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Machine-learning model predictions and rasters of arsenic and manganese in groundwater in the Mississippi River Valley alluvial aquifer Groundwater from the Mississippi River Valley alluvial aquifer (MRVA) 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 trace elements, including manganese and arsenic. Boosted regression trees, a type of ensemble-tree machine-learning method, were used to predict manganese concentration and the probability of arsenic concentration exceeding a 10 µg/L threshold throughout the MRVA. Explanatory variables for the BRT models included attributes associated with well location and construction, surficial variables (such as hydrologic position and recharge), variables extracted from a MODFLOW-2005... Categories: Data; Tags: Arkansas, Geochemistry, Hydrology, Illinois, Kentucky, All tags... Louisiana, Mississippi, Missouri, Tennessee, USGS Science Data Catalog (SDC), Water Quality, aquifer, boosted regression tree, groundwater, groundwater quality, machine learning, mathematical modeling, Fewer tags Predictive soil property map: Sodium adsorption ratio These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive... Tags: Arizona, Colorado, Colorado River, Colorado River Basin, Colorado River Basin above Hoover Dam, All tags... Hoover Dam, Nevada, New Mexico, Utah, Wyoming, accuracy and error estimated, available water capacity, bulk density, calcium carbonate, digital soil mapping, electrical conductivity, environmental conditions, environmental raster layers, erodibility, geoscientificInformation, gypsum, interpolate, machine learning, maps and atlases, organic matter, predicitve modeling, predictive mapping, predictive maps, random forest models, random forests, restrictive layer, rock, sodium adsorption ratio, soil conductivity, soil density, soil forming factors, soil pH, soil properties, soil property maps, soil sciences, soil texture, soils, surface rock cover, surface rock size, texture fractions, uncertainty, Fewer tags Predictive soil property map: Very fine sand content These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive... Tags: Arizona, Colorado, Colorado River, Colorado River Basin, Colorado River Basin above Hoover Dam, All tags... Hoover Dam, Nevada, New Mexico, Utah, Wyoming, accuracy and error estimated, available water capacity, bulk density, calcium carbonate, digital soil mapping, electrical conductivity, environmental conditions, environmental raster layers, erodibility, geoscientificInformation, gypsum, interpolate, machine learning, maps and atlases, organic matter, predicitve modeling, predictive mapping, predictive maps, random forest models, random forests, restrictive layer, rock, sodium adsorption ratio, soil conductivity, soil density, soil forming factors, soil pH, soil properties, soil property maps, soil sciences, soil texture, soils, surface rock cover, surface rock size, texture fractions, uncertainty, Fewer tags 4. Figure code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling This section provides code for reproducing the figures in Rahmani et al. (2023b). The full model archive is organized into these four child items: 1. Model code - Python files and README for reproducing model training and evaluation 2. Inputs - Basin attributes and shapefiles, forcing data, and stream temperature observations 3. Simulations - Simulation descriptions, configurations, and outputs [THIS ITEM] 4. Figure code - Jupyter notebook to recreate the figures in Rahmani et al. (2023b) The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling.... Tags: US, United States, deep learning, environment, inlandWaters, All tags... machine learning, modeling, streams, water resources, water temperature, Fewer tags Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 3 Model inputs This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge. Categories: Data; Tags: AL, Alabama, DE, Delaware, GA, All tags... Georgia, IA, ID, Idaho, Iowa, KS, Kansas, MA, MD, ME, MI, MS, Maine, Maryland, Massachusetts, Michigan, Mississippi, NC, NJ, NM, NV, NY, Nevada, New Jersey, New Mexico, New York, North Carolina, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, South Carolina, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 2 Water temperature observations Observed water temperatures from 1980-2019 were compiled for 2,332 lakes in the US. These data were used as training, test, and error-estimation data for process-guided deep learning models and the evaluation of process-based models. The data are formatted as a single csv (comma separated values) file with attributes corresponding to the unique combination of lake identifier, time, and depth. Data came from a variety of sources, including the Water Quality Portal, the North Temperate Lakes Long-Term Ecological Research Project, and digitized temperature records from the MN Department of Natural Resources. This dataset is part of a larger data release of lake temperature model inputs and outputs for these same lakes... Tags: 007, 012, IA, IL, Illinois, All tags... Iowa, MI, MN, Michigan, Minnesota, ND, North Dakota, OH, Ohio, SD, South Dakota, US, United States, WI, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Daily surface temperature predictions for 185,549 U.S. lakes with associated observations and meteorological conditions (1980-2020) Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from 1980 to 2020 generated using an entity-aware long short-term memory deep learning model. In-situ measurements used for model training and evaluation are from 12,227 lakes and are included as well as daily meteorological conditions and lake properties. Median per-lake estimated error found through cross validation on lakes with in-situ surface temperature observations was 1.24 °C. The generated dataset will be beneficial for a wide range of applications including estimations of thermal habitats and the impacts of climate change on inland lakes. Categories: Data; Tags: AL, AR, AZ, Alabama, Aquatic Biology, All tags... Arizona, Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, Ecology, FL, Florida, GA, Georgia, Hydrology, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, LA, Limnology, Louisiana, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, USGS Science Data Catalog (SDC), UT, United States, Utah, VA, VT, Vermont, Virginia, WA, WI, WV, WY, Washington, Water Quality, Water Resources, West Virginia, Wisconsin, Wyoming, climate change, deep learning, environment, inlandWaters, machine learning, modeling, reservoirs, surface temperature, temperate lakes, temperature, water, water resources, Fewer tags Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 7 thermal and optical habitat estimates Using predicted lake temperatures from uncalibrated, process-based models (PB0) and process-guided deep learning models (PGDL), this dataset summarized a collection of thermal metrics to