An Environmental Information System for Hypoxia in Corpus Christi Bay: A WATERS Network Testbed

1. An Environmental Information System for Hypoxia in Corpus Christi Bay: A WATERS Network Testbed Paul Montagna, Texas A&M University Corpus Christi Barbara Minsker, University of Illinois Urbana-Champaign David Maidment and Ben Hodges, University of Texas Austin Jim Bonner, Texas A&M University College Station

2. Acknowledgements • Funding for the CCBay Testbed comes from NSF. • Funding for data collection comes from Coastal Bend Bay and Estuary Program, Texas General Land Office, and the Texas Water Development Board. • Project teams are thanked for their contributions to the emerging EIS system. • The Consortium of Universities for the Advancement of Hydrologic Sciences, Inc (CUAHSI),Hydrographic Information Systems (HIS) Project. • National Center for Supercomputing Applications (NCSA), Environmental CyberInfrastructure Demonstrator (ECID) Project. • WATERS Network.

3. Corpus Christi Bay, Texas WATERS Testbeds

4. Testbeds in WATERS Network • WATer and Environmental Research Systems (WATERS) Network: • A proposed networked infrastructure of environmental field facilities working to promote multidisciplinary research and education on complex, large-scale environmental systems. • A network of instrumented field facilities • A facility that assists with and provides training on sensor deployments, measurement campaigns, and sensor development • Multidisciplinary synthesis of research and education to exploit instrumented sites and networked information • An environmental cyberinfrastructure

5. Cyberinfrastructure (CI) • Computers • Networks • Archives • Grid services • Collaboration services • Information technology services • Data management, mining, and visualization services

6. Why Corpus Christi Bay (CCB)? • A good question: • Can we forecast hypoxia? • Existing long-term data sets • Existing sensor networks • Manageable place to prototype CI

7. CCBay Goal and Questions: • To observe, model, and understand hypoxia in Corpus Christi Bay with advanced sensing and environmental information systems • Understand Hypoxia: • How is hypoxia interrelated with dissolved oxygen dynamics, hydrodynamics, and salinity? • How do engineered systems impact hypoxia? • Integrate the Observing System: • Can data from different sensors be combined to depict hypoxic conditions in real-time and guide sampling strategies? • Model the System: • Can hydrodynamic and salinity conditions occurring during hypoxic events be successfully simulated using known mechanisms and/or or machine learning (i.e., data mining)? • Build Environmental Information System (EIS): • How can the EIS for in Corpus Christi Bay be applied as a template for the investigation of hypoxia at other locations? • Can cyberinfrastructure elements of a digital bay be adapted for other water environments? • What data models best integrate observed and simulated information in three-dimensional water bodies?

8. Sensors in Corpus Christi Bay

10. CC Bay Researchers Currently Cannot Adapt Monitoring to Hypoxia Events • Oxygen data from continuous sondes are only downloaded weekly • Other sensor data are available in near-real-time, but correlations with oxygen levels have not been quantified • For example, wind speed & direction, water surface level, salinity, and temperature • Manual sampling should be increased when probability of hypoxia is high, but researchers cannot integrate diverse data and models to predict when to mobilize • Cyberinfrastructure can create an information system to enable near-real-time, adaptive monitoring

11. Solution is to Create a Digital Watershed A Digital Watershed integrates observed and modeled data from various sources into a single description of the environment

12. Observations Server* GIS Data Server Digital Watershed Weather Server Remote Sensing Server Environmental Information System Servers *Using the Observations Data Model (ODM)

13. Observations Data Model ODM = Observations Catalog + Values Table + Metadata Tables

14. EIS Server Architecture • Map front end – • ArcGIS Server 9.2 (being programmed by ESRI Water Resources) • Relational database – • SQL/Server 2005 or Express • Web services library – • VB.Net programs accessed as a Web Service Description Language (WSDL)

15. Integrated CI Supporting Technology Data Services Workflows & Model Services Knowledge Services Meta-Workflows Collaboration Services Digital Library Analyze Data &/or Assimilate into Model(s) Link &/or Run Analyses &/or Model(s) Create Hypo-thesis Obtain Data Discuss Results Publish Research Process Environmental CI Architecture

16. Sensor net C++ code D2K workflows Hypoxia Machine Learning Models Anomaly Detection Replace or Remove Errors Update Boundary Condition Models Hypoxia Model Integrator Hydrodynamic Model Visualize Hydrodynamics Water Quality Model Visualize Hypoxia Risk Fortran numerical models IM2Learn workflows Data Archive CC Bay Near-Real-Time Hypoxia Prediction Process

17. Workflow Using Cyberintergrator Development • Studying complex environmental systems like Corpus Christi Bay requires: • Coupling analyses and models • Real-time, automated updating of analyses and modeling with diverse tools • CyberIntegrator is a prototype technology to support modeling and analysis of complex systems

18. CyberIntegrator

19. Event-Driven Architecture • What is an event? • When something noteworthy happens in one component of the CI that should be broadcast to other components of the CI. • Applications in the cyberinfrastracture can produce or consume events. • For example, sensor anomaly detected, or predicted hypoxia requires focused manual sampling.

20. Sensor Anomalies • Sensors are not always reliable (see above wind data), and real-time data can be difficult to check by hand • We have developed machine learning anomaly detectors • Being implemented with data services in CyberIntegrator to automatically detect anomalies & alert data managers

21. Event Architecture Producer Anomaly Detection Detect anomaly in data from Sensors Event Broker (JMS Broker) Handle messages and their distribution Event: Anomaly Detected Event: Anomaly Detected Consumer Event: Anomaly Detected CyberIntegrator Visualize anomaly and previous ten values Event: Anomaly Detected Consumer System Tray Notification App Notify user of anomaly Consumer Portlet Visualize published events

22. How Will All This Help Researchers in CC Bay? • Consider the following scenario that defines what could be enabled …

23. Hypoxia Alert • John Doe gets a page saying that hypoxic conditions are predicted with 80% certainty in 24 hours • John logs into the CyberCollaboratory, where he joins an ongoing chat with researchers (both local and across the country), who also received the alert, and are looking at the data and model predictions • The researchers agree that the predictions appear to be reasonable given the current conditions • John mobilizes his research team to deploy detailed manual sampling of the affected region the next morning • He uses the CyberCollaboratory to notify students & volunteers from the local region who have indicated an interest in helping with field sampling

24. Hypoxia Alert • When the samplers and crews are mobilized, the data they collect are transmitted back to the data storehouse • Model predictions made by CyberIntegrator meta-workflows are updated automatically • Additional data needs are identified with CyberIntegrator meta-workflows and are transmitted back to the crews through event subscriptions • Others monitor visualizations of hypoxia in real time and discuss implications in the CyberCollaboratory • Useful to: • Regulators & stakeholders • Researchers and students across the country • Interested public (fisherman, teachers, journalists)

25. New Paradigm • Cyberinfrastructure can enable near-real-time adaptive monitoring, modeling, and management of large-scale environmental systems through: • Web services architecture to deliver diverse data quickly and easily • Event-based cyberenvironments enable users to easily link and adapt complex models and analyses