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Journal ArticleDOI

A Stochastic-Conceptual Analysis of One-Dimensional Groundwater Flow in Nonuniform Homogeneous Media

R. Allan Freeze
- 01 Oct 1975 - 
- Vol. 11, Iss: 5, pp 725-741
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TLDR
In this paper, the effects of stochastic parameter distributions on predicted hydraulic heads are analyzed with the aid of a set of Monte Carlo solutions to the pertinent boundary value problems, and the results show that the standard deviations of the input hydrogeologic parameters, particularly σy and σc, are important index properties; changes in their values lead to different responses for even when the means μy, μc, and μn are fixed.
Abstract
The most realistic representation of a naturally occurring porous medium is a stochastic set of macroscopic elements in which the values of the three basic hydrogeologic parameters (hydraulic conductivity K, compressibility α, and porosity n) are defined by frequency distributions. A homogeneous formation under this representation is one in which the frequency distributions do not change through space. All soils and geologic formations, even the ones that are homogeneous, show random variations in the values of the hydrogeological parameters through space; that is, they are nonuniform, and a measure of the nonuniformity is provided by the standard deviation of the frequency distributions. If K and α are log normally distributed and n is normally distributed, and if we define Y = log K and C = log α, then the parameters Y, C, and n can be generated from a multivariate normal density function with means μy, μc, and μn, standard deviations σy, σc, and σn, and correlation coefficients ρyc, ρyn, and ρcn The analysis of groundwater flow in nonuniform media requires a stochastic-conceptual approach in which the effects of stochastic parameter distributions on predicted hydraulic heads are analyzed with the aid of a set of Monte Carlo solutions to the pertinent boundary value problems. In this study, two one-dimensional saturated flow problems are analyzed: steady state flow between two specified heads and transient consolidation of a clay layer. The primary output is the statistical distribution of hydraulic head ϕ, through space and time, as indicated by the mean values and their standard deviations Sϕ¯(x, t) Results show that the standard deviations of the input hydrogeologic parameters, particularly σy and σc, are important index properties; changes in their values lead to different responses for even when the means μy, μc, and μn are fixed. The degree of uncertainty associated with hydraulic head predictions increases as the degree of nonuniformity of the porous medium increases. For large values of σy and σc it becomes virtually impossible to obtain meaningful hydraulic head predictions. For transient flow the output distribution of hydraulic head values is almost never normal; in some cases it approaches a uniform distribution. The results of this study throw into question the validity of the hidden assumption that underlies all deterministic groundwater modeling; namely, that it is possible to select a single value for each flow parameter in a homogeneous but nonuniform medium that is somehow representative and hence define an ‘equivalent’ uniform porous medium. For transient flow there may be no way to define an equivalent medium. The fact that nine index parameters rather than three are required to describe a nonuniform geologic formation, the large uncertainties in predicted hydraulic heads for relatively simple flow problems in nonuniform soils, and the contention that there may be no simple way to define an equivalent uniform porous medium all have important implications in the development of groundwater flow theory and in its most fundamental applications.

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Citations
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Connectivity Field: a Measure for Characterising Fracture Networks

TL;DR: The connectivity field (CF), as a useful measure to evaluate the spatial connectivity characteristics of fractures in a fracture network, is proposed and two extensions are proposed: the generalised connectivity field, a measure that is independent of support size, and the probabilistic connectivity field.
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Parameter-independent model reduction of transient groundwater flow models: Application to inverse problems

TL;DR: In this paper, a new methodology is proposed for the development of parameter-independent reduced models for transient groundwater flow models based on Galerkin projection of a highly discretized model onto a subspace spanned by a small number of optimally chosen basis functions.
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A furrow irrigation model to improve irrigation practices in the Gharb valley of Morocco

TL;DR: In this paper, a model based on the spatial and temporal variability of infiltration is developed to simulate the impact of irrigation practices on water saving during an irrigation season, which will later contribute to the elaboration of a modelling approach simulating fertilization and irrigation practices.
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Measurement of Soil Physical Properties in the Field

TL;DR: A summary of the characteristics of field soils gleaned from such measurements can be found in this article, where some criteria for field measurements are identified and predictive frameworks for interpreting and using field data are discussed.
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Probabilistic capture zone delineation based on an analytic solution.

TL;DR: A statistical theory was developed for including the uncertainty in the transmissivity and the magnitude and direction of the hydraulic head gradient in the analytic solution for both the ultimate and time-dependent capture zone for an arbitrary reliability level.
References
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Book

Dynamics of fluids in porous media

Jacob Bear
TL;DR: In this paper, the Milieux poreux Reference Record was created on 2004-09-07, modified on 2016-08-08 and the reference record was updated in 2016.
Journal ArticleDOI

Statistical Continuum Theories

Journal ArticleDOI

Stochastic dynamic prediction

TL;DR: Stochastic dynamic prediction as mentioned in this paper assumes the laws governing atmospheric behavior are entirely deterministic, but seeks solutions corresponding to probabilistic statements of the initial conditions, thus recognizing the impossibility of exact or sufficiently dense observations.