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2019

51 record(s)
 
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From 1 - 10 / 51
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    This dataset provides geospatial polygon boundaries for marine bivalve shellfish harvest area classification in Nova Scotia, Canada. These data represent the five classification categories of marine bivalve shellfish harvest areas (Approved; Conditionally Approved; Restricted; Conditionally Restricted; and Prohibited) under the Canadian Shellfish Sanitation Program (CSSP). Data are collected by Environment and Climate Change Canada (ECCC) for the purpose of making applicable classification recommendations on the basis of sanitary and water quality survey results. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO). These geographic data are for illustrative purposes only; they show shellfish harvest area classifications when in Open Status. The classification may be superseded at any time by regulatory orders issued by DFO, which place areas in Closed Status, due to conditions such as sewage overflows or elevated biotoxin levels. For further information about the current status and boundary coordinates for areas under Prohibition Order, please contact your local DFO office.

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    This dataset provides geospatial polygon boundaries for marine bivalve shellfish harvest area classification in Canada (British Columbia, New Brunswick, Newfoundland and Labrador, Nova Scotia, Prince Edward Island and Quebec). These data represent the five classification categories of marine bivalve shellfish harvest areas (Approved; Conditionally Approved; Restricted; Conditionally Restricted; and Prohibited) under the Canadian Shellfish Sanitation Program (CSSP). Data are collected by Environment and Climate Change Canada (ECCC) for the purpose of making applicable classification recommendations on the basis of sanitary and water quality survey results. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO). These geographic data are for illustrative purposes only; they show shellfish harvest area classifications when in Open Status. The classification may be superseded at any time by regulatory orders issued by DFO, which place areas in Closed Status, due to conditions such as sewage overflows or elevated biotoxin levels. For further information about the current status and boundary coordinates for areas under Prohibition Order, please contact your local DFO office.

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    This dataset provides geospatial polygon boundaries for marine bivalve shellfish harvest area classification in Newfoundland and Labrador, Canada. These data represent the five classification categories of marine bivalve shellfish harvest areas (Approved; Conditionally Approved; Restricted; Conditionally Restricted; and Prohibited) under the Canadian Shellfish Sanitation Program (CSSP). Data are collected by Environment and Climate Change Canada (ECCC) for the purpose of making applicable classification recommendations on the basis of sanitary and water quality survey results. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO). These geographic data are for illustrative purposes only; they show shellfish harvest area classifications when in Open Status. The classification may be superseded at any time by regulatory orders issued by DFO, which place areas in Closed Status, due to conditions such as sewage overflows or elevated biotoxin levels. For further information about the current status and boundary coordinates for areas under Prohibition Order, please contact your local DFO office.

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    The data consists of temperature indices based on homogenized daily maximum and minimum temperatures at 338 locations across Canada, and of precipitation indices based on adjusted daily rainfall, daily snowfall and daily precipitation amounts at 463 locations across the country. These indices were selected for their relevance to social and economic impact assessment in Canada and for the insights they could provide regarding changes in extreme climate conditions. Please refer to the papers below for detailed information regarding the adjustment procedures and the trends in the indices.

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    This dataset provides geospatial polygon boundaries for marine bivalve shellfish harvest area classification in British Columbia, Canada. These data represent the five classification categories of marine bivalve shellfish harvest areas (Approved; Conditionally Approved; Restricted; Conditionally Restricted; and Prohibited) under the Canadian Shellfish Sanitation Program (CSSP). Data are collected by Environment and Climate Change Canada (ECCC) for the purpose of making applicable classification recommendations on the basis of sanitary and water quality survey results. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO). These geographic data are for illustrative purposes only; they show shellfish harvest area classifications when in Open Status. The classification may be superseded at any time by regulatory orders issued by DFO, which place areas in Closed Status, due to conditions such as sewage overflows or elevated biotoxin levels. For further information about the current status and boundary coordinates for areas under Prohibition Order, please contact your local DFO office.

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    This dataset provides geospatial polygon boundaries for marine bivalve shellfish harvest area classification in New Brunswick, Canada. These data represent the five classification categories of marine bivalve shellfish harvest areas (Approved; Conditionally Approved; Restricted; Conditionally Restricted; and Prohibited) under the Canadian Shellfish Sanitation Program (CSSP). Data are collected by Environment and Climate Change Canada (ECCC) for the purpose of making applicable classification recommendations on the basis of sanitary and water quality survey results. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO). These geographic data are for illustrative purposes only; they show shellfish harvest area classifications when in Open Status. The classification may be superseded at any time by regulatory orders issued by DFO, which place areas in Closed Status, due to conditions such as sewage overflows or elevated biotoxin levels. For further information about the current status and boundary coordinates for areas under Prohibition Order, please contact your local DFO office.

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    This dataset provides geospatial polygon boundaries for marine bivalve shellfish harvest area classification in Prince Edward Island, Canada. These data represent the five classification categories of marine bivalve shellfish harvest areas (Approved; Conditionally Approved; Restricted; Conditionally Restricted; and Prohibited) under the Canadian Shellfish Sanitation Program (CSSP). Data are collected by Environment and Climate Change Canada (ECCC) for the purpose of making applicable classification recommendations on the basis of sanitary and water quality survey results. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO). These geographic data are for illustrative purposes only; they show shellfish harvest area classifications when in Open Status. The classification may be superseded at any time by regulatory orders issued by DFO, which place areas in Closed Status, due to conditions such as sewage overflows or elevated biotoxin levels. For further information about the current status and boundary coordinates for areas under Prohibition Order, please contact your local DFO office.

