Hydraulic conductivity of quaternary surficial units within the Eklutna Valley, southcentral Alaska

Journal of Hydrology 575 (2019) 166–174

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Research papers

Hydraulic conductivity of quaternary surficial units within the Eklutna Valley, southcentral Alaska

T

Amanda L. King , James S. Meyers, Jim M. Brown ⁎

Institute of Culture and Environment, Alaska Pacific University, 4101 University Drive, Anchorage, AK 99508, United States

ARTICLE INFO

ABSTRACT

Keywords: Hydraulic conductivity Particle size distribution Bottomless bucket Surficial geology Modified Brabets

Eklutna Lake is the primary water supply for Anchorage, Alaska; therefore the hydrogeologic properties of Eklutna surficial deposits are important to characterize. However, there is limited data on the hydraulic conductivity of surficial deposits in the Eklutna basin. This study presents hydraulic conductivity (Kfs and Ksat) measurements using two methods: the bottomless bucket and particle size distribution methods. Statistically, the two methods produced similar results; however, the large range in values obtained for Kfs and Ksat suggests there is a great degree of natural variation. One method used a bottomless bucket in place of a ring infiltrometer to measure field-saturated hydraulic conductivity (Kfs). The other method used a sieve-derived particle size distribution to solve a modified Hazen equation for Ksat. Of 47 samples collected in nine surficial geologic units, from poorly-sorted to well-sorted soils, the means of the two datasets were statistically similar: 49 m/day (Kfs) vs. 41 m/day (Ksat). Although the variability between the datasets was significantly different, with a standard deviation ranging from 44 m/day (Kfs) to 75 m/day (Ksat), the Wilcoxon paired-samples and paired t-test results suggest the datasets are statistically similar. Additionally, 98% of the 47 paired values were within one order of magnitude of each other. The methods presented herein can be used for surficial geologic unit delineation, planning purposes and optimizing hydrological models.

1. Introduction Hydraulic conductivity is a critical hydrogeologic parameter affecting fluid transport in soils and rocks. However, large spatial variations in soil hydraulic properties have led to challenges in accurately characterizing this parameter on a local scale, particulary within heterogeneous surficial deposits. Estimates of hydraulic conductivity are known to vary widely across different deposit types, and can take on values spanning over 13 orders of magnitude (Freeze and Cherry, 1979). In mountainous landscapes, hydraulic conductivity can be strongly influenced by topographic variations (Gebrelibanos and Assen, 2014) as well as geomorphic qualities such as slope aspect (Carey and Woo, 1999; Sun et al., 2019). Soil hydrologic properties can also exhibit significant spatial and temporal variation due to anthropogenic influences such as climate change (Jarvis et al., 2013), vegetation degradation (Pan et al., 2017), fire disturbance (Neary et al., 1999) and agriculture (Teague et al., 2011). Methods for measuring hydraulic conductivity also vary widely in their accuracy and ease of use. Laboratory-based methods typically require more time and effort, while field-based methods tend to be quicker and simpler. Measuring field-saturated hydraulic conductivity ⁎

Corresponding author. E-mail address: [email protected] (A.L. King).

https://doi.org/10.1016/j.jhydrol.2019.05.024

Available online 11 May 2019 0022-1694/ © 2019 Elsevier B.V. All rights reserved.

(Kfs) is usually accomplished in the field via apparati such as single and double ring infiltrometers or borehole flowmeters. If a sample is brought back to the lab, falling head or constant head permeameters can also be used to measure Kfs. In general, surface methods are most likely to accurately reflect infiltration capacities and runoff, as opposed to the permeameter and borehole flowmeter (Nimmo et al., 2009). The type of material can also influence the accuracy of different methods, and in poorly-sorted materials such as glacial till, estimates of K based on particle size distribution methods are known to be less accurate (Jenssen, 1990; Ronayne et al., 2012). On a global scale, estimates of hydraulic conductivity are important for landscape hydrologic modeling. However, these values are really only obtainable via pedotransfer functions (PTFs), which crudely estimate hydraulic conductivity from soil properties observed in the field, such as bulk density and soil texture (Zhang et al., 2018). The glacially-fed Eklutna Lake provides Anchorage with 88-percent of its water supply (27.4 billion liters in 2017; AWWU, 2018) and a small percentage of the city’s power supply via hydropower generation. However, the glacier has retreated significantly over the last 95 years (Karpilo, 2010) and consequently, the relative importance of snowmelt and rainfall has increased. In the long term, detailed studies of of the

