Using temperature modeling to investigate the temporal variability of riverbed hydraulic conductivity during storm events

Journal of Hydrology 388 (2010) 321–334

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Using temperature modeling to investigate the temporal variability of riverbed hydraulic conductivity during storm events Samuel Mutiti a,*, Jonathan Levy b a b

Biological and Environmental Sciences, Georgia College and State University, Milledgeville, GA 31061, United States Department of Geology, Miami University, Oxford, OH 45056, United States

a r t i c l e

i n f o

Article history: Received 5 January 2010 Received in revised form 1 May 2010 Accepted 6 May 2010 This manuscript was handled by G. Syme, Editor-in-Chief Keywords: Temperature modeling Riverbed hydraulic conductivity Riverbank filtration Surface water–ground water interactions

s u m m a r y Understanding the impact of storm events on riverbed hydraulic conductivity is crucial in assessing the efficacy of riverbank filtration as a water-treatment option. In this study, the variability of riverbed hydraulic conductivity and its correlation to river stage during storm events was investigated. Water levels and temperatures were continuously monitored in the river using creek piezometers screened beneath the riverbed, and monitoring wells located on the river bank. The range of values for water levels during the study period was from 161.3 to 163.7 m AMSL while temperatures ranged from 3.75 °C to 24 °C. During the duration of the study the Great Miami River was losing water to the underlying aquifer due to pumping in the adjacent municipal well field. Flow and heat transport were simulated in a groundwater heat and flow program VSH2D to determine the hydraulic conductivity of the riverbed. Hydraulic conductivity was estimated by using it as a calibration parameter to match simulated temperatures to observed temperatures in a monitoring well. Hydraulic heads in the aquifer responded to storm events at the same times but with dampened amplitudes compared to the river stage. The relative responses resulted in increased head gradients during the rising limb of the stage-hydrograph. Heat-flow modeling during five storm events demonstrated that a rise in head gradient alone was not sufficient to produce the temperature changes observed in the wells. Simulated temperatures were fitted to the observed data by varying both river stage (as measured in the field) and riverbed hydraulic conductivity. To produce the best fit temperatures, riverbed hydraulic conductivity consistently needed to be increased during the rising and peak stages of the storm events. The increased conductivity probably corresponds to a loss of fine sediments due to scour during high river stage. Hydraulic conductivity increases during storm events varied from a factor of two (0.0951–0.2195 m/d) to almost one order of magnitude (0.0007–0.00658 m/d). Despite these predicted changes the highest model-predicted hydraulic conductivity value was 0.66 m/ d, which is still much lower than the infiltration rate used in sand filtration systems (3.59 m/d). These low values suggest that storm events do not pose a significant risk to the water quality at this well field. There was a direct correlation between the duration of rising limb, rate of change of stage and maximum river stage and the magnitude of change of riverbed hydraulic conductivity. Published by Elsevier B.V.

1. Introduction Alluvial aquifers that are hydraulically connected to surfacewater bodies are used for drinking-water production throughout the world. These sites are selected because of the significant amount of groundwater that can be extracted at shallow depths (Hiscock and Grischek, 2002). Production wells placed near rivers take advantage of the high permeability of the aquifer sediments of the river valley and the high aquifer-recharge rates caused by induced infiltration from the river. Rivers that are normally gaining can become losing streams due to nearby pumping. Water leaving * Corresponding author. E-mail address: [email protected] (S. Mutiti). 0022-1694/$ - see front matter Published by Elsevier B.V. doi:10.1016/j.jhydrol.2010.05.011

the river traveling through the riverbed can undergo riverbank filtration (RBF) as it moves to the underlying aquifer on its way to the production wells. The riverbed and aquifer material act as filters for contaminants found in river water and, therefore, protect the production-well water from contaminants such as Cryptosporidium and Giardia. RBF is considered an efficient and low-cost treatment technology for drinking water supply. RBF has been shown to remove pathogens and pollutants from river water before it reaches the production wells (Ray and Prommer, 2006). Due to its capacity to clean surface water, RBF can be compared to slow-sand filtration (SSF) systems, which is a crucial step in the treatment of surface water used for drinking water. Ray et al. (2002a) provided a detailed explanation of why RBF is similar to SSF. In SSF systems,

