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Geology topics

E. P. Poeter

Publications and source records attributed to E. P. Poeter.

10 recordsLinked to original sources
USGS2010

Use of geochemical, isotopic, and age tracer data to develop models of groundwater flow for the purpose of water management, northern High Plains aquifer, USA

A prolonged drought in the High Plains of Nebraska prompted the use of groundwater for cooling at the largest coal-fired power plant in the State. Prior to the drought, groundwater was used primarily for irrigation and the power plant relied exclusively on surface water stored in a nearby reservoir for cooling. Seepage from the reservoir system during the past ∼75 a has resulted in the buildup of a large mound of water in the underlying unconfined aquifer. A well field was installed during the drought for the purpose of tapping the groundwater mound as a supplemental source of water for cooling. Concentrations of dissolved Cl − and SO 4 2 - "> SO 4 2- indicate 65–100% of shallow groundwater and 0–100% of deep groundwater (saturated thickness ∼115 m) in the immediate vicinity of the reservoir was from seepage out of the reservoir system. Hydrogen and O isotopic data indicate most surface-water seepage occurred in the late spring and early summer when reservoir stage was at its highest level. Tritium/ 3 He apparent groundwater ages imply horizontal flow velocities from the reservoir were on the order of 60–600 m/a. These diverse data provided information regarding the spatial distribution, timing, and rate of seepage from the reservoir that could not have been obtained from the available geologic, hydraulic head, and conductivity data. In particular, mixing fractions of surface water and regional groundwater in the aquifer could not have been determined using hydraulic information. Mixing fractions were of special interest in this study because of the management objective to maximize the capture of surface-water seepage in the cooling water wells. Groundwater-flow models developed as well-field management tools were calibrated using inverse modeling techniques and observations of groundwater age, surface-water flow, reservoir stage, and groundwater levels. The age data only accounted for 6 of the 2574 field observations used to calibrate the groundwater-flow models, yet they were among the most influential for refining estimates of hydraulic conductivity, recharge, and seepage from the reservoir. Results from this study demonstrate the benefits of using geochemical, isotopic, and age tracer data to develop conceptual and numerical models of groundwater flow for the purpose of water management.

Nebraska↗
USGS2007

Insights into the use of time-lapse GPR data as observations for inverse multiphase flow simulations of DNAPL migration

Perchloroethylene (PCE) saturations determined from GPR surveys were used as observations for inversion of multiphase flow simulations of a PCE injection experiment (Borden 9??m cell), allowing for the estimation of optimal bulk intrinsic permeability values. The resulting fit statistics and analysis of residuals (observed minus simulated PCE saturations) were used to improve the conceptual model. These improvements included adjustment of the elevation of a permeability contrast, use of the van Genuchten versus Brooks-Corey capillary pressure-saturation curve, and a weighting scheme to account for greater measurement error with larger saturation values. A limitation in determining PCE saturations through one-dimensional GPR modeling is non-uniqueness when multiple GPR parameters are unknown (i.e., permittivity, depth, and gain function). Site knowledge, fixing the gain function, and multiphase flow simulations assisted in evaluating non-unique conceptual models of PCE saturation, where depth and layering were reinterpreted to provide alternate conceptual models. Remaining bias in the residuals is attributed to the violation of assumptions in the one-dimensional GPR interpretation (which assumes flat, infinite, horizontal layering) resulting from multidimensional influences that were not included in the conceptual model. While the limitations and errors in using GPR data as observations for inverse multiphase flow simulations are frustrating and difficult to quantify, simulation results indicate that the error and bias in the PCE saturation values are small enough to still provide reasonable optimal permeability values. The effort to improve model fit and reduce residual bias decreases simulation error even for an inversion based on biased observations and provides insight into alternate GPR data interpretations. Thus, this effort is warranted and provides information on bias in the observation data when this bias is otherwise difficult to assess. ?? 2006 Elsevier B.V. All rights reserved.

