cl_maintenanceAndUpdateFrequency

RI_541

21 record(s)
 
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    Monitor variations in seabird numbers and colony size in the St. Lawrence system. Survey of over 20 species of seabirds and herons during the breeding season, in order to monitor population dynamics through time and space. Seabird populations are influenced by food abundance and quality. There is actually more than 1,000,000 birds from more than 20 different species that breed in nearly 1,000 active colonies. Plongeon du Pacifique/Pacific Loon/Gavia Pacifica, Plongeon catmarin/Red-throated Loon/Gavia stellata, Macareux moine/Atlantic Puffin/Fratercula arctica, Guillemot à miroir/Black Guillemot/Cepphus grylle, Guillemot marmette/Common Murre/Uria aalge, Guillemot de Brünnich/Thick-billed Murre/Uria lomvia, Petit Pingouin/Razorbill/Alca torda, Mouette tridactyle/Black-legged Kittiwake/Rissa tridactyla, Goéland marin/Great Black-backed Gull/Larus marinus, Goéland argenté/Herring Gull/Larus argentatus, Goéland à bec cerclé/Ring-billed Gull/Larus delawarensis, Mouette rieuse/Common Black-headed Gull/Larus ridibundus, Sterne caspienne/Caspian Tern/Sterna caspia, Sterne pierregarin/Common Tern/Sterna hirundo, Sterne arctique/ArcticTern/Sterna paradisaea, Sterne de Dougall/Roseate Tern/Sterna dougallii, Océanite cul-blanc/Leach's Storm-Petrel/Oceanodroma leucorhoa, Fou de Bassan/Northern Gannet/Morus bassanus, Grand Cormoran/Great Cormorant/Phalacrocorax carbo, Cormoran à aigrettes/Double-crested Cormorant/Phalacrocorax auritus, Eider à duvet/Common Eider/Somateria mollissima, Grand Héron/Great Blue Heron/Ardea herodias, Bihoreau gris/Black-crowned Night-Heron/Nycticorax nycticorax.

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    With the changing climate conditions, marine traffic along Canada’s coastal regions has increased over the past couple of decades and the need to improve our state of preparedness for oil-spill-related emergencies is critical. Baseline coastal information, such as shoreline form, substrate, and vegetation type, is required for prioritizing operations, coordinating onsite spill response activities (i.e. Shoreline Cleanup Assessment Technique [SCAT]), and providing information for wildlife and ecosystem management. Between 2010 and 2017, georeferenced high-definition videography and photos were collected for various study sites across coastal Canada. The study areas include Beaufort Sea, Mackenzie Delta channels and Banks Island in the western Canadian Arctic; James Bay, Hudson Bay, Nunavik, Resolute Bay, Victoria Strait, Baffin Island and Coronation Gulf in the eastern Canadian Arctic; Labrador, Bay of Fundy and Chedabucto Bay in Atlantic Canada and Kitimat, Haida Gwaii and Burrard Inlet in the northern Pacific. Data was collected during ice-free and low tide conditions (where applicable) between July and September. Low-altitude helicopter surveys were conducted at each study site to capture video of the shoreline characteristics. In addition to acquiring videography, ground-based observations were recorded in several locations for validation. Shoreline segmentation was then carried out by manual interpretation of the oblique videography and the photos aided by ancillary data. This involved splitting and classifying the shoreline vectors based on homogeneity of the upper intertidal zone. Detailed geomorphological information (i.e. shoreline type, substrate, slope, height, accessibility etc.) describing the upper intertidal, lower intertidal, supratidal and backshore zones was extracted from the video and entered into a geospatial database using a customized data collection form. In addition, biological characteristics like biobands, water features, fauna, human use etc. observed along the coast were recorded. The data was also validated through ground samples (when available) and a second interpreter QA (quality analysis) was performed on each dataset (excluding Nunavik) to ensure high quality and consistency. The final dataset contains segments ranging in length from 150 metres to 2500 metres. In total, from 2010 to 2017, within the 14 study sites, about 26,150 km of shoreline were mapped.

