66 research outputs found
TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications
Get PDFMarine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of timescales. Numerous studies have digitized termini manually, but this process is labor intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and synthetic aperture radar (SAR) satellite sensors. These techniques rely on high-quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39 060 individual terminus traces for 278 glaciers with a mean of 136 ± 190 and median of 93 of traces per glacier. Across all glaciers, 32 567 dates have been digitized, of which 4467 have traces from more than one author, and there is a duplication rate of 17 %. We find a median error of ∼ 100 m among manually traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of the Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet
Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos
Full text linkA surrogate model approximates a computationally expensive solver. Polynomial
Chaos is a method to construct surrogate models by summing combinations of
carefully chosen polynomials. The polynomials are chosen to respect the
probability distributions of the uncertain input variables (parameters); this
allows for both uncertainty quantification and global sensitivity analysis.
In this paper we apply these techniques to a commercial solver for the
estimation of peak gas rate and cumulative gas extraction from a coal seam gas
well. The polynomial expansion is shown to honour the underlying geophysics
with low error when compared to a much more complex and computationally slower
commercial solver. We make use of advanced numerical integration techniques to
achieve this accuracy using relatively small amounts of training data
Seasonal dynamic thinning at Helheim Glacier
Get PDFAbstractWe investigate three annual mass-balance cycles on Helheim Glacier in south-east Greenland using TanDEM-X interferometric digital elevation models (DEMs), bedrock GPS measurements, and ice velocity from feature-tracking. The DEMs exhibit seasonal surface elevation cycles at elevations up to 800 m.a.s.l. with amplitudes of up to 19 m, from a maximum in July to a minimum in October or November, concentrated on the fast-flowing areas of the glacier indicating that the elevation changes have a mostly dynamic origin. By modelling the detrended bedrock loading/unloading signal we estimate a mean density for the loss of 671±70 kgm−3 and calculate that total water equivalent volume loss from the active part of the glacier (surface flow speeds >1 m day−1) ranges from 0.5 km3 in 2011 to 1.6 km3 in 2013. A rough ice-flux divergence analysis shows that at lower elevations (<200 m) mass loss by dynamic thinning fully explains seasonal elevation changes. In addition, surface elevations decrease by a greater amount than field observations of surface ablation or surface-energy-balance modelling predict, emphasising the dynamic nature of the mass loss. We conclude, on the basis of ice-front position observations through the time series, that melt-induced acceleration is most likely the main driver of the seasonal dynamic thinning, as opposed to changes triggered by retreat
Positive mass balance during the late 20th century on Austfonna, Svalbard, revealed using satellite radar interferometry
Get PDFBrief communication: Thwaites Glacier cavity evolution
Get PDFBetween 2014 and 2017, ocean melt eroded a large cavity beneath and along the western margin of the fast-flowing core of Thwaites Glacier. Here we show that from2017 to the end of 2020 the cavity persisted but did not ex-pand. This behaviour, of melt concentrated at the groundingline within confined sub-shelf cavities, fits with prior observations and modelling studies. We also show that acceleration and thinning of Thwaites Glacier grounded ice continued, with an increase in speed of 400 m a−1and a thinning rate of at least 1.5 m a−1, between 2012 and 2020
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