Analysis of Shallow Water Ice Profiler (SWIP) Data of a Small Northern Quebec Lake for the Deployment Periods: September 2008- October 2009, October 2009 – May 2010 and October 2010 – December 2010. Prepared for Adam Lewis Nunavik Research Centre Makivik Corporation 1111 Dr. Frederik-Philips Blvd. 3rd Floor St. Laurent, Quebec H4M 2X By R. Bowen, A. Slonimer, E. Ross, D.B. Fissel ASL Environmental Sciences Inc. #1-6703 Rajpur Place Victoria BC, Canada V8M 1Z5 ASL File: PR-767 Final Version September 2012
Transcript
Analysis of Shallow Water Ice Profiler (SWIP) Data of a Small Northern Quebec Lake for the Deployment Perio ds: September 2008- October 2009, October 2009 – May 20 10 and October 2010 – December 2010.
Prepared for
Adam Lewis Nunavik Research Centre
Makivik Corporation 1111 Dr. Frederik-Philips Blvd.
3rd Floor St. Laurent, Quebec
H4M 2X
By
R. Bowen, A. Slonimer, E. Ross, D.B. Fissel
ASL Environmental Sciences Inc. #1-6703 Rajpur Place
1. Introduction This report describes the processing steps and the results derived from Shallow Water Ice Profiler (SWIP) deployments in a northern Quebec lake. Where appropriate, comments have been added to draw attention to some anomalous features in the dataset that were dealt with in the data processing and analysis. Some discussion has been included that contrasts the inter-annual variability of the ice drafts, formation dates, growth rates and comparison of ice drafts to predicted ice thicknesses based on accumulated freezing degree days.
Three datasets were analyzed spanning the following time periods:
Deployment 1 September 2008 – October 2009 Deployment depth ~ 9.1 m * Deployment 2 October 2009 – May 2010 Deployment depth ~ 9.2 m * Deployment 3 October 2010 –December 2010 Deployment depth ~ 9.4 m * * approximate deployment depths extracted from range data
1.1 Location The SWIP units were deployed in a small lake in northern Quebec at coordinates 58.92 N 65.39 W. This lake is situated near the Baudan River which connects to Ungava Bay. There is both an inflow and an outflow to this freshwater lake. The overall dimensions of the lake are about 2 km in the long axis and about 0.7 km in the short axis. Figure 1 depicts the location of the deployments.
.
Figure 1 . Location of SWIP deployments and weather station at Tasikallak Lake.
2
In each of the deployments, the SWIP was deployed in the same location in about 9 m of water. The SWIP units used were attached to the shore where an external battery pack and small junction box were kept in an insulated enclosure. The SWIP was anchored with a customer supplied deployment platform. Care was taken to protect the cable from ice scour using ABS drain pipe as a protective cover in the near-shore zone.
The SWIP instrument allows data to be viewed and/or downloaded from the shore-based enclosure via a USB interface. If required, the instrument could be reconfigured with the Ips5Link supplied software. Figure 2 illustrates the main components of the deployment.
Figure 2. Sketch of field deployment.
1.2 Instrumentation The SWIP is a real-time upward looking sonar that acoustically detects ice draft and is suitable for shallow water applications such as lakes and rivers. The instrument is non-invasive and is useful for in-situ monitoring of ice growth, decay and ice dynamics such as ice movement during break-up or tidal forcing. The underwater components include an acoustic transducer, a tilt sensor, a high-precision pressure sensor and a temperature sensor, all providing high resolution for shallow water ice draft measurements (Figure 3). The instrument can be operated in a self-contained internal recording mode with a connection to an underwater battery pack for power and an internal datalogger, or with an RS-422 serial output for real-time operation using an underwater cable which also allows for external power. All three deployments presented here used RS-422 connections for real-time operations. The SWIP is a shallow water version of the Ice Profiler Sonar developed for ocean applications (Melling et al., 1995).
3
Figure 3. SWIP unit used in deployment 1 and 2. The SWIP instrument used for deployment 3 had the same components but elements were housed in an aluminum pressure case. SWIP
components are list below.
(1) Persistor Computing Module (2) Pressure Port (3) Serial Data Cable Connector (4) Transducer Connector (5) Transducer Face (6) Latch (one of two) (7) Zinc Anode (8) Transducer Mounting Plate (Stainless Steel) (9) Polycarbonate Pressure Case (10) Orientation Guide (face of transducer points towards the water surface)
This instrument continues to evolve with recent upgrades that provide additional data capacity and improved resolution and accuracy (Fissel et al., 2008, see link provided for most recent updates: http://www.aslenv.com/SWIP.html ). The use of the shallow water version has been used in river applications since 2005 (Jasek et al., 2005; Marko and Jasek, 2008).
A systematic series of data procesing steps were carried out on each of the three datasets. As these steps were, for the most part, common to each dataset, they will be briefly described to explain the considerations required to obtain a final ice draft. This explanation is similar to that provided to the client in the report “Analysis of Shallow Water Ice Profiler (SWIP) Data of a Small Northern Quebec Lake for the Deployment Period of July 2007- June 2008” submitted November 2008. For a more detailed explanation of these steps, please refer to the IPS Processing Toolbox User’s Guide, July 2011. Following the general processing steps, each dataset was examined based on their unique attributes.
