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Cold Regions Science and Technology

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Spatial variability of ice thickness on stormwater retention pondsJeffrey E. Kemp, Evan G.R. Davies, Mark R. Loewen⁎

Department of Civil and Environmental Engineering, University of Alberta, 7-023 Donadeo Innovation Centre for Engineering, 9211 116 Street NW, Edmonton T6G 1H9,Alberta, Canada

A R T I C L E I N F O

Keywords:Stormwater pondIce thicknessGround penetrating radar

A B S T R A C T

As integrated components of urban drainage networks, stormwater ponds receive runoff year-round. In coldregions, flow beneath ice covers transports heat and influences ice processes such that the spatial variability inice thickness cannot be assumed to be similar to that of natural lakes. To evaluate the variability in ice thickness,four stormwater ponds were monitored over two winter seasons, including both direct measurements of icethickness and indirect spatial surveys of ice thickness using ground penetrating radar (GPR). The 18 GPR surveysof ice thickness confirmed significant spatial variability in the ice thickness at all 4 study sites, with coefficientsof variation in ice thickness ranging from 0.046 to 0.163.

Pond bathymetry, the locations of inlets and outlets, and the locations of cleared ice rinks were found to beassociated with changes in ice thickness. In 3 of the 4 study sites, ice covers at inlet basins were found to besignificantly thinner than elsewhere in the ponds by up to 35.1%, whereas ice covers over outlet forebays weregenerally thicker, by up to 25.3%, than the pond average. Cleared ice rinks were found to have increased localice thickness by 5–10 cm. Mid-winter thaw events were found to decrease the mean ice thickness and increasethe spatial variability, with the change in ice thickness variability found to be positively correlated to the extentof bedfast ice preceding the thaw event.

GPR was found to be an effective tool to map ice thickness on stormwater ponds; however, impurities andliquid water in the ice covers presented challenges and reduced the accuracy of the measurements. Values ofrelative permittivity ranging between 3.2 and 8.5 were calculated at locations where direct measurements of icethickness were made, with the higher values consistently associated with thaw events.

1. Introduction

Stormwater retention facilities, commonly referred to as stormwaterponds, have become a common feature in urban landscapes over thepast few decades. The combination of submerged inlets and outlets,naturalised landscapes, and recreational facilities conceal the func-tional purpose of the ponds as components of the urban drainage net-work and transform them into focal points within residential commu-nities. The interaction between the public and these facilities has been alongstanding concern for municipalities because of the potential fordrowning, resulting in the installation of physical barriers and signageat some facilities (Michaels et al., 1985). As components of a drainagenetwork that is designed to remain functional year-round (AlbertaEnvironmental Protection, 1999), these facilities can be expected toreceive run-off during mid-winter thaw events. It is suspected that thedesign and operation of the facilities influence the spatial and temporalvariability in the ice cover thickness; however, the extent of thevariability and the underlying mechanisms driving it are uncertain. This

research aims to evaluate the variability in ice thickness on stormwaterponds and to correlate ice thickness variability with the physicalcharacteristics of the ponds.

Lake ice is classified primarily as one of two types: congelation iceand superimposed ice. Congelation ice, sometimes referred to asthermal, dark, or columnar ice, grows in long vertical crystals down-ward from the surface as heat is transferred from the water to the at-mosphere. Superimposed ice forms on top of an established ice cover,most often when the weight of snow depresses the ice cover, resulting inupwelling that saturates the snow and then becomes incorporated intothe ice cover when it refreezes (Ashton, 1980). Natural waters alsocontain impurities, which may be rejected or entrapped during theformation of the ice cover. Dissolved ions and gases, in particular, canhave a significant effect on the composition of the ice cover. For ex-ample, brine pockets reduce in size as the ice cover gets colder; how-ever, they persist until the ions precipitate, which varies by the ion typeand relative concentration. In the case of sea water, brine with dis-solved ions remains in liquid form to temperatures below −50 °C

https://doi.org/10.1016/j.coldregions.2018.12.010Received 25 June 2018; Received in revised form 14 December 2018; Accepted 27 December 2018

⁎ Corresponding author.E-mail addresses: jkemp@ualberta.ca (J.E. Kemp), evan.davies@ualberta.ca (E.G.R. Davies), mrloewen@ualberta.ca (M.R. Loewen).

Cold Regions Science and Technology 159 (2019) 106–122

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(Stogryn and Desargant, 1985). The distribution of bubbles withincongelation ice is a function of the water's air content and the rate atwhich the ice cover forms. Ice formed rapidly typically entraps dis-solved gases and contains a multitude of tiny bubbles, whereas ice thatforms very slowly is typically clear, as rejected gases are able to diffuseaway at the advancing ice front. In ice that formed at a moderate rate,rejected gases may diffuse into long, vertically-oriented bubbles thatbecome incorporated into the ice cover – sometimes referred to as iceworms (Halde, 1980).

The spatial variability of lake ice thickness has been found to besignificant by some researchers (Adams and Roulet (1980); Bengtsson,1986) and insignificant by others (Gow and Govoni, 1983). Those whoobserved variability attributed it to exposure to solar radiation, theredistribution of the snow cover by wind, and currents at the base of theice cover. Many studies (Adams et al., 1980; Adams and Roulet, 1984;Bengtsson, 1986; Gow and Govoni, 1983) have found that even if thereis minimal spatial variability in total ice thickness, the individualcontributions of superimposed snow ice and congelation ice can varysignificantly. In their study of ice thickness on Elizabeth Lake in Lab-rador, Adams and Roulet (1980) found the coefficient of variation washighest for the superimposed ice (0.67) and lowest for the total icethickness (0.14).

Most studies of ice covers on stormwater ponds have focused onwater quality implications rather than ice processes or the resultingspatial variability in ice thickness. Wittgren and Mæhlum (1997) notedthat flow constriction caused by ice covers can lead to flooding andsuggested that raising the water level prior to freeze-up may be bene-ficial. Marsalek et al. (2003) studied an on-stream, 0.5 ha pond inKingston, Ontario and monitored ice thickness, currents, and waterquality parameters during the 1995–1996 winter season. Two surveysof ice thickness, measured at holes drilled on a 15 m grid, were com-pleted mid-winter and in the spring following a thaw event. Ice thick-nesses measured prior to the thaw in March ranged from 0.22 to 0.45 mand had a mean and standard deviation of 0.31 and 0.075 m, respec-tively (coefficient of variation of 0.24). After the thaw event, the meanand standard deviation were 0.23 and 0.012 m, respectively (coefficientof variation of 0.05). Thinner ice was observed near the inlet and alongthe primary flow path with a significant thinning of the ice cover duringthe thaw event which was attributed to the influx of warmer meltwater(Marsalek et al., 2003). Pressurised flow beneath the ice and flow acrossthe ice cover was sometimes observed during heavier runoff events.During one particularly sudden thaw event, high inflows caused the icecover to rise and crack while remaining anchored to the shore. The icecover was flooded by surface flow which subsequently froze whentemperatures fell again, thickening the ice cover (Marsalek et al., 2003).

