Drought is a deficiency in precipitation over an extended period, usually a season or more, resulting in a water shortage that has adverse impacts on vegetation, animals and/or people. The Climate Moisture Index (CMI) was calculated as the difference between annual precipitation and potential evapotranspiration (PET) – the potential loss of water vapour from a landscape covered by vegetation. Positive CMI values indicate wet or moist conditions and show that precipitation is sufficient to sustain a closed-canopy forest. Negative CMI values indicate dry conditions that, at best, can support discontinuous parkland-type forests. The CMI is well suited to evaluating moisture conditions in dry regions such as the Prairie Provinces and has been used for other ecological studies.Mean annual potential evapotranspiration (PET) was estimated for 30-year periods using the modified Penman-Monteith formulation of Hogg (1997), based on monthly 10-km gridded temperature data. Data shown on maps are 30-year averages. Historical values of CMI (1981-2010) were created by averaging annual CMI calculated from interpolated monthly temperature and precipitation data produced from climate station records. Future values of CMI were projected from downscaled monthly values of temperature and precipitation simulated using the Canadian Earth System Model version 2 (CanESM2) for two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century.Provided layer: mean annual Climate Moisture Index across Canada for a reference period (1981-2010).Reference: Hogg, E.H. 1997. Temporal scaling of moisture and the forest-grassland boundary in western Canada. Agricultural and Forest Meteorology 84,115–122.

The data represents the frost-free period in Alberta over the 30-year period from 1971 to 2000. A 30-year period is used to describe the present climate since it is enough time to filter out short-term fluctuations but is not dominated by any long-term trend in the climate. The frost-free period is the number of days between the last date of 00C in the spring and the first date of 00C in the fall. Frost free periods in Alberta vary from 125 days in the south to less than 85 days in higher elevation, non-agricultural areas.The frost-free period is presented as days above 0°.C in the following classes: less than 85, 85 to 95, 95 to 105, 105 to 115, 115 to 125 and greater than 125. This resource was created using ArcGIS.

This data shows the spatial extent of surface disturbance mapping projects completed by GIS contractors on behalf of Environment Yukon. Within these polygons, features were digitized using high resolution satellite imagery and orthophotos; outside these polygons, disturbance mapping is either incomplete or non-existent.Distributed from [GeoYukon](https://yukon.ca/en/statistics-and-data/mapping/explore-map-data-using-geoyukon) by the [Government of Yukon](https://yukon.ca/) . Discover more digital map data and interactive maps from Yukon's digital [map](https://yukon.ca/en/maps) data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

The “Thematic Soil Maps of Saskatchewan” is a revised and condensed version of the Saskatchewan Detailed Soils Database produced by CANSIS. It contains data relating to the soils slope, drainage, agricultural capability, erosion potential, and surface texture.

Historical earthquakes recorded by Earthquakes Canada. This dataset contains the earthquakes recorded in decade 2010. However, the National Earthquake Database makes available seismic bulletin data from 1985 and onward. For a complete listing of current and historical earthquakes, visit https://www.earthquakescanada.nrcan.gc.ca/.

