River water quality (WQ) parameters, such as suspended sediment concentration (SSC) is sparsely measured for most rivers, limiting our understanding of riverine WQ dynamics and the underlying mechanisms. On one hand, several empirical and physics-based models have been developed to get continuous estimates of riverine sediment, but these models themselves incur large uncertainties due to the simplification of watersheds scale erosion and hydrological processes, epistemic limitation, and the lack of input data. On the other hand, remote sensing (RS) provides an opportunity to measure WQ parameters throughout the year. However, RS data are intermittent in space and time, and also incur errors when used for WQ estimation due to errors in the inversion model and noise in the RS data. This paper presents a method to assimilate RS-derived and an empirical model-derived SSC concentration data, to improve the accuracy of the estimated SSC. Further, this method is being developed to apply at regional to continental scales. To this end, the Ohio River Basin (ORB) was selected as a test case. The Revised Morgan-Finney equation along with a simple routing scheme was used to simulate the SSC in ORB, and LANDSAT reflectance data was used to get RS-based estimates of SSC. In this presentation, comparisons of model-derived simulations and post-assimilation simulations with in-situ observed data will be discussed.
