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

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

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 D

etec

ted

Eve

nt:

Ano

mal

y D

etec

ted

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