Version 1
: Received: 2 March 2020 / Approved: 3 March 2020 / Online: 3 March 2020 (11:45:15 CET)
How to cite:
Shuler, C.; Mariner, K. Collaborative Groundwater Modeling: Open-Source, Cloud- Based, Applied Science at a Small-Island Water Utility Scale. Preprints2020, 2020030043. https://doi.org/10.20944/preprints202003.0043.v1
Shuler, C.; Mariner, K. Collaborative Groundwater Modeling: Open-Source, Cloud- Based, Applied Science at a Small-Island Water Utility Scale. Preprints 2020, 2020030043. https://doi.org/10.20944/preprints202003.0043.v1
Shuler, C.; Mariner, K. Collaborative Groundwater Modeling: Open-Source, Cloud- Based, Applied Science at a Small-Island Water Utility Scale. Preprints2020, 2020030043. https://doi.org/10.20944/preprints202003.0043.v1
APA Style
Shuler, C., & Mariner, K. (2020). Collaborative Groundwater Modeling: Open-Source, Cloud- Based, Applied Science at a Small-Island Water Utility Scale. Preprints. https://doi.org/10.20944/preprints202003.0043.v1
Chicago/Turabian Style
Shuler, C. and Katrina Mariner. 2020 "Collaborative Groundwater Modeling: Open-Source, Cloud- Based, Applied Science at a Small-Island Water Utility Scale" Preprints. https://doi.org/10.20944/preprints202003.0043.v1
Abstract
Recent advancements in cloud-computing and social-networking are influencing how we communicate professionally, work collaboratively, and approach data-science tasks. Here we show how the groundwater modeling field is well positioned to benefit from these advancements. We present a case study detailing a vertically-integrated, collaborative modeling framework jointly developed by participants at the American Samoa Power Authority and at the University of Hawaii Water Resources Research Center. The framework components include direct collection and analysis of climatic and streamflow data, development of a water budget model, and initiation of a dynamic groundwater modeling process. The framework is entirely open-source and applies newly available data-science infrastructure using Python-based tools compiled with Jupyter Notebooks and cloud computing services such as GitHub. These resources allow for seamless integration of multiple computational components into a dynamic cloud-based workflow that is immediately accessible to stakeholders, resource managers, or anyone with an internet connection
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
