Server-side workflow execution using data grid technology for reproducible analyses of data-intensive hydrologic systems

Bakinam T. Essawy, Jonathan L. Goodall, Hao Xu, Arcot Rajasekar, James D. Myers, Tracy A. Kugler, Mirza M. Billah, Mary C. Whitton, Reagan W. Moore

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Many geoscience disciplines utilize complex computational models for advancing understanding and sustainable management of Earth systems. Executing such models and their associated data preprocessing and postprocessing routines can be challenging for a number of reasons including (1) accessing and preprocessing the large volume and variety of data required by the model, (2) postprocessing large data collections generated by the model, and (3) orchestrating data processing tools, each with unique software dependencies, into workflows that can be easily reproduced and reused. To address these challenges, the work reported in this paper leverages the Workflow Structured Object functionality of the Integrated Rule-Oriented Data System and demonstrates how it can be used to access distributed data, encapsulate hydrologic data processing as workflows, and federate with other community-driven cyberinfrastructure systems. The approach is demonstrated for a study investigating the impact of drought on populations in the Carolinas region of the United States. The analysis leverages computational modeling along with data from the Terra Populus project and data management and publication services provided by the Sustainable Environment-Actionable Data project. The work is part of a larger effort under the DataNet Federation Consortium project that aims to demonstrate data and computational interoperability across cyberinfrastructure developed independently by scientific communities.

Original languageEnglish (US)
Pages (from-to)163-175
Number of pages13
JournalEarth and Space Science
Issue number4
StatePublished - 2016

Bibliographical note

Funding Information:
This work was supported by the National Science Foundation (NSF) under awards ACI-0940841, ACI-0940824, and ACI- 0940818 and by Amazon Web Services (AWS) through an Education Research Grant award. This research would not have been possible without assistance from the larger iRODS, DFC, SEAD, and TerraPop teams. The data used are listed in Table 1 and can be found in the SEAD repository at the DOIs provided in Table 1.

Publisher Copyright:
©2016. The Authors.


  • federation
  • hydrologic modeling
  • iRODS
  • reproducibility
  • workflows


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