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    Multi-model ensembles for a suite of variables based on projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) are available for 1850-2100 on a common 1x1 degree global grid. Climate projections vary across GCMs due to differences in the representation and approximation of earth systems and processes, and natural variability and uncertainty regarding future climate drivers. Thus, there is no single best climate model. Rather, using results from an ensemble of models (e.g., taking the average) is best practice, as an ensemble takes model uncertainty into account and provides more reliable climate projections. Provided on Canadian Climate Data and Scenarios (CCDS) are four types of products based on the CMIP6 multi-model ensembles: time series datasets and plots, maps and associated datasets, tabular datasets, and global gridded datasets. Monthly, seasonal, and annual ensembles are available for up to six Shared Socioeconomic Pathways (SSPs) (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0, and SSP5-8.5), four future periods (near-term (2021-2040), mid-term (2041-2060 and 2061-2080), end of century (2081-2100)), and up to five percentiles (5th, 25th, 50th (median), 75th, and 95th) of the CMIP6 ensemble distribution. The number of models in each ensemble differs according to model availability for each SSP and variable, see the model list resource for details on the models included in each ensemble. The majority of products show projected changes expressed as anomalies according to a historical reference period of 1995-2014. The products provided include global, national, and provincial/territorial datasets and graphics. For more information on the CMIP6 multi-model ensembles, see the technical documentation resource.

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    CANGRD is a set of Canadian gridded annual, seasonal, and monthly temperature and precipitation anomalies, which were interpolated from stations in the Adjusted and Homogenized Canadian Climate Data (AHCCD); it is used to produce the Climate Trends and Variations Bulletin (CTVB).

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    The purpose of the Long-Range Forecast Transient Intercomparison Project (LRFTIP) is to provide an archive of hindcast climatologies, describing the systematic behavior of models evolving from observation-based initial states, that can inform investigations into the transient behavior of initialized subseasonal to decadal climate predictions, the development of model biases, and the relative merits of different initialization methods. The archive is based on publicly available hindcast datasets including the Subseasonal to Seasonal Prediction Project (S2S), the Climate-system Historical Forecast Project (CHFP) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6). ECCC models include the GEPS-based contribution to S2S, the CanCM3, CanCM4, CanCM4i and GEM-NEMO seasonal prediction models, and the CanCM4 CMIP5 and CanESM5 CMIP6 decadal prediction models. Additional contributions are being added as they become available.

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    1. Provides public access to real-time instantatenous streamflow collected at over 1800 active locations in Canada. These data are collected under a national program jointly administered under federal-provincial and federal-territorial cost-sharing agreements; 2. Provides public access to archived daily streamflow for stations of interest using search criteria. These data include: daily and monthly mean, max and min of flow. For some sites, annual peaks and extremes are also recorded. Archived streamflow data are disseminated online; 3. Provides public access to a MS Access database file containing archived daily streamflow that users can download to their desktop. These data include: daily and monthly mean, max and min of flow. For some sites, annual peaks and extremes are also recorded. MS Access file is updated quarterly; 4. Provides public access to streamflow statistics for stations of interest using search criteria.

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    One of the ways of obtaining local-scale climate change scenarios is to use regression-based statistical downscaling of GCMs. In this approach, an empirical relationship between GCM predictors (i.e., near-surface and upper-level atmosphere circulation variables) and surface predictands (such as observed temperature or precipitation from a station) is derived by linear or non-linear transfer functions. For this purpose, an ensemble of daily predictor variables are produced from CanESM5, MPI-ESM1.2-HR, NorESM2-MM, and two reanalysis datasets. A total of 26 predictor variables are included in each ensemble, composed of both raw and derived variables, with multiple atmospheric variables available at three different pressure levels. Predictor variables are available at the daily scale on a 64 by 128 latitude-longitude global Gaussian grid with T42 spectral truncation. The historical simulation for 1979-2014 as well as the four Tier 1 Shared Socioeconomic Pathways (SSPs) prioritized by the Intergovernmental Panel on Climate Change (IPCC) and Scenario Model Intercomparison Project (ScenarioMIP) (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and SSP1-1.9 (due to its relevance for the Paris Agreement) for 2015-2100 are available for each GCM (O'Neill et al., 2016). Two reanalysis dataset options are available for the historical period 1979-2014 (ECMWF ERA5 and NCEP-DOE Reanalysis 2). GCMs chosen for inclusion into the CMIP6 predictors dataset was determined by three factors. Firstly, the equilibrium climate sensitivity (ECS) must have been calculated according to the Gregory methodology and the selected GCMs must cover a range of ECS values (see sections 1.1. and 1.2.). Secondly, the GCM must have run the historical simulation and as many of the five SSPs as possible (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Thirdly, for the relevant simulations, the seven base variables at all three included pressure levels (if applicable) must be available for download on Earth System Grid Federation (ESGF) website (https://esgf-node.llnl.gov/search/cmip6/).

