diff --git a/jose.00240/10.21105.jose.00240.crossref.xml b/jose.00240/10.21105.jose.00240.crossref.xml
new file mode 100644
index 0000000..f2ea584
--- /dev/null
+++ b/jose.00240/10.21105.jose.00240.crossref.xml
@@ -0,0 +1,195 @@
+
+
+
+ 20241224182406-5f77e1707cc0f9610f8d11180b9b39ade2e29e17
+ 20241224182406
+
+ JOSS Admin
+ admin@theoj.org
+
+ The Open Journal
+
+
+
+
+ Journal of Open Source Education
+ JOSE
+ 2577-3569
+
+ 10.21105/jose
+ https://jose.theoj.org
+
+
+
+
+ 12
+ 2024
+
+
+ 7
+
+ 82
+
+
+
+ Self-Guided Decision Support Groundwater Modelling with Python
+
+
+
+ Rui T.
+ Hugman
+
+ INTERA Geosciences, Perth, Western Australia, Australia
+
+ https://orcid.org/0000-0003-0891-3886
+
+
+ Jeremy T.
+ White
+
+ INTERA Geosciences, Perth, Western Australia, Australia
+
+ https://orcid.org/0000-0002-4950-1469
+
+
+ Michael N.
+ Fienen
+
+ U.S. Geological Survey, Upper Midwest Water Science Center, Madison, WI USA
+
+ https://orcid.org/0000-0002-7756-4651
+
+
+ Brioch
+ Hemmings
+
+ Wairakei Research Centre, GNS Science, Taupō, New Zealand
+
+ https://orcid.org/0000-0001-6311-8450
+
+
+ Katherine H.
+ Markovich
+
+ INTERA Geosciences, Perth, Western Australia, Australia
+
+ https://orcid.org/0000-0002-4455-8255
+
+
+
+ 12
+ 24
+ 2024
+
+
+ 240
+
+
+ 10.21105/jose.00240
+
+
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+
+
+
+ Software archive
+ 10.5281/zenodo.13933751
+
+
+ GitHub review issue
+ https://github.com/openjournals/jose-reviews/issues/240
+
+
+
+ 10.21105/jose.00240
+ https://jose.theoj.org/papers/10.21105/jose.00240
+
+
+ https://jose.theoj.org/papers/10.21105/jose.00240.pdf
+
+
+
+
+
+ Approaches to highly parameterized inversion: PEST++ version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis
+ White
+ 10.3133/tm7C26
+ 2020
+ White, J. T., Hunt, R. J., Fienen, M. N., & Doherty, J. E. (2020). Approaches to highly parameterized inversion: PEST++ version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis. US Geological Survey Techniques; Methods 7-C26. https://doi.org/10.3133/tm7C26
+
+
+ A python framework for environmental model uncertainty analysis
+ White
+ Environmental Modelling & Software
+ 85
+ 10.1016/j.envsoft.2016.08.017
+ 2016
+ White, J. T., Fienen, M. N., & Doherty, J. E. (2016). A python framework for environmental model uncertainty analysis. Environmental Modelling & Software, 85, 217–228. https://doi.org/10.1016/j.envsoft.2016.08.017
+
+
+ A python framework for environmental model uncertainty analysis
+ White
+ Environmental Modelling & Software
+ 85
+ 10.1016/j.envsoft.2016.08.017
+ 2016
+ White, J. T., Fienen, M. N., & Doherty, J. E. (2016). A python framework for environmental model uncertainty analysis. Environmental Modelling & Software, 85, 217–228. https://doi.org/10.1016/j.envsoft.2016.08.017
+
+
+ Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses
+ White
+ Environmental Modelling & Software
+ 139
+ 10.1016/j.envsoft.2021.105022
+ 2021
+ White, J. T., Hemmings, B., Fienen, M. N., & Knowling, M. J. (2021). Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses. Environmental Modelling & Software, 139, 105022. https://doi.org/10.1016/j.envsoft.2021.105022
+
+
+ MODFLOW 6 Modular Hydrologic Model
+ Langevin
+ 10.5066/F76Q1VQV
+ 2022
