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    Semi-structured interviews, focus groups, sharing circles, participatory mapping, participatory drawing (rich pictures), agent-based rule generation in sharing circles, evaluative data (counts) of scenarios modelled and characteristic of model runs in each community. This dataset includes: - Database and systematic review of effects of floods in Indigenous community from academic and grey literature - Transcripts of interviews, focus groups, sharing circles, with community members on effects of flooding on reserves - Maps created using participatory mapping activities and community tours - Rich pictures (participatory drawings) made with youth - Agent rules for agent-based models (ABMs) based on previous data - Evaluative data of ABM use in two communities Agent-based models are computer models for simulating the interactions of decision-makers in a network to assess their impacts on the system. ABMs model complex patterns and interactions between individuals, and provide insight into what drives behaviours. Flooding is an issue for many Indigenous communities. Saskatchewan communities have experienced much flooding. Incidents of evacuation occurred over a series of years. James Smith Cree Nation has experienced 4-5 consecutive years of flooding and has had to issue a state of emergency. At the same time, the Red Cross (CRC) was providing services to 6 Manitoba Nations impacted by ice-jam flooding on the Saskatchewan River. Flooding has long-term impacts on communities. In April 2013, 11 First Nations of the File Hills Qu’Appelle Tribal Council declared a state of emergency and as of 2016 were still dealing with impacts of flooding from 3 years previous. Assessments of the long-term needs of First Nations affected by flooding noted management and recovery needs require efforts to address physical, emotional and social health. Recommendations included actions aimed at advancing health, social and emergency plans and services. Effective management takes coordination from agencies including the First Nations and local, municipal, provincial and federal agents. These groups need to work together to provide five major pillars (promotion, prevention, protection, education and surveillance) of public health. We are using ABM to support planning and coordination for improved health risk management and decision making with two partnered communities. See also: www.gwfnet.net/Metadata/Record/T-2020-11-30-y1DzBd4Z7WEK8529mNxqP1Q

  • Data have been collected from several lakes the Dehcho Region of the Northwest Territories since 2013 (August and September) to investigate among-lake variability in fish growth rate and biomagnification of mercury as important controls of mercury concentrations in fish. Different levels of the food webs (i.e., from primary consumers (invertebrates) to tertiary consumers (fish)) and physicochemical properties of lakes (i.e., water chemistry and lake and watershed physical characteristics) have been monitored. Environmental drivers of growth rate in Walleye and Northern Pike is being investigated using fish bony structures and lake physicochemical characteristics. In addition to common chemical properties, water samples, as well as the samples from various levels of the food web, have been analysed for mercury, methylmercury, and stable isotopes, which will be used to study the among-lake variability in biomagnification of mercury. I, as part of team (joined in late 2018), sampled Fish Lake in the field season 2019.

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    Meteorological data from ground stations suffer from temporal discontinuities caused by missing values and short measurement periods. Gap filling and reconstruction techniques have proven to be effective in producing serially complete station datasets that are used for a myriad of meteorological applications (e.g., developing gridded meteorological datasets and validating models). We developed the serially complete Earth (SC-Earth) dataset, which provides global daily precipitation, mean temperature, temperature range, dew-point temperature, and wind speed data from 1950 to 2019. SC-Earth utilizes raw station data from the Global Historical Climatology Network-Daily (GHCN-D) and the Global Surface Summary of the Day (GSOD). There are three types of missing values that are infilled/reconstructed by this dataset: (1) Missing value during the observation period when the station still works. (2) Missing value beyond the observation period (reconstruction period) before the station is deployed or after the station ceases working. (3) Station measurements that fail quality control checks are treated as missing values and imputed. This dataset is useful for various purposes of applications that require: (1) Quality-controlled actual station observations; (2) Station observations without missing values in the observation period; (3) Serially complete station observations. Users should be cautious when using this dataset for trend analysis because it is possible that trends are not well reconstructed. The five variables are precipitation (prcp), mean daily temperature (tmean), daily temperature range (trange), dew-point temperature (tdew), and wind speed (wind). Daily minimum and maximum temperature can be inferred from tmean and trange. Humidity variables can be inferred from tdew. There are three files for each variable. "observation" contains quality controlled raw station observations. "estimate" contains SC-Earth estimates for all days (including days "observation" has values) by merging estimates from 15 strategies (quantile mapping, interpolation, machine learning, and multiple-strategy merging). "final" is the final SC-Earth output, which uses "estimate" to fill the gap that "observation" is not available. See also: www.gwfnet.net/Metadata/Record/T-2021-09-09-D1TsBuYFJZ0qVBNMPO09LQg

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    The MESH version r1589_RTE_ts450s was used. This version of MESH has a hybrid functionality which allows users to define different grid specifications for routing scheme and land surface scheme. Here, in our model, routing scheme has a cell resolution of 0.0083 degree and land surface scheme has a resolution of 0.09 degree. The model includes 13 Ground Response Unit (GRU) classes. The Digital Elevation Models (DEM) was downloaded from USGS HydroSHEDS database with 30 arc-second resolution. Soil data was obtained from Global Soil Dataset for Earth System Models (Shangguan et al. 2014) (http://globalchange.bnu.edu.cn/research/soilw) and the landcover data was obtained from Climate Change Initiative (CCI) Land Cover 2015. The forcing data was extracted from the Global Environmental Multiscale atmospheric model and the Canadian Precipitation Analysis (GEM-CaPA) which a high-resolution gridded database. The main goal of this project is to accurately estimate streamflow from creeks and tributaries to the main stem of Grand River. The estimated streamflow will be used to estimate non-point nitrogen loads to the main stem of the river and in order to improve the estimation of nitrogen loads from Grand River to Lake Erie. This project supports the Water Quality Modelling theme of the Core Modelling and Forecasting Team. See also: www.gwfnet.net/Metadata/Record/T-2021-09-29-u1iVu2kr7gxkSPOzT4tF1v1Q

