US Long-Term Ecological Research Network

Lake snow removal experiment snow, ice, and Secchi depth, 2019-2021

Abstract
Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents the snow depths, black and white ice thickness, and Secchi depths during the period of ice cover each winter.<br/>
Dataset ID
419
Data Sources
Date Range
-
LTER Keywords
Methods
Snow depth was determined by averaging ten random samples taken with a meter stick.<br/>
NTL Themes
Version Number
1

Lake snow removal experiment phytoplankton community data, under ice, 2019-2021

Abstract
Although it is a historically understudied season, winter is now recognized as a time
of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging
research points to a future of reduced ice cover duration and changing snow conditions that
will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice
conditions, and subsequent changes to under-ice light environment, might impact ecosystem
dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from
a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during
the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from
the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019
season served as a reference year, and snow was not removed from the lake. This dataset
represents phytoplankton community samples (pooled epilimnion and hypolimnion samples
representative of 7 m water column) both under-ice and during some shoulder-season (open
water) dates. Samples were collected into amber bottles and preserved with Lugol's solution
before they were sent to Phycotech Inc. (St. Joseph MI, USA) for phytoplankton taxonomic
identification and quantification.<br/>
Core Areas
Creator
Dataset ID
418
Data Sources
Date Range
-
Methods
Phytoplankton samples were obtained from the epilimnion and hypolimnion by
slowly lowering weighted Tygon tubing through the water column to a depth of 7 m, such
that the tubing was filled with a representative water column sample. Based on the inner
diameter of the tubing, 205 mL of water was pumped from the tubing for the epilimnion
sample. Next, 267 mL of water was pumped from the tubing for the hypolimnion sample. Each
sample was collected into a 250 mL amber bottle that contained 2 mL of Lugol’s solution.
Phytoplankton samples were pooled by sampling date and sent to Phycotech Inc. (St. Joseph
MI, USA) for phytoplankton identification, and concentration and biovolume
quantification.<br/>
Publication Date
Version Number
1

Lake snow removal experiment zooplankton community data, under ice, 2019-2021

Abstract
Although it is a historically understudied season, winter is now recognized as a time
of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging
research points to a future of reduced ice cover duration and changing snow conditions that
will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice
conditions, and subsequent changes to under-ice light environment, might impact ecosystem
dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from
a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during
the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from
the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019
season served as a reference year, and snow was not removed from the lake. This dataset
represents under ice zooplankton community samples (integrated tows at depths of 7 m) and some
shoulder-season (open water) zooplankton community samples. Zooplankton samples were preserved
in 90% ethanol and later processed to determine taxonomic classification at the species-level,
density (individuals / L), and average length (mm).<br/>
Contact
Core Areas
Creator
Dataset ID
414
Date Range
-
Methods
Our study lake, South Sparkling Bog (SSB) (46.003°N, 89.705°W), is a bog lake
located in Vilas County in Northern Wisconsin. South Sparkling Bog is a dystrophic,
dimictic lake with a maximum depth of 8 m, a mean depth of 3.6 m, and a surface area of
0.44 ha. South Sparkling Bog is surrounded by a sphagnum bog mat and has no shoreline
development. During the winters of 2019-2020 and 2020-2021, snow was removed from the
surface of South Sparkling Bog following any snow accumulation event. Removal was
conducted via a snowplow attached to the front of an ARGO all-terrain vehicle and a
snowblower. The winter of 2018-2019 served as a reference year, and snow was not removed
from South Sparkling Bog’s surface. While ice cover persisted, plankton samples were
collected at the deep spot for each lake during on a biweekly-to-monthly basis each
winter. On each sampling date, one integrated zooplankton tow was taken at a depth of 0-7
m using a 56 µm mesh Wisconsin net. All zooplankton samples were collected into glass
sample jars, preserved in 90% ethanol, and saved for laboratory analysis. In the lab,
zooplankton samples were filtered through 53 µm mesh and diluted to a known volume, and
three sub-sample replicates were taken using a 1 mL Hensen-Stempel pipette. Sub-sample
replicates were counted to at least 100 individuals, otherwise the entire sample was
quantified. Sub-sample data was then converted to the known diluted volume and finally
converted to total filtered volume (from the integrated tow sample) to estimate density
(individuals L-1). Zooplankton samples were processed using a Leica M8Z dissecting scope
and Leica imaging software. Replicate subsamples were averaged to estimate total abundance
and density, and average lengths (mm) for each sample taxa were calculated from measures
of the first 30 taxa found within a sample date.<br/>
Publication Date
Version Number
1

Lake Mendota at North Temperate Lakes LTER: Snow and Ice Depth 2009-2010

Abstract
Ice core data collected by Yi-Fang (Yvonne) Hsieh and collaborators for her PhD project, "Modeling Ice Cover and Water Temperature of Lake Mendota."; Part of the project was the development of a 3D hydrodynamic-ice model that simulated both temporal and spatial distributions of ice cover on Lake Mendota for the winter 2009-2010. The parameters from these ice core data were used as model inputs to run model simulations. Parameters measured include: blue ice, white ice, snow depth, and total ice. On February 13, 2009, ice cores were taken on Lake Mendota at four different stations. From January 14, 2010 through March 3, 2010 ice cores were taken on Lake Mendota at 31 different stations. In addition, ice cores were taken on other Yahara Lakes during February of 2009: Lake Kegonsa (4 stations_February 6), Lake Waubesa (4 stations_February 7), Lake Wingra (2 stations_February 8), and Lake Monona (4 stations_February 8). Only total ice measurements are reported for 2009. Included in this data set are the ice core data, and geospatial information for ice coring stations. Documentation: Hsieh, Y.-F., 2012a. Modeling ice cover and water temperature of Lake Mendota. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 157.
Dataset ID
283
Date Range
-
Maintenance
ongoing
Metadata Provider
Methods
Ice and snow sampling was conducted weekly from 14 January to 30 March, 2010 on Lake Mendota when the ice was safe to walk on. A Kovacs Mark III core drill, manufactured by Ice Coring and Drilling Service (ICDS), Space Science and Engineering Center (SSEC) UW Madison, was used to collect ice cores. Snow depth was also measured at the locations where ice cores were sampled. All measurements were made in centimeters. Blue ice can be defined as the portion of the ice core that is strictly frozen lake water. White ice can be defined as &ldquo;snow ice,&rdquo; which occurs when water rushes through cracks in the ice and soaks the overlying snow, resulting in a mixture of ice and snow that subsequently freezes. Total ice is blue ice + snow ice. Finally, snow depth was calculated as the average of 10 snow depth samples at each sampling location.
Version Number
19

North Temperate Lakes LTER: Snow and Ice Depth 1982 - current

Abstract
Snow and ice depth are measured during the winter months on the eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout lakes, unnamed lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Fish, Mendota, Monona and Wingra). 10 snow depth measurements are taken in a circle around the sampling location and averaged to single measurement. Sampling Frequency: every 6 weeks during ice-covered season in the north and typically once during the winter in the south. Number of sites: 11.
Dataset ID
34
Date Range
-
LTER Keywords
Maintenance
ongoing
Metadata Provider
Methods
Methods are described in the abstract.
Short Name
NTLPH06
Version Number
34
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