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2,495 results for “continentality”
KKE01 The Konza-Kruger Experiment: A cross-continental fire and grazing experiment at Konza Prairie
For more than a decade, we have compared responses of mesic (subhumid) savanna grasslands (>500 mm MAP in the tropics and >600 mm MAP outside the tropics) in North America and South Africa to alterations in both fire and grazing regimes. The long-term, comparative experiment that forms the centerpiece of this cross-continental research program is located in tallgrass prairie at the Konza Prairie Biological Station (Kansas, USA) and in knob-thorn marula savanna at the Kruger National Park (Limpopo and Mpumalanga provinces, South Africa). We refer to this study as the Konza-Kruger (K-K) Experiment. At both sites, we have been manipulating grazing by removing all large herbivores (>5 kg) from research plots with permanent exclosures (each with a paired plot that grazers can freely access). These exclosures were established in replicated fire frequency experiments ongoing at each site (treatments range from >25-50 yrs of annual burning, burning every 3-4 yrs, or complete fire exclusion).
Geochemical, physicochemical, and genomic data from a continental-scale survey of microbial diversity in Antarctic soils (2003-2023)
This data package offers comprehensive insights into Antarctic soil microbial diversity and composition. From 2003 to 2023, a total of 186 samples were collected from diverse locations spanning the Antarctic Peninsula to East Antarctica, representing a wide range of environmental gradients and climatic conditions. Soils were stored at -20°C to preserve their integrity for downstream analyses. This data package integrates cultivation-independent sequencing of prokaryotic and fungal communities alongside a robust cultivation-dependent culture collection to enable direct comparisons across microbial diversity assessment methods. Accompanying geochemical, physicochemical, and environmental parameters provide critical context for biogeographical analyses, offering a valuable resource for studying microbial adaptations and community dynamics in extreme Antarctic environments.
Analogue models testing the interaction between a propagating continental rift and inherited crustal fabrics
<p>This dataset presents the results of an experimental series of analogue models performed to investigate the interaction between a propagating continental rift and inherited crustal fabrics. Our experimental series was designed adopting a parametric approach, which consisted in the systematic variation of the orientation of various kinds of brittle discontinuities (e.g., faults, fractures, foliations, etc.). Structures of models have been analysed quantitatively by means of photogrammetric digital elevation model reconstruction and semi-automatic fault pattern quantification. In this dataset, we show the row data and specific elaborations supporting the interpretation of results.</p>
A 30m Topographic Wetness Index Dataset for the Continental United States
<p>The topographic wetness index was computed at a 30m resolution for the continental United States. This dataset was computed using the triangular multiple flow direction algorithm for computing upslope areas (see https://doi.org/10.1029/2006WR005128). We used the Shuttle Radar Topography Mission (SRTM; https://doi.org/10.1029/2005RG000183) digital elevation model (DEM) for all computations. This analysis was conducted in the R programming language using the System for Automated Geoscientific Analyses (SAGA; https://doi.org/10.5194/gmd-8-1991-2015) for back end computations. This dataset is in the NAD83(NSRS2007) / Conus Albers projection system, EPSG 5072.</p>
Output files corresponding to "Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to the output files that were produced for the study reported in:</p> <ul> <li>Sawyer, Audrey H., Cédric H. David, and James S. Famiglietti, (2016), Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities, Science, 353(6300), 705-707. DOI:10.1126/science.aag1058. </li> </ul> <p> </p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p> </p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Regions used are: Northeast (NE: 01), Mid-Atlantic (MA: 02), South-Atlantic North (SAN: 03N), South-Atlantic South (SAS: 03S), South-Atlantic West (SAW: 03W), Lower Mississippi (MS: 08), Texas (TX: 12), California (CA: 18), and Pacific Northwest (NW: 17).</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2010 Census dataset (CENSUS 2010), obtained from: http://www2.census.gov/geo/tiger/TIGER2010DP1/County_2010Census_DP1.zip.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>NHDFlowline_CONUS_coastline.zip. </em>This zip file contains a shapefile with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in regions used. </li> <li><em>Catchment_CONUS_coastline.zip. </em>This zip file contains a shapefile with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in regions used. </li> <li><em>Catchment_CONUS_coastline_centroid.zip</em>.