the usefulness of outcrop-analogue air-permeameter ... · tures and hydrogeological parameters for...

Hydrol. Earth Syst. Sci., 17, 5155–5166, 2013www.hydrol-earth-syst-sci.net/17/5155/2013/doi:10.5194/hess-17-5155-2013© Author(s) 2013. CC Attribution 3.0 License.

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The usefulness of outcrop-analogue air-permeameter measurementsfor analysing aquifer heterogeneity: testing outcrop hydrogeologicalparameters with independent borehole data

B. Rogiers1,2, K. Beerten1, T. Smeekens2, D. Mallants3, M. Gedeon1, M. Huysmans2,4, O. Batelaan2,4,5, andA. Dassargues2,6

1Institute for Environment, Health and Safety, Belgian Nuclear Research Centre (SCK·CEN), Boeretang 200,2400 Mol, Belgium2Dept. of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200e – bus 2410, 3001 Heverlee, Belgium3Groundwater Hydrology Program, CSIRO Land and Water, Waite Road – Gate 4, Glen Osmond, SA 5064, Australia4Dept. of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium5National Centre for Groundwater Research and Training (NCGRT), School of the Environment, Flinders University,G.P.O. Box 2100, Adelaide, SA 5001, Australia6Hydrogeology and Environmental Geology, Dept. of Architecture, Geology, Environment and Civil Engineering (ArGEnCo)and Aquapole, Université de Liège, B.52/3 Sart-Tilman, 4000 Liège, Belgium

Correspondence to:B. Rogiers ([email protected])

Received: 28 June 2013 – Published in Hydrol. Earth Syst. Sci. Discuss.: 23 July 2013Revised: 17 October 2013 – Accepted: 18 November 2013 – Published: 18 December 2013

Abstract. Outcropping sediments can be used as easily ac-cessible analogues for studying subsurface sediments, espe-cially to determine the small-scale spatial variability of hy-drogeological parameters. The use of cost-effective in situmeasurement techniques potentially makes the study of out-crop sediments even more attractive. We investigate to whatdegree air-permeameter measurements on outcrops of uncon-solidated sediments can be a proxy for aquifer saturated hy-draulic conductivity (K) heterogeneity. The Neogene aquiferin northern Belgium, known as a major groundwater re-source, is used as the case study.K and grain-size data ob-tained from different outcropping sediments are comparedwith K and grain-size data from aquifer sediments obtainedeither via laboratory analyses on undisturbed borehole cores(K and grain size) or via large-scale pumping tests (K only).This comparison shows a pronounced and systematic differ-ence between outcrop and aquifer sediments. Part of this dif-ference is attributed to grain-size variations and earth surfaceprocesses specific to outcrop environments, including rootgrowth, bioturbation, and weathering. Moreover, palaeoen-vironmental conditions such as freezing–drying cycles anddifferential compaction histories will further alter the initial

hydrogeological properties of the outcrop sediments. A lin-ear correction is developed for rescaling the outcrop data tothe subsurface data. The spatial structure pertaining to out-crops complements that obtained from the borehole coresin several cases. The higher spatial resolution of the out-crop measurements identifies small-scale spatial structuresthat remain undetected in the lower resolution borehole data.Insights in stratigraphic andK heterogeneity obtained fromoutcrop sediments improve developing conceptual models ofgroundwater flow and transport.

1 Introduction

Compared to core drilling for sample collection and analysis,outcropping sediments are easily accessible analogues forstudying subsurface sediments. This outcrop-analogue con-cept has been extensively applied in the oil industry for theanalysis and modelling of reservoirs (e.g. Flint and Bryant,1993; McKinley et al., 2004) resulting in various tools tocharacterize geological facies geometries, their connectivityand continuity (Pringle et al., 2004), and to create 3-D virtual

Published by Copernicus Publications on behalf of the European Geosciences Union.

5156 B. Rogiers et al.: Testing outcrop hydrogeological parameters with independent borehole data

outcrop models (Pringle et al., 2006). The concept has alsobeen used with small-scale outcrops in unconsolidated mate-rial (e.g. Teutsch et al., 1998; Bayer et al., 2011), collectingboth hydraulic and geophysical data. Most of these studiesare more concerned with defining the geological facies ge-ometry rather than determining the corresponding hydroge-ological parameters and hence direct quantification of theseparameters and certainly a comparison with the correspond-ing subsurface parameters is often lacking.

In slightly dipping unconsolidated stratigraphic settings,a very limited number of facies are generally encounteredin a single outcrop. The information contained within suchlithofacies type potentially represents key stratigraphic fea-tures and hydrogeological parameters for building concep-tual groundwater flow models. Furthermore, different out-crops may represent different parts of a stratigraphic or land-scape succession series (Beerten et al., 2012). The combina-tion of several outcrops can then be used to obtain a com-posite picture of an aquifer system containing the same orat least similar sediments. As demonstrated by Rogiers etal. (2013a), the use of a hand-held air permeameter is avery accurate and cost-effective approach for quantifying hy-draulic conductivity (K) and its spatial variability in situ onoutcropping sediments. The question that remains howeveris how representative the obtained outcrop parameters are forthe actual subsurface sediments.

In first instance, the outcrop sediments may differ insome aspects from their subsurface equivalents as a resultof slightly differing depositional contexts, e.g. with respectto the position in the basin (palaeogeographical conditions).Inherently, this problem is largely circumvented by compar-ing outcrop and subcrop sediments from one and the sameformation.

