ALBERT

Description: This data set describes surface water and late season snow melt physical and geochemical observations collected around the Greiner Lake Watershed (near Cambridge Bay, NU) over July 2018 and between April and June 2019, as well as several rain, river, lake, and groundwater samples collected opportunistically. Snow and surface water samples were collected as part of the project entitled "Development of a multi-scale cryosphere monitoring network for the Kitikmeot region and Northwest territories using in-situ measurements, modeling and remote sensing" led by Dr. Alex Langlois, Université de Sherbrooke. Snow density profiles were measured by extracting snow samples at 3 cm intervals using 192 cm3 and 100 cm3 density cutters. The samples were weighed using a Pesola light series scale (100 g) from which density was calculated. Snow temperature was determined using a digital temperature probe (+/- 0.1°C). Surface water and late season snow melt geochemical properties were also determined following the methods outlined in Levasseur et al., (submitted). Briefly, snow was collected into 1 L HDPE plastic snow containers using a clean plastic trowel. Snow samples were melted at room and/or fridge temperature, with melt progression checked at regular intervals. Once melted, samples were filtered through 0.22 μm Sterivex-GV filters into Wheaton 4 mL Amber Vials with TFE-Lined Caps for the analysis of stable isotope composition (δ18O-H2O and δ2H-H2O). Rain samples were collected using a funnel rain gauge at the Canadian High Arctic Research Station, whereas lake and pond samples were collected from surface waters at the shore or edge, respectively. Soil and pore water samples were collected by digging a hole, filling containers and pressing their contents through a filter with a pestle. All water samples were then processed identically to the snow melt samples. River water samples were also collected from Freshwater Creek (69.131°N, -104.991°E), which directly drains Greiner Lake. Surface water samples for the determination of stable water isotopes were collected according to methods developed by the Arctic Great Rivers Observatory (Arctic-GRO; http://www.arcticgreatrivers.org/) as described in detail by Brown et al., 2020. For samples collected in 2018, stable isotope analyses were conducted at the Environmental Chemistry Facility at Brown University (RI) using a Picarro L1102-i Isotopic Water Liquid Analyzer with a standard error of +/- 0.1 ‰ for δ18O-H2O and +/-1 ‰ for δ2H-H2O. For samples collected in 2019, stable isotope analyses were conducted at the University of Calgary using a Los Gatos Research Liquid Water Isotope Analyzer with a reported analytical precision of ± 0.2‰ for δ18O-H2O and ± 2‰ for δ2H-H2O.

Description: This data set describes snow physical and geochemical observations collected in the Canadian Arctic between March 2018 and June 2019. Snow samples were collected as part of the project entitled "Development of a multi-scale cryosphere monitoring network for the Kitikmeot region and Northwest territories using in-situ measurements, modeling and remote sensing" led by Dr. Alex Langlois, Université de Sherbrooke. Sampling was carried out at two primary locations in the Canadian sub-Arctic and Arctic: Wekweètì (NWT), located just south of tree line, and Greiner Lake Watershed (NU), situated well into Arctic tundra on Victoria Island (near Cambridge Bay, NU). Data are also included from Herschel Island (NWT) and Trail Valley Creek (NWT). Sampling was conducted primarily from spring into summer, with snow samples collected in Wekweètì over the month of March 2018 and Cambridge Bay over the months of April and July 2018, and April to June 2019. Trail Valley Creek was visited in winter, January 2019, whereas Herschel Island samples were collected in April and May 2019. Snow physical properties were measured following the methods outlined in Levasseur et al., (submitted). Briefly, snowpits were excavated along transects to conduct observations of snow stratigraphy, density, temperature, grain size, and grain type following Langlois et al., (2009). Density profiles were measured by extracting snow samples at 3 cm intervals using 192 cm3 and 100 cm3 density cutters. The samples were weighed using a Pesola light series scale (100 g) from which density was calculated. Temperature profiles were also measured at 3 cm intervals using a digital temperature probe (+/- 0.1°C). Snow Water Equivalent was also determined for some layers after Langlois et al., (2009). Snow geochemical properties were also determined following the methods outlined in Levasseur et al., (submitted). Briefly, snow was collected into 1 L HDPE plastic snow containers using a clean plastic trowel. Snow samples were melted at room and/or fridge temperature, with melt progression checked at regular intervals. Once melted, samples were filtered through 0.22 μm Sterivex-GV filters into Wheaton 4 mL Amber Vials with TFE-Lined Caps for the analysis of stable isotope composition (δ18O-H2O and δ2H-H2O). For samples collected in 2018, stable isotope analyses were conducted at the Environmental Chemistry Facility at Brown University (RI) using a Picarro L1102-i Isotopic Water Liquid Analyzer with a standard error of +/- 0.1 ‰ for δ18O-H2O and +/-1 ‰ for δ2H-H2O. For samples collected in 2019, stable isotope analyses were conducted at the University of Calgary using a Los Gatos Research Liquid Water Isotope Analyzer with a reported analytical precision of ± 0.2‰ for δ18O-H2O and ± 2‰ for δ2H-H2O.