4
2. Data Processing
2.1 Ice Draft The processing of the SWIP data involves the conversion of the time-of-travel measurement recorded internally by the SWIP unit into a calibrated time-series of ice draft in units of meters. The general approach to the data processing follows that of Melling et al. (1995). The implementation of the procedures, along with many further refinements, have been developed and carried out by ASL personnel over several years.
The raw data recorded by the SWIP instrument consists of measured parameters including the time-of-travel to a number of selected target echoes and the amplitude of these echoes. The basic operation of the SWIP instrument is outlined briefly below. Essentially, the SWIP operates in a pulsed mode with its acoustic beam directed toward zenith. A multi-faceted algorithm (Melling et al., 1995) identifies echoes which are intended to be that of the bottom of the ice as the strongest target as well as other strong targets within the water column. In the absence of ice, targets are returned from the lake-atmosphere interface. The following describes the target selection algorithm:
• Select those echoes that are returned from beyond a minimum range but within a maximum range whose amplitudes and durations exceed chosen minimum values (i.e. the echo start amplitude and the minimum persistence). The SWIP is capable of recording up to five targets with target 1 having the longest persistence.
• Choice of the control parameters for target 1 must be carried out with a view to minimizing the likelihood that the algorithm will select echoes from sources within the water column as opposed to the ice undersurface (Melling et al., 1995). The start amplitude, stop amplitude and persistance values selected for these deployments were based on pervious field experiences.
The time of travel value was converted to a one-way range by applying a first estimate of the speed of sound of freshwater, c, in m s-1. For these deployments the initial value of c was user-defined and was entered into the SWIP configuration xml file to be 1450.88 m s-1. This initial selection of sound speed is more appropriate for sea water conditions. Corrections were made during the processing steps to adjust c to reflect the in situ freshwater sound speed conditions.
2.2 Processing
2.2.1 Initial Processing Steps
The following steps were carried out to process the SWIP data.
1. Converted raw SWIP data from binary form in instrument units to nominal engineering units.
2. Prepared time-series plots of the raw range measurements. Sample plots are shown in Figure 4. Note that the plots show raw data presented exactly as measured without any corrections or changes. In addition to the anomalous values (spikes) evident in the raw
5
data, the data had not been corrected for variations in water levels due to water level fluctuations and other effects, nor for changes in the speed of sound in the water column nor for the effects of instrument tilt.
Figure 4. Example of raw range data.
3. Manually examined the time-series plots of range to determine where editing was
required. Segments of the dataset characterized by level ice errors, recurrent spikes to a specific range, and missing ice echoes were noted. SWIP range data was edited by using automated data editing algorithms described below. 3.1 Erroneous values were automatically removed based on the following attributes:
• range values < 0.01 m to remove range dropouts
• range values that exceed a reasonable value computed as ranges above the water depth plus 3 m.
3.2 Erroneous values, identified in the steps immediately above, were removed and replaced with linear interpolated data based on the adjacent boundary points. On completion of the automated editing, longer sequences of consecutive data values (10 points or more) that were removed were examined manually to ensure that the error detection algorithm had functioned as expected.
3.3 The boundary points of segments of consecutive null range records were compared. If the range values of these points differed by less than 10 cm then the range values of these records were calculated by linear interpolation of the boundary points. The remaining segments were investigated and edited manually using ASL_EDIT, an in-house software designed to carefully examine and edit spikes. This manual editing step was particularly useful in level ice conditions where spikes occurred in stationary ice conditions, a situation where large excursions in draft values would not be plausible.
3.4 As the ice was fixed during the winter months and was either progressively growing
or decaying, a threshold value for each phase was set to detect spikes that exceeded the selected threshold. This threshold was set to the maximum ice draft within the phase plus 20 cm.
6
3.5 The edited dataset was re-plotted and reviewed for any additional spikes or suspect
values. Further spikes (usually occurrences over five to 10 consecutive data values) were manually replaced using linear interpolation; occasionally, longer segments of suspect data were replaced with flag values which represented no valid data for the data samples within the segment.
2.2.2 Computation of Ice Draft
The next stages of data processing dealt with converting the measured ranges into ice drafts (the distance of the underside of the ice thickness from water level). The ice draft, d, was derived from the edited ice ranges, r, and the water level, η. The η value was computed from the measured lake bottom pressure, Pbtm, and atmospheric pressure, Patm, as follows:
( )1)(
gPP atmbtm
⋅= −ρη
where ρ is the density of freshwater based on SWIP bottom temperature and g is the calculated local acceleration due to gravity at the site latitude. Measurements of Patm were obtained from Environment Canada weather stations at Kangiqsualujjuaq and Kuujuaq Quebec and a HOBO weather station data logger provided an onsite pressure record for part of the second and third datasets. Creating a continuous atmospheric pressure curve for each year presented unique challenges that will be addressed individually within the dataset results.
The ice draft, d, was then computed from the edited range data as:
( )2Drd ∆−⋅−= βη
where β is a calibration factor for the actual mean speed of sound in freshwater, c, relative to the initially assumed value of 1450.88 m s-1, r is the measured acoustic range measured from the transducer to the underside of the ice surface and ∆D is the distance of the pressure sensor below the acoustic transducer. Note that the sign convention for ice draft is positive downwards, i.e., a draft of +5 m represents an ice keel, which extends 5 m below lake level.
