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Estimating restorable wetland water storage at landscape scales

Overview of attention for article published in Hydrological Processes, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

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13 tweeters

Citations

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19 Dimensions

Readers on

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64 Mendeley
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Title
Estimating restorable wetland water storage at landscape scales
Published in
Hydrological Processes, December 2017
DOI 10.1002/hyp.11405
Pubmed ID
Authors

Charles Nathan Jones, Grey R. Evenson, Daniel L. McLaughlin, Melanie K. Vanderhoof, Megan W. Lang, Greg W. McCarty, Heather E. Golden, Charles R. Lane, Laurie C. Alexander

Abstract

Globally, hydrologic modifications such as ditching and subsurface drainage have significantly reduced wetland water storage capacity (i.e., volume of surface water a wetland can retain) and consequent wetland functions. While wetland area has been well documented across many landscapes and used to guide restoration efforts, few studies have directly quantified the associated wetland storage capacity. Here, we present a novel raster-based approach to quantify both contemporary and potential (i.e., restorable) storage capacities of individual depressional basins across landscapes. We demonstrate the utility of this method by applying it to the Delmarva Peninsula, a region punctuated by both depressional wetlands and drainage ditches. Across the entire peninsula, we estimated that restoration (i.e., plugging ditches) could increase storage capacity by 80%. Focusing on an individual watershed, we found that over 59% of restorable storage capacity occurs within 20 m of the drainage network, and that 93% occurs within 1 m elevation of the drainage network. Our demonstration highlights widespread ditching in this landscape, spatial patterns of both contemporary and potential storage capacities, and clear opportunities for hydrologic restoration. In Delmarva and more broadly, our novel approach can inform targeted landscape-scale conservation and restoration efforts to optimize hydrologically mediated wetland functions.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 25%
Student > Master 11 17%
Student > Ph. D. Student 7 11%
Student > Bachelor 6 9%
Student > Doctoral Student 5 8%
Other 6 9%
Unknown 13 20%
Readers by discipline Count As %
Environmental Science 20 31%
Earth and Planetary Sciences 8 13%
Agricultural and Biological Sciences 8 13%
Engineering 5 8%
Social Sciences 2 3%
Other 2 3%
Unknown 19 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 18 February 2021.
All research outputs
#2,664,345
of 17,067,437 outputs
Outputs from Hydrological Processes
#169
of 1,576 outputs
Outputs of similar age
#80,922
of 418,249 outputs
Outputs of similar age from Hydrological Processes
#6
of 40 outputs
Altmetric has tracked 17,067,437 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,576 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 89% 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 418,249 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.