This dataset displays the Canadian geographic ranges of the priority species identified under the Pan-Canadian Approach for Transforming Species at Risk Conservation in Canada (“Pan-Canadian Approach”). These species include Barren-ground Caribou (including the Dolphin and Union population); Greater Sage-grouse; Peary Caribou; Wood Bison; Caribou, Boreal population (“Boreal Caribou”); and Woodland Caribou, Southern Mountain population (“Southern Mountain Caribou”). The priority species were chosen following a number of criteria and considerations in collaboration with federal, provincial, and territorial partners. These include, but were not limited to, the species' ecological role on a regional or national scale, their conservation status and achievability of conservation outcomes, their social and cultural value (particularly to Indigenous peoples), and the leadership/partnership opportunities that they present. Delivering conservation outcomes for targeted priority species can have significant co-benefits for other species at risk, and wildlife in general. For more information on the Pan-Canadian Approach and the priority species, see https://www.canada.ca/en/services/environment/wildlife-plants-species/species-risk/pan-canadian-approach.html. This dataset includes: 1) the range for the Boreal Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/2253); 2) the local populations for the Southern Mountain Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/1309); 3) the range for the Greater Sage-grouse (see https://species-registry.canada.ca/index-en.html#/consultations/1458); 4) local populations for the Peary Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/3657); 5) range for the Barren-ground Caribou (see https://www.maps.geomatics.gov.nt.ca/Html5Viewer/index.html?viewer=NWT_SHV English only); 6) range for the Barren-ground Caribou, Dolphin and Union population (https://www.maps.geomatics.gov.nt.ca/Html5Viewer/index.html?viewer=NWT_SHV English only); 7) range for the Wood Bison (see https://species-registry.canada.ca/index-en.html#/consultations/2914).

This national dataset contains geographic range data for 488 Species at risk based on NatureServe data, SAR recovery strategies, Environment Canada resources and COSEWIC status reports.

Precipitation measurements in the Environment and Climate Change Canada (ECCC) surface network are a necessary component for monitoring weather and climate and are required for flood and water resource forecasting, numerical weather prediction and many other applications that impact the health and safety of Canadians. Beginning in the late 1990s, the ECCC surface network began a transition from manual to automated precipitation measurements. Advantages to increased automation include enhanced capabilities for monitoring in remote locations and higher observation frequency at lower cost. However, transition to automated precipitation gauges has resulted in new challenges to data quality, accuracy, and homogenization. Automated weighing precipitation gauges used in the ECCC operational network, because of their physical profile, tend to measure less precipitation falling as snow because lighter particles (snow) are deflected away from the collector by the wind flow around the gauge orifice. This phenomenon of wind-induced systematic bias is well documented in the literature. The observation requires an adjustment depending on gauge and shield configuration, precipitation phase, temperature, and wind speed. Hourly precipitation, wind speed, and temperature for 397 ECCC automated surface weather stations were retrieved from the ECCC national archive. Climate Research Division (CRD) selected this sub-set of stations because they are critical to the continuity of various climate analysis. The observation period varies by station with the earliest data series beginning in 2001 (with most beginning in 2004). The precipitation data was quality controlled using established techniques to identify and flag outliers, remove spurious observations, and correct for previously identified filtering errors. The resulting hourly precipitation data was adjusted for wind bias using the WMO Solid Precipitation Inter-Comparison Experiment (SPICE) Universal Transfer Function (UTF) equation. A full description of this data set, including the station locations, data format, methodology, and references are included in the repository.

This dataset displays the geographic areas within which critical habitat (CH) for terrestrial species at risk, listed on Schedule 1 of the federal Species at Risk Act (SARA), occurs in Canada. Note that this includes only terrestrial species and species for which Environment and Climate Change Canada (ECCC) and Parks Canada Agency (PCA) lead. Under SARA, critical habitat is “the habitat that is necessary for the survival or recovery of a listed wildlife species and that is identified as the species’ critical habitat in the recovery strategy or action plan for the species.” To precisely define what constitutes critical habitat for a particular species it is essential that this geospatial information be considered in conjunction with complementary information provided in a species’ recovery document. Recovery documents are available from the Species at Risk (SAR) Public Registry (https://www.canada.ca/en/environment-climate-change/services/species-risk-public-registry.html) for two posting stages (proposed and final posting). The recovery documents contain important information about the interpretation of the geospatial information, especially regarding the biological and environmental features (“biophysical attributes”) that complete the definition of a species’ critical habitat. Within any defined critical habitat geospatial boundary, not all of the area is necessarily critical habitat. It is important to note that recovery planning documents (and, therefore, critical habitat) may be amended from time to time as new information becomes available, which may occur after a document has been posted as proposed or final on the SAR Public Registry. The SAR Public Registry should always be considered as the main source for critical habitat information. In cases where the data are sensitive, the geographic area within which critical habitat occurs may be represented as grids. These are coarse grids (1, 10, 50 or 100 square kilometres) that serve as indicators to locate critical habitat in the recovery planning document. More detailed information on critical habitat may be made available on a need-to-know basis by contacting Environment and Climate Change Canada – Canadian Wildlife Service at ec.planificationduretablissement-recoveryplanning.ec@canada.ca. The data is current as of the date of the most recent revision.

