Data file describing high frequency (approximately every 10 minutes), optial sensor-derived chemistry of river water from the Kuparuk River near Toolik Field Station, North Slope of Alaska. Data file includes date, time, dissolved organic carbon (DOC) concentration, and nitrate concentration. Sensors (V2 s::can uv-vis spectrophotometers) were continuously deployed from June through August or September and optically determined nitrate and dissolved organic carbon concentrations.
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In-situ Sensor Data Collection
We collected high-frequency biogeochemical data from the Kuparuk headwaters (90 km2), an Arctic headwater watershed on the North Slope of Alaska associated with the Arctic Long-Term Ecological Research (LTER) Toolik Field Station site for three consecutive years (2017-2019). We deployed submersible UV-visible spectrophotometers (s::scan Messtechnik GmbH, Vienna, Austria), that measure water chemistry data at 15-minute intervals. The spectrophotometers measured light absorbance at wavelengths from 220 through 750 nm (Edwards et al., 2001; Ruhala & Zarnetske, 2017) and spectra are normalized for optical path length (15 cm). In the minute prior to collecting the absorbance reading, the spectrophotometers automatically cleaned their lens with a rotating brush (s::can rucksack, Messtechnik GmbH, Vienna, Austria). Sampling sites were visited, at a minimum, biweekly to download data and guarantee proper functioning for the duration of the study. To protect the sensors, we housed them in PVC tubing anchored parallel to flow on the streambed. The deployment dates varied slightly each year, depending on weather and flow conditions.
To create site-specific calibration of the absorbance spectra (Ruhala and Zarnetske, 2017), we collected biweekly grab samples analyzed independently in the laboratory using standard analyses following the LTER protocol. Each field sample was syringe-filtered to 0.7 µm with a glass fiber filter (GF/F Whatman) into a clean HDPE bottle. The filtered water samples were acidified with hydrochloric acid to a final normality of 0.01N and refrigerated (DOC stored at 2°C) or frozen (NO3- stored at at -4°C) until laboratory analysis. We measured DOC on a Shimadzu TOC-L analyzer using a combustion catalytic method and NO3- on a QuickChem Lachat analyzer (2017) or a SEAL AA3 segmented flow analyzer (2018-2019). We did not subtract NO2- from these results, as concentrations are typically low to undetectable in these rivers. With the lab-measured concentrations, to account for large dimensionality between absorbance spectra, we used a partial least squares regression variable-selection approach to generate robust calibration relationships between spectra and observed grab samples (Etheridge et al., 2014; Vaughan et al., 2018). We used the packages pls (Mevik & Wehrens, 2007; R Core Team, 2014) and tools from plantspec (Griffith & Anderson, 2019) to fit the calibration in R. Each respective calibration allowed us to generate a time series of DOC and NO3- concentrations (mg/L) from the spectra obtained from the s::can spectrolyers. All values that were less then 0 / below detection based on the calibration are noted as -1111.
Edwards, A. C., Hooda, P. S., & Cook, Y. (2001). Determination of Nitrate in Water Containing Dissolved Organic Carbon by Ultraviolet Spectroscopy. International Journal of Environmental Analytical Chemistry, 80(1), 49–59. https://doi.org/10.1080/03067310108044385
Etheridge, J. R., Birgand, F., Osborne, J. A., Osburn, C. L., Burchell, M. R., & Irving, J. (2014). Using in situ ultraviolet-visual spectroscopy to measure nitrogen, carbon, phosphorus, and suspended solids concentrations at a high frequency in a brackish tidal marsh. Limnology and Oceanography: Methods, 12(1), 10–22. https://doi.org/10.4319/lom.2014.12.10
Griffith, D. M., & Anderson, T. M. (2019). The ‘plantspec’ r package: A tool for spectral analysis of plant stoichiometry. Methods in Ecology and Evolution, 10(5), 673–679. https://doi.org/10.1111/2041-210X.13143
Mevik, B.-H., & Wehrens, R. (2007). The pls Package: Principal Component and Partial Least Squares Regression in R. Journal of Statistical Software, 18(1), 1–23. https://doi.org/10.18637/jss.v018.i02
R Core Team. (2014). R: A language and environment for statistical computing. 3(1), 201–201.
Ruhala, S. S., & Zarnetske, J. P. (2017). Using in-situ optical sensors to study dissolved organic carbon dynamics of streams and watersheds: A review. Science of The Total Environment, 575, 713–723. https://doi.org/10.1016/j.scitotenv.2016.09.113
Vaughan, M. C. H., Bowden, W. B., Shanley, J. B., Vermilyea, A., Wemple, B., & Schroth, A. W. (2018). Using in situ UV‐Visible spectrophotometer sensors to quantify riverine phosphorus partitioning and concentration at a high frequency. Limnology and Oceanography: Methods, 16(12), 840–855. https://doi.org/10.1002/lom3.10287