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Direct measurements of meltwater runoff on the Greenland ice sheet surface

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, December 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
9 news outlets
blogs
8 blogs
twitter
138 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
60 Mendeley
citeulike
1 CiteULike
Title
Direct measurements of meltwater runoff on the Greenland ice sheet surface
Published in
Proceedings of the National Academy of Sciences of the United States of America, December 2017
DOI 10.1073/pnas.1707743114
Pubmed ID
Authors

Laurence C. Smith, Kang Yang, Lincoln H Pitcher, Brandon T. Overstreet, Vena W. Chu, Åsa K. Rennermalm, Jonathan C. Ryan, Matthew G. Cooper, Colin J. Gleason, Marco Tedesco, Jeyavinoth Jeyaratnam, Dirk van As, Michiel R. van den Broeke, Willem Jan van de Berg, Brice Noël, Peter L. Langen, Richard I. Cullather, Bin Zhao, Michael J. Willis, Alun Hubbard, Jason E. Box, Brittany A. Jenner, Alberto E. Behar

Abstract

Meltwater runoff from the Greenland ice sheet surface influences surface mass balance (SMB), ice dynamics, and global sea level rise, but is estimated with climate models and thus difficult to validate. We present a way to measure ice surface runoff directly, from hourly in situ supraglacial river discharge measurements and simultaneous high-resolution satellite/drone remote sensing of upstream fluvial catchment area. A first 72-h trial for a 63.1-km2 moulin-terminating internally drained catchment (IDC) on Greenland's midelevation (1,207-1,381 m above sea level) ablation zone is compared with melt and runoff simulations from HIRHAM5, MAR3.6, RACMO2.3, MERRA-2, and SEB climate/SMB models. Current models cannot reproduce peak discharges or timing of runoff entering moulins but are improved using synthetic unit hydrograph (SUH) theory. Retroactive SUH applications to two older field studies reproduce their findings, signifying that remotely sensed IDC area, shape, and supraglacial river length are useful for predicting delays in peak runoff delivery to moulins. Applying SUH to HIRHAM5, MAR3.6, and RACMO2.3 gridded melt products for 799 surrounding IDCs suggests their terminal moulins receive lower peak discharges, less diurnal variability, and asynchronous runoff timing relative to climate/SMB model output alone. Conversely, large IDCs produce high moulin discharges, even at high elevations where melt rates are low. During this particular field experiment, models overestimated runoff by +21 to +58%, linked to overestimated surface ablation and possible meltwater retention in bare, porous, low-density ice. Direct measurements of ice surface runoff will improve climate/SMB models, and incorporating remotely sensed IDCs will aid coupling of SMB with ice dynamics and subglacial systems.

Twitter Demographics

The data shown below were collected from the profiles of 138 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 43%
Researcher 9 15%
Student > Bachelor 6 10%
Student > Master 4 7%
Unspecified 4 7%
Other 11 18%
Readers by discipline Count As %
Earth and Planetary Sciences 38 63%
Unspecified 10 17%
Environmental Science 6 10%
Agricultural and Biological Sciences 1 2%
Computer Science 1 2%
Other 4 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 216. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 June 2018.
All research outputs
#50,769
of 12,381,579 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#1,271
of 77,415 outputs
Outputs of similar age
#2,951
of 353,338 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#55
of 943 outputs
Altmetric has tracked 12,381,579 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 77,415 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.1. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 353,338 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 943 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.