scholarly journals Controls on short-term variations in Greenland glacier dynamics

2013 ◽  
Vol 59 (217) ◽  
pp. 883-892 ◽  
Author(s):  
A.V. Sundal ◽  
A. Shepherd ◽  
M. van den Broeke ◽  
J. Van Angelen ◽  
N. Gourmelen ◽  
...  

AbstractShort-term ice-dynamical processes at Greenland’s Jakobshavn and Kangerdlugssuaq glaciers were studied using a 3 day time series of synthetic aperture radar data acquired during the 2011 European Remote-sensing Satellite-2 (ERS-2) 3 day repeat campaign together with modelled meteorological parameters. The time series spans the period March–July 2011 and captures the first ∼30% of the summer melting season. In both study areas, we observe velocity fluctuations at the lower ∼10 km of the glacier. At Jakobshavn Isbræ, where our dataset covers the first part of the seasonal calving-front retreat, we identify ten calving episodes, with a mean calving-front area loss of 1.29 ± 0.4 km2. Significant glacier speed-up was observed in the near-terminus area following all calving episodes. We identify changes in calving-front geometry as the dominant control on velocity fluctuations on both glaciers, apart from a <15% early-summer speed-up at Kangerdlugssuaq Glacier during a period of calving-front advance, which we attribute to enhanced surface melt-induced basal lubrication. Our 3 day velocity maps show new spatial characteristics of the ice melange flow variability in the Jakobshavn and Kangerdlugssuaq fjord systems, which are primarily controlled by calving-front dynamics and fjord geometry.

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1849 ◽  
Author(s):  
Mahmood Mahmoodian ◽  
Jairo Arturo Torres-Matallana ◽  
Ulrich Leopold ◽  
Georges Schutz ◽  
Francois H. L. R. Clemens

In this study, applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a dynamic surrogate model for a computationally expensive urban drainage simulator is investigated. Considering rainfall time series as the main driving force is a challenge in this regard due to the high dimensionality problem. However, this problem can be less relevant when the focus is only on short-term simulations. The novelty of this research is the consideration of short-term rainfall time series as training parameters for the GPE. Rainfall intensity at each time step is counted as a separate parameter. A method to generate synthetic rainfall events for GPE training purposes is introduced as well. Here, an emulator is developed to predict the upcoming daily time series of the total wastewater volume in a storage tank and the corresponding Combined Sewer Overflow (CSO) volume. Nash-Sutcliffe Efficiency (NSE) and Volumetric Efficiency (VE) are calculated as emulation error indicators. For the case study herein, the emulator is able to speed up the simulations up to 380 times with a low accuracy cost for prediction of the total storage tank volume (medians of NSE = 0.96 and VE = 0.87). CSO events occurrence is detected in 82% of the cases, although with some considerable accuracy cost (medians of NSE = 0.76 and VE = 0.5). Applicability of the emulator for consecutive short-term simulations, based on real observed rainfall time series is also validated with a high accuracy (NSE = 0.97, VE = 0.89).


2010 ◽  
Vol 56 (197) ◽  
pp. 415-430 ◽  
Author(s):  
Ian Joughin ◽  
Ben E. Smith ◽  
Ian M. Howat ◽  
Ted Scambos ◽  
Twila Moon

AbstractUsing RADARSAT synthetic aperture radar data, we have mapped the flow velocity over much of the Greenland ice sheet for the winters of 2000/01 and 2005/06. These maps provide a detailed view of the ice-sheet flow, including that of the hundreds of glaciers draining the interior. The focused patterns of flow at the coast suggest a strong influence of bedrock topography. Differences between our two maps confirm numerous early observations of accelerated outlet glacier flow as well as revealing previously unrecognized changes. The overall pattern is one of speed-up accompanied by terminus retreat, but there are also several instances of surge behavior and a few cases of glacier slowdown. Comprehensive mappings such as these, at regular intervals, provide an important new observational capability for understanding ice-sheet variability.


2021 ◽  
Author(s):  
Virginie Pinel ◽  
François Beauducel ◽  
Raditya Putra ◽  
Sulis Sulistiyani ◽  
Gusti Made Agung Nandaka ◽  
...  

&lt;p&gt;Despite the well-established interest of Synthetic Aperture Radar data for volcanoes study and monitoring, their integration to operational monitoring activities in volcanoes observatories remains limited so far. We here describe the effort in progress to integrate in near real time the information derived from Sentinel-1 satellites into the monitoring devices at BBPTKG in charge of Merapi volcano survey as well as the use of Sentinel-1 data during the recent period of &amp;#160;unrest. Merapi (7&amp;#176;32.5&amp;#8217; S and 110&amp;#176;26.5&amp;#8217; E) located in the densely populated Province of Yogyakarta in Central Java is one of the most active volcanoes in Indonesia. The eruptive history of Merapi is characterized by two eruptive styles: 1) recurrent effusive growth of viscous lava domes, with gravitational collapses producing pyroclastic flows known as &amp;#171; Merapi-type nu&amp;#233;es ardentes &amp;#187; (VEI 2); 2) more exceptional explosive eruptions of relatively large size (VEI 3-4), associated with column collapse pyroclastic flows reaching distances larger than 15 km from the summit. The eruptive periodicity is 4 to 5 years for the effusive events and 50 to 100 years for the explosive ones. The last explosive events (VEI 3-4) occurred in November 2010 and was followed by a period of limited activity. In August 2018, a new dome was observed inside the summit crater, thus marking the start of a new phase of effusive activity. A new period of unrest then started in mid-October 2020, characterized by an increase in seismic activity as well as large and localized displacements in the summit area. Magma finally reached the surface on 4&lt;sup&gt; &amp;#160;&lt;/sup&gt;January 2021. Deformation is currently recorded by EDM and tiltmeters together with a network of 10 permanent GNSS stations. GNSS data are automatically processed and inverted for a pressure source at depth. Both displacement time series as well as spatial probability distribution are directly available through WebObs (Beauducel et al., Frontiers, 2020), an integrated web-based system for monitoring. Sentinel-1 data are acquired over the volcano every 12 days on descending track 76 and every 6 days on ascending track 127. Since mid 2017, Sentinel-1 data are automatically downloaded on a local server at BPPTKG. Interferograms and coherence images are then produced using the NSBAS processing chain (Doin et al, 2012) and automatically integrated to WebObs to enable detection of potential rapid and significant changes in signal. Mean velocity maps are also produced as well as time series of surface displacement at given location enabling direct comparison with GNSS measurements. The descending InSAR time series shows a strong displacement away from the satellite in a 1.5 km wide area located on the north-eastern part &amp;#160;of the crater. This signal became significant in September 2020. It is consistent with field measurements recorded and allows to map the affected area. In mid-November 2020, Sentinel-1 data thus provided the first information on the spatial extent of the ongoing surface displacements, which was useful for crisis management.&lt;/p&gt;


2015 ◽  
Vol 14 (4) ◽  
pp. 463-472 ◽  
Author(s):  
Kei Oyoshi ◽  
Nobuhiro Tomiyama ◽  
Toshio Okumura ◽  
Shinichi Sobue ◽  
Jun Sato

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