Coupled inverse modelling of groundwater flow and mass transport and the worth of concentration data

By H. H. Franssen, J. Jaime Gómez-Hernández, A. Sahuquillo. 2003

89 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Geophysics

View Paper: https://www.semanticscholar.org/paper/2f3249e3c7475d8f424fc6871eda48366552a60e

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In Civil and Structural Engineering

In Environmental Engineering

In Geophysics

Citations in the corpus

Best unbiased ensemble linearization and the quasi‐linear Kalman ensemble generator (2009, methodology): “Typical numbers for ensemble-based methods range about 500 [e.g., Chen and Zhang, 2006; Hendricks Franssen and Kinzelbach, 2008b].”
Real‐time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem (2008, methodology): “…methodologies such as sequential selfcalibration, the Pilot Points method or the Representer Method [e.g., Sahuquillo et al., 1992; LaVenue et al., 1995; Gómez-Hernández et al., 1997; Oliver et al., 1997; Valstar, 2001; Hendricks Franssen et al., 2003; Wen et al., 2003; Alcolea et al., 2006].”
A Bayesian approach to stochastic capture zone delineation incorporating tracer arrival times, conductivity measurements, and hydraulic head observations (2003, background): “The SC has been extended to condition on concentration measurements [Hendricks Franssen et al., 2003] and tracer breakthrough curves [Wen et al., 2002].”
Calibration‐constrained Monte Carlo analysis of highly parameterized models using subspace techniques (2009, background): “However, the benefit in using concentration data together with groundwater heads for conditioning groundwater model parameters is well established [e.g., Hendricks Franssen et al., 2003].”
Role of the calibration process in reducing model predictive error (2005, background): “…this regard will be better served if it is calibrated against historical contaminant concentration data than if it is calibrated solely against groundwater heads [see, e.g., Harvey and Gorelick, 1995; Poeter and Belcher, 1991; Frederick and Doherty, 2003; Franssen et al., 2003; Feyen et al., 2003].”
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities (2012, methodology): “…have been made to address these parameter identifiability issues via Monte Carlo type inverse modeling techniques, which apply 4D-VAR techniques on a large number of stochastic realizations of one or more spatially distributed parameter fields (Sahuquillo et al., 1992; Franssen et al., 2003).”
Inverse methods in hydrogeology: Evolution and recent trends (2014, methodology): “The SCM was extended to incorporate concentration data (Hendricks Franssen et al., 2003; Wen329 et al., 2002a) for the characterization of hydraulic conductivity, and also to incorporate breakthrough data of330 both reactive and nonreactive data to characterize the spatial variability of the…”
A hybrid regularized inversion methodology for highly parameterized environmental models (2005, background): “This is particularly important where concentration data are included in the calibration data set since groundwater elevation data may contain little information on this heterogeneity [Franssen et al., 2003].”
A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments (2009, methodology): “[53] and further extended to transient inversion, including the joint calibration of transmissivity and storage coefficient [62], the three-dimensional inverse modelling of fractured media [64] and the coupled inversion of flow and transport [65].”