<em> </em>This zip file contains a shapefile with the centroids of the above catchments. </li> <li><em>SGD_Coastal_Vulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID. Unique feature identifier in NHDPlusV2 (-).</li> <li>LENGTHkm. Length of coastline feature (km).</li> <li>REACHCODE. Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature (-).</li> <li>AREAsqkm. Area of coastal catchment feature (km<sup>2</sup>).</li> <li>REGION. NHDPlusV2 region: NE = Northeast, MA = Mid-Atlantic, SAN = South Atlantic North, SAS = South Atlantic South, SAW = South Atlantic West, TX = Texas, MS = Lower Mississippi, CA = California, PN = Pacific Northwest (-).</li> <li>RLENGTHkm. Total length of coastline accumulated by REACHCODE (km).</li> <li>RAREAsqkm. Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>BGRUNkgpsqm. Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>SGDsqmpy. Average annual fresh SGD rate for REACHCODE (m<sup>2</sup>/y).</li> <li>RCOUNT. Number of features by REACHCODE (-).</li> <li>PDENpsqkm. Population density for coastal catchment feature (km<sup>-2</sup>).</li> <li>SWIVULN. Vulnerability to saltwater intrusion: - 1 = vulnerable, 0 = not vulnerable (-).</li> <li>PCTDEV11. Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>CONTVULN. Vulnerability to offshore contamination associated with direct groundwater discharge: - 1 = vulnerable, 0 = not vulnerable (-).</li> </ul> </li> </ul> <p> </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>No bugs have been unveiled since publication of this dataset or the associated manuscript. Vulnerability thresholds are subjective and could be adjusted for different applications, refer to published manuscript for approaches used here.</p> <p> </p> <p><strong>Funding</strong></p> <p>This work was supported by the Ohio State University School of Earth Sciences, and NSF grant EAR-1446724 (A.H.S); the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA, and grants from the NASA SWOT and Sea Level Science Teams (C.H.D. and J.S.F.).</p>
Dataset: Six years of surface remote sensing of stratiform warm clouds in marine and continental air over Mace Head, Ireland
<p>A total of 118 stratiform water clouds observed by ground-based remote sensing instruments at the Mace Head Atmospheric Research Station at the West coast of Ireland from 2009 to 2015 were analyzed in terms of microphysical and optical characteristics as well as the impact of aerosols on these properties. The microphysical and optical cloud properties in the files were obtained using the algorithm SYRSOC (SYnergistic Remote Sensing Of Clouds).</p>
Using RS-DAT to study continental-scale phenology at high-spatial resolution
This repository includes the notebooks employed to calculate and analyze a gridded phenological model, as computed from a set of daily meteorological variables.
Role of Volcano-Tectonic Interactions During Early-Phase Magma-Assisted Continental Rifting: Supplementary Model Files
<p>Input and output model files for the manual script titlted "Role of Volcano-Tectonic Interactions During Early-Phase Magma-Assisted Continental Rifting" submitted to Journal of Geophysical Research: Solid Earth.</p>
Data inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al.
<p>This data packet provides inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al., currently in the submission process. This repository will eventually be updated to link to the published manuscript.</p> <p> </p> <p>===============================================================================<br>===============================================================================<br>Overview<br>===============================================================================<br>===============================================================================</p> <p>Data and model products associated with the manuscript "Mammal niches are not <br>conserved over continental scales" by Goldstein et al. </p> <p>Files are organized into two subdirectories. The first, "model_inputs/", <br>contains 8 data files intended to be used as part of the reproducible code <br>repository at https://github.com/dochvam/Mammal_SVCs_ISDM_reproducible. <br>The second subdirectory, "model_outputs/", contains modeled products giving<br>estimated spatially varying niche relationships and predictions of relative<br>abundance.</p> <p>Below, we describe the contents of each file type. See the main manuscript <br>for full methodology, data sources, and discussions of spatial scales.</p> <p>NOTE: Version 1 of this dataset contained some errors that have been corrected<br>in Version 2. Version 2 was used as the input dataset for the analyses in the<br>associated manuscript. Version 1 should not be used.