AndreaBozzo/ceres-open-data-index huggingface dataset:
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cobb Island, VA, 2014

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, bayesian-network, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-erosion, coastal-habitat, coastal-hazards, coastal-processes, cobb-island, delmarva-peninsula, environment, erosion, geographic-information-systems, geomorphology, geoscientificinformation, geospatial-analysis, geospatial-datasets, gis, hazards, land-use-and-land-cover, mean-high-water, mhw, north-america, probability, sea-level-change, sea-level-rise, shoreline-change, st-petersburg-coastal-and-marine-science-center, transect-sampling, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37aae4b09fd3b0c9ceb3, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
Groundwater-quality data in the Redding-Red Bluff Shallow Aquifer Study Unit, 2018-2019: Results from the California GAMA Priority Basin Project

The U.S. Geological Survey collected groundwater samples from 50 wells used for domestic and small system drinking water supplies in Shasta and Tehama Counties, California in 2018-2019. The wells were sampled for the Redding-Red Bluff Shallow Aquifer Study Unit of the California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project’s assessment of the quality of groundwater resources used for domestic and small system drinking water supplies. Domestic and small-system wells are commonly screened at shallower depths than public-supply wells. Wells in the study unit generally tap unconsolidated to moderately consolidated coarse and fine-grained sediments derived from the northern Coast Ranges to the west and the Cascade Range to the east. The Redding-Red Bluff Shallow Aquifer study unit included 11 groundwater subbasins of the Sacramento Valley groundwater basin (California Department of Water Resources, 2003) and was divided into two study areas: Redding (Enterprise, Millville, South Battle Creek, Bowman, Rosewood, and Anderson) and Red Bluff (Bend, Antelope, Dye Creek, Los Molinos, and Red Bluff). In each study area, the collective area of all one-mile square sections estimated to contain domestic wells (Johnson and Belitz, 2015) was divided into equal-area grid cells, and a well was sampled in each cell: Redding, twenty-five 48-square kilometer cells, and Red Bluff, twenty-five 43 square kilometer grid cells. Groundwater samples from all 50 wells were analyzed for field water-quality parameters and microbial indicators, volatile organic compounds, pesticides and pesticide degradates, major ions and trace elements, chromium (VI), perchlorate, nutrients, gross alpha and gross beta particle activities, carbon-14 in dissolved inorganic carbon, stable isotopic ratios of water, tritium, sulfur hexafluoride, and noble gases. Groundwater levels were measured in 41 of the 50 wells. All results are presented in this data release, except results for sulfur hexafluoride and noble gases.
This data release supports the following publication: Groundwater quality in the Redding-Red Bluff Shallow Aquifer Study Unit of the Northern Sacramento Valley, California: U.S. Geological Survey Fact Sheet 2020–3025, 4 p., https://doi.org/10.3133/fs20203025.

Tags: california, environment, gama, geoscientificinformation, groundwater, groundwater-ambient-monitoring-and-assessment-program, shasta-county, tehama-county, united-states, usgs-5df944d7e4b0cdded73b4220, water-quality
Organization: Department of the Interior
Oyster habitat suitability modeling for the Alabama Barrier Island restoration assessment at Dauphin Island

A spatially explicit oyster habitat suitability index (HSI) model was developed for the Alabama barrier island restoration assessment at Dauphin Island. Based on previous oyster habitat suitability studies, seven water quality variables were selected and their relationships with habitat suitability were developed and incorporated into the oyster HSI model for Dauphin Island restoration assessment: 1) mean salinity, 2) minimum monthly mean salinity, 3) annual mean salinity, 4) annual mean dissolved oxygen, 5) annual mean total suspended solids, 6) annual mean water depth, and 7) annual mean water temperature. The final HSI score was calculated using the weighted geometric mean of the suitability scores of these individual variables. The oyster HSI model was calibrated and validated using field data on oyster density from the Alabama Department of Conservation and Natural Resources (ADCNR) Marine Resources Division (MRD and continuous water quality data from the Mobile Bay National Estuary Program. Then, the oyster HSI model was used to assess oyster habitat suitability changes with and without restoration under future storminess and sea level (SL) conditions. The barrier island restoration actions being assessed include beach and dune restoration, marsh restoration, and placement of sand in the littoral zone. The storminess bins included realizations with a “medium” storminess, which included 1 to 3 storms over a 10-year period (that is, ST2) and a “high” storminess, which included 4 to 5 storms over an equal period (that is, ST3). The two future sea levels included a SL of 0.3 m (that is, SL1) and a SL of 1.0 m (that is, SL3) above the contemporary SL. Specifically, the medium storminess was paired with the 0.3 m above the contemporary SL (that is, ST2SL1) and the “high” storminess bin was paired with the 1.0 m above the contemporary SL (that is, ST3SL3). To account for intertidal marsh vertical accretion as a component of marsh morphology evolution, two scenarios were included in modeling: the U.S. Army Corps of Engineers (USACE) high and intermediate SLR curves in which marsh kept pace with SLR through accretion (1 cm/yr) through 2022 under high SLR curve whereas marsh kept pace with SLR by accretion for the entirety of the USACE intermediate curve. Inputs of water quality conditions under future storminess and sea level conditions were provided by the CE-QUAL-ICM model that was coupled with a geomorphology model and a hydrodynamic model. This data release includes simulation results and metadata of oyster habitat suitability scores at each spatial unit (grid cell) across the study domain: estuarine waters near Dauphin Island.

