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    The integrated pan-Arctic snow melt onset dataset was generated by combining active and passive microwave satellite derived estimates for northern high latitude land surface, ice caps, large lakes, and sea ice (Wang et al., 2011). This dataset allows snow melt dynamics to be examined in a full pan-Arctic context and enables exploration of interactions between the terrestrial and marine components of the cryosphere. It provides a unique dataset for validating snow cover simulations from regional or global climate models during the spring transition period when snow cover exerts the strongest feedback to the climate system.

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    A high-resolution digital elevation model (DEM) is key to assessing habitat conditions, surface water connectivity, developing hydraulic models, and environmental change in the Peace-Athabasca Delta (PAD), Northern Alberta. Note that in this instance, DEM is a representation of the ground surface elevations and is thus considered synonymous with a digital terrain model. As part of the Oil Sands Monitoring (OSM) Program, the PAD DEM v1 was derived solely from airborne LiDAR surveys carried out since 2000 through multiple projects with different partners and collaborators, with the bulk of the area surveyed in 2019. Hence, the vertical accuracy varies spatially depending on the technology/method used in individual aerial surveys, as well as if a pixel was filled in via interpolation due to a lack of LiDAR ground data returns in very dense deltaic floodplain vegetation. Furthermore, it does not include traditional ground-based elevation survey data and bathymetric elevations, nor surface water extents for the multiple lakes, wetlands and channels – addition of these data will form part of a planned future version of this DEM product. Given that the DEM has yet to be tested and is a work in progress, the quality, accuracy or completeness is not warranted. The PAD DEM v1 is provided as a 4 m horizontal resolution product that is projected to the UTM NAD83 (CSRS) Zone 12 coordinate system and is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013) Epoch 2010.

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    The Hudson Bay Lowlands (HBL) is the wettest ecozone in Canada with 80% of its area covered by wetlands. It forms the third largest wetland in the world and is composed almost entirely of permafrost and non-frozen subarctic peatlands that store more carbon in the first 2 m of soil than the total carbon stored in any other ecozone in Canada. The HBL is also home to large mammals including polar bears, caribou, moose, and provides important breeding habitat for migratory birds. Surface water dynamics in the HBL are both a consequence and a driver of climate change, impacting evapotranspiration, permafrost thaw and carbon budgets under wetting / drying conditions as well as wildlife habitat. Previously, dynamic surface water products were generated from historical Landsat data to inform surface water trends in the HBL based on binary classifications of land versus water at 30 m spatial resolution (Olthof and Rainville, 2022). However, the HBL contains many water features smaller than 30 m, including streams and patterned fens that require a sub-pixel mapping approach to depict these small features as the percent water fraction within each pixel footprint. The annual surface water products in this dataset were created by leveraging an existing binary dynamic surface water product (Olthof and Rainville, 2022) to implement adaptive physical linear spectral unmixing models. The result is a spatially and temporally comprehensive Landsat sub-30m surface water time-series over the HBL from 1985 to 2021 that can be used to help researchers and policymakers address issues around climate change and wildlife. Following the Government of Canada Open Data initiative, these original dynamic surface water maps are available to the public. More information on the creation of this dataset can be found in the associated research paper at https://www.sciencedirect.com/science/article/pii/S0034425723004467?ref=pdf_download&fr=RR-2&rr=84402791cff87154.