The maps show a multiyear ground deformation rate caused by small-scale deformation processes in Canada, measured in meters per year. Horizontal-east and vertical deformation components were computed from data acquired on ascending and descending orbits. This horizontal-east/vertical 2D decomposition is approximate and assumes constant viewing geometry and the absence of horizontal-north deformation.In the line-of-sight (LOS) map computed from ascending orbit data, a negative signal approximately corresponds to either subsidence or eastward motion, while a positive signal corresponds to uplift or westward motion. In the LOS map computed from descending orbit data, a negative signal approximately corresponds to either subsidence or westward motion, while a positive signal corresponds to uplift or eastward motion.In the horizontal-east map, a negative signal corresponds to westward motion, while a positive signal corresponds to eastward motion. In the vertical map, a negative signal indicates subsidence, while a positive signal indicates uplift.The maps were calculated from Sentinel-1 Synthetic Aperture Radar data collected between 2017 and 2024 during the snow-free season. Interferometric analysis of Sentinel-1 data was performed using GAMMA Software (https://www.gamma-rs.ch), and the long-term deformation rate was computed with the Multidimensional Small Baseline Subset (MSBAS) Software Version 10 (https://doi.org/10.1080/07038992.2024.2424753) at the Canada Centre for Mapping and Earth Observation, Natural Resources Canada.Long-wavelength signals caused by postglacial rebound and tectonic motion were filtered to enhance the visibility of small-scale deformation processes, such as those originating from landslides and mining. Field studies have confirmed only a few of these processes to date. The maps are expected to contain processing artifacts, which will be addressed in future work.References:Samsonov, S. V., & Feng, W. (2023). Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges. Canadian Journal of Remote Sensing, 49(1). https://doi.org/10.1080/07038992.2023.2247095Samsonov, S. V. (2024). Multidimensional Small Baseline Subset (MSBAS) Software for Constrained and Unconstrained Deformation Analysis of Partially Coherent DInSAR and Speckle Offset Data. Canadian Journal of Remote Sensing, 50(1). https://doi.org/10.1080/07038992.2024.2424753Limitation of Liability :The information contained on this website is provided on an “as is” basis and Natural Resources Canada makes no representations or warranties respecting the information, either expressed or implied, arising by law or otherwise, including but not limited to, effectiveness, completeness, accuracy or fitness for a particular purpose. Natural Resources Canada does not assume any liability in respect of any damage or loss based on the use of this website. In no event shall Natural Resources Canada be liable in any way for any direct, indirect, special, incidental, consequential, or other damages based on any use of this website or any other website to which this site is linked, including, without limitation, any lost profits or revenue or business interruption.

This catalog contains annual 30 m spatial resolution snow dynamics metrics for each snow-year from 2018-2019 to 2023-2024 for all of Canada. We gather all Landsat and Sentinel-2 images collected over Canada and identify the status of each pixel observation on the image collection date: snow (and ice), non-snow (i.e., land, water), unclear (i.e., clouds, shadows). We built an algorithm to calculate snow cover metrics for each pixel during each winter: start date of the first (and biggest) snow period [startF, startB], end date of the last (and biggest) snow period [endL, endB], number of days with snow cover in total (or in the biggest snow period) [lengthT, lengthB], number of snow periods (i.e., separated times with multiple confirmed snow observations) [periods], and a status classification (e.g., continuous snow, snow free) [status]. We do not obtain a clear observation every day because of satellite orbit frequencies and clouds. This means that timing-based metrics are identified by the middle date between two clear observations, with uncertainty quantified as half the length of the gap (i.e., ± days) [startF_u, startB_u, endL_u, endB_u, lengthT_u, lengthB_u].

