Self-Guided Decision Support Groundwater Modelling with Python

The GMDSI tutorial notebooks repository provides learners with a comprehensive set of tutorials for self-guided training on decision-support groundwater modelling using Python-based tools.Although targeted at groundwater modelling, the tutorials are based around model-agnostic tools and readily transferable to other environmental modelling workflows.The tutorials are divided into three parts.The first covers fundamental theoretical concepts.These are intended as background reading for reference on an asneeded basis.Tutorials in the second part introduce learners to some of the core concepts of parameter estimation in a groundwater modelling context, as well as provide a gentle introduction to the PEST, PEST++ and pyemu software.Lastly, the third part demonstrates how to implement highly parameterized applied decision-support modelling workflows.The tutorials aim to provide examples of both "how to use" the software as well as "how to think" about using the software.A key advantage to using notebooks in this context is that the workflows described run the same code as practitioners would run on a large-scale real-world application.Using a small synthetic model facilitates rapid progression through the workflow.

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Publication Details

Journal
Journal of Open Source Education
Published
2024-12-24
DOI
https://doi.org/10.21105/jose.00240
Primary Topic
Groundwater flow and contamination studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Self-Guided Decision Support Groundwater Modelling with Python

Katherine H. Markovich, Brioch Hemmings, Jeremy T. White, Michael N. Fienen et al.
Journal of Open Source Education
Groundwater flow and contamination studies
article

Self-Guided Decision Support Groundwater Modelling with Python

Katherine H. Markovich, Brioch Hemmings, Jeremy T. White, Michael N. Fienen, Rui Hugman
article en

Abstract

The GMDSI tutorial notebooks repository provides learners with a comprehensive set of tutorials for self-guided training on decision-support groundwater modelling using Python-based tools.Although targeted at groundwater modelling, the tutorials are based around model-agnostic tools and readily transferable to other environmental modelling workflows.The tutorials are divided into three parts.The first covers fundamental theoretical concepts.These are intended as background reading for reference on an asneeded basis.Tutorials in the second part introduce learners to some of the core concepts of parameter estimation in a groundwater modelling context, as well as provide a gentle introduction to the PEST, PEST++ and pyemu software.Lastly, the third part demonstrates how to implement highly parameterized applied decision-support modelling workflows.The tutorials aim to provide examples of both "how to use" the software as well as "how to think" about using the software.A key advantage to using notebooks in this context is that the workflows described run the same code as practitioners would run on a large-scale real-world application.Using a small synthetic model facilitates rapid progression through the workflow.

Journal of Open Source EducationVol. 7(82)
GNS Science (NZ), United States Geological Survey (US)
U.S. Geological Survey
Clean water and sanitation
Openalex Percentile: Top 79%
Groundwater flow and contamination studies
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