Corrections were also made for the effect of instrument tilt on the measured ranges. Generally, if not corrected for, this source of uncertainty would result in errors of a few centimeters or less on the ice drafts. As the SWIP was deployed in lake conditions and anchored to the lake bed, tilts magnitudes had very little variation throughout the deployments.
2.2.3 Range Correction Factor
The factor β, applied to the measured range in equation (2) represents the ratio of the actual sound speed to the nominal value of 1450.88 m s-1. To determine β, open water segments in the range dataset were selected (i.e. d = 0 in equation 2 above) and β was empirically computed. The empirical values of β, as realized from open water periods of the range data, seem to follow reasonably well the variation in the local speed of sound as computed from the measured near-bottom temperatures obtained from the SWIP values at the measurement location.
7
A fit was made to determine the time varying β values over the full duration of the time-series data based on:
• the empirical computations of β realized from periods of open water; and
• the computed effect of near-bottom temperatures on c (using a fixed salinity and the speed of sound algorithm of Urick, 1983).
An example plot of the time varying ∆β values are shown in Figure 5 where:
( ) ( )310001 ×−=∆ ββ
The empirically derived values of β were applied through equation 2 to the edited range data to compute ice drafts (drafts are taken to be positive as the ice thickness extend downward from the lake surface).
2.2.4 Sound Speed Corrections
A fourth order polynomial equation (Figure 5) was fitting using measured data of temperature and corresponding sound speed in freshwater based on Table 1.
As there were no out-of-water data recordings to compare SWIP measured pressure with coincident atmospheric pressure data, it was not possible to correct for pressure drift over the deployment period.
9
As the SWIP measured pressure requires the removal of atmospheric pressure, a continuous atmospheric pressure record was needed for the entire deployment period. To assemble this curve required careful examination of the data available. A HOBO data logging station was installed on the shores of Tasikallak Lake which provided pressure data for part of the deployment (10/16/2009 12:43 – 01/25/2010 17:59). However there was no data available for the 2008/09 deployment. This necessitated the extraction and comparisons of two Environment Canada weather stations in the area. Kuujjuaq weather station, approximately 200 km away from the SWIP deployment, measured hourly pressure data for 24 hours a day, and the closest station, Kangiqsualujjuaq, about 45 km away, measured hourly pressure from 6 am to 5 pm only. Each pressure dataset was plotted and an overlay was constructed for comparison.
Figure 6. Atmospheric pressure curves. The Kangiqsualujjuaq station sampled pressure only 10 hrs per day. The Kuujjuaq station measured pressure
continuously. Both sampled at 1hr intervals.
The general pressure characteristics of the two curves were largely observed regionally with some variations in amplitude and phase. The Kujjuaq dataset was found to be offset 2 hours ahead of the Kangiqsualujjuaq data. It was also found that the Kujjuaq pressure was offset from the Kangiqsualujjuaq pressure on average by 0.0064 dbar over the course of the 2008/09 deployment. The Kujjuaq pressure was shifted in time and magnitude to correct for these differences.
10
Figure 7. Kujjuaq pressure correction to match Kangiqsualujjuaq pressure.
The shifted Kujjuaq matched the Kangiqsualujjuaq data very well. Figure 7 illustrates the overlay of these two datasets with the green curve indicating the final curve used in the removal of the atmospheric pressure from the SWIP pressure data. A hybridized curve between the two data sets was not used because although the match was close, it was not quite perfect. A hybridized curve would have resulted in sudden jumps and changes in the computed water levels.
Several interesting features were observed in the datasets that aid in the interpretation of the overall characteristics unique to this deployment period. These are briefly discussed below.
3.1.2 Air Temperature
As the air temperatures vary from year to year and contribute significantly to the presence or absence of ice, it is one factor that must be carefully examined as it provides insights into ice production and decay. This is a basic attribute that is constantly referred to during any interpretive assessments such as first ice formation.
The air temperature was not directly used in any of the acoustic data processing steps. However the Air Temperature was used to model the ice growth curve. The Kuujjuaq air temperature was a fair distance from the lake and so it would be inaccurate to model a curve using only this data set. Instead the Kuujuaq temperatures, like the pressure data, were shifted by 2 hours to better approximate the Kangiqsualujjuaq data. This was then grafted to fill the gaps in the temperature record.
11
Figure 8 . Air temperatures from the Environment Canada Kuujjuaq and Kangiqsualujjuaq weather stations
3.1.3 SWIP Bottom Temperature
An important consideration to the first appearance of ice is the water temperature. Although there were no measurements made at the air-water interface, the SWIP unit measured bottom temperatures throughout the deployment. At the beginning of phase 1, the SWIP bottom temperatures were nearly 12°C and displayed the over-turning event as they steadily dropped throughout the phase. During phase 2, the SWIP bottom temperature dropped to it lowest value of the winter of 1.6°C on November 3, 2008. It is not until nearly a week later, on November 9, 2008, where the bottom temperature reaches a stable plateau of approximately 2.0°C. This temperature was extremely stable with less than 0.1°C fluctuation until late April to early May, 2009 where a slow steady increase was observed that correlated well with an increasing air temperature.
