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1,133 results for “wetlands”
The evolution of hummock-depression micro-topography in an alpine marshy wetland in Sanjiangyuan as inferred from vegetation and soil characteristics
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Influence of water level management on vegetation and bird use of restored wetlands in the Montezuma Wetlands Complex
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Data from: Aggregation but not organo-metal complexes contributed to C storage in tidal freshwater wetland soils
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H-O isotopic compositions in China's wetlands
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Data for: Leaf N-S of wetland plants in western China
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Tipping the balance: the role of seed density, abiotic filters, and priority effects in seed-based wetland restoration
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Butterfly species diversity and their floral preferences in the Rupa wetland of Nepal
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Data from: Seasonal variability of groundwater level effects on the growth of Carex cinerascens in lake wetlands
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Long-term trends in the phylogenetic and functional diversity of Anatidae in South China coastal wetlands
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LPJ-EOSIM L2 Global Simulated Monthly Wetland Methane Flux V001
The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands. These wetland CH4 flux data will be used to support the United States Greenhouse Gas Center ([GHGC](https://earth.gov/ghgcenter)) and its mission to study natural GHG fluxes. The model will also be used to facilitate improved rapid detection and attribution of climate-carbon feedback and in strategic placement of measurement campaigns and monitoring systems as they relate to predicted biogeochemical hotspots. The LPJ-EOSIM L2 Global Simulated Monthly Wetland Methane Flux (LPJ_EOSIM_L2_MCH4E) Version 1 data product provides simulated monthly wetland CH<sub>4</sub> flux globally at a spatial resolution of 0.5 degrees. The monthly simulation data contains aggregate versions of the daily LPJ-EOSIM L2 Global Simulated Daily Wetland Methane Flux ([LPJ_EOSIM_L2_DCH4E](https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_DCH4E.001)) Version 1 data. The monthly data are presented in four Cloud Optimized GeoTIFF (COG) files: two based on the aggregated daily forcing datasets Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), and two containing the mean and standard deviation values calculated from the monthly aggregate data. Due to the latency of global carbon dioxide (CO<sub>2</sub>) concentration estimates required for computation of LPJ-EOSIM simulated monthly CH4 flux data products, low latency ([LPJ_EOSIM_L2_MCH4E_LL](https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_MCH4E_LL.001)) and high latency (LPJ_EOSIM_L2_MCH4E) collections are available. High latency data in this collection will be delivered around May of each year when National Oceanic and Atmospheric Administration’s (NOAA) Global Monitoring Laboratory (GML) publishes the previous year’s CO2 concentration and will have a lag of at least 5 months (January-May), and at most 17 months (January of the current year to May of the next year). Please refer to Section 2.0.1 of the User Guide for a more detailed explanation of estimated CO2 inputs and timing for scheduled updates to the collections.
Tidal Wetland Soil Carbon Stocks for the Conterminous United States, 2006-2010
This dataset provides modeled estimates of soil carbon stocks for tidal wetland areas of the Conterminous United States (CONUS) for the period 2006-2010. Wetland areas were determined using both 2006-2010 Coastal Change Analysis Program (C-CAP) raster maps and the National Wetlands Inventory (NWI) vector data. All 30 x 30-meter C-CAP pixels were extracted that are coded as estuarine emergent, scrub/shrub, or forested in either 2006 or 2010. A soil database for model fitting and validation was compiled from 49 different studies with spatially explicit empirical depth profile data and associated metadata, totaling 1,959 soil cores from 18 of the 22 coastal states. Reported estimates of carbon stocks were derived with modeling approaches that included (1) applying a single average carbon stock value from the compiled soil core data, (2) applying models fit using the empirical data and applied spatially using soil, vegetation and salinity maps, (3) relying on independently generated soil carbon maps from The United States Department of Agriculture (USDA)'s Soil Survey Geographic Database (SSURGO), and the NWI that intersected with mapped tidal wetlands, and (4) using a version of SSURGO bias-corrected for bulk density. Comparisons of uncertainty, precision, and accuracy among these four approaches are also provided.
