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A boosted regression tree (BRT) model was developed to predict pH conditions in three-dimensions throughout the glacial aquifer system (GLAC) of the contiguous United States using pH measurements in samples from 18,258 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and when coupled with long flow paths, results in the most alkaline conditions. Conversely, in areas where glacial sediments are thin and carbonate-poor, pH conditions remain acidic. At depths typical of drinking-water supplies, predicted pH > 7.5 – which is associated with arsenic mobilization – occurs more frequently...
Aim Assessing the influence of land cover in species distribution modelling is limited by the availability of fine-resolution land-cover data appropriate for most species responses. Remote-sensing technology offers great potential for predicting species distributions at large scales, but the cost and required expertise are prohibitive for many applications. We test the usefulness of freely available raw remote-sensing reflectance data in predicting species distributions of 40 commonly occurring bird species in western Oregon. Location Central Coast Range, Cascade and Klamath Mountains Oregon, USA. Methods Information on bird observations was collected from 4598 fixed-radius point counts. Reflectance data...
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The ascii grids associated with this data release are model inputs representing the Central Valley aquifer, California, and predicted nitrate concentrations (as NO3-N, mg/L) at two depth zones associated with private and public drinking water supply wells, respectively. The model input and prediction grids are bound by the alluvial bed boundary that defines the Central Valley. The prediction grids were produced with Boosted Regression Tree (BRT) modeling methods within a statistical modeling framework using the statistical modeling software R (R Core Team, https://www.r-project.org/) and linear interpolation within Oasis Montaj software (Geosoft, version 9.0.2). The response variable was a set of nitrate concentrations...
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The ascii grids represent regional probabilities that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL) for water quality. The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 meters (m). We utilized prediction modeling methods, specifically boosted regression trees (BRT) with a Bernoulli error distribution within a statistical learning...
Abstract (from http://onlinelibrary.wiley.com/doi/10.1111/gcb.12642/abstract): Predicting biodiversity responses to climate change remains a difficult challenge, especially in climatically complex regions where precipitation is a limiting factor. Though statistical climatic envelope models are frequently used to project future scenarios for species distributions under climate change, these models are rarely tested using empirical data. We used long-term data on bird distributions and abundance covering five states in the western US and in the Canadian province of British Columbia to test the capacity of statistical models to predict temporal changes in bird populations over a 32-year period. Using boosted regression...
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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...
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Habitat suitability was estimated for invasive Phragmites in the coastal Great Lakes region (shoreline to 10 km inland). These estimates were based on current distribution patterns and environmental conditions. Phragmites presence or absence was defined based on a distribution map produced by cooperative research between the GLSC and Michigan Technical Research Institute. Environmental variables were processed in a Geographic Information System (GIS) and came from existing publicly available sources. Variables include descriptors of soils, nutrients, topography, ecoregion, anthropogenic disturbance, and climate. Environmental conditions and Phragmites presence/absence were sampled in a GIS at points established...
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Data used to model and map pH and redox conditions in groundwater in the Northern Atlantic Coastal Plain aquifer system, eastern USA, are documented in this data release. The models use as input data measurements of pH and dissolved oxygen concentrations at about 3000 to 5000 wells, which were compiled primarily from U.S. Geological Survey and U.S. Environmental Protection Agency databases. The boosted regression trees machine learning method was used to build the models. Explanatory variables (predictors) describe geology, hydrology, chemistry, physical characteristics, anthropogenic influence, metrics from a groundwater flow model, and groundwater residence times in the aquifer system. Data for four models are...
    map background search result map search result map Phragmites Habitat Suitability Probability distribution grids of dissolved oxygen and dissolved manganese concentrations at selected thresholds in drinking water depth zones, Central Valley, California Groundwater nitrate data and ascii grids of predicted nitrate and model inputs for the Central Valley aquifer, California, USA Data used to model and map pH and redox conditions in the Northern Atlantic Coastal Plain aquifer system, eastern USA Machine-learning model predictions and rasters of groundwater salinity in the Mississippi Alluvial Plain Data for machine learning predictions of pH in the glacial aquifer system, northern USA Groundwater nitrate data and ascii grids of predicted nitrate and model inputs for the Central Valley aquifer, California, USA Probability distribution grids of dissolved oxygen and dissolved manganese concentrations at selected thresholds in drinking water depth zones, Central Valley, California Machine-learning model predictions and rasters of groundwater salinity in the Mississippi Alluvial Plain Data used to model and map pH and redox conditions in the Northern Atlantic Coastal Plain aquifer system, eastern USA Phragmites Habitat Suitability Data for machine learning predictions of pH in the glacial aquifer system, northern USA