12
Figure 9. SWIP bottom temperature record depicting the fall turn-over, extreme winter low, stable winter temperature and spring response to thermal heating. Also captured is the drop in temperature leading into the following autumn.
At the beginning of May a very low amplitude diurnal oscillation (~0.1°C) began. This intensified May 26-June3 to an ampltiude of 0.3°C. The mean temperatures then dropped by ~0.4°C on June 5 and stayed low through to June 12. The temperatures rapidly increase afterwards. Figure 9 displays the SWIP bottom temperature for the entire deployment.
Figure 10 . Warming bottom Temperatures
13
3.1.4 Unusual Features in the Range and Ice Draft D ata
The range data were generally of high quality in Phase 1 and the latter (ice-free) half of Phase 4. During the cold winter months in certain situations, multiple targets were recorded where the first and second targets alternated between two values which differed by just over 0.20 m. For instance, ranges from target 1 with consistent distances, were occasionally punctuated by spikes. In the same instance, target 2 ranges equaled the target 1 values before the spike. Under these conditions, target 1 spikes were conditionally replaced or switched with target 2 ranges to provide a consistent data record for each target type. After this was corrected very few spikes remained and the data quality was much improved. Automated despiking was unnecessary. Table 3 summarizes the extent of the editing of the data.
Table 3 . Summary of Data Editing Phase 1 Phase 2 Phase 3 Phase 4 Out of bounds and null values 227 58 5 98 Automated Threshold spikes - - - - Target Switch - 953 421393 1114800 Manual Spike Removal 9 11 22 898 Total Num of Records 402267 5270411 1036803 14626341 Final Number of Replaced Data Records 236 1022 421420 1115796 %Replaced Data 0.06% 0.02% 40.65% 7.63%
The high frequency of occurrences of the two alternate targets begins around March 5 2009, the extent of which is visible in Figure 11. It is also observed before this date but becomes nearly continuous at this time and onwards into the next phase. To ensure consistency in the interpretation of the range values used for computing ice drafts, the Target1 (Range1) data was redefined as ‘the first target above a threshold range of 7.6m.
Figure 11 . The red line indicates the original Range1 returns. The blue line shows how this was reorganized into a smooth and continuous record,
where the green dots indicate Range2 after applying the switch.
An automated replacement routine was used in Phase 3 and the first half of Phase 4. The automated replacement was not well suited to the data in Phase 2 nor the latter half of Phase 4
14
(after 2009/06/25 00:00:00) so a manual replacement was carried out instead. Phase1 did not contain any ice so no corrections were necessary.
The automated routine was split into 2 steps. The first was to switch larger ranges into Target2 and smaller ranges into Target1. The second step was to fix any wrongful switching that had been done. The newly redefined Target1 was checked for ranges less than the threshold (7.6m) and replace with Target2. This routine was especially helpful during the break up events (~06/09/21 17:00) as seen below in Figure 12.
Figure 12 . Comparison of raw Target1 data (top) to edited Target1 data (bottom) during the ice-break up period.
The cause of the Target Switching was investigated through the use of echoplots, which record the entire length of the acoustic return from a ping (Figure 13). Time progresses from right to left in the figures. In the upper plot of Figure 12, the acoustic return is very strong and exceeds the Start Amplitude, then dips slightly, and then rises again, afterwards falling below the Stop Amplitude. The lower plot in the figure is very similar, except that the signal amplitude after the initial return dips below the stop amplitude. It is then registered as a second target, and due to the longer duration, is recorded as the Range1 Target.
15
Figure 13 . Echoplots capturing the cause of the observed Target Switching. The upper plot shows Target1 as occurring closer, and the lower plot shows Target1 as occurring further away. The dashed green line is the Start Amplitude, the solid red line is the Stop Amplitude, and the dashed red line is the Maximum-Recorded Amplitude. Note the earliest echo returns occur on the right and the later echo returns are on the left.
3.1.5 Instrument Tilts
Tilt magnitudes were smoothed in those segments that contained many spikes or showed lots of noise. Typically ocean data is smoothed with a 30 minute window to remove high frequency wave effects. However the source of noise was more localized and very high frequency, so a 5 minute (15 point) window was found to be sufficient.
3.1.6 First Ice Formation
Ice first appeared October 15 2008, 11:40:08 during a relatively calm and cold period. This was short-lived and broken up by storm activity after which a small amount of ice was again observed. Storm activity then resumed and, accompanied by rising temperatures, the thin ice disappeared by September 21. The lake was ice free up until October 15 10:45:48 when temperatures were again cold enough to allow ice formation. The ice is observed only intermittently as it is broken up by waves and storm activity. It does not become fully established until near midnight on November 3.
The ice reaches a maximum thickness of 1.30m on May 5 2009 at 15:11:20. Ice break-up began June 21 2009 at 18:14 (ice drafts reduce from 0.30m to 0.01m) and the surface is absent of ice by the end of June 26.
16
There are many small changes observed throughout the ice record that correspond to changes in air temperature (evidenced by the model ice growth), precipitation, variations in daily solar insolation. It may also be a result of small changes in water levels.
