Spatial connectivity in a highly heterogeneous aquifer: From cores to preferential flow paths

Abstract

[1] This study investigates connectivity in a small portion of the extremely heterogeneous aquifer at the Macrodispersion Experiment (MADE) site in Columbus, Mississippi. A total of 19 fully penetrating soil cores were collected from a rectangular grid of 4 m by 4 m. Detailed grain size analysis was performed on 5 cm segments of each core, yielding 1740 hydraulic conductivity (K) estimates. Three different geostatistical simulation methods were used to generate 3-D conditional realizations of the K field for the sampled block. Particle tracking calculations showed that the fastest particles, as represented by the first 5% to arrive, converge along preferential flow paths and exit the model domain within preferred areas. These 5% fastest flow paths accounted for about 40% of the flow. The distribution of preferential flow paths and particle exit locations is clearly influenced by the occurrence of clusters formed by interconnected cells with K equal to or greater than the 0.9 decile of the data distribution (10% of the volume). The fraction of particle paths within the high-K clusters ranges from 43% to 69%. In variogram-based K fields, some of the fastest paths are through media with lower K values, suggesting that transport connectivity may not require fully connected zones of relatively homogenous K. The high degree of flow and transport connectivity was confirmed by the values of two groups of connectivity indicators. In particular, the ratio between effective and geometric mean K (on average, about 2) and the ratio between the average arrival time and the arrival time of the fastest particles (on average, about 9) are consistent with flow and advective transport behavior characterized by channeling along preferential flow paths.

A kriging approach based on Aitchison geometry for the characterization of particle-size curves in heterogeneous aquifers

“…Hydraulic conductivity values are then assigned to the blocks of a numerical flow and transport model upon projecting only the 10th and 60th quantiles of the observed PSCs on the computational grid through kriging. A similar approach has been employed, amongst other authors, by Bianchi et al (2011). In this sense, the information content embedded in the particlesize curve is only partially transferred to unsampled locations in the system, through few selected local features (in the example above, the 10th and 60th quantiles).…”

“…The functional approach allows modeling a global variogram for the functional process and the solution of the ensuing kriging system of equations is performed only once yielding the prediction (and associated prediction variance) of the complete particle-size curve at unsampled locations. On the other hand, typical geostatistical analyses [e.g., Riva et al (2006Riva et al ( , 2008Riva et al ( , 2010; Bianchi et al (2011) and references therein] treat each quantile separately (possibly introducing estimated crosscorrelations in terms of cross-variograms) and project their predictions through kriging on a computational grid. Such an approach, besides being methodologically different from the one we propose, can produce inconsistent results (Tolosana-Delgado et al 2008).…”

“…These types of data are typically based on standard grain sieve analysis of soil samples, yielding a discrete representation of the curves by measuring selected particle diameters which, in turn, correspond to quantiles of the particle-size curve. The information can then be employed to classify soil types [e.g., Riva et al (2006) and references therein], to infer hydraulic parameters such as porosity and hydraulic conductivity [e.g., amongst others, Lemke and Abriola (2003); Riva et al (2006Riva et al ( , 2008Riva et al ( , 2010; Bianchi et al (2011);Tong et al (2010); Barahona-Palomo et al (2011) and references therein], or, in the presence of inorganic compounds, to provide estimates of the porous medium sorption capacity [e.g., Hu et al (2004) and references therein].…”

A practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm

“…A TI is a conceptual image of the expected spatial structure and is often built based on prior information. Using a TI allows extracting multiple-point statistics and hence describing more complex patterns; this is especially important when spatial connectivity plays a key role in the model application (Bianchi et al, 2011;Gó mez-Herná ndez and Wen, 1998;Renard and Allard, 2012;Zinn and Harvey, 2003).…”

Probabilistic human health risk assessment of degradation‐related chemical mixtures in heterogeneous aquifers: Risk statistics, hot spots, and preferential channels

“…The spatial heterogeneity of the hydrogeological properties will dictate the relative importance of the degradation products to the total human health risk. In this context, the variability of the hydraulic properties typically leads to preferential flow channels and lowpermeability areas where contaminants can be temporarily trapped by rate-limited mass transfer [e.g., Gomez-Hernandez and Wen, 1998;Zinn and Harvey, 2003;Bianchi et al, 2011]. The formation of these fast flow channels is typically associated with the presence of well-connected, highly permeable geological bodies or structures that can concentrate flow and solute transport [e.g., Knudby and Carrera, 2005; Incorporating hydrogeological uncertainty in human health predictions has been a topic of intense research in the past [e.g., Andričević and Cvetković, 1996;de Barros and Rubin, 2008;Cvetković and Molin, 2012;Rodak and Silliman, 2011;Andričević et al, 2012;Siirila and Maxwell, 2012;Atchley et al, 2013;de Barros and Fiori, 2014].…”