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56 results for “ice cover”
Hubbard Brook Experimental Forest: Mirror Lake Ice Cover 1968 - ongoing
This data set reports ice on and ice off dates for Mirror Lake beginning in 1968 and continuing through the present. Mirror Lake is located within the Hubbard Brook valley in the White Mountains of New Hampshire, and has been the subject of numerous limnological investigations since the early 1960s. These Mirror Lake data are part of the Hubbard Brook Watershed Ecosystem Record (HBWatER), a long-term record of weekly sampling of nine gaged watersheds at Hubbard Brook which includes the stream draining Mirror Lake. The collection and management of the long-term record was initiated in 1963 by Gene E. Likens, F. Herbert Bormann, Robert S. Pierce, and Noye M. Johnson. HBWatER is currently sustained by Tammy Wooster (Cary IES) and Jeff Merriam (USFS) and the dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emily Bernhardt (Duke), Bill McDowell (UNH), Charley Driscoll (Syracuse U.), Keith Nislow (USFS), and Mark Green (Case Western). Current financial Support for HBWatER is provided by NSF LTREB # 2401760 and the USDA Forest Service Northern Research Station. 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.
Douglas Lake Ice Cover at the University of Michigan Biological Station, Pellston, MI 1931 to 2025
This dataset represent the ice on and ice off dates for Douglas Lake in Pellston, Michigan. The first observations are from the 1930 and were intermittently documented until the mid 1970s. The observations are complete since then to current.
University of Kansas Field Station: Water level and ice cover at Frank B. Cross Reservoir (Kansas, USA) 1993 - 2016
This database is from regular monitoring of water level and surface ice cover at Frank B. Cross Reservoir, a small freshwater impoundment in northeastern Kansas (USA). Cross Reservoir, located at the University of Kansas Field Station near Lawrence (KS), has a 3-ha surface area and a maximum depth of 12 m. Measurements of water elevation and estimates of ice cover were made at semi-monthly intervals (i.e., roughly every two weeks). The first data were taken in December 1993, shortly after the reservoir was constructed and first filled to capacity. Water levels were measured relative to a permanent water control structure. Ice cover observations were visual estimates of the percent (%) surface of the reservoir covered with ice. Water level measurements and ice cover estimates are made at the same time. This database is updated periodically and maintenance is ongoing.
Ice cover data for Falling Creek Reservoir and Beaverdam Reservoir, Vinton, Virginia, USA for December 2013-December 2022
Ice cover was recorded at Falling Creek Reservoir and Beaverdam Reservoir (both located in Vinton, Virginia, USA) using multiple methods. The ice cover record for Falling Creek Reservoir began in the winter of 2013-2014 and Beaverdam Reservoir beginning in the winter of 2020-2021. Falling Creek Reservoir and Beaverdam Reservoir are owned and operated by the Western Virginia Water Authority as a primary and secondary drinking water source, respectively, for Roanoke, Virginia. These two reservoirs are some of the southernmost waterbodies in the United States that exhibit ice cover during the winter. Given incipient climate change, Falling Creek Reservoir and Beaverdam Reservoir's ice cover data represent an important record of warming for waterbodies in the southeastern United States. Ice cover presence was assessed using multiple methods: visual observation of ice spanning the deep hole (from an observer standing on a reservoir's dam); visual observation via an automated camera deployed near the deep hole; water temperature data from a profile of sensors deployed at a reservoir's deepest site; upwelling shortwave radiation and albedo measured by a meteorological station deployed on a metal structure located over Falling Creek Reservoir's deep hole; and dissolved oxygen sensors deployed at multiple depths at the deep hole of both reservoirs.
Itasca Biological Station Lake Ice Cover Data
Established in 1907, the Itasca Biological Station and Laboratories is a field station focused on conducting and promoting biological and ecological research. Part of this mission consists of long-term ecological monitoring efforts taken on by station staff. This dataset is one of such long-term efforts. Station staff tracked and recorded dates when lakes completely froze each winter (ice on, recorded when >99% of the lake was covered) as well as when they completely thawed each spring (ice off, recorded when >99% of the lake was open). Monitoring focused on Lake Itasca, with several other lakes in the region being added as various staff became associated with the Itasca Station.