Secondly, the outcropping sediments could also be influ-enced by post-depositional processes such as surficial weath-ering and compaction due to slightly different overburdensedimentation and erosion histories. During the initial load-ing of sands, a rapid increase of packing density and soilstrength is expected due to grain reorganization (Pettersen,2007). As packing becomes tighter, further packing will beincreasingly more difficult to achieve, each packing level ismore stable than previous levels and deformation is perma-nent. This process should be visible in the porosity, bulk den-sity and eventuallyK data of a progressively compacted ma-terial. Overconsolidated sands should however not show di-lation properties, and unloading would thus have little effect.However, the amounts of silt and clay present throughoutthe aquifer sediments might initiate such dilation properties.Moreover, dissolution of certain mineral phases or frame-work grains by meteoric water might also enhance perme-ability, as shown by Lambert et al. (1997).

The objectives of this paper are therefore (i) to test whetherthe hydraulic conductivity and its spatial heterogeneity inoutcrops obtained through air permeametry are comparableto those of nearby aquifer and aquitard sediments, (ii) to

evaluate major differences between outcrop and aquifer sed-imentK heterogeneity including the transferability of infor-mation from outcrop to aquifer sediments, and (iii) to dis-cuss the scale effect and overall outcrop parameter represen-tativity for use in groundwater modelling. For this purposethe results from the outcrop study by Rogiers et al. (2013a)are compared with more standard borehole core analyses andpumping test results. Moreover, grain-size analyses are usedto verify the similarity between outcrop and subsurface sed-iments. In a final step, we provide possible explanations forthe observed differences inK behaviour and options on howto integrate air permeametry-based data with existing knowl-edge available from borehole and pump test analyses in viewof developing more reliable groundwater flow models.

2 Materials and methods

Table 1 provides an overview of all data used in this paper.The hydrogeological setting and the outcrop measurementsare discussed first. The data at each outcrop has been up-scaled to an equivalentK tensor. Next, the constant-headmeasurements on the borehole core samples are discussed.The procedure for obtaining grain-size distributions is de-scribed, and we shortly introduce the used pumping testmethods and analyses. Finally we outline the approach forvariography of the data to quantify spatial variability.

2.1 Hydrogeological setting and outcrop analyses

Rogiers et al. (2013a) proposed a methodology to measuresmall-scaleK variability from unconsolidated outcrop sed-iments and to calculate outcrop-scale equivalentK values.This methodology relies on air permeability measurementsthat are converted to saturatedK values using the empiricalequation from Iversen et al. (2003), and a subsequent numer-ical upscaling step. The air permeability measurements areperformed with a hand-held air permeameter, the Tinyperm II(New England Research & Vindum Engineering, 2011), on aregular grid of measurement locations at the outcrop face.The TinyPerm II device has an inner tip diameter of 9 mm,resulting in an investigation depth of 9–18 mm, correspond-ing to a maximum spatial support of∼ 24 cm3. Pressing thedevice plunger will create a vacuum to withdraw air from theoutcrop sediments. A microprocessor analyzes the pressureincrease, and returns air permeability. The resulting valuescannot be converted directly to saturated hydraulic conduc-tivity because corrections are needed in regards to (i) the po-lar characteristics of water, (ii) the fact that air at atmosphericpressure does not act as a true fluid continuum in soil (e.g. gasslippage might occur at the interface with solids), and (iii) thedifficulty in obtaining totally dry conditions in the investi-gated sediments. The use of empirical relationships like thatof Iversen et al. (2003) has proven to be very effective inconverting air permeability into hydraulic conductivity.

Hydrol. Earth Syst. Sci., 17, 5155–5166, 2013 www.hydrol-earth-syst-sci.net/17/5155/2013/

B. Rogiers et al.: Testing outcrop hydrogeological parameters with independent borehole data 5157

Table 1.Overview of the differentK and grain-size samples used in this paper.

Sediment Parameter Outcrop Borehole Pumpingtest

Mol formation

No. of K samples 32 161 9No. of grain-size measurements – 61 –Sample spacing 20 cm 2 m –Measurement support ∼ 24 cm3 100 cm3 large-scale

Kasterlee formation: sandy part

No. of K samples 112 96 9No. of grain-size measurements 6 12 –Sample spacing 10 cm 2 m –Measurement support ∼ 24 cm3 100 cm3 large-scale

Kasterlee formation: clayey part

No. of K samples 127 61 1No. of grain-size measurements 9 32 –Sample spacing 10 cm 2 m –Measurement support ∼ 24 cm3 100 cm3 large-scale

Diest formation: clayey part

No. of K samples 192 89 –No. of grain-size measurements 4 38 –Sample spacing 5 cm 2 m –Measurement support ∼ 24 cm3 100 cm3 large-scale

Diest formation: sandy part

No. of K samples 48 61 10No. of grain-size measurements – 42 –Sample spacing 10 cm 2 m –Measurement support ∼ 24 cm3 100 cm3 large-scale

Source: Rogiers et al. (2013a) and Beerten et al. (2010).

This methodology was tested on five outcrops from threekey formations of the Neogene aquifer in north-eastern Bel-gium (from top to bottom): the Mol formation (the abbrevi-ation Fm. will be used in the subsequent discussions), sandyand clayey parts of the Kasterlee Fm., and the clayey andsandy parts of the Diest Fm. For these five formations addi-tional geological and hydrogeological data is available froma recent characterization campaign (Beerten et al. 2010) ofthe shallow aquifer sediments in Mol/Dessel (up to about40 m depth), including seven cored boreholes (Fig. 2 inRogiers et al., 2013a). This lithostratigraphical successionand its main characteristics are presented in Fig. 1. Apartfrom the minimum and maximum unit thickness obtainedfrom this recent characterization campaign, a typical bore-hole core from the clayey Kasterlee Fm. is displayed, as wellas a grain-size and glauconite content profile through mostof the units. The most striking features are the high clayand fine silt contents within the aquitard represented by theclayey part of the Kasterlee Fm., the sudden increase of theglauconite content in the sediments below this unit, and thecontrast in coarse sand content between the upper and loweraquifers separated by the aquitard.

