Ts’udé Nilįné Tuyeta Established Protected Area

Department of ENR/ITI Administrative Boundaries

EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, acidity and contaminants. The chemicals chosen reflect importance to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data on Eutrophication and Acidity (14 parameters with quality flag indicators), and covers the Norwegian Sea, Barents Sea, Greenland Sea and Icelandic Waters with 114721 CDI stations. Data were aggregated and quality controlled by 'Institute of Marine Research - Norwegian Marine Data Centre (NMD)'. Regional datasets concerning eutrophication and acidity are automatically harvested and resulting collections are aggregated and quality controlled using ODV Software and following a common methodology for all Sea Regions ( https://doi.org/10.6092/9f75ad8a-ca32-4a72-bf69-167119b2cc12 ). When not present in original data, Water body nitrate plus nitrite was calculated by summing up the Nitrates and Nitrites. Same procedure was applied for Water body dissolved inorganic nitrogen (DIN) which was calculated by summing up the Nitrates, Nitrites and Ammonium. Parameter names are based on P35, EMODnet Chemistry aggregated parameter names vocabulary, which is available at: https://www.bodc.ac.uk/resources/vocabularies/vocabulary_search/P35/ Detailed documentation is available at: https://dx.doi.org/10.6092/4e85717a-a2c9-454d-ba0d-30b89f742713 Explore and extract data at: https://emodnet-chemistry.webodv.awi.de/eutrophication>Arctic The aggregated dataset can also be downloaded as ODV collection and spreadsheet, which is composed of metadata header followed by tab separated values. This spreadsheet can be imported to ODV Software for visualisation (More information can be found at: https://www.seadatanet.org/Software/ODV ) The original datasets can be searched and downloaded from EMODnet Chemistry Chemistry CDI Data and Discovery Access Service: https://emodnet-chemistry.maris.nl/search

Ecologically Based Landscape Classification Data

Mining Leases

Important Wildlife Areas In The NWT

This dataset shows locations that contain critical habitat for certain terrestrial species at risk across Canada. It is intended to provide general guidance only, not legally authoritative boundaries. The dataset includes active critical habitat records for terrestrial species listed on Schedule 1 of the Species at Risk Act (SARA) as Endangered, Threatened, or Extirpated, as well as archived records for species whose status has been downlisted to Special Concern, and for which Environment and Climate Change Canada (ECCC) and/or Parks Canada are responsible. Under SARA, critical habitat means 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. These documents, which are published in the Species at Risk Public Registry (https://www.canada.ca/en/environment-climate-change/services/species-risk-public-registry.html), describe the critical habitat and may include maps or geographic coordinates. Because new information becomes available over time, recovery documents—and the critical habitat they identify—can be updated or revised. The Species at Risk Public Registry is the main source for the most current official information. If there is any difference between this dataset and a recovery document, the recovery strategy or action plan is the authoritative source. Some habitat locations are considered sensitive. For these species, the data may be shown in a less detailed way, or at a broader geographic scale, to avoid increasing risks to the species. For more detailed information on critical habitat, contact the Canadian Wildlife Service at scf-geocarto-cws-geomapping@ec.gc.ca. The data is current as of the date of modification.

The Canadian Protected and Conserved Areas Database (CPCAD) is the authoritative source of data on protected and conserved areas in Canada. The database consists of the most up-to-date spatial and attribute data on marine and terrestrial protected areas in all governance categories recognized by the International Union for Conservation of Nature (IUCN), as well as Other effective area-based conservation measures (OECMs, or conserved areas) across the country. Indigenous Protected and Conserved Areas (IPCAs) are also included if they are recognized as protected or conserved areas. CPCAD adheres to national reporting standards and is freely available to the public. CPCAD is compiled and managed by Environment and Climate Change Canada (ECCC), in collaboration with federal, provincial, territorial, and other reporting authorities that provide the data. The database contains combined data from all these Canadian reporting authorities, who have determined that their areas meet the Canadian criteria as protected or conserved areas. CPCAD is used by a wide range of organizations, including governments, environmental non-governmental organizations (ENGOs), academia, land managers, industry, and the general public. CPCAD supports many of the Government of Canada’s priorities including Canada’s national reporting on protected areas, Canada’s international reporting on protected areas as a result of Canada’s commitments under the United Nations Convention on Biological Diversity, and Canada’s protected areas program by providing baseline information. More detailed information on CPCAD is available by downloading the User Manual. The data is current as of the date of the most recent revision. For prior years, please reach out to scf-geocarto-cws-geomapping@ec.gc.ca.

The Boreal Caribou data Package includes layers that are used for Boreal Caribou Range Planning in the NWT. This includes fire history, human disturbance, range planning regions as well as the 2020 Resource Selection Function layers for all seasons. Data sources and contact information can be found within each layer's metadata.