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    The remote Hudson Plains of northern Ontario is a flat, wetland-dominated area and acts as a drainage basin for several major rivers that flow to Hudson and James Bay. Relative to other parts of the province, the Hudson Plains has been the subject of few bird sampling efforts, with most of bird surveys conducted within a few kilometres of major rivers and near the coast. The Canadian Wildlife Service developed this study to begin to estimate the degree to which sampling between the major rivers was needed to properly represent bird distribution and abundance in the region. Autonomous recording units (ARUs) were deployed via helicopter at 35 locations along a 208 kilometre North-South transect between the Attawapiskat and Albany Rivers and 11 clustered locations near the Attawapiskat River. Included here are interpreted data from the sound files collected by the ARUs during the period June 1 to July 6, 2019. There were 91 bird species and 6 amphibian (frogs and toads) species detected in the segments.

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    The dataset contains large ensembles of bias adjusted daily climate model outputs of minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation, and incoming longwave radiation on a 0.5-degree grid over North America. Intended uses include hydrological/land surface impact modelling and related event attribution studies. The CanLEADv1 dataset is based on archived climate model simulations in the Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) https://open.canada.ca/data/en/dataset/83aa1b18-6616-405e-9bce-af7ef8c2031c and Canadian Earth System Model Large Ensembles (CanESM2 LE) https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c datasets. Specifically, CanLEADv1 provides bias adjusted daily climate variables over North America derived from 50 member initial condition ensembles of CanESM2 (ALL and NAT radiative forcings) and CanESM2-driven CanRCM4 (ALL radiative forcings) simulations (Scinocca et al., 2016; Fyfe et al., 2017). Raw CanESM2 LE and CanRCM4 LE outputs are bias adjusted (Cannon, 2018; Cannon et al., 2015) so that they are statistically consistent with two observationally-constrained historical meteorological forcing datasets (S14FD, Iizumi et al., 2017; EWEMBI, Lange, 2018). File names, formats, and metadata headers follow the recommended Data Reference Syntax for bias-adjusted Coordinated Regional Downscaling Experiment (CORDEX) simulations (Nikulin and Legutke, 2016). Multiple initial condition simulations can be used to investigate the externally forced response, internal variability, and the relative role of external forcing and internal variability on the climate system (e.g., Fyfe et al., 2017). Large ensembles of ALL and NAT simulations can be compared in event attribution studies (e.g., Kirchmeier-Young et al., 2017). Availability of bias adjusted outputs from the CanESM2-CanRCM4 modelling system can be used to investigate the added value of dynamical downscaling (Scinocca et al., 2016). Multiple observational datasets are used for bias adjustment to partly account for observational uncertainty (Iizumi et al., 2017). For CanESM2 LE, there are two sets of radiative forcing scenarios (ALL, which consists of historical and RCP8.5 forcings for the periods 1950-2005 and 2006-2100, respectively, and NAT, which consists of historicalNat forcings for the period 1950-2020), two observationally-constrained target datasets for bias adjustment (S14FD and EWEMBI), and 50 ensemble members, which gives a total of 2 × 2 × 50 = 200 sets of outputs. For CanRCM4 LE, historicalNat simulations were not run; hence, there are 2 × 50 = 100 sets of outputs. In both cases, CanLEADv1 provides variables on the CORDEX NAM-44i 0.5-degree grid. CanESM2 outputs (~2.8-degree grid) and CanRCM4 outputs (0.44-degree grid), are bilinearly interpolated onto the NAM-44i grid before bias adjustment. A multivariate version of quantile mapping (Cannon, 2018) is used to adjust the distribution of each simulated variable, as well as the statistical dependence between variables, so that these properties match those of the target observational dataset. Bias adjustment is performed on a grid cell by grid cell basis. Outside of the historical calibration period, the climate change signal simulated by the climate model is preserved (Cannon et al., 2015). References: Cannon, A. J. (2018). Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, 50(1-2), 31-49. Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015). Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate, 28(17), 6938-6959. Fyfe, J. C., Derksen, C., Mudryk, L., Flato, G. M., Santer, B. D., Swart, N. C., Molotch, N. P., Zhang, X., Wan, H., Arora, V. K., Scinocca, J., & Jiao, Y. (2017). Large near-term projected snowpack loss over the western United States. Nature Communications, 8, 14996. Iizumi, T., Takikawa, H., Hirabayashi, Y., Hanasaki, N., & Nishimori, M. (2017). Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes. Journal of Geophysical Research: Atmospheres, 122(15), 7800-7819. Kirchmeier-Young, M. C., Zwiers, F. W., Gillett, N. P., & Cannon, A. J. (2017). Attributing extreme fire risk in Western Canada to human emissions. Climatic Change, 144(2), 365-379. Lange, S. (2018). Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset. Earth System Dynamics, 9(2), 627-645. Nikulin, G., & Legutke, S. (2016). Data Reference Syntax (DRS) for bias-adjusted CORDEX simulations. http://is-enes-data.github.io/CORDEX_adjust_drs.pdf Scinocca, J. F., Kharin, V. V., Jiao, Y., Qian, M. W., Lazare, M., Solheim, L., Flato, G. M., Biner, S., Desgagne, & Dugas, B. (2016). Coordinated global and regional climate modeling. Journal of Climate, 29(1), 17-35.

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    The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. The climate change indicators data collection contains datasets that report on changes in climate variables such as temperature and precipitation. This information is provided in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables, and downloadable reports.