Tags: barrier-island, cedar-point, dauphin-island, environment, farming, habitat-suitability-index, imagerybasemapsearthcover, mobile, oyster, usgs-5dbb2ac7e4b06957974ec016
Organization: Department of the Interior
Geophysical and related field data from the West Fork of Dall Creek, AK 2017-2019

The West Fork of Dall Creek contains a burn scar from the 2004 Dall City Fire and is surrounded by otherwise unburned black spruce forest. Multi-season, multi-method geophysical data were collected both within the burned and unburned areas. Geophysical techniques used include Nuculear Magnetic Resonance (NMR) and Ground Penetrating Radar (GPR) data. NMR data were collected with a down borehole Dart tool (Vista Clara Inc.). GPR data were collected with a Sensors and Software pulseEKKO pro unit and 100 mHz antennas and a high power (100V) transmitter. Soil temperature data were collected from the unburned black spruce forest at depths of 10, 30, 50 and 70 cm. NMR data were collected in June of 2018 and April of 2019, GPR data were collected in April of 2019, and soil temperature data were collected continuously from May 2017 through June of 2018.

Tags: geophysics, hydrology, permafrost, usgs-5dbb0922e4b06957974ebf60
Organization: Department of the Interior
DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Monomoy Island, MA, 2013-2014

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, bayesian-network, biota, cape-cod, charadrius-melodus, cmgp, coastal-and-marine-geology-program, coastal-habitat, ecology, environment, foraging-ecology, geographic-information-systems, geography, geomorphology, geospatial-analysis, geospatial-datasets, gis, ma, massachusetts, mean-high-water, mhw, monomoy-island, monomoy-national-wildlife-refuge, north-america, piping-plovers, st-petersburg-coastal-and-marine-science-center, u-s-geological-survey, united-states, usa, usgs, usgs-5daa372be4b09fd3b0c9ce2a, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
Input and Output Data used to Compare Storm Runoff Models for a Small Watershed in an Urban Metropolitan Area, Albuquerque, New Mexico

Three separate hydrologic models were used to simulate storm runoff in the Hahn Arroyo Watershed, an urbanized watershed with concrete lined channels in the northeastern quadrant of Albuquerque, New Mexico that exhibits flashy, monsoonal-driven, storm runoff events. This data release contains the input and output files associated with the hydrologic simulations of each of the following models: Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS), Program for Predicting Polluting Particle Passage through Pits, Puddles and Ponds (P8), and Arid-Lands Hydrologic Model (AHYMO). The study was designed to compare three existing rainfall-runoff modeling software packages to determine which provided the best balance of accuracy and usability for simulating storm runoff in small watersheds in the Albuquerque urbanized area. Model simulations were completed for the years 2011-14. Results were used in the associated Scientific Investigations Report "Comparison of Storm Runoff Models for a Small Watershed in an Urban Metropolitan Area, Albuquerque, New Mexico" (Shephard and Douglas-Mankin, 2020).

Tags: albuquerque, bernalillo, new-mexico, precipitation-atmospheric, rio-grande, runoff, storms, stormwater-runoff, streamflow, streamflow-modeling, urban-runoff, usgs-5dba1220e4b06957974eb76d
Organization: Department of the Interior
shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Parramore Island, VA, 2014

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-processes, delmarva-peninsula, geomorphology, geospatial-analysis, geospatial-datasets, north-america, oceans, parramore-island, st-petersburg-coastal-and-marine-science-center, study-areas, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37e7e4b09fd3b0c9cef9, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Parramore Island, VA, 2014

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-habitat, coastal-processes, delmarva-peninsula, environment, erosion, geographic-information-systems, geomorphology, geospatial-analysis, geospatial-datasets, gis, hazards, image-analysis, mean-high-water, mhw, north-america, oceans, parramore-island, scientific-interpretation, sea-level-change, st-petersburg-coastal-and-marine-science-center, study-areas, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37e9e4b09fd3b0c9cefb, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Ship Shoal Island, VA, 2014

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-hazards, delmarva-peninsula, geographic-information-systems, geomorphology, geoscientificinformation, geospatial-analysis, geospatial-datasets, gis, mean-high-water, mhw, north-america, oceans, ship-shoal-island, st-petersburg-coastal-and-marine-science-center, transect-sampling, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37ede4b09fd3b0c9ceff, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Ship Shoal Island, VA, 2014

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, bayesian-network, biota, charadrius-melodus, cmgp, coastal-and-marine-geology-program, coastal-habitat, delmarva-peninsula, ecology, environment, foraging-ecology, geographic-information-systems, geography, geomorphology, geospatial-analysis, geospatial-datasets, gis, mean-high-water, mhw, north-america, piping-plovers, ship-shoal-island, st-petersburg-coastal-and-marine-science-center, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37efe4b09fd3b0c9cf02, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
DisOcean: Distance to the ocean: Ship Shoal Island, VA, 2014

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Tags: atlantic-ocean, barrier-island, cmhrp, coastal-and-marine-hazards-and-resources-program, coastal-habitat, delmarva-peninsula, environment, geographic-information-systems, geomorphology, geospatial-analysis, geospatial-datasets, gis, mean-high-water, mhw, north-america, oceans, ship-shoal-island, st-petersburg-coastal-and-marine-science-center, u-s-geological-survey, united-states, usa, usgs, usgs-5daa37f1e4b09fd3b0c9cf04, va, virginia, virginia-coast-reserve, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior