Code and Data for CAMELS-Chem Concentration-Discharge Analysis

Here we provide the data and R scripts to complete the analyses and create the figures presented in the manuscript titled, “Solute export patterns across the contiguous United States” by Kincaid et al. 2024 at Hydrological Processes. Importantly, this resource contains paired solute concentration (C) and discharge (Q) data for 11 solutes from CAMELS-Chem (Sterle et al. 2024; https://doi.org/10.5194/hess-28-611-2024). This relational database was built upon the CAMELS dataset (https://doi.org/10.5194/hess-21-5293-2017), an existing dataset of catchment and hydroclimatic attributes from relatively undisturbed catchments across the contiguous United States. The version of CAMELS-Chem provided here has US Geological Survey (USGS) National Water Information System (NWIS) C and Q data for 506 catchments. C and Q measurements span from 1898 to 2020 with the first paired C-Q sample occurring in 1924. Solutes include aluminum (Al), calcium (Ca), chloride (Cl), dissolved organic C and N (DOC, DON), magnesium (Mg), nitrate (NO3), potassium (K), silica (Si), sodium (Na), and sulfate (SO4). Of note, a shorter version of the CAMELS-Chem database that spans from 1980 to 2018, but includes data for more stream water quality constituents and atmospheric deposition data is described in CAMELS-Chem (Sterle et al. 2024; https://doi.org/10.5194/hess-28-611-2024) and available for download via Hydroshare (http://www.hydroshare.org/resource/841f5e85085c423f889ac809c1bed4ac).

The R scripts and data files provided in this resource are intended to allow users to replicate the tables and figures in the Kincaid et al. manuscript. Specifically, we provide all files to complete the analyses coded in in the R script 9_analyses_figures_for_manuscript.R. However, other R scripts and data files provided should allow users to replicate intermediate steps in the analyses as well. See the README file for more details, but analyses provided in the R scripts include: modeling C-Q relationships with the power-law function using data-driven Bayesian segmented regression; conducting hierarchical clustering to group catchments based on catchment attributes; building random forest models to select catchment attribute correlates of C-Q metrics; conducting flow-duration exceedance probability analyses; and general code for figures, tables, and other statistics presented in the Kincaid et al. manuscript.

The metadata for the CAMELS-Chem dataset (camels_chem_all_2022-02-25.csv) is available in camels_chem_metadata.csv