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raw water passes through a sand layer about 0.9-m thick at an average rate of about 3.6 m/d (0.15 m/h). An organic mat is allowed to grow as a way of increasing the effectiveness of the sand to filter out contaminants, especially biological contaminants. RBF systems are similar to SSF systems in that they also often contain a layer of low-hydraulic conductivity with a similar thickness (about 1 m) that filters out contaminants, especially during low flow conditions. RBF systems have different filtration rates depending on location, well type and construction, pumping rates and number of wells in the vicinity. The average flow rates, however, are typically lower than those in SSF systems (Ray et al., 2002a). With respect to contaminant removal, some RBF systems have been shown to achieve up to 3-log removal efficiencies (Gollnitz et al., 2004), 5log removal for E. coli (Schubert, 2002b) and up to 8-log reduction for viruses (Ray et al., 2002a). RBF proponents have, therefore, advocated for RBF to receive recognition as a legitimate treatment technology and be allocated treatment credits by the United States Environmental Protection Agency (USEPA). In January of 2006, the USEPA promulgated the Long Term 2 (LT2) Enhanced Surface Water Treatment Rule under which a 0.5-log or a 1.0-log filtration credit for Cryptosporidium can be allocated to water treatment plants using RBF and meet a specified criteria (USEPA 2006; Ray and Prommer, 2006). Higher log reductions may be granted by states with a demonstration of a performance study (USEPA, 2006). One of the principal reasons for the conservative 1-log credit allocation is the perceived issue of riverbed scour and the changing filtration capacity of the riverbed (Gollnitz et al., 2004). Riverbeds can undergo mechanical clogging, a process by which a low-hydraulic conductivity layer or lining (a colmation layer) forms within or on top of coarser sediments (Wilson, 1993; Schubert, 2002a; Hubbs, 2006a,b; Doppler et al., 2007). Clogging of the riverbed is caused by infiltration of river water carrying fine sediments that block the pore spaces. It is believed that riverbed clogging at RBF sites almost always occurs if the operations exist over long periods of time (Schubert, 2006). Under certain conditions an organic layer (a microbial mat) can also develop and act as a filter for surfacewater contaminants. High-stage events can erode the colmation and organic layers, thus reducing the filtration efficiency of the riverbed. Walton et al. (1967) showed that infiltration of water from the Great Miami River into the underlying aquifer was greatly influenced by river stage. This change in infiltration rate could be due to greater contact area between the river and ground surface, an increase in the head gradient and/or an increase in the riverbed vertical hydraulic conductivity (Kv) due to sediment scour at high river stage. Doppler et al. (2007) observed an increase in infiltration rate at an impounded section of a river after a major flood event. They attributed this change to a suspected increase in riverbed hydraulic conductivity and, therefore, recommended that groundwater flow models be recalibrated after large events. Increased riverbed Kv can have an impact on water quality. Ray and Prommer (2006) simulated the effect of increasing riverbed hydraulic conductivity on the concentration of atrazine at a site of RBF during a hypothetical flood event. They observed an increase in simulated atrazine concentration breaking through at the production well when the hydraulic conductivity was low and constant. Other researchers have noted the temporal variability riverbed hydraulic conductivity (i.e. Su et al., 2004; Constantz et al., 2006; Hubbs, 2006a,b; Cox et al., 2007). Cox et al. (2007) investigated the variability of riverbed Kv of the Russian River from 2003 to 2005 using specific conductance. They observed that for the period between April 2003 and January 2004, the effective horizontal hydraulic conductivity (Kh) was 2.1  104 m/s, from January 2004 to August 2004 the value was 1.4  104 m/s and between August 2004 and December 2005 it was 8.1  105 m/s (Cox et al., 2007). In another study

in the Russian river, Su et al. (2004) and Constantz et al. (2006) observed changes in hydraulic conductivity of more than 35% after mid-August. These changes were attributed to accumulation of organics and fine sediments behind an inflatable dam. They also concluded that river stage fluctuations were not responsible for the observed changes. None of the previous studies, however, studied the correlation between changes in riverbed Kv, stage, timing and duration of individual storm events. The effect that a rapid rise in river stage has on the riverbed Kv has not been examined. Barlow and Coupe (2009) successfully utilized heat modeling to constrain streambed fluxes and determine stage requirements for groundwater to surface water flow reversals during extreme events. There is still a need to study the impact of storm events on the riverbed Kv, and to assess the effect that these impacts might have on the quality of well water undergoing induced infiltration (Ray et al., 2002). During high-stage events, direct measurement of riverbed Kv, with such methods as seepage metering, is very difficult and potentially hazardous. An alternative approach is using heat-flow modeling (Anderson, 2005; Constantz, 2008). Estimating riverbed Kv using temperature data has advantages over other methods in that temperatures are robust and relatively easy and inexpensive to measure over periods of weeks or months. Fluxes between surface water and groundwater, vertical groundwater velocities and hydraulic conductivities have been estimated successfully using heat-flow models (Barlow and Coupe, 2009; Constantz, 2008; Constantz et al., 1994; Constantz and Thomas, 1997; Constantz et al., 1999a,b,2002,2006; Stonestrom and Constantz, 2003; Ronan et al., 1998; Bravo et al., 2002; Cox et al., 2002, 2007; Bartolino and Niswonger, 1999, Dowman et al., 2003; Prudic et al., 2003; Su et al., 2004; Arriaga and Leap, 2006; Conant, 2004; Schmidt et al., 2007). Some of these studies addressed temporal variations in infiltration rates, but most studies have considered riverbed Kv to be a constant during modeling; only a few studies have actually considered the temporal variability of Kv. In this investigation we used heat-flow modeling to quantify the temporal variation of riverbed Kv (at a fixed point in space) during a series of storm events and related the observed changes to variations in river stage. We defined the riverbed as the sediment immediately below the river that in this case, due to clogging, has a Kv that is low relative to the underlying and adjacent glacial-outwash aquifer. We hypothesized that storm events erode or suspend fine sediment in the riverbed, temporarily unclogging this layer, resulting in increased Kv values. It is also possible that Kv can decrease during storm events due to the increased suspended load that can clog the pore spaces (Schubert, 2006). This study, therefore, investigated the timing of the increase and decrease in the hydraulic conductivity during storm events. We also investigated whether the predicted changes in hydraulic conductivity were significant enough to pose a potential water-quality threat to the underlying aquifer by comparing Kv values to SSF infiltration rates. This question is especially important at sites of induced infiltration where there is potential for increased groundwater contamination when the riverbed is scoured away.

