Comparing statistical models of physical heterogeneity in buried‐valley aquifers

Ritzi, Robert W.; Dominic, David F.; Slesers, Andris J.; Greer, Christopher B.; Reboulet, E. C.; Telford, Jennifer A.; Masters, Robert W.; Klohe, Cheryl A.; Bogle, Jessica L.; Means, Brent P.

doi:10.1029/2000wr900143

Cited by 54 publications

(47 citation statements)

References 29 publications

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“…Numerous papers address the hydrogeological conceptualisation using hydrofacies: e.g. in glacial melt water-stream sediment and till (Anderson, 1989), buried valley aquifers (Ritzi et al, 2000); and alluvial fan depositional systems (Weissmann and Fogg, 1999). Comprehensive reviews and compilations of this issue can be found in e.g.…”

Section: What Is a Hydrogeological Conceptual Model?mentioning

confidence: 99%

“…Fogg (1996 and) present a transition probability geostatistical framework that can be conditioned to hard as well as soft data in simulating hydrofacies distributions. There are several examples on application which include simulation of alluvial fan systems (Fogg et al, 1998;Weissmann et al, 1999;Weissmann and Fogg, 1999), river valley aquifer systems (Ritzi et al, 1994;Ritzi et al, 2000), Quaternary aquifer complex (Troldborg et al, 2007 1 ) and sandlenses distribution within glacial till in (Sminchak et al, 1996;Petersen et al, 2004). Neuman and Wieranga (2002) present a generic strategy that embodies a systematically and comprehensive multiple conceptual model approach, including hydrogeological conceptualisation, model development and predictive uncertainty analysis.…”

Section: What Is a Hydrogeological Conceptual Model?mentioning

confidence: 99%

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Uncertainty in geological and hydrogeological data

Nilsson

Højberg

Refsgaard

et al. 2007

Hydrol. Earth Syst. Sci.

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Abstract. Uncertainty in conceptual model structure and in environmental data is of essential interest when dealing with uncertainty in water resources management. To make quantification of uncertainty possible is it necessary to identify and characterise the uncertainty in geological and hydrogeological data. This paper discusses a range of available techniques to describe the uncertainty related to geological model structure and scale of support. Literature examples on uncertainty in hydrogeological variables such as saturated hydraulic conductivity, specific yield, specific storage, effective porosity and dispersivity are given. Field data usually have a spatial and temporal scale of support that is different from the one on which numerical models for water resources management operate. Uncertainty in hydrogeological data variables is characterised and assessed within the methodological framework of the HarmoniRiB classification.

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Section: What Is a Hydrogeological Conceptual Model?mentioning

confidence: 99%

Section: What Is a Hydrogeological Conceptual Model?mentioning

confidence: 99%

Uncertainty in geological and hydrogeological data

Nilsson

Højberg

Refsgaard

et al. 2007

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…To fully characterize the geostatistical models for the log conductivity, Y = ln K, parameters such as the mean hY i, the variance s Y 2 , and the horizontal (I Y,h ) and vertical (I Y,v ) integral scales need to be determined. In many cases, more complex structures need to be identified [Ritzi et al, 2000]. Inferring such parameters is a challenging task, often requiring a large number of measurements at a prohibitive cost.…”

Section: Introductionmentioning

confidence: 99%

On the use of peak concentration arrival times for the inference of hydrogeological parameters

Bellin

Rubin

2004

Water Resources Research

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[1] Aquifer characterization is subject to large uncertainty due to spatial variability of the hydrologic parameters on the one hand, coupled with data scarcity and measurement error on the other. Traditional characterization methods rely on direct measurements of the hydrologic parameters, which usually are quite few and with large distances in between. In recent years, attempts have been made to evaluate low-cost, minimally invasive alternative sources of information that can be used to augment the direct measurements. This paper attempts to augment the data available for characterization by looking at tracer experiments. Specifically, we analyze the suitability of peak concentration arrival times to provide information useful for inference of geostatistical models of the conductivity. We found that the peak concentration arrival time is a good proxy for the advectiondominated travel time and that it is particularly useful for inference through comparison with advection-dominated, Lagrangian travel time moments when these models are expressed in terms of the geostatistical models of the conductivity. What makes the peak arrival time suitable for this is its limited sensitivity to pore-scale dispersion at early travel times and its resistance to errors induced by infrequent sampling especially at early and late arrival times. This paper analyzes this concept through analysis of a synthetic case study as well as an analysis of field data collected during a forced-gradient tracer test.