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hydrologic properties of Eklutna watershed surficial deposits will become increasingly important. The goal of this paper is to determine the range of hydraulic conductivity values for Eklutna Valley surficial deposits using two independent field-based methods, which will aid in the effective management of this water resource. Each of these methods mathematically derives K in different ways: field-saturated hydraulic conductivity (Kfs) uses on-site infiltration data (i.e., Nimmo et al., 2009), while saturated hydraulic conductivity (Ksat) uses sieve-derived particle size distribution data (i.e., Hazen, 1892). The Nimmo et al. (2009) method offers a decrease in the complexity of equipment required, as well as the ability to calculate Kfs values rapidly in the field. Therefore we ask the question – do these two methods, requiring distinctly different amounts of time and effort, produce statistically similar results? Moreover, through a statistical comparison of the output from these two methods, we seek to characterize the variability of hydraulic conductivity in a glaciated alpine setting. Finally, for the sake of comparison, our measured Kfs and Ksat datasets are evaluated against a publicly-available Ksat dataset estimated by the NRCS (2018) using pedotransfer functions.

accessibility. A similar technique was used by Smith et al. (2006) to compare remotely-sensed data to field mapping data, concluding that LiDAR is a relatively complete representation of the glacial geomorphology. 3. Methods 3.1. Sampling strategy To place constraints on hydraulic conductivity (K) for Eklutna basin surficial geologic units, two methods were employed: the Bottomless Bucket (Nimmo et al. 2009) and Particle Size Distribution (PSD) analysis (Hazen, 1892) techniques. The Bottomless Bucket method is an insitu process utilizing a bucket apparatus sampling technique, with measured values applied to Nimmo et al.’s (2009) equation:

Kfs =

LG tf

In 1 +

D0 LG +

(1)

where LG = ring-installation scaling length (m), tf = time when falling head is zero (seconds), Do = depth of ponding (m), and λ = macroscopic capillary length (m). The value of λ is related to the capillary forces in the soil, where λ = 0.03 m for extremely coarse and gravelly soils, λ = 0.08 m for soils with significant structural development, and λ = 0.25 m for fine-textured soils without macro-pores. Because estimated Kfs values show minimal sensitivity to the λ values used, the value of 0.08 m was chosen, which is appropriate for application to most soils (Elrick et al. 1989). The second method used was a PSD analysis, which involves an exsitu soil sieve gradation analysis to obtain the effective grain size (d10) in conjunction with Hazen’s (1892) equation for Ksat:

2. Study area Glacial history in the Eklutna watershed has been traced back to pre-Wisconsin age (before 130,000 years before present; BP), consisting of the Mount Susitna glaciation and the Caribou Hills glaciation. Subsequently, the Eklutna Glaciation occurred between 130,000 and 85,000 BP, and is identified by till and outwash deposits exposed in bluffs along the Knik Arm north of Eagle River Flats (Karlstrom, 1956). The Naptowne Glaciation was the last major episode of glaciation (between 45,000 BP and 7,000 BP) of the Wisconsin Ice Age (Karlstrom, 1956). Many smaller glacial advances are evidenced in the Eklutna Valley by the various lateral and end moraine deposits. Most important are the Elmendorf moraine, Dishno Pond moraine and the Fort Richardson moraine, as described and illustrated by Brabets (1993a), all of which are late-Pleistocene age. The till that makes up the moraine deposits is a diamicton consisting of massive, poorly sorted gravel, sand and silt mixtures with small amounts of clay and scattered boulders (Brabets, 1993a). The Eklutna Glacier terminated at about 366 m elevation in 1957, but in 1988 terminated at about 732 m elevation (Brabets, 1993a). While retreating 366 m vertically, the Eklutna Glacier also receded about 1.6 km horizontally over the same 30-year period (Brabets, 1993a). More recently, glacial melting was quantified by Sass et al. (2017), showing substantial mass loss over the past half-century. From 1957 to 2010, mass balance was –0.52 + 0.46 m w.e. per year, and increased to –0.74 + 0.46 m w.e. a-1 between 2010 and 2015. Approximately 7% of the inflow to Eklutna Lake is derived from net mass loss from Eklutna Glacier (Sass et al., 2017). Previous studies of the surficial geology in the Eklutna watershed are summarized in the surficial geological maps of Brabets (1993b) and NRCS (2018), but do not specifically address hydraulic conductivity. Meyerhofer (2016) described hydraulic conductivity in the Eklutna basin by characterizing the soil according to texture and then assigning a range of saturated hydraulic conductivity values defined by the U.S. Department of Agriculture. These results are largely similar to the broad ranges provided by the NRCS (2018). Surficial geologic units (defined by material type and expression of the topography) in the Eklutna watershed range from poorly-sorted mixed till to well-sorted river sands (Fig. 1). Detailed unit descriptions are included in Appendix A. The study area was defined based on Brabets (1993b) surficial geology map, restricted to include only the area between the Eklutna Glacier terminus and the point where the Eklutna River enters Eklutna Lake (Fig. 1). LiDAR (Light Detection and Ranging) images and aerial photographs were digitally overlain by Brabets’ (1993b) surficial geological map, thus allowing for a closer analysis of topography, glacial geomorphology and degree of

