AndreaBozzo/ceres-open-data-index huggingface dataset:
Mean annual groundwater recharge rates for the Island of Hawaiʻi for historical and future drought conditions, and three land-cover conditions

This shapefile represents the spatial distribution of mean annual groundwater recharge, in inches, for the Island of Hawaiʻi, Hawaiʻi for a set of drought and land-cover conditions represented in six water-budget scenarios. The six scenarios include: (1) historical drought rainfall and 2020 land cover, (2) future drought rainfall and 2020 land cover, (3) historical drought rainfall and Conversion 1 land cover, (4) future drought rainfall and Conversion 1 land cover, (5) historical drought rainfall and Conversion 2 land cover, and (6) future drought rainfall and Conversion 2 land cover. Historical drought rainfall is monthly rainfall during 2007–12 from Frazier and others (2016), whereas future drought rainfall is monthly rainfall during 2007–12 from Frazier and others (2016) adjusted for a Representative Concentration Pathway 8.5 2071–99 (RCP8.5 2071–99) projection from Elison Timm and others (2015). Monthly rainfall for historical and future drought conditions was disaggregated into daily values using daily rainfall during 2007–12 from Longman and others (2019). A 2020 land-cover map developed by Kāne and others (2024a, 2024b) was used to define the land-cover conditions and the model subareas. Conversion 1 land cover is a hypothetical land-cover condition in which roughly 50 percent of forest and shrubland areas within the cloud zone are converted to grassland. Conversion 2 land cover is a hypothetical land-cover condition in which 100 percent of forest and shrubland areas within the cloud zone are converted to grassland. Groundwater recharge for each model subarea was computed for each scenario using a water-budget model developed by Oki (2022). The shapefile attribute information associated with each subarea present an estimate of mean annual groundwater recharge, and select geographic and land-cover attributes. Brief descriptions of the groundwater recharge estimates and attributes are included in this metadata file. Refer to Mair and others (2024) for further details of the methods and sources used to determine groundwater recharge and the attributes.

Tags: cloud-water-interception, droughts, groundwater, hawaii, land-use-and-land-cover, pacific-islands, precipitation-atmospheric, state-of-hawaii, usgs-647e8c8cd34eac007b55ee16, water-budget, water-budget-model, water-cycle
Organization: Department of the Interior
Hawaii Island, carbon dioxide as a tool to manage invasive fish in anchialine pools, initial aquarium water conditions, 2020

This data release includes metadata and tabular data that documents initial water conditions (temperature, pH, dissolved oxygen and salinity) during aquarium trials testing the efficacy of carbon dioxide (CO2) to manage invasive fish in anchialine pools.

Tags: anchialine-pool, biota, carbon-dioxide, hawaii, hawaii-island, invasive-fish, invasive-species-management, koloko-honokohau-national-historical-park, puuhonua-o-honaunau-national-historical-park, usgs-647e9ee9d34eac007b55ee1d
Organization: Department of the Interior
Hawaii Island, carbon dioxide as a tool to manage invasive fish in anchialine pools, aquarium trials subject fate, 2020

This data release includes metadata and tabular data that documents the survival of fish or invertebrate exposed to carbon dioxide (CO2) gas treatment during aquarium trials. Aquarium trials were conducted with six taxa: guppies (Poecilia reticulata), western mosquitofish (Gambusia affinis), Mozambique tilapia (Oreochromis mossambicus), Opae ula shrimp (Halocaridina rubra), feeble shrimp (Palaemon debilis) and a species of thiarid snail (Thiaridae). These results were part of a larger study aimed to test the efficacy of CO2 as a tool for managing invasive fish in anchialine pools.

