Performance and Comparison of Water Level Forecasting Models for the Texas Ports and Waterways

Abstract

The ports and waterways of the Texas Gulf Coast are of vital importance to the shipping industry as well as the overall US economy. Safe navigation, particularly underkeel clearance, within these shallow, confined waterways requires accurate water level forecasts. While tide tables are tabulated for a number of locations along the Texas Gulf coast, they do not meet National Ocean Service (NOS) standards due to meteorological forcing. This paper presents and compares alternative models to improve real-time water level forecasts, including a new model based on Artificial Neural Networks (ANN). All models include real-time measurements collected by the Texas Coastal Ocean Observation Network (TCOON) and the forecasts are published on the World Wide Web. The new ANN model is shown to improve considerably upon the tide tables and the other models tested and to meet NOS criteria for many locations for up to 48-hour forecasts. Model performances are compared for Corpus Christi Bay and Galveston Bay and present model limitations and future improvements are discussed.