+ Langevin, C. D., Hughes, J. D., Provost, A. M., Russcher, M., Niswonger, R. G., Panday, S., Merrick, D., Morway, E. D., Reno, M. J., Bonelli, W. P., & Banta, E. R. (2022). MODFLOW 6 Modular Hydrologic Model (Version 6.4.1). https://doi.org/10.5066/F76Q1VQV
+
+
+ PEST and Its Utility Support Software
+ Doherty
+ 2015
+ Doherty, J. (2015). PEST and Its Utility Support Software. https://pesthomepage.org/
+
+
+ Scripting MODFLOW model development using python and FloPy
+ Bakker
+ Groundwater
+ 5
+ 54
+ 10.1111/gwat.12413
+ 2016
+ Bakker, M., Post, V., Langevin, C. D., Hughes, J. D., White, J. T., Starn, J. J., & Fienen, M. N. (2016). Scripting MODFLOW model development using python and FloPy. Groundwater, 54(5), 733–739. https://doi.org/10.1111/gwat.12413
+
+
+ AN EXERCISE IN GROUND-WATER MODEL CALIBRATION AND PREDICTION
+ Freyberg
+ Groundwater
+ 3
+ 26
+ 10.1111/j.1745-6584.1988.tb00399.x
+ 1988
+ Freyberg, D. L. (1988). AN EXERCISE IN GROUND-WATER MODEL CALIBRATION AND PREDICTION. Groundwater, 26(3), 350–360. https://doi.org/10.1111/j.1745-6584.1988.tb00399.x
+
+
+
+
+
+
diff --git a/jose.00240/10.21105.jose.00240.pdf b/jose.00240/10.21105.jose.00240.pdf
new file mode 100644
index 0000000..7306ed7
Binary files /dev/null and b/jose.00240/10.21105.jose.00240.pdf differ
diff --git a/jose.00240/paper.jats/10.21105.jose.00240.jats b/jose.00240/paper.jats/10.21105.jose.00240.jats
new file mode 100644
index 0000000..a3de9ec
--- /dev/null
+++ b/jose.00240/paper.jats/10.21105.jose.00240.jats
@@ -0,0 +1,541 @@
+
+
+
+
+
+
+
+Journal of Open Source Education
+JOSE
+
+2577-3569
+
+Open Journals
+
+
+
+240
+10.21105/jose.00240
+
+Self-Guided Decision Support Groundwater Modelling with
+Python
+
+
+
+https://orcid.org/0000-0003-0891-3886
+
+Hugman
+Rui T.
+
+
+
+
+https://orcid.org/0000-0002-4950-1469
+
+White
+Jeremy T.
+
+
+
+
+https://orcid.org/0000-0002-7756-4651
+
+Fienen
+Michael N.
+
+
+
+
+https://orcid.org/0000-0001-6311-8450
+
+Hemmings
+Brioch
+
+
+
+
+https://orcid.org/0000-0002-4455-8255
+
+Markovich
+Katherine H.
+
+
+
+
+
+INTERA Geosciences, Perth, Western Australia,
+Australia
+
+
+
+
+U.S. Geological Survey, Upper Midwest Water Science Center,
+Madison, WI USA
+
+
+
+
+Wairakei Research Centre, GNS Science, Taupō, New
+Zealand
+
+
+
+
+20
+9
+2023
+
+7
+82
+240
+
+Authors of papers retain copyright and release the
+work under a Creative Commons Attribution 4.0 International License (CC
+BY 4.0)
+2024
+The article authors
+
+Authors of papers retain copyright and release the work under
+a Creative Commons Attribution 4.0 International License (CC BY
+4.0)
+
+
+
+Python
+groundwater modelling
+environmental modelling
+decision-support
+uncertainty analysis
+
+
+
+
+
+ Summary
+
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 as-needed
+ 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.
+
+
+ Story of the Project
+
The Groundwater Modelling Decision Support Initiative
+ (GMDSI)
+ is an industry-backed and industry-aligned initiative. Established in
+ mid-2019, its primary goal is to enhance the role of groundwater
+ modelling in groundwater management, regulatory processes, and
+ decision-making. GMDSI promotes the improved use of modelling in
+ decision support, with activities focused on industry engagement,
+ education, practical examples, research, and software development. It
+ also emphasizes the importance of tools for uncertainty quantification
+ (UQ) and parameter estimation (PE) in these processes.