</p> <p>===============================================================================<br>===============================================================================<br>Subdirectory 1: "model_inputs/"<br>===============================================================================<br>===============================================================================</p> <p>Two versions of each of four files are provided, corresponding to analyses <br>that do or do not consider ancient genetic lineages as potential sources of <br>spatial nonstationarity in mammal niches. Each file type is formatted the same,<br>and the versions are differentiated by either the suffix "nolineage" or <br>"lineage" in the filename. </p> <p>===============================================================================<br>File 1: gridcell_covars_lineage.csv and gridcell_covars_nolineage.csv<br>===============================================================================<br>These files are .csvs giving spatial covariate data for each scale 2 cell<br>in North America, summarized to 5000 m. All percentage values are given in <br>10ths of a percent (scale of 0-1000). The following columns are provided:</p> <p>- grid_cell: Scale 2 cell ID<br>- Arable: pct arable land (Jung et al. 2020)<br>- EVI_mean: mean enhanced vegetation index (Didan 2021)<br>- EVI_Q95: 95th quantile of EVI (Didan 2021)<br>- Forest: Pct forest cover (Jung et al. 2020)<br>- Grassland: pct grassland (Jung et al. 2020)<br>- Pastureland: pct pastureland (Jung et al. 2020)<br>- Pop_den: Human population density, from Gridded Population of the World (CIESIN 2018)<br>- Precipitation: avg annual precip. (Vega et al. 2017)<br>- Shrubland: pct shrubland (Jung et al. 2020)<br>- Temp_max: Average maximum daily temperature (Vega et al. 2017)<br>- Terrain_roughness (Amatulli et al. 2018)<br>- Wetlands: pct wetlands (Jung et al. 2020)<br>- is_land: Whether or not the cell is on land vs. ocean, used for filtering<br>- Agriculture: Pct. agricultural land (Jung et al. 2020)<br>- Pop_den_sqrt: Square root of human population density (CIESIN 2018)<br>- EVI_variability: Distance btw the 95% inner quantiles of EVI (Didan 2021)</p> <p> </p> <p>===============================================================================<br>File 2: inat_cts_lineage.csv and inat_cts_nolineage.csv<br>===============================================================================</p> <p>These files give summaries of iNaturalist sampling effort and detections<br>for target species. The following columns are provided:</p> <p>- grid_cell: Scale 3 cell ID<br>- n: Total iNaturalist effort in the cell (number of obs. of all mammals)<br>- The remaining columns are named for species. Each column gives the count<br> of observations of the species in the cell.</p> <p>===============================================================================<br>File 3: ct_datlist_lineage.RDS and ct_datlist_nolineage.RDS<br>===============================================================================</p> <p>The ct_datlist files contain R objects that are lists of lists. These objects ultimately<br>contain all of the camera detection histories and camera-level covariate data used<br>in modeling. We use the nice data type "unmarkedFrameOccu" from the unmarked R package<br>to organize these detection data.</p> <p>Each outer list is of length equal to the number of species. The ith element of each<br>list contains the following named slots:</p> <p>- species: a string giving the name of the ith species<br>- umf: an unmarkedFrameOccu object. This object has three important slots:<br> - y: a (# deployments) x (max # replicates) matrix giving 1s, 0s, or NAs indicating<br> whether the target species was observed in that 10-day window;<br> - siteCovs: a (# deployments) x 2 data frame with the following columns:<br> - site_ID: A unique ID of the exact location, shared by deployments with the same<br> coordinates<br> - subproject_ID: A unique ID indicating which camera array is associated <br> with this deployment<br> - obsCovs: a (# deployments * max # replicates) x 6 data frame with the following columns:<br> - year: the year of survey, relative to 2020 (zero-year is 2020)<br> - yday_scaled: the (scaled) Julian date of the beginning of the window<br> - yday_scaled_sq: yday_scaled^2, for use in estimating a quadratic effect<br> - log_roaddist_scaled: Scaled distance to nearest road (Meijer et al. 2018)<br> - Canopy_height_scaled: Scaled canopy height (Potapov et al. 2021)<br> - obs_len_scaled: Scaled duration of window, to account for some windows <br> being cut off at < 10 days<br>- coords: a data frame. Originally, this file gave the exact position for each camera,<br> but these exact locations have been scrubbed for privacy. See the original sources<br> cited in the manuscript for full details. This data frame contains the following column:<br> - scale2_grid_ID: the ID of the Scale-2 5000 m grid cell containing the camera</p> <p>===============================================================================<br>File 4: grid_translator_wspecs_nolineage.csv and grid_translator_wspecs_lineage.csv<br>===============================================================================</p> <p>These files are used for bookkeeping to track the relationships between the <br>three spatial scales in this study. Each row corresponds to a single "scale 2"<br>cell, giving the ID of the corresponding S3 and S4 grid and also an ID for each<br>species indicating whether and where it is in the species' range. </p> <p>Note that the scale names in the code don't match the manuscript. In the code,<br>"scale 1" is the level of an individual camera, "scale 2" is the 5 km intensity<br>grid, "scale 3" is the 50 km iNaturalist grid, and "scale 4" is the 100 km<br>SVC grid.