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  • Kluane Lake (Lhù'ààn Mǟn) Study: This modeling study was motivated and required by Yukon Government following the retreat of the Kaskawulsh Glacier in 2016, causing the Slims River (Ä'äy Chù) Piracy and depriving Kluane Lake from its most important summer inflow and as a consequence left summer lake levels 1.6 metres lower than their normal. The MESH model was used here to simulate the hydrology of Kluane Lake during three periods: the 20th Century using EU WATCH forcing data, recent time (2000-15) using WRF forcing data bias corrected with GEM-CaPA, through the future using PGW RCP8.5 scenario by the end of the 21st Century. Model outputs include daily lake inflow, outflow, and water level. See also: www.gwfnet.net/Metadata/Record/T-2021-09-13-a1BPq3BfSokqJt1dzmkmGPA

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    Number of pelts and cash value of pelts for fur-bearing mammals in Saskatchewan, including muskrat, beaver, mink, and marten. Numbers are recorded per fur management zone. Fur harvest is reported for 1996-2016. Data were collected from fur dealers, Ministry of Environment field offices, and North American Fur Auctions. Data are published in a report summarizing wild fur harvest and cash value of pelts in Saskatchewan from 2015 to 2016. While specific counts are given per for each species in each fur management zone in 2015 and 2016, summary numbers of total pelts, cash value, and the number of observations of each species are provided from as early as 1970. Data are used for IMPC Theme B: Water Management Modelling, Coupling Human-driven and Natural Systems. The data contribute to IMPC subtheme B2: Developing a performance model for aquatic ecosystems based on hydro-ecologic metrics and environmental demands. See also: www.gwfnet.net/MetadataEditor/Record/T-2021-09-16-V15PS9u1ihEOgg8dbQIMArg

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    To date, passive flux meters have predominantly been applied in temperate environments for tracking the movement of contaminants in groundwater. This study applies these instruments to reduce uncertainty in (typically instantaneous) flux measurements made in a low-gradient, wetland dominated, discontinuous permafrost environment. This method supports improved estimation of unsaturated and over-winter subsurface flows which are very difficult to quantify using hydraulic gradient-based approaches. Improved subsurface flow estimates can play a key role in understanding the water budget of this landscape.

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    These data explored how plant community composition and traits change across the Scotty Creek Forest Dynamics plot in response to environmental variation, including active layer thickness, organic layer thickness, and forest structure (i.e., canopy cover and tree basal area). To do this, we used a random stratified design to select ten 20 m by 20 m grid cells belonging to each of four aboveground tree biomass categories for a total of 40 grid cells across the Scotty Creek Forest Dynamics Plot. Within each grid cell, we randomly placed two 1 m by 1m quadrats to assess community composition of vascular plants via stem counts and measure canopy cover, active and organic layer thickness. These data were averaged to provide an estimate at the grid cell level. Basal area was calculated at the level of the grid cell. Plant functional traits were collected from 3 replicate individuals per species within 2-3 grid cells per aboveground tree biomass category.

  • Rainbow darter, a type of fish, from both upstream and downstream sites of the wastewater treatment plants, and will be aged using their ear bones (otoliths). From this, their population structure and growth can be determined to compare potential impacts of wastewater effluent. Condition factor will also be computed using their lengths and weights to determine which endpoint is more sensitive (condition factor or growth). A minimum of 25 male and 20 female rainbow darter are being caught from various sites across the in the Grand River using an electrofisher between August and September each year. Fish length, weight, gonad weight, liver weight were all measured in the field. Frozen fish samples are used to extract otoliths, and process them for age determination. See also: www.gwfnet.net/Metadata/Record/T-2020-05-28-Z16cVw1HvbkOLrHZ24TB5adA

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    In the context of this research, several physical, chemical, and ecotoxicological parameters are determined in bottom sediments from reservoirs and lakes along the (South) Saskatchewan River in Saskatchewan. These data are collected to support the comprehensive ecotoxicological characterization of these sediments, with the ultimate goal of weighing different options for sediment restoration in the Saskatchewan River Delta. The investigated lakes and reservoirs include Lake Diefenbaker, Codette Reservoir, Tobin Lake, and Cumberland Lake. The measured parameters include sediment texture and total organic carbon, concentrations of common contaminants (metals, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, polychlorinated dibenzo-p-dioxins and -furans, pesticides), as well as ecotoxicological parameters (cytotoxicity, dioxin-like activity, mutagenicity, (anti-)estrogenicity, (anti-)androgenicity). This data was collected for the Global Water Futures project "We need more than just water: Assessing sediment limitation in a large freshwater delta". See also: www.gwfnet.net/Metadata/Record/T-2020-11-26-H1hpH1DQouyUKC9v67nBZ4Ow

  • Indigenous Self-governance to some is seen as the new way forward for Indigenous communities to govern their people. The Délįne Got’ine Government (DGG) is the first community in the Northwest Territories (NWT) to self-govern. Despite the power bestowed upon this Indigenous government, there are still challenges to effectively govern their people, as the community is still bound under the policies of the Land Claim agreement. While the Land Claim agreement set the foundation for self-governance, it is a policy that was co-created with the communities in the settlement area, the territorial government, and the federal government. In the summer of 2019, a country food processing training was operated out of a food processing facility to expose the community to new food processing techniques, increase food safety, and introduce muskox as a viable country food alternative. The people of Délįne are caribou people, but with declining herd populations, the harvesting rate has drastically declined, and traditional food consumption has thus reduced. As part of this study, an evaluation was conducted on both the country food training and the food processing facility to understand the community’s perceptions on these new foods and methods. The evaluation utilized a mixed method approach, with a pre and post survey, participant observation, and semi structured interviews. This need for this evaluation is the result of prior community based participatory research conducted with the community.

