The technology for integration of mathematical models and on-line monitoring in 
relation to flood forecasting has been available for almost a decade and the use of 
mathematical models for forecasting of flow has been adopted worldwide. 
The basic elements in flood warning system comprise water level and flow sensors, 
meteorological forecasts, SCADA systems and telemetry for online data processing and 
transmission, mathematical models for forecast simulations and finally the issue of 
warning the public. A prerequisite for reliable forecast is a data assimilation 
routine to improve forecast accuracy.  The measured and simulated water levels and 
discharges are compared and analyzed in the hindcast period and the simulations are 
corrected to minimize the discrepancy between the observations and the model 
simulations. In this context Ensemble Kalman filtering techniques have proven to be 
efficient for updating.  
When it comes to early warning systems for water quality the coupling with 
mathematical models and related technology for data assimilation has only been 
introduced recently. However, in line with the rapid development in sensor 
technology with respect to on-line monitoring on various compounds the possibility 
of coupling early warning systems with state of the art water quality modeling 
techniques and forecasting is becoming feasible both economically and 
technologically.
The benefit of the Ensemble Kalman filtering technique is that uncertainties (on 
boundary conditions and measured data) can be taken into account and hence a 
confidence intervals are provided with the forecasted pollutant concentration. 
Forecast can be hours, days ahead in time. Further the updating algorithm can 
provide information on the amount and location for water and pollutant updating. The 
latter is highly relevant when tracing pollutant sources.

The present paper presents the Ensemble Kalman filtering technique applied to 
coupled water quality processes with particular focus on the updating algorithm.  
Finally an example from forecasting of water quality in the complex canal system in 
Bangkok will be presented.
