CFPy-A Python Package for Pre- and Postprocessing of the Conduit Flow Process of MODFLOW
The conduit flow process (CFP) for MODFLOW's groundwater flow model is an advanced approach for investigating complex groundwater systems, such as karst, with coupled discrete-continuum models. CFP represents laminar and turbulent flow in a discrete pipe network coupled to a matrix continuum. H...
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Published in | Ground water Vol. 61; no. 6; pp. 887 - 894 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
United States
Ground Water Publishing Company
01.11.2023
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Subjects | |
Online Access | Get full text |
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Summary: | The conduit flow process (CFP) for MODFLOW's groundwater flow model is an advanced approach for investigating complex groundwater systems, such as karst, with coupled discrete-continuum models. CFP represents laminar and turbulent flow in a discrete pipe network coupled to a matrix continuum. However, the preprocessing demand is comparatively high to generate the conduit network and is usually performed with graphical user interfaces. To overcome this limitation and allow a scalable, reproducible, and comprehensive workflow, existing and new routines were aggregated to a Python package named CFPy, to allow script-based modeling that harmonizes well with the available and widely used FloPy package. CFPy allows information about the location and geometry of the conduit network to be considered by user-specific approaches or by sophisticated methods such as stochastic conduit network generators. The latter allows the automatic generation of many model variants with differing conduit networks for advanced investigations like multi-model approaches in combination with automatic parameter estimation. Additional postprocessing routines provide powerful control and valuable insights for CFP applications. In this methods note, a general technical description of the approach is complemented with two examples that guide users and demonstrate the main capabilities of CFPy. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0017-467X 1745-6584 |
DOI: | 10.1111/gwat.13331 |