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127 results for “snow depth”
Snow depth and snow water equivalent measurements along a road course and historic snow course in the Andrews Experimental Forest, 1978 to present
With an increase in emphasis on monitoring climate change impacts and change in the form of precipitation at HJ Andrews Experimental Forest, snow data collection within our climate monitoring program, a snow course to document depths of snow was designed around a dispersed sampling scheme rather than a point intensive scheme as previously employed in the historic Reference Stand snow course. Primary objectives are to document the presence/absence of snow, snow depth, and time of melt-off. Snow depths are verified using stakes placed near the road to allow for routine and frequent observation. Stakes are placed at different locations, elevations and aspects in paired forested/open sites. Time-lapse cameras were deployed at all the stakes to allow for daily measurements beginning in fall 2014. Truthing of points with snow core sampling for snow moisture content (snow water equivalent) is done when possible, usually 1-2 times per year. Cameras are set to take 3 readings per day (09:00, 12:00, 15:00 PST). One snow depth and coverage is extracted from the images per stake per day.
Sea ice thickness, snow depth, and sea ice freeboard in lagoon sites along the Alaska Beaufort Sea coast, 2019-ongoing
Physical parameters related to snow and sea ice have implications for lagoon circulation, sea-air heat exchange, and underwater light regimes. To understand these relationships and their greater effect on ecosystem function, the Beaufort Lagoon Ecosystem LTER (BLE LTER) uses in situ methods to assess snow depth, ice freeboard, and ice thickness in select water bodies across the Beaufort Sea coast (Elson Lagoon, Simpson Lagoon, Kaktovik Lagoon, Jago Lagoon, and Stefansson Sound). Sea ice thickness is the distance from sea ice bottom to top, not including snow. Freeboard, determined in the same drilled hole, is the distance from the surface of the water to the top of the ice, not including snow cover. These measurements are made annually, close to maximum ice thickness (typically April).
Snow depth data for Saddle grid, 1992 - ongoing.
The depth of snow was measured at 88 points on the saddle grid. The 500 x 350 m study area (17.5 ha) consisted of a grid of 8 rows of stakes in an east/west direction and 11 rows of stakes in a north/south direction (for a total of 88 stakes). The stakes were located 50 m apart. Each stake was given a point identification number starting with 1 in the southwest corner and progressing in an easterly direction for each of the east/west rows so that if head of this file represented the north compass point and the tail represented the south compass point, then the grid would look like this: 71 72 73 74 75 76 77 78 79 80 801(=80A) 61 62 63 64 65 66 67 68 69 70 701(=70A) 51 52 53 54 55 56 57 58 59 60 601(=60A) 41 42 43 44 45 46 47 48 49 50 501(=50A) 31 32 33 34 35 36 37 38 39 40 401(=40A) 21 22 23 24 25 26 27 28 29 30 301(=30A) 11 12 13 14 15 16 17 18 19 20 201(=20A) 1 2 3 4 5 6 7 8 9 10 101(=10A) Note that stakes along the east boundary of the grid, i.e. those ending with the "A", were given new designations to facilitate incorporation of the data into the Saddle GIS. Snow depths at each of the stakes were recorded on a weekly to biweekly basis throughout the period during which snow accumulation existed on the Saddle.
Snow depth data for saddle snowfence, 1992 - ongoing.
A snowfence was built in 1993 on the Niwot Ridge Saddle grid to determine the effects of changes in snowpack on a number of variables. The study area was 60m x 125m. Snow depths were measured at each of 147 points within the snowfence experiment area and at 6 control locations outside of but near the snowfence experiment area. Of the 147 points, 84 were located on the leeward (east) side of the snowfence, 56 were located on the windward (west) side of the snowfence, and 7 were located along the snowfence itself. These measurements were made weekly to biweekly. Sampling locations were each given a unique point identification number so that these data could be incorporated into the Saddle GIS. The snowfence was oriented in a north/south direction and was 60 m long. Each of the point identification numbers had a coordinate within the experiment area. The first number of the coordinate was the distance in m from the snowfence in an east/west direction, negative numbers being west of the snowfence and positive numbers being east of the fence. The second number in the coordinate was the distance in m from the southern terminus of the snowfence in a northerly direction.
