Data Releases
The data collected and the techniques used by USGS scientists should conform to or reference national and international standards and protocols if they exist and when they are relevant and appropriate. For datasets of a given type, and if national or international metadata standards exist, the data are indexed with metadata that facilitates access and integration.
Filter Total Items: 16325
Monthly and Annual population and water withdrawal maps of Rhode Island 2014-2021 Monthly and Annual population and water withdrawal maps of Rhode Island 2014-2021
This data release consists of multi-band 30-meter x 30-meter pixel rasters of estimated population and domestic self-supplied water withdrawals in Rhode Island between July 2014 and June 2021. Population raster data were generated using a national data product of 2010 population spatially distributed across land cover data and U.S. Census Bureau data of population growth estimates to...
Lower Salinas Valley Hydrologic Models: Future Climate Data Lower Salinas Valley Hydrologic Models: Future Climate Data
This digital dataset contains the gridded future climate data used for the Lower Salinas Valley Hydrologic Models. The monthly climate data for Lower Salinas Valley Hydrologic Models are based on the Salinas and Carmel River Basins Study (SCRBS) future climate scenarios [Henson and others, 2024). SCRBS considers one baseline climate scenario that represents recent historical climate...
Data for Gull-billed Tern and Black Skimmer Bayesian Network Model - Soil Textures and Topography Index Data for Gull-billed Tern and Black Skimmer Bayesian Network Model - Soil Textures and Topography Index
This U.S. Geological Survey (USGS) data release represents tabular and geospatial data for characterizing the soil texture and topography that may be relevant to Black Skimmer and Gull-billed tern nesting on bare ground sites across the U.S. portion of the Gulf of Mexico. These data characterize soil texture (e.g., sand or shell, loam), elevation, and distance to mean higher high water...
Data for Gull-billed Tern and Black Skimmer Bayesian Network Model Data for Gull-billed Tern and Black Skimmer Bayesian Network Model
This U.S. Geological Survey (USGS) data release represents tabular and geospatial data for the creation and application of a Bayesian network model that predicts Black Skimmer (Rynchops niger) and Gull-billed Tern (Gelochelidon nilotica) on bare ground sites across the U.S. portion of the Gulf of Mexico. Management plans with clear priorities can help to achieve Black Skimmer and Gull...
Assessing spatial variability of nutrients, phytoplankton and related water-quality constituents in the California Sacramento-San Joaquin Delta at the landscape scale: 2022 High-resolution mapping surveys Assessing spatial variability of nutrients, phytoplankton and related water-quality constituents in the California Sacramento-San Joaquin Delta at the landscape scale: 2022 High-resolution mapping surveys
Broad-area, high-resolution, boat-based water quality mapping surveys of the Sacramento-San Joaquin Delta and Suisun Bay (Delta) of California, USA were conducted under different environmental/flow conditions in May, July, and October of 2022. The spatial and temporal variability of nutrients, phytoplankton, and related water quality parameters were assessed. This dataset includes...
Environmental Sampling and Modeling Results to Characterize Surface-Water Quality at 32 Sites Across the Potomac River Watershed, 2022 (ver. 3.0, April 2025) Environmental Sampling and Modeling Results to Characterize Surface-Water Quality at 32 Sites Across the Potomac River Watershed, 2022 (ver. 3.0, April 2025)
This data release presents chemical results from investigations of surface-water quality in the Potomac River watershed (encompassing Washington, D.C. and parts of West Virginia, Virginia, Pennsylvania, and Maryland) conducted during low-flow conditions in July through September of 2022 and modeling results that support interpretative products. Water-quality sampling: A sampling campaign...