Tags: anchialine-pool, biota, carbon-dioxide, hawaii, hawaii-island, invasive-fish, invasive-species-management, koloko-honokohau-national-historical-park, puuhonua-o-honaunau-national-historical-park, usgs-647ea29cd34eac007b55ee22
Organization: Department of the Interior
Inventory of Managed Coastal Wetlands in Delaware Bay and Delaware's Inland Bays

This data release contains areas within Delaware Bay and Delaware Inland Bays that are within tidal elevations, as determined by the Highest Astronomical Tide (HAT), but that are classified as non-tidal or managed wetlands by the National Wetlands Inventory (NWI) or as non-estuarine by the 2016 Coastal Change Analysis Program (C-CAP) land cover dataset. These areas have been assigned the classification codes of NWI, where available, and C-CAP. These data are based on a 5m resolution elevation raster from Coastal National Elevation Database (CoNED), an interpolated surface from Highest Astronomical Tide (HAT) data from National Oceanographic and Atmospheric Administration (NOAA) tide gauges, and NWI and C-CAP digital wetland products. The area was determined by identifying non-tidal or non-wetland land covers at or below the interpolated HAT tidal elevation. The underlying wetland category from NWI or land cover type from C-CAP was then applied to the entire area to indicate areas for possible land or hydrologic management and assess current and future conditions of land within tidal elevations.

Tags: coastal-ecosystems, delaware, delaware-bay, elevation, environment, geospatial-datasets, inlandwaters, new-jersey, salt-marsh, united-states, usgs-647f5975d34eac007b562495, vegetation, wetland-ecosystems, wetland-functions
Organization: Department of the Interior
Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) Green Bay, U.S.: Composite Model Layers

This dataset contains all the layers associated with U.S. Geological Survey (USGS) Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) initiative for the Green Bay Restoration Assessment (GBRA) which aims to identify and rank coastal areas with the greatest potential for wetland habitat restoration. Each layer has a unique contribution to the identification of restorable wetlands. The 7 parameters (Parameter 0: Mask, Parameter 1: Hydroperiod, Parameter 2: Wetland Soils, Parameter 3: Flowlines, Parameter 4: Conservation and Recreation Lands, Parameter 5: Impervious Surfaces, and Parameter 6: Land Use) and Index Composite directly correlate to areas that are recommended for restoration. The dikes, degree flowlines, culverts, study area and DEM datasets are supplemental layers that provide additional information around the priority rank values. All layers were produced in collaboration with the USGS Upper Midwest Water Science Center, USGS Great Lakes Science Center, and by the New College of Florida.
For more information on the methodology used to create these parameters, view the metadata from GLCWRA: Green Bay Restoration Assessment (GBRA) and the GLCWRA webpage: https://glcwra.wim.usgs.gov/.

Tags: brown, brown-county-wi, delta, delta-county-mi, door, door-county-wi, environment, geoscientificinformation, great-lakes, green-bay, holocene, hydrology, inlandwaters, kewaunee, kewaunee-county-wi, lake, lake-michigan, lakeshore, manitowoc, manitowoc-county-wi, marinette, marinette-county-wi, menominee, menominee-county-mi, michigan, natural-resource-management, oconto, oconto-county-wi, remediation, resource-management, river, schoolcraft, schoolcraft-county-mi, stream, usgs-645baf2dd34ec179a838201f, water-quality, wetland, wetland-ecosystems, wetland-restoration, wisconsin
Organization: Department of the Interior
Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) Green Bay, U.S.: Degree Flowlines

This dataset is part of the U.S. Geological Survey (USGS) Great Lakes Coastal Wetland Restoration Assessment (GLCWRA) initiative. These data represent the flowline network in the Green Bay Restoration Assessment (GBRA). It is attributed with the number of disconnections (e.g., road crossings) between the reach and Lake Ontario. The more road crossings on a flowline the more disconnected that area is from the lake and the less suitable it will be for restoration. These data help identify the condition of hydrologic separation between potential restoration areas and Lake Ontario. Low numbers represent fewer disconnections, such as culverts, between the reach and the water body requiring no flow network modification to restore the area. Please refer to the process steps and https://glcwra.wim.usgs.gov/ for further explanation on the methods.
The GLCWRA initiative identifies coastal wetland areas that have the greatest habitat restoration potential. The data model uses seven parameters to identify and rank wetland restoration areas, resulting in a composite index raster that can be used by ecological managers and planners to assist with the selection of wetland restoration sites.
The parameters are 
Parameter 0: Mask
Parameter 1: Hydroperiod
Parameter 2: Wetland Soils
Parameter 3: Flowlines
Parameter 4: Conservation and Recreation Lands
Parameter 5: Impervious Surfaces
Parameter 6: Land Use (represents developed areas without impervious surfaces but high societal value)
The ancillary data include dikes, degree flowlines, study area, and culverts. These data layers are put through an ecological model, which results in a composite restoration index of ranked restoration areas.

Tags: brown, brown-county-wi, delta, delta-county-mi, door, door-county-wi, environment, great-lakes, green-bay, hydrology, inlandwaters, kewaunee, kewaunee-county-wi, lake, lake-michigan, lakeshore, manitowoc, manitowoc-county-wi, marinette, marinette-county-wi, menominee, menominee-county-mi, michigan, natural-resource-management, oconto, oconto-county-wi, remediation, resource-management, river, river-reaches, schoolcraft, schoolcraft-county-mi, stream, usgs-645baf08d34ec179a838201d, water-quality, wetland, wetland-ecosystems, wetland-restoration, wisconsin
Organization: Department of the Interior
Bumble bees and flowering plants on Montana rangelands, 2023-2024 (ver. 3.0, March 2025)

USGS personnel surveyed bumble bees and flowering plants on private and public lands in Central Montana during the spring and summer of 2023 and 2024. These data provide a summary of what was found, including the number of bumble bees caught during standardized netting surveys and a comprehensive list of all plants in flower within survey plots during each site visit.

Tags: agriculture, bees-and-wasps, biota, flowering-plants, usgs-647f7255d34eac007b562641
Organization: Department of the Interior
LANDFIRE Remap Fuel Vegetation Type (FVT) Palau

The LANDFIRE (LF) 2016 Remap (Remap) Fuel Vegetation Type (FVT) represents a modified pre-disturbance version of Existing Vegetation Type (EVT). FVT leverages fuel transition assignments related to disturbed areas by re-establishing  pre-disturbance vegetation. FVT was developed using the full suite of LF vegetation releases and the most recent 10 years of Annual Disturbance products and is a capable fuels product that calculates Time Since Disturbance (TSD) assignments for disturbed areas using an "effective year." For example, year 2020 fuels may be calculated for the year 2020. the new process considers all the existing disturbances included in LF Remap and adjusts the TSD for these to the effective year (2020 in the example), making the products "2020 capable fuels." More information about capable fuels can be found at https://www.landfire.gov/lf_remap.php. The FVT product is then evaluated through a series of QA/QC measures ensuring lifeform of the vegetation type assignment matches the lifeform of associated FVT attributes.

Tags: biota, evt, existing-vegetation-type, fires, fuel-vegetation-type, fvt, geographic-information-systems, geospatial-datasets, hazard-preparedness, ia, image-collections, imagerybasemapsearthcover, insular-areas, landfire-2016-remap, lf-2016-remap, oconus, palau, raster-digital-data, remote-sensing, u-s-forest-service-usfs, u-s-geological-survey-usgs, united-states, us, usgs-647f9f37-14a8-4f85-8246-8bebdd90b9f9
Organization: Department of the Interior
Hawaiʻi Island ʻōpeʻapeʻa random stand metrics, 2018–2021

Hawaiian hoary bats ('ōpe'ape'a; Lasiurus semotus) were captured and tracked back to roosting locations on Hawaiʻi Island. Roost stand metrics were observed and collected from 2018 to 2021. We observed a total of 123 roost stands used by 90 bats (29 female; 58 male; 3 unknown).
We examined roost preferences at the stand-level with discrete choice analysis. Discrete model choice sets were developed based on distinct selection events and served as the observational units at each level, that is roost tree and roost stand. The number of choice sets was determined both by the number of unique roost sites to which a bat was tracked and the duration of the sampling period over which it was confirmed at one or more roosts. A “basic” choice set was comprised of one used site and two random sites for each selection event. For bats observed for a short period (&lt;3 days) at only one roost, we produced a choice set limited to only a single selection event. For bats tracked to only one roost but confirmed at that roost on at least three days, we included an additional independent selection event for that roost. An additional selection event was also assigned to bats that returned to the same roost locations during more than one season (Reproductive season = May to September; non-reproductive season = October to April) and/or year. Bats that used multiple roosts were assigned an equivalent number of selection events, and additional events if confirmed at a particular roost on at least three days. The method estimates the probability of specific habitat attributes being used by comparing selected to available but unselected random sites. We modeled day-roost roost selection at the stand-level with 205 choice sets for 90 unique ‘ōpe‘ape‘a that included the habitat attributes of 123 unique stands. This data file includes data pertaining to random stand metrics including, stand canopy cover, and habitat classification.