2. Site description The site for this study was, the Charles M. Bolton (CMB) Well Field in southwest Ohio, located adjacent to the Great Miami River (Fig. 1). This well field comprises 12 wells that supply over 57 million liters of water per day to residents of northern Cincinnati. Groundwater at this well field, and all other well fields in this area, is withdrawn from the Great Miami River Buried Valley System (GMR-BVS). The GMR-BVS is one of the most productive aquifers in the Midwest (Sheets and Bossenbroek, 2005). It serves as the

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Fig. 1. Location of the Bolton Field site along the Great Miami River in Southwest Ohio.

main source of water to municipalities between Dayton and Cincinnati and capable of well yields up to 11.3 m3/min. It is composed mostly of sand-and-gravel outwash deposits that are approximately 60-m thick in the center of the valley. The valley walls are steep and composed of relatively impermeable interbedded Ordovician shale and limestone. The glacial-outwash aquifer is unconfined in this area and many studies have previously examined the interaction of the Great Miami River (GMR) and the aquifer (Dove, 1961; Smith, 1962; Walton et al., 1967; Gollnitz, 2002; Gollnitz et al., 2003,2004; Sheets et al. 2002; Sheets and Bossenbroek 2005). The study site (Site 6) was an area associated with production well 6 (PW6, Fig. 1). Previous studies at the CMB well field, have demonstrated that, natural RBF reduces concentrations of particulates and pathogens as well as (or even better than) engineered filtration (Gollnitz, 2002; Gollnitz et al., 2003,2004). Gollnitz et al. (2004) observed 3- to 5-log reductions in Cryptosporidium- and Giardia-sized particles, suggesting RBF at this site is efficient for these organisms. Aquifer horizontal hydraulic conductivity (Kh) at the CMB well field and the surrounding area has been measured in previous studies. Multiple-well aquifer pumping tests in the general area

yielded Kh values of 95–160 m/d (Dove, 1961; Smith, 1962). An aquifer pumping test was also performed in January 2000 (unpublished) using a CMB production well located approximately 1.2 km from Site 6. This test yielded Kh values ranging from 68 to 150 m/d with a geometric mean of 113 m/d. The analysis also yielded a rough estimate of 0.5 for the vertical-to-horizontal hydraulic conductivity anisotropic ratio (Kv/Kh). Slug tests performed at monitoring wells 6D-1, 6D-2, 6D-3 (closest monitoring wells to the site) and temporary drive-point piezometers installed in the riverbed yielded average aquifer hydraulic conductivity values of 56.2 m/d. At Site 6, the riverbed consists generally of poorly-sorted pebbles with an average median grain size of 18 mm and average silt and clay content of only 2.5% (Birck, 2006). Head gradient measurements between 2004 and 2006 consistently indicated downward flow from the river to the aquifer (Birck, 2006). Twenty-nine seepage meter tests were previously employed at Site 6 to measure the riverbed Kv (Birck, 2006). Values ranged from 0.017 to 1.7 m/d with a geometric mean of 0.19 m/d, a very low value relative to the aquifer conductivity, indicative of a clogged riverbed, probably exacerbated by strong downward gradients.

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for atmospheric pressure and converted to heights of the water column above the transducers.

Table 1 Monitoring-wells used in the VS2DH simulations. Well

Installation method Material

6B 6C 6E DP6-B1 DP6-4 DP6-5

Hollow-stem auger Hollow-stem auger Hollow-stem auger Hand driven Hand driven Hand driven

Diameter (cm) Screen depth (m)

PVC 5.1 PVC 5.1 PVC 2.5 Metal pipe 3.8 Metal pipe 3.8 Metal pipe 3.8

7.3–7.6 7.5–7.8 7.3–8.8 3.0–3.4 2.1–3.0 3.0–3.4

3. Methods 3.1. Instrumentation Site 6 was already instrumented with two off-shore monitoring wells, 6B and 6C, installed by the Greater City of Cincinnati Water Works. One additional on-shore piezometer, 6E, and three drivepoint piezometers – DP6-B1, DP6-4 and DP6–5 – were installed during this study (Table 1, Fig. 2). The on-shore piezometer was installed using a hollow-stem-auger drilling rig and screened between 7.31 and 8.84 m below ground surface. The drive-point piezometers were installed using a fence-post hammer. Drivepoint piezometers were 3.8-cm diameter steel pipe with 0.61-m long screens. DP6-4 and DP6-5 were screened from 2.26 to 2.44 m and 1.59 to 1.77 m below the riverbed, respectively. DP6B1 was screened between 2.5 and 3.5 m below ground surface. All wells used in the study were surveyed to establish absolute and relative elevations. Temperature and pressure data were collected in the piezometers at the screen depths and in the river using SolinstÒ Leveloggers, InsituÒ Trolls 1000 and One stepÒ Tidbit thermistors. Instruments in the river were mounted at known elevations on fence posts that were hammered into the riverbed. Temperatures and pressures were recorded every 15 min and downloaded monthly for the duration of the study from June 2005 through February 2006. Pressure data from the field were processed to adjust

