Conrad Blucher Institute Coastal Monitoring and Mapping

1. Conrad Blucher Institute Coastal Monitoring and Mapping Dr. Patrick R. Michaud December 6, 2007

2. Key points • Introduction to the Conrad Blucher Institute • Remote data collection and research capabilities • Long-term programs and partnerships

3. Gulf Monitoring Projects • Texas Coastal Ocean Observation Network • NOAA/NOS National Water Level Observation Network • Physical Oceanographic Real Time System (PORTS) • National/Global Ocean Observing System

4. TCOON Overview • Started 1988 • Over 50 stations • Primary Sponsors • Texas General Land Office • National Ocean Service • US Corps of Eng • Texas Water Development Board

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

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

11. Data Management • Automated Acquisition, Archive, Processing, Retrieval • 10-year Historical Database • Most processing takes place via Internet • Infrastructure for other observation systems

12. 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

13. Tidal Datums • Used for • Coastal property boundaries • Nautical charts • Bridge and engineering structures • National Ocean Service standards • Automated processing • Legally defensible datums • Technology transfer to/from NOS

14. Partnership with NOAA • “The value in TCOON's use of NOS data collection standards and formats is realized in the ability to display TCOON data on the NOS website with confidence that the data is accurate. The ongoing, partnership between NOS, GLO, and [CBI] over the last 16 years has resulted in enormous benefits to the agencies and to the user community.” http://tidesandcurrents.noaa.gov/tcoon.shtml

15. Expansion • NWLON stations in Texas • Houston/Galveston PORTS • Expansion to other states and systems

16. Other CBI Monitoring Projects • Nueces Bay Salinity • Real Time Navigation System (RTNS) • San Antonio Bay Water Monitoring • Bahia Grande Water Quality Monitoring • Mission Aransas National Estuarine Research Reserve • Corpus Christi Marina Weather Station • Red de Observaciones y Predicciones de Variables Oceanicas (ROPVO)

17. Uses of CBI/TCOON Data • Tidal Datums • Littoral Boundaries • Oil-Spill Response • Navigation • Storm Preparation/ Response • Water Quality Studies • Research

18. Research • Real-time Automated Data Processing • Tidal Datum Processing • Web-based Visualization and Manipulation of Coastal Data • Neural-Network-based forecasts from real-time observations • Specialized sensor and data acquisition system development • Support for other research efforts

19. Real-time surge information

20. Tide and surge predictions

21. Neural Network Forecasting • Use neural network to model non-tidal component of water level • Reliable short-term predictions CCNAS ANN 24-hour forecasting:

22. Texas Height Modernization • CBI lead in cooperation with NGS • Selected goals: • Re-establish NAVD 88 elevations to NGS standards • Connect NAVD 88 heights to TCOON • Expand use of GPS for elevation measurement • Build online data warehouse

23. Opportunity • Problem: The tide charts do not work for most of the Texas coast • Opportunity: We have extensive time series of water level and weather measurements for most of the Texas coast

24. Data Intensive Modeling • Real time data availability is rapidly increasing • Cost of weather sensors and telecommunication equipment is steadily decreasing while performance is improving • How to use these new streams of data / can new modeling techniques be developed

25. TCOON Data in CC Bay • 6 TCOON Stations Measuring: • Water levels (6) • Wind speeds (4) • Wind directions (4) • 10 x 8760 hourly measurements per year • Barometric pressure • Air temperature • Water temperature Port Aransas Aquarium Ingleside Nueces Bay Corpus Christi Bay Gulf of Mexico Naval Air Station Packery Channel Oso Bay Port of Corpus Christi Bob Hall Pier

26. Data Intensive Modeling • Classic models (large computer codes - finite elements based) need boundary conditions and forcing functions which are difficult to provide during storm events • Neural Network modeling can take advantage of high data density and does not require the explicit input of boundary conditions and forcing functions • The modeling is focused on forecasting water levels at specific locations

27. Neural Network Features • Non linear modeling capability • Generic modeling capability • Robustness to noisy data • Ability for dynamic learning • Requires availability of high density of data

28. BHP Performance Analysis harmonic forecasts (blue/squares), Persistence model (green/diamonds), ANN model without wind forecasts (red dashed/triangles) and ANN model with wind forecasts (red/circles)

29. CCNAS Performance Analysis Harmonic forecasts (blue/squares), Persistent model (green/diamonds), ANN model with only NAS data (red dashed/triangles) and ANN model with additional BHP data (red/circles)

30. Tropical Storms and Hurricanes • Need for short to medium term water level forecasts during tropical storms and hurricanes • Tropical storms and hurricanes are relatively infrequent and have each their own characteristics. • ANN model performance?

31. Forecasts in storm events • CCNAS ANN 12-hour Forecasts During 1998 Tropical Storm Frances • (ANN trained over 2001 Data Set)

32. CCNAS ANN 24-hour Forecasts During 1998 Tropical Storm Frances (ANN trained over 2001 Data Set)

33. Conclusions • Long-term, data-rich observation network • Web-based infrastructure for automated collection and processing of marine data • Research in datum computation and coastal forecasting