Abstract
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.
Highlights
For the last half-century, computational modeling has become a principal tool in the water resource manager’s toolbox
Because of the inherent complexity of numerical models and the significant time, effort, and expertise needed for their development, it is often challenging for stakeholders and water resources managers to access models that are appropriate for their needs (Essawy et al, 2018)
We developed the Tutuila water budget model, with the Soil-Water Balance 2 (SWB2) code developed by the U.S Geological Survey (USGS) (Westenbroek et al, 2018)
Summary
For the last half-century, computational modeling has become a principal tool in the water resource manager’s toolbox. Within the traditional model development paradigm, water management agencies usually take one of two approaches for obtaining hydrologic models to suit their needs, 1) dedicate significant resources to building internal modeling capacity or 2) contract with outside ‘experts’ to deliver models that typically cannot be interacted with once completed Drawbacks to the former approach include the high cost of training, software, and salary required for agencies to retain personnel with sufficient skills to assess the validity, conceptualization, calibration, and usefulness of existing models or to create and maintain effective modeling programs. The framework is intended to be portable, flexible, use small file sizes, and only include models with short run times, which are attributes that have been shown to enhance model adoption rates amongst managers (Argent and Grayson, 2003) By presenting this framework, we hope to demonstrate the ease of use and the applicability of modern code sharing and cloud-computing tools in a scientific modeling setting involving participants at remotely located institutions. The framework continues to evolve and change as we and our stakeholders continue to participate in discussion, raise concerns, and contribute new ideas
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