3.2. Geophysical investigation of the riverbed To estimate the thickness of the low-Kv riverbed, electrical resistivity surveys were conducted. Resistivity surveys provide subsurface stratigraphy. If the riverbed has more fines than the underlying sand-and-gravel aquifer, then resistivity sounding can pick out the boundary between the two materials. Resistivity surveys were carried out during the summer when the river stage was very low and parts of the riverbed that are normally under water were exposed. The Schlumberger electrode configuration was utilized to infer the thickness of the riverbed (Herman, 2001; Stummer et al., 2004). A MiniResÒ (L and R Incorporation) resistivity meter was used to collect vertical profiles of resistivity distribution at the study site. 3.3. Heat-flow modeling Heat-flow and temperature fluctuations in DP6-4 were simulated during 6 storm events that occurred between June 11, 2005 and February 6, 2006 (Table 2). Simulations were performed using the USGS program VS2DHI (Hsieh et al., 2000), which employs a finite-difference approximation to numerically solve the heat-flow governing equations (Healy and Ronan, 1996; Ronan et al., 1998; Bravo et al., 2002; Constantz et al., 2002). Riverbed Kv served as the principal calibration parameter for these simulations with the assumption that the value could change during the course of each event. Riverbed Kv was adjusted while aquifer hydraulic conductivity and other thermal and hydraulic parameters (Table 3) were kept constant during the simulations, until simulated temperatures matched field-observed temperatures. Simulations were conducted in two dimensions assuming that flow paths from the river to the aquifer were the results of both vertical and horizontal flow towards the production well. The two-dimensional (2D) model domain (Fig. 3) used for the numerical simulations was based on previously measured river

Fig. 2. Cross-sectional profile of the field site with the locations of monitoring wells (vertical exaggeration 13).

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S. Mutiti, J. Levy / Journal of Hydrology 388 (2010) 321–334 Table 2 Summary of the calibrated Kv values used in the storm-event, heat-flow simulations. Storm event

Time period

Early time (low stage) Kv (m/d)

Middle time (peak stage) Kv (m/d)

Late time (falling stage) Kv (m/d)

Increase factor (Max./Min.)

1 2 3 4 5 6

June 11–June 20, 2005 September 22–October 8, 2005 October 20–November 5, 2005 November 12–November 23, 2005 November 28–December 05, 2005 January 12–January 30, 2006

0.0059 0.00073 0.01 0.0095 0.008 NA

0.017 0.0066 0.023 0.059 0.053 0.044

0.015 0.0000073 NA 0.0000001 0.0095 0.012

2.9 9 2.3 6.2 6.6 NA

Table 3 Summary of the hydraulic properties used in the VS2DH simulations. Parameter

Riverbed

Aquifer

Source

Porosity Aquifer Kh (m/d) Longitudinal dispersivity (m) Transverse dispersivity (m) Cs (J m3 °C1) Kt (J h1 m1 °C1) Cw (J m3 °C1)

0.24 Calibration Calibration Calibration 2.18  106 7200 4.2  106

0.24 56.7 Calibration Calibration 2.18  106 7200 4.2  106

Site specific, Sun Kerang (2001) Site specific (slug tests) Prudic and Niswonger (2003) Prudic and Niswonger (2003) Su et al. (2004) Prudic and Niswonger (2003) Su et al. (2004) and Prudic and Niswonger (2003)

profiles conducted at Site 6 (Birck, 2006). The top portion of the model domain that is occupied by the river was modeled as a specified, variable-head boundary. The rest of the top portion of the domain (between the river and monitoring well 6B) was modeled as a no-flow boundary because the soils at this section had an extremely low permeability silt–clay layer. A core collected in 2005 from this section revealed a top layer of permeable sand underlain by a dark layer of impermeable clay (probably flood deposits). Laboratory determination of the permeability could not be completed as there was almost no flow through the material for a few days. The left hand side (south) of the model domain was also modeled as a specified, variable-head boundary. Heads and temperatures were continuously monitored in the top 9 m of the domain, and these data were extrapolated vertically along the rest of the boundary. Analysis indicated that model results were insensitive to boundary conditions below 9 m. The right hand side (middle of the river) was modeled as a no-flow boundary. Flow paths from the northern side of the river and from north of the river do not

Fig. 3. 2D Model domain for numerical simulation code VS2DH.

pass through DP6-4 (Fig. 2). They either end up in the river or pass far below DP6-4. Again, model results were completely insensitive to the placement of this no-flow boundary farther to the north at the bedrock contact versus the middle of the river. Therefore, the smaller and simpler model was used for the rest of the study. A no-flow boundary was used at the bottom to represent the limestone/shale bedrock. The riverbed thickness was based on results from the electrical resistivity surveys. Its hydraulic conductivity was used as the primary calibration parameter for the heat-flow modeling. The rest of the model domain (below the riverbed) was assumed to comprise sand-and-gravel aquifer material with a Kh value of 56.2 m/ d based on slug tests performed closest to field site (Windeler, 2006). Longitudinal and transverse thermal dispersivities (aL and aT) were initially based on the range of values (0.01–1 m) provided by Prudic and Niswonger (2003). Anderson (2005) discussed the different schools of thought that exist on the best estimate of thermal dispersivity. There is no consensus on how important this parameter is in temperature modeling. According to Anderson (2005) some researchers suggest that the effects of dispersivity are so negligible and that a value of zero can be used. To determine an appropriate value for these simulations, values of 0, 0.01, 0.1, 0.5 and 1 m were tested to see which produced the most reasonable results. The model value for Kv/Kh was initially estimated to be 0.5 based on one nearby aquifer pumping test. Much uncertainty is associated with this parameter estimate and was, therefore, also used as a calibration parameter. The values of Kv/Kh were adjusted from 0.1 to 1. Porosity values were based on laboratory analyses of sediment cores from the field site (Sun, 2001). Model parameters, thermal conductivity (Kt), water heat capacity (Cw) and sediment heat capacity (Cs), were estimated based on literature values (Table 3). Values used for Kt, Cw, Cs and porosity were 2.18  106, 7200, 4.2  106 and 0.24, respectively. While some uncertainty exists, the range of values and the effects of changing these values were minor compared to the effect of changing riverbed Kv. For all simulations, initial temperature and head (inputs) values were obtained from thermistors and pressure transducers in the river, piezometer DP6-4 and wells 6B, DP6-B1 and 6C. These initial point measurements were then interpolated to get the starting temperatures for the rest of the domain.

