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3,575 results for “2009”
Leaf Area Index on the GLBRC Biofuel Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (2009 to 2017)
Dataset AbstractThe leaf area index was measured to estimate the phenology and growth patterns of the different biofuel crops.original data source http://lter.kbs.msu.edu/datasets/225
Wisconsin Lake Historical Limnological Parameters 1925 - 2009
This dataset is a compilation of ten sources of data representing physical and chemical properties of 13,093 Wisconsin lakes. The goal was to compile a comprehensive resource of historical and more recent lake information which would be accessible by querying a single database. Due to the wide temporal extent (1925-2009), methods used for measuring lake parameters in this dataset have varied. A careful look at the available metadata and background information is recommended. Sampling Frequency: varies Number of sites: 13,093
Climate data for saddle data loggers (CR23X and CR1000), 2009 - 2021, hourly.
Climatological data were collected from the saddle climate station on Niwot Ridge (3525 m elevation) throughout the year. From 2000-06-24 to 2012-03-24, data were recorded using a Campbell Instruments CR23X data logger. Subsequently, data were recorded using a Campbell Instruments CR1000 data logger. This data set includes data beginning in 2009. Maximum and minimum values were recorded instantaneously, with a sampling interval of 5 seconds. Hourly means and totals were calculated from 720 individual measurements. The CR23X logger was programmed to generate both hourly and daily output. The CR1000 logger generated daily, hourly, and minute data until September 2014, and 10 minute and minute data thereafter. This dataset discontinued, see methods for instructions on accessing the 10 minute data instead.
Zooplankton collected with a 2-m, 700-um net towed from surface to 120 m, aboard Palmer Station Antarctica LTER annual cruises off the western Antarctic peninsula, 2009 - 2024.
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton and thus provide a link between primary producers and higher trophic levels. Zooplankton density and biovolume were determined at grid stations on the annual LTER cruises along the western Antarctic Peninsula (WAP). Annual cruises take place between late December to early February, except for the NBP21-13 cruise, which was November and December. Typically, zooplankton were collected with a 2x2 meter, 700um mesh net fitted with a flow meter and towed obliquely to 120m. Zooplankton distributions vary spatially due to water column characteristics, which affect their predators' distributions. As climate change continues to affect the WAP, the relative abundance of the various zooplankton components can also be expected to change.
Standard body length of Euphausia superba collected with a 2-m, 700-um net towed from surface to 120 m, collected aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 2009 - 2024.
Antarctic krill, Euphausia superba, are a critical food-web link between phytoplankton primary production and higher trophic levels, such as whales, penguins, and seals. Krill standard length was measured from LTER zooplankton tows along the western Antarctic Peninsula. Annual cruises take place between late December to early February, except for the NBP21-13 cruise, which was November and December. Length data provides estimates of age-class abundance and recruitment. Climate-induced changes in krill recruitment are an important consideration in the management and modelling of krill populations.
Length of Salpa thompsoni collected with a 2-m, 700-um net towed from surface to 120 m, collected aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 2009 - 2024.
Salps (Salpa thompsoni) are conspicuous gelatinous zooplankton capable of rapid population increases, enabling them to respond quickly to unpredictable phytoplankton blooms common in the Antarctic. Body length was measured on salps collected from LTER zooplankton tows along the western Antarctic Peninsula. Annual cruises take place between late December to early February, except for the NBP21-13 cruise, which was November and December. Salps have amongst the highest filtration rates of all zooplankton, and package their waste into large, fast sinking fecal pellets. These pellets provide a mechanism to export carbon fixed in the surface waters into the deep ocean. Since filtration rates and pellet size are positively related to the size of a salp, population estimates of grazing and exported carbon can be determined through length data.
Zooplankton collected with a 1.4-m2 frame, 500-μm mesh Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 2009-2017
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton in surface waters and thus provide a link between primary producers and higher trophic levels. Other zooplankton reside in the mesopelagic zone and feed on detritus or on other animals. Depth-discrete density of zooplankton taxa was determined at process study stations on the annual Palmer LTER cruises along the western Antarctic Peninsula. Samples were collected with a 1.4-m2 frame, 500-μm mesh Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) towed obliquely to the surface from a depth of typically 500 m. MOCNESS tows were conducted in consecutive day-night pairs at each process study station. Zooplankton depth distributions vary between day and night as these animals conduct diel vertical migrations. Depth distributions also vary among zooplankton taxa based on species feeding ecology and life history traits. Zooplankton diel vertical migration contributes to the export of carbon and nutrients from the surface ocean to the mesopelagic zone.
