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Scripting MODFLOW Model Development Using Python and FloPy

Overview of attention for article published in Ground Water, March 2016
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2 X users

Citations

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239 Dimensions

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389 Mendeley
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Title
Scripting MODFLOW Model Development Using Python and FloPy
Published in
Ground Water, March 2016
DOI 10.1111/gwat.12413
Pubmed ID
Authors

M Bakker, V Post, C D Langevin, J D Hughes, J T White, J J Starn, M N Fienen

Abstract

Graphical user interfaces (GUIs) are commonly used to construct and postprocess numerical groundwater flow and transport models. Scripting model development with the programming language Python is presented here as an alternative approach. One advantage of Python is that there are many packages available to facilitate the model development process, including packages for plotting, array manipulation, optimization, and data analysis. For MODFLOW-based models, the FloPy package was developed by the authors to construct model input files, run the model, and read and plot simulation results. Use of Python with the available scientific packages and FloPy facilitates data exploration, alternative model evaluations, and model analyses that can be difficult to perform with GUIs. Furthermore, Python scripts are a complete, transparent, and repeatable record of the modeling process. The approach is introduced with a simple FloPy example to create and postprocess a MODFLOW model. A more complicated capture-fraction analysis with a real-world model is presented to demonstrate the types of analyses that can be performed using Python and FloPy.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 389 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
Denmark 1 <1%
Canada 1 <1%
Unknown 386 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 87 22%
Student > Master 63 16%
Researcher 48 12%
Other 23 6%
Student > Bachelor 23 6%
Other 47 12%
Unknown 98 25%
Readers by discipline Count As %
Earth and Planetary Sciences 114 29%
Engineering 67 17%
Environmental Science 65 17%
Agricultural and Biological Sciences 10 3%
Computer Science 8 2%
Other 8 2%
Unknown 117 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 19 April 2022.
All research outputs
#16,147,353
of 24,561,012 outputs
Outputs from Ground Water
#730
of 883 outputs
Outputs of similar age
#176,300
of 305,671 outputs
Outputs of similar age from Ground Water
#5
of 8 outputs
Altmetric has tracked 24,561,012 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 883 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 16th percentile – i.e., 16% 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 305,671 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 8 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.