Sensor and nutrient data associated with the article Harrison et al. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression

This document describes a dataset used to produce Random Forests Regression models of stream nitrogen and phosphorus concentrations from high-frequency sensor data, as reported in: Harrison, J.W., Lucius, M.A., Farrell, J.L., Eichler, L.W., and Relyea, R.A. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of the Total Environment: https://doi.org/10.1016/j.scitotenv.2020.143005. The dataset consists of paired values of stream nitrogen and phosphorus concentrations and various high-frequency sensor parameters (water temperature, specific conductance, pH, fluorescent dissolved organic matter, turbidity, hydrostatic pressure, soil moisture) collected during baseflow and storm events from 2018 to 2019 as part of routine monitoring of eleven tributaries of Lake George, New York. This dataset does not include raw data; two levels of processing were performed: (1) erroneous values (extreme or otherwise outlying values with no apparent environmental cause) were removed from the sensor data as part of the routine QA/QC process of the Jefferson Project, and (2) one-hour rolling medians of the raw sensor data were calculated at a 1-minute timestep to maximize pairing of sensor data with nutrient concentrations. The resultant dataset was used to train and test the models presented in Harrison et al. 2020.