2015
Temperature Monitoring
Project Report
February 16, 2016
Lakes Environmental
Association
230 Main Street
Bridgton, ME 04009
207-647-8580
lakes@leamaine.org
Project Summary
LEA began using in-lake data loggers to acquire high resolution temperature measurements in 2013. We expanded in 2014 to include 15 basins on 12 lakes and ponds in the Lakes
Region of Western Maine and again in 2015, adding 2 more deep-water strings of loggers. The
loggers, also known as HOBO temperature sensors, allow us to obtain important information
that was impossible to collect through manual sampling. Using digital loggers to record temperature gives us both a more detailed and longer record of temperature fluctuations over time.
This information will help us better understand the structure, water quality, and extent and impact of climate change on the waterbody tested.
In 2015, the sensors recorded temperature readings from the spring through the fall.
Most of the lakes tested reached their maximum temperature between August 17th and August
20th. Surface temperature patterns were similar across all basins. The date of complete lake
mixing varied considerably, with shallower lakes destratifying in September and others not fully mixed until November. Similarities in yearly stratification were evident when comparing
this year’s data to the previous two years. A comparison with routine water testing data confirmed the accuracy of the HOBO sensors throughout the season. Keoka Lake’s two sets of
sensors demonstrated how results vary due to sensor placement and the number of sensors
used. Finally, a shallow sensor in Peabody Pond showed 2015 was a warmer summer than the
previous two years. Additional shallow sensors in Keoka Lake and Woods Pond had similar
patterns to Peabody Pond.
Deployment of temperature sensors on Trickey Pond
2
Introduction and Background
Because of its role in physical, chemical, and biological processes, temperature is an important and informative 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 measurements of temperature profiles using a handheld YSI meter.
This method is time consuming, resulting in at best 8 temperature profiles per year. While temperature sensors require an initial time investment, once deployed, the sensors record over
The data collected by these
15,000 profiles before they are removed in the fall.
temperature sensors provides much
greater detail and clarity than the
This wealth of data provides much greater detail and clartraditional method ever could.
ity 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.
The data collected by the sensors allow us to infer the effect of temperature on diverse lake characteristics such as stratification (lake layering), ecology, habitat, and internal nutrient dynamics. 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 additional lakes contained one sensor in a shallow area. For the second season of testing in 2014, a total of 16 sites
were tested. Thirteen basins at ten lakes and ponds contained sensors measuring the entire water column.
Three additional sensors measured shallow temperature on two ponds. The locations of deep water sensors
were clearly marked by regulatory-style buoys.
The 2015 setup was similar to 2014, with two strings of sensors added—one in Keyes Pond and another in the south basin of Long Lake, and a few changes to shallow temperature sensor placement (see Figure 1
and Table 1 on pages 4 and 5).
Lake Stratification
Most of the lakes LEA tests become stratified in the summer. This means that the lake separates into distinct layers – the epilimnion, metalimnion and hypolimnion – based on temperature and water density. The top layer, the epilimnion, is the warmest. Somewhere in the middle is a region where the temperature and density change
rapidly. This is known as the metalimnion and it defines the location of the thermocline. The hypolimnion contains
the coldest 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.
3
Figure 1. Map of temperature sensor sites. Yellow circles indicate shallow sensor placement; green squares show the location of multi-sensor temperature buoys. The red icon shows the location of our automated testing buoy on Highland Lake, and the orange triangle marks the second buoy set-up on Keoka Lake which has sensors located every meter in the water column.
4
Sampling Methods
Fifteen sites on eleven lakes
and ponds were outfitted with a
regulatory-style buoy attached by
rope to an anchor (figure 2). The
HOBO sensors (figure 3) were
attached to the line at 2 meter intervals, beginning 1 meter from
the bottom and ending approximately 1 meter from the top. Each
buoy apparatus was deployed at
the deepest point of the lake or
basin it monitored. The setup results in the sensors being located
at odd numbered depths throughout the water column (the shallowest sensor is 1 meter deep, the
next is 3 meters, etc.).
Peabody Pond, Keoka Lake,
and Woods Pond also contained a
temperature sensor located in a
shallow area of the lake. Keoka
Lake had a second deep sensor
string near the first, with HOBO
sensors located at 1-meter intervals. It was marked by a small
mooring buoy rather than a large
marker buoy.
Temperature sensors were deployed between April 27th and
July 15, 2015 and collected between September 21st and November 10, 2015.
