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Precipitation uncertainty estimation and rainfall-runoff model calibration using iterative ensemble smoothers

Overview of attention for article published in Advances in Water Resources, April 2024
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  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
1 X user

Readers on

mendeley
6 Mendeley
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Title
Precipitation uncertainty estimation and rainfall-runoff model calibration using iterative ensemble smoothers
Published in
Advances in Water Resources, April 2024
DOI 10.1016/j.advwatres.2024.104658
Authors

Davide Zoccatelli, Daniel B. Wright, Jeremy T. White, Michael N. Fienen, Guo Yu

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 3 50%
Researcher 2 33%
Student > Master 1 17%
Readers by discipline Count As %
Unspecified 3 50%
Environmental Science 1 17%
Engineering 1 17%
Unknown 1 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 February 2024.
All research outputs
#17,292,294
of 25,382,440 outputs
Outputs from Advances in Water Resources
#570
of 876 outputs
Outputs of similar age
#18,541
of 37,708 outputs
Outputs of similar age from Advances in Water Resources
#2
of 2 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 876 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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 37,708 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.