Ksat =

g N (n) de2 µ

(2) 3

where = 0.9981 (density of water in g/cm ), g = 980 (gravity in cm/ s2), μ = 0.0098 (centipoise (kinematic viscosity) in g/cm/s), N = 6 × 10-4 (a constant dependent on characteristics of the porous (n) = [1 + 10 (n − 0.26)] (function of porosity), medium), n = 0.255(1 + 0.83U ) (Porosity), and de = d10 (effective grain size in mm). Grain uniformity (U) is measured as the ratio of d60 to d10 where d60 equals grain size at which 60-percent of grains are finer and d10 is the grain size at which 10-percent of grains are finer. Fifty-two sampling points were systematically placed among the Modified Brabets surficial units (modification explained in Fig. 1). A minimum of three sample points were placed within each unit with the exception of the Talus Cone unit, where only two sample points were collected. Sample point locations were digitized in GIS software, establishing a general latitude and longitude, and then uploaded into a handheld Global Positioning System (GPS) unit with an average accuracy of ± 3 m. 3.2. Data collection At each sample site, the Bottomless Bucket technique was used to measure field-saturated hydraulic conductivity. This method employed a 19-liter bucket, with the bottom surface carefully cut out and dimensions of the bucket recorded for later calculations. All leaf litter and loose organic matter was cleared from the soil surface. The Bottomless Bucket was inserted into the ground to a depth of up to two inches. A pre-measured amount of water varying from 3.8 to 7.6 L was poured into the apparatus. The time it took for the water to completely drain into the soil’s surface was measured with a stopwatch for later use in Eq. (1). Soil samples were collected at each site and brought back for PSD analysis. From the 52 sample locations, five samples did not make it back to the lab due to transportation mishap. The 47 remaining samples were oven dried for 1.5 h at 65 °C on a foil sheet. Once dried, the sample 167

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Fig. 1. (a) Map of Alaska, showing geographical location of Eklutna watershed. (b) Detail of box in (a), showing Eklutna Glacier and Lake in relation to Anchorage and the Chugach Mountains. (c) Detail of box in (b), showing surficial units classified according to Brabets (1993b). Unit descriptions are provided in Appendix A. Sample locations (red circles) are shown with sample identification number indicated. For purposes of this investigation and to simplify, Brabets (1993b) surficial unit Qaa was modified to East Fork Eklutna River and West Fork Eklutna River, Qcm to Colluvium from Moraine, Qld to Delta Lake Deposits, Qame to Glacial Moraine, Qaco to Alluvial Cone, Qct and Qcta to Talus Cone, and Kjm to Glacially Modified Bedrock Surface. These modifications are referred to as “Modified Brabets” in this paper, corresponding to eight of the 32 total surficial units delineated by Brabets (1993b). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

was weighed to ± 0.1 g. The dried sample was placed in a container and de-aggregated with a rubber mallet until all visual clods of dirt were broken down, following the practices of ASTM D421 (2007). Clods present in the samples were assumed to be created during the drying process and not present in the field. After being de-aggregated, each sample was placed in a sieve stack (12.7 mm, 0.841 mm, 0.420 mm, 0.250 mm, 0.149 mm and 0.074 mm), run for 15-minutes on a sieve shaker (Gilson SS-15 Sieve Shaker) then emptied onto a tared plate and the weight of the soil sample retained in each sieve was recorded. Each sieve’s mesh fabric was cleaned with a coarse brush between samples. Size fractions were plugged into HydrogeoSieveXL (Devlin, 2015) in order to determine d10 for use in Eq. (2), producing Ksat values for each of the 47 samples.