In addition to the individual air-permeameter measure-ments (spatial support of∼ 24 cm3) and their statistics,the measurement grids were numerically upscaled to ob-tain equivalent horizontal and verticalK values at the scale

Minimum thickness

Maximum thickness

≈ 2 m

≈ 10 m

≈ 2 m

≈ 5 m

≈ 7 m

≈ 5 m

≈ 20 m

≈ 6 m

≈ 7 m

≈ 10 m

1 m

Typical glauconite

content

% wt

0 20 40 60

Typical grain-size

profile

% wt

0 50 100

Q

M

SK

CK

CD

Q

M

SK

CK

CD

Fig. 1. Overview of the studied lithostratigraphical succession withformation thicknesses, typical glauconite content (weight percent-age; % wt), and a typical grain-size profile. A picture of a boreholecore from the clayey part of the Kasterlee formation is providedto illustrate its heterogeneity. For more information, see Beerten etal. (2010).

of the outcrop (i.e. typically several m2; Rogiers et al.,2013a). This was done by using the approach of Li etal. (2011). The measurements on the sampling grid were con-verted into a numerical grid, with one extra grid cell at allsides. By invoking flow conservation for a combination of

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Fig. 2. Cumulative grain-size distributions for the outcrop (laser diffraction) and borehole data (mean value and 5–95 percentiles fromSediGraph or standard method; Beerten et al. 2010) for(A) the sandy Kasterlee Fm.,(B) the clayey Kasterlee Fm. and(C) clayey Diest Fm.

different boundary conditions an equivalentK tensor wasobtained. An overview of this approach for all outcropscharacterized by air-permeameter measurements within thestudy area is provided by Rogiers et al. (2013b). The in-dividual small-scale air-permeameter results show a corre-lation of 0.93 with independent constant-head laboratorypermeameter measurements on 100 cm3 ring samples takenfrom the same outcrop measurement grid (Rogiers et al.,2013a). The average ratio between both log-transformedK

data (air permeameter/constant head) equals 1.03, and is be-tween 0.78 and 1.24 for individual samples. Repeatability ofthe TinyPerm II measurements was tested on a set of differ-ent lithologies withK ranging from 10−3.5 to 10−6.5 m s−1,with maximum log10(K) error variance of 0.007. Given thishigh repeatability, and the absence of visible macropores inthe investigated outcrop faces, theK data obtained from theoutcrops is deemed accurate and unbiased.

2.2 Constant-headK measurements

To characterize the aquifer sediments’ hydraulic conductivityvariability, multiple undisturbed 100 cm3 ring samples (withdiameter of 53 mm) were taken from contiguous boreholecores (Beerten et al., 2010). The ring samples were pushedin the cores in horizontal or vertical direction, for characteri-zation of respectively horizontal or verticalK. The gathereddata enclose several hundred hydraulic conductivity mea-surements on such 100 cm3 ring samples from seven coredboreholes, representing 350 m of core material. Two sam-ples were taken each 2 m, for horizontal and verticalK, butthe anisotropy at the sample scale was generally negligible(Beerten et al., 2010). The average thickness of the Moland Kasterlee formations in these boreholes is respectively20 and 10 m. The highly stratified clayey part of the Kaster-lee Fm. – coarse sand layers alternate with heavy clay lenseswith thickness varying from less than a cm to several cm– varies in thickness from 2 to 6 m. The Diest formation isnot penetrated fully, but was characterized on average across15 m.

All 100 cm3 ring samples were analyzed in the lab us-ing the constant-head method (Klute, 1965), using a low-pressure device for coarse material and a high-pressure de-vice (approx. 6 bar) for the clay material expected to displaylow K values (see Beerten et al., 2010 for more details). To-tal porosity was also determined for most core samples, aswell as bulk density and volumetric moisture content for theoutcrop samples, by repeatedly weighing the samples afterdrying and complete saturation. The methodology is similarto that used by Rogiers et al. (2013a) to validate the outcropair-permeameter measurements.

2.3 Grain-size measurements

A SediGraph or a combination of standard sieving and a sus-pension cylinder (European standard EN 933-1) was used toquantify, respectively, 20 and eight grain-size fractions of theborehole core samples. All samples were prepared by remov-ing carbonates and organic matter. Clay samples were an-alyzed with the SediGraph, after removing particles largerthan 250 µm by sieving. For more details on the data, thereader is referred to Beerten et al. (2010) and Rogiers etal. (2012).

Grain-size analyses of outcrop samples were performedby laser diffraction with a Malvern Mastersizer (MalvernInstruments Ltd., UK). This method consists of monitoringthe amount of reflection and diffraction that is transmittedback from a laser beam directed at the particles, and quan-tifies 64 grain-size fractions. Each sample was divided into10 sub-samples by a rotary sample splitter to enable repeatedmeasurements on a single sample, and all samples were mea-sured at least twice. The final result was based on the averagegrain-size distribution of all sub-samples. Note that particlesizes are expressed as the size of an equivalent sphere withan identical diffraction pattern.

2.4 Pumping tests

Step drawdown, constant discharge and recovery tests wereperformed at different locations within the study area,including some of the borehole locations. The transient



groundwater head observations were interpreted with analyt-ical as well as numerical models (Meyus and Helsen, 2012).Results from these large-scale tests are used here to illustratethe scale effect for hydraulic conductivity determination onsubsurface sediments, and to compare such large-scale mea-surements with the numerically upscaledK values for theoutcrops.

