January 13, 2015 2014 Temperature Monitoring Project Report 230 Main Street Bridgton, ME 04009 207-647-8580 [email protected] Lakes Environmental Association
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January 13, 2015
2014 Temperature Monitoring
Project Report
230 Main Street Bridgton, ME 04009
207-647-8580
Lakes Environmental Association
2
LEA began using in-lake data loggers to acquire high resolution temperature measure-
ments in 2013. We expanded in 2014 to include 15 basins on 12 lakes and ponds in the Lakes
Region of Western Maine. The loggers, also known as HOBO temperature sensors, allow us to
obtain important information that was previously out of reach because of the high cost of man-
ual sampling. Using digital loggers to record temperature gives us both a more detailed and
longer record of temperature fluctuations. This information will help us better understand the
physical structure, water quality, and extent and impact of climate change on the waterbody
tested.
Most of the lakes tested reached their maximum temperature on July 23rd. Surface tem-
perature patterns were similar across all basins. The date of complete lake mixing varied con-
siderably, with shallower lakes destratifying in September and others not fully mixed until No-
vember. Differences in yearly stratification were seen in lakes with HOBO sensor chain data
from 2013 and 2014. A comparison with routine water testing data confirmed the accuracy of
the HOBO sensors throughout the season. A month-by-month comparison of temperature pro-
files in each lake showed the strongest stratification around the time of maximum temperature,
in late July. In addition, August stratification was stronger than June and early July stratifica-
tion. Shallow sensors showed similar average temperatures between 2013 and 2014.
Project Summary
Deployment of temperature sensors on Trickey Pond.
3
Because of its role in physical, chemical, and biological processes, temperature is an important and in-
formative lake measurement. In order to get a better idea of temperature patterns in and between lakes, LEA
began monitoring lake temperature using in-lake digital data loggers in 2013. These loggers, also known as
HOBO sensors, are programmed to record temperature readings every 15 minutes. The sensors are deployed in
the spring and the data is stored on the sensors until they are retrieved in the mid to late fall.
LEA serves six towns in western Maine, providing comprehensive lake monitoring for 40 lakes and
ponds. This comprehensive testing includes meas-
urements of temperature profiles using a handheld
YSI meter. This method is time consuming, resulting
in at best 8 temperature profiles per year. Using
HOBO sensors there is an initial time investment,
but once deployed, the sensors record over 12,000
profiles before they are removed in the fall.
This wealth of data provides much greater
detail and clarity than the traditional method ever
could. Daily temperature fluctuations, brief mixing
events caused by storms, the date and time of strati-
fication set up and breakdown, and the timing of
seasonal high temperatures are all valuable and informative events that traditional sampling can’t measure.
The measurements these sensors record allow us to infer the effect of temperature on diverse lake char-
acteristics such as stratification (lake layering), ecology, habitat, and nutrient loading. In addition, comparing
temperature data over a number of years allows us to make observations about climate change in our region.
During the first season of testing in 2013, four basins on three lakes (Highland Lake, Moose Pond, and
2 sites on Long Lake) contained sensors at 2 meter intervals measuring the entire water column. Nine addi-
tional lakes contained one sensor in a shallow (littoral) area. All sensors were attached to a rope held in place
by an anchor and a sub-surface buoy.
For the second season of testing in 2014, a total of 16 sites were tested (figure 1, next page). Thirteen
basins at ten lakes and ponds contained sensors measuring the entire water column (table 1, page 5). Three ad-
ditional sensors measured shallow temperature on two ponds. The locations of deep water sensors were clearly
marked by regulatory-style buoys.
Lake Stratification
Most of the lakes LEA tests become stratified in the summer. This means that the lake separates into dis-
tinct layers – the epilimnion, metalimnion and hypolimnion – based on temperature and water density. The top
layer, the epilimnion, is the warmest. Somewhere in the middle, there will be a large temperature and density shift.
This is known as the metalimnion and it defines the location of the thermocline. The hypolimnion contains the cold-
est water and reaches from the bottom of the metalimnion to the bottom of the lake.
Each lake’s stratification is unique and is affected by weather, as well as the lake’s size, depth, and shape.
Stratification sets up in the Spring and breaks down in the Fall. “Lake turnover” refers to the destratification of a
lake, when the water completely mixes and the temperature becomes uniform from top to bottom.
Introduction and Background
“The data collected by these temperature
sensors provides much greater detail and
clarity than the traditional method ever could.
