| Water quality modeling has been developed for more than three quarters of a century, but is limited to the study of trends instead of making accurate short-term forecasts. A major-barrier to water-quality modeling is the lack of an efficient system for water-quality monitoring. Traditional water quality sampling is time-consuming, expensive, and can only be taken for small size samples. Also, instant and accurate water quality data cannot always be provided to satisfy the demands of water quality modeling and parameter calibration. Remote sensing provides a revolutionary technique to monitor water quality repetitively over a large area. The major concerns regarding the use of remote sensing for water quality monitoring are: (1) suitable spectral channels for deriving various characteristics of water quality variables, and (2) appropriate and efficient image processing techniques to convert image brightness to traditional water quality indices.; The first objective of this research is to use SPOT satellite imagery to remotely detect water quality variables, such as chlorophyll a, Secchi depth, and phosphorus. A geographic information system (GIS) software--ERDAS IMAGINE--was integrated into the monitoring system to enhance the display of predictions from the water quality model QUAL2E. The short-term forecasting system was applied to a case study at the Te-Chi Reservoir, Taiwan. All water quality variables from simulations are displayed on a geographically registered map and in color to correspond with varying water quality levels. The visualizing technique is helpful for rapid understanding of water quality conditions. The complete integrated system being developed is designed to be economical and to efficiently answer "what-if" questions whenever pollutant sources change in the system.; The second objective of this research is to calibrate relevant biological parameters by incorporating remote sensing data into the modeling system. The most important biological parameters, the maximum growth rate and respiration, were calibrated by using least squares estimation and SPOT satellite data. The results yield data of algal biological parameters that provide engineers with fundamental information about the interactions within the ecosystem for use in controlling eutrophication. |
