The dataset is a compilation of the boundaries of the 19 NWT Electoral Districts based upon the 2012 Electoral Boundary Commission and the legal descriptions found in Bill 18 of Fifth Session, Seventeenth Legislative Assembly plus any Polling Divisions within each Electoral District as determined by Elections NWT.

Important Wildlife Areas In The NWT

Prospecting Permits

The Thaidene Nëné (thy-Den-ay nen-ay) area is a celebrated cultural landscape with rich wildlife populations and unique geography located at the eastern end of Great Slave Lake in the Northwest Territories. The initial Thaidene Nëné study area was approximately 33,690 km2. Thaidene Nëné means ‘Land of the Ancestors’ in the Dënesųłı̨né language.

Mining Leases

Coal Exploration Licences

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.

Mineral Claims

Department of ENR/ITI Administrative Boundaries

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.