scholarly journals The subglacial landscape and hydrology of Antarctica mapped from space

2021 ◽  
Author(s):  
Wei Leong

<p><b>To narrow uncertainties in the Antarctic ice sheet's contribution to sea level rise, we present a collection of novel machine learning and automated satellite remote sensing methods which use ice surface observations to infer the subglacial nature of Antarctica. A super-resolution deep neural network called DeepBedMap was designed and trained to produce a high-resolution (250 m) bed elevation model of Antarctica called DeepBedMap_DEM that preserves bed roughness details useful for catchment- to continent-scale ice sheet modelling. This DeepBedMap_DEM is compared with a smoother, medium-resolution (500 m) BedMachine topography in a basal inversion experiment over Pine Island Glacier, with results motivating more research into the interacting roles of subglacial hydrology which influences skin drag and high resolution bed topographies which influences form drag. Active subglacial lakes in Antarctica were mapped using an unsupervised density-based classification method on ICESat-2 point cloud data from 2018-2020, yielding 194 active subglacial lakes, including 36 new lakes in the 86-88°S area not detected by the previous ICESat (2003-2009) mission. This thesis showcases both the rich diversity in subglacial landscapes and the dynamic nature of subglacial hydrology in Antarctica, forming a foundation enabling the accurate modelling of overland ice flow in critical regions of the vulnerable West Antarctic Ice Sheet.</b></p> <p>Plain language summaryAntarctica has a lot of ice, but we're unsure how fast ice can slide into the sea and cause water to go up in beaches around the world. So we teach computers to solve hard math problems that tell us how fast sea water might go up. These computers are fed with lots of pictures taken from cameras up in the sky and space. Ice sits on top of rock in Antarctica, and with practice, the computers get pretty good at telling us how high and bumpy the rock is. The rock under the ice appears quite bumpy, and ice probably doesn't like sliding over bumpy rocks since it's rough. Sometimes though, ice may not mind sliding over rough bits of rock if the rock moves along with it, or if water gets in between the rock and ice to makes things slippery, but we ask our smart computers to be sure. There are also lasers from space shooting down at earth and bouncing back to tell us how ice in Antarctica is going up or down. Once in a while, they tell us that ice in parts of Antarctica moved up or down a bit too fast. Smart people think these are lakes hiding under the ice, filling up with water or draining, and we found many of these lakes over Antarctica, especially in an area called Whillans Ice Stream on the Siple Coast. We hope that the computers can keep learning faster because there's a lot of pictures showing ice moving pretty fast, and it doesn't look like there's much time before a big chunk of ice might break away in Antarctica and flood beaches around the world.</p> <p>Code availabilityPython code for reproducing the methods in this thesis are publicly available at https://github.com/weiji14/deepbedmap for Chapter 2 (DeepBedMap), https://github.com/weiji14/pyissm for Chapter 3 (Basal inversion); and https://github.com/weiji14/deepicedrain for Chapter 4 (ICESat-2 subglacial lakes).</p>

2021 ◽  
Author(s):  
Wei Leong

<p><b>To narrow uncertainties in the Antarctic ice sheet's contribution to sea level rise, we present a collection of novel machine learning and automated satellite remote sensing methods which use ice surface observations to infer the subglacial nature of Antarctica. A super-resolution deep neural network called DeepBedMap was designed and trained to produce a high-resolution (250 m) bed elevation model of Antarctica called DeepBedMap_DEM that preserves bed roughness details useful for catchment- to continent-scale ice sheet modelling. This DeepBedMap_DEM is compared with a smoother, medium-resolution (500 m) BedMachine topography in a basal inversion experiment over Pine Island Glacier, with results motivating more research into the interacting roles of subglacial hydrology which influences skin drag and high resolution bed topographies which influences form drag. Active subglacial lakes in Antarctica were mapped using an unsupervised density-based classification method on ICESat-2 point cloud data from 2018-2020, yielding 194 active subglacial lakes, including 36 new lakes in the 86-88°S area not detected by the previous ICESat (2003-2009) mission. This thesis showcases both the rich diversity in subglacial landscapes and the dynamic nature of subglacial hydrology in Antarctica, forming a foundation enabling the accurate modelling of overland ice flow in critical regions of the vulnerable West Antarctic Ice Sheet.</b></p> <p>Plain language summaryAntarctica has a lot of ice, but we're unsure how fast ice can slide into the sea and cause water to go up in beaches around the world. So we teach computers to solve hard math problems that tell us how fast sea water might go up. These computers are fed with lots of pictures taken from cameras up in the sky and space. Ice sits on top of rock in Antarctica, and with practice, the computers get pretty good at telling us how high and bumpy the rock is. The rock under the ice appears quite bumpy, and ice probably doesn't like sliding over bumpy rocks since it's rough. Sometimes though, ice may not mind sliding over rough bits of rock if the rock moves along with it, or if water gets in between the rock and ice to makes things slippery, but we ask our smart computers to be sure. There are also lasers from space shooting down at earth and bouncing back to tell us how ice in Antarctica is going up or down. Once in a while, they tell us that ice in parts of Antarctica moved up or down a bit too fast. Smart people think these are lakes hiding under the ice, filling up with water or draining, and we found many of these lakes over Antarctica, especially in an area called Whillans Ice Stream on the Siple Coast. We hope that the computers can keep learning faster because there's a lot of pictures showing ice moving pretty fast, and it doesn't look like there's much time before a big chunk of ice might break away in Antarctica and flood beaches around the world.</p> <p>Code availabilityPython code for reproducing the methods in this thesis are publicly available at https://github.com/weiji14/deepbedmap for Chapter 2 (DeepBedMap), https://github.com/weiji14/pyissm for Chapter 3 (Basal inversion); and https://github.com/weiji14/deepicedrain for Chapter 4 (ICESat-2 subglacial lakes).</p>


