Developing a particle tracking surrogate model to improve inversion of ground water – Surface water models - GéoHydrosystèmes COntinentaux - EA6293 Accéder directement au contenu
Article Dans Une Revue Journal of Hydrology Année : 2018

Developing a particle tracking surrogate model to improve inversion of ground water – Surface water models

Résumé

The inverse problem of groundwater models is often ill-posed and model parameters are likely to be poorly constrained. Identifiability is improved if diverse data types are used for parameter estimation. However, some models, including detailed solute transport models, are further limited by prohibitive computation times. This often precludes the use of concentration data for parameter estimation, even if those data are available. In the case of surface water-groundwater (SW-GW) models, concentration data can provide SW-GW mixing ratios, which efficiently constrain the estimate of exchange flow, but are rarely used. We propose to reduce computational limits by simulating SW-GW exchange at a sink (well or drain) based on particle tracking under steady state flow conditions. Particle tracking is used to simulate advective transport. A comparison between the particle tracking surrogate model and an advective–dispersive model shows that dispersion can often be neglected when the mixing ratio is computed for a sink, allowing for use of the particle tracking surrogate model. The surrogate model was implemented to solve the inverse problem for a real SW-GW transport problem with heads and concentrations combined in a weighted hybrid objective function. The resulting inversion showed markedly reduced uncertainty in the transmissivity field compared to calibration on head data alone.
Fichier non déposé

Dates et versions

hal-02308252 , version 1 (08-10-2019)

Identifiants

Citer

Yohann Cousquer, Alexandre Pryet, Olivier Atteia, Ty P.A. Ferré, Célestine Delbart, et al.. Developing a particle tracking surrogate model to improve inversion of ground water – Surface water models. Journal of Hydrology, 2018, 558, pp.356-365. ⟨10.1016/j.jhydrol.2018.01.043⟩. ⟨hal-02308252⟩
118 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More