2.5 Variography

The experimental variograms are all fitted with sphericalmodels, using a weighted least squares approach. Two ap-proaches are tested: (1) treating both data sets separately(variogram models for the outcrops are taken from Rogierset al., 2013a), and (2) using a pooled data set which com-bines both outcrop and borehole data. In the latter case equalweight is given to both data sets in the least squares fitting.In the former case individual experimental variogram pointsare weighted according to the number of point pairs they rep-resent. The initial variogram parameters for the nugget, to-tal sill and range were respectively set to the overall min-imum semivariance, the data variance, and the maximumlag distance. In certain cases singular model fits occurreddue to non-uniqueness (data does not allow to discriminatebetween different equivalent models, e.g. pure nugget vsspherical model with zero range). The responsible parame-ters were then fixed at their initial value, before re-initialisingthe model fitting procedures. All variography was performedwith the gstat package (Pebesma, 2004).

3 Results and discussion

3.1 Grain-size distributions

Prior to comparingK values obtained from different mea-surement methods, a comparison is made between grain-sizedistributions for the outcrop sediments and aquifer materialscollected from cored boreholes (Beerten et al., 2010). Thisevaluation is necessary to verify if the outcrop and aquifersediments represent the same lithostratigraphical units, andto highlight possible discrepancies between both to informthe comparison of their correspondingK values.

Overall there is good correspondence between out-crop/aquifer grain-size distributions for the sandy part of theKasterlee Fm. and clayey part of the Diest Fm. (Fig. 2a–c), with a somewhat larger fraction of fines (i.e. between2 and 22 µm) for the outcrop samples. Van Ranst and DeConinck (1983) suggested that post-depositional weatheringof glauconite material, a green iron-rich clay mineral, mightincrease the relative amount of fines. Kasterlee formationsamples collected from boreholes contain glauconite up toa few percent, but for the Diest formation it is at least 10 to20 % (Beerten et al., 2010). The disintegration of the glau-conite fractions in the outcrops could thus have increased thefines content.

The comparison further illustrates that the clay fraction(< 2 µm) of the clayey part of the Kasterlee Fm. is about20 % lower in the outcrop samples compared to the aquifermaterial. Since we are dealing with outcrop samples that areclose to the surface, post-depositional migration of clay outof the clay lenses (e.g. Mažvila et al., 2008) together withbioturbation in the outcrops is a plausible explanation for thelower clay content in the outcrop. Weathering of clay lensesor drapes close to the surface would be another plausible ex-planation. For the clayey Kasterlee Fm. outcrop, the individ-ual grain-size distribution curves (Fig. 2b) indicate a contin-uous gradation between two extreme cases, i.e. from a claylens texture (approximately 40 % clay) to coarse sand with-out fines (> 90 % sand). The corresponding grain-size distri-butions for boreholes show no overlap between the clay andsand samples, an illustration of the existence of two distinctlydifferent materials within the clayey part of the Kasterlee Fm.(i.e. heavy clay lenses embedded in coarse sands character-ized by a sharp interface) (Beerten et al., 2010).

In conclusion, weathering, clay migration, and biotur-bation may have influenced the lower end of the outcropsamples’ grain-size distribution considerably. Furthermore,dissimilarities in palaeogeographic conditions and sedimentsource regions between the outcrop and borehole locationsmay equally explain such differences. However, the consis-tent stratigraphic position of the clayey Kasterlee Fm. sedi-ments on top of the Diest Fm. and the relatively good corre-spondence in particle size for the sandy material (i.e. sandlayers within the Kasterlee Fm.), are sufficient underpin-ning arguments to support using the studied clayey KasterleeFm. outcrop at Heist-op-den-berg (for details of the outcropsee Rogiers et al., 2013a) as surrogate for the clayey Kaster-lee Fm. aquitard (Gulinck, 1963; Laga, 1973; Fobe, 1995).Additional insight could be obtained from tracing the exactorigin and initial composition of the outcrop materials; how-ever, this is beyond the scope of the current paper.

3.2 Hydraulic conductivity distributions

Figure 3 provides a comparison of outcrop and borehole(aquifer)K kernel density estimates of the probability den-sity functions (pdfs) for the five sediments. Statistically sig-nificant differences exist for all sediments, withp values forF tests all below 4× 10−3, while the correspondingt testsp values are all below 1× 10−5 indicating statistically sig-nificant differences for both the variance and mean. All out-crop pdfs have higher meanK values than their boreholecomplement. While most outcrop samples display conductiv-ities between 10−5 and 10−3 m s−1, borehole samples havetheir most frequentK values between 10−6 and 10−4 m s−1.Moreover, the standard deviations for the borehole samplesare consistently larger than those based on the outcrop sam-ples. The left tail of the pdfs tends to be much larger for theborehole data while the peaks tend to be wider (one to two or-ders of magnitude for the outcrops versus two to four orders



Fig. 3. Comparison between distributions (kernel density estimates of the probability density functions) for air-permeameter-based outcropK and constant-headK measurements on undisturbed samples from cored boreholes, for(A) the Mol Fm.,(B) the sandy Kasterlee Fm.,(C) the clayey Kasterlee Fm.,(D) the clayey Diest Fm. and(E) the sandy Diest Fm. Mean (µ) and standard deviation (σ ) are given for bothdata sources.

of magnitude for the borehole data), especially for the sandyKasterlee Fm. (Fig. 3b). Relative variability expressed as co-efficient of variation (CV) is approximately two times largerfor borehole pdfs than for outcrop pdfs (Mol Fm.:−13.4 %vs −5.9 %; Kasterlee Fm. sands:−24.5 % vs−12.9 %; Di-est Fm. sands:−23.9 % vs−18.8 %) while it is similar forthe clayey parts of the Kasterlee Fm. (−23.9 % vs−18.8 %)and Diest Fm. (−15.8 % vs−17.4 %). For the borehole data,sampling occurred over a large geographical area (severaltens of km2 vs as little as a few m2 to at most a few tensof m2 for the outcrops) and over a much larger depth (up to50 m) thus having the opportunity to sample a much largerspatial heterogeneity.