2012 ◽  
Vol 26 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jinho Ahn ◽  
Edward J. Brook ◽  
Logan Mitchell ◽  
Julia Rosen ◽  
Joseph R. McConnell ◽  
...  

1983 ◽  
Vol 4 ◽  
pp. 302-302 ◽  
Author(s):  
D. R. MacAyeal

A collapse of the West Antarctic ice sheet due to warming by atmospheric carbon dioxide is a potential threat to mankind because low-lying land would be flooded by rising sea-level. Intervention would be possible by creating one or several artifical ice rises on the floating ice shelves surrounding the West Antarctic ice sheet. Ice rises are places where the ice shelf has run aground locally on the sea bed. Existing ice rises obstruct ice flow and are responsible for maintaining the ice sheet in its present stable condition. An artificial ice rise could be created by (1) drilling a hole through the ice shelf in a choice position such as over a sea-bed ridge, (2) pumping large volumes of sea-water from beneath the ice shelf so as to flood the surface, and (3) continuing to pump until the frozen sea-water has thickened the ice shelf by 100 m over an area of 100 km2. Approximately 1.6 × 106 kW of power would be required to accomplish the task in ten years. This would cost approximately 20 × 109 US dollars.


2017 ◽  
Vol 59 (76pt1) ◽  
pp. 29-41 ◽  
Author(s):  
Jan T. M. Lenaerts ◽  
Stefan R. M. Ligtenberg ◽  
Brooke Medley ◽  
Willem Jan Van de Berg ◽  
Hannes Konrad ◽  
...  

ABSTRACTWest Antarctic climate and surface mass balance (SMB) records are sparse. To fill this gap, regional atmospheric climate modelling is useful, providing that such models are employed at sufficiently high horizontal resolution and coupled with a snow model. Here we present the results of a high-resolution (5.5 km) regional atmospheric climate model (RACMO2) simulation of coastal West Antarctica for the period 1979–2015. We evaluate the results with available in situ weather observations, remote-sensing estimates of surface melt, and SMB estimates derived from radar and firn cores. Moreover, results are compared with those from a lower-resolution version, to assess the added value of the resolution. The high-resolution model resolves small-scale climate variability invoked by topography, such as the relatively warm conditions over ice-shelf grounding zones, and local wind speed accelerations. Surface melt and SMB are well reproduced by RACMO2. This dataset will prove useful for picking ice core locations, converting elevation changes to mass changes, for driving ocean, ice-sheet and coupled models, and for attributing changes in the West Antarctic Ice Sheet and shelves to changes in atmospheric forcing.


1983 ◽  
Vol 4 ◽  
pp. 302 ◽  
Author(s):  
D. R. MacAyeal

A collapse of the West Antarctic ice sheet due to warming by atmospheric carbon dioxide is a potential threat to mankind because low-lying land would be flooded by rising sea-level. Intervention would be possible by creating one or several artifical ice rises on the floating ice shelves surrounding the West Antarctic ice sheet. Ice rises are places where the ice shelf has run aground locally on the sea bed. Existing ice rises obstruct ice flow and are responsible for maintaining the ice sheet in its present stable condition. An artificial ice rise could be created by (1) drilling a hole through the ice shelf in a choice position such as over a sea-bed ridge, (2) pumping large volumes of sea-water from beneath the ice shelf so as to flood the surface, and (3) continuing to pump until the frozen sea-water has thickened the ice shelf by 100 m over an area of 100 km2. Approximately 1.6 × 106 kW of power would be required to accomplish the task in ten years. This would cost approximately 20 × 109 US dollars.


