an environmental information system for hypoxia in corpus christi bay: a waters network testbed
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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 - PowerPoint PPT PresentationTRANSCRIPT
An Environmental Information System for Hypoxia in Corpus Christi Bay: A WATERS Network Testbed
Paul Montagna, Texas A&M University Corpus ChristiBarbara Minsker, University of Illinois Urbana-ChampaignDavid Maidment and Ben Hodges, University of Texas AustinJim Bonner, Texas A&M University College Station
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.
WATERS Testbeds
Corpus Christi Bay, Texas
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
Cyberinfrastructure (CI)
Computers Networks Archives Grid services Collaboration services Information technology services Data management, mining, and visualization
services
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
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?
Sensors in Corpus Christi BaySensors 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)USGS NCDC TCOON Dr. Paul Montagna TCEQ SERF
National Datasets (National HIS) Regional Datasets (Workgroup HIS)USGS NCDC TCOON Dr. Paul Montagna TCEQ SERF
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
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
Environmental Information System Servers
ObservationsServer*
GIS Data Server
Weather Server Remote SensingServer
Digital Watershed
*Using the Observations Data Model (ODM)
Observations Data ModelODM = Observations Catalog + Values Table + Metadata Tables
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)
Environmental CI Architecture
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 TechnologyIntegrated CI
CC Bay Near-Real-Time Hypoxia Prediction Process
DataArchive
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
Fortran numerical models
IM2Learn workflows
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
CyberIntegrator
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.
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
Event Broker(JMS Broker)
Handle messages and their distribution
Anomaly Detection
Detect anomaly in data from Sensors
CyberIntegrator
Visualize anomaly and previous ten values
System TrayNotification App
Notify user of anomaly
Event: Anomaly Detected
Event: Anomaly Detected
Portlet
Visualize publishedevents
Even
t: An
omal
y De
tect
ed
Eve
nt: A
nom
aly
Det
ecte
d
Producer
Consumer
ConsumerConsumer
Event Architecture
How Will All This Help Researchers in CC Bay?
Consider the following scenario that defines what could be enabled …
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
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)
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