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RI_623

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    Growing Degree Days (GDDs) are used to estimate the growth and development of plants and insects during the growing season. Insect and plant development are very dependent on temperature and the daily accumulation of heat. The amount of heat required to move a plant or pest to the next development stage remains constant from year to year. However, the actual amount of time (days) can vary considerably from year to year because of weather conditions. Base temperatures are a point below which development does not occur for the organism in question. Base 15 temperatures are commonly used for general insect development. These values are calculated across Canada in 10x10 km cells.

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    Description: Seasonal climatologies of the Canadian Pacific Exclusive Economic Zone (CPEEZ) were computed from a numerical simulation of the British Columbia continental margin (BCCM) model for the 1981 to 2010 period, which can be considered as a representation of the climatological state of the region. Methods: The BCCM model is an ocean circulation-biogeochemical model implementation of the Regional Ocean Modelling System (ROMS version 3.5). The horizontal resolution is eddy-resolving at 3 km and the vertical discretization is based on a terrain-following coordinate system with 42 depth levels of increasing resolution near the surface. A detailed description of the BCCM model is given in Peña et al. (2019). Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal climatology of temperature, salinity, current speed, nitrate, oxygen, total alkalinity, dissolved inorganic carbon, pH, aragonite saturation state, phytoplankton, and primary production. The data include 47 vertical levels (surface, bottom, and 45 more selected depths), except for total phytoplankton (surface values only) and primary production (depth-integrated values). A layer giving the bottom depth in metres at the centre of each grid point is also provided. Model grids were set to NaN values in regions where the model output is highly uncertain, such as inlets, nearshore areas, and the Strait of Georgia. Uncertainties: Model results have been compared against tide gauge data, altimetry, CTD and nutrient profiles, observed geostrophic currents, and seasonal temperature and salinity climatologies over the 1981 to 2010 period. The model is successful in reproducing the main features of the region including salient features of the seasonal cycle and the vertical structure of density and nutrients.

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    Description: Chlorophyll-a concentration (a proxy for phytoplankton biomass) was retrieved from the MODIS instrument on the Aqua satellite, with data distributed by the NASA Ocean Biology Processing Group, and averaged into monthly climatological composites. The data span the years 2003-2020; records were created for both 1 km and 4 km pixel resolutions to be consistent with other satellite products. Methods: MODIS-Aqua Chlorophyll-a (Chl-a) was acquired from the NASA Ocean Biology Processing Group where Chl-a concentration was calculated using the OC3/OCI method. The months of January and December were excluded from these datasets, as data in the winter months at higher latitudes are missing due to low sun angle preventing acquisition. The monthly geometric mean value at all pixels was calculated for individual years, then the geometric mean and geometric standard deviation factor of chlorophyll-a were calculated by month from these images. These methods of calculating mean and standard deviation were used due to the log-normal distribution of chlorophyll-a. The geometric standard deviation is a unitless factor, where the lower bound is the ratio of the geometric mean and geometric standard deviation, and the upper bound is the multiplication of the two. In addition to the geometric mean and geometric standard deviation factor the number of occurrences of valid data at each pixel over the period of observation were calculated. Pixels with fewer than two occurrences over the entire period of observation were removed from these maps and set to a NaN value in the tif files. All resulting rasters were cropped to the Canadian Exclusive Economic Zone, assigned to the NAD83 geographic coordinate reference system (EPSG:4269), and have final pixel resolutions of approximately 0.01 degrees and 0.0417 degrees. The monthly geometric mean, monthly geometric standard deviation factor, and number of occurrences for all pixels are provided. Data Sources: NASA Ocean Biology Processing Group. (2017). MODIS-Aqua Level 2 Ocean Color Data Version R2018.0. NASA Ocean Biology Distributed Active Archive Center. https://doi.org/10.5067/AQUA/MODIS/L2/OC/2018 Uncertainties: Satellite values have been evaluated against global datasets, and datasets of samples in the Pacific region (see references). However, uncertainties are introduced when averaging together images over time as each pixel has a differing number of observations. Short-lived or spatially limited events may be missed.

  • Level below which soil or rock is saturated with water, in the well and at the time the level has been measured, expressed in m above the sea level. Groundwater depth is measured on the field, using a water level meters. The depth is then subtracted from the elevation of the measurement site to obtain the water level elevation. The dataset is a general description of the measurement site including location and well elevation. It features a series of points of the surface elevation of the groundwater body.

  • High resolution 2D/3D seismic surveys performed for the Geological Survey of Canada to advance the use of seismic methods in hard rock environments.

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    The “Soils of Canada, Derived” national scale thematic datasets display the distribution and areal extent of soil attributes such as drainage, texture of parent material, kind of material, and classification of soils in terms of provincial Detailed Soil Surveys (DDS) polygons, Soil Landscape Polygons (SLCs), Soil Order and Great Group. The relief and associated slopes of the Canadian landscape are depicted on the local surface form thematic dataset. The purpose of the “Soils of Canada, Derived” series is to facilitate the cartographic display and basic queries of the Soil Landscapes of Canada at a national scale. For more detailed or sophisticated analysis, users should investigate the full “Soil Landscapes of Canada” product.

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    Growing Season Frost Free Period (-2 °C) is defined as the count of the number of days from the day after the last spring frost (-2 °C) to the day before the first fall frost (-2 °C). These values are calculated across Canada in 10x10 km cells.

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    Percent of Average Precipitation represents the accumulation of precipitation for a location, divided by the long term average value. The long term average value is defined as the average amount over the 1981 – 2010 period. Products are produced for the following timeframes: Agricultural Year, Growing Season, Winter Season, as well as rolling products for 30, 60, 90, 180, 270, 365, 730, 1095, 1460 and 1825 days.

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    Frost Day Count (0 °C) is defined as the number of days in a calendar month where the minimum daily temperature for the climate day was at or below 0 °C. These values are calculated across Canada in 10x10 km cells.

  • A measure of the intrinsic susceptibility of an aquifer representing the tendency or likelihood for contaminants to reach a specified position in the groundwater system after introduction at some location above the uppermost aquifer. The method used to create the dataset is described in the metadata associated with the dataset. The dataset is a general assessment of the vulnerability of the hydrogeological unit considered as a whole. It features the local and regional qualifiers in a controlled vocabulary list referring to the extent where the vulnerability value is valid. Because the vulnerability is assessed using contextual indices linked to the regional hydrogeological settings, it is very unlikely to have an homogeneous range of data throughout the various hydrogeologic units across the country for this dataset. Hence, the vulnerability dataset will not qualify as an homogeneous dataset. A more generic reclassification using for examples three vulnerability classes could then be used to solve this problem. Each sub layers used to create the global vulnerability index can be provided along with the final vulnerability index map.