Coupled process parameter estimation and prediction uncertainty using hydraulic head and concentration data

Gailey, Robert M.; Crowe, Allan S.; Gorelick, Steven M.

doi:10.1016/0309-1708(91)90041-l

Cited by 41 publications

(29 citation statements)

References 28 publications

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“…For applications to groundwater systems, the majority of methodologies are demonstrated using two-dimensional (2-D) confined groundwater flow models (e.g., Gailey et al, 1991;Hantush and Mariño, 1997;Hendricks Franssen et al, 1999;Drécourt et al, 2006;Hendricks Franssen and Kinzelbach, 2008;Fu and Gómez-Hernández, 2009;Bailey and Baù, 2010). Several studies have employed threedimensional steady-state flow models (Chen and Zhang, 2006;Liu et al, 2008), and several have estimated hydraulic parameters in variably-saturated flow conditions (Yeh and Zhang, 1996;Zhang and Yeh, 1997;Li and Yeh, 1999), although for the latter applications were limited to small 2-D vertical-plane systems.…”

Section: R T Bailey and D Baù: Estimating Geostatistical Parametersmentioning

confidence: 99%

“…In recognition that improved parameter estimation occurs when system-response data from more than one governing equation is used (Gailey et al, 1991), with the implication that each data type contains unique information regarding the parameter, numerous studies have employed two or more sets of dissimilar data to condition the parameter values. Such data sets typically include hydraulic head data as well as another data type such as solute concentration data (Gailey et al, 1991;Li and Yeh, 1999;Hendricks Franssen et al, 2003;Liu et al, 2008), groundwater temperature (Woodbury and Smith, 1988), groundwater travel time (Fu and Gómez-Hernández, 2009), groundwater discharge to surface water (Bailey and Baù, 2010), and tracer breakthrough data at observation wells (Wen et al, 2002).…”

Section: R T Bailey and D Baù: Estimating Geostatistical Parametersmentioning

confidence: 99%

“…Such data sets typically include hydraulic head data as well as another data type such as solute concentration data (Gailey et al, 1991;Li and Yeh, 1999;Hendricks Franssen et al, 2003;Liu et al, 2008), groundwater temperature (Woodbury and Smith, 1988), groundwater travel time (Fu and Gómez-Hernández, 2009), groundwater discharge to surface water (Bailey and Baù, 2010), and tracer breakthrough data at observation wells (Wen et al, 2002). Streamflow data, which carries information regarding the spatial structure of aquifer K due to groundwater-surface water interactions, has been used in data assimilation to improve model performance (Schreider et al, 2001;Aubert et al, 2003;Clark et al, 2008;Camporese et al, 2009Camporese et al, , 2010, although as yet has not been used to condition K.…”

Section: R T Bailey and D Baù: Estimating Geostatistical Parametersmentioning

confidence: 99%

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Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother

Bailey

Baù

2012

Hydrol. Earth Syst. Sci.

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Abstract. Groundwater flow models are important tools in assessing baseline conditions and investigating management alternatives in groundwater systems. The usefulness of these models, however, is often hindered by insufficient knowledge regarding the magnitude and spatial distribution of the spatially-distributed parameters, such as hydraulic conductivity (K), that govern the response of these models. Proposed parameter estimation methods frequently are demonstrated using simplified aquifer representations, when in reality the groundwater regime in a given watershed is influenced by strongly-coupled surface-subsurface processes. Furthermore, parameter estimation methodologies that rely on a geostatistical structure of K often assume the parameter values of the geostatistical model as known or estimate these values from limited data.In this study, we investigate the use of a data assimilation algorithm, the Ensemble Smoother, to provide enhanced estimates of K within a catchment system using the fullycoupled, surface-subsurface flow model CATHY. Both water table elevation and streamflow data are assimilated to condition the spatial distribution of K. An iterative procedure using the ES update routine, in which geostatistical parameter values defining the true spatial structure of K are identified, is also presented. In this procedure, parameter values are inferred from the updated ensemble of K fields and used in the subsequent iteration to generate the K ensemble, with the process proceeding until parameter values are converged upon. The parameter estimation scheme is demonstrated via a synthetic three-dimensional tilted v-shaped catchment system incorporating stream flow and variably-saturated subsurface flow, with spatio-temporal variability in forcing terms. Results indicate that the method is successful in providing improved estimates of the K field, and that the iterative scheme can be used to identify the geostatistical parameter values of the aquifer system. In general, water table data have a much greater ability than streamflow data to condition K. Future research includes applying the methodology to an actual regional study site.