3.3. Statistical analysis To evaluate the datasets (Bottomless Bucket and PSD), two statistical paired sample tests were performed on the 47 samples. The nonparametric Wilcoxon signed-rank test is a paired difference test and evaluates whether two datasets have similar or different population ranked means where the null hypothesis suggests that the two populations have similar distributions and means. The coefficient of variation (CV) was used as another comparative analysis between the variability of measured Kfs and Ksat datasets. The CV calculates the dispersion (variance) of data points around a mean. Deb and Shukla (2012) used CV analysis to evaluate the results of multiple sampling methods of various soil types. An F-test was used to evaluate the equality of variance between the datasets, which is a test used to compare the variance between two sample sets with the null hypothesis 168

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that the samples are from different distributions. A two-tailed paired ttest was also performed, assuming unequal variance, which is a parametric Wilcoxon equivalent. The two-tailed t-test was chosen because the direction of any relationship in the data is unknown, and unknown effects could be missed with a one-tailed test. The null hypothesis of a two-tailed t-test is that the difference between the true means of the datasets is equal to zero. To test for a relationship between measured Kfs and Ksat, correlation analyses were performed using Kendall’s tau test (non-parametric) and the Pearson product-moment correlation test (parametric). The closeness of the these coefficients to 1 or −1 reflects the strength of linear relationship between two variables, while the p value, at the chosen α level of significance, can suggest if a linear relationship exists. Also included is a comparison of NRCS-delineated surficial units and NRCS Ksat values with measured Kfs and Ksat values obtained in this study.

A Wilcoxon signed-rank test between Kfs and Ksat values suggests there is no significant difference (p = 0.08) between the datasets (Table 4). However, the Ksat values (PSD method) show greater variability than the Kfs values (Bottomless Bucket method), as indicated by the CV calculated for Kfs (0.8) vs. Ksat (1.8). Both the CV and an F-test performed on the Kfs and Ksat datasets suggests a statistically different variance (F = 8.4, P < 0.01, F Critical = 1.6). The t-test results, under the assumption of unequal variances (Kfs = 1,939 and Ksat = 5,599), with a null hypothesized mean difference of zero suggests the sample means (49 m/day vs. 41 m/day) do not differ significantly. The t-Stat of −0.61 lies within the critical t-Stat range of ± 1.99. These tests indicate that the null hypothesis should not be rejected, suggesting that the datasets can be treated as equal. Correlation coefficients for Kendall’s tau and Pearson’s correlation coefficient suggest that a positive linear relationship exists between the Kfs and Ksat datasets. Kendall’s tau coefficient (non-parametric correlation) was calculated at α = 0.01 and gave a τ value of 0.3 (p < 0.01). The Pearson’s product-moment correlation test (parametric) yielded a coefficient of r = 0.70 (p < 0.01) (Fig. 4). A statistical comparison between our measured Kfs and Ksat values and the Ksat values published by the NRCS (2018) indicate the datasets are significantly different. The Wilcoxon signed-rank test yielded a twotailed probability of p < 0.01 for measured Kfs vs. NRCS-Ksat (Table 4). T-test results on the measured Kfs vs. NRCS-Ksat data also suggest there is a significant difference between the datasets (t-Stat of 7.08 lies outside of the critical t-Stat of ± 2.02; Table 5). Wilcoxon (p < 0.01) and t-test results (t-Stat of 3.63 lies outside of the critical t-Stat of ± 2.01) on measured Ksat vs. NRCS-Ksat data suggests there is a significant difference between those datasets as well. Pearson’s product-moment correlation and Kendall’s tau coefficients calculated between NRCS-Ksat values and our measured Ksat and Kfs also indicate no correlation (r = −0.2 and −0.06; τ = 0.2 and −0.03, respectively).