Seasonality of in-lake and meteorological data from seven lakes, including daily measurements of water temperature, chlorophyll-a, dissolved oxygen, ice cover, air temperature, and solar radiation
This data product supports the manuscript "Seasons and seasonality in lakes: a synthesis amid global change" (Lewis et al. 2026; in review). Data were analyzed to understand how seasonality varies among diverse lakes and variables. Specifically, this data publication includes daily mean water temperature, chlorophyll-a, and dissolved oxygen at multiple depths, ice cover (binary), air temperature and solar radiation. Data availability and collection methods differ among lakes, as described in the Methods.
Lake Levels and Ice cover on local lakes at the Kellogg Biological Station, Hickory Corners, MI (1923 to 2022)
Dataset AbstractLake levels and ice cover have been measured on local lakes with the help cf citizen volunteers.original data source http://lter.kbs.msu.edu/datasets/122
ADCP data of ice-covered and open-channel (macro-turbulent) flow, Pulmanki River, 2016-2020
<p>README of ADCP_data_Lotsari_et_al_Water_opened.zip</p> <p><br> The ADCP data was the basis of the following paper:<br> Macro-turbulent flow and its impacts on sediment transport potential of a subarctic river during ice-covered and open-channel conditions <br> Eliisa Lotsari (1, 2), Michael Dietze (3), Maria Kämäri (4), Petteri Alho (2,5), Elina Kasvi (6,2)</p> <p>1 Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistokatu 2, P.O. Box 111, FI-80101, Joensuu, Finland. eliisa.lotsari@uef.fi<br> 2 Department of Geography and Geology, University of Turku, FI-20014 Turun yliopisto, Turku, Finland.<br> 3 Section 4.6 Geomorphology, German Research Centre for Geosciences GFZ Potsdam, D-14473 Potsdam, Germany. mdietze@gfz-potsdam.de<br> 4 Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland. maria.kamari@ymparisto.fi<br> 5 Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430, Masala, Finland. mipeal@utu.fi<br> 6 Turku University of Applied Sciences, Joukahaisenkatu 3, FI-20520, Turku, Finland. elina.kasvi@turkuamk.fi</p> <p>(Note: During the time of data gathering, Maria Kämäri worked at the University of Eastern Finland, and Elina Kasvi at the University of Turku)</p> <p>The data set has been measured with Sontek M9 or S5 sensors, depending on the time step (Table 1).</p> <p>Table 1. The measurement times, their acronyms (applied in the above mentioned publication)<br> and applied sensors. In the acronyms of the measurement times W=winter low flow period, S=spring<br> (snow-melt flood period), A=autumn low flow period. These information are presented also<br> in the Table 1 of the above-mentioned publication.<br> Date Acronym Sensor<br> 17.2.2016 W2016 M9<br> 25.5.2016 S2016 S5 <br> 10.9.2016 A2016 M9<br> 16.2.2017 W2017 M9 <br> 31.5.2017 S2017 M9 <br> 9.9.2017 A2017 M9 <br> 9.2.2018 W2018 M9 <br> 23.5.2018 S2018 S5 <br> 8.9.2018 A2018 S5 <br> 8.2.2019 W2019 M9 <br> 21.5.2019 S2019 M9 <br> 6.2.2020 W2020 M9 </p> <p>RiverSurveyor Live software, and its most recent version, was used each time.<br> The measurements have been done at Pulmanki River (69°55'59.09" N; 28° 2'34.32" E), Northern Finland, during 2016 - 2020. <br> The data is in directories of corresponding measurement times. The measurement<br> locations cs1, cs2, cs3, cs4, csA, csB and csC can be found in the paper (Figs. 1 and 2). <br> The data is in raw Matlab file format, as exported from the RiverSurveyor Live software.<br> The coordinate system is ENU.</p> <p>When used, the referencing to the paper and DOI ( 10.5281/zenodo.3855035 ) are required.</p>
Figure 5 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 5. Location of 34 descending tracks of satellites ERS-1 (phases C and G), ERS-2 and ENVISAT, which were used to analyze the position of the sea ice edge in the Barents Sea. The red line is the average climatic position of the sea ice edge. The green line shows the position of track N444, the purple line shows the track N360. Dashed line is a reference line used for calculation the distance to the ice edge along the tracks.
Figure 6 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 6. Interannual variability of the position of the ice edge along track N118 according to altimetry measurements of the ERS − 1/2, ENVISAT and SARAL/AltiKA satellites in 1992-2018. Dashed line shows a linear trend for the variability of the distance to the sea ice edge in the Barents Sea.