Several characteristics typical of heterogeneity inK arehowever visible in both the outcrop and boreholeK distri-butions. For the sandy part of the Kasterlee Fm. (Fig. 3b),a long tail towards low values is present both in the outcropand in the boreholes, while the majority of samples is withina much narrower distribution in the outcrop. For the clayeypart of the Kasterlee Fm. (Fig. 3c), a multi-modal distributionis present for both data sets and representative of samples be-longing mainly to clay lenses or sand layers. The clayey partof the Diest Fm. (Fig. 3d) displays a similar pdf in both datasets (ratio of borehole to outcrop CV = 0.91), and the sandyDiest Fm. data (Fig. 3e) shows the best absolute match interms of the meanK, although the second peak with lowerK values was not observed in the outcrop.

Validation of air permeameterK with core-based out-crop K demonstrated absence of systematic bias in the air-permeameterK estimates (Rogiers et al., 2013a). Therefore,differences inK distributions between outcrop and aquifersediments can be attributed to the scale of investigation (a

single outcrop with a typical measurement grid of a few m2

vs seven∼ 50 m-deep vertical transects through the differentlithostratigraphical formations, Fig. 1), different evolution-ary states of the outcropping and subsurface sediments, andpossibly different sedimentation conditions.

3.3 Linear rescaling correction

To investigate the (dis)similarities between the outcrop andborehole data across these five lithological units, the min-imum and maximum values are plotted in Fig. 4, with alldeciles (10th, 20th, . . . , 90th percentile) in between. Thisshows that linear scaling of the outcrop values to the corre-sponding borehole distributions is possible for all outcrops.The extreme values are however not always in line with thecentre of the distributions (as indicated by the deviation of theoverall shape of the first and last line segments). All outcropsexhibit a more or less similar trend for at least part of the data,which is supported by the linear model fit on all minimum,maximum and decile points (r2 = 0.7). The slope, larger than45◦, indicates that the deviation between outcrop and bore-holes is larger for lowK than for higherK values, whichis consistent with the previous observations. The sandy Di-est Fm. curve lies apart and above the other curves, and ismuch closer to the 1 : 1 line of perfect agreement. This isas expected based on the good correspondence in pdfs (seeFig. 3e). In other words, the Diest Fm. outcrop is well andtruly representative for the entire aquifer unit.

3.4 Porosity and compaction state

Weathering of clay layers at the surface has certainly con-tributed to produce higherK values for the fine material



Fig. 4. Outcrops versus borehole log10(K) deciles, and a fitted lin-ear correction model (y = 0.6938+ 1.5685x).

in the outcrops, but the systematic bias of about one or-der of magnitude that is also present for the sands remainsunexplained.

Trends in porosity or bulk density with depth are very hardto detect in the borehole data due to the extensive layering ofdifferent lithologies and grain-size distributions at the studyarea (the same lithology may occur at different depth depend-ing on the geographical location). Moreover, the data fromthe outcrops are hardly sufficient to prove differences withthe subsurface sediments are statistically significant. For ex-ample, the mean total porosity for the four Mol and Kaster-lee Fm. outcrop core samples is 43 % with a mean dry bulkdensity of 1.52 g cm3 (see Rogiers et al., 2013a), while theborehole values of the same two formations (43 samples) are40 % and 1.60 g cm−3 (samples between 2 and 28 m belowsurface). This is consistent with different compaction states(i.e. outcrop samples being less compacted than boreholesamples), but the differences remain very small and are onlysignificant for porosity at the 5 % significance level. How-ever, even small differences in porosity can yield large dif-ferences inK (see discussion below).

The impact of the degree of compaction onK values wasfurther investigated for the borehole data set only using to-tal porosity as proxy for compaction, as analyses in literatureshow that porosity has a high influence onK, given a homo-geneous grain-size distribution and chemistry (e.g. Bourbieand Zinszner, 1985). On an individual sample basis, it is hardto detect total porosity –K relationships within the boreholedata set, since these are very complex owing to the influenceof grain size (Rogiers et al., 2012), sorting, packing and even-tually the actual accessible pore throat radii (e.g. Bakke andØren, 1997; Øren et al., 1998). However, as indicated by thescatter plot in Fig. 5, if total porosity andK are averaged for

Fig. 5. Scatter plot of log10-transformed hydraulic conductivityKversus porosity (borehole data set only) for the five lithostratigraph-ical units with corresponding linear model fits. Each data point rep-resents the mean porosity and meanK of all measurements pertain-ing to one formation for one particular borehole.

each formation and for each borehole separately, some statis-tically significant relationships exist. The slopes of the linearmodel fits are consistently positive, and in several cases, achange of a few percent in porosity can changeK drastically.For instance, a 1 % decrease in porosity yields a decrease inK of minimum 0.14 and maximum 1.08 log10 units. This isa partial confirmation of the importance of the degree of con-solidation and compaction on ourK values; corroboratingevidence about the effect of grain-size, sorting and packingcharacteristics will be sought in future research.

An additional analysis of theK – depth below surface re-lationship was performed but did not yield any significantdependencies (results not shown). This is probably due tothe alternation of different lithologies and grain sizes withdepth, hence obscuring the influence of depth on compactionand thus on porosity andK.

