Collaborative groundwater modeling: Open-source, cloud-based, applied science at a small-island water utility scale

Recent advances 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 advances. We present a case study detailing a ve...

Full description

Saved in:
Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2020-05, Vol.127, p.104693, Article 104693
Main Authors: Shuler, Christopher K., Mariner, Katrina E.
Format: Article
Language:English
Subjects:
American Samoa
Cloud computing
Collaboration
Computer applications
GitHub
Groundwater
Groundwater modeling
Hydrologic data
Hydrologic monitoring network
Jupyter Notebooks
Python
Research facilities
Social organization
Stream discharge
Stream flow
Water budget
Water resources
Water utilities
Workflow
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recent advances 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 advances. 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 climate 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. •Used cloud-based methods to collaborate with remote stakeholders in American Samoa.•Applied open-source tools to develop a collaborative hydrologic modeling framework.•Used seamless framework to integrate multiple data collection and modeling components.•Components included monitoring network, water budget model, and groundwater model.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2020.104693