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The steep and unstable terrain found on debris-covered glaciers, rock glaciers, talus slopes, moraines and other proglacial features often make terrestrial ground-penetrating radar (GPR) surveys unsafe or cost-prohibitive. To address these challenges, this research introduces a novel approach for studying buried ice using multi-low-frequency drone-based GPR. Monostatic antennas of 50, 100, and 200 MHz were flown along a transect spanning a debris-covered glacier and an ice–debris complex at Shár Shaw Tagà (Grizzly Creek) in southwest Yukon, Canada. The drone-based results were compared to manual GPR at two locations along the transect. The two manual segments were conducted using the same radar system in a bi-static mode and included common mid-point (CMP) surveys. Overall, the drone-based radar successfully identified buried ice and enabled estimation of ice body thickness. Notably, CMP results confirmed layer characteristics and enabled depths to be measured across the entire drone-based transect. Discrimination of detail across a range of depths was made possible by comparing the three low frequencies, highlighting the possibility of using this method for future investigations of debris thickness in addition to quantifying buried ice. This study confirms the effectiveness of drone-based GPR combined with manual CMP for surveying ice beneath previously inaccessible terrain. [ABSTRACT FROM AUTHOR]

Debris-covered glaciers are an important component of the mountain cryosphere and influence the hydrological contribution of glacierized basins to downstream rivers. This study examines the potential to make estimates of debris thickness, a critical variable to calculate the sub-debris melt, using ground-based thermal infrared radiometry (TIR) images. Over four days in August 2019, a ground-based, time-lapse TIR digital imaging radiometer recorded sequential thermal imagery of a debris-covered region of Peyto Glacier, Canadian Rockies, in conjunction with 44 manual excavations of debris thickness ranging from 10 to 110 cm, and concurrent meteorological observations. Inferring the correlation between measured debris thickness and TIR surface temperature as a base, the effectiveness of linear and exponential regression models for debris thickness estimation from surface temperature was explored. Optimal model performance (R 2 of 0.7, RMSE of 10.3 cm) was obtained with a linear model applied to measurements taken on clear nights just before sunrise, but strong model performances were also obtained under complete cloud cover during daytime or nighttime with an exponential model. This work presents insights into the use of surface temperature and TIR observations to estimate debris thickness and gain knowledge of the state of debris-covered glacial ice and its potential hydrological contribution. [ABSTRACT FROM AUTHOR]

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Soil freeze-thaw processes play a fundamental role in the hydrology, geomorphology, ecology, thermodynamics and soil chemistry of a cold-regions landscape. In understanding these processes, the temperature of the soil is used as a proxy to represent the soil ice content through a soil freezing characteristic curve (SFCC). This mathematical construct relates the soil ice content to a specific temperature for a particular soil. SFCCs depend on many factors including soil properties (e.g., porosity, composition, etc.), soil pore water pressure, dissolved salts, (hysteresis in) freezing/thawing point depression, and degree of saturation, all of which can be site-specific and time varying. SFCCs have been measured using various methods for diverse soils since 1921, and to date this data has not been broadly compared, in part because it has not previously been compiled in a single data set. The dataset presented in this publication includes SFCC data digitized or received from authors, and includes both historic and modern studies. The data is stored in an open-source repository, and an R package is available to facilitate its use. Aggregating the data has pointed out some data gaps, namely that there are few studies of coarse soils, and comparably few in-situ measurements of SFCCs in mountainous environments. It is hoped that this dataset will aid in the development of SFCC theory, and improve SFCC approximations in soil freeze-thaw modelling activities. [ABSTRACT FROM AUTHOR]