3.5 The scale effect and vertical anisotropy

The representativity ofK measurements – whether for out-crop or aquifer sediments – for characterizing a lithostrati-graphical unit depends, among others, on the size of themeasurement scale (or measurement support) and the spatialextent and lithostratigraphic complexity of the sampled do-main. The effect of measurement scale for individualK mea-surements also impacts the overall variability, as measure-ments with a larger support volume, like pumping tests, av-erage out the small-scale variabilities (Mallants et al., 1997).It is thus important in the comparison between outcrop andboreholeK values to consider such scale-effects.



Fig. 6. Comparison of geometric meanK values obtained fromborehole core samples, pump tests, outcrop air-permeameter mea-surements and calculated equivalent values. The gray boxes repre-sent the data limits, and the arrows indicate the contrasting effectsof upscaling for the aquifer and aquitard units.

A comparison between the outcrop data (air-permeameter-based geometric meanK values and the calculated corre-sponding equivalent values) and the subsurface data (bore-hole core geometric meanK values and the pump test val-ues) is shown in Fig. 6. It reveals the overall range is small-est for the outcrop data, both at the smallest measurementscale (data for air-permeameter measurements spans 5 or-ders of magnitude versus 8 orders of magnitude for boreholecores) and at the largest scale (calculated equivalent outcropK values show a range of∼ 2 orders of magnitude versus∼ 5 orders of magnitude for pump tests). It is further evi-dent that the outcrop-based equivalentK values are system-atically higher than the mean borehole core values; a bettercorrespondence is achieved with the pump test values.

Because a pump test represents a large support volume,easily tens to hundreds of m3, small-scale heterogeneitieshave much less effect on such large-scaleKvalues, hencethe smaller data range. Furthermore, the support volume iscommensurate with the computational domains used to cal-culate equivalent outcrop values. Overall the pump test val-ues are generally only slightly smaller than the equivalentoutcrop values, except for the clayey part of the KasterleeFm. for which the discrepancy is about three to four orders ofmagnitude. This again emphasizes the need for a correctionif outcrop K values are used to inform building conceptualgroundwater models. Correction models such as those fromFig. 4 would account for impacts of different compaction

Fig. 7. Comparison of the vertical anisotropy factors derived fromthe geometric meanK values from Fig. 6. The pluses between roundand square brackets represent respectively the parameter value ob-tained by Gedeon and Mallants (2012) using regional inverse mod-elling and the value representing a part of the aquitard in the originalDessel 2 pump test interpretation by Lebbe (2002).

and/or weathering processes, especially for the more clay-bearing sediments.

The arrows in Fig. 6 indicate different effects of upscalingfor the aquifer and aquitard units. Moving from the sample(cm scale) to the pumptest scale (metre scale) in most casesincreases the aquifer geometric meanK values by one orderof magnitude, while the outcrop values remain more or lessconstant when geometric means are compared with effectivevalues. Unlike the other formations, upscaling the clayey partof the Kasterlee Fm. data results in a decrease of the aver-ageK values, for bothKv andKh pertaining to the aquiferand for outcropKv. This indicates that in both the outcropand aquifer sediments of this particular lithostratigraphic unita significant amount of small-scale heterogeneity is present(i.e. clay lenses) which significantly decreases the magnitudeof the calculated effectiveK values.

Faulting could be another process involved enhancing dis-crepancies between small and large measurement supports.However, this process is considered to be absent as the studyarea is known as a zone of low seismic and limited tectonicactivity (De Craen et al., 2012).

A comparison of the vertical anisotropy values (Kh/Kv)is shown in Fig. 7. TheKh/Kv ratios based on the geo-metric means of the 100 cm3 borehole cores lies between 1and 5. The two lithostratigraphical units with the highestKh/Kv values are the sandy parts of the Kasterlee and Di-est Fm., which are influenced by some outliers that probablybelong to the under- or overlying units. The equivalent out-cropKh/Kv values are less than the corresponding boreholecore anisotropy values, except for the clayey parts of the Di-est and Kasterlee Fm. For the latterKh/Kv increased more



Table 2. Overview of fitted spherical variogram model parame-ters for the vertical experimental variograms (range = correlationlength). The outcrop data is taken from Rogiers et al. (2013a). Theroot mean squared error (RMSE) is provided as a measure of good-ness of fit.

Sediment Parameter Outcrop Borehole Both

Mol formation

Nugget 0.05 0.13 0.04Sill – 0.41 0.41Range (m) – 19.66 12.46Type Spherical

RMSE 0.005 0.046 0.036

Kasterlee formation: sandy part

Nugget 0.16 0 0.25Sill 0.35 0.13 –Range (m) 1.36 2.9 –Type Spherical

RMSE 0.069 0.014 0.145

Kasterlee formation: clayey part

Nugget 0.4 2.07 0.6Sill 0.2 – 1.32Range (m) 0.36 – 2.2Type Spherical

RMSE 0.127 0.303 0.653

Diest formation: clayey part

Nugget 0.35 0.23∗ 0.33Sill 0.2 0.24 0.14Range (m) 2.07 1.17 1.12Type Spherical

RMSE 0.044 0.097 0.076

Diest formation: sandy part

Nugget 0.02 0.07 0.1Sill 0.18 0.11 0.06Range (m) 0.6 13.34∗ 13.34∗

Type Spherical

RMSE 0.015 0.019 0.044

∗ Fixed during variogram model fit.

than one order of magnitude, when moving from the boreholecore to the outcrop scale. The pump test anisotropy valuesmostly show larger values compared to those from the bore-hole cores, with a maximum vertical anisotropy of 10. Theoriginal Dessel 2 pump test interpretation by Lebbe (2002)yieldedK values for the clayey part of the Kasterlee Fm. andmentions a vertical anisotropy factor of 190 for part of theaquitard. This value was obtained by inverse modelling ofthe pump test, but due to a limited drawdown across theaquitard, the optimized parameter values remain highly un-certain. A more reliable estimate is probably obtained fromthe more regional modelling of the Neogene aquifer and theflow across the aquitard by Gedeon and Mallants (2012).They obtain a vertical anisotropy of 148 by inverse condition-ing on regional piezometric observations above and belowthe aquitard. The high vertical anisotropy determined fromthe outcrop supports these values, and indicates that suchlarge values might be more realistic at larger scales.

