The impact of storm events on a riverbed system and its hydraulic conductivity at a site of induced infiltration

Journal of Environmental Management 92 (2011) 1960e1971

Contents lists available at ScienceDirect

Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman

The impact of storm events on a riverbed system and its hydraulic conductivity at a site of induced infiltration Jonathan Levy a, *, Matthew D. Birck b,1, Samuel Mutiti a, 2, Kathryn C. Kilroy a, 3, Britton Windeler b, 4, Ominigho Idris b, 5, Lauren N. Allen a, 6 a b

Department of Geology, Miami University, Oxford, OH 45056, USA The Institute of Environmental Sciences, Miami University, Oxford, OH 45056, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 11 June 2010 Received in revised form 8 February 2011 Accepted 10 March 2011 Available online 14 April 2011

The spatial and temporal variability of riverbed vertical hydraulic conductivity (Kv) was investigated at a site of induced infiltration, associated with a municipal well field, to assess the impact of highstage events on scour and subsequently the riverbed Kv. Such impacts are important when considering the potential loss of riverbank filtration capacity due to storm events. The study site, in and along the Great Miami River in southwest Ohio, overlaid a highly productive glacial-outwash aquifer. A three-layer model for this system was conceptualized: a top layer of transient sediment, a second layer comprising large sediment resistant to scour, but clogged with finer sediment (the armor/colmation layer), and a third layer that was transitional to the underlying higher-Kv aquifer. One location was studied in detail to confirm and quantify the conceptual model. Methods included seepage meters, heat-flow modeling, grain-size analyses, laboratory permeameter tests, slug tests and the use of scour chains and pressure-load cells to directly measure the amount of sediment scour and redeposition. Seepage meter measured riverbed Kv ranged from 0.017 to 1.7 m/d with a geometric mean of 0.19 m/d. Heat-transport model-calibrated estimates were even lower, ranging from 0.0061 to 0.046 m/d with a mean of 0.017 m/d. The relatively low Kv was indicative of the clogged armor layer. In contrast, slug tests in the underlying riverbed sediment yielded Kv values an order of magnitude greater. There was a linear relationship between scour chain measured scour and event intensity with a maximum scour of only 0.098 m. Load-cell pressure sensor data over a 7-month period indicated a total sediment-height fluctuation of 0.42 m and a maximum storm-event scour of 0.28 m. Scour data indicated that the assumed armor/colmation layer almost always remained intact. Based on measured layer conductivities and thicknesses, the overall Kv of this conceptualized system was 1.6 m/d. Sensitivity analyses indicated that even complete scour of the armor/colmation layer would likely increase the overall Kv only by a factor of 1.5. Most scour events observed removed only the transient sediment, having very little effect on the entire system indicating low risk of losing filtration capacity during storms. The research, however, focused on the point bar, depositional side of the river. More research of the entire river profile is necessary. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Riverbed hydraulic conductivity Riverbed scour Heat-flow modeling Seepage meters

* Corresponding author. Tel.: þ1 513 529 1803; fax: þ1 513 529 1542. E-mail addresses: [email protected] (J. Levy), mdb@paynefirm.com (M.D. Birck), [email protected] (S. Mutiti), [email protected] (K.C. Kilroy), [email protected] (B. Windeler), [email protected] (O. Idris), [email protected] (L.N. Allen). 1 Present address: The Payne Firm, 11231 Cornell Park Dr., Cincinnati, OH 45242, USA. 2 Present address: Dept. of Biological & Environmental Sciences, Georgia College & State University, Milledgeville, GA 31061, USA. 3 Present address: Department of Geoscience, Minot State University, Minot, ND 58707, USA. 4 Present address: Naval Science, Millett Assembly Hall, Miami University, Oxford, OH 45056, USA. 5 Present address: Procter and Gamble Co., 11810 East Miami River Rd., Cincinnati, OH 45252, USA. 6 Present address: Department of Teacher Education, Miami University, Oxford, OH 45056, USA. 0301-4797/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2011.03.017

J. Levy et al. / Journal of Environmental Management 92 (2011) 1960e1971

1. Introduction Alluvial aquifers that are hydraulically connected to surfacewater bodies are used as drinking-water production sites throughout the world because of the relative ease of shallow groundwater withdrawal (Hiscock and Grischek, 2002). Wells placed in such locations have generally high production capacities because of the high permeability of the aquifer sediment and high aquifer-recharge rates caused by induced infiltration from the surface-water body. As surface water travels through the riverbed and aquifer on its way to the production well, it undergoes riverbank filtration (RBF), a term that describes the attenuation of potential contamination that can occur because of physical filtration, sorption, or degradation (Ray et al., 2002). Contaminant attenuation can also occur from dilution as water from a river mixes with water coming from farther away with longer aquifer residence times. All these processes can eliminate or lessen the concentrations of particulates (Wang et al., 1995), total and dissolved organic carbon (Miettinen et al., 1994; Sontheimer, 1991; Wang et al., 1995), turbidity (Mikels, 1992), bacteria (Miettinen et al., 1996), viruses (Havelaar et al., 1995), and synthetic organic compounds (Wilderer et al., 1985; Ray et al., 1998, 2002). RBF has been highly effective in filtering out particles, bacteria, viruses, and parasites (Kuehn, 2000). Gollnitz et al. (2005), for example, found that for a system in Wyoming, RBF resulted in up to 4.0 log (99.99%) reduction in algae and diatoms and at least 2.0 log (99%) reduction in both Giardia and Cryptosporidium surrogates (no Giardia or Cryptosporidium were found in the samples). Many RBF systems in the United States are classified by the US Environmental Protection Agency (USEPA) as groundwater under the direct influence of surface water (GWUDISW). Historically, GWUDISW has been regulated in the same manner (i.e., required the same treatment processes) as surface water. Regulations promulgated in 2006 in the Long Term 2 Enhanced Surface Water Treatment Rule (Federal Register, 2006) grant a 0.5-log or a 1.0-log credit for use of RBF depending on whether certain criteria are met. Higher log reductions may be granted by states with a demonstration of a performance study (Federal Register, 2006). Substantial treatment of RBF-derived water is still required to potentially remove, kill, or inactivate Cryptosporidium. Public water utilities can demonstrate on a case-by-case basis the efficacy of their RBF systems in removing these pathogens, but this option has had limited application (Gollnitz et al., 2005) because of the length and cost associated with RBF studies. The USEPA has been generally reluctant to grant more than a 1.0-log credit despite studies indicating that a 3.0- or 4.0-log credit is warranted (Gollnitz et al., 2004). Hesitance toward greater acceptance of riverbank filtration as an alternative treatment option has stemmed at least in part from concern over the stability of the infiltration rate and attenuation capacity of the system during high-stage events. The concern is that high-stage events can trigger scouring or removal of riverbed layers critical to the natural attenuation of surface-water contaminants (Hiscock and Grischek, 2002; Ray et al., 2002; Wett et al., 2002). The sediment layer potentially most at risk from scouring is the biologically active colmation layer of the riverbed (Hiscock and Grischek, 2002). This layer is the product of deposition of fine particles into the interstitial spaces, often beneath a protective armor layer of larger-sized particles (Velickovic, 2005; Grischek and Ray, 2009). This layer is sometimes referred to as the mechanical clogging layer (Schubert, 2002, 2006). Because it is composed of suspended solids deposited in the upper layer of a rivereaquifer interface, it is often assumed to have a lower hydraulic conductivity and greater filtering capacity than do the underlying