While reasonably accurate, drilling holes to measure ice thickness isintrusive, requires significant time and effort, and has limited repeat-ability. An alternative approach, ground penetrating radar (GPR),provides a non-destructive means to measure the ice thickness rapidly,which enables the spatial variability to be better captured. GPR hasbeen used extensively for measurements of sea, lake, and glacial icethickness and is frequently deployed from aircraft, surface vehicles, andsatellites (Daniels, 2004). GPR exploits the reflections of electro-magnetic pulses at the interfaces of materials of contrasting electro-magnetic properties to delineate boundaries. When the speed, V, atwhich an electromagentic pulse propagates changes at the interface oftwo materials, the result is a partial reflection of that pulse. Recordingthe strength of the reflected pulse with respect to the time since thepulse was transmitted enables the location of material interfaces. Theelectromagnetic permittivity of a material is often expressed in relationto the speed of an electromagnetic pulse through a vacuum, c, as therelative permittivity, εr, where (Davis and Annan, 1989),

= cVr (1)

Typical values of relative permittivity are provided in Table 1. Ice

density, temperature, air content, and its liquid water content aregenerally the most significant factors affecting its permittivity(Bogorodsky et al., 1985; Cooper et al., 1975). The permittivity of ice isalso a function of the frequency of the electromagnetic wave propa-gating through it; however, over the frequency range of 106 to1013 MHz and at a temperature of 0 °C, it remains relatively constant atapproximately 3.2 for pure polycrystalline ice (Bogorodsky et al.,1985). In a calibration of ice thickness measurements by radar at fre-quencies of 1 to 18 GHz, Vickers (1975) found permitivities of 3.17,3.08, and 2.99 for clear ice, ice with small air bubbles, and ice withlarge air bubbles, respectively. In degrading ice covers, water may ac-cumulate at the crystal interfaces, resulting in increased permittivitiessuch as those measured by Moorman and Michel (1997) that rangedbetween 4.6 and 5.1. In ice covers that form from saline or brackishwater, the presence of brine pockets has a significant influence on therelative permittivity (Liu et al., 2014; Stogryn and Desargant, 1985). Intests of a GPR system on Lake Saroma, a brackish lagoon in Japan withchloride concentrations of 17–18 ppt, Liu et al. (2014) estimated per-mitivities of 4 to 5.5. In the case of sea water, permittivities have beenmeasured ranging between 10 and 53, varying as a function of icetemperature and antenna frequency (Stogryn and Desargant, 1985).

A number of studies have used ground-truthing measurements toestimate the accuracy of the ice thicknesses derived from GPR surveysand evaluate potential sources of error. Cooper et al. (1975) estimated apermittivity of 3.1 with an average error of 0.1%. Fitting the GPR datato the same augered measurements gave a range in permittivity of 2.8to 3.8. Arcone (1991) calculated relative permittivity values rangingbetween 2.8 and 3.6. Li et al. (2010) found a standard deviation of4.3 cm in their comparison of 24 ice thicknesses measured at drilledholes to GPR measurements calculated using a theoretical relationshipand attributed the variability in GPR ice thickness measurements tobubble content. Testing of a short-pulse radar system by Cooper et al.(1976) indicated that the results were accurate to within 3.5 cm, withdifferences between the GPR and ice thicknesses measured with anauger of less than 9.8%. There was no measureable influence of snowcover, adverse weather conditions, or the speed at which the antennaewere travelling. Two GPR cross-sections of the Tanana River, Alaska,collected using 600 and 900 MHz antennae, were within 9.0% and 7.0%of the values obtained from drilled holes (Arcone and Delaney, 1987).In their recent study of the spatial variability of ice thickness on thePulmanki River in Finland, Kämäri et al. (2017) found the GPR mea-surements most closely matched borehole measurements when a re-lative permittivity of 3.5 was applied to calculate ice thickness. Basedon a comparison against 103 direct measurements of ice thickness, theirGPR survey was found to have a mean absolute error of 3 cm and meanabsolute percentage error of 5%.

The high reflectivity of water on the ice surface has been found toimpede the collection of ice measurements using GPR (eg. Cooper et al.,1976; Arcone, 1991). Lower frequency antennae are able to penetrate agreater depth of surface water (Arcone, 1991). For example, using an S-band (2–4 GHz) radar, Cooper et al. (1975) found that water layersexceeding 1 mm in depth prevented the measurement of ice thicknessand layers of slush within ice covers could yield erroneous measure-ments. However, Arcone (1991) was able to measure the ice thicknesson the Yukon River with a 500 MHz antenna despite an estimated1–5 mm of water on the ice surface. He then calculated upper limits of8.2 mm and 4.4 mm of surface water for surveys using a 500 MHz and900 MHz systems, respectively.

2. Methodology

The City of Edmonton is located in a region with a cool, dry, andtemperate climate, with a mean annual air temperature of 4.2 °C, anaverage of 1150°-freezing degree-days (FDD) during the winter, a meanannual snowfall of 123.5 cm, and an average of 82.6 days per year withmaximum air temperatures below freezing (Environment Canada,

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2016). Four stormwater retention facilities were selected for study overthe 2013–2014 and 2014–2015 winter seasons: MacTaggart 2 (MT2),Silver Berry 4 (SB4), South Terwillegar 2 (ST2), and Terwillegar Towne2 (TT2). The ponds were selected to represent a range of morphometriesand, based on information provided by the City of Edmonton, an an-ticipated range in ice conditions. With the exceptions noted in Table 2,all the inlets and outlets were submerged at a depth of at least 1 m andonly MT2 received inflow from an upstream pond. The local topo-graphy of each site was surveyed using a Trimble Real-Time KineticGlobal Positioning System (RTK GPS). Bathymetric data were collectedby wading where the depth was 0.6 m or less and using a boat andSonarMite-BT echo-sounder where depths exceeded 0.4 m. The bathy-metry and physical characteristics are provided in Table 2 and Fig. 1.

Ice thickness measurements were made between January and Marchin both monitoring seasons by drilling holes in the ice covers and byspatial mapping with GPR. Although the ice covers began forming inNovember, ice thickness measurements began when the ice cover wasconsidered safe to work on in January and were continued until lateMarch when the ice cover began to deteriorate rapidly.

Direct measurements of ice thickness, local snow depth, and, whenconditions permitted, superimposed ice thickness were made at augeredholes. Once per season, ice cores were collected and examined in thefield and, for cores from the 2013–2014 season, brought to a cold roomfacility at the University of Alberta for crystallography. To evaluate thespatial variability of the ice thickness, GPR surveys were conducted.Two surveys at each of ST2 and TT2 were combined into single surveysand four surveys were discarded due to poor quality data. The re-maining 18 surveys (6, 4, 5, and 3 surveys completed at MT2, SB4, ST2,and TT2, respectively) were processed and analysed. The GPR systemconsisted of Geophysical Survey Systems, Inc. (GSSI) 400 and 900 MHz

antennae (models 5103A and 3101A) and a SIR-3000 control unit.Location was logged every 0.25 m using a Trimble RTK GPS and the twodatasets were paired in post-processing. The GPR antenna was securedin a plastic sled with the RTK GPS antenna mounted vertically atop it.The sled was then manually towed back and forth across the ice cover ina grid-like pattern as shown in Fig. 2. Processing of the GPR data, asoutlined in Fig. 3, was required to extract the raw data from the pro-prietary format, georeference it with the RTK GPS data, identify thepeak reflections associated with the ice-water interface, establish cali-brations, and filter the results.

After each pulse is transmitted by the GPR, scans of the reflectedsignal by the receiving antenna measure the reflected signal strengthsampled at a constant rate. Each series of scans is compiled into a traceof the reflected signal strength which can then be plotted as a functionof distance as shown in Fig. 4. The ice-water interface in the GPR datawas identified by searching for and evaluating the peaks in each trace.In the ice thickness survey data, the ice-water interface can be expectedto return the strongest reflection (Daniels, 2004). The thresholds weredetermined by visually examining a small portion of each survey wherethe antenna transitioned between bedfast and floating ice. The largedifference in the electromagnetic emissivity of ice and water results in astrong reflection at the interface of the two materials, whereas an in-terface of ice-rock or ice-soil would have a much weaker reflection. Asshown in Fig. 4, the first peak logged was consistently from the directpulse while a second strong peak was only detected if there was an ice-water interface at the bottom of the ice cover. By selecting a thresholdvalue exceeded by only the direct pulse and ice-water interface peaks, itwas possible to identify when the ice was bedfast, and how thick it wasif not bedfast.