Little Rock Lake Experiment at North Temperate Lakes LTER: Snow and Ice Depth 1984 - 2000
The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. Snow and ice depth are measured during the winter months on the reference and treatment basins of Little Rock Lake. Sampling Frequency: varies - Number of sites: 4
Lake Mendota at North Temperate Lakes LTER: Snow and Ice Depth 2009-2010
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.
Retrieved snow depth in Mainland Norway (2018.10-2022.10) based on ICESat-2 ATL08 and DEMs
<h3><strong>Introduction</strong></h3> <p>This dataset's snow depth data was derived using elevation differencing, which is simply the snow surface elevation (ICESat-2 ATL08) minus the reference surface elevation (obtained from Digital Elevation Models):</p> <ol> <li><strong>DEM Co-registration</strong>: DEMs are co-registered to ICESat-2 ATL08 snow-off reference without vertical bias adjustment.</li> <li><strong>Elevation Bias Correction</strong>: The elevation bias between the DEMs and ICESat-2 is corrected using ICESat-2 ATL08 snow-off segments.</li> <li><strong>Snow Depth Calculation</strong>: Determining snow depth by subtracting <strong>the bias-free reference ground elevation(from Step 2)</strong> from ICESat-2 ATL08 snow-on segments.</li> </ol> <p>This dataset is presented in a tabular format, which simplifies the preprocess for machine learning models. While co-registration has been done (1), users have the flexibility to train a bias correction model again (2) and retrieve snow depth measurements anew (3). Alternatively, the snow depth can be directly used for various analytical purposes. Detailed methodologies for the co-registration, bias correction, and snow depth determination are thoroughly documented in the paper (under submission) to support users in leveraging this dataset for their research needs.<br> </p> <h3><strong>Meta Information</strong></h3> <ul> <li><strong>Study Area</strong>: Mainland Norway</li> <li><strong>Acquisition Period (ICESat-2)</strong>: October 2018 to October 2020</li> <li><strong>ICESat-2 data source</strong>: ATL08 (level3, version 5)</li> <li><strong>Reference DEMs</strong>: Norway DTM1, Norway DTM10, Copernicus GLO30, FABDEM. (see reference links)</li> <li><strong>Reference snow depth: </strong>ERA5 Land (hourly), ERA5 Land (monthly).</li> <li><strong>Snow condition</strong>: The dataset contains snow depth retrieved (snow_on_alt08_segments_and_snow_depth.csv) and snow-free observations (snow_free_alt08_segments_and_dems.csv).</li> <li><strong>Data Cleaning</strong>: No, this is a raw dataset that may contain outliers.</li> <li><strong>Mask</strong>: Excluded water surface and permanent ice at a spatial resolution of 100 m. </li> </ul> <h3><strong>Description</strong></h3> <p>This dataset encapsulates a wide array of attributes derived from ICESat-2 observations, alongside measurements pertinent to snow depth, terrain, and environmental conditions across Mainland Norway. For detailed attribute descriptions, refer to the <a href="https://nsidc.org/data/atl08/versions/5#anchor-2">ICESat-2 ATL08 documentation</a>. The dataset is structured into several columns, each representing a specific attribute:</p> <ol> <li>'latitude': Latitude coordinates of the data points in WGS 84.</li> <li>'longitude': Longitude coordinates of the data points in WGS 84.</li> <li>'segment_landcover': Land cover classification for each segment.</li> <li>'segment_snowcover': Snow cover classification for each segment.</li> <li>'h_te_best_fit': Best-fit elevation of the terrain.</li> <li>'h_te_std': Standard deviation of terrain elevation.</li> <li>'n_te_photons': Number of photons used for terrain elevation estimation.</li> <li>'subset_te_flag': Quality flag (5 = all geosegments available, 4 = four geosegments...).</li> <li>'segment_cover': Woody vegetation fractional cover derived from the 2019 Copernicus 100m shrub and forest fractional cover data product.</li> <li>'h_canopy': Canopy height above terrain from ICESat-2 (only for snow-off segments).