ABoVE: Wetland Inundation Coverage at Yukon Flats, AK and PA Delta, Canada, 2017-2019
This dataset provides time series of wetland inundation coverage maps and corresponding inundation frequency maps at ~10-meter resolution estimated every 12 days during the free-water period (May to October) for the years 2017-2019 over the Yukon Flats (YK) portion of the Yukon River, Alaska, USA, and the Peace-Athabasca Delta (PAD), Alberta, Canada. Wetland inundation coverage was determined by a two-step modified decision-tree classification approach that first used Sentinel-1 C-band SAR to identify likely inundated areas across a study site and was followed by a decision-tree classification step with C-band SAR backscatter statistics thresholds to distinguish among different inundation components. The result of this process was five classes for each inundation map, namely Open Water (OW), Floating Plants (FP), Emergent Plants (EP), Flooded Vegetation (FV), and Dry Land (DRY). After all the individual (every 12 days) inundation coverage maps were derived for a study site, they were generalized to two-class maps which maintained only inundation status. These generalized maps were then stacked and summarized to produce the inundation frequency map for the site. In these maps, higher values signify more frequently inundated areas, with the maximum value representing permanently inundated pixels. The Sentinel-1 inundation mapping capability demonstrated here provided frequent, broad-scale mapping of different wetland inundation components. Integration of such products with process-based methane (CH4) models would improve simulation of CH4 emissions from wetlands.
LBA Regional Wetlands Data Set, 1-Degree (Matthews and Fung)
This database, compiled by Matthews and Fung (1987), provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. This subset, for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America, retains all five arrays at the 1-degree resolution but only for the area of interest (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N). The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type. The data subsets are in both ASCII GRID and binary image file formats.The data base is the result of the integration of three independent digital sources: (1) vegetation classified according to the United Nations Educational Scientific and Cultural Organization (UNESCO) system (Matthews, 1983), (2) soil properties from the Food and Agriculture Organization (FAO) soil maps (Zobler, 1986), and (3) fractional inundation in each 1-degree cell compiled from a global map survey of Operational Navigation Charts (ONC). With vegetation, soil, and inundation characteristics of each wetland site identified, the data base has been used for a coherent and systematic estimate of methane emissions from wetlands and for an analysis of the causes for uncertainties in the emission estimate.The complete global data base is available from NASA/GISS [http://www.giss.nasa.gov] and NCAR data set ds765.5 [http://www.ncar.ucar.edu]; the global vegetation types data are available from ORNL DAAC [http://www.daac.ornl.gov].
Global Wetland Methane Emissions derived from FLUXNET and the UpCH4 Model, 2001-2018
This dataset provides monthly globally gridded freshwater wetland methane emissions from 2001-2018 in nmol CH4 m-2 s-1, g C-CH4 m-2 d-1, and TgCH4 grid cell-1 month-1. The data were derived from a six-predictor random forest upscaling model (UpCH4) trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites covering bog (8), fen (8), marsh (10), swamp (6), and wet tundra (11) wetland classes and distributed across Arctic-boreal (20), temperate (16), and (sub)tropical (7) climate zones. Weekly mean CH4 fluxes were computed from half-hourly FLUXNET-CH4 Version 1.0 fluxes. Each grid cell CH4 flux prediction was weighted by fractional grid cell wetland extent to estimate CH4 emissions using the primary global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) product and an alternate Global Inundation Estimate from Multiple Satellites GIEMS version 2 global wetland map. Both WAD2M and GIEMS-2 maps were modified with several correction data layers to represent the monthly area covered by vegetated wetlands, excluding open water and coastal wetlands. The data products are: mean daily fluxes with no adjustment for wetland area (i.e., flux densities assuming hypothetical 100% wetland cover); mean daily fluxes adjusting for WAD2M or GIEMS-2 wetland area; and by-pixel monthly sum of freshwater wetland methane emissions adjusting for WAD2M or GIEMS-2 wetland area. The data are provided in NetCDF4 format.
