An integrated water quality modeling system with dynamic remote sensing feedback

A coupled hydrodynamic-optical water quality modeling system based on Dynamic Data Driven Applications Systems (DDDAS) concepts that assimilates remote sensing data into a hydrodynamic model was developed and tested. The modeling system includes the hydrodynamic model (ALGE), a radiative transfer model (Hydrolight), and remote imagery (MODIS) as a dynamic feedback. The DDDAS was implemented through an Ensemble Kalman Filter (EnKF) with a small ensemble space. Large scale thermal structure and circulation patterns in Lake Ontario were simulated during the spring and summer seasons. High-resolution stream plume studies were performed in Conesus Lake and for the plume of the Niagara River in Lake Ontario. This work provided validation of the capabilities of the ALGE code to simulate the transport of sediment and passive tracer. Although the ALGE model produces predictions of the distribution of the TSS constituents, visual examination of MODIS 250 m reflectance data clearly shows discrepancies between themodel TSS output and the remote sensing data. These errors are due to the uncertainties in model physics, parameters, and forcing conditions. A Kalman filter-based method was implemented in this research to provide a better estimate of the modeled TSS. MODIS 250 m reflectance data was used as a dynamic feedback in EnKF. A test was performed at the single simulation grid point at the Genesee River mouth to validate the performance of the EnKF method. The EnKF estimate and the ensemble mean had similar and lower RMSE than any single run. Further validation was undertaken to examine the effects of assimilating MODIS data for all grid points to estimate the plume dissipation. Results show that the spatial filtering via an EnKF is capable of capturing the episodic nature of storm events by usingMODIS data as feedback. In this case the EnKF estimate RMSE is considerably smaller than the ensemble mean RMSE