4. Results Presented are the results of measured field-saturated hydraulic conductivity (Kfs) and saturated hydraulic conductivity (Ksat) from surficial units (Brabets, 1993b) within the Eklutna valley. Overall, the range of Kfs and Ksat values spanned one to two orders of magnitude (minimum of 1 m/day to a maximum of 390 m/day, respectively). Mean K values ranged from 49 (Kfs) to 41 (Ksat) m/day, and median K values ranged from 34 (Kfs) to 10 (Ksat) m/day with standard deviation ranging from 44 to 75, m/day, respectively (Table 1). Of the 47 samples, 25 were in the same order of magnitude, 21 were within one order of magnitude, and one sample was within two orders of magnitude (Delta Lake Deposit). Kfs and Ksat were also analyzed for each of the surficial units (Brabets, 1993b) in the study area (Table 2 and Fig. 2). Among these units, mean Kfs values ranged from 11 m/day (Outwash Deposits) to 104 m/day (Talus Cone). Kfs standard deviation values ranged from 7.0 to 129.7 m/day, and CV values ranged from 0.2 to 1.2. The mean Ksat values ranged from 4 m/day (Bedrock surface glacially modified) to 201 m/day (Talus Cone). Ksat standard deviation values ranged from 2.3 to 267.9 m/day, and the CV ranged from 0.5 to 2.4. The d10 grain size for each sample ranged from 0.67 mm (coarse sand) to 0.03 mm (silt) with an average grain size of 0.15 mm (fine sand). Particle size distribution graphs for each of the Modified Brabets surficial units are shown in Appendix C. For comparison with Brabets’ (1993b) surficial units, descriptive statistics for all measured Kfs and Ksat values were also calculated for each of the NRCS (2018) surficial units. Mean Kfs exhibited values ranging from 9 m/day (Clam Gulch silt loam) to 87 m/day (TalkeetnaChugach-Deneka complex) (Table 3 and Fig. 3). The Kfs standard deviations ranged from 11.0 to 48.2 m/day, and the CV ranged from 0.8 to 1.2. The mean Ksat ranged from 2 m/day (Talkeetna-Chugach-Deneka complex) to 92 m/day (River Bed), with a standard deviation ranging from 3.9 to 99.7 m/day, and CV values ranging from 0.8 to 2.4. In comparison, the NRCS Ksat values (determined by NRCS, 2018) exhibit a range from 0.03 m/day to 3.6 m/day, over all six units in the study area.

5. Discussion This paper provides ranges of hydraulic conductivity values for surficial deposits of the Eklutna Valley, which is important for understanding the hydrogeology of the Eklutna Glacier and Lake. These reservoirs provide an essential water source for nearly half the population of Alaska, and must continue to be studied for their long-term water storage potential. This paper also answers the question of whether a relatively quick and easy, field-based method can produce the same results as a time-intensive, primarily lab-based method for measuring hydraulic conductivity. The results presented herein suggest that these datasets are indeed statistically similar. Statistical analyses indicate that the values measured for Kfs and Ksat are not statistically different datasets, as evaluated by a Wilcoxon signed-rank test (α = 0.01) and Paired t-tests. The Kendall’s tau and Pearson’s Product-Moment Correlation coefficients also suggest that these methods can bea used to establish a range of possible K values. A magnitude comparison between the 47 samples show 53-percent of paired values were within the same order of magnitude, 45-percent were within one order of magnitude, and one sample was within two orders of magnitude. However, as Freeze and Cherry (1979) show, various geologic units can range widely in orders of magnitude of K (Glacial Till up to 6 and Clean Sand up to 4 orders of magnitude). Examination of Kfs and Ksat values suggest statistical similarity (α = 0.01), and significant correlation. These results strongly suggest all measured K values are comparable and representative of K in the sampled surficial geologic units of the Eklutna watershed. Although the evaluations suggest similarity between measured Kfs and Ksat, the variability between these two datasets are statistically different, as suggested by the F-test and CV evaluation. Variability is inherent with the measurement of K (Freeze and Cherry, 1979; Mohanty et al., 1994; Deb and Shukla, 2012; Ronayne et al., 2012). Deb and Shukla (2012) indicate CV values between 0.9 and 1.7 are

Table 1 Descriptive statistical analysis (n = 47) of measured Kfs, Ksat (this study) and Ksat (NRCS) in m/day.

Average Standard Deviation Minimum Maximum Median 95% CI for the median Interquartile range

Ksat

Kfs

Ksat (NRCS)

41 74 0.8 390 10 4.5 to 26 2.3 to 42

49 44 3.2 196 34 23 to 55 18 to 69

0.6 0.3 0.04 1.2 0.4 0.43 to 0.54 0.43 to 0.54

169

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Table 2 Mean hydraulic conductivity values for each of Brabets (1993b) surficial geologic units in the study area. For comparison, the published NRCS Ksat range for each unit is also shown. Brabets (1993b) surficial unit

Mean Ksat (m/day)

St Dev

CV

Count (n)

Mean Kfs (m/day)

St Dev

CV

Count (n)

NRCS Ksat range (m/day)

Alluvial Cone Bedrock surface glacially modified Colluvial from Moraine Delta Lake Deposits Glacial Moraine Outwash Deposits related to Alaskan moraines River Channel East Fork River Channel West Fork Talus Cone