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.
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 (3)
3416
Main basin/west
shore
Deep/Shallow
Keyes Pond
3232
Main basin
Deep
6
Long Lake
5780
North basin
Deep
9
Long Lake
5780
Middle basin
Deep
8
Long Lake
5780
South basin
Deep
8
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
Sand Pond
3130
Main basin
Deep
7
Trickey Pond
3382
Main basin
Deep
9
Woods Pond (2)
3456
Main basin
Deep/Shallow
6/12/1
4/1
Figure 2. (Left) Diagram
showing the buoy apparatus
with temperature sensors
attached.
Figure 3. (Below) A HOBO
temperature sensor.
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.
5
Results and Discussion
General Patterns in 2015
There were a number of common patterns in the temperature data collected in 2015 (Figures 4 and
5, next page). The magnitude and exact timing of the events differ, but they are present in all of the lakes
tested. We were able to catch the onset of stratification in data from the first buoys that were deployed.
They showed stratification beginning in late April. Two events, around May 13th and May 23rd, caused a
temporary breakdown of stratification and re-mixing of lake water
throughout much of the water column of many lakes. In some lakes, particularly in the south basin of Long Lake, these events caused a significant warming of water at the bottom of the lake. Some lakes also show
less dramatic mixing events around June 6th and again on June 25th.
The overall temperature trend is that of a gradual increase from
May through early July. There was an early spike in water temperature
Individual reports for
each lake can be found
in LEA’s 2015 Water
Testing Report on our
website,
www.mainelakes.org
in late May. A warm period began in mid-July, when there were a series
of temperature spikes culminating in the peak in temperature on almost all basins occurring in mid- to late
August, almost a month later than in 2014 (Table 2). This warm period lasted approximately 2 months.
Beginning in mid-September, surface waters began to cool and deeper, cooler water started to mix in. This
is similar to the timing in previous years, despite the fact that air temperatures were unusually warm
throughout much of the fall. The date of full mixing across the lakes tested differed greatly based on basin
size and shape, ranging from mid-September to early November (See table 3 on page 12).
The maximum water temperatures in 2015, on average, were higher than in 2014 by 0.6 oC. In
comparing the data from the shallow temperature sensor on Peabody Pond from June 17 to September 24
of the past three years, the average temperature was 0.8 oC higher in 2015 than over the same period in
2014, and 0.6 oC higher than the 2013 average. Water temperatures were higher in the latter half of the
summer in 2015 compared to the last two years, as is evident in the graph comparing yearly temperature
patterns in Peabody Pond (See Figure 11 on Page 16).
For detailed reports on individual lakes’ temperature data, please refer to LEA’s 2015 Water Testing Report, which can be found at our website, www.mainelakes.org.
6
Results and Discussion
Temperature Peak
Beginning of cooling and
mixing period
Temperature Spike
Mixing
throughout
the water
column
Lake fully mixed
Warming at depth
Figure 4. Graph of 2015 temperature data from McWain Pond, recorded between 5/8 and 10/27. Each line represents a different sensor’s data; the topmost line (red) is data from the sensor at 1 meter below the water surface, the orange line represents 3 meters below
the surface, and so on. 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.
7
Results and Discussion
Deep Sensor Data
Table 2. Date when the maximum temperature was reached at 1 meters’ depth on each lake, 2013—2015.
*Data from Highland Lake in 2014 and 2015 are from LEA’s Automated Testing Buoy.
Date of Peak Temperature by Year
LAKE
2013
2014
2015
Back Pond
N/A
7/23
8/20
Hancock Pond
N/A
7/24
8/29
Highland Lake
7/18
7/23*
8/18*
Island Pond
N/A
7/23
8/21
Keoka Lake
N/A
7/17
8/19
Keyes Pond
N/A
N/A
8/19
Long Lake North
7/18 & 7/20
7/23
N/A
Long Lake Middle
7/17 & 7/18
7/18
8/19
Long Lake South
N/A
N/A
8/17
McWain Pond
N/A
7/23
8/19
Moose Pond Main
7/19
7/23
8/17
Moose Pond North
N/A
7/3
8/20
Moose Pond South
N/A
7/3
8/17
Sand Pond
N/A
7/24
8/20
Trickey Pond
N/A
7/24
8/20
Woods Pond
N/A
7/23
8/18
The main driver of surface water temperature is air temperature. This is why similar patterns are
seen across many of the lakes in the Lakes Region – they are close enough to each other to be affected by
the same weather patterns. Changes in ambient air temperature directly influence the temperature of sensors located one meter from the water surface. As the water gets deeper, it is more cut off from the surface, and therefore less affected by air temperature.