3.1.7 Sound Speed, Beta Corrections and Ice Draft
There were open water periods throughout phase 1, and through most of phase 2, including those periods between the intermittent formation of ice. These were used to force the ice draft to zero. The final adjusted delta beta curve is in Figure 14 below. These were derived from open water, auto beta calculations, and SWIP bottom temperatures.
Figure 14 . Adjust delta beta curve based on open water segments, auto
beta calculations and SWIP bottom temperatures.
With the beta adjustments applied to the range data, a draft curve was generated. Some minor despiking was required and the four ice draft phases were combined to create a corrected draft curve for the entire deployment. This ice draft curve is presented in Figure 15.
17
Figure 15. Final ice draft curve for 2008-2009 SWIP deployment
Linear fits of each phase of the draft curve were estimated and these rates are listed in Table 4.
Table 4. Ice draft growth rates based on linear fits of phase segments.
Phase Growth rate (mm/hr) Growth or decay 1 Open water Open water 2 7.8 x10-3 Growth 3 3.0 x10-3 Growth 4 14.0 x10-3 Decay
Ice drafts from the SWIP data were converted to total ice thickness based on the density differentials between freshwater and ice (assumed density for freshwater ice 916.8 kg/m3 and liquid freshwater 1000 kg/m3). SWIP total ice thickness was compared against two simple ice thickness equation curves (Zubov 1943; Lebedev 1940)(Figure 16). The two model generated curves were based on freezing degree days and were considering sea ice environments. The Lebedev curve does not consider the insulation effect of snow.
18
Figure 16 . Density corrected ice thickness curve with a comparison to the Lebedev modeled ice thickness based on freezing degree days
For Lebedev’s equation the initial growth is modeled well but the overall thickness is overestimated. This is likely because the equation does not take insulation by snow cover into account.
3.1.8 Water Levels
Another interesting facet of this environment is the water levels. These were plotted to examine the seasonal variations throughout the deployment period. The water level curve displayed in Figure 17 shows some of the common processes associated with lake basins. The fall period had high frequency oscillations likely set up by storm events with precipitation runoff from the steep hills that surround the lake. Starting in early May, a steep influx from snow and ice melt increased the water level by as much as 40 cm. Unlike what is seen in some of the later years of data, water levels do not experience large pulses of water influx during the ice covered months.
19
Figure 17. Water levels for 2008-2009
3.2 Deployment 2 (October 2009 – May 2010)
The 2009 dataset was set up to record data using phase parameters listed in Table 5.
Table 5. Phase Summary
Phase Start date (yyyy/mm/dd) Time (hr:mm:ss) Duration (days) Sample interval (sec)
The rationale for these phase events was primarily to capture the ice formation and break-up processes (phases 2 and 4) at relatively high sampling rates. These high sampling rates were not required for the ice free wave events that typified phase 1 or the slow ice growth rates of phase 3.
3.2.1 Pressure Considerations
As there were no out-of-water data recordings to compare SWIP measured pressure with coincident atmospheric pressure data, it was not possible to correct for pressure drift over the deployment period.
As the SWIP measured pressure requires the removal of atmospheric pressure, a continuous atmospheric pressure record was needed for the entire deployment period. To assemble this curve required careful examination of the data available. A HOBO data logging station was installed on the shores of Tasikallak Lake which provided pressure data for part of the
20
deployment (10/16/2009 12:43 – 01/25/2010 17:59). To fill in the missing intervals required the extraction and comparisons of two Environment Canada weather stations in the area. Kuujjuaq weather station, approximately 200 km away from the SWIP deployment, measured hourly pressure data and the closest station, Kangiqsualujjuaq, about 45 km away, measured hourly pressure from 6 am to 5 pm only. Each pressure dataset was plotted and an overlay was constructed for comparison. The general pressure characteristics of all three curves were largely observed regionally with some variations in amplitude and phase. The Kangiqsualujjuaq dataset was interpolated to produce a continuous 1 hour interval plot for the entire deployment. This curve matched the HOBO data very well and where needed, the HOBO data were graphed into the Kangiqsualujjuaq curve to respect local pressure conditions. Figure 18 illustrates the overlay of these two datasets with the red curve indicating the final hybrid curve used in the removal of the atmospheric pressure from the SWIP pressure data.
Figure 18. Atmospheric pressure curves. The Kangiqsualujjuaq hybrid curve was used to remove atmospheric pressure from the SWIP bottom pressure.
Several interesting features were observed in the datasets that aid in the interpretation of the overall characteristics unique to this deployment period. These are briefly discussed below.
3.2.2 Air Temperature
As the air temperatures vary from year to year and contribute significantly to the presence or absence of ice, it is one factor that must be carefully examined as it provides insights into ice production and decay. This is a basic attribute that is constantly referred to during any interpretive assessments such as first ice formation. As with the pressure data mentioned
21
above, the onsite HOBO weather station also measured air temperatures but not for the entire deployment period. HOBO air temperature data were plotted and several gaps and large spikes were detected. This required a similar approach of comparison to Environment Canada weather data. Figure 19 shows the HOBO data overlaid onto the Kuujjuaq continuous air temperature data. Several values of +139°C, +114°C and -69°C from the HOBO air temperature data were removed from this plot and a number of gaps in the data made it difficult to interpolate a plausible curve based solely on local conditions. As air temperature was not directly used in any of the data processing steps, the Kuujjuaq air temperature was considered a reasonable approximation of air temperatures.
