1. Corpus Christi Bay Observatory Testbed Source: David Maidment, Univ. of Texas

2. Hypoxia in CCBay Occurs when dissolved oxygen (DO) in aquatic environments is reduced to levels harmful to organisms System is hypoxic when DO < 30% saturation or (~ 2 mg/L) Most fish cannot live below 30% DO saturation. http://www.epa.qld.gov.au/images/environmental_management/water/fishkill2.jpg

3. Corpus Christi Bay Testbed Objectives An interdisciplinary team of hydrologists, environmental engineers, and biologists are collaborating to: –Improve understanding of hypoxia Correlated with salinity-induced stratification Causes of stratification and spatial and temporal patterns of hypoxia are currently uncertain –Explore how sensor data can be used to guide adaptive sampling –Create improved models of hypoxia, coupling numerical hydrodynamic and oxygen models with data mining methods; and –Demonstrate how these information sources can be integrated into emerging cyberinfrastructure tools to create an environmental information system (EIS) for collaborative research and decision support.

4. How Will An EIS Help the Researchers in Corpus Christi Bay? Consider the following scenarios that define what could be enabled….

5. Hypoxia Alert Data from agency and observatory sensors stream into the EIS, which provides near-real-time hypoxia forecasts George Smith gets a page saying that hypoxic conditions are predicted with 80% certainty in 24 hours George 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 –George 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

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

7. Hypoxia Alert When the samplers and crews are mobilized, the data they collect are transmitted back to the HIS data store –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 CyberCollaboratory subscriptions Others monitor visualizations of hypoxia in real time & discuss implications in the CyberCollaboratory –Regulators & stakeholders –Students across the country

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

9. Daily Fluctuations in CCBay Sonde Data Source: Paul Montagna, Univ. of Texas Oxygen Hypoxia Events

10. Corpus Christi Bay Environmental Info System Workgroup HIS implementation Uses ODM to store hydrology and environmental data from state agencies and academic investigators. Contains web-services to regional data repositories (e.g. TCOON). Water quality data sites in Corpus Christi Bay (maps by Tyler Jantzen) Demo: TXHIS ODM webservice

11. Sensors in Corpus Christi Bay Montagna stations SERF stations TCOON stations USGS gages TCEQ stations Hypoxic Regions NCDC station National Datasets (National HIS)Regional Datasets (Workgroup HIS) USGSNCDCTCOONDr. Paul MontagnaTCEQSERF

12. Data hosted by other regional research agency Interaction between Workgroup HIS server and Regional Datasets TCOON Web server CRWR Workgroup HIS server Regional data stored on server in ODM schema Dr. Paul Montagna TCEQ Webservices ODM webservices Webscraper Webservices The Scientist Data Request Data Response Workgroup HIS works both as a gateway and warehouse for regional datasets.

13. Benefits to the scientist Flow vectors provided by Paula Kulis, student of Dr. Ben Hodges. Ingleside TCOON station provides wind and tide data Preliminary velocity vectors from hydrodynamic model (P. Kulis)

14. ExcelCUAHSI Web service How Excel connects to ODM Obtains inputs for CUAHSI web methods from relevant cells. Available Web methods are GetSiteInfo, GetVariableInfo GetValues methods. converts standardized request to SQLquery. imports VB object into Excel and graphs it converts response to a standardized XML. Observations Data Model SQL query Response HydroObjects converts XML to VB object parses user inputs into a standardized CUAHSI web method request.

15. Demo of CCBay Workgroup HIS by Ernest To

16. http://www.ncdc.noaa.gov/oa/ncdc.html

17. mm / 3 hours Precipitation Evaporation North American Regional Reanalysis of Climate Variation during the day, July 2003 NetCDF format

18. Series and Fields Features Point, line, area, volume Discrete space representation Series – ordered sequence of numbers Time series – indexed by time Frequency series – indexed by frequency Surfaces Fields – multidimensional arrays Scalar fields – single value at each location Vector fields – magnitude and direction Random fields – probability distribution Continuous space representation

19. Demo of weather data ingestion for CCBay (Cedric David and Tim Whiteaker)

20. Space, L Time, T Variable, V D Data Cube – What, Where, When What Where When A data value

21. Continuous Space-Time Data Model -- NetCDF Space, L Time, T Variables, V D Coordinate dimensions {X} Variable dimensions {Y}

22. Space, FeatureID Time, TSDateTime Variables, TSTypeID TSValue Discrete Space-Time Data Model

23. Geostatistics Time Series Analysis Multivariate analysis Hydrologic Statistics How do we understand space-time correlation fields of many variables?