Early Warning Systems in surface waters - Data ...

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XVI International Conference on Computational Methods in Water Resources (CMWR-XVI) Ingeniørhuset

Early Warning Systems in surface waters - Data assimilation techniques in relation to water quality processes
Author:Johan Hartnack <jnh@dhi.dk> (DHI)
Mads Madsen <mm@dhi.dk> (DHI)
Henrik Madsen <hem@dhi.dk> (DHI)
Presenter:Johan Hartnack <jnh@dhi.dk> (DHI)
Date: 2006-06-18     Track: Special Sessions     Session: Data assimilation in water resources modelling
DOI:10.4122/1.1000000240

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.