</li> <li>'h_mean_canopy': Mean canopy height ICESat-2 (only for snow-off segments).</li> <li>'canopy_openness': Canopy openness from ICESat-2 (only for snow-off segments).</li> <li>'h_canopy_winter': Canopy height above terrain from ICESat-2 (only for snow-on segments).</li> <li>'h_mean_canopy_winter':Canopy mean height from ICESat-2 (only for snow-on segments).</li> <li>'canopy_openness_winter':Canopy openness from ICESat-2 (only for snow-on segments).</li> <li>'tree_presence': the presence of trees in the segment (1 = tree, 0 = no tree, binary of h_canopy).</li> <li>'pair': Pair flag for ICESat-2.</li> <li>'beam': Beam flag for ICESat-2.</li> <li>'p_b': Pair and beam flag for ICESat-2.</li> <li>'region': Region identifier for ICESat-2.</li> <li>'cloud_flag_atm': Atmospheric cloud flag for ICESat-2.</li> <li>'urban_flag': Urban area flag for ICESat-2.</li> <li>'h_te_skew': Skewness of terrain elevation of segments.</li> <li>'snr': Signal-to-noise ratio for ICESat-2.</li> <li>'terrain_slope': Slope of the terrain from ICESat-2.</li> <li>'h_te_uncertainty': Uncertainty in terrain elevation estimation.</li> <li>'night_flag': Flag indicating nighttime data.</li> <li>'brightness_flag': Brightness flag for ICESat-2.</li> <li>'h_te_interp': Interpolated terrain elevation.</li> <li>'E': Easting coordinate in EPSG 32633.</li> <li>'N': Northing coordinate in EPSG 32633.</li> <li>'slope': Terrain slope computed from DTM10.</li> <li>'aspect': Terrain aspect computed from DTM10.</li> <li>'planc': Plan curvature computed from DTM10.</li> <li>'profc': Profile curvature computed from DTM10.</li> <li>'curvature': Overall terrain curvature computed from DTM10.</li> <li>'tpi': Terrain Position Index computed from DTM10.</li> <li>'tpi_9': TPI with a 90-meter radius.</li> <li>'tpi_27': TPI with a 270-meter radius.</li> <li>'wf_positive': Positive wind aspect index.</li> <li>'wf_negative': Negative wind aspect index.</li> <li>'smlt_acc': Snowmelt accumulation calculated from ERA5 Land monthly snow melting (currently not in use).</li> <li>'sf_acc': Snowfall accumulation calculated from ERA5 Land monthly snowfall (currently not in use).</li> <li>'sd_era': Snow depth from ERA5 Land reanalysis, coupled with ICESat-2 measurements at daily resolution,</li> <li>'sde_era': Snow depth linear interpolated from ERA5 Land reanalysis.</li> <li>'date': Date of data acquisition.</li> <li>'date_': Date in Pandas Datatime data dype.</li> <li>'month': Month of data acquisition.</li> <li>'difference': The elevation difference between segment and subsegment at the midpoint ( 'h_te_best_fit_20m_2' minus 'h_te_best_fit'). If you want to use h_te_best_fit_20m_2 instead of h_te_best_fit as elevation from ICESat-2, you can do it by df_after_dtm1 - difference, snowdepth_dtm1 - difference.</li> </ol> <p>Columns on elevation difference and snow depth (in meters):</p> <ol> <li>'<strong>dh_after_dtm1</strong>': The elevation difference between the snow-free segment and DTM1 (ICESat-2 minus DTM1). This serves as an independent variable y in the bias correction model for DTM1. Here, 'after' means after co-registration.</li> <li>'<strong>snowdepth_dtm1</strong>': The elevation difference between the snow-on segment and DTM1 (ICESat-2 minus DTM1), representing the raw snow depth as measured against DTM1.</li> <li>'<strong>sd_correct_dtm1</strong>': Corrected snow depth using DTM1, adjusted by bias correction model.</li> <li>'<strong>df_dtm1_era5</strong>': Difference betwen 'sd_correct_dtm1' and 'sde_era'. (sd_correct_dtm1 minus sde_era), providing a comparison between corrected snow depth from DTM1 and snow depth from ERA5 Land reanalysis</li> <li><strong>'dh_after_dtm10'</strong>: The elevation difference between the snow-free segment and DTM10 (ICESat-2 minus DTM10), used in bias correction for DTM10.</li> <li><strong>'snowdepth_dtm10'</strong>: The elevation difference between the snow-on segment and DTM10 (ICESat-2 minus DTM10).</li> <li><strong>'sd_correct_dtm10'</strong>: Corrected snow depth using DTM10, adjusted by bias correction model.