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National-Scale Geophysical, Geologic, and Mineral Resource Data and Grids for the United States, Canada, and Australia: Data in Support of the Tri-National Critical Minerals Mapping Initiative National-scale geologic, geophysical, and mineral resource raster and vector data covering the United States, Canada, and Australia are provided in this data release. The data were compiled as part of the tri-national Critical Minerals Mapping Initiative (CMMI). The CMMI, established in 2019, is an international science collaboration between the U.S. Geological Survey (USGS), Geoscience Australia (GA), and the Geological Survey of Canada (GSC). One aspect of the CMMI is to use national- to global-scale earth science data to map where critical mineral prospectivity may exist using advanced machine learning approaches (Kelley, 2020). The geoscience information presented in this report include the training and evidential... Categories: Data; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: ARDS, Alaska, Alaska Mineral Resource Data, Australia, Canada, All tags... Critical Minerals Mapping Initiative, GIS, Geographic Information Systems (GIS), Geological Survey of Canada, Geology, Geophysics, and Geochemistry Science Center, Geoscience Australia, MRDS, Mineral Resources Data System, Mineral Resources Program (MRP), Mississippi Valley-type deposits, Pb-Zn, U.S. Geological Survey (USGS), USGS Science Data Catalog (SDC), United States, ["Economic Geology" "Geophysics" "Mineral Resources"], airborne magnetics, ancient passive margins, assessment, basin, black shale, clastic-dominated deposits, critical minerals, economic geology, faults, geologic maps, geologic time periods, geology, geophysics, geospatial, geospatial analysis, geospatial data, gravity, lead, lithosphere-asthenosphere boundary, machine learning, magnetics, mineral occurrence, mineral prospectivity, mineral resources, modeling, modeling, satellite gravity, zinc, Fewer tags Random forest classification data developed from multitemporal Landsat 8 spectral data and phenology metrics for a subregion in Sonoran and Mojave Deserts, April 2013 – December 2020 These data were compiled for the creation of a continuous, transboundary land cover map of Bird Conservation Region 33, Sonoran and Mojave Deserts (BCR 33). Objective(s) of our study were to, 1) develop a machine learning (ML) algorithm trained to classify vegetation land cover using remote sensing spectral data and phenology metrics from 2013-2020, over a large subregion of the Sonoran and Mojave Deserts BCR, 2) Calibrate, validate, and refine the final ML-derived vegetation map using a collection of openly sourced remote sensing and ground-based ancillary data, images, and limited fieldwork, and 3) Harmonize a new transboundary classification system by expanding existing land cover mapping resources from the United... Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: Arizona, Baja California, Botany, California, Ecology, All tags... Forestry, Geography, Hermosillo, Information Sciences, Landsat images, Mexico, Mojave Desert, Phoenix, Remote Sensing, Sonora, Sonoran Desert, Southwest, US-Mexico Transboundary region, USGS Science Data Catalog (SDC), United States, artificial intelligence, biogeography, biota, classification, conservation, data release, desert ecosystems, ecosystem management, imageryBaseMapsEarthCover, land use and land cover, machine learning, modeling, natural resource management, phenology, phenology metrics, random forest model, remote sensing, remote sensing spectral data, time series datasets, transboundary, vegetation, Fewer tags Data for Machine Learning Predictions of Nitrate in Shallow Groundwater in the Conterminous United States An extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in shallow groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution at a depth below the water table of 10 m. The model builds off a previous XGB machine learning model developed to predict nitrate at domestic and public supply groundwater zones (Ransom and others, 2022) by incorporating additional monitoring well samples and modifying and adding predictor variables. The shallow zone model included variables representing well characteristics, hydrologic conditions, soil type, geology, climate, oxidation/reduction, and nitrogen inputs. Predictor... Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Environmental Health, Hydrology, USGS Science Data Catalog (SDC), United States, Water Quality, All tags... environment, geoscientificInformation, groundwater, machine learning, nitrate, water quality, water well, Fewer tags [Prospectivity Models] Prospectivity models - clastic-dominated (CD) and Mississippi Valley-type (MVT) GeoTIFF grids for the United States, Canada, and Australia GeoTiff grids of models of prospectivity for clastic-dominated (CD) and Mississippi Valley-type (MVT) Pb-Zn mineralization for the US and Canada (combined) and Australia that used data provided in this report are provided here. The models are the result of a study by Lawley and others (2022) that used a data-driven machine learning approach called Gradient Boosting to predict the mineral prospectivity for clastic-dominated (CD) and carbonate-hosted (MVT) deposits across the United States, Canada, and Australia. The study was part of a tri-national collaboration between the U.S. Geological Survey, the Canadian Geological Survey, and Geoscience Australia called the Critical Minerals Mapping Initiative. The original... Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Australia, CMMI, Canada, Critical Minerals Mapping Initiative, Exploring for the Future Program, All tags... GA, GGGSC, GSC, Geological Survey of Canada, Geology, Geophysics, and Geochemistry Science Center, Geoscience Australia, MRP, MVT, Mineral Resources Program, Mississippi Valley-type, Pb-Zn, TGI, Targeted Geoscience Initiative, U.S. Geological Survey, USGS, United States, Zn-Pb, clastic dominated, critical minerals, geoscientificInformation, gradient boosting, lead, machine learning, metallic mineral resources, mineral prospectivity, mineral resources, prospectivity modelling, sedimentary basin, zinc, Fewer tags Examining the influence of deep learning architecture on generalizability for predicting stream temperature in the Delaware River Basin This data release and model archive provides all data, code, and modelling results used in Topp et al. (2023) to examine the influence of deep learning architecture on generalizability when predicting stream temperature in the Delaware River Basin (DRB). Briefly, we modeled stream temperature in the DRB using two spatially and temporally aware process guided deep learning models (a recurrent graph convolution network - RGCN, and a temporal convolution graph model - Graph WaveNet). The associated manuscript explores how the architectural differences between the two models influence how they learn spatial and temporal relationships, and how those learned relationships influence a model's ability to accurately predict... Categories: Data; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: DE, Delaware, Hydrology, MD, Maryland, All tags... NJ, NY, New Jersey, New York, PA, Pennsylvania, US, USGS Science Data Catalog (SDC), United States, Water Quality, Water Resources, climate change, domain shift, environment, explainable AI (XAI), generalization, hybrid modeling, inlandWaters, machine learning, modeling, process guided deep learning, stream temperature, temperature, water, water resources, Fewer tags High-Resolution Land Cover Maps of Lāna‘i, Hawai‘i, 2020 - Class Probability Stack This raster stack contains 15 probability layers representing the pixel-level predicted probability of membership in each species-specific vegetation class from 0 to 1. These probability layers can be used to generate class membership uncertainty maps or probabilistic class cover maps from the model outputs. They provide additional information beyond the discrete categorial land cover assignments. Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: Conservation, Hawaiian Islands, Hawai‘i, Invasive species, Land cover, All tags... Lāna‘i, Machine learning, Mapping, Pacific Islands, Remote sensing, Vegetation, biota, climatologyMeteorologyAtmosphere, Fewer tags Supporting data and model outputs for hydrologic alteration modeling in the Pearl and Pascagoula river basins Anthropogenic hydrologic alteration threatens the health of riverine ecosystems. This study assesses hydrologic alteration in the Pearl and Pascagoula river basins using modeled daily streamflow. Machine learning was used to identify locations that have undergone statistically significant streamflow alteration, quantify the volume of the alteration, and predict alteration using cubist models. Statistically significant alteration was determined by hypothesis testing. The pre- and post-alteration flow duration curves were used to calculate the net change across 60 years. Cubist models were developed for both basins to predict hydrologic alteration and to identify important basin characteristics. This data release... Categories: Data; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Alabama, Florida, Gulf Coast, Hydrology, Louisiana, All tags... Mississippi, RESTORE, Texas, USGS Science Data Catalog (SDC), United States, Water Resources, cubist, hydrologic alteration, machine learning, model tree, southeast, streamflow, streamflow alteration, Fewer tags Machine-learning model predictions and rasters of groundwater salinity in the Mississippi Alluvial Plain Groundwater from the Mississippi River Valley alluvial aquifer (MRVA), coincident with the Mississippi Alluvial Plain (MAP), is a vital resource for agriculture and drinking-water supplies in the central United States. Water availability can be limited in some areas of the aquifer by high concentrations of salinity, measured as specific conductance. Boosted regression trees (BRT), a type of ensemble-tree machine-learning method, were used to predict specific conductance concentration at multiple depths throughout the MRVA and underlying aquifers. Two models were created to test the incorporation of datasets from a regional aerial electromagnetic (AEM) survey and evaluate model performance. Explanatory variables... Categories: Data; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Arkansas (AR), Illinois (IL), Kentucky (KY), Louisiana (LA), Mississippi (MS), All tags... Missouri (MO), Tennessee (TN), USGS Science Data Catalog (SDC), alluvial aquifer, aquifer, boosted regression trees, groundwater, groundwater quality, machine learning, mathematical modeling, water quailty, Fewer tags Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Perpetual Hazards Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal,... Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: Acadia National Park, ArcGIS Pro, Arcpy, Autoclassification, Automation, All tags... Bathymetry, Cape Cod National Seashore, Coastal Change Hazard Assessment, Coastal Fabric, Coastal Hazards, Coastal Vulnerability Index, Coastal and Marine Hazards Mission Area, Connecticut, Decision Tree Framework, Delaware, Elevation, Gateway National Recreation Area, George Washington Birthplace National Monument, High Tide Flooding, Interpretation, Land Cover, Landcover, Machine Learning, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Northeast US, Rhode Island, Sea Level Rise, Shoreline Change, St. Croix Island International Historic Site, Storm Overwash, Storm Recurrence, Supervised Classification, Support Vector Machine, Topography, Training Samples, U.S. Geological Survey, USGS, UVVR, Unvegetated-Vegetated Ratio, Virginia, Wave Power, Woods Hole Coastal and Marine Science Center, geoscientificInformation, marine geology, oceans, Fewer tags