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446 results for “Water bodies”
A Comprehensive Global Aquatic N2O Emission Database (GANED): Unravelling N2O Emission Patterns from Different Water Bodies, 1980-2023
The Global Aquatic Nitrous Oxide Emission Database (GANED) is a comprehensive synthesis of empirical observations of N2O concentration measurements and flux records, spanning the period 1980-2023. GANED advances N2O research by providing the first global systematic emission mechanisms among the different aquatic system types, including rivers, streams, estuaries, reservoirs, ponds, lakes, open seas and coastal areas. The N2O data in GANED is further interconnected with biogeochemical metadata on dissolved oxygen, dissolved organic carbon, ammonium, nitrate, nitrite, total nitrogen, water temperature, salinity and pH, along with site data (latitude, longitude, codes of channel type, depth, surface area, elevation). The dataset explains the discrepancy that emission of N2O in aquatic bodies is determined mainly by substrate availability, and not by climatic factors, and reveals the systematic biases of concentration-only measurements, which can result in an underestimation of fluxes in effluent water of dynamically changing aquatic waters. Consequently, GANED constitutes a crucial transition “where” emissions occur to understanding “why” they differ across systems, and thus enabling targeted mitigation interventions. GANED includes 5130 records of N2O concentration and 7386 flux measurements from 3,002 unique sites, most of which are resolved to the daily time scale.
Mask at 300 m of water-body locations more than 5, 15 and 20 km distant from land
<p>Locations of water-body locations remote from land: This dataset is a latitude-longitude grid indicating the locations of water-body locations more distant from land than 5, 15 and 20 km. It is derived from Carrea et al., 2016, which in turn was derived from the ESA Climate Change Initiative for Land Cover Water Bodies product released in October 2014. 3 = distance greater than 20 km; >=2 = distance greater than 15 km; >=1 = distance greater than 5 km. Paper describing underlying distance-to-land dataset: Carrea, L., Embury, O., Merchant, C.J. (2016) Datasets related to inland water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates. Geoscience Data Journal, 2(2). pp. 83-97. doi: https://doi.org/10.1002/gdj3.32. This work done within the project: ESA Climate Change Initiative Lakes, by University of Reading, UK. </p> <p> 'geospatial_lat_min': -90.0,\<br> 'geospatial_lat_max': 90.0,\<br> 'geospatial_lon_min': -180.0,\<br> 'geospatial_lon_max': 180.0,\<br> 'geospatial_lat_units': 'degrees_north',\<br> 'geospatial_lat_resolution': 0.0027777778,\<br> 'geospatial_lon_units': 'degrees_east',\<br> 'geospatial_lon_resolution': 0.0027777778,\<br> 'spatial_resolution': '300m'</p>
S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images
<p>The S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM.</p><p>This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project "Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungsunterstützung im Katastrophenfall" (AIFER) under Grant 13N15525, and by the Helmholtz Artificial Intelligence Cooperation Unit through the project "AI for Near Real Time Satellite-based Flood Response" (AI4FLOOD) under Grant ZT-IPF-5-39. </p>
Annual and 33-year water body frequency maps of the contiguous US from 1984 to 2016
<p>There are 50 5-by-5 degree tiles covering the entire CONUS. In each zipped tile folder, there are 33 annual water body frequency images (e.g. freq_2016_-070_040.tif), one 33-year water body frequency image (e.g. 33YearFreq_1984_2016_-070_040.tif), and one 33-year good observation image (e.g. 33YearGoodObs_1984_2016_-070_040.tif). </p> <p>The annual and 33-year water body frequency are defined as the ratio of water observations to total good observations in a year and in 1984-2016, respectively. The frequency stored in these image is compressed in 8 bits (1-255). To get the frequency in floating point (0-1.0), use the equation: f = (F-1)/254.0, where F is in 8 bits while f is in floating point. </p> <p>For each Landsat image, the CFmask band was used as a quality control band to remove the cloud, cloud shadow, and snow pixels. The solar azimuth and zenith angles of each image were used along with the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) to simulate terrain shadows and remove them. The remaining pixels were considered as good observations that can be used for water body detection. The total good observation number during 1984-2016 is stored in the 33-year good observation images.</p> <p>Projection is WGS84, while spatial resolution is 0.000269494585236.</p> <p>For additional details, please go to our lab server (http://mangrove.rccc.ou.edu/eomfftp/conus_water_dataset/).<br> To use this data, please cite our articles: <br> Zou, Z., Xiao, X., Dong, J., Qin, Y., Doughty, R.B., Menarguez, M.A., Zhang, G., Wang, J. Divergent Trends of Open Surface Water Body Area in the Contiguous United States from 1984 to 2016, PNAS, doi: 10.1073/pnas.1719275115</p> <p>Zou, Z., J. Dong, M. A. Menarguez, X. Xiao, Y. Qin, R. B. Doughty, K. V. Hooker, and K. David Hambright (2017), Continued decrease of open surface water body area in Oklahoma during 1984-2015, Sci Total Environ, 595, 451-460, doi: 10.1016/j.scitotenv.2017.03.259.</p>
Environmental, community and trait data of small water bodies in Zijin Mountain, Nanjing, Jiangsu, China, 2022