Daily temperature fluctuations, brief mixing
events caused by storms, the date and time of
stratification set up and breakdown, and the
timing of seasonal high temperatures are all
valuable and informative events that
traditional sampling can’t measure.”
4
Figure 1. Map of temperature sensor sites. Yellow circles indicate shallow sensor placement; green squares show
the location of multi-sensor temperature buoys.
5
Thirteen sites on ten lakes
were outfitted with a marked,
regulatory-style buoy attached by
rope to an anchor (figure 2). The
HOBO sensors (figure 3) were
attached to the rope at 2 meter in-
tervals, beginning 1 meter from
the bottom and ending approxi-
mately 1 meter from the top. Each
buoy apparatus was deployed at
the deepest point of the lake or
basin it monitored. The setup re-
sults in the sensors being located
at odd numbered depths through-
out the water column (the shal-
lowest sensor is 1 meter deep, the
next is 3 meters, etc.).
Two additional ponds, Pea-
body and Stearns, contained tem-
perature sensors at shallow loca-
tions. These were attached to a
small mooring buoy and deployed
so that they were located approxi-
mately 1 meter below the surface
of the water, at a total depth of
approximately 3 meters.
Temperature sensors were de-
ployed between June 6th and July
9th, 2014 and collected between
October 30th and November 25th,
2014.
Each sensor was configured to
take temperature readings at 15
minute intervals. This results in 96
readings from each sensor every
day, and thousands of readings
during the course of deployment.
Sampling Methods
Name Midas # Location Type # sensors
Back Pond 3199 Main basin Deep 5
Hancock Pond 3132 Main basin Deep 9
Island Pond 3448 Main basin Deep 6
Keoka Lake 3416 Main basin Deep 6
Long Lake 5780 North basin Deep 9
Long Lake 5780 Middle basin Deep 9
McWain Pond 3418 Main basin Deep 6
Moose Pond 3134 North basin Deep 3
Moose Pond 3134 Middle basin Deep 11
Moose Pond 3134 South basin Deep 6
Peabody Pond 3374 Western shore Shallow 1
Peabody Pond 3374 Outlet Shallow 1
Sand Pond 3130 Main basin Deep 7
Stearns Pond 3234 Western shore Shallow 1
Trickey Pond 3382 Main basin Deep 8
Woods Pond 3456 Main basin Deep 4
Table 1. Details of HOBO temperature data logger deployment, including
lake/pond name, location of sensor string, type of deployment, and number
of sensors per string.
Figure 3. (Below) A HOBO
temperature sensor.
Figure 2. (Left) Diagram
showing the buoy apparatus
with temperature sensors
attached.
6
Out of a total of 92 sensors deployed, only four were unable to provide data. Two sensors
failed due to water intrusion, one was dislodged from the buoy line and lost, and a fourth sensor re-
corded faulty data. This section will compare data from a variety of lakes. For a report focusing on
temperature data from a specific lake, please see the Lake Informa-
tion page on our website, www.mainelakes.org, and click on the lake
you want to know more about.
There were a few temperature patterns that were common
across all basins in 2014, although they varied in intensity. This simi-
larity makes sense because surface temperature is affected strongly
by the weather, and these lakes and ponds have similar weather pat-
terns due to their proximity to one another. Figure 4 (next page)
shows the complete dataset for Hancock Pond, with each sensor’s
data graphed in a different color. It labels the common patterns seen across most of the lakes. Figure 5
is a more visual representation of the same data from McWain Pond, showing the thermal dynamics of
the lake over time.
A sharp spike in surface temperature was seen on all basins on July 2nd and/or 3rd. This was, in
most cases, the second highest temperature reading for the season. The spike occurred around the time
of heavy storms on the 3rd, so it is reasonable to assume the storm caused the subsequent cooling of
water temperatures. Another sharp spike in temperature was not seen, though temperatures did gradu-
ally increase over the month of July. The data showed that most basins reached their maximum tem-
perature on or around the same date, which was July 23rd.
The beginning of seasonal temperature decline across all basins occurred around the 8th and 9th
of September. This date marked the beginning of a steady drop in temperature leading to lake turn-
over. There were two brief warm periods where calm conditions allowed for slight water restratifica-
tion, these being between the 27th and 29th of September and the 17th and 18th of October.
Results and Discussion
General Patterns in 2014
Individual reports for
each lake can be found
on the Lake Information
page of our website,
www.mainelakes.org
7
Results and Discussion
Figure 4. Graph of 2014 temperature data in Hancock Pond from 6/24 to 11/11. Each line represents a different sensor’s data; the top-
most line is from the sensor at 1 meter, etc. This graph is representative of data from other lakes with HOBO temperature monitoring.
