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Coastal Flood Forecasting at TAMU-CC/TCOON

Coastal Flood Forecasting at TAMU-CC/TCOON. Dr. Philippe Tissot ptissot@cbi.tamucc.edu. Dr. Patrick R. Michaud pmichaud@sci.tamucc.edu. Collaborators:. Dr. Daniel Cox, Dr. Alex Sadovski, Scott Duff, Jessica Tishmack, Deidre Williams, Zack Bowles,

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Coastal Flood Forecasting at TAMU-CC/TCOON

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  1. Coastal Flood Forecastingat TAMU-CC/TCOON Dr. Philippe Tissot ptissot@cbi.tamucc.edu Dr. Patrick R. Michaud pmichaud@sci.tamucc.edu Collaborators: Dr. Daniel Cox, Dr. Alex Sadovski, Scott Duff, Jessica Tishmack, Deidre Williams, Zack Bowles, Aimee Mostella, Jeremy Stearns, Kelly Torres, Alex Drikitis January 30, 2003

  2. TCOON overview • Started 1988 • Over 50 stations • Primary Sponsors • General Land Office • Water Devel. Board • US Corps of Eng • Nat'l Ocean Service Gulf of Mexico

  3. TCOON overview • Measurements • Precise Water Levels • Wind • Temperature • Barometric Pressure • Follows NOAA/NOS standards • Real-time, online database

  4. Wind anemometer Radio Antenna Satellite Transmitter Solar Panels Data Collector Water Level Sensor Water Quality Sensor Current Meter Typical TCOON station

  5. Other real-time observing systems • Real-time Navigation • Port of Corpus Christi • Port Freeport • NOAA PORTS • Water-Quality • Nueces Bay Salinity • Oso Creek Dissolved Oxygen • Offshore Weather

  6. Data management design principles • Preserve source data • Annotate instead of modify • Automate as much as possible • Maintain a standard interchange format • Avoid complex or proprietary components • Emphasize long-term reliability over short-term costs

  7. Uses of observation data • Tidal Datums • Littoral Boundaries • Oil-Spill Response • Navigation • Storm Preparation/ Response • Research

  8. Water-level graph

  9. Water-level prediction …what will happen next?

  10. Tide predictions tide: The periodic rise and fall of a body of water resulting from gravitational interactions between Sun, Moon, and Earth. Tide and Current Glossary, National Ocean Service, 2000 According to NOS, changes in water level from non-gravitational forces are not “tides”.

  11. Harmonic analysis • Standard method for tide predictions • Represented by constituent cosine waves with known frequencies based on gravitational (periodic) forces • Elevation of water is modeled as h(t) = H0 +  Hc fy,c cos(act + ey,c – kc) h(t) = elevation of water at time t H0 = datum offset ac = frequency (speed) of constituent t fy,c ey,c = node factors/equilibrium args Hc = amplitude of constituent c kc = phase offset for constituent c

  12. Harmonic tide predictions To predict tides using harmonic analysis: • Obtain amplitudes and phases of harmonic constituents from trusted sources (e.g., NOS) or • Perform a least-squares analysis on observations to determine amplitudes and phases of harmonic constituents

  13. Harmonic prediction Apply the amplitudes/phases to get:

  14. Prediction vs. observation It’s nice when it works…

  15. Prediction vs. observation …but it often doesn’t work in Texas

  16. Water level != tide In Texas, meteorological factors have a significant effect on water elevations

  17. Uses of harmonic predictions However, harmonic predictions can still be useful! Consider… Isidore begins to (re-)enter the Gulf… …what will happen next?

  18. Uses of harmonic predictions If we add harmonic prediction… …what will happen next?

  19. Uses of harmonic prediction

  20. Uses of harmonic prediction

  21. Uses of harmonic prediction landfall

  22. Isidore & JFK Causeway • Effect of Isidore at JFK causeway

  23. Harmonic WL prediction -present capabilities • Automated system for computing harmonic constituent values from observations database • Harmonic predictions available via query page for many TCOON stations

  24. Harmonic WL prediction –(near) future capabilities • Persistent model forecast • Apply difference between latest observation and harmonic prediction to future predictions • Forecasts page on DNR web site • Obtain forecasts from different models • Harmonic predictions • Persistent model • Neural-network model • Linear-regression/statistical model • Hybrid models • Information about water-level forecasting methods • Statistics on previous forecasts

  25. Neural-network forecasts Dr. Philippe Tissot

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