Journal of Contaminant Hydrology↗
USGS2005

Two-dimensional advective transport in ground-water flow parameter estimation

Nonlinear regression is useful in ground-water flow parameter estimation, but problems of parameter insensitivity and correlation often exist given commonly available hydraulic-head and head-dependent flow (for example, stream and lake gain or loss) observations. To address this problem, advective-transport observations are added to the ground-water flow, parameter-estimation model MODFLOWP using particle-tracking methods. The resulting model is used to investigate the importance of advective-transport observations relative to head-dependent flow observations when either or both are used in conjunction with hydraulic-head observations in a simulation of the sewage-discharge plume at Otis Air Force Base, Cape Cod, Massachusetts, USA. The analysis procedure for evaluating the probable effect of new observations on the regression results consists of two steps: (1) parameter sensitivities and correlations calculated at initial parameter values are used to assess the model parameterization and expected relative contributions of different types of observations to the regression; and (2) optimal parameter values are estimated by nonlinear regression and evaluated. In the Cape Cod parameter-estimation model, advective-transport observations did not significantly increase the overall parameter sensitivity; however: (1) inclusion of advective-transport observations decreased parameter correlation enough for more unique parameter values to be estimated by the regression; (2) realistic uncertainties in advective-transport observations had a small effect on parameter estimates relative to the precision with which the parameters were estimated; and (3) the regression results and sensitivity analysis provided insight into the dynamics of the ground-water flow system, especially the importance of accurate boundary conditions. In this work, advective-transport observations improved the calibration of the model and the estimation of ground-water flow parameters, and use of regression and related techniques produced significant insight into the physical system.

Massachusetts↗
USGS2005

Inverse models: A necessary next step in ground-water modeling

Inverse models using, for example, nonlinear least-squares regression, provide capabilities that help modelers take full advantage of the insight available from ground-water models. However, lack of information about the requirements and benefits of inverse models is an obstacle to their widespread use. This paper presents a simple ground-water flow problem to illustrate the requirements and benefits of the nonlinear least-squares regression method of inverse modeling and discusses how these attributes apply to field problems. The benefits of inverse modeling include: (1) expedited determination of best fit parameter values; (2) quantification of the (a) quality of calibration, (b) data shortcomings and needs, and (c) confidence limits on parameter estimates and predictions; and (3) identification of issues that are easily overlooked during nonautomated calibration.

Groundwater↗
USGS2005

Iterative use of the Bruggeman-Hanai-Sen mixing model to determine water saturations in sand

The accuracy of the Bruggeman-Hanai-Sen (BHS) mixing model has been previously demonstrated for two-material mixtures during BHS model development. Using permittivities determined from modeling ground-penetrating radar (GPR) data, the BHS model has been iteratively applied to three-material mixtures of water, sand, and a dense, nonaqueous-phase liquid (DNAPL). However, the accuracy of this application has not been verified. A 10-cm air-line system driven by a network analyzer is used to measure bulk permittivitities when the water saturations in a sand are varied (frequency range of 20 to 200 MHz). Through iterative use of the BHS mixing model, the measured permittivities are used to calculate water saturations, which are compared to known saturation values. An iterative BHS mixing model for an air/water/sand system must consider which two-material end member (air/sand or water/sand) represents the matrix term in the original two-material BHS model. An air/sand matrix provides the best accuracy for low water saturations, and a water/sand matrix provides the best accuracy for high water saturations; thus, a new weighted model is developed. For a given porosity and a measured bulk permittivity, water saturation is most accurately determined by proportionally weighting the water saturation values determined using air/sand as the matrix and water/sand as the matrix in the BHS model. ?? 2005 Society of Exploration Geophysicists. All rights reserved.

Geophysics↗
USGS2002

Residual bias in a multiphase flow model calibration and prediction

When calibrated models produce biased residuals, we assume it is due to an inaccurate conceptual model and revise the model, choosing the most representative model as the one with the best-fit and least biased residuals. However, if the calibration data are biased, we may fail to identify an acceptable model or choose an incorrect model. Conceptual model revision could not eliminate biased residuals during inversion of simulated DNAPL migration under controlled conditions at the Borden Site near Ontario Canada. This paper delineates hypotheses for the source of bias, and explains the evolution of the calibration and resulting model predictions.