45 4 6 12 39 24 5 96 201

85.8 2.3 4.5 19.4 36.4 21.5 8 87.5 267.9

1.9 0.5 0.8 1.6 0.9 0.9 1.6 0.9 1.3

8 3 3 3 13 4 6 5 2

56 77 75 58 54 11 19 44 104

58.4 13.4 44.3 47.9 27.3 7 16.5 38.6 129.7

1 0.2 0.6 0.8 0.5 0.7 0.9 0.9 1.2

8 5 4 4 14 4 6 5 2

0.1–1.2 0.1–1.2 0.03–1.2 0.3–3.6 0.3–3.6 0.3–1.2 NM NM 0.1–1.2

NM = Not Measured - NRCS does not have a Ksat value assigned to river bed sediments Ksat = Hydraulic conductivity from Hazen (1892) equation Kfs = Field-saturated hydraulic conductivity from Bottomless Bucket Method NRCS Ksat = Saturated hydraulic conductivity from NRCS (2018) St Dev = Standard deviation CV = Coefficient of Variation (Relative Standard Deviation)

considered acceptable, and our results were within ± 10% of that defined range. Variability between different methods, as well as the spatial variability of K over relatively small scales, can be quite large. Factors that can affect K values include: the variability of soil properties over a given area, hydrophobic properties of soils, air trapped in pore spaces and variable water temperatures (Nimmo, 2009). Freeland (2013) also notes that roots and burrowing near the surface will increase surface soil conductivity, while soil weathering products such as clays, sesquioxides, carbonates and silicates accumulating in the soil beneath will decrease conductivity. As such, there is typically a substantial decrease in conductivity as water passes from the topsoil to the subsoil horizon (Freeland, 2013). Whereas the thickness and extent of these two horizons may vary considerably over a small geographic area, this may also contribute to the variability in measured K values. In other subarctic regions, methods for measuring hydraulic conductivity also vary considerably, but similar to our sampling strategy, are generally limited to infiltrometry tests in the field combined with

lab-based methods. Carey and Woo (1999) used pumping tests conducted in wells and piezometers, as well as constant head permeameter tests in the laboratory, to measure hydraulic conductivity of soils in Yukon Territory, Canada. Sharratt et al. (2006) measured the saturated hydraulic conductivity of soils in interior Alaska using the falling head method (Klute and Dirksen, 1986), but measured infiltration using insitu field-based methods. These studies from similar geographic regions and/or similar glaciated terrain report K values on the same order of magnitude as our measured K values (Sharratt et al., 2006), or within one order of magnitude (Mohanty et al., 1994; Carey and Woo, 1999; Ronayne et al., 2012). Hydraulic conductivity values provided by the Bottomless Bucket (Kfs) method (Nimmo et al. 2009) give a reasonable estimate of K using simple equipment, procedures and calculations. Inherent to this method, Kfs values will fall somewhere between unsaturated and saturated soils (between 0 and 100% saturation) because the degree of saturation at the beginning and end of the field procedure is not

Fig. 2. Boxplots showing the hydraulic conductivity measured via the Particle Size Distribution (red = Ksat) and Bottomless Bucket (blue = Kfs) methods, grouped by Brabets’ (1993b) surficial units (x-axis). Whiskers are plotted at the maximum and minimum values for each distribution. All measured K values are plotted as filled circles to the left of each boxplot. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 170

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Table 3 Mean hydraulic conductivity values for each of NRCS (2018) surficial geologic units in the study area. For comparison, the published NRCS Ksat range for each unit is also shown. NRCS (2018) surficial unit

Mean Ksat (m/day)

St Dev

CV

Count (n)

Mean Kfs (m/day)

St Dev

CV

Count (n)

NRCS Ksat range (m/day)

Andic Humicryods-Rock outcrop association Clam Gulch silt loam Deception-Estelle-Kichatna complex Eklutna very cobbly sand Kashwitna-Kichatna complex River Bed Talkeetna-Chugach-Deneka complex

57 10 5 13 28 92 2

99.7 – 3.9 16.9 66.2 74.5 –

1.8 – 0.8 1.4 2.4 0.8 –

14 1 4 6 14 7 1

62 9 68 37 48 40 87

48.2 – 41.2 43.0 46.7 31.9 11.0

0.8 – 0.6 1.2 1.0 0.8 0.1

14 1 5 7 15 8 2

0.1–1.2 0.03–0.1 0.3–1.2 1.2–3.6 0.3–1.2 NM 0.1–1.2

NM = Not Measured - NRCS does not have a Ksat value assigned to River Bed Sediments Ksat = Hydraulic conductivity from Hazen (1892) equation Kfs = Field-saturated hydraulic conductivity from Bottomless Bucket Method NRCS Ksat = Saturated hydraulic conductivity from NRCS (2018) St Dev = Standard deviation CV = Coefficient of Variation (Relative Standard Deviation)