2020 ◽  
Author(s):  
Wei Ji Leong ◽  
Huw Joseph Horgan

Abstract. To better resolve the bed elevation of Antarctica, we present DeepBedMap – a novel machine learning method that produces realistic Antarctic bed topography from multiple remote sensing data inputs. Our super-resolution deep convolutional neural network model is trained on scattered regions in Antarctica where high resolution (250 m) groundtruth bed elevation grids are available. The model is then used to generate high resolution bed topography in less well surveyed areas. DeepBedMap improves on previous interpolation methods by not restricting itself to a low spatial resolution (1000 m) BEDMAP2 raster image as its prior. It takes in additional high spatial resolution datasets, such as ice surface elevation, velocity and snow accumulation to better inform the bed topography even in the absence of ice-thickness data from direct ice-penetrating radar surveys. Our DeepBedMap model is based on an adapted Enhanced Super Resolution Generative Adversarial Network architecture, chosen to minimize per-pixel elevation errors while producing realistic topography. The final product is a four times upsampled (250 m) bed elevation model of Antarctica that can be used by glaciologists interested in the subglacial terrain, and by ice sheet modellers wanting to run catchment or continent-scale ice sheet model simulations. We show that DeepBedMap offers a more realistic topographic roughness profile compared to a standard bicubic interpolated BEDMAP2 and BedMachine Antarctica, and envision it to be used where a high resolution bed elevation model is required.


2020 ◽  
Vol 14 (11) ◽  
pp. 3687-3705
Author(s):  
Wei Ji Leong ◽  
Huw Joseph Horgan

Abstract. To resolve the bed elevation of Antarctica, we present DeepBedMap – a novel machine learning method that can produce Antarctic bed topography with adequate surface roughness from multiple remote sensing data inputs. The super-resolution deep convolutional neural network model is trained on scattered regions in Antarctica where high-resolution (250 m) ground-truth bed elevation grids are available. This model is then used to generate high-resolution bed topography in less surveyed areas. DeepBedMap improves on previous interpolation methods by not restricting itself to a low-spatial-resolution (1000 m) BEDMAP2 raster image as its prior image. It takes in additional high-spatial-resolution datasets, such as ice surface elevation, velocity and snow accumulation, to better inform the bed topography even in the absence of ice thickness data from direct ice-penetrating-radar surveys. The DeepBedMap model is based on an adapted architecture of the Enhanced Super-Resolution Generative Adversarial Network, chosen to minimize per-pixel elevation errors while producing realistic topography. The final product is a four-times-upsampled (250 m) bed elevation model of Antarctica that can be used by glaciologists interested in the subglacial terrain and by ice sheet modellers wanting to run catchment- or continent-scale ice sheet model simulations. We show that DeepBedMap offers a rougher topographic profile compared to the standard bicubically interpolated BEDMAP2 and BedMachine Antarctica and envision it being used where a high-resolution bed elevation model is required.


2020 ◽  
Author(s):  
George Malczyk ◽  
Daniel Goldberg ◽  
Noel Gourmelen ◽  
Jan Wuite ◽  
Thomas Nagler

&lt;p&gt;Active subglacial lakes have been identified throughout Antarctica, offering a window into subglacial environments and into controls on ice dynamics. Between June 2013 and January 2014 a system of connected subglacial lakes drained in unison under the Thwaites glacier in the West Antarctic ice sheet, the first time that such a system has been observed in the Amundsen Sea Sector. Estimates based on catchment scale melt production suggested that lake drainages of this type should occur every 20 to 80 years. We collected elevations from January 2011 to December 2019 over the Thwaites lake region using the CryoSat-2 swath interferometric mode and ICEsat-2 land ice elevations, as well as ice velocity from the Sentinel-1 SAR mission since 2014. Using various elevation time series approaches, we obtain time dependent elevations over each lake. Results indicate that the upstream lakes undertake a second episode of drainage during mid-2017, only 3 years after the previous event, and that a new lake drained. Unlike the 2013-2014 episode, this new drainage episode contributed to filling one of the downstream lake with no evidence of further downstream activity. This new sub-glacial lake activity under Thwaites offer the possibility to explore lake connectivity, subglacial melt production and the interaction with ice dynamics.&lt;/p&gt;


2018 ◽  
Vol 16 (1) ◽  
pp. 112-119
Author(s):  
VLADIMIR GLEB NAYDONOV

The article considers the students’ tolerance as a spectrum of personal manifestations of respect, acceptance and correct understanding of the rich diversity of cultures of the world, values of others’ personality. The purpose of the study is to investgate education and the formation of tolerance among the students. We have compiled a training program to improve the level of tolerance for interethnic differences. Based on the statistical analysis of the data obtained, the most important values that are significant for different levels of tolerance were identified.


2017 ◽  
Author(s):  
Gabin Archambault

This 5 km resolution grid presents groundwater storage in Africa (in mm). This parameter was estimated by combining the saturated aquifer thickness and effective porosity of aquifers across Africa. For each aquifer flow/storage type an effective porosity range was assigned based on a series of studies across Africa and surrogates in other parts of the world. Groundwater storage is given in millimeters. Detailed description of the methodology, and a full list of data sources used to develop the layer can be found in the peer-reviewed paper available here: http://iopscience.iop.org/article/10.1088/1748-9326/7/2/024009/pdf The raster and a high resolution PDF file are available for download on the website of British Geological Survey (BGS): http://www.bgs.ac.uk/research/groundwater/international/africanGroundwater/mapsDownload.html Groundwater Storage


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