</p> <p>The following columns are provided:<br>- scale2_grid_ID: unique ID for each cell in the 5 km intensity grid<br>- scale3_grid_ID: unique ID for each cell in the 50 km iNaturalist aggregation<br>- scale4_grid_ID: unique ID for each cell in the 100 km SVC grid<br>- GRID_ID_[species]: for each species, a column is provided on the S4 scale<br> counting each cell in the species' modeled range. NAs<br> indicate that the S2 cell defined in the row is not<br> included in the species' modeled range.</p> <p>===============================================================================<br>===============================================================================<br>Subdirectory 2: "model_outputs/"<br>===============================================================================<br>===============================================================================</p> <p>===============================================================================<br>File 1: svc_estimates.csv<br>===============================================================================</p> <p>This file gives an estimate of the effect of each covariate on each species'<br>intensity, and the uncertainty in that estimate, for each species/covariate<br>pair. Results correspond to lineage models for species with phylogeographies<br>and non-lineage species otherwise. Each row represents the effect of one <br>covariate on one species' relative intensity process within one 100 km cell g. <br>Note that many estimates of beta_g are uncertain even for strong spatial <br>effects---the model is often confident that a spatial process is supported <br>while estimates of the realized process are uncertain.</p> <p>The following columns are provided:<br>- x: the x-coordinate of the 100 km cell<br>- y: the y-coordinate of the 100 km cell<br>- species<br>- parname: the name of the covariate<br>- mean: the mean of the posterior samples of beta_g<br>- 2.5%: the 2.5th quantile of the posterior samples of beta_g<br>- 50%: the 50th quantile of the posterior samples of beta_g<br>- 97.5%: the 97.5th quantile of the posterior samples of beta_g</p> <p>The following spatial projection is used to define X/Y coordinates:<br>"+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83"</p> <p>===============================================================================<br>File 2: predicted_intensity.tif<br>===============================================================================</p> <p>This file contains a raster "brick" giving the predicted intensity surface <br>and uncertainty in this surface for each species. All predictions are generated<br>using models that do *not* account for lineage information---this means that <br>predictions for species with lineages are not from the models reported in the<br>main manuscript. The reason for this is that we found that lineages were <br>overall unsupported, so better predictions can be arrived at by excluding this<br>source of uncertainty in the underlying intensity process.</p> <p>The raster brick has 66 layers. Each layer provides either the mean predicted<br>log intensity in each grid cell across the species range or else provides<br>the standard error of that predicted log intensity. Layer names indicate output<br>type and species associated with each layer.</p> <p><br>===============================================================================<br>===============================================================================<br>References<br>===============================================================================<br>===============================================================================</p> <p>Camera data are obtained from the following sources, which can be consulted to<br>obtain the original raw camera data</p> <p>- Cove, Michael V., et al. "SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States." (2021): e03353.<br>- Kays, Roland, et al. "SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID‐19 pandemic." (2022): e3775.<br>- Shamon, H., et al. “SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.” Ecology, 105.6 (2024): e4318.<br>- Rooney, B., et al. “SNAPSHOT USA 2019–2023: The first five years of data from a coordinated camera trap survey of the United States.” In Press (2024).