Canadian Homogenized Precipitation – Version 2 (CanHomP V2) The CanHomP V2 datasets were developed for climate trend analysis and include long-term monthly and daily precipitation series for 425 stations across Canada (Wang et al., 2026; Wang & Feng, 2026). Key improvements in Version 2 over its predecessor, CanHomPmlyV1 (Wang et al., 2023), include: • Expanded and improved source datasets, incorporating adjusted data from automated gauge stations and the Collaborative Rain, Hail and Snow (CoCoRaHS) network (https://www.cocorahs.org/canada.aspx) for recent decades. • Additional quality control procedures to remove false zeros in station data records for data-sparse regions or periods. • Enhanced changepoint detection, including tests on both untransformed and log-transformed data, and improved methods for identifying station-joining and variance inhomogeneities. • Use of more complete metadata for better accuracy. • Improved adjustment procedures to eliminate inhomogeneity by adjusting untransformed data series. • Development of homogenized daily precipitation series consistent with corresponding monthly totals (Wang & Feng, 2026). Key processing steps for both V1 and V2 include: • Merging observations from nearby sites to create long records, primarily using updated Adjusted Daily Rainfall and Snowfall data corrected for known issues such as unrealistic snow-water equivalent conversion, gauge wetting loss, and wind-induced undercatch (Wang et al., 2017). • Infilling data gaps using advanced spatial modeling of available data for the gap period. • Comprehensive quality control of data (Cheng et al., 2024). • Detecting non-climatic shifts using homogeneity tests with reference and station metadata. • Using up to four best neighbor stations as references, along with data derived from advanced spatial modeling of adjusted precipitation (MacDonald et al., 2021) and the Twentieth Century Reanalysis (20CRv3) ensemble-mean series of monthly precipitation (Slivinski et al., 2019). • Applying Quantile-Matching (QM) adjustments without reference data to correct non-climatic changes (Wang et al., 2026; Wang et al., 2023; Wang et al., 2010). Wang et al. (2023) also developed a dynamic QM adjustment method that uses the most strongly correlated homogeneous segment of nearby station data as a reference. They found that when station density is too low to identify suitable reference stations, adjustments without a reference could perform better than those with one, although results are similar for most stations. This approach may not be applicable to precipitation datasets with much higher station density or to variables with greater spatial coherence (e.g., surface air temperature). The QM method adjusts the entire distribution of data in one segment to match another (Wang et al., 2026; Wang et al., 2023; Wang & Feng, 2013; Wang et al., 2010), ensuring that distribution changes—including variance shifts—at identified changepoints are homogenized. Differences from AHCCD adjusted precipitation data • CanHomP V2: Adjusted, gap-filled, homogenized monthly and daily precipitation data for 425 core stations for the period up to 2023. • AHCCD adjusted precipitation: Adjusted but unhomogenized monthly and daily precipitation data for 464 manual stations for the period up to 2012 (Mekis & Vincent, 2011). References Wang, X. L., Feng, Y., Zwiers, F. W., & Cheng, V. Y. S. (2026). Precipitation trends in version 2 of Canadian homogenized monthly precipitation dataset. Atmosphere-Ocean, 1-16. https://doi.org/10.1080/07055900.2026.2617861. Wang, X. L., & Feng, Y. (2026). Observed trends in precipitation extreme indices as inferred from a homogenized daily precipitation dataset for Canada. Weather and Climate Extremes, 51, 100860. https://authors.elsevier.com/sd/article/S2212-0947(26)00011-3. Wang, X.L, Y. Feng, V. Y. S. Cheng, H. Xu, 2023: Observed precipitation trends inferred from Canada’s homogenized monthly precipitation dataset, J. Clim., 36, 7957-7971. DOI: 10.1175/JCLI-D-23-0193.1. Wang, X. L., H. Xu, B. Qian, Y. Feng, E. Mekis, 2017: The adjusted daily rainfall and snowfall data for Canada. Atmos.-Ocean, 55:3, 155-168, DOI:10.1080/07055900.2017.1342163. Cheng, V. Y. S., Wang, X.L., and Y. Feng, 2024: A quality control system for historical in situ precipitation data. Atmosphere-Ocean, 62(4), 271-287, https://doi.org/10.1080/07055900.2024.2394836. Wang, X. L. and Y. Feng, published online July 2013: RHtestsV4 User Manual. Climate Research Division, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment Canada. 28 pp. [Available online at https://github.com/ECCC-CDAS] DOI: 10.13140/RG.2.2.17309.17125. Wang, X. L., H. Chen, Y. Wu, Y. Feng, and Q. Pu, 2010: New techniques for detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteor. Climatol., 49, 2416-2436. DOI: 10.1175/2010JAMC2376.1. MacDonald, H., D. W. McKenney, X. L. Wang, J. Pedlar, P. Papadopol, K. Lawrence, M. F. Hutchinson, 2021: Spatial Models of adjusted precipitation for Canada at varying time scales. J. Appl. Meteor. And Climatol., 60, 291-304. DOI: 10.1175/JAMC-D-20-0041.1. Slivinski, L. and coauthors, 2019: Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q. J. R. Meteor. Soc., 2876-2908, https://doi.org/10.1002/qj.3598. Mekis, E., & Vincent, L. A. (2011). An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmosphere-Ocean, 49, 163–177. https://doi.org/10.1080/07055900.2011.583910.