3.6 Spatial variability

The vertical spatial variability for the outcrop and boreholedata (Kh only) is compared in Fig. 8 and Table 2. For the Mol

Fig. 8. Comparison between vertical experimental and modelledsemivariograms (fitted using a least squares approach) for out-crop and borehole data.(A) Mol Fm., (B) sandy Kasterlee Fm.,(C) clayey Kasterlee Fm.,(D) clayey Diest Fm., and(E) sandy Di-est Fm. The fit diagnostics are provided in Table 2.

Fm., the outcrop data overall shows less variability (smallersemi-variance) than the borehole core samples; but corre-spond well with the experimental borehole variogram at thecentimetre to metre scale. The larger total sill for borehole(0.13+ 0.41) compared to outcrop (0.05) is a reflection ofthe larger variability captured by the borehole data. Thislarger variability is caused in part by combining two localstratigraphical subunits into the Mol Fm. (see Beerten et al.,2010) with thin gravel layers and clay lenses at their inter-face. The borehole data also displays a larger vertical spatialrange (i.e. 20 m) than the outcrop (i.e. pure nugget), owingto samples being collected from a much larger vertical sam-pling window (up to 20 m) and multiple boreholes spread



over several km2. As both experimental semivariograms arecompatible, fitting the joint data set seems to result in a morerobust variogram model.

For the Kasterlee Fm. sands, the borehole and outcrop datashow a large difference, which might be due to the rather lim-ited number of borehole core samples identified as the sandypart of the Kasterlee Fm. or an increased amount of hetero-geneity in the outcrop due to weathering processes. The over-all variability (total sill) is more or less similar for both out-crop and borehole data, suggesting that the variability cap-tured by the outcrop samples may be used as surrogate forthe variability in boreholes. Despite the presence of spatialcorrelation in the both data sets, the joint model fit shows apure nugget because of the high semivariance values for theoutcrop data.

The clayey Kasterlee Fm. shows the largest spatial vari-ability of all lithological units for both the outcrop and bore-hole data. While the outcrop shows some spatial correlation,the borehole model shows a pure nugget. The borehole coresshow higher variability due to the clay-rich lenses and corre-spondingly lowK values, which are altered in the outcrops,but only the first data point at 0.5 m is contradicting the out-crop data. The joint model fit does reveal their compatibility,and shows spatial correlation up to a few metres. This modelmight be more useful than the individual variogram modelsdue to the integration of different scales.

Most of the clayey Diest Fm. outcrop data seems to becompatible with the borehole core spatial variability. Allthree model fits show a range of one to two metres, and sim-ilar total sills. The sandy Diest Fm. also exhibits similar to-tal sill in all three cases, with a larger spatial range for theborehole data. The joint model fit is compatible with thatof the borehole data, but shows a higher nugget due to thehigher semivariances in the outcrop data. The sill and rangefor the variograms that have not reached a constant semi-variance within a lag distance of 14 m (Fig. 8a and e), arehighly uncertain as a linear model would provide an equallypoor description of the data as the used spherical model. Thesemivariance within the distance range of the experimentaldata (up to 10–15 m) is, however, hardly affected by this.

Overall, the borehole data exhibit larger correlationlengths than the outcrop data. The total sills are mostly sim-ilar, except for two cases were the borehole data clearly en-compasses more heterogeneity. Three out of five experimen-tal variograms are overlapping at certain locations, indicat-ing that at certain scales both data sets exhibit similar spatialvariability. Fitting of the joint data sets results in these casesin more robust variogram models. For the Mol formation, thevariogram model root mean squared errors (RMSE; Table 2)show that fitting both data sets simultaneously improves thefit, mainly due to the very low outcrop semivariances that arecompatible with the borehole data. For the sandy part of theKasterlee formation, the data sets are not compatible and thejoint pure nugget fit shows the highest RMSE. For the clayeyKasterlee formation, both data sets seem to be compatible,

except for the borehole data point with the smallest lag dis-tance. For the clayey Diest formation, the variogram modelsare very similar, as are the RMSE values. For the sandy Diestformation, the range is very different, but the sill values aresimilar.

This indicates that small-scale structural information, suchas alternation of relatively thin clay and sand layers, and itseffect on spatial variability inK may be preserved in outcropsediments. Therefore, analysis of outcrop stratigraphy andhydraulic conductivity variability can yield valuable qualita-tive and quantitative insight about such properties for similaraquifer and aquitard sediments.

4 Perspectives

Despite the limitations of and systematic differences betweenthe outcrop and borehole data sets, we have demonstratedthat outcrop studies can provide useful information for devel-oping more reliable groundwater flow and contaminant trans-port models. Because of the systematic differences observedhere between outcrop and subsurface sediments, the obtainedoutcropK values are not directly applicable in groundwa-ter flow modelling, unless a correction is applied. Further-more, the differentK distributions are comparable at leastin a relative way, and linear scaling based on deciles wasshown to be relatively accurate. In other words, results suchas the spatial heterogeneity models, the equivalent verticalanisotropy factors, and relative differences between the dif-ferent sediments provide us with information useful to guideconceptual groundwater flow model building and constrain-ing model parameterization.