+
The roots of the materials making up the tutorial notebooks were
+ from a traditional, week-long classroom course curriculum developed
+ for internal training at the US Geological Survey (USGS) by a subset
+ of the authors of this paper. After three iterations of teaching the
+ in-person class, the authors, with support from the GMDSI, endeavored
+ to build on the positive aspects of using Jupyter Notebooks and
+ explore alternative teaching environments. The first major change was
+ to add sufficient narration and explanation to the notebooks to
+ improve possibilities for self-study. The next change was to
+ reorganize the content from a strictly linear progression to a
+ three-part structure. This led to a hybrid model of self-study
+ accompanied by discussion and background lectures online.
+
+
+ Statement of Need
+
Many groundwater modelers typically rely on Graphical User
+ Interfaces (GUIs) for their modelling needs. However, each GUI has its
+ unique characteristics and varying degrees of compatibility with
+ external software like PEST
+ (Doherty,
+ 2015) and PEST++
+ (Jeremy
+ T. White et al., 2020). Creating educational materials for
+ these GUIs would necessitate tailoring content to each GUI’s specific
+ features, obtaining cooperation from the GUI developers themselves and
+ potentially lagging behind the latest developments. Many GUIs are
+ commercial products as well which limits accessibility.
+
Decision-support modelling often demands capabilities that surpass
+ what current GUIs can offer. Thus, the use of Python for environmental
+ modelling has increased in recent years, due to its open-source
+ nature, user-friendly syntax, and extensive scientific libraries.
+ Python-based tools have been developed to facilitate UQ and PE
+ analyses, such as pyemu
+ (Jeremy
+ T. White et al., 2016b;
+ Jeremy
+ T. White et al., 2021). pyemu is a Python package that provides
+ a framework for implementing UQ and PE analyses with PEST and PEST++.
+ It offers a range of capabilities, including parameter estimation,
+ uncertainty analysis, and management optimization. Although initially
+ designed for groundwater modelling, pyemu’s methodologies are
+ versatile and can be applied to diverse numerical environmental
+ models, Anecdotally, we have seen that more modelers are turning to
+ Python packages like FloPy
+ (Bakker
+ et al., 2016) and pyemu
+ (Jeremy
+ T. White et al., 2016a) for model and PEST++ setup.
+ Unfortunately, the adoption of this approach is hindered by a steep
+ learning curve primarily due to the scarcity of user-friendly training
+ materials.
+
The GMDSI tutorial notebooks aim to address this gap by providing a
+ comprehensive, self-guided, and open-source resource for learning
+ decision-support modelling workflows with Python. They are designed to
+ be accessible to a broad audience, including students, researchers,
+ and practitioners who aim to undertake applied environmental
+ decision-support modelling.
+
+
+ Contents and Instructional Design
+
The tutorial notebooks are structured into three main parts:
+
+ Part 0: Introductory Background
+
Part 0 serves as the foundation, providing essential background
+ material. Each notebook in Part 0 is standalone and covers a unique
+ topic. These include:
+
+
+
Introduction to a synthetic model known as the “Freyberg”
+ model
+ (Freyberg,
+ 1988). This model is used as a consistent example
+ throughout the tutorial exercises, allowing learners to apply
+ concepts in a practical context.
+
+
+
An introduction to the pyemu Python
+ package that is used to complement and interface with
+ PEST/PEST++.
+
+
+
Explanation of fundamental mathematical concepts that are
+ relevant and will be encountered throughout the tutorial
+ notebooks.
+
+
+
Pre-requisites for Part 0 include a basic understanding of
+ Python, Jupyter Notebooks, and MODFLOW 6
+ (Langevin
+ et al., 2022). Familiarity with git is a bonus but not
+ fundamental.
+
+
+ Part 1: Introduction to <monospace>PEST</monospace> and the
+ Gauss-Levenberg Marquardt Approach
+
Part 1 focuses on the Gauss-Levenberg Marquardt (GLM) approach to
+ parameter estimation and associated uncertainty analysis in a
+ groundwater modelling context.
+
Part 1 is designed to be accessible without strict sequential
+ dependencies. Learners have the flexibility to explore its contents
+ in any order that suits their preferences or needs. These
+ include:
+
+
+
Introduction to concepts such as non-uniqueness,
+ identifiability, and equifinality.
+
+
+
Introduction to the PEST control file
+ and the PEST/PEST++ interface.
