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Estimating Summer Nutrient Concentrations in Northeastern Lakes from SPARROW Load Predictions and Modeled Lake Depth and Volume

Overview of attention for article published in PLOS ONE, November 2013
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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1 blog
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Title
Estimating Summer Nutrient Concentrations in Northeastern Lakes from SPARROW Load Predictions and Modeled Lake Depth and Volume
Published in
PLOS ONE, November 2013
DOI 10.1371/journal.pone.0081457
Pubmed ID
Authors

W. Bryan Milstead, Jeffrey W. Hollister, Richard B. Moore, Henry A. Walker

Abstract

Global nutrient cycles have been altered by the use of fossil fuels and fertilizers resulting in increases in nutrient loads to aquatic systems. In the United States, excess nutrients have been repeatedly reported as the primary cause of lake water quality impairments. Setting nutrient criteria that are protective of a lakes ecological condition is one common solution; however, the data required to do this are not always easily available. A useful solution for this is to combine available field data (i.e., The United States Environmental Protection Agency (USEPA) National Lake Assessment (NLA)) with average annual nutrient load models (i.e., USGS SPARROW model) to estimate summer concentrations across a large number of lakes. In this paper we use this combined approach and compare the observed total nitrogen (TN) and total phosphorus (TN) concentrations in Northeastern lakes from the 2007 National Lake Assessment to those predicted by the Northeast SPARROW model. We successfully adjusted the SPARROW predictions to the NLA observations with the use of Vollenweider equations, simple input-output models that predict nutrient concentrations in lakes based on nutrient loads and hydraulic residence time. This allows us to better predict summer concentrations of TN and TP in Northeastern lakes and ponds. On average we improved our predicted concentrations of TN and TP with Vollenweider models by 18.7% for nitrogen and 19.0% for phosphorus. These improved predictions are being used in other studies to model ecosystem services (e.g., aesthetics) and dis-services (e.g. cyanobacterial blooms) for ~18,000 lakes in the Northeastern United States.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 44 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 27%
Student > Ph. D. Student 11 24%
Other 4 9%
Student > Master 4 9%
Student > Bachelor 3 7%
Other 3 7%
Unknown 8 18%
Readers by discipline Count As %
Environmental Science 14 31%
Agricultural and Biological Sciences 7 16%
Engineering 4 9%
Earth and Planetary Sciences 3 7%
Psychology 2 4%
Other 3 7%
Unknown 12 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 01 December 2016.
All research outputs
#3,072,869
of 22,731,677 outputs
Outputs from PLOS ONE
#40,116
of 194,033 outputs
Outputs of similar age
#36,868
of 302,097 outputs
Outputs of similar age from PLOS ONE
#978
of 5,172 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 194,033 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one has done well, scoring higher than 79% 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 302,097 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 87% of its contemporaries.
We're also able to compare this research output to 5,172 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.