassFlow2D for networks and local floodplains, coupled with hydrological model(s)
The forward model is based on the 2D SW equations, solved by a Finite Volume schemes (either first or second order). The conservative variables are the water depth h and the local discharge q = hu, where u = (u,v) is the depth-averaged velocity field.
A few examples are presented below.
Garonne river (south-west of France). This is the same river portion as those considered in the HiVDI algorithm presentation. The 2D mesh cointains 436 264 nodes, 867 498 cells. The flood plain topography data derives from LIDAR + STRM information. Datasets have been post-treated by IMFT Toulouse (D. Dartus et al.).
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