Improving operational water quality forecasting with ensemble data assimilation

Dong-Jun Seo, Sunghee Kim and Hamideh Riazi, University of Texas Arlington, TX, USA and Changmin Shin, National Institute of Environmental Research, Incheon, Korea


For effective control and management of water quality, real-time forecasting of key water quality variables is necessary. Such forecasting provides the water managers with the predictive information necessary to take proactive actions. As in any forecasting, water quality forecasting is subject to various sources of error. Of the errors involved, those in the model initial conditions may be readily reduced, often very significantly, by adjusting or updating them in real time based on the observations available in real time. Such an operation, or data assimilation (DA), keeps the model states in line with what is being observed for improved short-range prediction and hence constitutes an integral component in any real-time environmental forecasting system. Due to the combination of a large number of state variables and sparse observations, however, DA for water quality forecasting is particularly challenging. In this paper, we describe the application and evaluation of maximum likelihood ensemble filter (MLEF)-based DA to the Hydrologic Simulation Program – Fortran (HSPF) model in support of real-time water quality forecasting at the Water Quality Control Center of the National Institute of Environmental Research in Korea.

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