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    This dataset contains the code and data files needed to produce the analyses and figures in Shea et al. (2021), doi:10.3389/frwa.2021.604275. The data used in this study are derived from publicly available data sets. These include global elevation data, river basin boundaries, climate normals, automated snow pillows, and manual snow course observations. The Cold Regions Hydrological Model (CRHM, v.05/15/19) was used to produce daily estimates of snowmelt for 50 basins using identical elevation ranges and bands, identical accumulation gradients, and identical climate inputs. Only the hypsometry (area-elevation distribution) was varied for each model run. Analysis of hypsometry, climate inputs, and CRHM outputs is given in a Jupyter notebook running Python 3.7.6. See also: www.gwfnet.net/Metadata/Index/T-2021-06-09-21dykb21xDw0aVRAng0DFOHw

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    The objectives of this study are testing the HYPE model for biogeochemical modelling in Canadian catchments, and exploring methodologies to improve biogeochemical prediction in view of uncertainty analysis and scenario investigation. Total phosphorus concentration is simulated using two approaches: 1) the sequential calibration approach that calibrates the hydrology first and then uses the best hydrological model to infer the biogeochemical model parameters, and 2) the joint approach that infers the hydrological and biogeochemical parameters simultaneously. The joint approach uses the total phosphorus load and streamflow as optimization variables. The two approaches were compared concerning identifiability of both the hydrological and biogeochemical model parameters, and the predictive power of both the hydrological and biogeochemical models. A daily computational time step was used. Outputs of the model are total phosphorus concentration and streamflow. The input data used to set up the model are provided below. -Topographic information source is USGS: Hydro1K (https://lta.cr.usgs.gov/HYDRO1K) -Soil data source: Harmonized World Soil Database V1.2, Nachtergaele et al. (2010) -Land use data source: ESA CCI LC 2010 v1.4, ESA Climate Change Initiative -Lake and wetland data source: Global Lake and Wetland Database (GLWD), Lehner and Doll (2004) -Reservoirs data source: Global reservoir and Dam database (GRanD) v1.1, Lehner et al. (2011) -Discharge data source: HYDAT, 2. USGS, Environment Canada and USGS -Meteorological data source: Hydro-GFD, Berg et al. (2017) -Snow data source: GlobSnow (www.globsnow.info) -Glacier fluctuations data source: World Glacier Monitoring Service (WGMS), Zemp et al. (2009) -Evapotranspiration data source: FLUXNET (fluxnet.ornl.gov) -Point source pollution data source: USGS and Benoy et al. (2016), https://www.sciencebase.gov/catalog/item/5ba54362e4b08583a5c9d4e5 -Fertilizer and manure data source: Red-Assiniboine SPARROW model, Benoy et al. (2016) The total phosphorus load and streamflow produced by our HYPE model is used to test HYPE in the Red-Assiniboine River basin as part of the broader objective of evaluating the suitability of biogeochemical modelling in Canadian basins and to explore improved calibration strategy of biogeochemical model for uncertainty quantification and management scenarios. This dataset supports the water quality modelling (WQM) theme of the GWF Core Modelling and Forecasting Team. See also: www.gwfnet.net/MetadataEditor?q=T-2020-11-25-J1jql9TQRnUu8a1WNl7QMbw

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    MESH model is used for calibration/ validation of the streamflow. MESH is a physics-based, land-surface hydro- logical modelling system developed by Environment and Climate Change Canada. MESH performs both water and energy balances and is best suited to cold-region, large-scale catchments due to its ability to simulate snow processes, such as snow accumulation, redistribution, and melt. MESH is used to simulate important hydrological processes (like runoff generation, evapotranspiration, and soil moisture). SED model is a physically based watershed sediment transport model which is developed based on empirical equations and the sediment mass balance equation (for overland and instream flow). It includes different sediment classes and is suitable for large scale cold regions catchments. The model simulates hourly sediment load and concentration. The following input data were used for hydrological modelling: - Meteorological data: For model calibration and validation, seven forcing data (precipitation, longwave radiation, shortwave radiation, wind speed, air temperature, barometric pressure and specific humidity) is taken from GEM-CaPA. (https://wiki.usask.ca/display/MESH/Forcing+Datasets+for+MESH#ForcingDatasetsforMESH-GEM-CaPA) - Drainage basin data: Digital Elevation data (Geobase database as Canadian Digital Elevation Data), Landcover data (http://cec.org/tools-and-resources/map-files/land-cover-2010-landsat-30m), soil data (Soil Landscapes of Canada (version 2.2), eco-regions shape files for Canada, and other relevant data was obtained from different sources. The drainage basin was created using GreenKenue. - Hydrological data: The ECCC hydat stations for Athabasca River Basin is used for the discharge data. - Sediment concentration data: Surface water quality data website/ Long Term River Data http://environment.alberta.ca/apps/EdwReportViewer/LongTermRiverStation.aspx See also: www.gwfnet.net/Metadata/Record/T-2021-03-15-C15sibC10qjEKgqIhEm9LC34g