Impact of changes in grain size and pore space on the hydraulic conductivity and spectral induced polarization response of sand

Koch, K.; Kemna, Andreas; Irving, James; Holliger, Klaus

doi:10.5194/hess-15-1785-2011

Cited by 60 publications

(44 citation statements)

References 43 publications

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“…Hence, subsurface flow would remain shallow and converge quickly into down-slope flow channels, each with low flow accumulation requirements. In Area 3, the terrain has high hydraulic conductivities (Koch et al, 2011), since it cuts across ice-river alluvium, which is mostly composed of sand, gravel, and boulders, and the adjacent slopes are dissected by steep and deep ravines. Here, (i) water would drain quickly and deeply (Aneblom and Persson, 1979), (ii) more flow accumulation drainage area would be required for water to re-appear within the surface channels, and (iii) only the areas immediately next to the water-filled channels would remain wet.…”

Section: Discussionmentioning

confidence: 99%

Evaluating digital terrain indices for soil wetness mapping – a Swedish case study

Ågren

Lidberg

Strömgren

et al. 2014

Hydrol. Earth Syst. Sci.

136

117

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Abstract. Trafficking wet soils within and near stream and lake buffers can cause soil disturbances, i.e. rutting and compaction. This -in turn -can lead to increased surface flow, thereby facilitating the leaking of unwanted substances into downstream environments. Wet soils in mires, near streams and lakes have particularly low bearing capacity and are therefore more susceptible to rutting. It is therefore important to model and map the extent of these areas and associated wetness variations. This can now be done with adequate reliability using a high-resolution digital elevation model (DEM). In this article, we report on several digital terrain indices to predict soil wetness by wet-area locations. We varied the resolution of these indices to test what scale produces the best possible wet-areas mapping conformance. We found that topographic wetness index (T WI ) and the newly developed cartographic depth-to-water index (D TW ) were the best soil wetness predictors. While the T WI derivations were sensitive to scale, the D TW derivations were not and were therefore numerically robust. Since the D TW derivations vary by the area threshold for setting stream flow initiation, we found that the optimal threshold values for permanently wet areas varied by landform within the Krycklan watershed, e.g. 1-2 ha for till-derived landforms versus 8-16 ha for a coarsetextured alluvial floodplain.

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Section: Discussionmentioning

confidence: 99%

Evaluating digital terrain indices for soil wetness mapping – a Swedish case study

Ågren

Lidberg

Strömgren

et al. 2014

Hydrol. Earth Syst. Sci.

136

117

View full text Add to dashboard Cite

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“…Our laboratory measurements of inhomogeneous sandy aquifers show a weak relationship between the measured hydraulic conductivity and the maximum, median and weighted average relaxation time. Independently of the soil texture, soil compaction has an influence on both the conductive properties (Seladji et al, 2010) and the polarization response (Koch et al, 2011) of porous media. Accordingly, the weak correlation between K and τ can be attributed to the compaction and sieving of the core samples from preparing the laboratory measurements (Koch et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…Independently of the soil texture, soil compaction has an influence on both the conductive properties (Seladji et al, 2010) and the polarization response (Koch et al, 2011) of porous media. Accordingly, the weak correlation between K and τ can be attributed to the compaction and sieving of the core samples from preparing the laboratory measurements (Koch et al, 2011). Consequently, other physical parameters, which show a relation with the measured K, are required to support the K-τ relationship.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 8 shows slight differences between field data inversion results and laboratory data up to the till layer (∼ 13 m depth). Koch et al (2011) stated that changes in compaction and sorting of samples cause a certain shift in the SIP response but did not fundamentally alter the overall picture. Accordingly, the modification of pore space and grain size characteristics of original samples through compaction of preparing the lab sample will change the SIP response, while the chemistry of the saturation pore fluid is kept constant.…”

Section: Discussionmentioning

confidence: 99%

“…It is generally accepted that τ (tau) is controlled by the diffusion process (Revil and Cosenza, 2010) in the electrochemical double layer (EDL). A power law relationship between some relaxation time constants and permeability (k) has been reported in published data sets (Binley et al, 2005;Kemna et al, 2005;Tong et al, 2006a, b;Koch et al, 2011):…”

Section: Methodsmentioning

confidence: 99%

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Spectral induced polarization measurements for predicting the hydraulic conductivity in sandy aquifers

Attwa

Günther

2013

Hydrol. Earth Syst. Sci.