The general patterns seen in 2015 seem to be mainly due to temperature changes. Precipitation and
wind associated with storms did not appear to directly cause mixing events seen in the early part of the
season nor destratification at the end of the season, although they certainly contributed to the magnitude
and timing of these events.
8
Results and Discussion
The mixing events that occurred in May were seen across a number of lakes to varying degrees.
Looking at wind, temperature, and precipitation data on and around the mixing event on May 23rd, it appears that the mixing was mainly due to a drop in air temperature. There was no precipitation or high
winds immediately preceding the mixing. Because it was early in the season, stratification was not very
stable or strong. The lower air temperature meant that the surface waters did not heat up, causing internal
currents to overcome any density difference and allowing the waters to mix.
Stratification and temperature patterns within lakes seemed to be similar between 2014 and 2015,
although the exact temperatures and surface temperature patterns differed. The upper waters stayed warm
longer into the summer of 2015, staying relatively steady through August, whereas in 2014 the surface
temperature dropped after the peak in July. While later season temperatures were warmer than average,
the date of full mixing was similar to the previous year on many lakes (Table 3). A few basins were exceptions – Long Lake’s middle basin, Hancock Pond, Moose Pond’s north basin, and Woods Pond all mixed
more than a week later than in 2014.
The average temperature of the 1 meter deep sensor was slightly warmer in 2015 than in 2014 (and
2013, where applicable) in each of the lakes tested. This is a little misleading, however, because in most
cases these averages only take into account the temperature between June and late October or early November, so they do not include the cooler spring 2015 temperatures. Nonetheless, this shows that the temperature, on average, was similar to previous years, even if the spring was cooler than normal and the fall
was warmer than normal.
A comparison of Sand Pond data (Figure 6) shows the similarity of temperature patterns within the
lake between 2014 and 2015. However, the graphs of Moose Pond data from 2014 and 2015 (Figure 7)
show the thermocline being somewhat deeper in 2015 (around 7 meters for much of the summer versus 56 in 2014). Most of the other lakes tested, like Sand Pond, had similar temperature patterns at depth in
both years. Additionally, manual testing data show that the average thermocline depth on Moose Pond
was similar in both years.The reason the HOBO data suggest a deeper thermocline in Moose Pond in 2015
is likely due to the placement of the sensors themselves. Moose Pond’s bottom temperatures are warmer
in 2015, suggesting a less deep location. Also, the data from the sensor that is supposed to be at 1 meter
deep shows a lot of spikes in daytime temperatures across the season, which suggests that the sensor was
heating up due to solar radiation, and was therefore close to the surface. Comparing manual testing data to
the sensor data (see next section) suggests that the sensors on Moose Pond are about 0.5 meters shallower
than intended.
Changes in the temperature at the bottom of a lake from year to year can be indicative of changes
in ice out date and the timing of stratification. They can also be affected by changes in sensor placement
from year to year, as seen on Moose Pond. In general, the bottom temperatures in each of the lakes tested
were similar to those of 2013 and 2014, with some being warmer in 2015 and others being slightly cooler.
9
Results and Discussion
Figure 6. Sand Pond data from 2014 (top) and 2015 (bottom) with the same scales on the x and y axes. Corresponding depth lines are
have the same coloring in each graph. While the surface temperature patterns differ, the placement of the lines is similar, indicating a
similar stratification pattern in both years.
10
Results and Discussion
2014
2015
Figure 7. Moose Pond data from 2014 (top) and 2015 (bottom) with the same scales on the x and y axes. Corresponding depth lines
have the same coloring in each graph. These graphs show stratification appearing to be deeper in 2015, although the spikiness of the
1 meter data (red line) suggests the sensors were closer to the water surface than intended.
11
Results and Discussion
Table 3. Date when lakes destratified, by year. When “after” a certain date is specified, this indicates that sensors were removed before full mixing occurred. The date stated is when the sensors were removed. *Data from Highland Lake in 2014 and 2015 are from
LEA’s Automated Testing Buoy.