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Section: R T Bailey and D Baù: Estimating Geostatistical Parametersmentioning

confidence: 99%

Section: R T Bailey and D Baù: Estimating Geostatistical Parametersmentioning

confidence: 99%

Section: R T Bailey and D Baù: Estimating Geostatistical Parametersmentioning

confidence: 99%

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Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother

Bailey

Baù

2012

Hydrol. Earth Syst. Sci.

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“…The importance of including concentrations in the objective function for flow-and-transport model calibration is discussed by Strecker and Chu [1986], Wagner and Gorelick [1987], Gailey et al [1991], Franssen et al [2003], and Carrera [1996, 2003]. These studies suggest that including observations in the calibration that are of the same type as the model prediction(s) may improve the reliability of the calibrated model.…”

Section: Observation Datamentioning

confidence: 99%

“…[61] Gailey et al [1991], Sun [1994], Weiss and Smith [1998], and Barlebo et al [2004], among others, discuss alternative weighting strategies. Moore and Doherty [2005] discuss selective weight assignment for reducing the error variance of model predictions.…”

Section: Observation Datamentioning

confidence: 99%

A hybrid regularized inversion methodology for highly parameterized environmental models

Tonkin¹,

Doherty

2005

Water Resources Research

186

215

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[1] A hybrid approach to the regularized inversion of highly parameterized environmental models is described. The method is based on constructing a highly parameterized base model, calculating base parameter sensitivities, and decomposing the base parameter normal matrix into eigenvectors representing principal orthogonal directions in parameter space. The decomposition is used to construct super parameters. Super parameters are factors by which principal eigenvectors of the base parameter normal matrix are multiplied in order to minimize a composite least squares objective function. These eigenvectors define orthogonal axes of a parameter subspace for which information is available from the calibration data. The coordinates of the solution are sought within this subspace. Super parameters are estimated using a regularized nonlinear Gauss-Marquardt-Levenberg scheme. Though super parameters are estimated, Tikhonov regularization constraints are imposed on base parameters. Tikhonov regularization mitigates over fitting and promotes the estimation of reasonable base parameters. Use of a large number of base parameters enables the inversion process to be receptive to the information content of the calibration data, including aspects pertaining to small-scale parameter variations. Because the number of super parameters sustainable by the calibration data may be far less than the number of base parameters used to define the original problem, the computational burden for solution of the inverse problem is reduced. The hybrid methodology is described and applied to a simple synthetic groundwater flow model. It is then applied to a real-world groundwater flow and contaminant transport model. The approach and programs described are applicable to a range of modeling disciplines.Citation: Tonkin, M. J., and J. Doherty (2005), A hybrid regularized inversion methodology for highly parameterized environmental models, Water Resour. Res., 41, W10412,

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Reliability in Estimating Urban Groundwater Recharge Through the Vadose Zone

Friedel

NATO Science Series

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Coupled process parameter estimation and prediction uncertainty using hydraulic head and concentration data

Cited by 41 publications

References 28 publications

Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother

Estimating geostatistical parameters and spatially-variable hydraulic conductivity within a catchment system using an ensemble smoother

A hybrid regularized inversion methodology for highly parameterized environmental models

Reliability in Estimating Urban Groundwater Recharge Through the Vadose Zone

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