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Abstract
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.
Highlights
Soil moisture is a key variable in the hydrologic cycle, through its crucial role in the partitioning of the available radiation into latent and sensible heat fluxes, and the partitioning of rainfall into surface runoff and infiltration
The Nash-Sutcliffe Efficiency (NSE) values for these three catchments are within one standard deviation of the average, and can be considered as values typically obtained for the 59 catchments
When the means are different, different bias values result, but still with a relatively high agreement in the temporal evolution of the bias. These results provide support to a framework in which both observation bias and model forecast bias are estimated, but the joint identifiability of both biases remains a topic to be further explored in more studies
Summary
Soil moisture is a key variable in the hydrologic cycle, through its crucial role in the partitioning of the available radiation into latent and sensible heat fluxes, and the partitioning of rainfall into surface runoff and infiltration. Through these mechanisms, forecasted precipitation is highly sensitive to the land surface wetness conditions (Betts et al, 1996). Because of observation noise and errors in the inversion algorithm, remotely sensed soil moisture values will always be prone to a certain level of error For these reasons, it has been suggested that the best way to estimate soil moisture values is the merging of satellite remote sensing and hydrologic modeling (Kostov and Jackson, 1993), which is commonly referred to as soil moisture data assimilation. Since the pilot study of Entekhabi et al (1994), a large number of studies have put soil moisture data assimilation into practice
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