3. GPR calibration

Although there are approximate values for the electromagneticpermittivity of pure ice in the literature (Table 1), the potentially sig-nificant influence of impurities necessitates a calibration under local iceconditions to ensure an accurate conversion of the GPR data to icethicknesses. Therefore, a calibration against physical measurementswas performed to estimate the speed of the electromagnetic pulsethrough the ice on the stormwater ponds. During each trace, the GPRscanned and logged the reflected signal at a constant rate of 0.04885 or0.02444 ns/scan for the 400 MHz or 900 MHz antenna, respectively. Anumber of scans can therefore be directly converted to a length of timeand used to calculate how long it takes the electromagnetic pulse totravel to, reflect at, and return from the ice-water interface, ∆t.

Table 1Typical values for relative permittivity and specific conductivity.

Material Relative permittivity, εr Specific conductivity (μS/cm) Propagation velocity (m/ns) Source

Air 1.00 0 0.3 Davis and Annan (1989)Pure ice 3.20 10 0.16 Bogorodsky et al. (1985)Distilled water 88–56a 10 0.033 Malmberg and Maryott (1956)Fresh water 88–56a 500 0.033 Malmberg and Maryott (1956)Sea water 85–76b 30000c 0.01 Klein and Swift (1977);

Davis and Annan (1989)Dry snowd 1.6–1.8 – 0.22–0.24 Previati et al. (2011)Rotting lake ice 4.6–5.1 – 0.13–0.14 Moorman and Michel (1997)Brackish lagoon ice 4.0–5.5 – – Liu et al. (2014)Impure snow icee – 2700 – Matzler and Wegmuller (1987)Sea ice with brine pocketsf 10–53 – – Stogryn and Desargant (1985)

a Decreasing with temperature from 88 at 0 °C to 56 at 100 °C.b Decreasing with temperature from 85 at 5.5 °C to 76 at 30 °C.c Assumed to be for sea water at 0 °C.d Measurements made on a snowpack with an average depth of 2.13 m (depth ranging between 0.2 and 3.3 m and density between 250 and 600 kg/m3).e Snow ice formed on sea ice cover.f Varies as a function of temperature (0 to −25 °C) and frequency (7.5 to 40 GHz) with the dielectric constant measured to be higher at warmer temperatures and

when measured at lower frequencies.

Table 2Study site characteristics.

MT2 SB4 ST2 TT2

Type of pond Wet Pond Wetland Wet Pond Wet PondYear of construction 2005 2001 2008 1999Surface area (ha) 1.03 1.82 0.90 2.18Volume (m3) 7800 8100 7700 38,900Mean depth (m) 0.76 0.44 0.85 1.78Number of inlets 3a 4b 3 2Number of outlets 1 1 1 1Total catchment area (ha) 67.8 92.9 40.8 91.4

a One inlet is a submerged siphon connection from an upstream pond.b One inlet is a surface inlet.

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Fig. 1. Bathymetry and inlet configurations of each study site.

Fig. 2. GPR survey in progress at MacTaggart 2 pond (March 6, 2014).

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Fig. 3. Flow chart outlining the processing of the GPR ice thickness survey data.

Fig. 4. Profile view of the raw GPR data showing a series of trace profiles through bedfast and floating ice. Scan number and trace number are analogous to depth anddistance, respectively.

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Multiplying by the constant speed at which the electromagnetic pulsetravels through ice, ci, then provides the distance it travelled. For GPRsystems that employ monostatic antennae, which use a single antennaboth to transmit and receive, the thickness of ice, hice is then given by,

=h c t2ice

i(2)

The GSSI antennae used in this study, however, had separate an-tennae to transmit and receive the signal, and this physical separationbetween the transmission and reception antennae meant that the re-flection was not perpendicular to the ice-water interface. The angle ofreflection was then a function of the distance of the boundary from theantennae, as shown in Fig. 5. For the 400 MHz and 900 MHz antennaeused in this study, the antennae separations, dS, were 16.00 cm and15.24 cm, respectively (Kesik, 2015), which is between approximately15% and 50% of the anticipated distance to the ice-water interface.

For a bistatic antenna situated on the boundary of two materialswith different rates of electromagnetic transmission, such as air and ice,there would be two direct pulses between the transmitting and re-ceiving antennae, one through the air and a second through the ice. Inthe setup used to survey the stormwater ponds, the antenna wasmounted in a sled that had a corrugated, plastic bottom such that therewas a gap of snow (if present), air, and thin plastic between the antennaand ice surface. As a result, there was a direct pulse through the air atthe base of the antenna and a reflection at the top of the ice cover, asshown in Fig. 5. However, the difference in travel time between the two

pathways was less than the duration of the pulse, thereby resulting inan overlap between the peaks in the received signal. This was addressedby calibrating each survey individually based on field measurementsunder the assumption of uniform conditions over the course of eachsurvey. In the analysis described below the gap between the antennaand the ice cover was assumed to be a layer comprised only of air ofconstant thickness. In addition, the two strongest peaks in the tracewere treated as reflections at the air-ice interface and then at the ice-water interface, allowing ice thickness to be calculated by the followingmethod, as outlined by Cooper et al. (1975).

Using the scan number of each peak, the constant scan rate, rscan,and the scan numbers scana−i and scani−w at which pulses reflected atthe air-ice and ice-water interfaces were detected, respectively, the timeelapsed between the two pulses, ∆t, can be calculated as,

= ×t scan scan r( )i w a i scan (3)

=t t t( )2 1 (4)

where t1 and t2 are the time it takes the pulse to travel from thetransmitting antenna, Tx, to the receiving antenna, Rx. Since the directpulse is travelling through air at a constant speed, c, the time it takesthe first pulse to travel from the transmitting antenna to the receivingantenna, reflecting at the air-ice interface, can be calculated for a givendistance between the antennae, ds, as,

= +tc

h d22ant

s1

22

(5)

where c is the speed of light in air, hant is the height of the antennaeabove the air-ice interface, and ds is the distance between the trans-mitting and receiving antennae. Using the geometry and notation ofFig. 5, it can be shown that,

+ =h h dtan( ) tan( ) /2ant ice S (6)

and,

+ =h h ctsec( ) sec( ) /2ant ice r 2 (7)

which, rearranged, can be expressed as,

= +t hc

h2cos

2c cos

ant ice2

i (8)

where θ is the incident angle of the pulse at the ice surface and φ isthe angle of reflection at the ice-water interface. These two angles are afunction of the difference in relative permittivity between the air andthe ice cover and are related by Snell's law:

=sin 1 sin(9)

Substitution of Eqs. (5) and (8) into Eq. (4) yields,

= + +t hc

hc

d h2cos

2c cos

22

( )ant ice sant

i

22

(10)

Solving for the ice thickness,

=+ +

hc t d h h

c( /2) ( )

2 cosc cosice

s ant ant2 2

i

(11)

Using a measured elapsed time, ∆t, with Eqs. (6), (9), and (11)permits calculation of the three unknown values of ice thickness, hice,the incident angle at the air-ice interface θ, and the angle of reflectionat the ice-water interface, φ.

In order to apply the calibration, values needed to be established foreach of the input parameters of antenna height, hant, and the relativepermittivity, ε′. The antenna height was estimated as 75% of the meanmeasured snow depth, which ranged between 0 and 16 cm, with anadditional 5 cm to account for the thickness of the sled and antennahousing. The reduction in snow depth by 25% is equivalent to assuming

Fig. 5. Measurement of ice thickness using a bistatic GPR system (adapted fromCooper et al., 1975).

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a mean density of 1.33 g/cm3, a value that falls midway between esti-mates for freshly fallen snow of 0.7 to 1.65 g/cm3 (Pomeroy and Brun,2001) and a deep, packed snow cover of 1.6 to 1.8 g/cm3 (Previatiet al., 2011). These estimates and adjustments allowed the space abovethe ice cover to be treated as a single material (air) of constant thick-ness. The precise thicknesses and relative permittivities of the com-pacted snow, sled, and antenna housing (a combination of plastics andair) are unknown but are assumed to be relatively constant for theduration of a single ice thickness survey (3–4 h). Including the gapbetween the antennae and ice surface in the calculations resulted in avery minor improvement of the fit of the calibration over the assump-tion of direct contact.