Figure 4 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 4. Interannual variability of the Barents Sea level anomalies according to satellite altimetry measurements of the ERS − 1/2, ENVISAT and SARAL/AltiKa for the period 1992–2018 for June, July, August and September. Dashed lines show linear trends.
Figure 2 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 2. The position of the tracks of the ERS − 1/2, ENVISAT and SARAL/AltiKA (a) satellites with a repetition period of 35 days and the Sentinel – 3A / 3B satellites (b) with a repetition period of 27 days in the Barents Sea.
Figure 3 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 3. Interannual variability of the Barents Sea level anomalies according to satellite altimetry measurements of the ERS − 1/2, ENVISAT and SARAL/AltiKa for the period 1992 –2018. Dashed line shows a linear trend.
Data for 'Controls on Ice Cliff Distribution and Characteristics on Debris-Covered Glaciers'
<p>This dataset contains ice cliff, pond, debris, glacier and crevasse zone outlines (as shapefiles) derived for the study 'Controls on Ice Cliff Distribution and Characteristics on Debris-Covered Glaciers'.</p> <p>They were outlined from 14 Pléiades multi-spectral images (see Methods and tables in corresponding manuscript and SI) acquired at the dates indicated in the corresponding folder names.</p> <p>The majority of the Pléiades stereo-pairs used were provided by Etienne Berthier via the Pléiades Glacier Observatory (PGO) initiative of the French Space Agency (CNES). The remaining images were acquired through the CNES ISIS Programme. </p>
Canopy cover data from the Ice Storm Experiment (ISE) plots
Large-scale disturbances such as ice storms may increase in frequency and intensity as climate changes. While disturbances are a natural component of forest ecosystems, climatically driven alteration to historical patterns may impart fundamental change to ecosystem function. At Hubbard Brook Experimental Forest, NH, experimental ice storms of varying severity were applied to replicate plots of mature northern hardwoods to quantify their effects on forested ecosystems. We assessed ice storm treatment effects on insectivorous foliage-gleaning birds and their interactions with larval Lepidoptera. These birds are charismatic, of conservation concern, and are a major predator of caterpillars. In turn, lepidopterans are the dominant herbivores in temperate forests and are integral to ecosystem function. We predicted that avian abundance would increase due to additional structural heterogeneity caused by ice treatments, with a concomitant increase in caterpillar predation. Point counts were used to measure insectivorous bird activity in the ice storm experiment plots and additional control plots before and after treatments. Point counts were conducted in June 2015 and June 2016. Icing occurred in January-February 2016. We deployed and retrieved plasticine model caterpillars and estimated predation from characteristic marks to these surrogates. Abundance of foliage-gleaning birds was higher in the ice storm plots and birds responded to treatments as a single diffuse disturbance rather than on an individual plot level. All species except one were observed both before and after the ice treatments. Surprisingly, predation on caterpillar models was unaffected by ice storm treatments but rather was a function of caterpillar density. The increase in avian abundance in the ice storm treatment plots corroborates other studies of bird responses to relatively small-scale disturbances in forests and the limited change in species composition was expected given the plot size. We conclud
Phenological changes in stratification and overturning of Northern Hemisphere ice-covered lakes
<p>The data and codes to reproduce analysis and visualization in the paper entitled "Projected phenological shifts in stratification and overturning of ice-covered Northern Hemisphere lakes"</p>
Glacier_inventory_debris_cover_ice_thickness_dataset_Chandra_Bhaga_basin_Himalaya
<p>This is a multitemporal glacier dataset over ChandraBhaga basin in western Himalaya which includes glacier inventory (for 251 glaciers with area > 0.5 km<sup>2</sup>) for year 1993, 2000, 2010 and 2019 (.shp files); debris cover for year 1993 and 2019 (.tif files) and ice thickness (.tif files). Details of files are included in data_description.docx. These datasets form part of manuscript submitted to Earth Systems and Science Data.</p>
Figure 1 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 1. Map of the Barents Sea. The red dashed line shows the boundaries of the Barents Sea.
SWAN simulations: Local wind-waves in partial sea ice cover
<p>This repository contains data and scripts associated with the study: <em>Scaling simulations of local wind-waves amid sea ice floes </em>by S. Brenner & C. Horvat.</p>
The sensitivity of primary productivity in Disko Bay, a coastal Arctic ecosystem to changes in freshwater discharge and sea ice cover
<p>Data for figure 4 in <a href="https://doi.org/10.5194/egusphere-2022-916">https://doi.org/10.5194/egusphere-2022-916</a></p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.