</li> <li><strong>'df_dtm10_era5'</strong>: Difference between 'sd_correct_dtm10' and 'sde_era'.</li> <li><strong>'dh_after_cop30'</strong>: The elevation difference between the snow-free segment and Copernicus GLO30 (ICESat-2 minus Copernicus GLO30).</li> <li><strong>'snowdepth_cop30'</strong>: The elevation difference between the snow-on segment and Copernicus GLO30.</li> <li><strong>'sd_correct_cop30'</strong>: The adjusted snow depth using Copernicus GLO30, adjusted by bias correction model.</li> <li><strong>'df_cop30_era5'</strong>: The discrepancy between 'sd_correct_cop30' and 'sde_era'.</li> <li><strong>'dh_after_fab'</strong>: The elevation difference between the snow-free segment and FABDEM (ICESat-2 minus FABDEM), used in bias correction for FABDEM.</li> <li><strong>'snowdepth_fab'</strong>: The elevation difference between the snow-on segment and FABDEM, representing the uncorrected snow depth.</li> <li><strong>'sd_correct_fab'</strong>: The corrected snow depth using FABDEM, adjusted by bias correction model.</li> <li><strong>'df_fab_era5'</strong>: The difference between 'sd_correct_fab' and 'sde_era'.</li> </ol> <p>More explanation (especially on how the parameters are calculated, such as wind aspect index) is available in related works and blog posts on<a href="https://zhihaol.eu.org/blog/2023/subgrid/"> snow depth</a>, and <a href="https://zhihaol.eu.org/blog/2023/dataset/">DEM bias correction</a>.</p> <p>This dataset includes a comprehensive collection of snow depth data and correlated environmental variables for Mainland Norway. Researchers can use this dataset to investigate the following:</p> <ul> <li>The difference between ICESat-2 and DEMs. For example, how 'df_after_dtm1'<strong> </strong>relates to terrain parameters.</li> <li>The residual bias of ICESat-2 derived snow depth, for example, snowdepth_dtm1 and bias-corrected sd_correct_dtm1. You can train a better bias correction to retrieve snow depth again. You can compare your model with my model by 'dh_reg_dtm1', 'dh_reg_dtm10', 'dh_reg_cop30', and 'dh_reg_fab', which are the elevation differences after bias correction for each DEM.</li> <li>The difference between ICESat-2-derived snow depth and snow depth from ERA5 Land, for example, 'df_dtm1_era5'.</li> <li>The spatial distribution of snow depth or subgrid variability.</li> </ul>
Marcell Experimental Forest biweekly snow depth, frost depth, and snow water equivalent, 1962 - ongoing
This data table contains snowpack and frost data measured at the Marcell Experimental Forest from 1962–ongoing. The data came from five peatland/upland forest watersheds instrumented for hydrologic monitoring. Frost thickness and snowpack (snow water content, snowpack depth) are measured at 10 snowcourses that encompass three cover types (conifer, deciduous, open). The Marcell Experimental Forest in Itasca County, Minnesota, is operated and maintained by the USDA Forest Service, Northern Research Station, and was formally established in 1962 to study the ecology and hydrology of peatlands.
Bonanza Creek LTER: Hourly Snow Depth Measurements from 1988 to Present in the Bonanza Creek Experimental Forest near Fairbanks, Alaska
Snow depth is measured hourly at both the LTER1 and LTER2 research stations within Bonanza Creek Experimental Forest using a Campbell Scientific data logger and a SR50 sonic snow depth sensor.
Bonanza Creek LTER: Hourly Snow Depth Measurements from 2006 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
Hourly snow depth measured in meters from the CRREL and CARSNOW climate data stations within CPCRW using a Campbell Scientific data logger and a SR50 sonic snow depth sensor.
Lake Mendota water temperature secchi depth snow depth ice thickness and meterological conditions 1894 - 2007
Data for water temperature at different depth and different frequencies assembled from various sources by Dale Roberson. A table with additional parameters collected at the same time is also provided for dates when available. These parameters are weather observations, secchi depth, snow and ice depths.
