Evaluation of State and Bias Estimates for Assimilation of SMOS Retrievals Into Conceptual Rainfall-Runoff Models

For an accurate estimation of land surface state variables through remote sensing data assimilation, it is important to estimate the forecast and observation biases as well. This study focuses on the evaluation of a methodology to estimate land surface state variables, together with model forecast and observation biases. Two conceptual rainfall-runoff models (HBV and GRKAL) are used for this purpose. Soil moisture data, retrieved by the Soil Moisture Ocean Salinity (SMOS) mission, are assimilated into these models for 59 unregulated sub-basins of the Murray-Darling basin in Australia. When both models simulate similar soil moisture values, the methodology results in similar forecast and observation bias estimates for both models. The same behavior is obtained when the temporal evolution of the soil moisture simulations is different, but with a similar long-term mean climatology. However, when the long-term mean climatology of both models is different, but with a similar temporal evolution, the bias estimates from both models have a different climatology as well, but with a high temporal correlation. The overall conclusion from this paper is that observation bias estimation is of key importance when updating internal state variables in a conceptual rainfall-runoff system that is calibrated to produce realistic discharge output for possibly biased internal state variables, and that the relative partitioning of bias into forecast and observation bias remains a model-dependent challenge.