ABoVE: Lake and Wetland Classification from L-band SAR, Alaska and Canada, 2017-2019
This dataset contains a high-resolution land cover classification focused on water and wetland vegetation classes over three NASA ABoVE Campaign regions: Yukon Flats, Alaska, USA; the Peace-Athabasca Delta, Alberta; and the Canadian Shield, Northwest Territories (NWT), Canada. The product was derived from L-band synthetic aperture radar (SAR) acquisitions from the airborne NASA UAVSAR instrument in 2017-2019. The classification was trained and validated from field visits, UAV images, satellite imagery as well as other ABoVE datasets. Classifications in all regions are provided as both preliminary 13-class versions and final, simplified 5-class versions. Training and test data used for the classifier are also included as well as characteristics of lakes in the study area. This land cover classification was developed to support a project focusing on potential methane emissions from the shallow near-shore, or littoral, regions of lakes. The emergent aquatic vegetation classes can be used as a proxy for these littoral zones. Wetland vegetation classifications are provided as gridded raster files with an approximately 5-meter spatial resolution and aligned with the original UAVSAR footprints. Composite mosaics that aggregate these UAVSAR scenes by region and day of acquisition, if applicable, are also provided. Classifications in all regions are provided as both preliminary 13-class versions and final 5-class versions.
SAFARI 2000 Wetlands Data Set, 1-Deg (Matthews and Fung)
This database, compiled by Matthews and Fung (1987), provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. The subset retains all five arrays at the 1-degree resolution but only for the area of interest. The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type. The data subsets are in both ASCII GRID and binary image file formats. The data base is the result of the integration of three independent digital sources: (1) vegetation classified according to the United Nations Educational Scientific and Cultural Organization (UNESCO) system (Matthews, 1983), (2) soil properties from the Food and Agriculture Organization (FAO) soil maps (Zobler, 1986), and (3) fractional inundation in each 1-degree cell compiled from a global map survey of Operational Navigation Charts (ONC). With vegetation, soil, and inundation characteristics of each wetland site identified, the data base has been used for a coherent and systematic estimate of methane emissions from wetlands and for an analysis of the causes for uncertainties in the emission estimate. The complete global data base is available from NASA/GISS [http://www.giss.nasa.gov] and NCAR data set ds765.5 [http://www.ncar.ucar.edu]; the global vegetation types data are available from ORNL DAAC [http://www.daac.ornl.gov]. More information can be found at: ftp://daac.ornl.gov/data/safari2k/vegetation_wetlands/giss_wetlands/comp/matt_fung_readme.pdf.
Delta-X: Soil Properties for Herbaceous Wetlands, MRD, Louisiana, 2021, V3
This dataset contains properties of soil core samples for herbaceous wetlands collected in the Atchafalaya and Terrebonne basins in southeastern coastal Louisiana for the period 2021-03-21 to 2021-04-02 and on 2021-08-19. Field measurements were conducted at six sites in the Atchafalaya (N = 3) and Terrebonne (N = 3) basins. Five sites were adjacent to sites from the Coastwide Reference Monitoring System (CRMS). The other site is in the Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. Herbaceous wetland sites in both basins were chosen to represent a salinity gradient including freshwater, brackish and saline ecosystems. Soil properties include bulk density, organic matter content, total densities of carbon, nitrogen, phosphorus, along with 13C and 15N isotopic signatures. The data are provided in comma-separated values (.csv) format.