Description: This data set describes the stable water isotope (δ18O-H2O, δ2H-H2O), Dissolved Organic Carbon (DOC), and Nutrient (Nitrate + Nitrite, Phosphate, Silicate) data collected from 53 rivers, lakes, and glaciers throughout the Canadian Arctic Archipelago (CAA) and Hudson Bay as part of the Canadian Arctic Archipelago Rivers Program (CAA-RP; 2016 – 2019); ArcticNet / Amundsen Science biogeochemical surveys (2017-2019); the Canada 150 C3 Expedition (2017); and the BaySys project (2018). Water samples were collected according to methods developed by the Arctic Great Rivers Observatory (http://www.arcticgreatrivers.org/), described in detail in Brown et al., 2020. Water collected for stable water isotope, DOC, and Nutrient analyses was filtered through 0.22 μm Sterivex cartridges (Millipore) into triply rinsed glass (isotopes) or HDPE (Nutrients, DOC) vials; HDPE vials were acid cleaned prior to use. Samples for the determination of DOC and Nutrients were frozen until analyses, whereas stable isotope samples were stored in the dark at room temperature or refrigerated until analyses. Analytical methods are described in the accompanying metadata file. Where indicated, water Temperature and Conductivity at the time of sampling were determined as described in the dataset metadata file.

Description: This data set describes geochemical samples collected from 25 rivers and 11 lakes throughout the Canadian Arctic Archipelago (CAA). CAA rivers were sampled as part of the Canadian Arctic Archipelago Rivers Program (CAA-RP) and the Canadian Arctic GEOTRACES program with access via land, water, and air during the summer seasons of August 1-September 9, 2014, and August 11-19, 2015. Time series observations were also collected from the Coppermine River in Kugluktuk, Nunavut (NU) (year-round; August 5, 2014 to August 23, 2016), and from Freshwater Creek in Cambridge Bay, NU (open water only; June 19, 2014 to September 16, 2016). Lake samples were collected opportunistically during float plane air-surveys in 2014 and 2015 as part of the CAA-RP study in the southern CAA. Precipitation was collected during two significant rain events in Kugluktuk, NU (August 25, 2015) and Cambridge Bay, NU (August 20, 2016). River water samples were collected according to methods developed by the Arctic Great Rivers Observatory (Arctic-GRO; http://www.arcticgreatrivers.org/); lake sampling followed the same general methods, with collection carried out in deeper waters away from the shore; rain samples were collected using an HCl cleaned plastic box and processed immediately the morning following the rain event in order to limit the influences of evaporation. Sampling and analytical methods are described in detail in Brown et al., 2020. This data set includes the raw data supplied in Supplementary Tables S2 (Geochemical Data for the CAA-Rivers Project, collected from 2014 - 2016) and S3 (Geochemical Time Series Data for the Coppermine River and Freshwater Creek collected from 2014 - 2016) that accompany Brown et al., 2020. In addition to geochemical observations, the following calculated parameters can be found in Supplementary Table S2 of Brown et al., 2020: drainage basin area; predominant bedrock lithology; percent coverage of lakes; and surficial geology characteristics. These parameters were determined for each river drainage basin as described in the text and associated references found in Brown et al., 2020.

Description: Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Biogeosciences 125(1), (2020): e2019JG005414, doi:10.1029/2019JG005414.