Using Eqs. (6), (9), and (11), values for the relative permittivitywere calculated by fitting calculated ice thicknesses to measured icethicknesses and are listed in Table 3. A total of 156 locations where icethickness was measured both manually and with the GPR were avail-able to calibrate the relative permittivity, although 20 of these wereexcluded as outliers because the calculated permittivity values wereoutside the range of 2.5–10, which includes reasonable estimates forboth freshwater and brackish ice (eg. Bogorodsky et al., 1985; Cooperet al., 1975; Liu et al., 2014). Of these outliers, 18 were measuredduring three surveys conducted in the midst of a thaw event on March15, 2015, and were likely the result of liquid water in the ice cover. Ifsafety concerns due to unknown ice conditions prevented direct mea-surement of the ice thickness and there were no calibration pointsavailable for a given survey, then the median of all the calculated re-lative permittivities from surveys conducted with the same antenna wasapplied. An exception to this rule was made for the survey at TT2 onJanuary 12, 2015, as there were three other surveys conducted withintwo days which yielded consistently higher relative permittivities,which is thought to be associated with a period of warmer air tem-peratures. In Fig. 6, measured ice thicknesses from 136 locations in theponds are plotted versus the ice thicknesses computed from the GPRdata. A linear least squares fit gives a slope of 1.002, an intercept of−0.008 and a coefficient of determination of 0.948 (Fig. 6). A directcomparison of the measured ice thicknesses and those computed fromthe GPR data yielded a mean absolute error of 3.0 cm and mean abso-lute percentage error of 8.6%.

4. GPR data processing

The presence of slush and layers of water in the ice cover presenteda significant challenge to the GPR data processing. An example of in-ternal layers of water from a section of raw GPR scan data is shown inFig. 7, where echoes below the internal layers obscure the bottom of theice cover. The internal layers of water were always near the surface ofthe ice cover, so a minimum ice thickness of 10–15 cm was applied toprevent falsely identifying internal water layers as the bottom of the icecover. As outlined in Fig. 3, this was then followed by a series of

Table 3Calibration parameters used in the calculation of ice thickness.

Survey Antenna frequency(MHz)

Snow depth(m)

Compressed snowdepth (m)

Sled thickness(m)

Estimated antennaheight hant (m)

Number of calibrationpoints

Relative permittivity used inanalysis, εr

MT2_20140306 400 0.10 0.08 0.05 0.13 14 3.69a

MT2_20140325 400 0.00 0.00 0.05 0.05 0 3.86b

MT2_20150113 400 0.12 0.09 0.05 0.14 8 4.70a

MT2_20150129 400 0.00 0.00 0.05 0.05 15 4.10a

MT2_20150224 900 0.03 0.02 0.05 0.07 5 4.88a

MT2_20150315 900 0.00 0.00 0.05 0.05 12 7.62a

SB4_20140227 400 0.06 0.04 0.05 0.09 0 3.86b

SB4_20140326 400 0.10 0.08 0.05 0.13 0 3.86b

SB4_20150113 400 0.12 0.09 0.05 0.14 13 5.20a

SB4_20150315 900 0.00 0.00 0.05 0.05 15 7.66a

ST2_20140307 400 0.12 0.09 0.05 0.14 19 3.92a

ST2_20140325 400 0.00 0.00 0.05 0.05 0 3.86b

ST2_20150114 400 0.12 0.09 0.05 0.14 7 5.73a

ST2_20150129 400 0.00 0.00 0.05 0.05 9 3.54a

ST2_20150315 900 0.00 0.00 0.05 0.05 20 6.27a

TT2_20140307 400 0.10 0.08 0.05 0.13 19 3.49a

TT2_20140326 400 0.10 0.08 0.05 0.13 0 3.86b

TT2_20150112 400 0.16 0.12 0.05 0.17 0 5.19c

a Median of the relative permittivity values calculated using the calibration points for that survey.b Median of the relative permittivity values calculated using the combined calibration points of all 400 MHz surveys.c Median of the relative permittivity values calculated using the combined calibration points of all 400 MHz surveys conducted on January 13, 2015 and January

14, 2015.

Fig. 6. Comparison of estimated ice thicknesses from the GPR surveys to fieldmeasurements, with a linear fit that yields a slope of 1.002, an intercept of−0.008, and a coefficient of determination of 0.948.

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algorithms designed to eliminate measurements for which no locationdata was available, identify traces through bedfast ice, and eliminateoutliers. Since the reflection of the GPR signal at the ice-water interfacewas significantly stronger than that at ice-soil (bedfast) interfaces, thealgorithms were able to differentiate between the two most of the time.Peaks identified as bedfast ice in locations where the water depth wasmore than 25 cm greater than the ice thickness measurement wereflagged as outliers and removed. A one-dimensional median filter wasapplied to eliminate the notable outliers.

In order to proceed with a statistical analysis, the bedfast regionswere delineated manually using ERSI's ARCMAP software, and the re-sults were interpolated onto a 0.25 by 0.25 m grid using the naturalneighbour method. The manual delineation of bedfast ice was com-pleted by tracing bathymetric contours between locations where GPRmeasurements indicated transitions between floating and bedfast ice.Interior layers of meltwater in the top 10 cm were sometimes falselyidentified as the bottom of the ice cover and no direct measurements ofice thickness exceeded 80 cm, therefore any GPR ice thickness mea-surements less than 10 cm or greater than 90 cm were eliminated fromthe data to remove the remaining outliers. A minimum threshold of 20valid GPR measurements within a 5 m radius of each grid point wasused to ensure that interpolation did not conceal areas where poor fieldconditions resulted in sparse data. Finally, all grid points within themanually delineated bedfast regions were defined as such and a twodimensional median filter was applied to the gridded floating ice coverresults to remove any outliers that had been generated during the two-dimensional interpolation of the data.

5. Validation of measurements and sources of error

To validate the raw GPR data, the survey path was intentionallymade to cross previous sample locations to form a grid (Fig. 2), yieldingmultiple measurements at the same location, and for five of the surveysa short portion of the path was repeated. Pairs of non-sequential pointscollected at the same location were identified and compared to oneanother as a measurement error for each survey. Four of the surveysreturned very poor results in the validation step, most likely due towarm weather conditions at the time of data collection; these were alsothe surveys that had yielded most of the outliers in the calibration. Theywere excluded from further analysis. For the remaining 20 surveys, theabsolute mean percentage error of all the intersection points was 5.6%.

The results of the GPR calibration and estimates of electromagneticpermittivity were acceptable for the surveys performed with the400 MHz antenna, but marginal for three of the four 900 MHz antennasurveys performed in the midst of a thaw on March 15, 2015. An ex-amination of the estimated relative permittivities, listed in Table 3,show that the surveys on March 15, 2015 at MT2, SB4, and TT2 had thehighest relative permittivities with values of 7.62, 7.66, and 6.27, re-spectively. Likewise, three surveys at the same ponds performed duringmild weather in mid-January 2015 also had values for relative

permittivity ranging between 4.70 and 5.73, which are greater thanwould be expected for pure freshwater ice (eg. Stogryn and Desargant,1985) and closer to both the values of 4.6–5.1 measured in degradedlake ice by Moorman and Michel (1997) and the values of 4.0–5.5 fromthe ice cover of a brackish lagoon measured by Liu et al. (2014). Fig. 8shows the frequency distribution of all of the calculated relative per-mittivities that fell within the filter range of 2.5 and 10 (i.e. valuesoutside this range were considered outliers, see Fig. 6). There were 156calibration points and 20 were identified as outliers. Of these, oneoutlier fell below the lower relative permittivity limit of 2.5 and 19were above the upper limit of 10, which indicates that the GPR cali-bration almost always over-estimated the ice thickness at these loca-tions. These measurements were made in the midst of a thaw event, sothe relative permittivity of the ice cover may have been higher becauseof increased water content. Therefore, when the median relative per-mittivity value was applied, the increased attenuation of the GPR pulsein the wet ice manifested itself as an overestimation of ice thickness.The distribution of measured relative permittivities begins sharply at3.2, with a primary peak at 3.5, secondary peak at 4.9, and there aresome scattered values between 5.5 and 8.5. The minimum of 3.2 andpeak value of 3.5 were consistent with values from the literature forfreshwater ice (eg. Stogryn and Desargant, 1985; Cooper et al., 1975;Bogorodsky et al., 1985) and the secondary peak was consistent withmeasurements on a brackish lagoon (Liu et al., 2014) and of a degradedice cover (Moorman and Michel, 1997). With one exception, all of therelative permittivity estimates above 5.5 were for points measuredduring either mild weather in mid-January 2015 or in the midst of thethaw in March 2015, and the relative permittivity estimates above 7.0were limited to measurements made during the thaw event in March2015.