LPJ-EOSIM L2 Global Simulated Daily Wetland Methane Flux Low Latency V001
The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands. These wetland CH4 flux data will be used to support the United States Greenhouse Gas Center ([GHGC](https://earth.gov/ghgcenter)) and its mission to study natural GHG fluxes. The model will also be used to facilitate improved rapid detection and attribution of climate-carbon feedback and help with strategic placement of measurement campaigns and monitoring systems as they relate to predicted biogeochemical hotspots. The LPJ-EOSIM Level 2 Global Simulated Daily Wetland Methane Flux Low Latency (LPJ_EOSIM_L2_DCH4E_LL) Version 1 data product provides simulated daily wetland CH4 flux globally at a spatial resolution of 0.5 degrees. The daily data are presented in four Cloud Optimized GeoTIFF (COG) files: two based on the forcing datasets Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), and two containing the mean and standard deviation values. Due to the latency of global carbon dioxide (CO2) concentration estimates required for computation of LPJ-EOSIM simulated daily CH4 flux data products, low latency (LPJ_EOSIM_L2_DCH4E_LL) and high latency ([LPJ_EOSIM_L2_DCH4E](https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_DCH4E.001)) collections are available. Low latency data are delivered on a two-month cadence throughout the year. Granules will also be updated as new CO2 input data become available. Please refer to Section 2.0.1 of the User Guide for a more detailed explanation of CO2 estimate inputs and timing for scheduled updates to the collections.
LPJ-EOSIM L2 Global Simulated Daily Wetland Methane Flux V001
The Lund-Potsdam-Jena Earth Observation SIMulator (LPJ-EOSIM) model estimates global wetland methane (CH4) emissions using simulated wetland extent and characteristics including soil moisture, temperature, and carbon content. For this dataset, wetlands are defined as land areas that are either permanently or seasonally saturated, excluding small ponds, lakes, and coastal wetlands. These wetland CH4 flux data will be used to support the United States Greenhouse Gas Center ([GHGC](https://earth.gov/ghgcenter)) and its mission to study natural GHG fluxes. The model will also be used to facilitate improved rapid detection and attribution of climate-carbon feedback and help with strategic placement of measurement campaigns and monitoring systems as they relate to predicted biogeochemical hotspots. The LPJ-EOSIM Level 2 Global Simulated Daily Wetland Methane Flux (LPJ_EOSIM_L2_DCH4E) Version 1 data product provides simulated daily wetland CH4 flux globally at a spatial resolution of 0.5 degrees. The daily data are presented in four Cloud Optimized GeoTIFF (COG) files: two based on the forcing datasets Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5), and two containing the mean and standard deviation values. Due to the latency of global carbon dioxide (CO2) concentration estimates required for computation of LPJ-EOSIM simulated daily CH4 flux data products, low latency ([LPJ_EOSIM_L2_DCH4E_LL](https://doi.org/10.5067/Community/LPJ-EOSIM/LPJ_EOSIM_L2_DCH4E_LL.001)) and high latency (LPJ_EOSIM_L2_DCH4E) collections are available. High latency data in this collection will be delivered around May of each year when National Oceanic and Atmospheric Administration’s (NOAA) Global Monitoring Laboratory (GML) publishes the previous year’s CO2 concentration and will have a lag of at least 5 months (January-May), and at most 17 months (January of the current year to May of the next year). Please refer to Section 2.0.1 of the User Guide for a more detailed explanation of CO2 estimate inputs and timing for scheduled updates to the collections.
Wetland Salinity Maps of Select Estuary Sites in the United States, 2020
This dataset provides gridded average annual wetland salinity concentrations in practical salinity units (PSU) at 30-meter resolution within 24 coastal estuary sites in the United States predicted for 2020. Salinity in estuaries can serve as a proxy for sulfate concentration, which can inhibit methanogenesis. Data were derived from a hybrid approach to mapping salinity as a continuous variable using a combination of physical watershed and stream characteristics, optical remote sensing based on vegetation characteristics, and climate variables. Data are provided in cloud-optimized GeoTIFF format covering 33 Hydrologic Unit Code 8-digit (HUC8) watersheds to the extent of palustrine and estuarine wetlands as defined by NOAA's 2016 Coastal Change Analysis Program (C-CAP) Coastal Land Cover layer. Additionally, model outputs are provided in comma separated values (CSV) files, and code scripts are provided in a compressed (*.zip) file.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.