1961

sediments (Velickovic, 2005). The majority of particulate material is removed in the first two feet of the riverbed (Wang et al., 2002; Gollnitz et al., 2003). Loss of the colmation layer resulting from scour could potentially allow contaminants to reach production wells (Schubert, 2000). Understanding the effects of storm events on riverbeds is important not only in the US but also worldwide as shallow aquifers in alluvial settings represent groundwater resources yet to be developed in many countries (Grischek and Ray, 2009). The goal of this study was to evaluate the impact of storms and high-stage events on the thickness and hydraulic conductivity of a riverbed at a site of induced infiltration. The working hypothesis of the research was that the riverbed comprised a colmation/ clogged-armor layer that generally has a lower hydraulic conductivity than does the underlying aquifer. During storm events, scour takes place, removing sediment and cleansing the colmation layer of some of its clogging, thereby increasing its hydraulic conductivity and decreasing its thickness. The net effect therefore is to increase the amount and velocity of infiltrating river water into the aquifer. This effect might be especially important because scouring can occur during the time of maximum hydraulic-head gradient and therefore during times with the greatest driving force of river water infiltration. The hypothesis was tested at a site of induced infiltration associated with Charles M. Bolton Municipal Well Field, located along the Great Miami River in southwest Ohio (Fig. 1) with the goal of quantifying the impact of storm events on the overall hydraulic conductivity of the riverbed system. 2. Description and previous studies of the research area The Great Miami River is 273 km in length, discharging into the Ohio River just west of Cincinnati. Its watershed covers 10,196 km2, 80% of which is agricultural. More than 1.3 million people reside within the watershed, 97% of those rely on groundwater for their primary drinking-water source (MCD, 2005). The bulk of this groundwater is procured from the Great Miami Buried Valley Aquifer, an alluvial-outwash, mostly unconfined aquifer ranging from 3.2- to 4-km wide and 24- to 61-m deep. Hydraulic conductivities generally range from 60 to 120 m/d (Gollnitz et al., 2003). The nearest US Geological Survey gaging station is in Hamilton, OH, 9.5 km northeast of the Bolton well field. During 2005 and 2006, the mean discharge at the Hamilton station was 144 m3/s. The region receives an annual precipitation total of about 1 m (Walton et al., 1967). At the Charles M. Bolton Municipal Well Field, the Greater Cincinnati Water Works operates a drinking-water production facility featuring 12 wells pumping between 56 and 113 ML/d, roughly 12% of the City of Cincinnati’s drinking water. As a result of this pumping, river water undergoes induced infiltration into the aquifer. This research focused on Site 6, an area within the well field associated with one of the production wells, PW6 (Fig. 2). PW6 is located about 0.3 km south of the river. During baseflow conditions, the river at this point is nearly 120 m in width and 3 m at its greatest depth. Most of the riverbed in this area is visibly covered with pebble- to cobble-sized sediments. The sediments generally appear imbricated and clast supported. Most of this investigation focused on the southern side of the river (nearest PW6) on a migrating depositional point bar (Fig. 2). Because of the influence of the municipal production wells, the area is dominated by strong downward hydraulic gradients. Previously, an aquifer-pumping test was performed using municipal well PW1, located approximately 1.2 km east of Site 6 (Fig. 2). Drawdowns were monitored in three nearby monitoring wells and analyzed with the Neuman method (Neuman, 1975).

1962

J. Levy et al. / Journal of Environmental Management 92 (2011) 1960e1971

mean of 46 m/d. No spatial pattern to the values was observed either horizontally or vertically. Gollnitz (2002) estimated that the rate of river infiltration into the aquifer at the Bolton well field could potentially range four orders of magnitude, with a likely scenario of the rate varying one order of magnitude throughout a year. This variability was based on the variability of river stage, area of pumping influence, water viscosity and riverbed permeability. The impact of infiltration rate variability on natural filtration and water quality was investigated at the Bolton well field (Gollnitz et al., 2004). Two production wells with relatively long and short associated flowpaths from the river were monitored for surface water indicators such as Giardia, Cryptosporidium, turbidity, and aerobic spores. Out of more than 200 groundwater samples, Giardia and Cryptosporidium were not detected, turbidity remained well below 1.0 ntu, and aerobic spores experienced a 4.0-log reduction compared with that in river water. There was no direct evidence that increased infiltration rates had any adverse effects on water quality. Gollnitz et al. (2004) asserted that there was a need for further investigation into riverbed characteristics, particularly the impact of high-stage events on riverbed hydraulic conductivity. 3. Hypothesized riverbed conceptual model

Fig. 1. Riverbed research field area at Charles M. Bolton Water Plant in southwest Ohio.

Resulting horizontal hydraulic conductivity (Kh) values ranged from 68.3 to 150 m/d with a geometric mean of 113 m/d. The analysis also yielded a vertical to horizontal hydraulic conductivity anisotropic ratio (Kv/Kh) of 0.5. Slug tests were performed on 12 monitoring wells, four associated with PW1, four associated with PW8, three at nest 6D and one additional well. Slug test derived Kh values (assuming Kv/Kh of 0.5) ranged from 6.9 to 360 m/d with a geometric

A conceptual model of the riverbed system overlying the glacialoutwash aquifer sediments was developed to guide this research based on the model by Schalchli (1995) and research by Wilcock and DeTemple (2005). A three-layer system was hypothesized with some probable ranges of layer thicknesses: a thin, transient-sediment layer (between 0- and 100-cm thick), an armor and colmation layer (0- to 100-cm thick), and a bottom layer that is spatiallytransitional to the underlying outwash sediments (1- to 5-m thick) (Fig. 3). The transient-sediment layer was thought to comprise the top-most silt, sand and gravel that is continuously scoured and re-deposited during high-stage events. Underlying these sediments was thought to be a relatively stable armor and colmation layer that remains in place during most storm events. Lisle and Madej (1992)

Fig. 2. Location of research site (Site 6) along the Great Miami River.

J. Levy et al. / Journal of Environmental Management 92 (2011) 1960e1971

1963

installed by the Greater Cincinnati Water Works (Fig. 2). Well nest 6D, comprising three monitoring wells, was installed about halfway between the river and the municipal well PW6 (Fig. 2) via hollowstem-auguring. Drive-point piezometers were installed directly into the riverbed at five separate locations; at three of these locations, two nested piezometers were installed at depths of 0.61 and 1.22 m (Fig. 4). At the other locations, piezometers were at depths of 2.13 and 0.76 m. All eight drive-point piezometers were 4.3-cm in diameter and with well screens ranging between 0.30 and 0.61-m long. In addition, nine previously-installed monitoring wells throughout the Bolton well field were used to measure aquifer hydraulic conductivity. Fig. 3. Conceptual diagram of three distinct riverbed layers. Adapted from Schubert (2002).

define the armor layer as a coarse layer of particles greater than 4 mm in diameter that is mobile only in annual floods. Due to the constant downward gradients induced by pumping, this layer was thought to be tightly clogged by finer material within and beneath the armor layer (internal colmation, Velickovic, 2005) resulting in a relatively low hydraulic conductivity. Below the influence of colmation, there was a hypothesized zone of sediments that are intermediary between the low-conductivity colmation layer and the much more conductive glacial outwash. By quantifying the thicknesses, hydraulic conductivities and dynamics of these layers, we hoped to be able to better understand the influence of storm events on the entire system. 4. Methods 4.1. Monitoring river and groundwater River stage and temperature at Site 6 were measured using Solinst LeveloggersÒ (referred to as stream gages in Fig. 4) mounted at known elevations on fence posts hammered into the riverbed. When Solinst Leveloggers were not in place, river stage at Site 6 was estimated using the data recorded at the USGS gage in Hamilton, Ohio. Predictions were based on a linear regression relationship determined by using the USGS and the Solinst LeveloggerÒ data. Monitoring wells were installed both on-shore and beneath the riverbed to measure hydraulic conductivity via slug tests and provide temperature data for heat-flow modeling. Already existing monitoring wells near the riverbed site included 6B and 6C