Figure 5. Heat map of McWain Pond showing temperature variation in the water column over time. The Y-axis represents with depth
from the surface, with the top of the graph representing the top of the lake. Stratification deepens over time (block of color extends
further down) until it breaks down. The uniform color on the right side of the graph indicates lake mixing.
Temperature Peak
Beginning of temperature
decline
Slight Restratifica-
tion
Lake Turnover
Temperature Spike
8
Results and Discussion
Lake Turnover
Measurements such as the date of lake turnover (See “Lake Stratification” box), are dependent on
an individual lake’s characteristics and will therefore vary more widely than surface temperature patterns.
The earliest destratification (“turnover”) occurred on September 12th, with the latest occurring on Novem-
ber 3rd. Shallower lakes, such as Woods Pond, and those that are exposed to heavy winds, like Long Lake,
mixed the earliest. The smaller and/or deeper lakes destratified later. Interestingly, most lakes turned over
during one of three 2-day periods, either in mid-September, mid-October, or the beginning of November.
Precipitation records from NOAA Climate Data Online (www.ncdc.noaa.gov) show significant rainfall
over the days when destratification occurred on lakes in October and November, and a large precipitation
event a few days before the destratification of a number of lakes in mid-September. This rainfall likely
weakened stratification, allowing for lake turnover a few days later. Wind speed data were not available,
though it is reasonable to assume that winds played a factor in the timing of turnover.
Monthly Temperature Profiles
Monthly HOBO sensor temperature pro-
files from each basin were also graphed to show
how stratification changed over time (figure 6).
These profiles showed that late July had the
strongest stratification, followed by late August,
June, and September. The pattern, which shows
the strengthening and weakening of stratifica-
tion over time, was similar for most of the ba-
sins tested.
Figure 6. Monthly temperature profiles from Moose Pond, showing the changes stratification at monthly intervals between June and
October. Stratification is strongest in late July, as evidenced by the large temperature difference between the surface (at the top of the
graph) and the bottom (bottom of the graph).
9
Three basins contained strings of sensors in 2013 and 2014. These were Moose Pond’s main basin
and the north and middle basins of Long Lake. In each of these basins, the maximum temperature was
higher in 2013 than in 2014, with a clear “spike” in temperature, unlike the more gradual peak in 2014.
Each basin mixed completely around the same date in 2013 as in 2014, although in all cases the 2014 turn-
over was slightly earlier.
Both Moose Pond and the north basin of Long Lake had much more pronounced daily temperature
fluctuation than in 2013. In 2014, the temperature at 7 meters depth on Moose Pond often fluctuates by 5 oC or more in a single day, whereas in 2013 the difference is rarely more than 2 oC (figure 7, next page).
One reason for this difference may be the new buoy setup. Slack in the line allows the buoy to be moved
by wind and water currents, changing the sensors’ depths slightly, which then impacts the temperature. In
2013, the buoys were under the surface of the water, which kept the sensor lines relatively straight. Graph-
ing daily averages for 2014 (figure 7, bottom) allows for a clearer picture of temperature patterns over the
season, although compared to 2013 daily averages (not shown) there is still much more variability in the
2014 data.
Both basins of Long Lake showed differences between 2013 and 2014 sampling. The north basin’s
bottom temperature was consistently about 0.5 oC cooler in 2014. This may be the result of earlier stratifi-
cation in 2014, or it could be related to a difference in the location of the temperature sensors from year to
year. In the middle basin there was less of a difference between the top and bottom temperatures in 2014
compared to 2013, possibly a result of a later date of stratification. Because the middle basin is exposed to
more mixing than other basins, it makes sense that stratification would occur later. This is also evident in
the much warmer bottom temperatures commonly seen in this basin.
Moose Pond appeared to have deeper stratification in 2014 than 2013, which is corroborated by the
water testing data. A sample of epilimnetic (top layer) water known as a core sample, the depth of which
usually indicates the position of the thermocline, was on average deeper in 2014 than in 2013. This is also
the case, albeit less pronounced, in Long Lake’s middle basin. The average core depth for Long Lake’s
north basin was the same in both years.
Results and Discussion
Deep Sensors: Year-On-Year Comparisons
10
Results and Discussion
Figure 7. Moose Pond data from 2013 (top) and 2014 (middle) show much more daily variability in 2014. Averaging each day’s
2014 temperature readings (bottom) dampens this variability and gives a clearer picture of temperature patterns.