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    Flow data from the MESH model were used to develop presumptive standards for flow at 35 hydrologic gauge stations in the Saskatchewan River basin. Data used were daily flows from 1979-2010 (historic gauge data) and MESH flows with no management under 15 climate scenarios for the time periods 1979-2010 (e.g., naturalized historic flow), 2025-2055 (naturalized future flow), and 2070-2100 (naturalized future flow). For each climate scenario, calculations for presumptive standards included median, minimum, maximum and standard deviation of flow, median +/- 5%, 7.5%, 10%, and 20%, Q95, seasonal Q95, seasonal Q75, seasonal Q50, and seasonal Q10, deviation of actual flow from naturalized, and % deviation of actual flow from naturalized. Flow and presumptive standards were plotted. Presumptive standards provide guidelines for acceptable flows to maintain ecosystem function. Using naturalized flows, i.e., flows with the effects of anthropogenic activities removed, we calculated these flow guidelines for 35 stations in the Saskatchewan River basin, based on estimates from 1979-2010. These naturalized flows were compared with actual flow over the same period to examine deviations from expected flows in the absence of human activity. Naturalized future flows under 15 climate scenarios were also examined to evaluate how flow is expected to change, and what impact that will have on seasonality, timing, and magnitude of flows. 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/Metadata/Record/T-2021-09-16-K1tqOhDN5G0K1GeNBK3Ln2N1Q

  • Secondary data are extracted by doing a large literature review where we search through relevant published articles, government reports and websites, conference presentations, and unpublished work. Based on these data, we calculate the costs of eutrophication to each impact category with certain assumption on the degree of eutrophication. Different data sources are used to account for calculation uncertainty (we provide a minimum and a maximum of the estimates according to different sources). In most cases, we first calculate the total value of a specific ecosystem service, then get an estimate of eutrophication days in the area, and finally compute the degree the ecoservice is affected by eutrophication, i.e. the economic cost of eutrophication. When the total value of the ecosystem service cannot be retrieved, we use the costs of using substitute products due to deteriorated water, e.g., costs of bottled water for water treatment costs, and government budgets to recover endangered aquatic species for biodiversity costs. Further, assuming the costs to each individual lake is proportional to the population in each lake basin, we allocate the costs to each of the four great lakes in Canada.

  • Digital Elevation Model data and climate data for this project were downloaded from publicly available online sources from Government of Canada Open Data Catalogue GEOGRATIS (http://geogratis.cgdi.gc.ca/) and Environment Canada and Climate Change (http://climate.weather.gc.ca/) websites respectively; data will also be collected during fieldwork. Field techniques included collection of various tracers such as isotopes, geochemistry, and temperature. Data includes surface water and shallow groundwater quality and chemistry, 18O, 2H, 3H, 87Sr, and 13C isotopes, as well as sampling locations, measurements of active layer thickness, hydraulic conductivity and geology. Data is in spreadsheet and shapefiles/geospatial data formats.

  • The current data bring together maps (flowcharts of relationships) created by participants at three workshop series, held in 2009, 2010, and 2013-2014. The maps depict relationships among concepts (+ or -; relationship strength of 1-5 with 5 being strong and 1 being weak) identified as important by workshop participants. Maps were then translated into sparse matrices by project HQP, where the value in Cij represents the strength of the relationship between concepts i and j, which is ith row and jth column of the matrix. The data from each workshop have been combined into an overall consensual fuzzy cognitive map using matrix algebra, depicting a broad understanding of eutrophication in Lake Erie as of 2014. Future workshops are being planned to update this dataset. The updated dataset will allow an assessment of how our understanding of the causes of eutrophication has evolved over time, as well as comparisons of the perceptions of various groups (stakeholders, researchers, etc.).

  • The Soil Landscapes of Canada were downloaded from Agriculture and Agri-Food Canada website (http://sis.agr.gc.ca/cansis/nsdb/slc/v3.2/index.html). Agriculture and Agri-Food Canada developed the Soil Landscapes of Canada (SLC) version 3.2 to provide information about the country's agricultural soils. For this project data was cropped to Great Lakes and then converted into the NetCDF format. Data are then made available to the project collaborators on a private GitHub. Researchers interested in finding more about the data can email Juliane Mai (University of Waterloo; juliane.mai@uwaterloo.ca)

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    The Ensemble Meteorological Dataset for North America (EMDNA) contains daily precipitation, mean daily temperature, and daily temperature range at the 0.1-degree resolution from 1979 to 2018. Minimum and maximum temperature can be calculated from mean temperature and temperature range. EMDNA merges station observations and reanalysis model outputs to improve the quality of estimates. The dataset is expected to be useful for hydrological and meteorological applications in North America. Two types of datasets are provided by EMDNA, including the probabilistic dataset and the deterministic dataset. The probabilistic dataset has 100 equally plausible ensemble members, which can be used to evaluate the impact of the uncertainties in a myriad of applications. The deterministic dataset is generated during the production of ensemble members and can be applied in studies that do not need uncertainty estimation. See also: www.gwfnet.net/Metadata/Record/T-2020-11-25-i1Fwxi32sBMU2GDhUZ6gAJEg