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“…However sedimentology studies [see e.g. Miall, 1996;Ritzi et al, 1995Ritzi et al, , 2000Ritzi and Allen-King, 2007] have shown that unconsolidated sedimentary formations are much more complex with the spatial variability of hydraulic conductivity dominated by the intertwined arrangement of hydrofacies, i.e. geological facies with contrasting hydraulic properties.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, since lithological data are typically more abundant than hydraulic conductivity measurements, with relatively few conductivity data it is still possible to characterize at least the large-scale geological features of the formation. For example, Ritzi et al [2000] showed that spatial variability of permeability in a buriedvalley aquifer follows a bimodal distribution. Furthermore, a bimodal field is much more suitable than a Gaussian field for capturing the spatial organization of interconnected high-conductivity zones, which constitutes the backbones through which solute propagates as preferential pathways [Zinn and Harvey, 2003].…”

Section: Introductionmentioning

confidence: 99%

Spatial moments analysis of kinetically sorbing solutes in aquifer with bimodal permeability distribution

Massabò

Bellin

Valocchi

2008

Water Resources Research

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[1] We analyze the influence of the formation structure upon transport of nonreactive and kinetically sorbing solutes in heterogeneous porous formations. Formation structure is represented through a bimodal distribution of hydraulic conductivity with two scales of spatial variability, which is a simplified, yet realistic, model of spatial variability. Kinetic sorption is modeled using a linear reversible rate expression. We derive new closed-form solutions of the second ergodic spatial moments of a kinetically sorbing solute. Although these solutions are obtained through a perturbation expansion in term of logconductivity variance, they are not limited to any particular model of spatial variability. We show that the formation structure, as described by the bimodal model, has a significant impact on plume moments of a non-reactive tracer at moderate contrast between mean hydraulic conductivity of the two hydrofacies, when the two scales of variability have comparable weight. On the contrary, the impact diminishes when bimodality weakens at both low and high contrast. As expected, the large time asymptotic longitudinal macrodispersion coefficient is influenced by the structure only through the variance and integral scale of log-conductivity, and thus the result for the bimodal model is identical to that of a unimodal Gaussian field sharing the same second-order statistics. The impact of kinetic sorption depends upon the plume travel time since injection. At early times, sorption kinetics tends to accentuate the difference between the longitudinal macrodispersivity in bimodal and unimodal fields, while at later times the difference is minimal because overall macrodispersivity is dominated by the spreading caused by nonequilibrium sorption. In a bimodal formation transverse macrodispersivity peaks later and declines to zero slower than in a Gaussian unimodal field with the same second-order statistics. These results demonstrate that even a very simplified facies model, such as the bimodal model relying on a statistical distribution of high-and low-conductivity zones, has important impacts upon global parameters describing the migration of nonreactive and reactive tracers. Consequently, larger effects are expected for quantities such as early arrival times and BTC tailing which are more directly influenced by the spatial patterns of high hydraulic conductivity than global parameters, such as macrodispersivity. Furthermore, these solutions contribute to bridge the gap between stochastic modeling and applications, since bimodal fields can be obtained to honor soft data typically available in applications, for example through conditional simulations using stratigraphic information from well logs.Citation: Massabó, M., A. Bellin, and A. J. Valocchi (2008), Spatial moments analysis of kinetically sorbing solutes in aquifer with bimodal permeability distribution, Water Resour. Res., 44, W09424,

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Comparing statistical models of physical heterogeneity in buried‐valley aquifers

Cited by 54 publications

References 29 publications

Uncertainty in geological and hydrogeological data

Uncertainty in geological and hydrogeological data

On the use of peak concentration arrival times for the inference of hydrogeological parameters

Spatial moments analysis of kinetically sorbing solutes in aquifer with bimodal permeability distribution

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