measured. Some additional variation in Kfs values when compared to the PSD method (Ksat) is due to the variables taken into consideration: only the water is considered during Kfs testing and the soil properties are empirically defined. The PSD analysis provided the information needed to calculate Ksat with Eq. (2). Possible inaccuracy introduced during the particle-size sieving process could be caused by the clogging of sieve fabric openings (blinding) that fail to allow the passing of smaller soil particles, overestimating the particle size and causing a narrower PSD (Hogg et al., 2008). The Hazen (1892) equation is the basis of the PSD to K relation that others have built upon. Similar to the Bottomless Bucket method, there are shortcomings in the Hazen equation due to the inherent assumptions within. For example, the equation considers only the d10 of a soil and omits other larger particles (cobbles, boulders, etc.) that might provide macro-pore pathways, which often increases K. Such overall soil sampling discrepancies and K measurement inaccuracies are surely present within our datasets, and are represented in the Eklutna valley surficial units. Ten particle size distribution graphs grouped by Modified Brabets

surficial geology units show some notable patterns (Appendix C). The glacial moraine and talus cone PSDs showed a heterogeneous mixture of grain sizes (gravel to fines) while the alluvial cone PSD showed a mixture of coarse (primarily gravel) to primarily sand type grain size curves. The PSD for the West Fork of the Eklutna River is very similar to the samples from the glacial moraine areas, suggesting the river is reworking moraine deposits in the area. The higher flow rate of the West Fork of the Eklutna River (Larquier, 2010) is reflected in its PSD by the higher distribution of coarse grains (fine gravel to medium sand) in comparison to the East Fork, with grain size concentrations primarily in the medium to fine sand range. The higher degree of sorting in the East Fork reflects a lower flow rate, capable of carrying only smaller grains. Overall, PSDs from all samples displayed a bimodal distribution – showing either a poorly-sorted PSD or a well-sorted PSD, with very few in between. Poor-sorting near the channel reflects the eroding of moraine deposits, with well-sorted material deposited during flood events farther from the main channel. The broad characterization of surficial units by the NRCS (2018) likely contributes to some of the large within-unit variation (e.g.,

Fig. 3. Boxplot of soil hydraulic conductivity obtained via the Particle Size Distribution (red = Ksat) and Bottomless Bucket (blue = Kfs) methods, grouped by NRCS (2018b) surficial units (x-axis). Whiskers are plotted at the maximum and minimum values for each distribution. All measured K values are plotted as filled circles to the left of each boxplot. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 171

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Table 4 Wilcoxon paired sample test results.

Number of positive differences Number of negative differences Large sample test statistic Z Two-tailed probability

Ksat vs. Kfs

Ksat (NRCS) vs. Ksat

Ksat (NRCS) vs. Kfs

32 15 −1.77 p = 0.08

46 1 −5.95 p < 0.01

46 1 −5.71 p < 0.01

Fig. 4. Hydraulic conductivity measured via the Particle Size Distribution method (Ksat) vs. the Bottomless Bucket method (Kfs). Linear regression line is shown (dotted blue), along with Pearson correlation coefficient (r = 0.70), and 1:1 identity line (dashed red). Points are categorized and colored according to Brabets (1993b) surficial units (see legend). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3). The NRCS (2018) surficial geological unit with the highest measured CV of 2.4 (Kashwitna-Kichatna complex) falls outside the recommended CV range of 0.9–1.7 as established by Deb and Shukla (2012). This unit coincides with four Modified Brabets units: Alluvial Cone, Glacial Moraine, River Channel Deposits and Bedrock surface glacially modified, suggesting the NRCS could subdelineate the Kashwitna-Kichatna complex. Another source of uncertainty stems from the methods used by the NRCS (2018) to determine hydraulic conductivity. NRCS-Ksat values are calculated by selecting a soil bulk density class,

then assigning the soil to a range of Ksat values provided in an NRCS textural triangle. This provides a quick and generalized estimate. Qualitative data of this type is empirically derived from NRCS agency experience throughout similar depositional environments and gives an approximate value for similar soil classes over a broad area; these values are “not meant to be used at a specific site but for a class range comparison of soils” (NRCS, 2012). Statistical comparison of measured Kfs and Ksat with NRCS-Ksat average values suggest no statistical similarity and no correlation

Table 5 Statistical comparison of K datasets using t-Test (two-sample assuming unequal variances). Ksat

Kfs

Ksat (NRCS)

Ksat (NRCS)*100

Mean Variance Observations

41 5599 47 Ksat vs. Kfs

49 1939 47 Ksat vs. Ksat(NRCS)

0.6 0.1 39 Kfs vs. Ksat (NRCS)

