Ts’udé Nilįné Tuyeta Established Protected Area

Established Protected and Conservation Areas in the NWT

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

The Canadian National Wetlands Inventory (CNWI) is a comprehensive, publicly available national geodatabase developed by the Canadian Wildlife Service (CWS) of Environment and Climate Change Canada (ECCC), in collaboration with federal, provincial, and territorial governments, academia, Indigenous groups, and Non-Governmental Organizations (NGOs). It consists of the best available wetland mapping data, along with its metadata, published in a standardized manner. The CNWI is continuously updated through the compilation of existing data and the acquisition of new high-resolution datasets to address coverage gaps, with an emphasis on peatlands and coastal wetlands, which are key habitats for greenhouse gas (GHG) sequestration. ECCC plans to use the CNWI to train and validate machine-learning algorithms to delineate and classify wetlands at a national scale and to measure trends over time. This will directly support Canada’s Nature-Based Climate Solutions by informing biodiversity conservation, guiding climate change mitigation and adaptation strategies, and supporting GHG emissions reporting. The CNWI was initially released in February 2024 with 13 source datasets. In June 2025, the Inventory was updated to include 14 additional datasets. Collectively, these 27 source datasets comprise approximately 12.1 million wetland polygon features, covering a total area of roughly 640,000 square kilometers across ten provinces and territories (BC, MB, NB, NL, NS, PE, ON, QC, SK, YT). These source datasets were cross-walked into a standardized CNWI classification schema, which is based on two foundational documents: the Canadian Wetland Classification System (National Wetlands Working Group, 1997) and the Canadian Wetland Inventory Data Model (2016). The CNWI Schema contains five major wetland classes (Bog, Fen, Swamp, Marsh, and Shallow/Open Water) and eight subclasses (Rich Fen, Poor Fen, Organic Swamp, Mineral Swamp, Organic Marsh, Mineral Marsh, Shallow Water, and Open Water). Non-conforming wetlands can be categorized into three groups: Peatland, Mixed, and Unclassified. For more information on the CNWI and the related database, please refer to the CNWI User Manual and other supporting documents that accompany this publication. The User Manual provides detailed information on how data are collected, managed, and distributed to meet CNWI data standards.

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.

Canadian Homogenized Surface Air Temperature – Version 3.1 (CanHomT V3.1) This dataset is an update of the Third Generation Homogenized Temperature dataset (Vincent et al. (2020), extended through 2023. It was developed for climate trend analysis and includes long-term daily maximum, minimum, and mean temperature series for 780 stations (776 locations) in Canada. The key enhancement in V3.1 is the inclusion of both Original and Adjusted data, together with their associated data flags and the source station climate ID, providing improved traceability, reproducibility, and transparency. Key processing steps include: • Merging observations from nearby sites to create long records • Quality control of data • Adjusting daily minimum temperatures since July 1961 for the observing-time (climatological day definition) change at 96 principal stations (Vincent et al., 2009) • Detecting non-climatic shifts in annual and seasonal average temperature data series using homogeneity tests with reference and station metadata. • Applying Quantile-Matching (QM) adjustments with reference data (including parallel observations) to correct non-climatic changes (Vincent et al., 2018; Wang et al., 2010). Parallel observations —simultaneous measurements from old and new instruments or locations—were used whenever available because they provide the most reliable basis for data homogenization. Adjustments made without suitable reference data carry greater uncertainty, and this increased uncertainty should be quantified and clearly communicated (WMO, 2020). The QM method adjusts the entire distribution of data in one segment to match another (Wang et al., 2026; Vincent et al., 2018; Wang & Feng, 2013; Wang et al. 2010), ensuring that distribution changes—including variance shifts—at identified changepoints are homogenized. The gridded version of monthly CanHomT V3.1, called CanGridT mlyV3.1 [https://open.canada.ca/data/en/dataset/781e02cc-6c1b-462e-b61b-f96c607b23bd], was found to represent Canada’s warming trend reasonably well since 1900, despite changes in data availability over time (Wang et al., 2026, see their Figure S3). References Vincent, L.A., M.M. Hartwell and X.L. Wang, 2020: A Third Generation of Homogenized Temperature for Trend Analysis and Monitoring Changes in Canada’s Climate. Atmosphere-Ocean, 58(3), 173–191. https://doi.org/10.1080/07055900.2020.1765728. Vincent, L.A., E.J. Milewska, R. Hopkinson and L. Malone, 2009: Bias in minimum temperature introduced by a redefinition of the climatological day at the Canadian synoptic stations. J. Appl. Meteor. Climatol, 48, 2160-2168. DOI: 10.1175/2009JAMC2191.1. Vincent, L.A., E.J. Milewska, X. L. Wang, and M. M. Hartwell, 2018. Uncertainty in homogenized daily temperatures and derived indices of extremes illustrated using parallel observations in Canada, Intl. J. Climatol., 38:2, 692-707. DOI: 10.1002/JOC.5203. 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. WMO. (2020). Guidelines on Homogenization (2020 edition). World Meteorological Organization. WMO-No. 1245. https://library.wmo.int/records/item/57130-guidelines-on-homogenization?offset=1. Wang, X. L., Feng, Y., Zwiers, F. W., & Cheng, V. Y. S. (2026). Precipitation trends in version 2 of the Canadian homogenized monthly precipitation dataset. Atmosphere-Ocean, 1–16, https://doi.org/10.1080/07055900.2026.2617861.