Small water bodies (SWBs) are vulnerable to drought and play a vital role in the conservation of aquatic biodiversity. Currently climate change is intensifying the seasonal drought of SWBs in monsoonal east Asia. However, little is known about the response of benthic macroinvertebrates of small ponds and streams that simultaneously suffer from climate-induced extreme drought. This study aimed to explore the taxonomic and functional response of macroinvertebrates in ponds and streams, either respectively or jointly, to extreme summer drought. We calculated taxonomic and functional diversity indices of communities in 11 streams and 12 ponds across three seasons: spring, summer and winter in 2022. We performed a permutational multivariate analysis of variance, Moran’s eigenvector maps, Moran Spectral Randomization based variation partitioning and convex hull analysis of trait space to examine temporal compositional and functional, as well as trait changes, and the contributions of environmental and spatial factors in shaping communities. The responses of taxonomic and functional diversity in ponds and streams were contrasting during the summer drought. Ponds showed increased taxonomic richness (TR), functional richness (FRic), functional richness (FRed) and trait space volume, while streams experienced decreased TR, FRic and trait space volume but increased FRed. The taxonomic and functional increases of ponds were driven by an influx of generalist taxa from streams, while the increase of FRed in streams resulted from the loss of species with strong dispersal and lentic adaptation traits. Dispersal played a more significant role than environmental filtering in shaping community structure during the drought, especially for streams lacking hydrological connectivity. This study provides the first insights into the complex response of macroinvertebrates to summer drought of SWBs in east Asia monsoonal region. Our results underscore the refuge effect of ponds during summer
GIS21 GIS Coverages Defining Water Bodies on Konza Prairie (1972-present)
This Coverage Contains the Locations of Streams (GIS210) and Waterbodies (GIS211) within the Konza Prairie Biological Station. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).
Random-Phase Approximation in Many-Body Noncovalent Systems: Methane in a Dodecahedral Water Cage
<p>Supplementary information and raw data for Random-Phase Approximation in Many-Body Noncovalent Systems: Methane in a Dodecahedral Water Cage. </p>
Water Body Checklists 2019: Adriatic Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Adriatic Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: Ceram Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Ceram Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Data for Li et al., Coupling remote sensing and particle tracking to estimate trajectories in large water bodies, International Journal of Applied Earth Observation and Geoinformation, 2022
<p>This data set contains four parts:</p> <p>1) compressed folder with input parameters and results for the hydrodynamic model</p> <p>2) compressed folder with input parameters and results for the particle tracking</p> <p>3) compressed folder with satellite data </p> <p>4) code used in the article for hydrodynamic model, particle tracking and image processing</p> <p>Each folder contains a readme file,</p>
CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)</p> <p>Source data:<br>- CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m</a> <br>- CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m</a></p> <p>Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management.<br>The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices.</p> <p>Processing steps:<br>To cover the complete time period from 2019 to 2023 two data products of the Water Bodies product group are processed. Up to December of 2020 the Water Bodies at 10-daily resolution have been used, from January 2021 the Water Bodies at monthly resolution have been used. Both original datasets have been downloaded for the area of Mauritania (NUTS MR) within Latitude-Longitude/WGS84 spatial reference system. Then both datasets have been downsampled to 30 arc seconds (ca. 1000 meter) using the most frequent occuring value. The 10-daily data have been aggregated to monthly resolution using the most frequent occurring value.</p> <p>File naming:<br>Until December 2020: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_2020_12_01T00_00_00.tif</code> <br>From January 2021 on: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_2023_12_01T00_00_00.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.<br>NOTE: data for 2023-04 are missing, since they are not available from CLMS</p> <p>Pixel values:<br>0: Sea<br>70: Water<br>255: No water</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 27:17:30N<br>south: 14:43:30N<br>west: 17:04:30W<br>east: 04:48:00W</p> <p>Temporal extent:<br>January 2019 - December 2023 (except: April 2023)</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original dataset license:<br>Generated using European Union's Copernicus Land Monitoring Service information</p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
LakeSST: Lake Skin Surface Temperatures in French inland water bodies
<p>The data set LakeSST contains skin surface temperature data for 442 French water bodies for the period 1999-2016 obtained from archives of Landsat 5 and Landsat 7 thermal infrared images. The overall accuracy of the satellite-derived temperature measurements is about 1.2 ºC, similar to other applications of satellite images to estimate freshwater surface temperatures. The spatial and temporal coverage of the data set makes it an ideal resource for studies on the temporal evolution of lake surface temperatures and for geographical studies of temperature patterns.</p>
Water Body Checklists 2019: Yellow Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Yellow Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: Tyrrhenian Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Tyrrhenian Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: Tasman Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Tasman Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: Sulu Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Sulu Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: Strait of Gibraltar Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Strait of Gibraltar using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: Timor Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Timor Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: White Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the White Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: South Pacific Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the South Pacific Ocean region using effechecka and a modified polygon from the International Hydrographic Association.
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Allen Brain Atlas
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International Brain Laboratory public data
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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.