As part of a scientific assessment of critical habitat for boreal woodland caribou (Environment Canada 2011, see full reference in accompanying documentation), Environment Canada's Landscape Science and Technology Division was tasked with providing detailed anthropogenic disturbance mapping across known caribou ranges. This data allowed researchers to better understand the attributes that have a known effect on caribou population persistence. The mapping process was established to create a nationally consistent, reliable and repeatable geospatial dataset that followed a common methodology. The methods developed were focused on mapping disturbances at a specific point of time, and were not designed to identify the age of disturbances, which can be of particular interest for disturbances that can be considered non-permanent, for example cutblocks. The resultant datasets were used for caribou resource selection function,habitat modeling, and assess overall disturbance levels on each caribou ranges. Anthropogenic disturbances within 57 caribou ranges across Canada were mapped. The ranges were defined by individual Provinces and Territories across Canada. Disturbances were mapped across these ranges using 2008-2010 Landsat-5 satellite imagery to provide the most up to date data possible. Originally some areas were mapped to match the date of collected caribou demographic data, however more recent imagery was used and additional disturbance features that were seen since the original mapping date were added. Within the context of this project, anthropogenic disturbance was defined as any human-caused disturbance to the natural landscape that could be visually identified from Landsat imagery at a viewing scale of 1:50,000. A minimum mapping unit (MMU) of 2 ha or approximately 22 contiguous Landsat pixels was selected. Each disturbance feature type was represented in the database by a line or polygon depending on their geometric description. Polygonal disturbances included: cutblocks, mines, reservoirs, built-up areas, well sites, agriculture, oil and gas facilities, as well as unknown features. Linear disturbances included: roads, railways, powerlines, seismic exploration lines, pipelines, dams, air strips, as well as unknown features. For each anthropogenic feature type, a clear description was established (see Appendix 7.2 of the science assessment) to maintain consistency in identifying the various disturbances in the imagery by the different interpreters. Various ancillary vector datasets were used as aids in detecting, classifying and digitizing disturbances on the Landsat imagery (a table listing these datasets and their sources has been included in a separate file). Ancillary data was used to guide interpretation and feature labelling since the ancillary data was often variable across the country in terms of completeness as well as scale. As a result, features were only digitized if they were visible in the Landsat imagery at a viewing scale of 1:50,000. A 2nd interpreter quality control phase was carried out to ensure high quality, completete and consistent data collection. A quality assessment analysis, since an actual accuracy assessment was not possible, using high resolution SPOT imagery was carried out on a sample basis. Results are included in accompanying documentation. The vector data was buffered by 500m (radius) representing the zone of influence impacting boreal caribou herds in order to calculate range disturbance levels as well as for use in the integrated risk assessment analysis. Fire polygons were merged into the anthropogenic footprint in order to create an overall disturbance footprint.

A database of verified tornado occurrences across Canada has been created covering the 30-year period from 1980 to 2009. The tornado data have undergone a number of quality control checks and represent the most current knowledge of past tornado events over the period. However, updates may be made to the database as new or more accurate information becomes available. The data have been converted to a geo-referenced mapping file that can be viewed and manipulated using GIS software.

The AdjDlyRS dataset contains adjusted daily rainfall (R) and snowfall (S) data from all Canadian stations reporting rainfall and snowfall for which we have metadata to do the adjustments (Wang et al. 2017). The processing includes inspection and adjustments using quality control procedures customized for producing gridded datasets (Wang et al. 2017), including: (1) conversion of snowfall ruler measurements to their water equivalents; (2) corrections for gauge undercatch and evaporation due to wind effect, for gauge specific wetting loss, and for trace precipitation amount; and (3) treatment of flags (e.g. accumulation flags). Version 2020 or later versions of this dataset also includes identification and correction of random erroneous values, including false zeros, which usually arose from missing values being misrecorded as 0 precipitation in the climate Archive (Cheng et al. 2022). All the identified erroneous daily values are set to missing. A total of 3346 stations were processed, but the data series are not homogenized. Most of the stations are located in southern Canada and have short and/or seasonal data records. The number of stations changes over time: there are 512-958 stations in the period 1948-1964, 1012-2038 stations in the period 1965-2008, and only around 300 stations in the recent years. Note that the unadjusted/raw total precipitation data in Environment and Climate Change Canada's digital Archive underestimate more than 25% of the total precipitation in northern Canada, and about 10-15% in most of southern Canada (Wang et al. 2017). References: (1) Wang, X. L., Xu, B. Qian, Y. Feng, E. Mekis, 2017: Adjusted daily rainfall and snowfall data for Canada, Atmosphere-Ocean, 55:3, 155-168, DOI:10.1080/07055900.2017.1342163. (2) Cheng, V. Y.S., X. L. Wang, Y. Feng, 2022: A quality control system for historical in situ precipitation data. Atmosphere-Ocean (submitted)

This dataset contains 21 statistics of wind and waves calculated from hourly reanalysis data of historical surface winds and ocean surface waves for the Canadian East Coast for the period 1954-2018 (each statistic has an annual, 4 seasonal, and 12 monthly values). These data can be used for characterization of marine surface wind and wave climate conditions, trends and variability for Canadian waters (East Coast), and for use of coastal and offshore operations/risk management (e.g., oil platforms, shipping). The hourly wind and wave’s data may be obtained upon request from the Meteorological Service of Canada's Climate Services.

A database of verified tornado tracks across Canada has been created covering the 30-year period from 1980 to 2009. The tornado data have undergone a number of quality control checks and represent the most current knowledge of past tornado events over the period. However, updates may be made to the database as new or more accurate information becomes available. The data have been converted to a geo-referenced mapping file that can be viewed and manipulated using GIS software.

A database of verified tornado tracks across Canada has been created covering the 30-year period from 1980 to 2009. The tornado data have undergone a number of quality control checks and represent the most current knowledge of past tornado events over the period. However, updates may be made to the database as new or more accurate information becomes available. The data have been converted to a geo-referenced mapping file that can be viewed and manipulated using GIS software.