<br>- Kays, Roland, et al. "Does hunting or hiking affect wildlife communities in protected areas?." Journal of Applied Ecology 54.1 (2017): 242-252.<br>- Roberts, R. California Department of Fish and Wildlife, Bobcat Program Initiative. wildlifeinsights.org (2023).<br>- Lasky, Monica, et al. "CAROLINA CRITTERS: a collection of camera trap data from wildlife surveys across North Carolina." Ecology 102.7 (2021): e03372.<br>- Forrester, T. (2000). Urban to Wild Project. http://n2t.net/ark:/63614/w12004302. Accessed via wildlifeinsights.org on 2024-08-29.<br>- McMurry, S. et al. In review (2024).<br>- Forrester, T. (2011) Okaloosa S.C.I.E.N.C.E. Project. http://n2t.net/ark:/63614/w12004287. <br>- Myers, J. (2014) Tyson Research Center ForestGEO Project. http://n2t.net/ark:/63614/w12004295.<br>- McMurry, S., and Kays, R.(2023). Calloway Forest Preserve. http://n2t.net/ark:/63614/w12006449. Accessed via Wildlife Insights on 2024-08-29.<br>- McMurry, S., Parsons, A., Lasky, M., Luongo, K., Clark, J., McShea, W., Scher, L., Kays, R., Spurlin, J., Martin, G., Frech, G., Barajas-Salazar, K., Snider, M. (2022). Last updated October 2023. Calloway Forest Preserve. http://n2t.net/ark:/63614/w12004251. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Kays, R.. (2008). Last updated March 2024. Albany Area Camera Trapping Project. http://n2t.net/ark:/63614/w12003860. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Kays, R., Snider, M., McMurry, S., Alyetama, M. (2024). Last updated April 2024. Pilot Mountain Density 2024. http://n2t.net/ark:/63614/w12007160. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Malleshappa, V., Smithsonian, E., Kays, R., Schuttler, S. (2015). Last updated December 2022. Museums Connect Mexico. http://n2t.net/ark:/63614/w12004298. Accessed via wildlifeinsights.org on 2024-08-29.</p> <p>Covariate data are obtained from the following sources:<br>- Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017).<br>- Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020).<br>- Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).<br>- Center For International Earth Science Information Network-CIESIN-Columbia University. Documentation for the Gridded Population of the World, Version 4 (GPWv4), Revision 11 Data Sets. (2018) doi:10.7927/H45Q4T5F.<br>- Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center https://doi.org/10.5067/MODIS/MOD13A2.061 (2021).<br>- Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018).<br>- Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).<br>- Jensen, A. J. et al. Geographic barriers but not life history traits shape the phylogeography of North American mammals. Glob. Ecol. Biogeogr. e13875 (2024).</p> <p>iNaturalist data are obtained from inaturalist.org via the data exporter (see manuscript for details).</p>
OpenStreetMap+ Protected nature areas in continental Europe (IUCN status + Natura 2000)
<p>Twelve maps of continental Europe indicating the protected nature area status in 2019 according to <a href="https://ec.europa.eu/environment/nature/natura2000/index_en.htm">Natura 2000</a> and the <a href="https://www.iucn.org/">International Union for Conservation of Nature</a> (IUCN). The IUCN status was extracted from crowdsourced data obtained from OpenStreetMap through geofabrik.de.</p> <p>This dataset contains:</p> <ul> <li>3 raster maps representing Natura 2000 protection status (A, B and C), named <strong>Natura2000_[status].tif</strong></li> <li>8 raster maps representing OSM-derived IUCN protection status (1a, 1b, 2, 3, 4, 5, 6, and 'other'), named <strong>OSM_IUCN_[status].tif</strong></li> <li>1 aggregated map (<strong>adm_protected.area_natura2000.osm_p_30m_0..0cm_2019..2021_eumap_epsg3035_v0.1</strong>) where each of the 11 protection statuses, as well as pixels where multiple statuses apply, are assigned a unique value. This map can also be accessed interactively at <a href="https://maps.opendatascience.eu/?base=OpenStreetMap%20(grayscale)&layer=Natura2000-OSM%20Protected%20areas&zoom=4&eye=5000000&center=53.7139,17.0066&opacity=45">maps.opendatascience.eu</a>.</li> </ul> <p>All files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files for the aggregated raster are provided in both <strong><em>SLD</em></strong> and <strong><em>QML</em></strong> format.</p>
Phanerozoic continental climate and Köppen–Geiger climate classes
<p>General circulation model simulations have been performed over the Phanerozoic (from 540 to 0 million years ago, every 20 million years) using the FOAM model. Simulated continental climatic fields and Köppen-Geiger climatic zones are shown in PDF file "figures.pdf". Numerical values are provided in the form of individual files (1 per time slice, 2 formats: NetCDF and CSV), and corresponding .zip archives containing NetCDF and CSV files for all time slices.</p>
SoildiverAgro Continental Region Coordinator Stefan Schrader