Description: A survey of 25 coastal‐draining rivers across the Canadian Arctic Archipelago (CAA) shows that these systems are distinct from the largest Arctic rivers that drain watersheds extending far south of the Arctic circle. Observations collected from 2014 to 2016 illustrate the influences of seasonal hydrology, bedrock geology, and landscape physiography on each river's inorganic geochemical characteristics. Summertime data show the impact of coincident gradients in lake cover and surficial geology on river geochemical signatures. In the north and central CAA, drainage basins are generally smaller, underlain by sedimentary bedrock, and their hydrology is driven by seasonal precipitation pulses that undergo little modification before they enter the coastal ocean. In the southern CAA, a high density of lakes stores water longer within the terrestrial system, permitting more modification of water isotope and geochemical characteristics. Annual time‐series observations from two CAA rivers reveal that their concentration‐discharge relationships differ compared with those of the largest Arctic rivers, suggesting that future projections of dissolved ion fluxes from CAA rivers to the Arctic Ocean may not be reliably made based on compositions of the largest Arctic rivers alone, and that rivers draining the CAA region will likely follow different trajectories of change under a warming climate. Understanding how these small, coastal‐draining river systems will respond to climate change is essential to fully evaluate the impact of changing freshwater inputs to the Arctic marine system.

Description: This work was only possible through a network of enthusiastic and devoted collaborators. Partners included Polar Knowledge Canada and the Canadian High Arctic Research Station, the Arctic Research Foundation, the Kugluktuk Angoniatit Association, and the Canadian Arctic GEOTRACES Program. We acknowledge support from the Department of Fisheries and Oceans Canada, the Woods Hole Oceanographic Institution Coastal Ocean Institute, The G. Unger Vetlesen Foundation, Jane and James Orr, and the Woods Hole Research Center. Many thanks go to Austin Maniyogena, Angulalik Pedersen, Adrian Schimnowski, JS Moore, Les Harris, Oksana Schimnowski, as well as Barbara Adjun, Amanda Dumond, and Johnny Nivingalok, and the captains and crew of the research vessels CCGS Amundsen and R/V Martin Bergmann, all of whom supported our research and helped with sample collection. Special thanks also go to Valier Galy, Zhaohui “Aleck” Wang, Marty Davelaar, Michiyo Yamamoto‐Kawai, Hugh McLean, Mike Dempsey, Baba Pedersen, Maureen Soon, Katherine Hoering, Sean Sylva, Ekaterina Bulygina, and Anya Suslova for their invaluable contributions during field program planning, preparations, and laboratory analyses. Robert Max Holmes is thanked for many fruitful discussions. We also thank several anonymous reviewers for their helpful comments on the paper's content and structure. All of the data presented in this paper can be found at https://doi.org/10.1594/PANGAEA.908497.

Description: Spatial variability of 13 foliar nutrients was assessed within and between individual black cottonwood (Populustrichocarpa Torr. & Gray ex Hook.) trees at seven alluvial sites in coastal British Columbia to help in the development of foliar sampling protocol for the determination of black cottonwood nutrient status. Foliar nutrients were divided into two groups based on concentration differences in foliage samples from within black cottonwood canopies: group 1 included N, P, K, S, SO4-S, Cu, and possibly Mg and B, mostly macronutrients that are mobile in the phloem; group 2 included mostly immobile micronutrients (Mn, Zn, Ca, and possibly Fe and active Fe, although these last two might best be included in a third group). Group 1 nutrient concentrations were significantly higher in the upper canopy, while group 2 nutrient concentrations were higher in the lower canopy at the two sites studied. Numbers of samples required for several combinations of accuracy (including both α and β significance) and precision were calculated based on mean coefficient of variation estimates from the seven stands. Fifteen foliar samples collected from the most recently matured late leaves on lateral branches in the top one-third of the canopy of dominant or codominant black cottonwood trees will estimate the mean with an allowable error of 10% at an α significance level of 0.95 for most macronutrients (N, P, K, S, Mg, and Ca). Including a β significance level of 0.95 at this level of precision and α significance would increase the number of required samples to at least 20 for these nutrients. Between 21 and 56 samples would be required to estimate the mean concentrations of SO4-S, Cu, Zn, Mn, B, active Fe, and Fe at the same levels of accuracy and precision.