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To match simulated temperatures and hydraulic heads to observed values at DP6-4, all storm events were divided into several time periods based on when different Kh values were required by the model to reproduce the observed temperatures and heads. For each time period, a separate model was created. The initial model simulation for each event began just prior to the beginning of the storm event. The Kh value was adjusted until simulated temperatures and heads matched observed values for the early part of the event. The next simulation started at the point where the initial simulation’s predicted temperatures and heads began to deviate from the observed values. The time, sediment height (when available and applicable), hydraulic heads and temperatures at the times of temperature deviation were noted and used at the beginning of the next simulation allowing it to begin with the correct initial conditions. The next simulation was recalibrated with a new Kh value. This procedure was repeated throughout the storm event so that each event generated a series of successive simulations. The goodness of fit of the simulated to the observed temperatures was initially determined visually to select at least three Kv values that produced the closest matches of simulated to observed temperatures. Analyses of the root mean square errors (RMSE) were then conducted to select the best fits. 3.4. Correlation between stage and riverbed Kv To determine the correlation between river stage and change in Kv, regression analyses were carried out in MINITAB statistical and Microsoft Excel Software (Minitab Inc., Microsoft Corporations). The results were utilized to create mathematical models describing the correlation between river stage (peak stage, change in stage, and duration of the storm event) and the riverbed Kv (peak Kv, change in Kv, ratio of peak Kv/pre-peak Kv). 3.5. Variability of head gradient Storms can also influence (increase) infiltration of river water into the underlying aquifer. The increase in flux during storms could be attributed to increased head gradients between the river and the aquifer and/or an increase in riverbed hydraulic conductivity during high stage. This increase in flux can result in an increased risk of aquifer contamination by surface-water contaminants, especially if there is no sufficient contaminant filtration by the riverbed. The increase in head gradients would occur when there is a relatively faster rise in river stage compared to the hydraulic head in the underlying aquifer. To determine whether there is an increase in the head gradients, and the timing of these changes, the difference between river stage and the pressure in the monitoring wells during storm events were analyzed and compared to the timing of Kv changes. 4. Results 4.1. Electrical resistivity The resistivity survey was conducted on a dry riverbed with the water table just a few centimeters below the surface. Any sharp changes observed in resistivity values at depths greater than 30 cm were, therefore, assumed to be due to changes in lithology. Resistivity data inversion was performed using the computer programs IX1D v 3Ò (CopyrightÓ 2004 Interpex Ltd.) and SounderÒ (CopyrightÓ 1991, Steve Sherrif). Based on the results of the inversion, the riverbed thickness near DP6-4 and DP6-5 was estimated to be between 1 and 1.6 m, and a value of 1.3 was used to represent the riverbed in all model simulations in this study. The values of Kv

provided in this paper therefore refer to the effective hydraulic conductivity of only the top 1.3 m of sediment material directly below the river. Sediments deeper than 1.3 m were considered part of the sand-and-gravel outwash aquifer, and were therefore assigned a value of 56.7 m/d. 4.2. Sensitivity of the modeling to secondary parameters A value of 0.01 m for both the longitudinal and transverse thermal dispersivities produced slightly visually-superior fit of the simulated-to-observed temperatures at well DP6-4. All models in this study were, therefore assigned aL and aT values of 0.01 m. Likewise, a visually-superior fit was consistently obtained with a Kv/Kh anisotropy ratio of 0.1 (example shown in Fig. 4). This value consistently produced simulated temperatures that matched the observed temperatures better, both the degree of oscillation and the absolute temperatures. Also, increasing the anisotropy ratio from 0.1 to 0.5 and 1 caused the model simulations to run extremely slowly and produced results that oscillated around the observed values. This value was close to the ratio obtained by Su et al. (2004) who determined that a value of 0.2 gave a better fit compared to values of 1 and 0.5. The model results were insensitive to changes in the riverbed thickness that were simulated to match the amounts of scour observed during the storm events. Simulation results from the original model were almost the same as those from scour-adjusted models. The model domain was, therefore, kept the same throughout the study. 4.3. Heat-flow modeling during individual storms events 4.3.1. Storm Event 1: June 11 to June 20, 2005 A storm event beginning on June 12, 2005 caused the river level to rise over 0.6 m in less than five hours (Fig. 5). During this time, the river-water temperature was consistently higher than the groundwater temperature but was decreasing while undergoing diurnal fluctuations of roughly 1.5 °C. Due to induced infiltration, the groundwater temperature was increasing at well DP6-4. With the advent of the June 12 storm, the rate of temperature increase at DP6-4 increased (Fig. 5). To match simulated to observed temperatures at DP6-4, the heat-flow simulation of this time period was split into three parts. In the time preceding the storm event (when stage was low) the best fit was achieved with a riverbed Kv value of about 0.0059 m/d. As the stage rose, however (even accounting for the increased gradient) this Kv value under-predicted the temperature change at DP6-4 (Fig. 5). To match the increasing rate of temperature increase during the peak-stage period, it was necessary to use a higher Kv value of 0.017 m/d beginning on the morning of June 13. As the stage decreased, a slightly lesser value of 0.015 m/d provided the best fit beginning on the morning of June 15. While the apparent Kv did not fall back to its original pre-storm-event value, neither did the river stage. The selected best-fit Kv values for the different parts of this event produced RMSE of 0, 0.001 and 0.002 respectively. The modeling indicates that the apparent Kv of the riverbed increased by a factor of about 2.7 during this storm event. 4.3.2. Storm Event 2: September 21 to October 11, 2005 The second storm event studied occurred between September 21 and October 11, 2005 (Fig. 6). During this period, the river temperature underwent diurnal fluctuation of about 2 °C, and the groundwater in DP6-4 was approximately the same temperature as the average river temperature. As the river stage rose on September 25, the river temperature fell steeply from 25 °C to 18 °C. Due to induced infiltration, the temperature at DP6-4 began to fall