<p>SoildiverAgro Continental Region Coordinator Stefan Schrader</p> <p>In this interview, Stefan Schrader from Thünen-Institute of Biodiversity (Continetal Region) introduces himself and explains his role in the SoildiverAgro project and what farmers can expect from the case studies developed in his region.</p> <p>This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].</p>
EOOffshore: CCMP v0.2.1.NRT Wind Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p>This particular catalog data set (<em>eooffshore_ics_ccmp_v02_1_nrt_wind.zarr</em>) contains 2015-2021 Cross-Calibrated Multi-Platform (CCMP) v0.2.1.NRT 6-hourly wind products for the ICS region, where wind speed and direction are calculated from the <em>uwnd</em> and <em>vwnd</em> variables. The source data products are generated by <a href="https://www.remss.com/measurements/ccmp/">Remote Sensing Systems (RSS)</a>. This CCMP data set was used in the EOOffshore project outputs presented (<em><a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a></em>) at the <em><a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a></em> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <p>Example usage of the CCMP data set in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/CCMP_ICS_Wind_Data.html">CCMP Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>Note:</p> <ul> <li>This <a href="https://rda.ucar.edu/datasets/ds745.1/">NCAR/UCAR Research Data Archive page</a> states that the CCMP license is CC-BY-4.0. A separate CCMP data set has been previously used in the <a href="https://gallery.pangeo.io/repos/cgentemann/pangeo_ccmp/">NASA CCMP Winds Pangeo Gallery notebook</a>.</li> </ul>
EOOffshore: Sentinel-1 Wind Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p>The <a href="https://www.copernicus.eu/en/about-copernicus">European Union Copernicus Earth Observation (EO) programme</a> and services are based on data collected from EO satellites, in particular, the <a href="https://sentinels.copernicus.eu/web/sentinel/home">Sentinel satellite missions</a>. This includes the <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1">Sentinel-1 mission</a>, which consists of C-band Synthetic Aperture Radar (SAR) imaging satellites in polar orbit. One of its main objectives is the provision of ocean monitoring services, where its <a href="https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-2">Level-2 Ocean (OCN)</a> products include an Ocean WInd field (OWI) component. This provides gridded estimates of wind speed and direction at 10 m above the surface, with a typical spatial resolution of 1 km. This particular catalog data set (<em>eooffshore_ics_level3_sentinel1_ocn.zarr.tar.gz</em>) contains 2015-2021 OCN wind products for the ICS region, which were retrieved from the <a href="https://scihub.copernicus.eu/">Copernicus Open Access Hub (COAH)</a> and the <a href="https://search.asf.alaska.edu/#/">Alaska Satellite Facility (ASF)</a>. The data set was used in the EOOffshore project outputs presented (<em><a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a></em>) at the <em><a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a></em> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <p>Description and example usage of the Sentinel-1 data set in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/Sentinel-1_ICS_Wind_Data.html">Sentinel-1 Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>As requested by the <a href="https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice">Legal Notice on the use of Copernicus Sentinel Data and Service Information</a>, this data set:</p> <ul> <li>Contains modified Copernicus Sentinel data [2015 - 2021]</li> </ul>
EOOffshore: New European Wind Atlas (NEWA) Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io/">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p>The <a href="https://www.neweuropeanwindatlas.eu/">New European Wind Atlas (NEWA)</a> provides wind statistics covering onshore Europe, 100km offshore over European seas, and the complete North and Baltic Seas, based on <a href="https://map.neweuropeanwindatlas.eu/about">30 years of mesoscale simulations</a>. These catalog data sets contain 2009-2018 products for the ICS region, provided by the <a href="https://map.neweuropeanwindatlas.eu/">NEWA Map Layers and Datasets</a> website, featuring variables at multiple heights (metres above surface level). They were used in the EOOffshore project outputs presented (<a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html"><em>Scalable Offshore Wind Analysis With Pangeo</em></a>) at the <a href="https://meetingorganizer.copernicus.org/EGU22/session/42046"><em>Meeting Exascale Computing Challenges with Compression and Pangeo</em></a> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <ul> <li><em>eooffshore_ics_newa_celticsea.zarr.tar.gz</em> <ul> <li>Data set for a North Celtic Sea area of interest.</li> </ul> </li> <li><em>eooffshore_ics_newa_irishsea.zarr.tar.gz</em> <ul> <li>Data set for an Irish Sea area of interest.