Acta Universitatis Carolinae, Geologica↗
USGS1999

UCODE, a computer code for universal inverse modeling

This article presents the US Geological Survey computer program UCODE, which was developed in collaboration with the US Army Corps of Engineers Waterways Experiment Station and the International Ground Water Modeling Center of the Colorado School of Mines. UCODE performs inverse modeling, posed as a parameter-estimation problem, using nonlinear regression. Any application model or set of models can be used; the only requirement is that they have numerical (ASCII or text only) input and output files and that the numbers in these files have sufficient significant digits. Application models can include preprocessors and postprocessors as well as models related to the processes of interest (physical, chemical and so on), making UCODE extremely powerful for model calibration. Estimated parameters can be defined flexibly with user-specified functions. Observations to be matched in the regression can be any quantity for which a simulated equivalent value can be produced, thus simulated equivalent values are calculated using values that appear in the application model output files and can be manipulated with additive and multiplicative functions, if necessary. Prior, or direct, information on estimated parameters also can be included in the regression. The nonlinear regression problem is solved by minimizing a weighted least-squares objective function with respect to the parameter values using a modified Gauss-Newton method. Sensitivities needed for the method are calculated approximately by forward or central differences and problems and solutions related to this approximation are discussed. Statistics are calculated and printed for use in (1) diagnosing inadequate data or identifying parameters that probably cannot be estimated with the available data, (2) evaluating estimated parameter values, (3) evaluating the model representation of the actual processes and (4) quantifying the uncertainty of model simulated values. UCODE is intended for use on any computer operating system: it consists of algorithms programmed in perl, a freeware language designed for text manipulation and Fortran90, which efficiently performs numerical calculations.

Computers & Geosciences↗
USGS1998

Documentation of UCODE: A computer code for universal inverse modeling

This report documents the computer program UCODE, which performs inverse modeling, posed as a parameter-estimation problem, using nonlinear regression. Any application model or set of models can be used; the only requirement is that they have numerical (ASCII or text only) input and output files and that the numbers in these files have sufficient significant digits. Application models can include pre-processors and post-processors as well as models related to the processes of interest (physical, chemical, and so on), making UCODE extremely powerful. An estimated parameter can be a quantity that appears in the input files of the application model(s), or that can be used in conjunction with user-defined functions to calculate a quantity that appears in the input files. Observations to be matched in the regression can be any quantity for which a simulated equivalent value can be produced, and simulated equivalent values are calculated using values that appear in the application model output files and a set of additive and multiplicative functions. Prior, or direct, information on estimated parameters also can be included in the regression. The nonlinear regression problem is solved by minimizing a weighted least- squares objective function with respect to the parameter values using a modified Gauss-Newton method. Sensitivities needed for the method are calculated approximately by forward or central differences, and problems and solutions related to this approximation are discussed. Statistics are calculated and printed for use in (1) diagnosing inadequate data and identifying parameters that probably cannot be estimated; (2) evaluating estimated parameter values; (3) evaluating the model representation of the actual processes; and (4) quantifying the likely uncertainty of model simulated values. UCODE is intended for use on any computer operating system: it consists of algorithms programmed in perl, a freeware language designed for text manipulation, and Fortran90, which efficiently performs numerical calculations.

Water-Resources Investigations Report↗
USGS1996

Unrealistic parameter estimates in inverse modelling: A problem or a benefit for model calibration?

Estimation of unrealistic parameter values by inverse modelling is useful for constructed model discrimination. This utility is demonstrated using the three-dimensional, groundwater flow inverse model MODFLOWP to estimate parameters in a simple synthetic model where the true conditions and character of the errors are completely known. When a poorly constructed model is used, unreasonable parameter values are obtained even when using error free observations and true initial parameter values. This apparent problem is actually a benefit because it differentiates accurately and inaccurately constructed models. The problems seem obvious for a synthetic problem in which the truth is known, but are obscure when working with field data. Situations in which unrealistic parameter estimates indicate constructed model problems are illustrated in applications of inverse modelling to three field sites and to complex synthetic test cases in which it is shown that prediction accuracy also suffers when constructed models are inaccurate.

IAHS-AISH Publication↗