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C3F: Collaborative Container-based Model Coupling Framework

Conference · · Practice and Experience in Advanced Research Computing
 [1];  [2];  [3];  [2];  [3];  [2]
  1. Purdue Univ., West Lafayette, IN (United States); Penn State
  2. Purdue Univ., West Lafayette, IN (United States)
  3. Univ. of New Hampshire, Durham, NH (United States)
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 limited 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 effectively. For example, in solving the global sustainability problems, 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 organizations, (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 convergence 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 researchers 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 cyberinfrastructure solution to support the linking of Water Balance Model (WBM) and SIMPLE-G agricultural economic model in an NSF funded INFEWS 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.
Research Organization:
Purdue Univ., West Lafayette, IN (United States); Univ. of New Hampshire, Durham, NH (United States)
Sponsoring Organization:
USDOE; National Science Foundation (NSF)
DOE Contract Number:
SC0016162; SC0022141
OSTI ID:
1901589
Conference Information:
Journal Name: Practice and Experience in Advanced Research Computing
Country of Publication:
United States
Language:
English

References (10)

Continental scale models of water balance and fluvial transport: An application to South America journal September 1989
Quantifying the impacts of compound extremes on agriculture journal January 2021
Achieving sustainable irrigation water withdrawals: global impacts on food security and land use journal October 2017
Closing the loop: Reconnecting human dynamics to Earth System science journal August 2017
Invisible water, visible impact: groundwater use and Indian agriculture under climate change journal August 2016
MyGeoHub—A sustainable and evolving geospatial science gateway journal May 2019
Simulating regional hydrology and water management: An integrated agent-based approach conference December 2015
SIMPLE-G: A multiscale framework for integration of economic and biophysical determinants of sustainability journal November 2020
Reconstructing 20th century global hydrography: a contribution to the Global Terrestrial Network- Hydrology (GTN-H) journal January 2010
Cloud-enabling a Collaborative Research Platform
  • Kalyanam, Rajesh; Campbell, Rob; Kearney, Derrick
  • Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact https://doi.org/10.1145/3093338.3093353
conference July 2017

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