</li> </ul> </li> <li><em>eooffshore_ics_newa_m3.zarr.tar.gz</em> <ul> <li>Data set for the area surrounding the <a href="http://www.marine.ie/Home/site-area/data-services/real-time-observations/irish-weather-buoy-network-imos">Irish Weather Buoy Network - M3 buoy</a> coordinates.</li> </ul> </li> <li><em>eooffshore_ics_newa_m4.zarr.tar.gz</em> <ul> <li>Data set for the area surrounding the <a href="http://www.marine.ie/Home/site-area/data-services/real-time-observations/irish-weather-buoy-network-imos">Irish Weather Buoy Network - M4 buoy</a> coordinates.</li> </ul> </li> </ul> <p>Description and example usage of the NEWA data sets in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/NEWA_ICS_Wind_Data.html">NEWA Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>As requested by the <a href="https://map.neweuropeanwindatlas.eu/about">NEWA Terms of use</a>, the following attribution is declared:</p> <ul> <li>Data [2009 - 2018] obtained from the New European Wind Atlas (NEWA), a free, web-based application developed, owned and operated by the NEWA Consortium. For additional information see <a href="http://www.neweuropeanwindatlas.eu/">www.neweuropeanwindatlas.eu</a>.</li> </ul>
EOOffshore: ASCAT Wind Data for the Irish Continental Shelf Region
<p><a href="https://eooffshore.github.io">EOOffshore</a> is a <a href="https://www.seai.ie/">Sustainable Energy Authority of Ireland (SEAI)</a> funded <a href="https://www.seai.ie/data-and-insights/seai-research/research-projects/details/building-upon-copernicus-earth-observation-services-to-augment-wind-measurement-coverage-of-the-oredp-offshore-renewable-energy-assessment-areas">project</a>, which commenced in June 2020 in the <a href="https://www.ucd.ie/physics/">School of Physics</a> in <a href="https://www.ucd.ie/">University College Dublin (UCD)</a>. It presents a case study that demonstrates the utility of the <a href="https://pangeo.io/">Pangeo</a> software ecosystem in the development of offshore wind speed and power density estimates, increasing wind measurement coverage of offshore renewable energy assessment areas in the <a href="https://www.marine.ie/Home/site-area/irelands-marine-resource/real-map-ireland">Irish Continental Shelf (ICS)</a> region. It has involved the creation of a new <a href="https://eooffshore.github.io/datasets.html">wind data catalog</a> for this region, consisting of a collection of analysis-ready, cloud-optimized (ARCO) datasets featuring up to 21 years of available in situ, reanalysis, and satellite observation wind data products.</p> <p>The <a href="https://marine.copernicus.eu/">Copernicus Marine Service (CMS), or Copernicus Marine Environment Monitoring Service (CMEMS)</a>, is the marine component of the <a href="https://www.copernicus.eu/en/about-copernicus">European Union Copernicus Earth Observation (EO) programme</a>. It provides free, regular and systematic ocean data products on a global and regional scale. The CMS <a href="https://marine.copernicus.eu/about/producers/wind-tac">Surface Wind Thematic Assembly Center (Wind TAC)</a> is responsible for the collection, processing, qualification and distribution of surface winds data products derived from scatterometer satellite missions, including near-real time (NRT) and delayed mode (REP) processing of global wind observations. These catalog data sets contain CMS wind speed and direction data products generated using the Advanced SCATterometer (ASCAT) instruments deployed on the Metop satellites.</p> <ul> <li><em>eooffshore_ics_cmems_WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005_MetOp_ASCAT.zarr.tar.gz</em> <ul> <li>2007-2021 data products from the <a href="https://resources.marine.copernicus.eu/product-detail/WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005/INFORMATION"><em>Global Ocean Daily Gridded Reprocessed (REP) Level-3 Sea Surface Winds from Scatterometer</em></a><em> </em>data set.</li> </ul> </li> <li><em>eooffshore_ics_cmems_WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002_MetOp_ASCAT.zarr.tar.gz</em> <ul> <li>2016-2021 data products from the <a href="https://resources.marine.copernicus.eu/product-detail/WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002"><em>Global Ocean Daily Gridded Near Real Time (NRT) Level-3 Sea Surface Winds from Scatterometer</em></a><em> </em>data set.</li> </ul> </li> </ul> <p>The products feature 0.125 degree grids, based on 12.5 km scatterometer swath observations, for all combinations of Metop A/B (REP) and Metop A/B/C (NRT) satellites and ASCending, DEScending passes. These ASCAT data sets were used in the EOOffshore project outputs presented (<em><a href="https://meetingorganizer.copernicus.org/EGU22/EGU22-2746.html">Scalable Offshore Wind Analysis With Pangeo</a></em>) at the <em><a href="https://meetingorganizer.copernicus.org/EGU22/session/42046">Meeting Exascale Computing Challenges with Compression and Pangeo</a></em> <a href="https://www.egu22.eu/">2022 EGU General Assembly</a> session.