4.2. Sediment sampling and grain-size analyses Riverbed sediment samples were collected for grain-size analysis using three techniques. First, a 5-cm diameter split-spoon sampler lined with polycarbonate tubing was used to obtain six intact cores from the top 0.12 m of the riverbed during low-flow conditions and in the river about 22.7 m north of the riverbank. Second, three backhoe trenches were dug during low-flow conditions on-shore, both parallel to the river and extending from shore out perpendicularly into the river. Thirty samples were obtained from the trenches taken from four depths: Shallow, Upper-middle, Lower-middle, and Deep corresponding to 0e0.076 m, 0.076e0.15 m, 0.15e0.23 m, and 0.23e0.30 m, respectively. Third, during a low-flow period, eight samples between 1 and 5 kg were collected with a shovel from approximately the top 0.08 m of the riverbed. Samples were collected on a transect starting on-shore about 10.4 m south of the riverbank and heading north, 106 m offshore in the river. Grain-size distribution analysis was performed on all sediment samples using wet-sieving techniques. Grain-size distribution curves were used to calculate the effective grain-size (d10), the median grain-size (d50) for classification according to the Wentworth scale, and the uniformity coefficient (Cu ¼ the ratio of the d60 to the d10) for quantifying the degree of sorting. A sample with a Cu < 4 was considered well sorted; a Cu between 4 and 6, medium sorted; and a Cu > 6, poorly sorted (Fetter, 2001). Statistical analyses (t-test, analysis of variance, and regression analysis) were used to investigate the spatial variability and distribution in sediment samples obtained from the trenches and also to relate the grain-size distribution to sample location and depth. 4.3. Measures of riverbed and aquifer hydraulic conductivity

Fig. 4. Spatial distribution of instrumentation and seepage meter experiments at Site 6.

Direct measurements of vertical riverbed hydraulic conductivity (Kv) were conducted throughout a variety of river stages and river temperatures using seepage meters spatially distributed across the site (Fig. 4). The seepage meters were modeled after Idaho seepage meters (ANCID, 2004) and fabricated at the Miami University Instrumentation Laboratory. Seepage meters were placed in close proximity to mini-piezometers (Lee and Cherry, 1978) to measure vertical hydraulic-head gradients. To minimize the head loss caused from river current flowing over the bags (Murdoch and Kelly, 2003; Libelo and MacIntyre, 1994), the water-collection bladders were housed in plastic bins sheltered from the river current but with openings large enough that the head in the bin was the same as in the river. Controlled laboratory tests were conducted to determine the accuracy of the seepage meters in an artificial sand tank that allowed the seepage meter estimates of Kv to be compared to those derived from constant-head vertical pumping tests. The pumpingtest value was assumed to be a more accurate measure as it represented the most direct application of Darcy’s law with maximum

1964

J. Levy et al. / Journal of Environmental Management 92 (2011) 1960e1971

pumping-rate and gradient-measurement errors of about 5%. Eight seepage meter tests yielded Kv values ranging from 6.1 to 12 m/d with a geometric mean Kv of 8.2 m/d compared to the pumping test average of 17.4 m/d, 2.1 times the seepage meter average. We assumed that this ratio represented a systemic error, the cause for which was probably head loss due to the small diameter of tubing (0.6 cm) between the bladder and seepage meter and friction associated with the bag itself. Kv values derived from our seepage meters were therefore multiplied by this factor of 2.1 to correct for head loss through tubing. Falling-head slug tests were performed by pouring water in six of the riverbed drive-point piezometers screened at depths of 0.61 and 2.13 m. All data collected via pressure transducers were analyzed using the BouwereRice (Bouwer, 1989; Bouwer and Rice, 1976; Zlotnik, 1994) method in AQTESOLV (www.aqtesolv.com) assuming a vertical to horizontal anisotropy ratio of 0.5 (based on previous pumping tests in the Bolton well field). Each test was performed in triplicate, examined for goodness of fit to the Bouwer and Rice model, and geometric averages at each piezometer were used. Three of the split-spoon cores collected from the riverbed were incorporated into a constant-head permeameter. Kv was determined in the laboratory according to Darcy’s law using three different gradients (Fetter, 2001). Heat-flow modeling was used to indirectly estimate riverbed Kv. The USGS program VS2DHI (Hsieh et al., 2000) was used to solve the transient, two-dimensional governing equation (in profile) for temperature change in response to thermal convection, conduction and dispersion (Constantz et al., 2002). The upper boundary, occupied by the river, and the left-hand (southern) boundary between the river and the production well, were modeled as specified, variable head and temperature boundaries. A no-flow boundary was used along the model bottom at a depth of 46 m. The right-hand (northern) boundary was placed far enough away from the area of interest to not affect the simulations and was a no-flow boundary. The period from June 2005 through January 2006 was simulated using continuous temperature and hydraulic-head data obtained in the river and in monitoring wells at various depths and locations using Stowaway TidbiTÒ temperature loggers and InSitu TrollsÒ. Simulated groundwater temperatures were matched to the observed temperatures at piezometer P6-4, 3.4-m below the river. Initial conditions were those observed in all the wells, including P64, in June 2005. Non-site-specific hydraulic properties used in the simulations were based on literature values (Table 1). Sediment porosity was estimated to be 0.24 based on previous investigations at the Bolton well field (Sun, 2001). The principle calibration parameters were the Kh value and the Kv/Kh anisotropy ratio for the top 1-m of the model domain representing the relatively low-K riverbed. The rest of the model domain was assumed to comprise Table 1 Hydraulic properties used in the VS2DH simulations. Parameter

Riverbed

Aquifer

Source

Longitudinal dispersivity (m) Transverse dispersivity (m) Csa (J m3  C1) b KT (J h1 m1  C1)

0.1

0.1

0.1

0.1

2.18  106 7200

2.18  106 7200

4.2  106

4.2  106

Niswonger and Prudic (2003) Niswonger and Prudic (2003) Su et al. (2004) Niswonger and Prudic (2003) Sue et al. (2004) Niswonger and Prudic (2003)

Cwc (J m3  C1)

a b c

Cs is volumetric heat capacity of the bulk sediment. KT is thermal conductivity. CW is heat capacity for water.

aquifer material of a higher Kh value of 57 m/d, corresponding to the mean value derived from slug tests at the nearest monitoring wells (Windeler, 2006). 4.4. Measures of riverbed thickness, scour, and deposition To characterize changes in the overall riverbed profile over time, four cross-sectional profiles were conducted between December 2004 and May 2006. The riverbed elevation was surveyed at 6-m intervals along a transect between monitoring wells FP6B and FP6C (Fig. 2). After the initial profiling, comparison profiles were conducted following three large storm events. Scour chains of known length were installed at five locations (Fig. 4) to estimate total scour and re-deposition associated with individual events. To install a scour chain, a temporary pipe was first driven into the riverbed to a depth of about 1 m. The chain was lowered into the pipe and then the pipe was removed, leaving the chain vertically in place below the riverbed. The chain length above the riverbed was measured and laid flat. In theory, during a highstage event, scour will expose more of the chain, reducing the length lying vertically. Deposition may then occur over the chain. Upon retrieval, the change in the vertical chain length was measured and assumed to be the amount of scour, and the depth to the vertical section was assumed to be the depth of re-deposition. From November of 2005 through June 2006, riverbed thickness was measured with a RoctestÒ vibrating-wire load-cell pressure sensor and thermistor (Roctest, 2005). This sensor was buried in a river trench approximately 0.7 m below the riverbed. The sensor cable was buried in a separate trench was connected to a Campbell ScientificÒ CR-10X data logger. The load-cell sensor measured total pressure comprising pressures from the saturated sediment load, the water-column height and air pressure. River stage and atmospheric pressure were subtracted from the load-cell pressure sensor data to isolate the pressure changes resulting from changes in sediment height above the sensor. Using an estimated wet-bulk sediment density, the pressure was converted to a sediment height. One complication of this process was that to calculate scour and deposition, we needed to know the change in the height of the water column above the load cell and yet, if there was scour or deposition, then a change in the height of the water column was not fully accounted for by the Solinst LeveloggerÒ pressure transducer. An iterative solution was required in which the change of sediment height was first calculated based on the load-cell and the pressuretransducer data. The sediment-height change was then used to calculate the true water-column height which was then used to recalculate the sediment-height change. This process continued until it converged on a solution. To verify the accuracy of the loadcell data and to calculate a reasonable wet-bulk sediment density, the load cell was excavated in June 2006 and placed in a 170-L plastic storage container. It was then covered in increasing amounts of water. A SolinstÒ Levelogger and In SituÒ Mini-Troll were also placed in the container to compare depth measurements with the load-cell reported pressure. After the storage container was full of water, the load cell was covered in 15 cm of sediment from the riverbed to compute wet-bulk density of the riverbed sediment. 5. Results and discussion 5.1. River stage River stage at Site 6 was measured from September 2004 through December 2006 (Fig. 5). Gollnitz et al. (2004) defined a high-stage event on the Great Miami River as a rise and fall of river stage of at least 1.52 m within a 24-h period. According to this