Date of Fall Turnover (Complete Mixing) by Year
LAKE
2013
2014
2015
Back Pond
N/A
after 10/25
10/26
Hancock Pond
N/A
11/3
after 11/10
Highland Lake
after 10/11
10/12*
10/11*
Island Pond
N/A
11/2
after 10/27
Keoka Lake
N/A
10/22
10/23
Keyes Pond
N/A
N/A
10/26
Long Lake North
10/25
10/23
N/A
Long Lake Middle
9/16
9/12
9/28
Long Lake South
N/A
N/A
10/11
McWain Pond
N/A
10/19
10/18
Moose Pond Main
11/3
11/2
11/2
Moose Pond North
N/A
9/12
9/22
Moose Pond South
N/A
10/22
10/3
Sand Pond
N/A
after 10/30
10/31
Trickey Pond
N/A
11/2
after 11/5
Woods Pond
N/A
9/13
9/30
Lake Turnover
The date of full lake mixing, also known as destratification or lake turnover (See “Lake Stratification”
box, page 3) is dependent on the each lake’s unique characteristics, including surface area, depth, shape, and
morphology. Of the lakes studied, the earliest destratification occurred on September 22nd in the north basin of
Moose Pond and the latest took place after November 10th on Hancock Pond. This range of dates is slightly
later than in 2014, however many lakes mixed on or about the same date in both years (Table 3). Shallower
lakes, such as Woods Pond, and those that are exposed to heavy winds, like Long Lake, mixed the earliest.
Turnover dates were more spread out in 2015 than in 2014, when groups of lakes tended to mix around
the same dates. These grouped mixing events were correlated with high-rainfall events. However, in 2015
there was no such pattern. Although there was a large rainfall event from September 30 – October 1, only one
pond turned over on or near these dates. The rainfall did cause some mixing of deeper waters to occur in several lakes, but the event likely occurred too early in the fall to induce full turnover in deeper lakes. In general, a
gradual decrease in temperatures and increase in wind speeds over the fall months were the main drivers of
lake turnover.
12
Results and Discussion
Comparison with Water Testing Data
In order to verify sensor depth and accuracy of temperature readings, temperature profiles collected
by the HOBO sensors were compared with manual testing profiles collected with YSI handheld meters.
LEA conducts routine lake monitoring bi-monthly in the summer, which includes taking temperature
measurements at 1-meter intervals from the top of each lake to its deepest point. The resulting temperature
profiles were graphed along with HOBO sensor profiles from the same date and time of manual testing
and compared to each other.
The stated depth of the individual HOBO sensors is an estimate based on the depth of the lake. The
actual depth of the sensors varies depending on the specific placement of the anchor, the lake level, and
the amount of slack in the line. When setting up the HOBO sensor lines, sensor placement is measured
relative to the bottom, with the first sensor being 1 meter from the bottom of the lake and each additional
sensor being 2 meters above the last. However, when graphing the results, the sensors are labelled from
the top of the line, near the water’s surface, and we assume that the first sensor is at 1 meter deep. In a
lake that is 14.5 meters deep, the sensors might be labeled 1, 3, 5, 7, 9, 11, and 13 meters, but in reality
they may be closer to 1.5, 3.5, 5.5 meters, and so on.
These subtle differences in depth can be seen when comparing the manual and digital temperature
profiles. Adjusting the HOBO sensor profiles to be 1 meter deeper or shallower, for example, can provide
a much better data fit (Figure 8), indicating sensors are actually deeper or shallower than marked, for the
reasons discussed above. Manually collected temperature profiles are collected from a boat, and depths are
measured from the surface of the water down.
The YSI meter used to collect the data has a
probe marked at each meter and effort is made to
keep the line straight up and down in the water
during sampling to ensure accuracy.
Despite the potential sources of error, HOBO sensor temperature profiles from all the lakes
tested matched closely with YSI meter profiles
after adjusting for actual lake depth.
Figure 8. Graph of manually collected water testing data
(blue line) versus HOBO sensor data (red and green circles).
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.
13
Results and Discussion
Keoka Lake’s Two Buoys
Keoka Lake contained two HOBO-sensor buoys. The first was set up like the others: sensors every two
meters and a large marker buoy. The second line of sensors was attached to a small mooring buoy with sensors
located at each meter. This buoy was deployed close to the first one, but far enough away to prevent the lines
from becoming tangled. The purpose of the second buoy was to provide quality control for the first string of
sensors and to assess the value of the additional data collected by deploying sensors at every meter.