6. Results

The two monitoring seasons, 2013–2014 and 2014–2015, had sub-stantially different climatic conditions, with 1532 and 1036 cumulativeFDD, respectively. A comparison to the 1980–2010 climate normal of1150 cumulative FDD (Environment Canada, 2016) shows that the2013–2014 and 2014–2015 winters were colder and warmer thanaverage, respectively. Edmonton experienced 11 thaw events in each ofthe two monitoring seasons. Thaw events were defined as periods of atleast 6 continuous hours when the air temperature was above 0 °C,preceded and followed by at least 48 continuous hours of temperaturesbelow freezing. The magnitude of each thaw event was calculated bysumming the degree-days of thaw on an hourly time step. Both yearsexperienced major mid-winter thaw events, each exceeding 40° thawingdegree-days (TDD), on very similar dates in late January and mid-March; however, the thaw events in the second monitoring season wereof greater magnitude with higher temperatures persisting over longerperiods. The January thaw events for 2013–2014 and for 2014–2015totalled 42.5 and 47.7° (TDD), respectively, while the March thaw

Fig. 7. Sample of a GPR survey (SB4 2014-03-26) showing reflections at the internal layers of water.

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events were 51.5 and 66.6° (TDD), respectively. Smaller thaw eventsoccurred during December of both monitoring seasons and in February2015. The impact of contrasting winter climates of 2013–2014 and2014–2015 and mid-winter thaw events became evident in the resultsof the ice thickness measurements and GPR surveys.

The measurements of ice thickness made at 143 and 191 holes au-gered in 2014 and 2015, respectively, are summarised in Table 4. Withthe exception of TT2, the thickest ice measurements were consistentlymade in the vicinity of each pond's outlet while the thinnest ice wasmeasured near inlets. This difference was most pronounced at the NWinlet of ST2, which was consistently 10–20 cm thinner than other lo-cations at the pond, with a mean ice thickness 41% less on average thanat the SE inlet. At MT2, the mean ice thickness at the NW and SE inletswas consistently thinner than the ice cover over the SW outlet of thepond by an average of 16%. Likewise, the mean ice thickness at the NEinlet of SB4 was consistently less, by an average of 27%, than that at theNW outlet. The range of ice thicknesses was generally smallest at TT2,where the locations of the thickest and thinnest measurements wereinconsistent.

To assess ice composition, measurements of superimposed icethickness were made at 118 of the augered holes. Superimposed iceconstituted an average of 42% of the total ice thickness, with a standarddeviation of 10%; however, its total range was from 0% to 85% of thetotal ice thickness, with considerable variability within ponds and noconsistent difference between the study sites. For example, Fig. 9 showsthree ice cores collected at ST2 on the 12 March 2015, with super-imposed ice composing 36% to 85% of the total ice thickness.

The relative contributions of superimposed ice and congelation iceincluded measurements of 5 ice cores collected in 2014 and 15 coresphotographed in the field in 2015. Cores collected at ST2 (Fig. 9) showthat the thickness of superimposed ice is generally constant while thecongelation ice thickness varies. Representative samples of congelation(Fig. 10a) and superimposed ice (Fig. 10b) extracted from the coresshow the complex internal structure and distinct difference between thetwo ice types. The congelation ice samples were generally clear withvisible layers oriented horizontally. Many samples, such as the oneshown in Fig. 10a, also contained strings of tiny bubbles oriented ver-tically, while horizontal interfaces in the ice structure sometimes

Fig. 8. Distribution of electromagnetic permittivities calculated for 156 field measurements of ice thickness.

Table 4Ice thicknesses measured at augered holes.

Ice thickness measurement (cm)Minimum – maximum (count)

Season 1 Season 2

Location 2014-02-112014-02-13

2014-02-26 2014-03-062014-03-07

2014-03-11 2014-04-04 2015-01-122015-01-132015-01-14

2015-01-282015-01-29

2015-02-172015-02-18

2015-02-24 2015-03-12 2015-03-15

MT2NWb 43–56 (3) 53–68 (7) 40–68 (7) 40–40 (3) 10–36 (6) 30–45 (5) 41–41 (1)SWa 60–61 (3) 71–76 (7) 41–41 (1) 42–49 (3) 47–47 (1) 45–49 (4)SEb 51–51 (2) 59–63 (6) 35–35 (1) 34–37 (5) 40–41 (3) 38–54 (2) 23–35 (7)Centre 40–40 (2) 42–42 (1)

SB4NE+ 49–60 (3) 16–50 (11) 19–35 (6) 11–35 (4) 35–38 (5) 35–40 (2) 27–37 (6)N 45–45 (1) 20–40 (4)NWa 56–60 (3) 37–39 (3) 40–42 (2) 48–48 (1) 53–53 (1)ENEb 58–66 (4) 25–36 (4) 3–40 (6)ESE 42–46 (4) 18–18 (1)

ST2NWb 40–43 (6) 43–54 (9) 27–30 (5) 22–28 (8) 26–27 (2) 30–34 (5) 27–27 (1) 19–29 (5)SWb 52–58 (4) 57–71 (11) 38–45 (3) 37–37 (1) 46–48 (2) 46–49 (2) 48–48 (1) 15–46 (8)SEa 53–60 (4) 39–46 (3) 50–50 (1) 56–56 (1)

TT2NEa 55–66 (6) 63–73 (9) 42–42 (1) 44–51 (3) 50–57 (2) 54–62 (2) 40–50 (3)SEa 59–65 (5) 65–75 (14) 27–40 (8) 48–48 (1) 41–52 (6)Centre 65–69 (5) 44–44 (1) 52–52 (1) 50–51 (2)NWa 56–59 (4) 40–42 (3) 49–50 (2) 54–54 (1) 41–45 (2)SW 45–45 (1)

a Outlet location.b Inlet location.

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contained a dense layer of tiny bubbles. In contrast, samples of super-imposed ice, as shown in Fig. 10b, contained a higher concentration ofbubbles of varying sizes, making the ice more opaque. Although thesebubbles appeared to be randomly distributed in the horizontal direc-tion, the superimposed ice actually contained layers characterised bydistinct variations in bubble size. Samples from five cores collected in2014 were thawed and the specific conductivities of congelation andsuperimposed ice components of the cores measured, the results of

which are summarised in Table 5. The specific conductivities of con-gelation ice and superimposed ice were found to range between 29 and57 μS/cm and 77 and 250 μS/cm, respectively. An additional sample,tested separately due to its unique appearance of cloud-like inclusionsin clear ice, was found to have a specific conductivity of 311 μS/cm.Typical freshwater ice would be expected to have a specific con-ductivity on the order of 10 μS/cm (Davis and Annan, 1989).