J. Levy et al. / Journal of Environmental Management 92 (2011) 1960e1971

1965

5.3. Direct measurements of riverbed hydraulic conductivity

Fig. 5. River stage at Site 6, September 2004 through December 2006.

definition, eight high-stage events occurred in 2005 and three in 2006 (Fig. 5). 5.2. Grain-size analyses Sample penetration during split-spoon sampling did not exceed 0.12 m because of contact with cobbles larger than the sampler diameter (5 cm) at this depth. We inferred that the split-spoon cores comprised the sediments representing the top transient sediment overlying the large cobble layer. These top sediments were poorlysorted pebbles and well-sorted coarse sand. Generally speaking, sediments were coarse-grained with less than 5% fines (silt þ clay) (Fig. 6). Almost all of the sediment samples within the channel had a pebble-sized median grain size. Outside the channel, to the south of or very close to the southern riverbank, finer samples (fine and medium sands) were found, probably representing over-bank flood deposits. All but one split-spoon sample were poorly sorted with uniformity coefficients ranging from 8 to 42. Sediments became coarser as their distance from the bank increased as was indicated by significant differences in median grain size between trenches. Also, regression analysis indicated that the silt þ clay content of the sediments from the trenches decreased with distance from the bank toward the river (p-value of 0.00031 and R2 of 38%). The relationship between grain-size distribution and depth of the sediment samples was also explored. We hypothesized that as a result of clogging, the shallow sediments would have more silt and clay. Surprisingly, no significant difference was found in the median grain size (d50), or the amount of silt þ clay between the shallow and deep sediment samples in any trench. Grain-size distribution did not vary in any systematic way with depth below the surface of the river. However, sampling occurred to a maximum depth of only 1 m. It is possible that depth of colmation extends as deep as 1 m. 166

100

164

163 1 162

0.1 -20

Elevation (m above msl)

Median grain size (mm)

165 10

161 -10

0

10

20

30

40

50

Consistent downward vertical gradients ranging from 0.010 to 0.14 were measured during riverbed seepage meter tests from September 2005 to January 2006. Seepage meter results spanned two orders of magnitude with uncorrected values ranging from 0.0080 to 0.82 m/d. Applying the tank-to-seepage meter correction factor of 2.1 yielded corrected values ranging from 0.017 to 1.7 (Table 2). These data were approximately log-normally distributed with a geometric mean of 0.19 m/d. The 95% confidence interval for this mean (based on a log-normal distribution) was 0.13e0.30 m/d. Regression analysis found no significant relationship of Kv to river stage, distance from bank or to river temperature. The seepage metering was testing the overall Kv of the upper part of the riverbed and probably reflected the most limiting layer of the system, hypothesized to be the colmation later beneath the transient sediment. Three intact split-spoon sediment samples from the top transient-sediment layer were incorporated in a laboratory permeameter. Kv values for these cores were 3.5, 4.3 and 10 m/d, with a geometric mean Kv of 5.3 m/d. Slug tests were performed in drive-point piezometers hammered directly into the riverbed at depths between 0.61 and 2.13 m below the river and therefore represented the third layer of the conceptual model. A total of eight tests yielded Kh values ranging from 2.9 to 35.4 m/d with a geometric mean of 10.5 m/d. This mean was significantly less than the measured hydraulic conductivity of the underlying aquifer measured either by pumping test (110 m/d) or slug test (46 m/d) (p < 0.05). Hydraulic conductivity was not correlated to depth below the riverbed. To obtain a Kv for comparative purposes, we assumed an anisotropy ratio between 0.1 and 0.5 to estimate a mean Kv value of between 1.1 and 5.3 m/d. The anisotropy ratio estimate was based on values derived in unconsolidated sediments including 0.5 from a pumping test in the Bolton well field, 0.1 to 0.5 (Freeze and Cherry, 1979), 0.34 for beach sediments (Li et al., 2010), 0.20 to 0.33 for streambed sediments (Su et al., 2004; Chen, 2000), 0.22 to 0.59 for a sandy till (Rayne, 1993), 0.086 to 0.15 for streambed sands and gravels (Chen, 2007). 5.4. Heat-flow modeling The calibrated, best-fit Kh values for the entire period of simulation (June 2005 to February 2006) ranged from 0.061 to 0.46 m/d (Fig. 7). The best-fit Kv/Kh value was consistently 0.1 yielding Kv values from 0.0061 to 0.046 m/d. The comparison of simulated and observed temperature, 3.4-m below the river, suggest that the most appropriate Kv value varied temporally. For example, 0.0061 m/d was the best value during June, but a value of 0.046 m/d was generally better in September and October 2005 after which time an intermediate value of about 0.027 m/d was best. The median Kv value for the entire study period was about 0.027 m/d, a value that was about seven times lower than the corrected geometric mean value for the seepage meters of 0.19 m/d, but still within the range of values measured by the seepage meters. The heat-flow modeling confirmed the hypothesized very low riverbed Kv. Discrepancies between the two methods may be explained by the fact that the heat-flow modeling represented only one physical location and flowpath whereas the seepage meters measured Kv at 29 locations. 5.5. Scour and deposition

Distance north from bank (m) Median grain size

Land surface / riverbed elevation

Fig. 6. Median grain size in relation to distance from riverbank on the south shore.

