Observational data and model output for sediment nutrient cycling at Patagonian fjords
Glacier meltwater supplies a significant amount of silicon (Si) and iron (Fe) sourced from weathered bedrock to downstream ecosystems. However, the extent to which these essential nutrients reach the ocean is regulated by the nature of the benthic cycling of dissolved Si and Fe within fjord systems, given the rapid deposition of reactive particulate fractions at fjord heads. The dataset is used to examine the benthic cycling of the two nutrients at Patagonian fjord heads through geochemical analyses of sediment pore waters and reaction-transport modeling for Si. The dataset contains: (i) pore water redox-sensitive nitrate (NO3-) and dissolved manganese (DMn) concentration data, nutrient dissolved silicon (DSi) and iron (DFe) concentration and isotope data (delta30 Si, delta56 Fe); (ii) mild alkaline leachable (Si-Alk) and acid leachable (Si-HCl) sediment silica content and isotope data; and (iii) reaction transport model output for the benthic cycling of Si. The pore water and sediment samples were collected from four sites: SJ (48.228o S, 73.502o W, 106 m depth), SH (47.679 S, 73.715 W, 203 m depth), SP (48.179 S, 73.347 W, 248 m depth) and SB (47.787 S, 73.610 W, 151 m depth) in the Baker-Martinez Fjord Complex on the research vessel Sur-Austral in February 2017.
Funded by NERC-CONICYT grant NE/P003133/1-PII20150106.
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- Date (Creation)
- 2022-06-27
- Date (Revision)
- 2022-06-27
- Date (Publication)
- 2022-06-27
- Date (released)
- 2022-06-27
- Edition
- 1.0
- Unique resource identifier
- https://doi.org/10.5285/5af9e5cf-657d-4640-8ff3-dd3ec2f43367
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- GB/NERC/BAS/PDC/01647
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- https://data.bas.ac.uk/
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- NE/P003133/1
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- Please cite this item as: Ng, H., Hawkings, J., Wadham, J., Hendry, K., Conway, T., Bertrand, S., Sieber, M., Summers, B., Freitas, F., Ward, J., Pryer, H., & Arndt, S. (2022). Observational data and model output for sediment nutrient cycling at Patagonian fjords (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/5af9e5cf-657d-4640-8ff3-dd3ec2f43367
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- Theme
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- Early diagenesis
- fjord
- nutrient cycling
- reaction transport model
- stable isotopes
- Place
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- Patagonia, Baker-Martinez Fjord Complex Chile
- GEMET - INSPIRE themes, version 1.0
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- Environment
- Oceans
- Begin date
- 2018-01-18
- End date
- 2020-11-28
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- It is recommended that careful attention be paid to the contents of any data, and that the author be contacted with any questions regarding appropriate use. If you find any errors or omissions, please report them to polardatacentre@bas.ac.uk.
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Methodology:
Silicon and nitrate concentrations were measured with spectrophotometry. Dissolved iron concentration, manganese concentration, iron isotopes and silicon isotopes were measured with inductively coupled plasma-mass spectrometry.
The reaction transport model employed is the Biogeochemical Reaction Network Simulator. The model was run under steady state assumption.
Pore water data depth resolution: 5 cm - 15 cm
Sediment data depth resolution: 10 cm - 50 cm
Model data depth resolution: 0.1 cm - 1 cm
Data collection:
(1) Thermo Scientific X-Series 2 quadrupole inductively coupled plasma-mass spectrometer (dissolved iron and manganese concentration measurements)
(2) Thermo-Finnigan Neptune multicollector-inductively coupled plasma-mass spectrometer (silicon and iron isotope measurements)
(3) Maple computing environment version 2016.0 (compile inputs for reaction transport modeling)
Data quality:
The accuracies of the pore water elemental concentration and isotope analyses were assessed with measurements of standard solutions. Errors associated with sediment and pore water analyses were assessed with replicate measurements of samples and standards. Analytical errors for: (i) NO3- concentration is about 0.5%; (ii) dMn concentration and dFe concentration are 0.7% - 8.9%; (iii) DSi concentration is about 2.5%; (iv) Si-Alk content is 25%; (v) Si-HCl content is 15%; (vi) delta30 Si is 0.08 per-mille - 0.34 per-mille, and (vii) delta56 Fe is 0.04 per-mille - 0.08 per-mille. Model's best fit with observational data was determined by minimizing the root-mean-square error.
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