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Fig. 4. Sensitivity analysis results for the anisotropy ratio of Kv/Kh.

Fig. 5. Storm Event 1, June 11 to June 20th. The river stage, observed and simulated temperatures at well DP6-4 are shown.

more steeply 2.5 days after the steep temperature drop in the river, just after the peak of the storm. During the river-stage falling limb, the temperature rose in the river and the temperature in DP6-4 leveled off. To simulate the temperature in DP6-4 in the period leading up to the storm, a Kv value of 0.00073 m/day was required. This low value was used until midnight on September 28, immediately following the peak river stage. From September 28 to midnight on October 3 a higher Kv value of 0.0066 m/day was used to better match the rate at which the temperature fell in DP6-4. This value was about nine times greater than the pre-storm value.

Once river stage had returned to lower levels, as of October 3rd, a lower Kv of 0.0000073 m/day provided the best fit of simulated to the observed temperatures (Fig. 6). This value was 16.5 times lower than the peak-stage value. 4.3.3. Storm Event 3: October 20 to November 5, 2005 Prior to the third simulated storm event in late October 2005, the river temperature was falling and it continued to fall during the event (Fig. 7). The groundwater temperature at DP6-4 was also falling (less steeply) before the event and then fell more steeply

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Fig. 6. Storm Event 2, September 21 to October 5 2005. The river stage, river temperatures, observed and simulated temperatures at well DP6-4 are shown.

Fig. 7. Storm Event 3, October 20 to November 5 2005. The river stage, river temperatures, observed and simulated temperatures at well DP6-4 are shown.

during the rising limb of the river-stage hydrograph. As the river temperature rose towards the end of the event, the groundwater temperature at DP6-4 leveled off (by about November 4). The river continued to exhibit diurnal fluctuation of between 0.6 and 1.2 °C, and these fluctuations did not propagate down to DP6-4. To simulate the observed temperatures at DP6-4 before the storm event, a relatively low riverbed Kv value of 0.010 (m/d) was used. The river stage began to increase mid-day on October 21 and then rose steeply beginning at the end of October 24. As the river stage increased, the steepness of the drop in temperature also increased starting about the beginning of October 24. To fit the observed temperature data at DP6-4, it was necessary to use an increased riverbed Kv of 0.023 m/d also starting at the beginning of October 24 (Fig. 7), about three times higher than the pre-storm Kv value.

4.3.4. Storm Event 4: November 12 to November 22, 2005 During Storm Event 4 (mid-November, 2005), the temperaturechanges at DP6-4 were similar to those during Storm-Event 3, but with a smaller magnitude (Fig. 8). Before the storm event on November 15, the river temperature was fairly steady. The river temperature began dropping as the stage began to rise on November 15 and continued to drop through November 18. The temperature at DP6-4 was higher than the river temperature during this period. As a result, the temperature at DP6-4 was gently dropping before the storm event and then began to drop more steeply at the beginning of November 16 as the river stage rose. To simulate the observed temperatures at DP6-4, this event was divided into four parts. The calibrated pre-event Kv was 0.0095 m/d. As the temperature dropped more steeply at the beginning of November 16, a

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higher Kv value of 0.0585 m/d was required. This higher value was used for only one day after which time the Kv value was lowered to 0.022 m/d. As the stage continued to drop, and late on November 18, it was again necessary to lower the Kv value to 1  107 m/d (Fig. 8). 4.3.5. Storm Event 5: November 28 to December 5, 2005 During Storm-Event 5, from November 28 through December 5, the groundwater temperature at DP6-4 continued to be higher than the river temperature. The temperature in the river began to fall steeply as the river stage began to rise on November 28 and continued to fall through December 2 (Fig. 9). As with the previous events, the groundwater temperature was gently falling prior to the event and fell more steeply as the river stage rose. To simu-