Four cross-sectional profiles were taken (Fig. 8). The first two profiles in December 2004 and February 2005 were taken before and after a 60-year recurrence-interval flood event that crested on

1966

J. Levy et al. / Journal of Environmental Management 92 (2011) 1960e1971 Table 2 Riverbed seepage meter results with accompanying measured parameters. Values are corrected based on sand-tank investigations. Test number

River stage above baseflow (m)

River temperature ( C)

Distance from bank (m)

Intrinsic permeability (darcy)

Kv (m/d)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

0.74 0.74 1.4 1.4 1.27 1.27 0.94 0.94 1.22 1.22 0.97 0.92 0.92 0.95 0.95 1.31 1.31 1.2 1.2 1.24 1.24 1.41 1.41 0.98 0.98 1.66 1.66 1.49 1.49

30.98 30.98 22.63 22.63 22.47 22.47 25.03 25.03 20.97 20.97 23.31 20.48 20.48 17.25 17.25 11.01 11.01 14.25 14.25 11.31 11.31 6.98 6.98 7.59 7.59 6.20 6.20 6.67 6.67

43 43 44 44 47 47 41 41 44 44 49 45 45 50 50 44 44 47 47 44 44 44 44 44 44 44 44 42 42

0.76 0.53 0.082 0.082 0.045 0.045 0.075 0.10 0.049 0.13 0.06 0.13 0.13 0.75 0.65 0.090 0.051 0.026 0.031 0.53 0.50 0.19 0.096 0.091 0.085 0.013 0.096 0.20 0.83

1.7 1.2 0.15 0.15 0.085 0.085 0.15 0.20 0.09 0.24 0.11 0.22 0.24 1.2 1.1 0.13 0.070 0.040 0.047 0.73 0.70 0.23 0.12 0.11 0.11 0.017 0.11 0.25 1.0

Arithmetic mean Geometric mean Minimum Maximum

1.18 1.16 0.74 1.66

16.24 14.27 6.2 30.98

44 45 41 50

0.22 0.12 0.013 0.83

0.37 0.19 0.017 1.7

January 6, 2005, with a peak discharge of 2016 m3/s and a maximum stage height about 6 m above low-flow stage. As a result of this one event, the cutbank was eroded horizontally nearly 8 m. Together, the four profiles indicate a gradual migration of the thalweg to the north and continuous erosion of the northern bank. Conversely, net deposition occurred out to approximately 70 m from the southern shore. Over the course of the study period, scour chains were placed in five different locations in the riverbed (Fig. 4) and scour data were collected from three of these. Although the southern half of the river was predominantly a depositional site, scour chain data indicated that high-stage events were responsible for a small

amount of temporary scouring (Table 3). (Depth of re-deposition turned out to be difficult to measure with any certainty, and those data were omitted.). It was not always possible to get to the scour chains after each storm event, and there were times when the scour chains were temporarily lost or measurements were not taken for other logistical and safety reasons. Between measuring times, there were sometimes several rises and falls of river stage. The scourchain data represented the maximum amount of scour that occurred during the time period between measurements. We assume that the maximum amount of scour corresponded to the highest river stage during that same period. The maximum stages and dates of those maxima are also shown in Table 3 as peak stage above a low-flow stage of 162.18 m above mean sea level (msl).

Fig. 7. Best-fits of riverbed temperatures, 3.4-m below the river, to estimate Kh using USGS program VS2DHI.

Fig. 8. Four cross-sectional survey profiles of the riverbed taken at Site 6 looking downstream. Vertical exaggeration is 10.

J. Levy et al. / Journal of Environmental Management 92 (2011) 1960e1971

1967

Table 3 Scour chain data and peak stage during measurement period. Chain #

Date of previous measure

Date of new measure

Peak stage above baseflow stage (m)

Date of peak stage

Scour depth (m)

SC-1 SC-1 SC-1 SC-1 SC-1 SC-1 SC-1 SC-1 SC-1 SC-1 SC-1 SC-1 SC-1 SC-1 SC-1

? 11-Nov-04 18-Nov-04 6-Dec-04 21-Dec-04 3-Feb-05 17-Apr-05 18-Aug-05 15-Sep-05 18-Sep-05 22-Sep-05 6-Oct-05 5-Nov-05 8-Nov-05 16-Feb-06

11-Nov-04 18-Nov-04 6-Dec-04 21-Dec-04 3-Feb-05 17-Apr-05 18-Aug-05 15-Sep-05 18-Sep-05 22-Sep-05 6-Oct-05 5-Nov-05 8-Nov-05 16-Feb-06 19-Feb-07

0.63 0.44 1.21 0.82 6.12 3.73 2.64 0.83 0.95 0.75 1.47 1.68 1.04 2.08 4.31

4-Nov-04 12-Nov-04 26-Nov-04 9-Dec-04 6-Jan-05 28-Mar-05 24-Apr-05 31-Aug-05 17-Sep-05 18-Sep-05 27-Sep-05 26-Oct-05 8-Nov-05 5-Feb-06 15-Jan-07

0.009 0.000 0.009 0 0.061 0.030 0.015 0.009 0.009 0.030 0.015 0.006 0.003 0.012 0.098

SC-4 SC-4 SC-4 SC-4 SC-4 SC-4 SC-4 SC-4

15-Sep-05 18-Sep-05 22-Sep-05 6-Oct-05 5-Nov-05 8-Nov-05 16-Feb-06 26-Oct-06

18-Sep-05 22-Sep-05 6-Oct-05 5-Nov-05 8-Nov-05 16-Feb-06 26-Oct-06 1-Feb-07

0.95 0.75 1.47 1.68 0.74 2.08 4.31 4.33

17-Sep-05 18-Sep-05 27-Sep-05 26-Oct-05 8-Nov-05 5-Feb-06 12-Mar-06 15-Jan-07

0.015 0.009 0 0 0 0.012 0.012 0.091

SC-6 SC-6 SC-6

20-Dec-05 7-Sep-06 26-Oct-06

7-Sep-06 26-Oct-06 15-Feb-07

4.31 2.35 4.33

12-Mar-06 17-Oct-06 15-Jan-07

0.015 0 0.076

In general, there was a linear relationship between the stage and depth of scour (Fig. 9). Regression analysis indicated that the river stage accounted for only about 48% of the observed variability, but the relationship was significant at p ¼ 5  105. The load-cell sensor was used to derive more or less continuous measurement of total pressure at a single location from November 2005 through June 2006 (Fig. 10). The controlled laboratory tests using tubs with water and riverbed sediment yielded values of wetbulk density of the sediment of between 2.04 and 2.20 g/cm3, corresponding to porosities between 0.27 and 0.38. For the calculations discussed below, we assumed a wet-bulk sediment density of 2.17 g/cm3, a value at the higher end of the measured range reflecting the fact that the natural sediments are probably more compact than those placed in the test tubs. For clarity, river stage in this section refers to the height of the river above the SolinstÒ Levelogger set at an elevation of 161.49 m above msl. Calculated sediment heights above the load-cell ranged from 0.15 to 0.68 m. As expected, scour occurred during the rising

Fig. 9. Relation between scour and river stage for 3 scour chains, November 2004 through February 2007.

limb of storm events and deposition occurred as the stage fell back to baseflow conditions. The most severe measured scour was from January 10 through 27, 2006 (Fig. 10) which apparently resulted from the presence of a 0.9-m by 1.2-m sheet of plywood wedged, perpendicular to flow, against a fencepost about 3 m upstream of the load cell. The plywood probably formed an eddy on its downstream side that caused the severe erosion. We therefore do not believe this was representative of typical scour. Excluding that period, the minimum sediment height was 0.26 m. The maximum scour during any one storm event was about 0.28 m during May 2006 (Fig. 10). Most storm events resulted in <0.1 m of scour, in general agreement with the scour-chain data. Examination of individual storm events helps gain insight into the complex relationships between stage and scour and re-deposition. Three such events are examined here as examples. The first high-stage event recorded was from November 28 to 30 (Fig. 11). Immediately prior to this event, on November 26, approximately 0.15 m of sediment was deposited on the load cell. This sediment was subsequently scoured away during the high-stage event. Scour continued until the river stage dropped to about 1.3 m on December 8. About 0.16 m of sediment was then deposited over the next six days, most of which was then scoured again during a slight rise in

Fig. 10. River stage and load-cell-calculated sediment height, November 21, 2005 through June 6, 2006.

1968

J. Levy et al. / Journal of Environmental Management 92 (2011) 1960e1971

Fig. 11. River stage and load-cell-calculated sediment height, November to December 2005.

