| Abstract: |
Chesapeake Bay Environmental Observatory (CBEO) is an NSF-supported project focused on studying hypoxia in Chesapeake Bay using advanced cyberinfrastructure (CI) technologies. The project is organized around four concurrent and interacting activities: 1) CBEO:S provides science and management context for the use of CI technologies, focusing on hypoxia and its non-linear dynamics as affected by management and climate; 2) CBEO:T constructs a locally-accessible CBEO test bed prototype centered on spatio-temporal interpolation and advanced querying of model runs; 3) CBEO:N incorporates the test bed CI into national environmental observation networks, and 4) CBEO:E develops education and outreach components of the project that translate observational science for public consumption.
CBEO:N activities, which are the focus of this presentation, include:
- constructing an online project portal to enable researchers to publish, discover, query, visualize and integrate project-related datasets of different types. The portal is based on the technologies developed within the GEON (the Geosciences Network) project;
• developing a CBEO node within the WATERS network, taking advantage of the CUAHSI Hydrologic Information System (HIS) Server technology that supports online publication of observational data as web services, and ontology-assisted data discovery;
• developing new data structures and metadata in order to describe water quality observational data, and model run output, obtained for the Chesapeake Bay area, using data structures adopted and modified from the Observations Data Model of CUAHSI HIS;
• prototyping CBEO tools that can be re-used through the portal. These tools include a collection of analytical workflows for studying hypoxia in the bay, OLAP-based visualization and animation tools for rapid aggregation of results from a 10-year run of the Chesapeake Bay Program’s 13k Water Quality Model, and map-based applications for finding, aggregating and interpolating observational data.
The paper describes recent accomplishments in these four development areas, and demonstrates how CI approaches transform research and data sharing in environmental observing systems.
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