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Unmanned aerial vehicle structure from motion and lidar data for sub-canopy snow depth mapping
Federated Research Data Repository / dépôt fédéré de données de recherche
Harder, Phillip; Pomeroy, John; Helgason, Warren — 2024-03-27 Unmanned Aerial Vehicles (UAV) have had recent widespread application to capture high resolution information on snow processes and the data herein was collected to address the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV Structure from Motion (SfM) and airborne-lidar have focused on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds, measure returns from a wide range of scan angles, and so have a greater likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV-lidar and UAV-SfM in mapping snow depth in both open and forested terrain was tested with data collected in a 2019 field campaign in the Canadian Rockies Hydrological Observatory, Alberta and at Canadian Prairie sites near Saskatoon, Saskatchewan, Canada. The data archived here comprises the raw point clouds from the UAV-SfM and UAV-lidar platforms, generated digital surface models, and survey data used for accuracy assessment for the field campaign in question as reported in Harder et al., 2019. This dataset was generated by the work of the Smart Water Systems Laboratory within the Centre for Hydrology at the University of Saskatchewan. This contributes to the objectives of a number of Pillar 3 Global Water Futures projects including Mountain Water Futures and the Transformative Technology and Smart Watersheds. https://creativecommons.org/licenses/by/4.0/
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Hydrometeorological data from Marmot Creek Research Basin, Canadian Rockies
Federated Research Data Repository / dépôt fédéré de données de recherche
Fang, Xing; Pomeroy, John; DeBeer, Chris; Harder, Phillip; Siemens, Evan — 2025-01-23 Meteorological, snow survey, streamflow, and groundwater data are presented from Marmot Creek Research Basin, Alberta, Canada. The basin is a 9.4 km2, alpine-montane forest headwater catchment of the Saskatchewan River Basin that provides vital water supplies to the Prairie Provinces of Canada. It was heavily instrumented, experimented upon and operated by several federal government agencies between 1962 and 1986, during which time its main and sub-basin streams were gauged, automated meteorological stations at multiple elevations were installed, groundwater observation wells were dug and automated, and frequent manual measurements of snow accumulation and ablation and other weather and water variables were made. Over this period, mature evergreen forests were harvested in two sub-basins, leaving large clear-cuts in one basin and a “honeycomb” of small forest clearings in another basin. Whilst meteorological measurements and sub-basin streamflow discharge weirs in the basin were removed in the late 1980s, the federal government maintained the outlet streamflow discharge measurements and a nearby high elevation meteorological station, and the Alberta provincial government maintained observation wells and a nearby fire weather station. Marmot Creek Research Basin was intensively re-instrumented with 12 automated meteorological stations, four sub-basin hydrometric sites and seven snow survey transects starting in 2004 by the University of Saskatchewan Centre for Hydrology. The observations provide detailed information on meteorology, precipitation, soil moisture, snowpack, streamflow, and groundwater during the historical period from 1962 to 1987 and the modern period from 2005 to the present time. These data are ideal for monitoring climate change, developing hydrological process understanding, evaluating process algorithms and hydrological, cryospheric or atmospheric models, and examining the response of basin hydrological cycling to changes in climate, extreme weather, and land cover through hydrological modelling and statistical analyses. **Please note: This dataset is linked to an ESSD paper at https://doi.org/10.5194/essd-11-455-2019.  The authors kindly request that you reference this paper in addition to the dataset. https://creativecommons.org/licenses/by/4.0/
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Water-Energy-Crop Observations (2015-2021) in Saskatchewan, Canada
Federated Research Data Repository / dépôt fédéré de données de recherche
Harder, Phillip; Helgason, Warren; Johnson, Bruce; Pomeroy, John W. — 2024-07-22 Beginning in 2015, field observations were initiated to quantify crop growth and land-atmosphere energy and water exchanges over agricultural surfaces in the semi-arid south central Saskatchewan prairies. Early research focussed on sites in the Brightwater Creek Research Basin (BWC) near Kenaston, 80 km south of Saskatoon, Saskatchewan (Tetlock et al., 2019). Additional sites were instrumented in 2018 at the University of Saskatchewan’s Livestock and Forage Centre for Excellence (LFCE) near Clavet, 30 km southeast of Saskatoon, Saskatchewan. Cumulatively this data collection effort comprises 17 site years for 7 different crop types. This dataset is comprised of micrometeorological, energy balance (radiation and turbulent fluxes), precipitation, soil moisture and crop growth observations aggregated to the daily scale. Stations were located within agricultural fields to satisfy the homogeneity assumptions required for eddy covariance observations and span the interval between planting and harvest. Data gapfilling and QA/QC procedures are described in the associated submitted manuscript. https://creativecommons.org/licenses/by/4.0/
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UAV-borne gamma spectrometry and lidar observations of prairie snow water equivalent
Federated Research Data Repository / dépôt fédéré de données de recherche
Harder, Phillip; Helgason, Warren; Pomeroy, John W — 2024-03-27 Gamma and lidar observations from a UAV were collected over two seasons from shallow, wind-blown, prairie snowpacks in Saskatchewan, Canada with validation data collected from manual snow depth and density observations. The data included herein includes the snow survey point observations, the gamma spectrometer count rates, and raster’s of snow depth (at 0.25m resolution) and snow water equivalent from combining lidar snow depth and manual snow density observations at 0.25 and 22.5m resolutions, and snow water equivalent directly from gamma observations at 22.5m. This dataset provides the means to test the ability of UAV-gamma spectroscopy to resolve the areal average and spatial variability of snow water equivalent directly. Data processing and analysis is described in the associated submitted manuscript: Harder, P., Helgason, W.D., Pomeroy, J.W. 2023. Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar. Submitted to The Cryosphere November 2023. https://creativecommons.org/licenses/by/4.0/

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(Author: Harder, Phillip)

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