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

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

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.

How Will An EIS Help the Researchers in Corpus Christi

Bay?Consider the following scenarios

that define what could be enabled….

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

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

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

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

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

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

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

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.

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)

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.

Demo of CCBay Workgroup HIS by Ernest To

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

mm / 3 hours

Precipitation Evaporation

North American Regional Reanalysis of Climate

Variation during the day, July 2003

NetCDF format

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

Demo of weather data ingestion for CCBay (Cedric David and Tim

Whiteaker)

Space, L

Time, T

Variable, V

D

Data Cube – What, Where, When

“What”

“Where”

“When”

A data value

Continuous Space-Time Data Model -- NetCDF

Space, L

Time, T

Variables, V

D

Coordinate dimensions

{X}

Variable dimensions{Y}

Space, FeatureID

Time, TSDateTime

Variables, TSTypeID

TSValue

Discrete Space-Time Data Model

Geostatistics

Time Series Analysis

Multivariate analysis

Hydrologic Statistics

How do we understand space-time correlation fields of many variables?