Potential applications of our findings for building con-ceptual and numerical models of groundwater flow include(i) where possible highly structured heterogeneity should ei-ther be represented explicitly in the models or use should bemade of appropriate geostatistical tools (e.g. multiple pointstatistics) based on detailed structural information visiblein and quantifiable from outcrops; (ii) use of the obtainedequivalent vertical anisotropy factors can influence concep-tual model choices for isotropy/anisotropy for certain units,and the actual value represents a minimum of the parame-ter range in larger scale groundwater flow simulations (es-pecially in a layered stratigraphical setting); (iii) to avoidover-parameterization, ratios betweenK values of differentunits can be fixed during model optimization (e.g. Gedeonand Mallants, 2012) using the ratios obtained from equiva-lent outcrop estimates; and (iv) use of the obtained outcropvariogram models can complement information from a largerscale (e.g. boreholes), or be used for small-scale geostatisti-cal simulations for detailed local transport simulations. Allthese applications will be most beneficial when combinedwith the traditional borehole coring and measurements andother invasive and non-invasive subsurface characterizationtechniques.



5 Conclusions

Analysis of outcrop sediments considered to be analoguesfor various lithostratigraphical units within a sedimentaryaquifer provided a qualitative understanding of aquifer andaquitard stratigraphy and a quantitative estimate aboutK

variability at the centimetre–metre scale. Comparison be-tween outcrop and independent borehole coreK values re-vealed significant differences between both data sets. Suchdifferences are believed to be induced mainly by weather-ing, different palaeoenvironmental conditions and differen-tial compaction, and can be corrected for as was demon-strated on the basis of a linear model. Hence, outcrop infor-mation can be used for building better stratigraphic modelsincluding determination of spatial structure by variogram fit-ting for further use in geostatistical simulations. Moreover,the relative variability inK values with similar coefficientsof variation for borehole and outcropK, and the derivedanisotropy values are very useful to get a more complete un-derstanding of the heterogeneity within the Neogene aquifer.

Comparison of outcrop and boreholeK values demon-strated the boreholeK probability density functions hadbroader peaks, longer tails towards low values, and the pres-ence of a systematic bias. The reasons behind this discrep-ancy are manifold, and include weathering of the outcropsediments and a lesser degree of consolidation and associatedstress states in outcrops. Also, measurements performed onoutcrops sometimes several tens of kilometres away from themain study site may further invoke differences inK. Grain-size analyses showed that the sediments from the investigatedoutcrops and boreholes are similar but not necessarily ex-actly the same. Clay migration and bioturbation in the out-crop sediments probably contributed to the observed discrep-ancies, as well as slight differences in palaeoenvironmentalsettings. The degree of (over)consolidation and stress statesmight also have an impact, but further research is needed toconfirm or quantify this, as trends with the current depth ofthe sediments are hard to detect due to the alternation of dif-ferent lithologies.

Based on all data a linear scaling relationship was derived(r2 = 0.7) that permits rescaling of outcropK values to theirsubsurface equivalents. For most individual units, the differ-ences between outcrop and subsurface sediments were simi-lar (except for the extremes of the distributions). The sandypart of the Diest Fm. however showed a considerably betterfit between outcrop and aquifer than the other cases.

In a comparison withK values obtained through othermeans, outcrop-based equivalentK values were systemati-cally higher than those from pump tests (especially for theclayey part of the Kasterlee Fm.), whose support volumesare considerably larger than the simulation domains consid-ered in the outcrops. Mean borehole core samples resulted inthe overall smallestK values. Smaller compaction at shallowdepth and long-term biophysical weathering processes pre-sumably contributed to outcrop equivalentK values being

larger than any other estimate of large-scaleK available inthis study.

In most cases the semivariograms for the outcrop andborehole data are compatible. Only for the sandy KasterleeFm. the outcrop data clearly shows higher variability than theborehole data. Spatial correlation (i.e. increasing semivari-ance with distance) is present in most cases, either in the out-crop or borehole data, or both. The clayey Diest Fm. showshowever a pure nugget effect for both data sets. For the MolFm. and the clayey Kasterlee Fm. both data sets complementeach other resulting in more robust semivariogram model fits.For the sandy Diest Fm. there seems to be a discrepancy inthe range between both data sets.

Given the small number and limited size of the studiedoutcrops, transfer of information from outcrops to the cor-responding aquifer sediments can be improved by expand-ing the number of outcrops for the same lithostratigraphi-cal units. In addition, more complementary aquifer informa-tion could be collected for developing a depth dependencyin aquiferK that incorporates effects of compaction whichcould then be used to rescale outcropK values to sedimentvalues at a given depth. Such information, together with geo-statistical parameters, may be used as input or prior informa-tion to stochastic flow models.

Next to the quantitative information tested in this paper,information about facies geometry, like the alternating clayand sand layers within the clayey Kasterlee Fm., cannot berevealed easily using available in situ methods, and repre-sents very important qualitative knowledge obtained fromoutcrops.

Acknowledgements.The authors wish to acknowledge the Fundfor Scientific Research – Flanders for providing a PostdoctoralFellowship to Marijke Huysmans. ONDRAF/NIRAS, the BelgianAgency for Radioactive Waste and Enriched Fissile Materials,is acknowledged for providing the borehole data. Findings andconclusions in this paper are those of the authors and do notnecessarily represent the official position of ONDRAF/NIRAS.

Edited by: P. Grathwohl

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http://dx.doi.org/10.1016/j.catena.2012.03.005

http://dx.doi.org/10.1007/s11004-012-9416-3

http://www.vindum.com/TinyPermManual.pdf

http://dx.doi.org/10.1144/1354-079305-677

http://dx.doi.org/10.1144/1354-079305-677

http://dx.doi.org/10.1007/s11004-012-9409-2

http://dx.doi.org/10.1007/s11004-012-9409-2

http://dx.doi.org/10.1002/hyp.10007

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