corpus christi bay observatory testbed source: david maidment, univ. of texas
TRANSCRIPT
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Corpus Christi Bay Observatory Testbed
Gulf of Mexico
Laguna Madre
OsoBay
Nueces Bay
Corpus Christi Bay
SERF
Tide gage
HF radar
Water quality
Observedhypoxia
Wastewater discharge
Barney DavisPower Plant
Gulf of Mexico
Laguna Madre
OsoBay
Nueces Bay
Corpus Christi Bay
SERF
Tide gage
HF radar
Water quality
Observedhypoxia
Wastewater discharge
Barney DavisPower Plant
Source: David Maidment, Univ. of Texas
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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
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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.
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How Will An EIS Help the Researchers in Corpus Christi
Bay?Consider the following scenarios
that define what could be enabled….
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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
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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
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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
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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 TechnologyIntegrated CI
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Daily Fluctuations in CCBay Sonde Data
11:16 20:16 05:16 14:16 23:16 08:167.607.727.847.968.088.20
pH
11:16 20:16 05:16 14:16 23:16 08:1636.3037.1638.0238.8839.7440.60
Sa
lin
ity
(pp
t)
11:16 20:16 05:16 14:16 23:16 08:169.0
30.051.072.093.0
114.0
DO
%(%
)
11:16 20:16 05:16 14:16 23:16 08:160
1.42.84.25.67.0
DO
Co
nc
(mg
/L) 11:16 20:16 05:16 14:16 23:16 08:16
2.5902.6882.7862.8842.9823.080
De
pth
(m)
11:16 20:16 05:16 14:16 23:16 08:1629.2029.7630.3230.8831.4432.00
Te
mp
(C)
DateTime(M/D/Y)
07/05/05 07/06/05 07/08/05 07/09/05 07/10/05 07/12/05
B4070505.DAT
Source: Paul Montagna, Univ. of Texas
Oxygen
Hypoxia Events
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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
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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)
USGS NCDC TCOON Dr. Paul Montagna TCEQ SERF
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Data hosted by other regional research
agency
Interaction between Workgroup HIS server and Regional Datasets
TCOONWeb 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.
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Benefits to the scientist
Flow vectors provided by Paula Kulis, student of Dr. Ben Hodges.
Ingleside TCOON stationprovides wind and tide data
Preliminary velocity vectors from hydrodynamic model (P. Kulis)
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Excel CUAHSI 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.
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Demo of CCBay Workgroup HIS by Ernest To
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http://www.ncdc.noaa.gov/oa/ncdc.html
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mm / 3 hours
Precipitation Evaporation
North American Regional Reanalysis of Climate
Variation during the day, July 2003
NetCDF format
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Series and FieldsFeatures
Point, line, area, volumeDiscrete space representation
Series – ordered sequence of numbersTime series – indexed by time
Frequency series – indexed by frequency
Surfaces Fields – multidimensional arrays
Scalar fields – single value at each locationVector fields – magnitude and direction Random fields – probability distribution
Continuous space representation
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Demo of weather data ingestion for CCBay (Cedric David and Tim
Whiteaker)
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Space, L
Time, T
Variable, V
D
Data Cube – What, Where, When
“What”
“Where”
“When”
A data value
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Continuous Space-Time Data Model -- NetCDF
Space, L
Time, T
Variables, V
D
Coordinate dimensions
{X}
Variable dimensions{Y}
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Space, FeatureID
Time, TSDateTime
Variables, TSTypeID
TSValue
Discrete Space-Time Data Model
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Geostatistics
Time Series Analysis
Multivariate analysis
Hydrologic Statistics
How do we understand space-time correlation fields of many variables?