+
+
+
Exploring the challenges of parameterization schemes on
+ predictive ability, as well as how to mitigate them.
+
+
+
Introducing first-order second-moment (FOSM) and prior Monte
+ Carlo uncertainty analysis approaches.
+
+
+
Pre-requisites for Part 1 include a basic understanding of
+ numerical groundwater modelling and familiarity with MODFLOW 6.
+ Familiarity with Python and Jupyter Notebooks is assumed.
Part 2 expands on the foundational knowledge gained in Part 1 and
+ delves into advanced topics related to ensemble-based parameter
+ estimation, uncertainty analysis and optimization methods. These
+ advanced topics include management optimization and sequential data
+ assimilation and assume a highly parameterized approach, as
+ motivated in Part 1. Topics are laid out in manner that reflects
+ real-world workflows, with a focus on practical application of
+ concepts and problem solving.
+
Learners have the option to explore various sequences, in line
+ with real-world applied workflows, such as:
+
+
+
Prior Monte Carlo analysis
+
+
+
Highly parameterized Gauss-Levenberg Marquardt history
+ matching and associated Data Worth analysis using First Order,
+ Second Moment (FOSM) technique,
+
+
+
Ensemble-based history matching and uncertainty analysis with
+ the iterative ensemble smoother approach as implemented in
+ PEST++IES,
+
+
+
Sequential data assimilation with
+ PEST++DA, and
+
+
+
Single-objective and multi-objective optimization under
+ uncertainty with PEST++OPT and
+ PEST++MOU.
+
+
+
Each of these sequences comprises multiple notebooks to be
+ executed in a specified order. They demonstrate how to execute the
+ workflow, interpret results, and apply the concepts to real-world
+ problems.
+
The flowchart below gives an example of a curated learning flow
+ for a common decision support modelling application. Over time,
+ referring back through Part 1 will provide a deeper understanding of
+ some concepts and techniques taken for granted in the highly
+ parameterized, largely ensemble-based approaches of Part 2.
+
+
Example notebook learning flow demonstrating a
+ comprehensive workflow for an applied, ensemble-based management
+ optimization
+ .
+
+
+
Pre-requisites for Part 2 include a basic understanding of
+ PEST/PEST++ and the PEST interface, as well
+ as familiarity with the Freyberg model. Familiarity with Python and
+ Jupyter Notebooks is assumed.
+
+
+
+ Experience of use in teaching and learning situations
+
The notebooks were employed during the
+ Applied
+ Decision Support Groundwater modelling With Python: A Guided
+ Self-Study Course hosted by GMDSI. This self-guided course
+ comprised 5 online sessions, each lasting 1 to 2 hours and focused on
+ the workflows of Part 2. During each session the instructors go
+ through a section of the tutorials and expand on some of the concepts.
+ Sessions were recorded and can be accessed
+ on
+ the GMDSI YouTube channel. Beyond the live online sessions,
+ learners were encouraged to make use of the GitHub
+ Discussions
+ feature to retain a search-engine findable record of common questions
+ that persist beyond the time frame of the course .
+
Feedback from the 65 students who participated in the course was
+ anecdotal but informative.
+ [fig:responses]
+ summarizes the responses by 34 respondents to four questions,
+ comprising 52%. The majority of respondents indicated a preference for
+ this hybrid self-guided/online instruction approach over an in-person
+ week-long intensive class.
+
Open-ended feedback from the participants was generally positive
+ and also included some constructive criticism. Participants
+ appreciated the opportunity to ask questions and several reported
+ hearing the discussion around other peoples’ questions as being
+ valuable and clarifying aspects of the material.
+
+
Summary of responses to post-course survey based on 34
+ responses. Panel A summarizes whether respondents would prefer an
+ intensive in-person workshop or this hybrid option. Panel B
+ summarizes how much of the notebooks respondents were able to
+ complete throughout the course. Panel C summarizes respondent
+ comfort level with PEST++ before and after
+ the course. Panel D highlights individual changes in comfort level
+ reported due to the
+ course.
+
+
+
+
+ Acknowledgements
+
The tutorials were originally developed with support from the US
+ Geological Survey (USGS) and support from USGS continues through the
+ HyTest training project. Continued development and support is funded
+ by the Groundwater Modelling Decision Support Initiative (GMDSI).