</p> <p>Description and example usage of the ASCAT data sets in EOOffshore:</p> <ul> <li><a href="https://eooffshore.github.io/ASCAT_ICS_Wind_Data.html">ASCAT Wind Data for Irish Continental Shelf region</a></li> <li><a href="https://eooffshore.github.io/Offshore_Wind_AOI.html">Offshore Wind in Irish Areas Of Interest</a></li> <li><a href="https://eooffshore.github.io/Comparison_Wind_Power.html">Comparison of Offshore Wind Speed Extrapolation and Power Density Estimation</a></li> </ul> <p>As requested by the <a href="https://marine.copernicus.eu/user-corner/service-commitments-and-licence">Copernicus Marine Service Service Commitments and Licence</a>, these Zarr stores were:</p> <ul> <li> <p>Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00182">https://doi.org/10.48670/moi-00182</a>; <a href="https://doi.org/10.48670/moi-00183">https://doi.org/10.48670/moi-00183</a>;</p> </li> </ul>
Baseline map of 137Cs inventories in reference soil sites at the continental scales of South America
<p>This dataset contains the baseline map of <sup>137</sup>Cs inventories in reference soil sites (Bq m<sup>-2</sup>, decay-corrected to 2020) estimated by Partial Least Square Regression (PLSR) with a spatial resolution of 2 km at the continental scale of South America, as well as the prediction uncertainties of the baseline map (coefficient of variation, %).<br> Details information regarding this dataset can be found in the original publication:<br> Mapping the spatial distribution of global <sup>137</sup>Cs fallout in soils of South America as a baseline for Earth Science studies, Earth-Science Reviews, Volume 214, 2021, 103542, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2021.103542.</p>
Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian sub-continental river basins
<p>We have simulated water budget and energy budget over Indian subcontinental basins, using three scenarios from the Variable Infiltration Capacity (VIC) model by including irrigation and reservoir practices in it:</p> <p>1). No irrigation and reservoir (VIC-NATURAL)<br> 2). Free irrigation and no reservoir (VIC-FREE)<br> 3). Reservoir and restricted irrigation (VIC-MANAGED)</p> <p>Here, we have shared results in below folders.</p> <p>Fig1: Annual precipitation (P) and reservoir locations used in study.<br> Fig2: Satellite (MODIS and GLEAM) based annual evapotranspiration (ET) and VIC-MANAGED simulated annual ET.<br> Fig3: Annual land surface temperature (LST) from MODIS, AATSR and VIC-MANAGED.<br> Fig4: Mean monthly observed and simulated reservoir storage.<br> Fig5: P, ET, total runoff (TR) and LST from one grid.<br> Fig6: Annual ET change between VIC-NATURAL and VIC-MANAGED run.<br> Fig7: Same as Fig6 but for TR.<br> Fig8: Same as Fig6 but for LST.<br> Fig9: Annual ET change between VIC-FREE and VIC-MANAGED run.<br> Fig10: Annual latent heat flux and sensible heat flux change between VIC-NATURAL and VIC-MANAGED run.</p> <p>More detail is available in "Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian sub-continental river basins" paper in JGR-Atmosphere. Or contact at harsh.lovekumar.shah@iitgn.ac.in</p> <p>Harsh Shah</p>
Estimated individual methane emission rates for oil and gas facilities from the continental United States in 2021
<p>File containing 500 separate estimates of 673,940 individual facility-level methane emission rates for oil and gas facilities for the year 2021 in the continental United States. Each column contains one full estimate of the individual facility-level emissions, presented in units of kilograms per hour of methane per facility. The facility categories included in these estimates are production well sites, gathering and boosting compressor stations, transmission and storage compressor stations, processing plants, and flares. This data can be used to recreate the 500 emission distributions presented in Figure 3 in the following manuscript (link: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1402) which is currently under review. This dataset may be updated as the review stages progress</p>
DATASETS and OUTCOMES - Assessment of intrinsic aquifer vulnerability at continental scale through a critical application of the DRASTIC method: the case of South America
<p>A robust and comprehensive assessment of intrinsic aquifer vulnerability at continental scale map may represent an essential initial step towards a more sustainable land-use and water management.</p> <p>This repository contains the outcomes of an intrinsic aquifer vulnerability assessment of South America, performed by the DRASTIC method. The assets included in this repository are mainly raster maps (.tif, .geotif), created and georeferenced in QGIS (v3.16). Coordinate reference system (CRS) of the dataset is WGS84.</p> <p>Technical specifications of all graphical outcomes are stored in a dedicated file (README.txt).</p>
ScienceDex guides
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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