Fig. 13. River stage and load-cell-calculated sediment height, April to June 2006.

stage beginning on December 15. This general pattern repeats throughout the study period, but usually with less sediment fluctuation and with some anomalous behavior. For example, between January 30 and February 6, 2006, there was net scour of about 0.1 m (Fig. 12), corresponding to a rising stage that began on January 29, dipped slightly from January 31 to February 3, and rose dramatically through February 5. There was an anomalous sediment-height drop on February 1 during a period of slight falling stage. Net deposition of 0.07 m then occurred beginning on February 14 as the stage fell below 1.6 m. Scour occurred again as the stage began its next rising phase and deposition began again on February 21 as the stage again dropped to about 1.7. From February 22 through 28, the sediment height underwent unexplained large fluctuations that may have been caused by sand waves, large cobbles or other bed load being transported over the load cell. The months of April and May 2006 nicely exemplified the general stageescour relationships (Fig.13). As the river stage fell below about 1.6 m on April 29, about 0.2 m of deposition occurred followed by 0.28 m of scour associated with the high-stage event beginning on May 11, the largest amount of measured scour associated with a single storm during the study period. It is interesting to note that the amount of maximum scour measured with the load-cell was more than three times the maximum scour measured with the scour chains. The chains and the

load cell were in different locations, but it is not known if this was the reason for this large discrepancy.

Fig. 12. River stage and load-cell-calculated sediment height, January to February 2006.

6. Quantification of the riverbed conceptual Model based on field data Measurements of riverbed K (summarized in Table 4) and sediment-height fluctuations were used to confirm and to quantify the conceptual model of the riverbed system overlying the glacialoutwash aquifer sediments. Based on this quantification, we can estimate the system’s overall effective Kv and the possible effect of storms and scour on the system. The measured and inferred values of Kv and the thicknesses of each layer of the conceptual riverbed model are summarized in Table 5. The transient-sediment layer comprised the top-most silt, sand and gravel sampled with the split-spoon core sampler retrieved only to depths of 0.12 m before a layer with larger cobbles was encountered. Scour chains and the load-cell pressure sensor confirmed that these sediments were continuously scoured and redeposited during high-stage events. The maximum storm-event scour observed was about 0.1 m with the scour chains and 0.28 m with the load-cell pressure sensor, with more typical scour depths of <0.17 m. Based on the laboratory permeameter tests on the splitspoon core samples, the Kv of these transient sediments averaged 5.3 m/d (Table 4). The transient-sediment thickness was observed to fluctuate between 0 and 0.28 m. The average thickness was therefore inferred to be 0.14 m. The conceptualized second layer was a stable armor and colmation layer beneath the transient sediment. The large sediment size of this layer prevented penetration by the 5-cm diameter split-spoon sampler past about 0.12 m. Such an armor layer would also explain why height fluctuations of the transient sediment, as measured by the load-cell sensor, rarely exceeded 0.17 m and why scour during high-stage events, as measured by scour chains, did not exceed 0.09 m. The seepage meters and the heat-flow modeling confirmed that the riverbed had consistently low Kv (corrected mean of 0.19 m/d for the seepage meters and 0.017 m/d for the heat-flow modeling, Table 4) probably resulting from vertical flow through the lowconductivity colmation layer located within and possibly immediately beneath the riverbed armor. The seepage meter mean was selected for the conceptual model value as it represented the most spatial and temporal coverage. The clogging that created this low-Kv layer was likely augmented by the consistent downward hydraulic gradients induced by municipal pumping. The persistence of the

J. Levy et al. / Journal of Environmental Management 92 (2011) 1960e1971

1969

Table 4 Summary of hydraulic conductivity estimates using a variety of methods. Method

Location

Part of system measured

Kv or Kh

Minimum

Maximum

Geometric mean

Pumping test Pumping test On-shore slug tests

PW1 PW1 FP1A, B, C, D, FP8A, B, C, D, 6D-1, 6D-2, 6D-3 CWWC-1 Site 6 riverbed Site 6 riverbed Site 6 riverbed Site 6 riverbed Site 6 riverbed

Outwash aquifer Outwash aquifer Outwash aquifer

Kh Kv Kh

68 34 6.9

150 75 360

113 57 55

Upper 1-m of riverbed 0.51e2.13 m below riverbed Top 0.12 m of riverbed Top 1 m of riverbed Top 1 m of riverbed

Kv Kh Kv Kh Kv

Seepage meter-corrected Riverbed slug tests Lab permeameter with intact cores Temperature modeling over long time period Temperature modeling over long time period

clogged armor layer was further supported by the finding that no temporal variation was shown in seepage meter estimates of Kv taken before and after large storm events. The thickness of the armor/colmation layer is unknown. The riverbed slug tests indicated that the hydraulic conductivity increases dramatically even at depths as shallow as 0.51-m below the river; 0.51 m therefore represents a maximum thickness of the armor/colmation layer. A reasonable assumption is that the colmation layer corresponded to just one layer of cobbles, in which case the thickness would be approximately 0.10 m. Velickovic (2005) suggested that the colmation depth can be estimated using the empirical formula:

dc ¼ 3dm þ 0:01½m

(1)

where dc is the colmation depth and dm is the mean grain size. Using the average mean grain size, this equation yields a colmation depth of about 0.06 m. The third layer of the conceptual model, the transitional bottom layer of the riverbed, was assumed to be represented by the K values derived from the riverbed slug tests. Those tests and an assumed anisotropy ratio of 0.1e0.5, yielded an average Kv value of about 2.3 m/d. This layer was presumed to extend at least to the depth of the deepest slug test or 2.1 m. Using the measured and inferred values in Table 5, the overall Kv of the hypothetical three-layer system was estimated to be 1.6 m/d. This overall average was obtained by using the harmonic mean, the mean appropriate when water flows through multiple layers in series (Fetter, 2001).

0.017 2.9 3.5 0.61 0.0061

1.7 35 10 0.46 0.046

0.19 11 5.3 0.17 0.017

the cobble layer of the riverbed had a higher silt and clay content than did the sediment 0.3 m below the riverbed surface. In other words, the grain-size analyses provided no direct-physical evidence of clogging to form a thin colmation layer. The existence of a colmation layer was hypothesized based on the induced, strong downward hydraulic gradients and confirmed by the very low Kv values, measured by seepage metering and heat-flow modeling, that were in contrast with the higher slug test results in the underlying sediment. Likewise, assuming the colmation layer does exist, there is uncertainty regarding its thickness. The colmation layer could have a much greater thickness than 0.1 m if clogging occurs over time as new gravels, pebbles, and cobbles are deposited. Additional sources of uncertainty include the facts that: the transient-sediment Kv value was based on only three measurements; we do not know the actual maximum depth of the transitional sediments (with a minimum estimated depth of 2.1 m); there was great variability in the seepage meter estimates that spanned two orders and the slug test estimates that spanned one order of magnitude; and there were limited data with which to estimate a Kv/Kh ratio. All these uncertainties have been incorporated in the sensitivity analysis that follows. The layer thicknesses and mean layer Kv values were varied through reasonable ranges while noting the effects of those changes on the overall, effective Kv. Despite the magnitude of the uncertainties, the sensitivity analyses are still able to provide a greater understanding of this riverbed system. 7.2. Sensitivity to estimated layer thicknesses The thickness of the armor/colmation layer was varied from an absence of clogging (thickness of 0 m) to substantial clogging of the layer through a thickness of 1 m, (bottom x-axis, Fig. 14). The

7. Sensitivity analysis 7.1. Principal sources of uncertainty A sensitivity analysis was used to assess the importance of the some of the uncertainties in the conceptual model, especially with respect to estimating the overall Kv value of the system and the possible effect of storms on the overall riverbed Kv. Substantial uncertainty exists in the simplified conceptual model and in the estimation of all the estimated mean parameter values (Table 5). First, based on the grain-size analyses, we found no evidence that Table 5 Hypothesized riverbed layers with measured vertical hydraulic conductivities and a calculated effective riverbed Kv. Layer

Mean Kv (m/d)

Estimated thickness (m)

Transient sediment Armor/colmation Transitional bottom

5.3 0.19 2.3

0.14 0.10 2.10

Overall effective mean (for Kv) or total (for thickness)

1.5

2.34 Fig. 14. Sensitivity analysis showing the effect of changes of layer thickness on the estimate of the overall effective Kv of the riverbed system.