This data shows anthropogenic polyline disturbance features. Features were digitized using high resolution satellite imagery and orthophotos. Features from the National Road Network (NRN) and the National Railway Network (NRWN) were adapted and included. The following data was not included in the dataset: proposed features.Table 1. A list of attributes, associated domains, and descriptions.AttributeData TypeDomainsDescriptionREF_IDText (20) Unique feature reference IDDATABASEText (20)Historic, Most Recent, RetiredSub-database to which the feature belongsTYPE_INDUSTRYText (50)Table 2.3.2Major classification of disturbance feature by industryTYPE_DISTURBANCEText (50)Table 2.3.2Sub classification of disturbance featureWIDTH_M*Double Width of feature in metersWIDTH_CLASS**Text (5)HIGH, MED, LOWWidth of feature by classificationSCALE_CAPTUREDLong Scale at which the feature was digitizedDATA_SOURCEText (10)Imagery, GPS, OtherData source: digitized from imagery, captured by GPS, or obtained by other meansIMAGE_NAMEText (100) Filename of source imageryIMAGE_DATEDate Date that imagery was captured (YYYYMMDD)IMAGE_RESOLUTIONDouble Resolution of source imagery in metersIMAGE_SENSORText (35) Name of sensor that captured source imagery\*WIDTH_M: Linear features must be attributed with a width measurement. The width of the feature can be estimated in meters, rounded to the nearest whole number.\*\*WIDTH_CLASS: This field employs a classification scheme used by previous contractors. This classification scheme was discussed and agreed upon by Mammoth Mapping and the Project Manager in 2011-2013. The width values are the following.Table 2. Width classification breakdown.WIDTH_CLASSAnticipated Value Range (meters)LOW<4MED4-8HIGH>8Table 3. A list of disturbance feature types and their descriptions.TYPE_INDUSTRYTYPE_DISTURBANCEDESCRIPTIONMiningSurvey / CutlineA linear cleared area through undeveloped land, used for line-of-sight surveying; impossible to distinguish whether associated with quartz or placer mining (overlapping or unclear claims information)Survey / Cutline - PlacerA linear cleared area through undeveloped land, used for line-of-sight surveying; associated with placer mining (identified using claims information and/or other indicators)Survey / Cutline - QuartzA linear cleared area through undeveloped land, used for line-of-sight surveying; associated with quartz mining (identified using claims information and/or other indicators)TrenchA long, narrow excavation dug to expose vein or ore structureUnknownUnknown linear mining disturbanceOil and GasPipelineVisible pipeline or pipeline Right-of-Way (above- or below-ground)Seismic LineSeismic linesRuralDrivewayA driveway in a rural areaFenceA fence in a rural areaTransportationAccess AssumedA linear feature that is assumed to be an access road, but could also be a trailAccess RoadA road or narrow passage whose primary function is to provide access for resource extraction (i.e. mining, forestry) and may also have served in providing public access to the backcountry.Arterial RoadA major thoroughfare with medium to large traffic capacityLocal RoadA low-speed thoroughfare, provides access to front of properties, including those with potential public restrictions such as trailer parks, First Nations land, private estate, seasonal residences, gravel pits (NRN definition for Local Street/Local Strata/Local Unknown). Shows signs of regular use.Right of WayFor Road Rights as attributed in the land parcels ancillary dataTrailPath or track (typically <1.5 m wide) used for walking, cycling, ORV, or other backcountry activities. (Note: trails used for mining activities are Access Roads.)Unpaved RoadDirt or gravel road (typically >1.5 m wide) that does not necessarily access remote resourcesUnknownRight of WayA right of way with unknown industry typeSurvey / CutlineA linear cleared area through undeveloped land, used for line-of-sight surveying. A cutline may not always be associated with mineral exploration, therefore, Type: Unknown was used to differentiate all cutlines that were outside of mineral exploration.UnknownUnclassified, or unable to identify type based on imagery, but suspected to be anthropogenicUtilityElectric Utility CorridorCorridor usually running parallel to highway, where transmission lines or other utilities are visibleUnknownUnknown linear feature assumed to be a utility corridor; ancillary data is unclear.Distributed from [GeoYukon](https://yukon.ca/en/statistics-and-data/mapping/explore-map-data-using-geoyukon) by the [Government of Yukon](https://yukon.ca/) . Discover more digital map data and interactive maps from Yukon's digital [map](https://yukon.ca/en/maps) data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)       

Growing degree days (GDDs) are used to estimate the growth and development of plants and insects during the growing season. Growing Degree Day are computed by subtracting a base value temperature from the mean daily temperature and are assigned a value of zero if negative. Base temperatures are a point below which development does not occur for the organism in question. Growing Degree Day products are created for base 0, 5, 10 and 15 degrees Celsius.GDD values are only accumulated during the Growing Season, April 1 through October 31.

Jurisdictional boundaries for the Ministry of Emergency Management and Climate Readiness (EMCR). This dataset represents the regional operational boundaries for the former Provincial Emergency Program (PEP).