Our model coupling work to showcase at PEARC22
A paper describing our recent work on model coupling co-authored by Jungha Woo, Lan Zhao and Carol Song of the MyGeoHub team, and their research collaborators Iman Haqiqi of Purdue Agro Economics, Danielle Grogan and Richard Lammers from the Institute for the Study of Earth, Oceans and Space at the University of New Hampshire, will be presented at the annual PEARC conference (Practice and Experience in Advanced Research Computing), July 10-14, 2022, in Boston, MA. An open access copy of the paper can be found at https://doi.org/10.1145/3491418.3530298.
Solving complex real-world grand challenge problems requires in-depth collaboration of researchers from multiple disciplines. Such collaboration often involves harnessing multiscale and multi- dimensional data and combining models from different fields to simulate systems. However, the progress on this front has been lim- ited mainly due to significant gaps in domain knowledge and tools that are typically employed in silos of the domains. Researchers from different fields face considerable barriers to understanding and reusing each other’s data/models in order to collaborate ef- fectively. For example, in solving the global sustainability prob- lems, researchers from hydrology, climate science, agriculture, and economics need to run their respective models to study different components of the global and local food, energy and water systems while, at the same time, need to interact with other researchers and integrate the results of one model with another. Developing this kind of model coupling workflow calls for (1) a large amount of data being processed and exchanged across domains and organi- zations, (2) identifying and processing the output of one model to make it ready for integration into another model, (3) controlling the workflow dynamically so that it runs until a certain conver- gence condition or other criteria is met, and (4) close collaboration among the modelers to explore, tune, and test the configuration and data transformation needed to link the models. We have developed C3F, a flexible collaborative model coupling framework to help re- searchers accelerate their model integration and linking efforts by leveraging advanced cyberinfrastructure such as high-performance computing and virtual containers. In this paper, we describe our experience and lessons learned in developing this cyberinfrastruc- ture solution to support the linking of Water Balance Model (WBM) and SIMPLE-G agricultural economic model in an NSF funded IN- FEWS project and a DOE-funded Program on Coupled Human and Earth Systems (PCHES) to study the implications of groundwater scarcity for food-energy-water systems. The C3F model coupling framework can be extended to facilitate other model linkages as well.
• Computing methodologies → Simulation environments.
Model coupling workflow, containerization, WBM, SIMPLE-G, Food-Energy-Water (FEW)
ACM Reference Format:
Jungha Woo, Lan Zhao, Danielle S. Grogan, Iman Haqiqi, Richard Lam- mers, and Carol X. Song. 2022. C 3 F : Collaborative Container-based Model Coupling Framework. In Practice and Experience in Advanced Research Com- puting (PEARC ’22), July 10–14, 2022, Boston, MA, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3491418.3530298