Significant contribution of small icebergs to the freshwater budget in Greenland fjords

被引:14
|
作者
Rezvanbehbahani, Soroush [1 ,2 ]
Stearns, Leigh A. [1 ,2 ]
Keramati, Ramtin [3 ]
Shankar, Siddharth [1 ,2 ]
van der Veen, C. J. [4 ]
机构
[1] Univ Kansas, Dept Geol, Lawrence, KS 66045 USA
[2] Univ Kansas, Ctr Remote Sensing Ice Sheets, Lawrence, KS 66045 USA
[3] Stanford Univ, Inst Computat & Math Engn, Stanford, CA 94305 USA
[4] Univ Kansas, Dept Geog & Atmospher Sci, Lawrence, KS 66045 USA
来源
COMMUNICATIONS EARTH & ENVIRONMENT | 2020年 / 1卷 / 01期
关键词
SIZE DISTRIBUTIONS; MODEL;
D O I
10.1038/s43247-020-00032-3
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Icebergs represent nearly half of the mass loss from the Greenland Ice Sheet and provide a distributed source of freshwater along fjords which can alter fjord circulation, nutrient levels, and ultimately the Meridional Overturning Circulation. Here we present analyses of high resolution optical satellite imagery using convolutional neural networks to accurately delineate iceberg edges in two East Greenland fjords. We find that a significant portion of icebergs in fjords are comprised of small icebergs that were not detected in previously-available coarser resolution satellite images. We show that the preponderance of small icebergs results in high freshwater delivery, as well as a short life span of icebergs in fjords. We conclude that an inability to identify small icebergs leads to inaccurate frequency-size distribution of icebergs in Greenland fjords, an underestimation of iceberg area (specifically for small icebergs), and an overestimation of iceberg life span. Small icebergs comprise a significant fraction of icebergs in Greenland fjords and have fallen below the detection and resolution limit of earlier satellite data, suggest analyses of high-resolution imagery with deep neural networks.
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页数:7
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