53 1442 6 Ksat vs. Ksat(NRCS)*100

Kfs vs. Ksat(NRCS)*100

Hypothesized mean difference df t Stat P(T < =t) one-tail t Critical one-tail P(T < =t) two-tail t Critical two-tail Pearson’s correlation coefficient Kendall’s tau coefficient

0 74 −0.61 0.27 1.67 0.54 1.99 0.7 0.3

0 38 3.63 0.00 1.68 0.00 2.01 −0.2 0.2

0 38 7.08 0.00 1.69 0.00 2.02 −0.06 −0.03

0 8 −1.94 0.04 1.86 0.09 2.31 0.02 0.0

0 9 −0.07 0.47 1.83 0.94 2.26 0.20 0.28

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between the datasets. However, when the mean NRCS-Ksat value was multiplied by 100 (two orders of magnitude) and compared to the mean measured Kfs and Ksat values within NRCS units, paired-sample t-test results suggested these datasets are similar. The need for this two-orderof-magnitude multiplier might be explained by the NRCS reporting permeability instead of Ksat. The original NRCS sampling metadata is unavailable. This was known to be an issue in the past when permeability and Ksat were used synonymously, as noted by the NRCS in technical note six (NRCS, 2004). After multiplying the mean NRCS-Ksat for each of their six surficial units by 100 and comparing averaged measured Kfs and Ksat, using a paired-sample t-test, results suggest these datasets are equivalent. T-test results for measured Ksat averages and NRCS-Ksat averages give a t-Stat of −1.94 that was within the critical tStat of ± 2.31. T-test results for the measured Kfs averages and NRCSKsat averages give a t-Stat of −0.07 that was within the critical t-Stat of ± 2.26. These results suggest the averages for measured Ksat and Kfs compare with the averages of NRCS-Ksat ranges after multiplying by 100. Insufficient data was available to perform a Wilcoxon test. Results suggest that our measured Kfs and Ksat are statistically similar datasets. Meanwhile, the averaged NRCS-Ksat range was shown to be statistically different when compared to our measured Kfs and Ksat datasets. However, the averaged range of measured Kfs and Ksat within NRCS units were within one order of magnitude of the averaged NRCSKsat range if multiplied by 100. Therefore a range of possible K values for surficial units in the Eklutna watershed is established by measured Kfs and Ksat values in this evaluation.

misrepresentative. For example, the surficial geologic maps delineated by Brabets (1993b) and the NRCS (2018) were key to this study and allowed accurate sample placement within specific geologic units. However, owing to the heterogeneity between Brabets (1993b) and the NRCS (2018) delineated surficial units, the creation of a unified version or comprehensive map is outside of the scope of this project. This paper presents two simple techniques for obtaining a range of K values in heterogeneous point samples, given that soil property data is commonly a limiting factor in many hydrogeologic assessments. As such, the methods presented in this paper could be used for surficial unit delineation and water resource management in any landscape. Moreover, the methods presented could potentially be used by other researchers to obtain the necessary data for creation of an effective localized hydrological model.

5.1. Recommendations for future work

The authors would like to thank Alaska Pacific University for supporting this project. Partial support was provided by the Alaska Space Grant Program (ASGP). This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Thanks to Edward Moran and the Alaska-Pacific River Forecast Center for editing and assistance with manuscript preparation. Additional gratitude to Dr. Chris Evans for valuable suggestions and comments on the manuscript, as well as Bace Poplawski for help in the field. This paper benefitted from the careful review and constructive suggestions provided by the anonymous reviewers.

CRediT authorship contribution statement Amanda L. King: Project administration, Resources, Supervision, Visualization. James S. Meyers: Investigation, Formal analysis. Jim M. Brown: Conceptualization, Supervision. Declaration of Competing Interest None. Acknowledgements

To better define the variability of hydraulic conductivity in the Eklutna area, future work should focus on increased sample coverage, particularly in the less accessible areas. Also, evaluation of PSD data using other empirical equations (e.g., Slichter, 1898, Zamarin, 1928, Zunker, 1930, Carman, 1937) in HydrogeoSieveXL (Devlin, 2015) could provide an increased understanding of the K variability. We also acknowledge that additional methods exist for testing hydraulic conductivity that could place further constraints on Eklutna surficial units. However, the primary focus of this paper is on increased efficiency in field-sampling and analytical techniques and as such, variation in K values are to be expected. Furthermore, measured K values presented in this research are site-specific and restricted to shallow sampling depths, and are not to be used for interpolation purposes until additional coverage is obtained.

Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jhydrol.2019.05.024. References

6. Conclusions

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