The second buoy was inadvertently placed at a slightly shallower depth than the first buoy, making its
sensors closer to the water surface. This can be seen in Figure 9, which shows data from each buoy’s top-most
sensor. The temperatures follow similar patterns, but the second buoy has much higher temperature spikes during the day. This is due to the increased solar radiation experienced close the water surface. The deeper sensors
in the second buoy line also show slightly higher temperatures at each depth compared to the first buoy.
The temperature patterns at each depth differed due to the depth difference but also because of the type
of buoy used. The regular buoy had more variation in temperature around the thermocline – located at approximately 5 meters’ depth – likely due to slack in the line allowing the sensors to move around in an area where
the temperature drops sharply. In contrast, the second buoy line was tied to a small mooring buoy, which better
facilitates tying a tighter line with less slack. While the second buoy provided more data, the first buoy was
adequate for determining key temperature features on Keoka Lake.
Figure 9. Close-up of Keoka Lake temperature data from sensors 1 meter below the surface on the regular buoy chain (with large
regulatory buoy, blue line) and the second buoy located close by (with small mooring buoy, orange line) between June 25 and July
25, 2015. The second buoy’s data follows a similar pattern but the daily temperature fluctuations are more pronounced. This is most
likely because this sensor was closer to the surface of the lake.
14
Results and Discussion
Shallow Sensor Data
Three lakes had single temperature sensors placed in a near-shore area (Figure 10). Peabody Pond
contained a sensor on the western shore about one meter below the surface and two meters from the bottom of the pond. Woods Pond and Keoka Lake both contained sensors attached to buoys or docks near the
surface. 2015 was the third year the Peabody sensor was deployed and the first year for the other sensors.
In general, the temperature patterns in Keoka Lake, Woods Pond, and Peabody Pond were similar.
The slight differences seen in Figure 10 are mostly due to the placement of the sensors. The amount of
sunlight hitting the sensor (The Keoka Lake and Woods Pond sensors were likely often partially shaded
by the dock/buoy they were attached to), for instance, can affect the temperature readings, as can the depth
of the sensor, especially in highly colored water such as in Woods Pond. Other differences are due to inlake factors, such as the influence of a boat wake or runoff from rain events entering the lake.
Year-on-year variation in temperature patterns on Peabody Pond were assessed between June 17
and September 24, as the sensor was in place between those dates in all three years (Figure 11). 2015 had
the highest average and minimum temperature of the three years, thanks to a warmer-than-usual August
and September. 2014 was the coldest year, with the lowest average, maximum and minimum temperatures
during the period specified. 2013 had the highest maximum temperature.
Figure 10. Comparison of daily temperature fluctuations from August 21 to September 1 in the three lakes with shallow temperature
sensors deployed in 2015: Keoka Lake, Woods Pond, and Peabody Pond. The sensors follow the same basic temperature patterns.
Differences are largely due to depth of sensor placement and within-lake variations.
15
Results and Discussion
Overall the average temperature in Peabody Pond over the same period in 2013, 2014 and 2015
varied by less than one degree Celsius. 2014 did not have a clear peak in temperature, while the 2013 and
2015 peaks were about a month apart. The major difference in the temperature patterns between the three
years is the sustained warmer temperatures in August and September of 2015.
Figure 11. Comparison of Peabody Pond shallow data from each of the last 3 years. This graph is zoomed in to show only days with
data from all three years.
Looking Ahead to 2016
Temperature monitoring with HOBO sensors will continue in 2016 following the successful 2015
season. LEA plans to deploy buoys at the same sites in 2016 as in 2015 using the same methods. The goal
each year is to deploy sensors as early as possible in order to capture the entirety of each lake’s summer
stratification. In 2015, stratification set up in late April, so deploying sensors before May is a goal for
2016.
LEA’s automated monitoring buoy collects temperature data on Highland Lake in the same way as
our HOBO sensor buoys. It was removed in November 2015 and replaced with a string of HOBO sensors
which will measure temperature continuously over the winter, until the buoy is deployed again in the
spring. This data will help us understand over-winter temperature dynamics and allow us to have continuous, year-round temperature data from Highland Lake.
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.
16
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
Keyes Pond Environmental Protection Association
An anonymous foundation
and all of our members
…for making this project possible with their generous support!
17
Lakes Environmental
Association
230 Main Street
Bridgton, ME 04009
207-647-8580
lakes@leamaine.org
18