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    In situ observations of snow water equivalent (SWE) from manual snow surveys and automated sensors are made at ~1000 sites across Canada in support of water resource planning for flood control and hydroelectricity production. These data represent an important source of information for research (e.g. validation of hydrological and climate model models), for applied studies (e.g. snow loads) and for climate monitoring. A data rescue effort in 1998 generated the first version of the Canadian historical snow survey dataset (MSC, 2000) which was subsequently updated in 2004. This update includes data to March 2017 and is provided as bi-weekly 0.1 x 0.1 degree grid box averages to facilitate research applications. Data for individual snow survey sites are also provided but exclude most data from Quebec after 1985 that have access restrictions. Information on data format and contact information for the Quebec data are provided in the "ReadMe" file.

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    With the changing climate conditions, marine traffic along Canada’s coastal regions has increased over the past couple of decades and the need to improve our state of preparedness for oil-spill-related emergencies is critical. Baseline coastal information, such as shoreline form, substrate, and vegetation type, is required for prioritizing operations, coordinating onsite spill response activities (i.e. Shoreline Cleanup Assessment Technique [SCAT]), and providing information for wildlife and ecosystem management. Between 2013 and 2019, georeferenced high-definition videography and photos were collected for various study sites along the west coast. The study areas include the mainland, inlets, channels and islands along the BC coast starting from Kitimat in the north to Quadra Island in the south, including Haida Gwaii and North Vancouver Island in the west and Burrard Inlet in the extreme south. Data was collected during low tide conditions (where applicable) between July and September. Low-altitude helicopter surveys were conducted at each of the study site to capture video of the shoreline characteristics. In addition to acquiring videography, ground-based observations were recorded in several locations for validation. Shoreline segmentation was then carried out by manual interpretation of the oblique videography and the photos aided by ancillary data. This involved splitting and classifying the shoreline vectors based on homogeneity of the upper intertidal zone. Detailed geomorphological information (i.e. shoreline type, substrate, slope, height, accessibility etc.) describing the upper intertidal, lower intertidal, supratidal and backshore zones was extracted from the video and entered into a geospatial database using a customized data collection form. In addition, biological characteristics like biobands, water features, fauna, human use etc. observed along the coast were recorded. The data was also validated through ground samples (when available) and a second interpreter QA (quality analysis) was performed on the dataset to ensure high quality and consistency. The final dataset contains segments ranging in length from 150 metres (45 metres for study areas surveyed in 2018-19) to 2500 metres. In total, from 2013 to 2019, about 15,000 km of shoreline were segmented.

  • Categories  

    With the changing climate conditions, marine traffic along Canada’s coastal regions has increased over the past couple of decades and the need to improve our state of preparedness for oil-spill-related emergencies is critical. Baseline coastal information, such as shoreline form, substrate, and vegetation type, is required for prioritizing operations, coordinating onsite spill response activities (i.e. Shoreline Cleanup Assessment Technique [SCAT]), and providing information for wildlife and ecosystem management. Between 2010 and 2016, georeferenced high-definition videography and photos were collected for various study sites along the north coast of Canada. The study areas include Beaufort Sea, Mackenzie Delta channels and Banks Island in the western Canadian Arctic and James Bay, Hudson Bay, Nunavik, Resolute Bay, Victoria Strait, Baffin Island and Coronation Gulf in the eastern Canadian Arctic. Data was collected during ice-free and low tide conditions (where applicable) between July and September. Low-altitude helicopter surveys were conducted at each study site to capture video of the shoreline characteristics. In addition to acquiring videography, ground-based observations were recorded in several locations for validation. Shoreline segmentation was then carried out by manual interpretation of the oblique videography and the photos aided by ancillary data. This involved splitting and classifying the shoreline vectors based on homogeneity of the upper intertidal zone. Detailed geomorphological information (i.e. shoreline type, substrate, slope, height, accessibility etc.) describing the upper intertidal, lower intertidal, supratidal and backshore zones was extracted from the video and entered into a geospatial database using a customized data collection form. In addition, biological characteristics like biobands, water features, fauna, human use etc. observed along the coast were recorded. The data was also validated through ground observations (when available) and a second interpreter QA (quality analysis) was performed on each dataset (excluding Nunavik) to ensure high quality and consistency. The final dataset contains segments ranging in length from 150 metres to 2500 metres. In total, from 2010 to 2016, within the 8 study sites, about 16,800 km of shoreline were segmented.