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Abstract. Field and laboratory spectral induced polarization (SIP) measurements are integrated to characterize the hydrogeological conditions at the Schillerslage test site in Germany. The phase images are capable of monitoring thin peat layers within the sandy aquifers. However, the field results show limitations of decreasing resolution with depth. In comparison with the field inversion results, the SIP laboratory measurements show a certain shift in SIP response due to different compaction and sorting of the samples. The SIP data are analyzed to derive an empirical relationship for predicting the hydraulic conductivity (K). In particular, two significant but weak correlations between individual real resistivities (ρ ) and relaxation times (τ ), based on a Debye decomposition (DD) model, with measured K are found for the upper groundwater aquifer. The maximum relaxation time (τ max ) and logarithmically weighted average relaxation time (τ lw ) show a better relation with K values than the median value τ 50 . A combined power law relation between individual ρ and τ with K is developed with an expression of A · (ρ ) B · (τ lw ) C , where A, B and C are determined using a least-squares fit between the measured and predicted K. The suggested approach with the calculated coefficients of the first aquifer is applied for the second. Results show good correlation with the measured K indicating that the derived relationship is superior to single phase angle models as Börner or Slater models.

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Predicting permeability from the characteristic relaxation time and intrinsic formation factor of complex conductivity spectra

Revil

Binley

Mejus

et al. 2015

Water Resources Research

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Low-frequency quadrature conductivity spectra of siliclastic materials exhibit typically a characteristic relaxation time, which either corresponds to the peak frequency of the phase or the quadrature conductivity or a typical corner frequency, at which the quadrature conductivity starts to decrease rapidly toward lower frequencies. This characteristic relaxation time can be combined with the (intrinsic) formation factor and a diffusion coefficient to predict the permeability to flow of porous materials at saturation. The intrinsic formation factor can either be determined at several salinities using an electrical conductivity model or at a single salinity using a relationship between the surface and quadrature conductivities. The diffusion coefficient entering into the relationship between the permeability, the characteristic relaxation time, and the formation factor takes only two distinct values for isothermal conditions. For pure silica, the diffusion coefficient of cations, like sodium or potassium, in the Stern layer is equal to the diffusion coefficient of these ions in the bulk pore water, indicating weak sorption of these couterions. For clayey materials and clean sands and sandstones whose surface have been exposed to alumina (possibly iron), the diffusion coefficient of the cations in the Stern layer appears to be 350 times smaller than the diffusion coefficient of the same cations in the pore water. These values are consistent with the values of the ionic mobilities used to determine the amplitude of the low and high-frequency quadrature conductivities and surface conductivity. The database used to test the model comprises a total of 202 samples. Our analysis reveals that permeability prediction with the proposed model is usually within an order of magnitude from the measured value above 0.1 mD. We also discuss the relationship between the different time constants that have been considered in previous works as characteristic relaxation time, including the mean relaxation time obtained from a Debye decomposition of the spectra and the Cole-Cole time constant.

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Impact of changes in grain size and pore space on the hydraulic conductivity and spectral induced polarization response of sand

Cited by 60 publications

References 43 publications

Evaluating digital terrain indices for soil wetness mapping – a Swedish case study

Evaluating digital terrain indices for soil wetness mapping – a Swedish case study

Spectral induced polarization measurements for predicting the hydraulic conductivity in sandy aquifers

Predicting permeability from the characteristic relaxation time and intrinsic formation factor of complex conductivity spectra

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