Model inversion of transient nonlinear groundwater flow models using model reduction

Vermeulen, Peter; Stroet, Chris B. M. te; Heemink, A.W.

doi:10.1029/2005wr004536

Cited by 22 publications

(14 citation statements)

References 30 publications

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“…However, characterization of field heterogeneity is always difficult due to economical and technical limitations. Inverse methods are commonly used to calibrate the model parameter distributions on the basis of observation data and provide more reliable model predictions, as summarized in several review articles (Bohlin and James 2001;Carrera et al 2005;Delay 1991;Huang et al 2009;Mclaughlin and Townley 1996;Neuman 1980;Vermeulen et al 2006;Zimmerman et al 1998). Many inverse algorithms have been developed in the field of hydrogeology (Hoeksema and Kitanidis 1984;Poeter and Hill 1997;Sun 1994;Sun and Yeh 1992;Vrugt et al 2005b;Vermeulen et al 2006;Yeh and Liu 2000;Zhang 1996, Zhu andYeh 2005;Chen and Zhang 2006).…”

Section: Introductionmentioning

confidence: 99%

Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

Tong

Yang

2010

Stoch Environ Res Risk Assess

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A data assimilation method is developed to calibrate a heterogeneous hydraulic conductivity field conditioning on transient pumping test data. The ensemble Kalman filter (EnKF) approach is used to update model parameters such as hydraulic conductivity and model variables such as hydraulic head using available data. A synthetical two-dimensional flow case is used to assess the capability of the EnKF method to calibrate a heterogeneous conductivity field by assimilating transient flow data from observation wells under different hydraulic boundary conditions. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating continuous hydraulic head measurements and the hydraulic boundary condition will significantly affect the simulation results. For our cases, after a few data assimilation steps, the assimilated conductivity field with four Neumann boundaries matches the real field well while the assimilated conductivity field with mixed Dirichlet and Neumann boundaries does not. We found in our cases that the ensemble size should be 300 or larger for the numerical simulation. The number and the locations of the observation wells will significantly affect the hydraulic conductivity field calibration.

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Section: Introductionmentioning

confidence: 99%

Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

Tong

Yang

2010

Stoch Environ Res Risk Assess

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“…In some studies, surrogate models after being developed are treated as if they are high-fidelity representations of the real-world systems of interest and fully replace the original models [e.g., McPhee and Yeh, 2008;Siade et al, 2010;Vermeulen et al, 2005Vermeulen et al, , 2006. Such a strategy can be deemed analogous to the strategy for hydrologic model calibration which identifies and solely relies on a representative short data period.…”

Section: Relations To Surrogate Modelingmentioning

confidence: 99%

An efficient framework for hydrologic model calibration on long data periods

Razavi

Tolson

2013

Water Resour. Res.

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.[1] Long periods of hydrologic data records have become available in many watersheds around the globe. Hydrologic model calibration on such long, full-length data periods is typically deemed the most robust approach for calibration but at larger computational costs. Determination of a representative short period as a ''surrogate'' of a long data period that sufficiently embeds its information content is not trivial and is a challenging research question. The representativeness of such a short period is not only a function of data characteristics but also model and calibration error function dependent. Unlike previous studies, this study goes beyond identifying the best surrogate data period to be used in model calibration and proposes an efficient framework that calibrates the hydrologic model to full-length data while running the model only on a short period for the majority of the candidate parameter sets. To this end, a mapping system is developed to approximate the model performance on the full-length data period based on the model performance for the short data period. The basic concepts and the promise of the framework are demonstrated through a computationally expensive hydrologic model case study. Three calibration approaches, namely calibration solely to a surrogate period, calibration to the full period, and calibration through the proposed framework, are evaluated and compared. Results show that within the same computational budget, the proposed framework leads to improved or equal calibration performance compared to the two conventional approaches. Results also indicate that model calibration solely to a short data period may lead to a range of performances from poor to very well depending on the representativeness of the short data period which is typically not known a priori.

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“…Fine scale details at grid points are encoded in this global basis. In the context of fluid flow in porous media, POD with Galerkin projection has been used as a model reduction procedure in many previous investiga-tions such as [90,92,91,57] for groundwater flow, [42,56,29,17,16] for immiscible two-phase (oil-water) reservoir simulation, and [36,79,80,35] for miscible flow for the enhanced oil recovery (EOR) process. In the case of flows described by linear governing equations, e.g [90], the POD-Galerkin technique substantially reduces the computational complexity and simulation time.…”

Section: Introductionmentioning

confidence: 99%