Jesusita Fire Perimeter (Santa Barbara County, CA), May 10, 2009 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Jesusita Fire burned from 2008-05-05 to 2008-05-18, Northwest of Mission Canyon and Santa Barbara City, Santa Barbara County. Approximately 8733 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-05-10, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Wave data for Hog Island Bay, Fowling Point and Upsur Neck, Virginia, 2009
An RBR wave gauge was deployed at 3 locations on the coast of Virginia during 2009.
ADCP wave and current data at Hog Island and Chimney Pole Marsh, VA 2009
Two acoustic doppler profilers were deployed in on Hog Island and Chimney Pole Marsh in 2009. all quantites (velocity, waves, water elevation, attenuation) are described in the file *.hdr (header file)
Winds on Hog Island Virginia 2009-2019
An RM Young wind monitor was installed at the Machipongo Station n the northern part of Hog Island, on the western side of the island upland. It was initially installed on top of the tower on the station at a height of approximately 20m. Subsequent to the demolition of the Machipongo Station in 2012, the monitor was moved to near the top of an abandoned Coast Guard flag tower (approx. 15 m high) located adjacent to the former site of the Machipongo Station. Measurements of wind speed and direction were taken each hour. The mean wind vector magnitude and direction are recorded based on 60 measurements made each hour. Standard deviation of the wind direction was calculated using the Campbell Scientifics formula. Starting in 2013 peak wind speeds were also recorded. Collection of this data ended in 2019. It was replaced by the dataset knb-lter-vcr.25 (Hourly Meteorological Data for the Virginia Coast Reserve LTER 1989-present) when a full-fledged meteological station was installed atop a 20-meter tall lookout tower on southern Hog Island at the site of the former town of Broadwater.
Quantifying the magnitude of storm events that have impacted the Virginia Coast Reserve (2009-2024) using the Cumulative Storm Impact Index (CSII)
This dataset contains a record of storm events along with quantified magnitudes that have impacted the Virginia Coast Reserve between 2009- 2024, minus 2010. We retrieved hourly water level data and monthly datums from the NOAA Tides and Currents database (tidesandcurrents.noaa.gov) for the tide station located in Wachapreague, VA (Station 8631044) to quantify the magnitude of storms using 1) the Storm Erosion Potential Index (SEPI; Zhang et al. 2001), and 2) the Cumulative Storm Impact Index (CSII; Fenster and Dominguez 2022). CSII incorporates the timing and magnitude of previous storms as a measure of cumulative impact, or "storminess". We identified storm events based on storm surge that exceeded two standard deviations (> 2SD) of the average surge and storm tide that exceeded the annual average Mean High Water (MHW) of a semi-diurnal tide (12 hours; SEPI). We then calculated the CSII for each storm as the sum of the SEPI and an exponentially decaying weighting factor (delta) from the previous storm's CSII that accounts for beach recovery that may have occurred between storm events. Here we use delta = 0.3 to best capture storm clustering during the 15 year period (Fenster and Dominguez 2022). Years missing >10% of data were excluded. For detailed methods on the data retrieval process, identifying storms, and quantifying storm magnitude, see Fenster and Dominguez (2022) and Dominguez et al. (2024). We identified a total of 208 storm events with an average of 14.3 events per year +/- 2.3 (SD) and an average annual CSII of 428.1 (m2hr) +/- 196.1 (SD).
