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Evaluation of Bias Associated with Capture Maps Derived from Nonlinear Groundwater Flow Models

Overview of attention for article published in Ground Water, September 2017
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4 tweeters
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1 Facebook page

Citations

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4 Dimensions

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12 Mendeley
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Title
Evaluation of Bias Associated with Capture Maps Derived from Nonlinear Groundwater Flow Models
Published in
Ground Water, September 2017
DOI 10.1111/gwat.12597
Pubmed ID
Authors

Cara Nadler, Kip Allander, Greg Pohll, Eric Morway, Ramon Naranjo, Justin Huntington

Abstract

The impact of groundwater withdrawal on surface water is a concern of water users and water managers, particularly in the arid western United States. Capture maps are useful tools to spatially assess the impact of groundwater pumping on water sources (e.g., streamflow depletion) and are being used more frequently for conjunctive management of surface water and groundwater. Capture maps have been derived using linear groundwater flow models and rely on the principle of superposition to demonstrate the effects of pumping in various locations on resources of interest. However, nonlinear models are often necessary to simulate head-dependent boundary conditions and unconfined aquifers. Capture maps developed using nonlinear models with the principle of superposition may over- or underestimate capture magnitude and spatial extent. This paper presents new methods for generating capture difference maps, which assess spatial effects of model nonlinearity on capture fraction sensitivity to pumping rate, and for calculating the bias associated with capture maps. The sensitivity of capture map bias to selected parameters related to model design and conceptualization for the arid western United States is explored. This study finds that the simulation of stream continuity, pumping rates, stream incision, well proximity to capture sources, aquifer hydraulic conductivity, and groundwater evapotranspiration extinction depth substantially affect capture map bias. Capture difference maps demonstrate that regions with large capture fraction differences are indicative of greater potential capture map bias. Understanding both spatial and temporal bias in capture maps derived from nonlinear groundwater flow models improves their utility and defensibility as conjunctive-use management tools.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 42%
Student > Ph. D. Student 2 17%
Student > Master 1 8%
Student > Doctoral Student 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 2 17%
Readers by discipline Count As %
Engineering 4 33%
Earth and Planetary Sciences 4 33%
Agricultural and Biological Sciences 1 8%
Environmental Science 1 8%
Unknown 2 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 29 September 2017.
All research outputs
#3,223,412
of 12,304,228 outputs
Outputs from Ground Water
#111
of 552 outputs
Outputs of similar age
#85,266
of 273,303 outputs
Outputs of similar age from Ground Water
#4
of 7 outputs
Altmetric has tracked 12,304,228 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 552 research outputs from this source. They receive a mean Attention Score of 3.8. 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 273,303 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.