Figure 19. Air temperatures from the onsite HOBO data logger and the Environment Canada Kuujjuaq weather station.
3.2.3 SWIP Bottom Temperature
An important consideration to the first appearance of ice is the water temperature. Although there were no measurements made at the air-water interface, the SWIP unit measured bottom temperatures throughout the deployment. At the beginning of phase 1, the SWIP bottom temperatures were just over 4°C and displayed the over-turning event as they steadily dropped throughout the phase. During phase 2, the SWIP bottom temperature dropped to it lowest value of the winter of 0.5°C on November 6, 2009. Within 1.5 days, an adjustment was made to the stable winter bottom temperature of approximately 1.1°C. This temperature was extremely stable with less than 0.1°C fluctuation until April, 2010 where a slow steady increase was observed that correlated well with an increasing air temperature. During
mid- May, a diurnal oscillation of amplitude in the order of 0.6°C was observed over a sixperiod (May 13-19). Figure 20
Figure 20. Swip bottom temperature record depicting the fall turnlow, stable winter temperature and spring response to thermal heating.
Note that this winter bottom temperature is almost a degree cooler than the One possible explanation for this difference could be related to the average summer air temperatures. The mean air temperature from May 110.0°C. The mean air temperature over the same period of time for the summer of 2009 was 7.0°C. The relatively warmer 2008 summer could have created a greater thermal reserve in the water body resulting in a warmer winter bottom temperature.
May, a diurnal oscillation of amplitude in the order of 0.6°C was observed over a six20 displays the SWIP bottom temperature for the entire deployment.
Swip bottom temperature record depicting the fall turn-over, extreme winter low, stable winter temperature and spring response to thermal heating.
winter bottom temperature is almost a degree cooler than the One possible explanation for this difference could be related to the average summer air
ean air temperature from May 1- Sept 30 in the summer of. The mean air temperature over the same period of time for the summer of 2009 was The relatively warmer 2008 summer could have created a greater thermal reserve in the
water body resulting in a warmer winter bottom temperature.
22
May, a diurnal oscillation of amplitude in the order of 0.6°C was observed over a six-day displays the SWIP bottom temperature for the entire deployment.
over, extreme winter low, stable winter temperature and spring response to thermal heating.
winter bottom temperature is almost a degree cooler than the 2008-2009 season. One possible explanation for this difference could be related to the average summer air
Sept 30 in the summer of 2008 was . The mean air temperature over the same period of time for the summer of 2009 was The relatively warmer 2008 summer could have created a greater thermal reserve in the
23
3.2.4 First Ice Formation
Upon close examination of the range data, ice first appeared to set up on November 3 at about 16:00 and grows for a 26 hour period. This period of slow growth was characterized by relatively calm winds and an average air temperature of -2°C. Immdiately following this brief ice formation event, strong winds induced high frequency waves that had sufficient energy to break-up the thin ice. This storm event persisted for two days during which the average air temperature was _5.3°C. This storm event coincides with the SWIP bottom temperature extreme, suggesting the rapid removal of heat through the agitational mixing caused by the surface waves and below freezing temperatures. On Novemebr 6 at 18:00, the ice sheet sets up again and initially grew quite quickly due to the cold air temperatures that persist below -10°C for 14 hours. It was during this initial growth period that the SWIP bottom temperature stablizes to its winter value.
3.2.5 Sound Speed, Beta Corrections and Ice Draft
Several open water periods were noted during phase 1 and only a few were detected just prior to freeze-up in phase 2. These open water events are used to force the draft to zero. Figure 21 displays the final adjusted delta beta curve which is derived from open water, auto beta calculations and SWIP bottom temperatures.
Figure 21. Adjust delta beta curve based on open water segments, auto beta calculations and SWIP bottom temperatures.
With the beta adjustments applied to the range data, a draft curve was generated. Some minor despiking was required and the four ice draft phases were combined and sub-sampled to create a corrected draft curve for the entire deployment. This ice draft curve is presented in Figure 22.
24
Figure 22. Final ice draft curve for 2009-2010 SWIP deployment.
Linear fits of each phase of the draft curve were estimated and these rates are listed in Table 6.
Table 6. Ice draft growth rates based on linear fits of phase segments.
Phase Growth rate (mm/hr) Growth or decay 1 Open water Open water 2 6.7 Growth 3 3.7 Growth 4 10.0 Decay
Ice drafts from the SWIP data were converted to total ice thickness based on the density differentials between freshwater and ice (assumed density for freshwater ice 916.8 kg/m3 and liquid freshwater 1000 kg/m3). SWIP total ice thickness was compared against two simple ice thickness equation curves (Zubov 1943; Lebedev 1940)(Figure 23). The two model generated curves were based on freezing degree days and were considering sea ice environments. The Lebedev curve does not consider the insulation effect of snow.
25
Figure 23. Density corrected ice thickness curve with Zubov and Lebedev modeled ice thickness based on freezing degree days.
Zubov’s equation represents the over thickness quite well although it underestimates the relatively rapid growth in the initial 0.5 m. The converse is true for Lebedev’s equation as the initial growth is modeled well but the overall thickness is overestimated.