7. Spatial and temporal variations in ice thickness

A summary of the processed GPR survey results is presented inTable 6. In general, the GPR ice thickness surveys extended over 90% ofthe ice cover, with the exception of TT2 where 65 to 75% of the icecover was surveyed and one survey of SB4 where only 80.3% (the largernorthern region) was surveyed. Five maps of the gridded ice thicknesssurvey results are presented in Fig. 11, showing the results of twosurveys at SB4 (a and b) and three at ST2 (c, d, and e). Similar mapswere produced for each of the 18 surveys and used to assess the resultsof the ice thickness surveys and identify significant trends.

The spatial variability found in the ice thickness surveys rangedfrom a relatively uniform ice cover at TT2 to highly variable ice coversat MT2 and ST2. The lowest coefficient of variation was 0.046, mea-sured at TT2 on March 7, 2014. The highest coefficient of variation was0.163, measured at both MT2 and ST2 on March 25, 2014. In many ofthe ponds there were locations that were either consistently thicker orthinner than elsewhere on the ponds. Ice thicknesses at inlet and outletbasins are summarised in Table 7. With several notable exceptions, theinlet basins of MT2, SB4, and ST2 have thinner ice covers than the pondas a whole and thinner ice covers than at the outlet basins. In 37 of 46instances, the mean ice thickness over inlet basins was less than thatover outlet basins, with 6 exceptions on TT2 and 3 at the SARM inlet ofST2, where local residents had cleared skating rinks. For those 37 in-stances, the mean percentage reduction in ice thickness over inlet ba-sins (as compared to outlet basins at the same pond) was 13%. The icethicknesses over all basins (both inlets and outlets) were compared tothickness measurements of the remainder of the ice cover (Table 7) andfound to be significantly different (p≤ 0.01) on 60 of 64 occasions. Forinstance, the ice cover over the inlet basins at MT2 was thinner than therest of the pond in the mid-winter surveys, but thicker than the pondaverage after the major thaw events in March 2014 and March 2015.The NE and ENE inlets at SB4 were consistently thinner than the pondaverage and outlet bay. The most significant and consistent reduction ofice thickness at an inlet basin was found at the NW inlet of ST2, wherethe ice cover was found to be 9.3%–35.1% thinner than the pondaverage. Unlike the other three ponds, in TT2 the ice cover over the NWoutlet was consistently thinner than both inlet basins and the pondaverage.

Notable exceptions to the trends described above include the post-thaw survey of MT2 in March 2014 where a significant reduction in ice

Fig. 9. Ice cores collected at South Terwillegar 2 on March 12, 2015.

Fig. 10. Samples of a) congelation ice from MacTaggart 2 (Core #2) and b)superimposed ice from MacTaggart 2 (Core #1).

Table 5Conductivity measurements of thawed ice core samples.

Pond Core ID Ice type Ice thickness(cm)

Specific conductivity(μS/cm)

SB4 Core 1 Congelation 24 57Superimposed 32 250

SB4 Core 2 Congelation 32 48Superimposed 26 113

MT2 Core 1 Congelation 45 38Congelation withcloudy inclusions

311

Superimposed 35 190MT2 Core 2 Congelation 26 33

Superimposed 37 90MT3 Core 3 Congelation 28 29

Superimposed 34 77

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Table 6Summary of GPR survey results.

Survey Surface area at normal water level(m2)

Area frozen to bed(m2)

Percent of facility frozen tobed

Percent of total areasurveyed

Ice thickness (m)b

Mean Std. dev Coef. of variation

MT2 2014-03-06 9284 6219 67.0% 97.7% 0.66 0.043 0.0662014-03-25 9284 3797 40.9% 97.7% 0.56 0.092 0.1632015-01-13 9284 313 3.4% 92.8% 0.39 0.023 0.0602015-01-29 9284 313 3.4% 91.8% 0.37 0.028 0.0742015-02-24 9284 848 9.1% 90.2% 0.43 0.024 0.0582015-03-15 9284 313 3.4% 93.6% 0.36 0.030 0.084

SB4 2014-02-27 18,378 14,268 77.6% 98.6% 0.58 0.029 0.0512014-03-26 18,378 12,901 70.2% 98.2% 0.51 0.064 0.1262015-01-13 18,378 10,715 58.3% 99.0% 0.33 0.023 0.0672015-03-15 18,378 8532 46.4%a 80.3% 0.39 0.057 0.146

ST2 2014-03-07 8444 2903 34.4% 98.3% 0.66 0.076 0.1152014-03-25 8444 2948 34.9% 99.6% 0.63 0.103 0.1632015-01-14 8444 1665 19.7% 93.5% 0.35 0.030 0.0872015-01-29 8444 1628 19.3% 90.1% 0.43 0.022 0.0512015-03-15 8444 1704 20.2% 95.1% 0.42 0.048 0.115

TT2 2014-03-07 22,822 4063 17.8% 75.0% 0.71 0.033 0.0462014-03-26 22,822 4632 20.3% 68.7% 0.67 0.078 0.1172015-01-12 22,822 2475 10.8% 69.2% 0.39 0.027 0.068

a The shallow regions south of the ENE inlet are not included in this survey, so these statistics for area frozen to bed only represent a portion of SB4.b Statistics calculated for TT2 may be biased as measurements were concentrated in the corners of the pond where the inlets and outlets are located.

Fig. 11. Results of ice thickness surveys conducted at SB4 (a and b) and at ST2 (c, d, and e). Surveys a and c were made preceding a major thaw event in March 2014,while surveys b and d were made following the thaw event. Inlets are named with cardinal coordinates, with the exception of SARM (South Arm).

Table 7Mean ice thicknesses measured at inlet and outlet basins, defined as the ice cover within 30 m of each inlet or outlet location where the total depth exceeds 1.5 m, andthe average ice thickness over the entire pond. Percentage differences between mean ice thicknesses at basins and the remainder of the pond are provided belowaverage measurements in brackets. Differences found to be significant at p≤ 0.01 are indicated in bold and those found not to be significant indicated in grey.

(continued on next page)

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Table 7 (continued)

Loca

tion

2014

-02-

27

2014

-03-

06

2014

-03-

07

2014

-03-

25

2014

-03-

26

2014

-03-

27

2015

-01-

12

2015

-01-

13

2015

-01-

14

2015

-01-

29

2015

-02-

24

2015

-03-

15

MT2

NW

(Inlet)

0.65

(0.7%)

0.58

(7.9%)4

0.39

(0.9%)

0.36

(-3.3%)

0.43

(2.8%)

0.35

(-0.1%)

MT31

(Inlet)

0.61

(-4.7%)

0.64

(18.4%)4

0.37

(-5.6%)

0.33

(-11.7%)

0.41

(-4%)

0.35

(-0.2%)

SE

(Inlet)

0.61

(-4.6%)

0.60

(10.1%)4

0.36

(-7.8%)

0.35

(-6.7%)

0.42

(-1.3%)

0.38

(5.9%)

SW

(Outlet)

0.71

(10.9%)

0.68

(25.3%)4

0.39

(1.8%)

0.42

(13%)

0.46

(9.5%)

0.42

(17.5%)

Pond Average 0.66 0.56 0.39 0.37 0.43 0.36

SB4

ENE

(Inlet)

0.56

(-3.7%)

0.43

(-16.1%)

0.33

(-1.0%)

0.38

(-3.8%)

NE

(Inlet)

0.53

(-8.9%)

0.44

(-14.7%)

0.30

(-10.4%)

0.31

(-21.2%)

NW

(Outlet)

0.58

(-0.8%)

0.54

(6%)

0.35

(4.9%)

0.46

(17.6%)

Pond Average0.58

(-0.8%)

0.54

(6%)

0.35

(4.9%)

0.46

(17.6%)

ST2

NW

(Inlet)

0.49

(-26.4%)

0.41

(-35.1%)

0.29

(-15.6%)No Data2

0.38

(-9.3%)

SARM3

(Inlet)

0.72

(8.4%)

0.66

(4.2%)

0.37

(8.9%)

0.45

(4.1%)

0.41

(-1.8%)

SW

(Inlet)

0.63

(-4.3%)

0.61

(-2.5%)

0.36

(6.1%)

0.43

(-0.3%)

0.45

(8.3%)

SE

(Outlet)0.69 (4.6%) 0.65 (3.4%) 0.38 (9.6%)

0.44

(1.2%)

0.47

(13%)

Pond Average 0.66 0.63 0.35 0.43 0.42

TT2

NE

(Inlet)

0.7

(-0.4%)

0.65

(-2.1%)

0.4

(2.6%)

SE

(Inlet)

0.74

(4.2%)

0.74

(10.4%)

0.4

(2.7%)

NW

(Outlet)

0.67

(-5%)

0.59

(-12%)

0.39

(-0.4%)

Pond Average 0.71 0.67 0.39

1Siphon from adjacent, upstream pond.2Flooded ice cover prevented data collection at this location.3An ice rink was cleared at this location in 2014, and nearby in 2015.

