ABSTRACT:

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

ABSTRACT:

The CAMELS-Chem dataset is a comprehensive collection of stream water chemistry data, atmospheric deposition data, and catchment attribute data for 516 minimally impacted headwater catchments across the continental United States. The dataset spans a period of 39 years, from 1980 through 2018, and includes 18 common stream water chemistry constituents, such as Al, Ca, Cl, Dissolved Organic Carbon, Total Organic Carbon, HCO3, K, Mg, Na, Total Dissolved Nitrogen, NO3, Dissolved Oxygen, pH, Si, SO4, and water temperature. Additionally, the dataset provides annual wet deposition loads for several key components. The dataset is based on the existing CAMELS dataset, which provides catchment attribute data such as topography, climate, land cover, soil, and geology. In CAMELS-Chem, this catchment attribute data is paired with atmospheric deposition data from the National Atmospheric Deposition Program and water chemistry data and instantaneous discharge from the US Geological Survey. The dataset also includes paired instantaneous and discharge measurements for all chemistry samples.
The catchment attribute data files used in the CAMELS-Chem dataset were downloaded from the CAMELS website (https://ral.ucar.edu/solutions/products/camels

ABSTRACT:

These high-frequency (15-minute) data were collected in situ from 2014 to 2018 at the following Vermont EPSCoR stream monitoring stations in Vermont, USA (formerly of the North East Water Resources Network [NEWRnet]):
Hungerford Brook (agricultural)
Potash Brook (urban)
Wade Brook (forested)
Nitrate and SRP concentrations were measured using s::can spectro::lyser UV-Visible spectrophotometers (s::can Messtechnik GmbH, Vienna, Austria). Also included in this dataset are hydrograph delineations (condition: event flow vs. baseflow) as described in the journal article below.
For site details and collection and event delineation methods see Kincaid DW, Seybold EC, Adair EC, Bowden WB, Perdrial JN, Vaughan MCH, & Schroth AW. (2020). Land use and season influence event-scale nitrate and soluble reactive phosphorus exports and export stoichiometry from headwater watersheds. (DOI to go here upon publication).
The dataset includes 2014-2015 discharge and nitrate data from Vaughan, M. (2017). Vermont NEWRnet stations: 2014-2015 high-frequency DOC, nitrate, and discharge data, HydroShare, http://www.hydroshare.org/resource/faac1672244c407e9c9c8644c8211fd6.

ABSTRACT:

These high-frequency (15-minute) data were collected in situ from 2014 to 2018 at the following NEWRnet stream monitoring stations in Vermont, USA:
Hungerford Brook (agricultural)
Potash Brook (urban)
Wade Brook (forested)
Nitrate and SRP concentrations were measured using s::can spectro::lyser UV-Visible spectrophotometers (s::can Messtechnik GmbH, Vienna, Austria). Indicated in this dataset are hydrograph delineations (condition: event flow vs. baseflow) as described in the journal article below. For collection methods, site details, and event delineation methods see Kincaid DW, Seybold EC, Adair EC, Bowden WB, Perdrial JN, Vaughan MCH, & Schroth AW. (2020). Land use and season influence event-scale nitrate and soluble reactive phosphorus exports and export stoichiometry from headwater watersheds. (DOI to go here upon publication).
The dataset includes 2014-2015 discharge and nitrate data from Vaughan, M. (2017). Vermont NEWRnet stations: 2014-2015 high-frequency DOC, nitrate, and discharge data, HydroShare, http://www.hydroshare.org/resource/faac1672244c407e9c9c8644c8211fd6.