Tags: bat-roost, bats, biota, endangered-species, hawaiian-hoary-bat, usgs-647ff0e0d34eac007b56a7b4, wildlife-biology
Organization: Department of the Interior
Hawaiʻi Island ʻōpeʻapeʻa random tree metrics, 2018–2021

Hawaiian hoary bats ('ōpe'ape'a; Lasiurus semotus) were captured and tracked back to roosting locations on Hawaiʻi Island. Roost tree metrics were observed and collected from 2018 to 2021. We observed a total of 56 roost trees used by 46 bats (18 female; 25 male; 3 unknown). 
We examined roost preferences at the tree-level with discrete choice analysis. Discrete model choice sets were developed based on distinct selection events and served as the observational units at each level, that is roost tree and roost stand. The number of choice sets was determined both by the number of unique roost sites to which a bat was tracked and the duration of the sampling period over which it was confirmed at one or more roosts. A “basic” choice set was comprised of one used site and two random sites for each selection event. For bats observed for a short period (&lt;3 days) at only one roost, we produced a choice set limited to only a single selection event. For bats tracked to only one roost but confirmed at that roost on at least three days, we included an additional independent selection event for that roost. An additional selection event was also assigned to bats that returned to the same roost locations during more than one season (Reproductive season = May to September; non-reproductive season = October to April) and/or year. Bats that used multiple roosts were assigned an equivalent number of selection events, and additional events if confirmed at a particular roost on at least three days. The method estimates the probability of specific habitat attributes being used by comparing selected to available but unselected random sites. We modeled day-roost selection at the tree-level with 91 choice sets for 45 (18F, 24M, 3 unknown) unique ‘ōpe‘ape‘a that included the habitat attributes of 55 unique trees. This data file includes data pertaining to random tree metrics including, height, canopy cover, and habitat classification.

Tags: bat-roost, bats, biota, endangered-species, hawaiian-hoary-bat, usgs-647ff147d34eac007b56a7b7, wildlife-biology
Organization: Department of the Interior
Hawaiʻi Island ʻōpeʻapeʻa roost perch metrics, 2019–2021

Hawaiian hoary bats ('ōpe'ape'a; Lasiurus semotus) were captured and tracked back to roosting locations on Hawaiʻi Island. Roost perch metrics were observed and collected from 2019 to 2021. We observed a total of 69 perches used by 52 bats (16 female; 22 male; 14 unknown) within 44 roost trees. This data file includes data pertaining to roost perch locations, dates, height, canopy cover, and aspect.

Tags: bat-roost, bats, biota, endangered-species, hawaiian-hoary-bat, usgs-647ff20bd34eac007b56a7ba, wildlife-biology
Organization: Department of the Interior
Soil-Water-Balance forecasted climate model output for simulations of water budget components in the Mississippi Embayment Regional Aquifer System, 2019 to 2055