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late the temperature changes at DP6-4, the simulation period was divided into three parts. The best-fit pre-storm Kv value was 0.0080 m/d. To fit the more steeply-falling temperature at DP6-4, a greater Kv value of 0.053 m/d was required and was used through the high-stage period until the morning of December 1. As the river stage declined from an elevation of 163.3–162.6 m, the best-fit Kv value fell to 0.0095 m/d, a value that was roughly an average of the pre-storm and peak-stage values (Fig. 9). 4.3.6. Storm Event 6: January 16 to January 30, 2006 During the period from January 16 to 30, 2006, the river temperature fluctuated between 4 and 7 °C while the observed temperature at DP6-4 remained relatively constant (Fig. 10). During the time when river stage was rising rapidly a relatively higher

Fig. 8. Storm Event 4, November 11 to November 23rd 2005. The river stage, observed and simulated temperatures at well DP6-4 are shown.

Fig. 9. Storm Event 5, November 28 to December 5 2005. The river stage, observed and simulated temperatures at well DP6-4 are shown.

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riverbed Kv of 0.044 m/d produced the best fit to the observed temperatures at DP6-4. Just after the peak stage, a lower Kv value of 0.012 m/d produced the best fit. As the river stage decreased, and the river temperature dropped, a still lower Kv value of 0.0066 m/ d was needed to prevent the simulated temperature at DP6-4 from dropping too low (Fig. 10).

hydraulic head values had to be simultaneously matched to observed values. Good matches of hydraulic heads (Fig. 11) were obtained at the same time as when good matches of temperature were produced during the six storm events. 4.5. Temporal variability of hydraulic-head gradients during storm events

4.4. Matching simulated hydraulic heads to observed heads at DP6-4 Since heat transfer and water flow are fully coupled in the governing equation used in VS2DH, both simulated temperature and

Head differences between the river and the aquifer were greatest during the rising limb of the storm, just before the peak (exemplified in Fig. 12 for Storm-Event 3). This corresponded to the same

Fig. 10. Storm Event 6, November 11 to November 23rd 2005. The river stage, observed and simulated temperatures at well DP6-4 are shown.

Fig. 11. Simulated hydraulic heads at the time of good fit temperatures results.

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Fig. 12. Head difference between the river and the underlying sediments at DP6-4.

Table 4 Summary of storm event’s pre-peak and peak head gradients, Kv and specific discharge. Date

Pre-storm high dh/dl

Storm high dh/dl

Pre-storm Kv (m/d)

Peak Kv (m/d)

Pre-storm q (m/d)

Peak-q (m/d)

Change in q (m/d)

k factor of increase

q factor of increase

11-January 28-September 20-October 12-November 27-November 11-January

0.16 0.28 0.35 0.31 0.22 0.38

0.18 0.33 0.46 0.33 0.35 0.44

0.0059 0.00073 0.0102 0.0095 0.008 N/A

0.017 0.0066 0.024 0.059 0.053 0.016

0.0009 0.0002 0.0026 0.0031 0.0011 N/A

0.0031 0.0022 0.011 0.020 0.018 0.0070

0.0021 0.0020 0.0085 0.016 0.017 N/A

2.9 9.0 2.3 6.2 6.6 N/A

3.2 10.7 4.3 6.3 16.3 N/A

time when the increase in the riverbed Kv occurred. This observation was consistent in all six events even though only results from one event are provided in this paper as an example. The combined effect of Kv and head gradient increase during storm events was an increase in the specific discharge (q) from the river to the aquifer (Table 4). The magnitudes of increase in q during individual storm events are similar to the increase in Kv. The largest increase is by a factor of 16 during the month of November in 2005 (Storm-Event 5) while the lowest increase was a factor of 3 in June 2005. The factors of increase in q for storm events 1, 2, 3, and 4 were about 3, 11, 4 and 6 respectively.

quired conductivity during the first part. This was due to the fact that the model simulations were started immediately after the end of a rapid decline in stage from a previous minor event. The increase in Kv during each storm event was a probably result of unclogging of the riverbed during the rising limb. The regression analyses indicate that the degree of unclogging is controlled partially by the duration of the rising limb of the river stage and to a lesser degree by the absolute river levels. Apparently, the longer the rising stage, the more time there is to agitate the sediments and remove the fine particles that clog up the riverbed.

5.2. Implications of storm-event modeling results 5. Analysis and discussion 5.1. Correlation between estimated effective riverbed Kv and river stage Regressions analysis of the storm events indicated positive but very weak correlations between the peak stage and the simulated highest Kv (Fig. 13), and between the peak stage and the simulated change in Kv (ratio of peak Kv to pre-storm Kv) between pre-storm and simulated peak stage Kv (Fig. 14). These correlations had adjusted R2 values of 0.34 and 0.12 with p-values of 0.13 and 0.58, respectively. Much more significant was the correlation between the duration of the rising limb of the river stage and the ratio of the peak Kv value to pre-peak Kv value:

Peak to pre-peak K v ratio ¼ 0:51 þ 0:140 DurationðhÞ This correlation had an adjusted R2 of 0.76 and a p-value of 0.030 (Fig. 15). Only the first five storm events were used in these regression analyses because the sixth event had the highest re-

