Graph-based visual analysis for large-scale hydrological modeling

Lorne Leonard, Alan M. MacEachren, Kamesh Madduri

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


This article reports on the development and application of a visual analytics approach to big data cleaning and integration focused on very large graphs, constructed in support of national-scale hydrological modeling. We explain why large graphs are required for hydrology modeling and describe how we create two graphs using continental United States heterogeneous national data products. The first smaller graph is constructed by assigning level-12 hydrological unit code watersheds as nodes. Creating and cleaning graphs at this scale highlight the issues that cannot be addressed without high-resolution datasets and expert intervention. Expert intervention, aided with visual analytical tools, is necessary to address edge directions at the second graph scale: subdividing continental United States streams as edges (851, 265, 305) and nodes (683, 298, 991) for large-scale hydrological modeling. We demonstrate how large graph workflows are created and are used for automated analysis to prepare the user interface for visual analytics. We explain the design of the visual interface using a watershed case study and then discuss how the visual interface is used to engage the expert user to resolve data and graph issues.

Original languageEnglish (US)
Pages (from-to)205-216
Number of pages12
JournalInformation Visualization
Issue number3
StatePublished - Jul 2017

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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