Improving modelled streamflow using time-varying multivariate assimilation of remotely sensed soil moisture and in-situ streamflow observations

Abstract

Hydrological models are widely used to estimate and forecast streamflow for various applications. Given the inherent uncertainties in these models, there is a pressing need to enhance the current state of the modelling strategies. Historically, hydrological models showed significant improvement through soil moisture (SM) assimilation. Particularly, when SM observations are combined with streamflow observations from the interior catchment locations during multivariate assimilation, models showed better performance. Recent studies also emphasized the importance of updating the parameters due to the transient nature of the catchment during the assimilation period. Additionally, it is crucial to determine whether it is necessary to assimilate all available observations. To address these issues, this study introduces five data assimilation (DA) scenarios ingesting advanced scatterometer (ASCAT) SM and in-situ streamflow observations into a conceptual hydrological model, aiming to enhance its performance. The findings reveal that while univariate assimilation improves both SM and streamflow estimates, but a substantial enhancement is observed in multivariate data assimilation (MVDA). Time-varying MVDA (TV-MVDA) substantially improves the model's performance compared to the open-loop scenario case. However, this improvement is only marginal when compared to the TV-MVDA scenario. Finally, the sensitivity-based TV-MVDA scenario exhibits comparable performance in streamflow estimation, while assimilating just 30 % of observations. These results suggest that Sensitivity based TV-MVDA can improve the model efficiently with minimal observation requirements.