characterize lake temperature impacts on fish habitat for 881 lakes. Included in the metrics are daily thermal optical habitat areas and a set of over 172 annual thermal metrics. Categories: Data; Tags: MN, Minnesota, SD, South Dakota, US, All tags... United States, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal habitat, thermal profiles, water, water resources, Fewer tags Prediction grids of pH Groundwater is a vital resource to the Mississippi embayment region of the central United States. Regional and integrated assessments of water availability that link physical flow models and water quality in principal aquifer systems provide context for the long-term availability of these water resources. An innovative approach using machine learning was employed to predict groundwater pH across drinking water aquifers of the Mississippi embayment. The region includes two principal regional aquifer systems; the Mississippi River Valley alluvial (MRVA) aquifer and the Mississippi embayment aquifer system that includes several regional aquifers and confining units. Based on the distribution of groundwater use for... Tags: Alabama, Arkansas, Illinois, Kentucky, Louisiana, All tags... Mississippi, Tennessee, aquifer, boosted regression tree, groundwater, groundwater quality, machine learning, mathematical modeling, Fewer tags Machine-learning model predictions and groundwater-quality rasters of specific conductance, total dissolved solids, and chloride in aquifers of the Mississippi embayment Groundwater is a vital resource in the Mississippi embayment of the central United States. An innovative approach using machine learning (ML) was employed to predict groundwater salinity—including specific conductance (SC), total dissolved solids (TDS), and chloride (Cl) concentrations—across three drinking-water aquifers of the Mississippi embayment. A ML approach was used because it accommodates a large and diverse set of explanatory variables, does not assume monotonic relations between predictors and response data, and results can be extrapolated to areas of the aquifer not sampled. These aspects of ML allowed potential drivers and sources of high salinity water that have been hypothesized in other studies to... Categories: Data; Tags: Alabama, Arkansas, Geochemistry, Hydrology, Illinois, All tags... Kentucky, Louisiana, Mississippi, Tennessee, USGS Science Data Catalog (SDC), Water Quality, Water Resources, aquifer, boosted regression tree, groundwater, groundwater quality, machine learning, mathematical modeling, Fewer tags Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) This dataset provides model specifications used to estimate water temperature from a process-based model (Hipsey et al. 2019). The format is a single JSON file indexed for each lake based on the "site_id". This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD). Categories: Data; Tags: 007, 012, Fish, MN, Minnesota, All tags... Northeast CASC, Rivers, Streams and Lakes, US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of... Categories: Data; Tags: 007, 012, Fish, Northeast CASC, Rivers, Streams and Lakes, All tags... US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added... Categories: Data; Tags: 007, 012, Fish, MN, Minnesota, All tags... Northeast CASC, Rivers, Streams and Lakes, US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Process-guided deep learning water temperature predictions: 3c All lakes historical inputs This dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs... Categories: Data; Tags: 007, 012, Fish, MN, Minnesota, All tags... Northeast CASC, Rivers, Streams and Lakes, US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Predictive soil property map: Organic matter These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive... Tags: Arizona, Colorado, Colorado River, Colorado River Basin, Colorado River Basin above Hoover Dam, All tags... Hoover Dam, Nevada, New Mexico, Utah, Wyoming, accuracy and error estimated, available water capacity, bulk density, calcium carbonate, digital soil mapping, electrical conductivity, environmental conditions, environmental raster layers, erodibility, geoscientificInformation, gypsum, interpolate, machine learning, maps and atlases, organic matter, predicitve modeling, predictive mapping, predictive maps, random forest models, random forests, restrictive layer, rock, sodium adsorption ratio, soil conductivity, soil density, soil forming factors, soil pH, soil properties, soil property maps, soil sciences, soil texture, soils, surface rock cover, surface rock size, texture fractions, uncertainty, Fewer tags Predictions for the presence of submersed aquatic vegetation in the upper Mississippi River, USA, from years 2010-2019 The datasets are to accompany a manuscript describing the prediction of submersed aquatic vegetation presence and its potential vulnerability and recovery potential. The data and accompanying analysis scripts allow users to run the final random forests predictive model and reproduce the figures reported in the manuscript. Files from several data sources (aqa_2010_lvl3_pct_oute_joined_VEG_BARCODE.csv, eco_states_near_SAV.csv, ltrm_vegsrs_thru2019_GEOMORPHIC_METRICS_final.csv, vegetation_data.csv, and water_full.csv) were combined into a single .csv file (analysis_data_for_SAV_RandomForest.csv) used as the input for the random forest model. When intersecting points with geomorphic metrics some sites were moved slightly... Categories: Data; Tags: Aquatic Biology, Ecology, Mississippi River, Pool 13, Pool 4, All tags... Pool 8, USGS Science Data Catalog (SDC), Water Resources, biota, ecosystem state, machine learning, macrophyte, predictive model, random forest, resilience, submergent plant, submersed plant, upper Mississippi River, vulnerability, Fewer tags 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 Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Maximum Change Likelihood 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 Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin Daily maximum water temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish species. This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper DRB. The modeling approach includes process-guided deep learning and data assimilation (Zwart et al., 2023). The model is driven by weather forecasts and observed reservoir releases and produces maximum water temperature forecasts for the issue day (day 0) and 7 days into the future (days 1-7). In combination with data provided in Oliver et al. (2022),... Tags: NY, New York, PA, Pennsylvania, US, All tags... USGS Science Data Catalog (SDC), United States, Water Resources, deep learning, environment, forecast, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, streams, temperature, water, water resources, Fewer tags Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Event 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 1 2 3 4 5 ... 9 »