This data release provides 64 forecasted water budget simulations for the Mississippi Embayment Regional Aquifer System (MERAS) during the period 2019 to 2055. Gridded daily data (1-kilometer resolution) include net infiltration (potential groundwater recharge), rejected net infiltration, interception, runoff, runoff outside (runoff that cannot be routed downslope), irrigation, actual evapotranspiration, minimum and maximum temperatures, and gross precipitation. The gridded representations of water budget components are output from USGS Soil-Water-Balance (SWB) model (Nielsen and Westenbroek, 2023; Westenbroek and Nielsen, 2023) simulations in netcdf4 format, and all water budget components are in inches.  The precipitation, maximum air temperature, and minimum air temperature data used as climatic input to the SWB model application were derived from Coupled Model Intercomparison Project Phase 5 (CMIP5) projections (Brekke and others, 2013), downscaled using Localized Constructed Analogs (LOCA; Pierce and others, 2014) to a 1/16 degree spatial resolution. The SWB model produced output based on 64 CMIP5 climate projections, half of which are Representative Concentration Pathway (RCP) 4.5 and half of which are RCP 8.5 greenhouse gas concentration trajectories. 
All 64 forecasted climate model outputs can be accessed through the child items: RCP_4_5 and RCP_8_5. Outputs for each climate model scenario are housed in a zipped folder named after the respective climate scenario. Each zipped folder contains ten files: actual_et__2019-01-01_to_2055-12-31__989_by_661.nc, gross_precipitation__2019-01-01_to_2055-12-31__989_by_661.nc, interception__2019-01-01_to_2055-12-31__989_by_661.nc, irrigation__2019-01-01_to_2055-12-31__989_by_661.nc, net_infiltration__2019-01-01_to_2055-12-31__989_by_661.nc, rejected_net_infiltration__2019-01-01_to_2055-12-31__989_by_661.nc, runoff__2019-01-01_to_2055-12-31__989_by_661.nc, runoff_outside__2019-01-01_to_2055-12-31__989_by_661.nc, tmax__2019-01-01_to_2055-12-31__989_by_661.nc, and tmin__2019-01-01_to_2055-12-31__989_by_661.nc.
Further details about the SWB model used to produce the water budget forecasts can be found in Nielsen and Westenbroek (2023).

Tags: environment, geoscientificinformation, groundwater, groundwater-recharge, groundwater-recharge-model, hydrology, inlandwaters, irrigation, irrigation-water-use, mississippi, mississippi-river-delta, net-infiltration, soil-water-balance-model, swb, usgs-6480aef7d34eac007b57a684, usgssoilwaterbalancemodel, water-availability, water-budget
Organization: Department of the Interior
Early Estimates of Exotic Annual Grass (EAG) in the Sagebrush Biome, USA, 2023 (ver. 2.0, May 2023)

These datasets provide early estimates of 2023 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a weekly basis from May to early July. The EAG estimates are developed typically within 7-13 days of the latest satellite observation used for that version. Each weekly release contains four fractional cover maps along with their corresponding confidence maps for: 1) a group of 16 species of EAGs, 2) cheatgrass (Bromus tectorum); 3) medusahead (Taeniatherum caput-medusae); and 4) Sandberg bluegrass (Poa secunda). These datasets were generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) data plots; Harmonized Landsat and Sentinel-2 (HLS) based Normalized Difference Vegetation Index (NDVI); other relevant environmental, vegetation, remotely sensed, and geophysical drivers; and artificial intelligence/machine learning techniques. A total of 28,901 AIM plots from years 2016–2022 were used to train an ensemble of five-fold regression-tree models using a cross-validation approach (each observation was used as test data once and as training data four times) that developed all the fractional cover maps. The geographic coverage includes arid and semi-arid rangelands in the western U.S classified as grassland/herbaceous by the 2019 National Land Cover Database at or below 2350-m elevation.
Note:
Maps of May 1st, 2023 were developed using satellite observation data no later than April 28.
Maps of May 8th, 2023 were developed using satellite observation data no later than May 05.
Releases: 
First Release: May 01, 2023 (ver. 1.0)
Revised: May 08, 2023 (ver. 2.0)

Tags: annual-grass, annual-herbaceous, arizona, california, cheatgrass, colorado, exotic, great-basin, harmonized-landsat-sentinel, idaho, invasive-species, kansas, medusahead, montana, ndvi, nebraska, nevada, new-mexico, nonindigenous-species, north-dakota, noxious, oklahoma, oregon, perennial-grass, poa-secunda, red-brome, remote-sensing, rye-brome, sagebrush, soft-brome, south-dakota, texas, texas-brome, united-states, usgs-645b938ad34ec179a8381a1c, utah, washington, western-united-states, wyoming
Organization: Department of the Interior