[1] Geology is ubiquitously heterogeneous, exhibiting both discrete and continuous spatial variations on a multiplicity of scales. It is therefore natural to expect that hydrogeologic and other geophysical variables would do likewise. We present evidence that hydrogeologic variables exhibit isotropic and directional dependencies on scales of measurement (data support), observation (extent of phenomena such as a dispersing plume), sampling window (domain of investigation), spatial correlation (structural coherence), and spatial resolution (descriptive detail). We then show that it is possible to interpret these multiple scale dependencies within a unified theoretical framework. This and similar theoretical frameworks may be applicable to a wider range of geophysical scale issues. INDEX TERMS: 3250 Mathe-

Abstract. An interpretation is offered for the observation that the log hydraulic conductivity of geologic media often appears to be statistically homogeneous but with variance and integral scale which grow with domain size. We first demonstrate that the power (semi)variogram and associated spectra of random fields, having homogeneous isotropic increments, can be constructed as weighted integrals from zero to infinity (an infinite hierarchy) of exponential or Gaussian variograms and spectra of mutually uncorrelated homogeneous isotropic fields (modes). We then analyze the effect of filtering out (truncating) high-and low-frequency modes from this infinite hierarchy in the real and spectral domains. A low-frequency cutoff renders the truncated hierarchy homogeneous with an autocovariance function that varies monotonically with separation distance in a manner not too dissimilar than that of its constituent modes. The integral scales of the lowest-and highest-frequency modes (cutoffs) are related, respectively, to the length scales of the sampling window (domain) and data support (sample volume). Taking each relationship to be one of proportionality renders our expressions for the integral scale and variance of a truncated field dependent on window and support scales in a manner consistent with observations. The traditional approach of truncating power spectral densities yields autocovariance functions that oscillate about zero with finite (in one and two dimensions) or vanishing (in one dimension) integral scales. Our hierarchical theory allows bridging across scales at a specific locale, by calibrating a truncated variogram model to data observed on a given support in one domain and predicting the autocovariance structure of the corresponding multiscale field in domains that are either smaller or larger. One may also venture (we suspect with less predictive power) to bridge across both domain scales and locales by adopting generalized variogram parameters derived on the basis of juxtaposed hydraulic and tracer data from many sites.

Abstract. It has been shown by Di Fededco and Neuman [1997, 1998a, b] that observed multiscale behaviors of subsurface fluid flow and transport variables can be explained within the context of a unified stochastic framework, which views hydraulic conductivity as a random fractal characterized by a power variogram. Such a random field is statistically nonhomogeneous but possesses homogeneous spatial increments. Di Federico and Neuman [1997] have demonstrated that the power variogram and associated spectra of a statistically isotropic fractal field can be constructed as a weigh. ted integral from zero to infinity (an infinite hierarchy) of exponential or Gaussian variograms and spectra of mutually uncorrelated fields (modes) that are homogeneous and isotropic. We show in this paper that the same holds true when the field and its constituent modes are statistically anisotropic, provided the ratios between principal integral (spatial correlation) scales are the same for all modes. We then analyze the effect of filtering out (truncating) modes of low, high, and intermediate spatial frequency from this infinite hierarchy in the real and spectral domains. A low-frequency cutoff renders the truncated hierarchy homogeneous. The integral scales of the lowest-and highest-frequency cutoff modes are related to length scales of the sampling window (domain) and data support (sample volume), respectively. Taking the former to be proportional to the latter renders expressions for the integral scale and variance of the truncated field dependent on window and support scale (in a manner previously shown to be consistent with observations in the isotropic case). It also allows (in principle) bridging across scales at a specific locale, as well as among locales, by adopting either site-specific or generalized variogram parameters. The introduction of intermediate cutoffs allows us to account, in a straightforward manner, for lacunarity due to gaps in the multiscale hierarchy created by the absence of modes associated with discrete ranges of scales (for example, where textural and structural features are associated with distinct ranges of scale, such as fractures having discrete ranges of trace length and density, which dissect the rock into matrix blocks having corresponding ranges of sizes). We explore mathematically and graphically the effects that anisotropy and lacunarity have on the integral scale, variance, covariance, an d spectra of a truncated fractal field. We then develop an expression for the equivalent hydraulic conductivity of a box-shaped porous block, embedded within a multiscale log hydraulic conductivity field, under mean-uniform flow. The block is larger than the support scale of the field but is smaller than a surrounding sampling window. Consequently, its equivalent hydraulic conductivity is a random variable whose variance and spatial autocorrelation function, conditioned on a known mean value of support-scale conductivity across the window, are given explicitly by our multiscale theory.

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