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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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1 tweeter

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148 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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
Canada 1 <1%
Unknown 146 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 32%
Student > Master 28 19%
Researcher 22 15%
Unspecified 17 11%
Other 10 7%
Other 24 16%
Readers by discipline Count As %
Earth and Planetary Sciences 53 36%
Environmental Science 34 23%
Unspecified 29 20%
Engineering 22 15%
Agricultural and Biological Sciences 4 3%
Other 6 4%

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 12 March 2018.
All research outputs
#10,105,602
of 12,633,553 outputs
Outputs from Ground Water
#504
of 561 outputs
Outputs of similar age
#255,092
of 370,810 outputs
Outputs of similar age from Ground Water
#8
of 12 outputs
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