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thickness occurred across the centre of the pond and the ice cover overthe SARM inlet at ST2, which was in the footprint of a cleared ice rink.Ice rinks were cleared and maintained by local residents at SB4, ST2,and TT2; however, their impact on ice thickness was only evident atST2 where the thickest ice was consistently measured within theirfootprints, and was often 5–10 cm thicker than adjacent ice covers(Fig. 11). The post-thaw survey of ST2 conducted on March 26, 2014(Fig. 11d), revealed the most variable ice cover observed at any of thefacilities with ice thicknesses ranging from 0.1 m near the northwestinlet to over 0.7 m at cleared rinks. Note that the mean values sum-marised in Table 7 do not accurately portray the very localised thinningthat was observed directly over many of the inlets (Kemp et al., 2019),some of which were open water. For safety and practical reasons, sur-veys with the GPR equipment did not always capture these thermallyeroded holes in the ice covers which, when visible, were estimated to be0.3–1.0 m in diameter.

In ice surveys completed prior to the major mid-winter and springthaw events, ice thickness was found to be thinner along the shorelinesthan in the middle of the pond. This trend was evident at all four ponds,with results for MT2 and TT2 presented in Fig. 12. At TT2, the positivecorrelation between ice thickness and total depth is maintained to adepth of approximately 2.1 m, which is consistent with the slopedshoreline that extends to a depth of 2.3 m. A similar positive correlationbetween ice thickness and total depth is apparent at MT2 however, it isonly maintained to a total depth of approximately 0.6 m, which is themean depth of the of the central region of the pond.

The influence of seasonal climate variations was clearly evident atall four ponds. Mean ice thicknesses (Table 6) were 15–25 cm greaterduring the colder 2013–2014 winter compared to the 2014–2015winter. Colder temperatures also significantly increased the peak extent

of bedfast ice, which was most pronounced at MT2 where 67.0% of theice cover was bedfast prior to the March 2014 thaw event, compared to9.1% leading up to the thaw in March 2015. Similarly, 70.2% of SB4was bedfast in March 2014, whereas only 46.4% was bedfast in thesurvey conducted in March 2015.

Pairs of ice thickness maps preceding and following the major thawevent in March 2014 are presented in Fig. 11 (SB4 and ST2) and inFig. 13 (MT2). At all three ponds, there is a reduction in the extent ofbedfast ice, an increase in the range of ice thicknesses, formation ofchannels through shallow regions, and localised reduction in icethickness at some inlets. Most apparent at MT2 (Fig. 13b), but alsovisible south of the NW inlet at ST2 (Fig. 11d) and west of the NE inletat SB4 (Fig. 11b), is the concentration of thermal erosion of the icecovers in shallow regions downstream of significant inlet basins; incontrast, ice covers over the basins experienced less thinning. At MT2(Fig. 13), for example, floating and bedfast ice downstream of the inletsthat was 50–60 cm thick before the March 2014 thaw event thinned byapproximately 20%. The large reduction in ice thickness from ap-proximately 60 to 35 cm across the centre of MT2 resulted from flowacross the surface of the pond. It was found that although the ice coverat TT2 was thicker and less variable than at the other three facilities,the ice cover thickness did still vary. A comparison of surveys con-ducted before and after the thaw in March 2014 showed that the icecover in the centre of the pond had decreased by up to 10 cm, whereasice thickness along the shoreline increased by as much as 10 cm.

Relative frequency distributions of ice thickness for surveys con-ducted shortly before and immediately following the March 2014 thawevent are presented in Fig. 14. For all four ponds, there is a clear de-crease in overall thickness and increase in the spatial variability fol-lowing the thaw event. This increase in variability is also evident in the

Fig. 12. Ice thicknesses from mid-winter ice thickness surveys of TT2 and MT2 overlain with the bathymetry of the two ponds and plotted as a function of depth.

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universal increases in coefficients of variation provided in Table 5 forthese pairs of surveys. With the exception of ST2, which experiencedonly a slight increase, the coefficients of variation for ice thicknesses ateach facility increased by a factor of approximately two. Likewise,Fig. 15 provides relative frequency distributions of the ice thicknesssurveys performed in mid-January and mid-March 2015. Because thepre-thaw thicknesses in the 2015 distributions are from much earlier inthe season, there is a general increase in the mean ice thickness at SB4and ST2 in the mid-thaw surveys on March 15. As was the case in the2014 thaw, the ice thickness variability increase following the 2015thaw event, as can be confirmed by the consistent increases in thecoefficients of variation (Table 6). Comparison of the two years showsthat the spatial variability was much higher in the first monitoringseason. This trend is visible in the ice thickness distributions (Figs. 14and 15) as well as in the coefficients of variation (Table 6). For the threeshallower ponds, the extent of bedfast ice in the pre-thaw surveys waspositively correlated to the change in spatial variability for the thawevents of March 2014 and March 2015 (R2 = 0.67), as shown in Fig. 16.TT2 was excluded from the correlation because of its significantly dif-ferent bathymetry and the potential introduction of bias error by thetargeted surveying of inlet basins.

8. Discussion

The mean ice thicknesses presented in Table 6 are consistent withthe climates of two winters, with thinner ice covers in the warmersecond winter. The mean thicknesses were also found to be inverselyrelated to the mean depth of each pond when comparing surveys col-lected within a day or two of one another. However, in some cases thebathymetry and physical layout appear to have significantly impactedthe ice covers on the study ponds. In the three shallower ponds, MT2,SB4, and ST2, variations in ice thickness were strongly associated withbathymetry and with the locations of inlets and outlets. Mid-wintersurveys showed reduced ice thickness along shorelines (Fig. 12), whereshallower sediments may have released stored heat and inhibited icegrowth. The thinner ice observed over inlets and along the primary flowpaths, as well as flows across ice surfaces, are consistent with ob-servations made at a stormwater retention pond in Kingston, Ontario byMarsalek et al. (2003). The association of reduced ice thickness withincreased flow rates beneath the ice cover has also been observed inmeasurements of river ice (Kämäri et al., 2017), which suggests thatsome of the mechanisms driving variability in ice thickness on storm-water ponds may be more analogous to those on rivers than to natural

lakes. Although the trend of reduced ice thickness at inlet basins wasnot observed at TT2, the ice thickness was consistently thinner in thenorthwest region of the pond. The depth of TT2 appears to have re-duced the influence of bathymetry and submerged inlets on its iceprocesses, suggesting that the dominant processes affecting ice thick-ness in deeper ponds may be different from those at shallower ponds.

The extent of bedfast ice appears to significantly affect ice thicknessvariability during thaw events. As shown in Figs. 14 and 15, andsummarised in Table 6, the March thaw events had a greater influenceon spatial variability at the facilities with the greater areas of bedfastice. At MT2 and SB4, where the bedfast ice was extensive in 2014, theincrease in the coefficient of variability during the 2014 thaw event wasproportional to the decrease in the extent of bedfast ice. At ST2 andTT2, however, the extent of bedfast ice remained relatively constantthrough the thaw events of both 2014 and 2015. Shallower facilitiesthat were subject to increased areas of bedfast ice were more likely tohave greater increases in the spatial ice thickness variability following athaw event, as indicated by the results presented in Fig. 16.