(Note: this spike
was caused by
temporary sensor
removal and
should be ignored)
11
The data from each chain of temperature sensors were compared to LEA’s temperature data from
traditional manual water testing. Routine lake monitoring is done on 24 basins bi-monthly in the summer,
and temperature data is collected with a handheld YSI probe at every meter from the top of the lake to its
deepest point. The resulting temperature profiles were graphed. In all cases, both the sensor and monitor-
ing profiles followed the same curve. However, in 5 out of the 12 basins compared, there was a discrep-
ancy in the depth of the sensors, evidenced by the two curves not being aligned with one another (figure
8).
The YSI handheld probe used for routine water testing is attached to a long cable which is marked
every meter. Therefore, we know that the water testing depth is accurate as long as the cable remains
straight in the water. The temperature sensor buoy placement is based on the known lake depth, however
the actual depth of the sensors is difficult to determine and will never be exact due to buoy line movement,
anchor shift, water level fluctuation, and wind/current effects. It is likely that in the 5 basins where a dis-
crepancy was found that the sensors were actually deeper or shallower than was estimated.
When adjusted for depth, the temperatures from the HOBO sensors and manual water testing were
always within 3 oC of each other, and most often within 0.5 oC. This level of accuracy is acceptable con-
sidering the numerous sources of error associated with the set up, including the accuracy of the HOBO
sensors, the calibration of the YSI probe, and the inevitable differences in the depth, location and position
of the sensors vs. the probe at the time of testing.
Results and Discussion
Comparison with Water Testing Data
Figure 8. Graph of water testing data (blue
line) versus HOBO sensor data (red squares
and green triangles). The sensor data fits the
water testing data much better if the assumed
sensor depth is decreased by one meter. This
indicates that the sensors were closer to the
surface of the water than assumed.
12
Results and Discussion
Shallow Sensor Data
The two ponds with shallow sensors, Peabody Pond and Stearns Pond, also contained sensors in 2013. In
each pond, the 2013 and 2014 averages were very similar (±0.1-0.2 oC) over the June – October period
analyzed (figure 9). Both ponds had higher maximum temperatures in 2013. The minimums were similar
between 2013 and 2014 in both ponds (±0.1 in Peabody Pond and ±0.7 in Stearns). Both ponds had similar
temperature patterns in both years, with a distinct temperature peak in 2013 and a more level period of ele-
vated temperatures in 2014 occurring in mid-July.
Peabody Pond’s second shallow water sensor, located near the outlet of the pond, had much more
variable temperatures than the western shore sensor, though the average temperatures differed by a frac-
tion of a degree. This sensor was closer to the water’s surface and in a more open location, giving it more
exposure to sunlight than the sensor near the shore and likely causing the higher variability.
Figure 9. Comparison of Stearns Pond shallow temperature data in 2013 (black line) and 2014 (blue line). The maximum tempera-
ture was higher in 2013, however the average over the course of deployment was almost identical.
13
Overall, 2014 HOBO sensor temperature monitoring was successful. The new buoy setup worked
well and most of the materials can be re-used in 2015. Buoy line slack appeared to be an issue this season,
causing excessive variability in temperature readings. Next season we will reduce the amount of slack
each line is given. Next summer, LEA plans to continue sampling in all of the basins monitored in 2014.
Additionally, we will deploy a new temperature buoy in Keyes Pond.
We hope to deploy the buoys earlier in the spring of 2015 if possible. This would allow us to deter-
mine the date of the onset of stratification, another important lake measurement. We are also planning to
use more weather data, including rainfall, wind speed and wind direction measurements, in our analysis.
This data will come from our weather station, which was purchased this year as part of our remote sensing
buoy project.
Additionally, LEA gratefully acknowledges the help and expertise we have received from Dr. Dan
Buckley at the University of Maine, Farmington over the course of this project.
LEA Would Like to Thank...
Five Kezar Ponds Watershed Association
Hancock and Sand Ponds Association
Island Pond Association
Keoka Lake Association
McWain Pond Association
Moose Pond Association
Peabody Pond Association
Residents of Woods Pond
Trickey Pond Association
An anonymous family foundation
and all of our members
…for making this project possible with their generous support!
Looking Ahead to 2015
14
230 Main Street Bridgton, ME 04009
207-647-8580
Lakes Environmental Association
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