Storm Events 1 through 5 all had similar patterns with respect to the values of Kv needed to match the observed temperatures at DP6-4 (Table 2). For each simulation, an initial relatively low Kv value was required before the rise in river stage. In each case, except Storm-Event 2, to optimize the calibration, an increased Kv value had to be employed during the rising limb of the river stage. For Storm-Event 2, the increased Kv value was not employed until immediately after peak stage. For almost all the events, a lower Kv value was then required either while the river stage was falling or after the stage had returned to pre-storm levels. Storm-Event 6 deviated from this general pattern in that the early, low-Kv was missing because this event occurred immediately after another minor event and, therefore, the starting Kv had not yet dropped back to lower levels. Calibrated Kv values ranged from 1  107 to 0.059 m/d. During individual storm events, the calibrated Kv values underwent changes by factors ranging from 2.3 (Storm-Event 3) to 9 (Storm-Event 5, Table 2, Fig. 16). The observed increase in model-estimated riverbed Kv values is probably due the cleansing of the riverbed by increased river flow and velocity as fine particles

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Fig. 13. Correlation between the maximum storm-event river stage and the simulated peak (maximum) Kv.

Fig. 14. Correlation between the maximum storm-event river stage and the simulated change in riverbed Kv (early low Kv minus peak-storm Kv).

Fig. 15. Correlation between the duration of the storm-event rising limb and the simulated change in riverbed Kv.

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Fig. 16. Temporal variability of model predicted Kv during storm events. Arrows indicate the magnitude of change in Kv during storm events, as determined through temperature modeling.

and organics that clog the riverbed are removed. During the waning stages of the storm, as the river velocity decreases, the riverbed once again get clogged with fine particles which were abundant during the preceding high turbidity period. The combined impacts of low streambed permeability and pumping at RBF sites is desirable for contaminant filtration, but can also lead to the formation of an unsaturated zone below the riverbed (Su et al., 2008). Hydraulic heads in the aquifer responded to storm events just as quickly as the river stage. Even so, the downward hydraulichead gradients between the river and the underlying aquifer did increase during the rising limb of the storm hydrograph (Table 4). The largest increase in head gradients was when the stage was rising rapidly, which coincided with the time when the largest Kv values where estimated. The combination of the increased Kv values and hydraulic gradients results in an increased rate of infiltration (flux) and risk of aquifer contamination. This flux increase would result in a larger portion of river water being withdrawn at the production well and, therefore, increasing the potential for surfacewater contaminants in the pumped water. Since this occurs at the time when riverbed scour is most likely to take place, there is increased risk for both particulate and soluble surface-water contaminant-breakthrough at the production. If contaminants such as protozoa, algae and insects parts, among other indicators, from the river are not filtered out due to this increase in Kv and flux then the water at the production well would be considered groundwater under the direct influence of groundwater (GWUDISW). GWUDISW is required by EPA to be treated as surface water. The largest change in estimated riverbed Kv (from pre-storm low to peak high values) during the study period was about one order of magnitude, from 7.0  104 to 0.0066 m/day in September 2005. The largest estimated value of Kv for the study period was 0.059 m/d in November 2005. The average hydraulic gradient between the river and DP6-4 during this period was 0.38. Assuming a porosity of 0.24 (Sun, 2001; Levy et al., 2007), water infiltrated into the riverbed at a vertical flow velocity of 0.093 m/d. This value is about 40 times smaller than the average infiltration rate of 3.6 m/d (0.15 m/h) used in SSF systems. It appears that while there is a consistent increase in estimated riverbed Kv during storm events, the increase at this site does not indicate an unacceptable risk of contamination. It is important to note here that there is no evidence at Bolton, both from this study and previous water quality studies, that this particular site has GWUDISW.

6. Summary and conclusions Characterizing river water and groundwater exchanges is important at sites of induced infiltration especially during storm events when the river stage is high enough to potentially scour and agitate the riverbed sediment. Such agitation and scour can result in the loss of fine particles increased connectivity between the river and aquifer. This can in turn result in an increase in the risk of aquifer contamination if the river is contaminated. In this study, the temporal variability of the riverbed Kv during storm events was investigated using heat-flow modeling at a site of induced infiltration adjacent to a well field along the Great Miami River. During six storm events the riverbed Kv increased. During five of those events, the increase occurred during the river stage rising limb. During one event, the increase in Kv occurred immediately after peak stage. It is probable that the increase in river stage and velocity resulted in the loss of the clogging layer on top of the riverbed and, therefore, increased riverbed Kv. The hydraulic head gradient between the river and the underlying aquifer also increased during the rising limb. This increase, coupled with the model-estimated increase in Kv could potentially increase the risk of aquifer contamination. At the Bolton site, maximum velocities were still around 40 times lower than those used in SSF systems. This finding is consistent with results from previous studies (Gollnitz et al., 2003, 2004) that have shown RBF to work very well in filtering contaminants at this site. The change in model-estimated riverbed Kv was correlated to the duration of the storm-event’s rising limb (R2 = 0.76, p = 0.030) and not correlated to the peak stage (R2 = 0.12, p = 0.58). This study was an investigation of the effects that storms have on the riverbed Kv. In future work, more locations will be investigated to gain more understanding of both the temporal and spatial variability of riverbed Kv. Role of the funding sources The funding agencies had no involvement in the preparation, analysis, interpretation, writing and decision to publish this manuscript. Acknowledgements Funding was provided by, the Ohio Water Development Authority, the Hamilton New Baltimore Groundwater Consortium and

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