3.2.6 Water Levels
Another interesting facet of this environment is the water levels. These were plotted to examine the seasonal variations throughout the deployment period. The water level curve displayed in Figure 24 shows some of the common processes associated with lake basins. The fall period had high frequency oscillations likely set up by storm events with precipitation runoff from the steep hills that surround the lake. Starting in early May, a steep influx from snow and ice melt increased the water level by as much as 30 cm. There are a number of large pulses worth noting where water levels increased and decreased in the order of 15-20 cm during the ice- covered months.
26
Figure 24. Water levels for 2009-2010.
3.3 Deployment 3 (October 2010 –December 2010) The 2010 dataset was set up to record data during four phases. These data, however, abruptly ended at the close of the first week in December. Table 7 lists the two phases and their setup parameters that were available for processing.
Table 7 . Phase summary
Phase Start date (yyyy/mm/dd) Time (hr:mm:ss) Duration (days) Sample interval (sec)
During the decoding step of this dataset, it became obvious that the thresholds selected for the range targets were set too high in the setup xml parameter file. Figure 25 displays the maximum target strength for each ping in the phase 2 dataset. Note the line drawn on the figure indicates the cutoff threshold where range values are rejected based on the start threshold limit, in this case 12000 counts (amplitude units). As can be seen, this threshold criterion would remove most of the range data and the resultant decoded file would contain mostly flags values. As the maximum amplitude is measured for every ping, this channel was used in the data processing steps. Ranges and ancillary data were plotted for initial assessment.
Figure 25. Amplitude threshold setting for phase 2 data.
3.3.1 Pressure Considerations
Fortunately, there was out-of-water SWIP data at the beginning of phase 1 which allowed for direct comparison of atmospheric pressure to the pressure values measured by the SWIP. Although the HOBO weather station was set up for part of this deployment, data logging did not begin until eight days after the SWIP was submerged. Figure 26 displays the pressure data from Environment Canada’s Kuujjuaq and Kangiqsualujjuaq weather stations as well as pressure values measured by the SWIP. For best comparisons, the SWIP unit must be as close to horizontal as possible to measure the vertical pressure exerted by the atmospheric column. Note the interval where the tilts are near zero. Pressure values from the Kangiqsualujjuaq station and the SWIP data line up almost exactly.
28
Figure 26 . Out-of-water pressure comparisons illustrating the good agreement between SWIP pressure sensor and Kangiqsualujjuaq pressure data.
When looking at the HOBO pressure data starting November 13 and onward, the Kangiqsualujjuaq data maps almost exactly on top of the HOBO data. As these two pressure sensors track both the pressure magnitude and fluctuation responses in unison, it seemed reasonable to assume that since the SWIP and Kangiqsualujjuaq pressure data were almost exact matches during the out-of-water segment and the HOBO and Kangiqsualujjuaq data matched well during the in-water phases, the HOBO pressure sensor data could be used to remove atmospheric pressure from the SWIP data with no offset needed. Figure 27 shows these pressure comparisons for phase 1.
29
Figure 27. Atmospheric pressure comparisons between Environment Canada pressure data and onsite HOBO pressure data.
3.3.2 Air Temperatures
Air temperatures in the fall of 2010 remained for the most part above zero (only brief dips below zero) until the end of the first week in November (Figure 28). This was a considerably warmer fall than the 2007/08, 2008/09 and 2009/10 deployment seasons.
Figure 28. Air temperatures for 2010 SWIP deployment period.
SWIP bottom temperatures had a similar temperature decline, as observed in previous years, throughout the fall, dropping to its lowest value of 0.77°C. This occurred on November 18 at 01:00. Temperatures slowly leveled out to a stable winter bottom temperature of 1.2°C by November 24 at 19:00. Figure 29 displays the SWIP bottom temperature decline until the established stable winter bottom temperature.
Figure 29. SWIP bottom temperatures for 2010 deployment period.
3.3.4 First Ice Formation
Phase 1 range data was characterized by high frequency wave events and both air and bottom temperatures were too high to support ice formation. Air temperatures in November appear to have had an oscillation of two days above zero followed by two days below zero ranging through +5°C to -5°C until November 16. On the November 16, the temperature dropped again and only briefly rose to above zero for about 3 hours before it again dropped below zero. The below zero air temperature and the lowest bottom temperature occurring on the 18th created ideal ice formation conditions. It is thought that the first ice appeared on November 18th at about 01:00. Zooming in on plots of the range data at this point in time confirmed the appearance of the first discernible ice formation.
Upon closer examination of the range data during the ice covered portion of phase 2, the data appeared to be quite noisy with stepping occurring in the order of 10-20 cm record to record
SWIP Phase 2 Bottom Temperature (2010)
0
1
2
3
4
5
6
7
October 7, 2010 October 17,
2010
October 27,
2010
November 6,
2010
November 16,
2010
November 26,
2010
December 6,
2010
Date
Tem
pera
ture
(deg
C)
Phase 1
Phase 2
31
even in very thin ice conditions. The use of maximum amplitude (used here as xml thresholds set too high) to determine the range is not as precise as an appropriately defined threshold value based on target strength. Fortunately, the SWIP recorded acoustic profiles every 3 minutes during this phase. A program was written to extract ranges from the profiles by setting a user-defined threshold. The defined thresholds used to extract new ranges from profiles were as follows:
The assumed sound speed selected here was based on the bottom temperature range within this phase of +3.5°C to 1.2°C which represent a sound speed variation of 1419.8 to 1409 m/s based on the 4th order polynomial derived from Figure 5.