1970

J. Levy et al. / Journal of Environmental Management 92 (2011) 1960e1971

The sensitivity analyses indicated that greatly-increased Kv values of the overall system are not likely to occur as a result of the type of scour observed at this site. Fig. 14 indicates that under conditions of a scour event larger than anything observed, one that results in the complete loss of the armor/colmation layer, the overall Kv increases to a maximum of 2.3 m/d. Most scour events observed would have removed only the transient sediment, having very little effect on the entire system. 8. Conclusions

Fig. 15. Sensitivity analysis showing the effect of changes of individual layer Kv on the estimate of the overall effective Kv of the riverbed system.

estimated overall riverbed Kv was sensitive to the armor/colmation layer thickness estimate; it increased by a factor of five from 0.50 m/d, with an armor/colmation layer of 1 m to 2.4 m/d with the total loss of this low-conductivity layer (i.e., the removal of all clogging). Compared with the base-case value of 1.6 m/d for a thickness of 0.1 m, a complete removal of clogging represented an increase of the overall Kv by a factor of 1.5. Alternatively, if the layer thickness was 0.45 m, the overall effective Kv of the system would be approximately one-half that of the base-case estimate. Further increases in layer thickness had relatively less impact on the estimated overall Kv. The overall effective Kv was less sensitive to changes in the thicknesses of the other layers. When the thickness of the transitional bottom layer was varied from 1 to 5 m (top x-axis, Fig. 14), the overall Kv varied from only 1.2e1.9 m/d. When the thickness of the overlying transient sediment was varied from 0 to 0.84 m (bottom x-axis, Fig. 14), more than twice the maximum range observed during the study period, the resulting overall Kv only varied from 1.5 to 1.9 m/d. This analysis also served to indicate that even complete scour of this top layer would have a negligible impact on the overall rate of induced infiltration or the capacity of the system for riverbank filtration. 7.3. Sensitivity to estimated layer Kv values Also examined was the sensitivity of the system’s overall Kv estimate to the uncertainty and spatial variability associated with the mean Kv values used for the three layers (Fig. 15). Because of its relatively high thickness and associated uncertainty, the estimated overall Kv was most sensitive to estimates of the transitional bottom layer Kv (top x-axis, Fig. 15). In response to a range of values from 0.29 to 18 m/d, the overall Kv varied an order of magnitude from 0.30 to 3.3 m/d. The armor/colmation layer Kv was varied from 0.0061 to 1.7 m/d (bottom x-axis, Fig. 15) which includes the range observed with both the seepage meters and the heat-flow modeling. The overall Kv responded with values differing by a factor of 17, from 0.14 to 2.3 m/d. On the other hand, when just the 95% confidence interval for the mean seepage meter Kv estimate was applied (0.13e0.30 m/d), the resulting overall Kv ranged only from 1.4 m/d to 1.8 m/d. The overall effective Kv was insensitive to the uncertainty associated with estimates of the transient-sediment Kv value (top x-axis, Fig. 15).

The USEPA has allowed for providing treatment credit for riverbank filtration under the Long Term 2 Enhanced Surface Water Treatment Rule. This allowance remains limited, however, in part because of the concern that scouring away riverbed layers leads to increased overall riverbed hydraulic conductivity. This study evaluated the impact of event-driven scour at one specific field site. Direct observations of riverbed sediment height fluctuations coupled with measurements of very low river Kv indicated the presence of an armor/ colmation layer that remained intact throughout the study period. The scour and deposition of sediment above this layer was minimal and the sensitivity analysis indicated that its loss and gain was likely to have little impact on the overall Kv of the system. For this field site, we believe that scour was not important and did not degrade the site’s capacity for riverbank filtration. The study, however, was limited to one location on the depositional side of the river. Greater-magnitude riverbed scour may occur at other locations along this reach of the Great Miami River, particularly in the thalweg where cross-sectional profiles have indicated loss of riverbed elevation of up to 1 m during large storm events. Further research is needed in these low-accessibility areas of the river to assess the spatial variability of riverbed scour and hydraulic conductivity. It is yet to be determined whether scour in the thalweg moves a greater volume of transient sediment or actually removes the critical armor/colmation layer. The supposition that scour at this site does not result in loss of system’s capacity for riverbank filtration is supported by a previous water quality investigations known as the flowpath study (Gollnitz et al., 2004) in which no increases in concentrations of surface water indicators were observed at monitoring wells during and after large storm events. This was true even though one of the flowpathstudy sites was at a higher-velocity, channelized part of the river. However, even if we assume that scour had minimal effect in the study area, scour occurs with greater magnitude along other sectors of the river profile, particularly in the thalweg, where our crosssectional profiles indicated scouring as great as 1 m with large events. A more complete assessment of the temporal variability of riverbed hydraulic conductivity would require instrumentation of entire profiles and along other reaches. Heat-flow modeling based on wells at different depths and locations might provide a tool for such an assessment. Until a more complete assessment is made, scour will remain a concern for regulators. In the meantime, riverbank filtration should continue to receive treatment credit, particularly enhanced credit in cases where demonstration studies have validated the consistent removal of surface water indicators at levels exceeding that of conventional treatment options. Acknowledgments Funding was provided primarily the Ohio Water Development Authority and was administered by the City of Hamilton, Ohio. Funding of some student work was also provided by Miami University’s College of Arts and Science and Office for the Advancement of Research and Scholarship. We received invaluable help and cooperation from Bruce Whitteberry from the City of Cincinnati Water Works, William Gollnitz, formerly from the City of Cincinnati