Snow depth sensor measurement data for Upper Sub Alpine site, 2010 - 2015.
Effects of infrared heaters on snow accumulation, snowmelt, and snow–atmosphere energy exchange were examined at Niwot Ridge, Colorado (CO). These .zip data files contains hourly snow depth measurements collected using Judd snow depth sensors for water year 2010-2015 (1 October 2009 – 30 September 2015) at the Upper Sub Alpine site, located just southeast of the Tundra Lab, below treeline in the Niwot Ridge Long-Term Ecological Research (NWTLTER) project area. The file contains both level 0 and level 1 (see details in “Process Description” below) hourly snow depth data measured in centimeters, and an accompanying metadata file.
Spatial distribution of snow depth for the Green Lakes Valley, 1997 - 2019
Climate warming represents an abiotic driver for change in alpine ecosystems, potentially altering the seasonal snowpack and thus water availability into the surrounding landscape. Future changes in snow accumulation and snowmelt distribution may have profound impacts on the flora and fauna of alpine ecosystems. In this regard, recent research has leveraged multi-year estimates of the spatial distribution of snow water equivalent (SWE) toward understanding alpine ecosystem function. The purpose of this project is to investigate the spatial variability of maximum snow depth at Niwot Ridge on an inter-annual basis.
Snow depth, snow water equivalent, ice thickness in Fuglebekken and Revdalen catchments collected in the SnowPilot campaign in Spring 2022
<p>File SnowPilot_snowdepth_along_the_GPR_profile_2022 contains snow depth measurements taken along the GPR profile performed during the SIOS SnowPilot campaign in Spring 2022. File SnowPilot_snowdepth_swe_2022 contains depth, snow water equivalent and basal ice thickness. Snowpits were dug on GPR profile crossings in the Fuglebekken and Revdalen catchments in the Hornsund fiord, Spitsbergen catchment. Snow density was measured with an IG PAS snow tube, and snow depth and basal ice (ice forming on the ground surface) thickness were measured with an avalanche probe. Point locations measured. with handheld GPR reciever.</p>
Grand Mesa 2017-02-01 snow depth estimate
<p>Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series of very-high-resolution commercial stereo imagery. More formal releases of data products will be available in the coming years.<br> <br> Analysis of this dataset is presented in the following publication:</p> <ul> <li>McGrath, D., Webb, R., Shean, D., Bonnell, R., Marshall, H-P., Painter, T., Molotch, N., Elder, K., Hiemstra, C., Brucker, L., (2019), Spatially Extensive Ground-Penetrating Radar Snow Depth Observations During NASA's 2017 SnowEx Campaign: Comparison With In Situ, Airborne, and Satellite Observations, Water Resources Research, 55 (19), 10026-10036, doi:<a href="https://doi.org/10.1029/2019WR024907">10.1029/2019WR024907</a>.</li> </ul> <p>If you use these data, please cite the above publication.</p>
New Hampshire Soil Sensor Network: Snow depth (2012-2022)
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes data from snow depth sensors. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): CiPEHR snow depth manual data 2009-2025
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This data set includes manual measurements of snow depth collected in early spring on winter warming and control treatment plots.
Snow depth, soil frost depth and snow water content along an elevation gradient at the Hubbard Brook Experimental Forest.
Snow depth, soil frost depth and snow water content have been measured at several locations at the Hubbard Brook Experimental Forest (HBEF). In October 2010, as part of a study of the relationships between snow depth, soil freezing and nutrient cycling (http://www.ecostudies.org/people_sci_groffman_snow_summary.html), we established 6 20 x 20-m plots (intensive plots) and 14 10 x 10-m plots (extensive plots) following an elevation gradient, with eight of the plots facing north and twelve facing south. Snow and frost depth, and snow water equivalent sampling started in December 2010. Measurements on the extensive plots ended at the conclusion of snow coverage in spring, 2012. Measurements at the 6 intensive plots are ongoing and measurement frequency was increased from approximately bimonthly to approximately weekly beginning in the 2019-2020 snow cover season. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Average glacier stake height and snow depth measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes stake height and snow depth measurements to the surface of six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys of Antarctica. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
North Temperate Lakes LTER: Snow and Ice Depth 1982 - current
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.
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