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 variables derived from empirical or numerical process-based models were also included to integrate information on controlling processes and conditions. This data release documents the model and provides the model results. Included in this data release are, 1) a model archive of the R project: source code, input files (including model training and testing data, rasters of all final predictor variables, and an output raster representing predicted nitrate concentration in the shallow zone), 2) a read_me.txt file describing the model archive and an explanation of its use and the modeling details, and 3) a table describing the model variables.

[
  "010:12"
]

[
  "geospatial"
]