DEM and associated kinematic GPS coordinates of September 2009 survey of the salar de Uyuni, Bolivia
<p>This dataset consists of two parts: 1) the post-processed kinematic GPS coordinates of a September 2009 survey of a 45 x 54 km region of the salar de Uyuni, Bolivia. 2) a digital elevation model (DEM) of the salar de Uyuni surface derived from those kinematic GPS data.</p> <p>Details of the survey design are identical to that from an earlier survey in 2002 and can be found in the manuscript, "Topography of the salar de Uyuni, Bolivia from kinematic GPS" (doi: 10.1111/j.1365-246X.2007.03604.x). The DEM is described in the manuscript "A Terrestrial Validation of ICESat Elevation Measurements and Implications for Gloval Reanalysis" (doi: 10.1109/TGRS.2019.2909739). The DEM was generated from fitting two-dimensional Fourier basis set with parameters: L_x = L_y = 70000 meters, m = n = 10. This results in a basis set with a nominal resolution of 7 km.</p> <p>The attached "salar_de_uyuni_2009_dem" files duplicate Figure 1 from the authors' "A terrestrial validation of ICESat elevation measurements and implications for global reanalyses," whose caption is: </p> <p>Landsat image of the salar de Uyuni, showing ICESat tracks 85, 241, 360 and 1320 (red) and the GPS-derived DEM from 2009 (color-coded with respect to mean elevation). The portion of each track plotted in Figure 2 is boxed in black. Total relief on the GPS DEM is less than 1 m over 50 km.</p>
Data for: Impact of SO2 injection profiles on simulated volcanic forcing for the Sarychev 2009 eruptions - investigating the importance of using high vertical resolution methods when compiling SO2 data
<p>The files are data assosicated with the study High-resolution stratospheric volcanic SO2 injections in WACCM. The files are associated with four differnt simulaions described in the paper: M16, S21-1D, S21-3D and No-Volc. The files with "input" in the name are the SO2 input files used in the WACCM (Whole Atmosphere Community Climate Model) simulations in the paper. The files with "monthly_averages" in the filenames are monthly averages of model output data the variables used in the paper. </p> <p>The CALIOP_monthly_averages.nc file is monthly average of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) satellite data used in the study to evaluate the WACCM simulations. </p> <p> </p>
2006_2009_MODIS_Phenology
<p>This dataset supercedes the previousl MODIS Phenology datasets based on MODIS V4 data lebelled 'Green up' and 'senescence'.</p> <p>A number of coverages have been processed and extracted from the global MODIS Land Cover Dynamics datasets derived by Boston University (http://www.bu.edu/lcsc/research/land-cover-dynamics) from the NASA MOD12Q2 MODIS tiled phenology layers (https://lpdaac.usgs.gov/products/modis_products_table/mcd12q2). A detailed user guide (http://www.bu.edu/lcsc/files/2012/08/MCD12Q2_UserGuide.pdf, and http://modis.gsfc.nasa.gov/data/atbd/atbd_mod12.pdf) has been released by the Boston University team which provides detailed technical specifications and describes the methods used to calculate the layer values.</p> <p>In summary, these datasets for version 5 were produced twice a year (January and June) between 2001 and early 2010. For each date, a series of parameters were calculated of which five are provided here for 2006 to 2010: Green Up and Senescence , which represent the dates when new green vegetation started be detected at the beginning of a growth cycle, and when the fall in greenness stopped at the end of the cycle.</p> <p>Because some areas have more than once cycle a year, two images are produced for each date: Cycle 1 and Cycle2 . Thus, the Cycle 1 green up image for January 2006 contains the date (expressed as days from January 1 2000) of the green up event for the first cycle occurring between the end of June 2005 and the beginning of July 2006, whilst the Cycle 2 image contains the date for the green up event of the second cycle occurring in that period.</p> <p>This can be confusing, for a detailed explanation with examples please refer to the document included within this download "MODIS PHENOLOGY DATA DESCRIPTION_v2.doc".</p>
Inter-Chemical Correlation results for the study: NHANES20092010 (NHANES Survey 2009-2010)
Title: NHANES Survey 2009-2010 <br>Species: Homo sapiens <br>Number of samples: 9717 <br>Number of named analytes: 186 <br>Datasource url: https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Laboratory <br>
18S V4 rDNA sequences organized at the OTU level for the SOMLIT-Astan time-series (2009-2016)
<p>The present file includes metadata for each 18S V4<strong> rDNA OTU</strong> from the SOMLIT-Astan time series (2009-2016) including the following fields: <strong>amplicon</strong> = identifier of the representative (most abundant) sequence; <strong>total</strong> = total number of reads; <strong>spread </strong>= number of samples in which the OTU has been found; <strong>cloud </strong>= number of unique sequences constituting the OTU; <strong>sequence</strong> = nucleic acid sequence of the representative sequence; <strong>length</strong> = length of the representative sequence; <strong>quality </strong>= minimum expected error observed for the representative sequence, divided by sequence length; <strong> taxonomy</strong> = taxonomic path assigned to the representative sequence; <strong>identity</strong> = percentage of identity of the representative sequence to the closest reference sequence from PR2; <strong>references</strong> = best hit reference sequence(s) ; <strong>RA090107_02:RA161222_3 </strong>= 375 samples from January 2009 to December 2016, the first two number are the year followed by the month and the day (sampling twice a month during 8 years). Values after “_” indicate the size of the filter used for the filtration: 02 for 0.2 µm and 3 for 3 µm.