+ GMDSI is jointly funded by BHP and Rio Tinto. We thank Dr. John
+ Doherty for his tireless and pioneering efforts starting
+ PEST and continuing to innovate and Dr. Randall
+ Hunt for his leadership in PEST and
+ PEST++ applications and development and
+ contributions to the initial curriculum for this material and the
+ early version of the notebooks. We thank Kalle Jahn (USGS),
+ Ines
+ Rodriguez and
+ codyalbertross
+ who made reviews that improved this manuscript. Lastly, we thank users
+ and stress-testers for their valuable feedback and continued community
+ contributions to the repository.
+
+
+ Disclaimer
+
Any use of trade, firm, or product names is for descriptive
+ purposes only and does not imply endorsement by the US Government.
+
+
+
+
+
+
+
+
+ WhiteJeremy T
+ HuntRandall J
+ FienenMichael N
+ DohertyJohn E
+
+ Approaches to highly parameterized inversion: PEST++ version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis
+ US Geological Survey Techniques; Methods 7-C26
+ 2020
+ 10.3133/tm7C26
+
+
+
+
+
+ WhiteJeremy T
+ FienenMichael N
+ DohertyJohn E
+
+ A python framework for environmental model uncertainty analysis
+ Environmental Modelling & Software
+ Elsevier
+ 2016
+ 85
+ 10.1016/j.envsoft.2016.08.017
+ 217
+ 228
+
+
+
+
+
+ WhiteJeremy T.
+ FienenMichael N.
+ DohertyJohn E.
+
+ A python framework for environmental model uncertainty analysis
+ Environmental Modelling & Software
+ 201611
+ 85
+ 10.1016/j.envsoft.2016.08.017
+ 217
+ 228
+
+
+
+
+
+ WhiteJeremy T
+ HemmingsBrioch
+ FienenMichael N
+ KnowlingMatthew J
+
+ Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses
+ Environmental Modelling & Software
+ Elsevier
+ 2021
+ 139
+ 10.1016/j.envsoft.2021.105022
+ 105022
+
+
+
+
+
+
+ LangevinChristian D.
+ HughesJoseph D.
+ ProvostAlden M.
+ RusscherMartijn
+ NiswongerRichard G.
+ PandaySorab
+ MerrickDamian
+ MorwayEric D.
+ RenoMichael J.
+ BonelliWesley P.
+ BantaEdward R.
+
+ MODFLOW 6 Modular Hydrologic Model
+ 202212
+ https://github.com/MODFLOW-USGS/modflow6
+ 10.5066/F76Q1VQV
+
+
+
+
+
+ DohertyJ.
+
+ PEST and Its Utility Support Software
+ 2015
+ https://pesthomepage.org/
+
+
+
+
+
+ BakkerM.
+ PostV.
+ LangevinC. D.
+ HughesJ. D.
+ WhiteJ. T.
+ StarnJ. J.
+ FienenM. N.
+
+ Scripting MODFLOW model development using python and FloPy
+ Groundwater
+ 2016
+ 54
+ 5
+ https://ngwa.onlinelibrary.wiley.com/doi/abs/10.1111/gwat.12413
+ 10.1111/gwat.12413
+ 733
+ 739
+
+
+
+
+
+ FreybergDavid L.
+
+ AN EXERCISE IN GROUND-WATER MODEL CALIBRATION AND PREDICTION
+ Groundwater
+ 1988
+ 26
+ 3
+ https://ngwa.onlinelibrary.wiley.com/doi/abs/10.1111/j.1745-6584.1988.tb00399.x
+ 10.1111/j.1745-6584.1988.tb00399.x
+ 350
+ 360
+
+
+
+
+
diff --git a/jose.00240/paper.jats/64cf776d53a854baf2005b4ee29e529237149890.png b/jose.00240/paper.jats/64cf776d53a854baf2005b4ee29e529237149890.png
new file mode 100644
index 0000000..db89573
Binary files /dev/null and b/jose.00240/paper.jats/64cf776d53a854baf2005b4ee29e529237149890.png differ
diff --git a/jose.00240/paper.jats/responses.png b/jose.00240/paper.jats/responses.png
new file mode 100644
index 0000000..9c3c2f1
Binary files /dev/null and b/jose.00240/paper.jats/responses.png differ