The coefficient of variation for measurements of total ice thicknessof 0.14 observed on Elizabeth Lake by Adams and Roulet (1980) fallswithin the range of coefficients of variation determined for the storm-water ponds (Table 6). In fact, only three of the stormwater pond icethickness surveys, conducted at MT2 and ST2 following the thaw eventin March 2014 and at SB4 following the thaw event in March 2015,yielded greater variability. Spatial variability in the ice thickness atElizabeth Lake was thought to be significantly influenced by spatialvariations in the snow depth (Adams et al., 1980), and was also dis-tributed over a much larger area of 11 ha.

The variability in ice thickness on a stormwater pond in Kingstonwas measured by Marsalek et al. (2003) both preceding and following athaw event. The pre-thaw and post-thaw coefficients of variation of0.24 and 0.05, respectively, were of similar magnitudes to those ob-served at the four facilities in Edmonton; however, unlike the facilitiesin Edmonton, the spatial variability in the Kingston pond was smallerafter the thaw event. This opposing trend may be due to differences insize and morphometry, or simply due to the limited number of mea-surements taken at the Kingston stormwater pond. Although the icethickness variability on the four study ponds in Edmonton was similarin magnitude to the variability at Elizabeth Lake and at the Kingstonstormwater pond, the mechanisms driving the variability may be dif-ferent. In addition to the formation of superimposed ice by snow coversdepressing the ice cover, ice covers on stormwater ponds are oftenflooded when urban runoff is impounded. Freezing of this flood water

Fig. 13. Results of the GPR ice thickness surveys conducted in March of 2014 with bathymetric contours.

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also adds to the superimposed ice cover, and is a primary mechanismfor ice cover thickening later in the winter. Thermal erosion of theunderside of the ice cover may also be intensified by increased ad-vective heat transport in stormwater ponds and a greater heat contentin the water column (Kemp et al., 2019).

Despite its requirements of extensive data processing and filtering ofmeasurements, GPR proved an effective means for measuring icethickness on stormwater ponds at a high spatial resolution. Estimates ofrelative permittivity for surveys conducted under colder weather con-ditions, ranging between 3.2 and 5.5, were consistent with values forice formed from freshwater and brackish water found in the literature(e.g., Bogorodsky et al., 1985; Kämäri et al., 2017; Vickers, 1975; Liuet al., 2014; Moorman and Michel, 1997). Surveys conducted in milderweather yielded higher estimates that were consistent with measure-ments of degraded ice (Moorman and Michel, 1997) and may be asso-ciated with partially thawed ice and pockets of brine in the ice cover.Because extremely high values of relative permittivity were excludedfrom the calibration, the survey results may overestimate the icethickness in locations where the ice cover had elevated water content.Visual examinations of ice core samples confirmed the presence of airbubbles within the ice covers while conductivity measurements in-dicated the presence of entrapped pockets of brine (She et al., 2016).Bubbles, inclusions of brine, and meltwater at crystal interfaces are allfactors that affect electromagnetic permittivity (Bogorodsky et al.,1985; Cooper et al., 1975; Liu et al., 2014; Stogryn and Desargant,1985; Vickers, 1975). The association of increased permittivities withsurveys collected during thaw events indicates that liquid water in theice cover is the most probable explanation for the variability in thecalculated values of permittivity. The presence of a water or slush layerwithin the ice cover may result in false measurements or prevent thedetection of the bottom of the ice cover all together (Cooper et al.,

1975). These layers, typical of recently or partially formed super-imposed ice covers, were encountered at all of the facilities followingthaw events, and led to extensive filtering of the data and the discardingof entire surveys.

The results of this study emphasize the importance of selecting theappropriate antenna frequency to suit the ice conditions. Beginning inFebruary 2015 the 900 MHz antenna was used exclusively in an attemptto improve the precision of ice thickness measurements. Most of the2015 surveys performed using the 900 MHz antenna were completedduring milder weather and, as a result, the survey data were often muchnoisier than surveys performed with the 400 MHz antenna under bothmild and colder conditions. This is consistent with the theory and cal-culations outlined by Arcone (1991), which state that the lower fre-quency 400 MHz antenna better penetrates layers of water, whereas thehigher frequency 900 MHz antenna cannot penetrate layers of waterdeeper than 4.4 mm. Given the variable ice conditions encountered inthis study, the increased precision of the 900 MHz antenna did notsignificantly improve the accuracy of the results. It is recommendedthat lower frequency antennas, such as the 400 MHz used for most ofthe surveys in this study, be used for ice thickness surveys on storm-water ponds to reduce the susceptibility to interference by slush, brinepockets, and internal layers of water.

The wide range of calculated relative permittivity values indicatesthat ice conditions varied considerably between ponds and withchanges in climatic conditions. This suggests that it is important toconsider local ice conditions when collecting GPR measurements of icecovers and reinforces the value of calibrating against concurrent directmeasurements. Additionally, positioning the antennas at a sufficientheight above the ice surface to capture the snow cover and top of icecover would likely improve the accuracy of the measurements andprovide additional information on the snow depth.

Fig. 14. Pre-thaw and post-thaw ice thickness distributions for the March 2014 thaw event.

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9. Conclusions

The GPR surveys of ice thickness confirmed significant spatial andtemporal variability in the ice thickness at all four study sites. The lo-cations of thinner and thicker ice covers were often consistent anddetermined by the bathymetry, the locations of inlets and outlets, andthe locations of cleared ice rinks. Early in the season, there was a po-sitive correlation between the depth of water and the ice thickness innear-shore regions likely due to the release of heat from the sediments.Throughout the winter and especially following thaw events, reduced

ice thickness was found directly over inlets, downstream of inlet basins,and at locations of restricted flow.

Spatial variability in ice thickness quantified using the coefficient ofvariability was found to range from 0.046 to 0.163. The spatial varia-bility consistently increased following thaw events, indicating thatrunoff through the stormwater ponds was driving a non-uniform de-gradation of the ice covers. Although these values are consistent withother measurements reported in the literature (Adams et al., 1980;Marsalek et al., 2003), it is suspected that the mechanisms driving thevariability differ from natural lakes, as discussed in greater detail byKemp et al. (2019).

The extent of bedfast ice was found to have a profound impact onthe ice cover as a whole, exhibiting a positive correlation with thechange in spatial variability in ice thickness following thaw events. Thisis largely attributed to the constriction of flow as evidenced by ther-mally eroded channels in the ice covers, flooding of anchored icecovers, and flows across the ice covers. Additionally, the clearing of icerinks, which resulted in local increases in ice thickness of 5–10 cm,contributed to flow constriction when constructed in shallow regions,as was observed at ST2 and SB4. As a result, the spatial variability in theice cover thickness was greater in shallower facilities, in facilities withshallow regions downstream of major inlets, and during colder winters.

Acknowledgements

This research was funded through the Natural Sciences andEngineering Research Council of Canada (NSERC), and City ofEdmonton (Collaborative Research and Development Grant (CRDPJ451210-13)). This support is gratefully acknowledged. The authorswould also like to thank Mahyar Mehdizadeh, Laurel Richards, Rocio

Fig. 15. Mid-winter and mid-thaw ice thickness distributions from the 2015 winter season. Although a survey was conducted at TT2 on March 15, 2015, there wastoo much interference from internal water layers at the time of survey (late afternoon).

Fig. 16. Effect of the extent of bedfast ice on the change in ice thicknessvariability over the course of a thaw event. A correlation coefficient of 0.67 isfound if the point from TT2 is excluded. Including TT2 yields a correlationcoefficient of 0.52.

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Fernandez, Yuntong She, Stefan Emmer, Perry Fedun, and VincentMcFarlane for their assistance in various aspects of the data collectionprogram.

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