Although this produced better quality data, the sampling interval dropped from 2 seconds to 3 minute pings. As ice growth in lakes typically is characterized by slow growth and decay, it was thought that this reduced sampling interval would not adversely affect the final ice draft curve.
3.3.5 Sound Speed, Beta Corrections and Ice Draft
The re-processed range file extracted from the ping profiles was saved as an csv file and an ASL format file was constructed and despiked. This edited range file was converted to an uncorrected draft file using the ASL calculate draft program (ASL_CalculatedDraft). This first look at draft required beta corrections as open water segments were not at zero draft. Calm open water events were identified and an adjustment beta curve was constructed and is illustrated in Figure 30. As the sound speed used in the range extraction process was reasonable based on the temperature ranges, the adjustments to the uncorrected drafts were relatively minor.
32
Figure 30. Beta correction curve for SWIP 2010 deployment.
The draft file was combined with the SWIP bottom temperature to produce a 3 minute corrected draft file. This file was extracted and an Excel spreadsheet was constructed that contained the following channels: Time, Draft, SWIP bottom Temperature, Sound Speed (based on SWIP bottom temperature) and Adjusted Draft. The adjusted draft was calculated using the following formula:
Corrected draft = Uncorrected draft x Calculated sound speed based on bottom temperature Assumed sound speed
Draft adjustments were made to the ice covered segments of the data to produce the following ice draft curve (Figure 31).
Figure 31. Ice draft curve for 2010 SWIP deployment.
This curve displays a rapid initial growth reflecting the impact of the relatively cold air temperatures. In fact, the ice growth curve is well correlated by the fluctuations in air temperatures showing responses in growth during below zero temperatures and decay when temperatures increase to below zero. The maximum ice draft of 18 cm occurred December 1 at 14:00.
Using Lebedev`s ice thickness model, ice growth and decay based on freezing degree days produces a somewhat smoothed curve similar to the SWIP derived ice thickness (Figure 32). Note that the ice draft was converted to total ice thickness based on the density differential between liquid freshwater and ice.
Figure 32. Lebedev`s ice thickness model based on freezing degree days.
3.3.6 Water Levels
The water levels were plotted in Figure 33. Water levels drop about 12-15 cm between the start of phase 1 to first ice. Note the dip in the water level November 18.This coincides with the first ice formation. This dip spans 1.25 days with amplitude of about 6 cm.
4. Summary and Conclusions A shallow water ice profiler was used to collect ice draft data from a lake in northern Quebec for three deployments spanning September 2008 through till December 2010. The datasets were generally of high quality and processing steps have been briefly described along with unique details for each deployment year. The SWIP unit, along with the acoustic transducer that measures instrument to ice range or water surface, was equipped with a series of sensors that provided pressure, temperature and tilt data. These ancillary data were useful for converting acoustic ping data to ice drafts. The processed data revealed the ice growth and decay during the deployment seasons, as well as providing interesting details about ice formation and water level anomalies. . Final ice draft curves are presented which show inter-annual variations in thickness. Weather data also varied significantly year to year.
5. References Fissel, D.B., J.R. Marko and H. Melling, 2008. Advances in upward looking sonar technology for studying the processes of change in Arctic Ocean ice climate. Journal of Operational Oceanography: 1(1), 9-18.
Jasek, M., J.R. Marko, Fissel, D., Clarke, M., Buermans, J., Paslawski, K., 2005 Instrument for detecting freeze-up, mid-winter and break-up processes in rivers. In Proceedings of 13th Workshop on Hydraulic of Ice-Covered Rivers (sponsored by CGU HS Committee on River Ice Processes and the Environment), Hanover, NH. 34p.
Lebedev, V. V. 1940. New formulas on the growth of ice in arctic rivers and seas. Meteorologiya i Gidrologiya 6:40-51.
Marko, J. and M. Jasek, 2008. Acoustic detection and study of frazil ice in a freezing river during the 2007-2008 winter. 19th IAHR International Symposium on Ice, Vancouver, Canada.
Melling, H., P.H. Johnston and D.A. Riedel, 1995. Measurements of the underside topography of sea ice by moored subsea sonar. J. Atmospheric and Oceanic Technology 13, 589-602
Urick, R.J. 1983. Principles of Underwater Sound, Third Edition. McGraw-Hill Inc., New York, 423pp.
Zubov, N. N., 1943: Arctic Ice (in Russian). Glavsevmorputi, 360 pp. (English translation, Transl. 103, U.S. Navy. Electr. Lab., San Diego, CA.)
![Deployment Package – Software Requirements Analysis€¦ · Web viewThe word deployment is sometimes used to mean the same thing [crm.com]. The importance of having this process](https://static.documents.pub/doc/80x56/5fc75810c2a2c80ee8535973/deployment-package-a-software-requirements-analysis-web-view-the-word-deployment.jpg)