J. Levy et al. / Journal of Environmental Management 92 (2011) 1960e1971

Water Works and Tim McLelland from the Hamilton to New Baltimore Groundwater Consortium. Our thanks go to Miami University’s Instrumentation Laboratory for their design and production of the seepage meters and for numerous other equipment modifications. Thanks also to Dr. Jason Rech from Miami University for his reading of the manuscript and his wise recommendations. References ANCID, 2004. Channel Seepage Management Tool, Point Measurement: Principle, Method. Australian National Committee on Irrigation and Drainage. http:// ancid.org.au/seepage/3_3_31_pointMeasPrinc.html. Bouwer, H., 1989. The bouwer and rice slug test e an update. Ground Water 27, 304e309. Bouwer, H., Rice, R.C., 1976. A slug test method for determining hydraulic conductivity of unconfined aquifers with completely or partially penetrating wells. Water Resources Research 12, 423e428. Chen, X.H., 2000. Measurement of streambed hydraulic conductivity and its anisotropy. Environmental Geology 39, 1317e1324. Chen, X.H., 2007. Hydrologic connections of a streameaquifer-vegetation zone in south-central Platte River valley, Nebraska. Journal of Hydrology 333, 554e568. Constantz, J., Stewart, A.E., Niswonger, R., Sarma, L., 2002. Analysis of temperature profiles for investigating stream losses beneath ephemeral channels. Water Resources Research 38 (no. 12), 1316. doi:10.1029/2001WR001221. Federal Register, 2006. Long term 2 interim enhanced surface water treatment rule. Environmental Protection Agency Rules and Regulations 71, 654e786. Fetter, C.W., 2001. Applied Hydrogeology. Prentice-Hall, Inc, Upper Saddle River, NJ. Freeze, R.A., Cherry, J.A., 1979. Groundwater. Prentice Hall, Englewood Cliffs, NJ. Gollnitz, W.D., 2002. Infiltration rate variability and research needs. In: Ray, C., Schubert, J., Linsky, R.B., Melin, G. (Eds.), Riverbank Filtration. Kluwer Academic Publishers, The Netherlands, pp. 281e290. Gollnitz, W.D., Clancy, J.L., Whitteberry, B.L., Vogt, J.A., 2003. RBF as a microbial treatment process. Journal of the American Water Works Association 95, 56e66. Gollnitz, W.D., Whitteberry, B.L., Vogt, J.A., 2004. Riverbank filtration: induced infiltration and groundwater quality. Journal of the American Water Works Association 96, 98e110. Gollnitz, W.D., Clancy, J.L., McEwen, J.B., Garner, S.C., 2005. Riverbank filtration for IESTWR compliance. Journal of the American Water Works Association 97, 64e76. Grischek, T., Ray, C., 2009. Bank filtration as managed surface e groundwater interaction. International Journal of Water 5, 125e139. Havelaar, A.H., van Olphen, M., Schijven, J.F., 1995. Removal and inactivation of viruses by drinking water treatment processes under full-scale conditions. Water Science Technology 31, 5e6. Hiscock, K.M., Grischek, T., 2002. Attenuation of groundwater pollution by bank filtration. Journal of Hydrology 266, 139e144. Hsieh, P.A., Wingle, W., Healy, R.W., 2000. VS2DHIdA Graphical Software Package For Simulating Fluid Flow And Solute Or Energy Transport In Variable Saturated Porous Media. U.S. Geological Survey Water-Resources Investigations Report 94130. USGS, Denver. Kuehn, W., 2000. Riverbank filtration: an overview. Journal of the American Water Works Association 92, 60. Lee, D.R., Cherry, J.A., 1978. A field exercise on groundwater flow using seepage meters and mini-piezometers. Journal of Geological Education 27, 6e9. Li, H., Sun, P., Chen, S., Xia, Y., Liu, S., 2010. A falling-head method for measuring intertidal sediment hydraulic conductivity. Ground Water 48, 206e211. Libelo, E.L., MacIntyre, W.G., 1994. Effects of surface-water movement on seepagemeter measurements of flow through the sediment-water interface. Applied Hydrogeology 4, 49e54. Lisle, T.E., Madej, M.A., 1992. Spatial variation in armouring in a channel with high sediment supply. In: Billi, P., Hey, R.D., Thorne, C.R., Tacconi, P. (Eds.), Dynamics of Gravel-Bed Rivers. John Wiley, New York, pp. 277e296. MCD, 2005. Miami Conservancy District, Water Basics: Great Miami River Watershed. http://www.miamiconservancy.org/water/gmrw.asp.

1971

Miettinen, I.T., Vartiainen, T., Martikainen, P.J., 1994. Humus transformation at the bank filtration water plant. Water Science Technology 30, 179e187. Miettinen, I.T., Martikainen, P.J., Vartiainen, T., 1996. Bacterial enzyme activities in ground water during bank filtration of lake water. Water Research 30, 2495e2501. Mikels, M.S., 1992. Characterizing the influence of surface water on water produced by collector wells. Journal of the American Water Works Association 84, 77e84. Murdoch, L.C., Kelly, S.E., 2003. Factors affecting the performance of conventional seepage meters. Water Resources Research 39, 2e10. Neuman, S.P., 1975. Analysis of pumping test data from anisotropic unconfined aquifers considering delayed gravity response. Water Resources Research 11, 329e342. Niswonger, R.G., Prudic, D.E., 2003. Appendix B–Modeling heat as a tracer to estimate streambed seepage and hydraulic conductivity. In: Stonestrom, D.A., Constantz, J. (Eds.), Heat as a Tool for Studying the Movement of Ground Water Near Streams. U.S. Geological Survey Circular, C 1260, pp. 81e89. Ray, C., Soong, T.W., Roadcap, G.S., Borah, D.K., 1998. Agricultural chemicals: effects on wells during floods. Journal of the American Water Works Association 90, 90e100. Ray, C., Soong, T.W., Lian, Y., Roadcap, G.S., 2002. Effect of flood-induced chemical load on filtrate quality at bank filtration sites. Journal of Hydrology 266, 235e258. Rayne T., 1993. Variability of Hydraulic Conductivity in a Sandy Till: The Effect of Scale and Method. PhD. Dissertation, University of Wisconsin-Madison, Madison, WI. Roctest, 2005. Instruction Manual: Vibrating Wire Pressure Cells, Model Tpc and EPC (St. Lampert, Quebec, Canada). pp. 28. Schalchli, U., 1995. Basic equations for siltation of riverbeds. Journal of Hydraulic Engineering 121, 274e286. Schubert, J., 2000. How does it work?. In: Field Studies of Riverbank Filtration, 2000 International Riverbank Filtration Conference Proceedings (Dusseldorf, Germany). Schubert, J., 2002. Hydraulic aspects of riverbank filtration e field studies. Journal of Hydrology 266, 145e161. Schubert, J., 2006. Significance of hydrologic aspects on RBF performance. In: Hubbs, S.A. (Ed.), Riverbank Filtration Hydrology. Springer, Netherlands, pp. 1e20. Sontheimer, H., 1991. Trinkwasser aus dem Rhein?: Bericht ueber ein vom Bundsminister fuer Forschung und Technologie gefoerdertes Verbundforschungsvorhaben zur Sicherheit der Trinkwassergewinnung aus Rheinuferfiltrat bei Stossbelastungen. Academia Verlag, St. Augustin. Su, W.G., Jasperse, J., Seymour, D., Constantz, J., 2004. Estimation of riverbed hydraulic conductivity in an alluvial system using temperatures. Ground Water 42, 890e901. Sun, K., 2001. Bromide and Bacterial Transport Through Different Porous Media: Controlling Sediment Characteristics and Mathematical Modeling. PhD. Dissertation, Miami University, Oxford, OH. Velickovic, B., 2005. Colmation as one of the processes in interaction between the groundwater and surface water. Architecture and Civil Engineering 3, 165e172. Walton, W.C., Hills, D.L., Grundeen, D.L., 1967. Recharge From Induced Streambed Infiltration Under Varying Groundwater-level and Stream-gage Conditions. In: Bulletin 6. Water Resources Research Center, University of Minnesota, St. Paul, Minnesota. Wang, J., Smith, J., Dooley, L., 1995. Evaluation of riverbank infiltration as a process for removing particles and DBP precursors. In: 1995 Water Quality Technology Conference Proceedings. American Water Works Association, Denver, CO. Wang, J., Hubbs, S.A., Song, R., 2002. Evaluation of Riverbank Filtration as a Drinking Water Treatment Process. AWWA Research Foundation, Denver. Wett, B., Jarosch, H., Ingerle, K., 2002. Flood induced infiltration affecting a bank filtrate well at the River Enns, Austria. Journal of Hydrology 266, 222e234. Wilcock, P.R., DeTemple, B.T., 2005. Persistence of armor layers in gravel-bed streams. Geophysical Research Letters 32, 1e4. Wilderer, P.A., Foerstner, U., Kuntschik, O.R., 1985. The role of riverbank filtration along the Rhine River for municipal and industrial water supply. In: Asano, T. (Ed.), Artificial Recharge of Groundwater. Butterworth Publishers, Boston, MA, pp. 509e528. Windeler, B., 2006. Temporal Variability of Riverbed Hydraulic Conductivity Along the Great Miami River, Southwest Ohio: A Continuance of Data Gathering and Instrumentation. M.En.S. Practicum Report. Miami University, Oxford, Ohio. Zlotnik, V., 1994. Interpretation of slug and packer tests in anisotropic aquifers. Ground Water 32, 761e766.