A Southeast Regional Testbed for Integrating Complex Coastal and Ocean Information Systems M. Fletcher, J. R. Pournelle, D. Ramage School of the Environment, University of South Carolina, Columbia, SC 29208 USA D. E. Porter Baruch Institute for Marine and Coastal Sciences, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208 USA V. Shervette Baruch Institute for Marine and Coastal Sciences, University of South Carolina, Columbia, SC 29208 USA SCDHEC Office of Ocean & Coastal Resource Management, Charleston, SC 29405 USA R. H. Kelsey NCBO-Cooperative Oxford Laboratory, 904 South Morris Street, Oxford, MD 21654 USA Abstract— Government agencies, non-governmental organizations, commercial enterprises, and academic research programs have established a vast array of environmental databases for programmatic and research purposes. Most of these use different data management infrastructures that prevent repurposing or integrating information to serve other uses. Creating capacity to integrate and access disparate data streams enormously increases their value and application potential. Building on existing environmental information management expertise, USC has launched a Southeast Regional Integration Testbed (SRIT), and used this new capacity to implement an end- to-end decision support application that links monitoring data with predictive models in order to provide advance warning of impending beach contamination. The Water Quality Portlet project design reported herein serves as an exemplar toward establishing a sustained Center for Integrated Information Systems and Coastal Ocean Observations (CIISCOO) that will promote flexible, real-time data interoperability, across a wide user base, to efficiently meet evolving future needs. I. INTRODUCTION Many government agencies—federal, state, county, municipal, and local; non-governmental organizations, commercial enterprises, and academic research programs collect environmental data, and many have established a vast array of environmental databases for programmatic or research purposes. Most of these were developed separately to address specific needs. For example, coastal monitoring programs may generate impaired water quality monitoring, beach contamination monitoring, and fisheries resource management data. Because of their separate origins, such programs tend to use different data management infrastructures, which stand as formidable barriers against repurposing or integrating information to serve other needs. By enabling seamless data availability for synthetic analyses, building capacity to integrate and access disparate data streams enormously increases their value and application potential. To address that need, building on our growing environmental information management expertise, USC's School of the Environment (SOE) has therefore established a Southeast Regional Integration Testbed (SRIT), and used this new capacity to implement an end-to-end decision support application for an important state agency that serves public health and safety. By explicitly linking monitoring data with predictive models to provide advance warning of impending beach contamination, this project provides a model for (and serves as a first step toward) establishing a sustained Center for Integrated Information Systems and Coastal Ocean Observations (CIISCOO)—a proposed partnership between the University of South Carolina (USC) and Raytheon. CIISCOO's advanced software and services infrastructure will enable broad access to environmental data, for use by a wide range of stakeholders. In partnership with the South Carolina Department of Health and Environmental Control (SCDHEC), the SRIT team has developed a user application for prediction and analysis of beach hazards. The initial demonstration incorporates data required for supporting SCDHEC decisions to issue beach advisories. Utilizing real-time (streamed) data, delayed mode data, and predictive models, it provides a mechanism to disseminate information to a variety of potential users, including SCDHEC and other, local government officials. GIS- based tools allow direct access to monitoring data, the models, and user-friendly presentations to provide processed information required for making closure decisions. Recent research has demonstrated that predictive models can serve the essential purpose of providing a decision support tool for preemptive advisory issuance [1, 2, 8, 12]. Such models use a variety of input data—most important being rainfall and salinity—to assess the probability of beach contamination by enterococci, which are commonly used as an indicator of microbial contamination. Applying new predictive models can significantly improve the timeliness and accuracy of decisions to issue beach swimming advisories. However, the specific data required can vary considerably depending upon the site in question, the types of models used, the types of data available, and specific user needs. Thus, enhancing data management and access through a common services architecture—the