</p> <p>Generation of 18S V4 rDNA Operational Taxonomic Units (OTUs) from the raw sequencing reads and their assembly into a OTUtable was obtained according to the following pipeline (https://doi.org/10.5281/zenodo.5791089). The V4 region was extracted from the 18S rDNA reference sequences from PR2 v4.12 (Guillou et al., 2013) with Cutadapt. The representative sequences of each OTU were compared to these V4 reference sequences by pairwise global alignment (usearch_global VSEARCH’s command). Each OTU inherits the taxonomy of the best hit or the last common ancestor in case of ties. OTUs with a score below 80% similarity were considered as unassigned (Mahé et al., 2017; Stoeck et al., 2010).</p> <p>The final dataset (filtered OTU table) contains 375 samples (sampled twice per month from 2009 to 2016) with a total of ~30 million sequence reads and 21,418 OTUs.</p>
Particulate organic carbon (POC) concentration in meltwater runoff of Leverett Glacier, Russell Glacier, and Isunnguata Sermia, southwest Greenland (2009-2018)
<p>This dataset describes particulate organic carbon (POC) and particulate carbon (PC) concentrations of suspended sediments in the proglacial rivers of 3 land-terminating glaciers in the Kangerlussuaq area, Southwest Greenland: Leverett Glacier (LG), Leverett River; Russell Glacier (RG), Akuliarusiarsuup Kuua; and Isunnguata Sermia (IS), Isortoq River. Both the Leverett River and Akuliarusiarsuup Kuua are tributaries of the Qinnguata Kuussua (also known as Watson River). The data have already been part of 3 different publications (Lawson et al. 2014, Kohler et al. 2017, and Vrbická et al. 2022) but are archived here for the first time.</p> <p>POC data was collected for LG during the 2009 and 2010 melt seasons (Lawson et al. 2014) as well as 2015 (Kohler et al. 2017). For the 2018 melt season, only total carbon concentrations of suspended sediments (PC) is archived as opposed to POC (see Vrbická et al. 2022).</p>
Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2009-2011)
<p>This data is part of the <em>Monthly aggregated Water Vapor MODIS MCD19A2 (1 km)</em> dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.</p><p><strong>General Description</strong></p><p>The monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:</p><ul><li><strong>Monthly time-series:</strong><br>Derived from <em>MCD19A2 v061</em>, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li><strong>Yearly time-series:</strong><br>Derived from <em>monthly time-series</em>, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li><strong>Long-term data (2000-2022):</strong><br>Derived from <em>monthly time-series</em>, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul><p><strong>Data Details</strong></p><ul><li><strong>Time period:</strong> 2009–2011</li><li><strong>Type of data:</strong> Water vapor column above the ground (0.001cm)</li><li><strong>How the data was collected or derived:</strong> Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li><strong>Statistical methods used:</strong> Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li><li><strong>Coordinate reference system:</strong> EPSG:4326</li><li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.00081, 179.99994, 87.37000)</li><li><strong>Spatial resolution:</strong> 1/120 d.d. = 0.008333333 (1km)</li><li><strong>Image size:</strong> 43,200 x 17,924</li><li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li></ul><p><strong>Support</strong></p><p>If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:</p><ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul><p><strong>Name convention</strong></p><p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p><ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20090101 = 2009-01-01</li><li>Time reference end time: 20111231 = 2011-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>
Time Series of Water Levels in a Coastal Barrier-Lagoon System, NW Spain (2009-2012)
<p>This repository contains the data recorded by water-level loggers (survey-pressure transducers) deployed in a barrier-lagoon coastal system, which were used in the study by</p> <p><strong>R. González-Villanueva, M. Pérez-Arlucea, and S. Costas titled 'Lagoon Water-Level Oscillations Driven by Rainfall and Wave Climate,' published in Coastal Engineering, Volume 130, 2017, Pages 34-45, ISSN 0378-3839, available at <a href="https://doi.org/10.1016/j.coastaleng.2017.09.013">https://doi.org/10.1016/j.coastaleng.2017.09.013</a></strong></p> <p>The repository consists of three text files:</p> <ol> <li><strong>lagoon_water_level.txt</strong></li> <li><strong>sea_level.txt</strong></li> <li><strong>phreatic_level.txt</strong></li> </ol> <p>Each file includes a header with metadata and information for each column in the data file, as follows:</p> <ul> <li><strong>pt_id</strong>: ID of the individual record</li> <li><strong>pt:</strong> instrument used</li> <li><strong>lat</strong>: Latitude in WGS84</li> <li><strong>long</strong>: Longitude in WGS84</li> <li><strong>units</strong>: Indicates the measurement unit for the water level recordings</li> <li><strong>temporal resolution</strong>: Indicates the time interval between two consecutive measurements</li> <li><strong>column 1</strong>: Description of the data contained in column 1</li> <li><strong>column 2</strong>: Description of the data contained in column 2</li> <li><strong>column n</strong>: Description of the data contained in column n</li> </ul>
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