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.

中文翻译：

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利用遥感土壤湿度和原位水流观测的时变多元同化改进模拟水流

水文模型广泛用于估计和预测各种应用的水流。鉴于这些模型固有的不确定性，迫切需要增强建模策略的当前状态。从历史上看，水文模型通过土壤水分（SM）同化显示出显着的改善。特别是，在多元同化过程中，当 SM 观测与内部流域位置的水流观测相结合时，模型表现出更好的性能。最近的研究还强调了由于同化期间流域的瞬态性而更新参数的重要性。此外，确定是否有必要吸收所有可用的观察结果也至关重要。为了解决这些问题，本研究引入了五种数据同化（DA）场景，将先进散射仪（ASCAT）SM和现场水流观测纳入概念水文模型，旨在提高其性能。研究结果表明，虽然单变量同化改善了 SM 和水流估计，但多变量数据同化 (MVDA) 也得到了显着增强。与开环场景情况相比，时变 MVDA (TV-MVDA) 显着提高了模型的性能。然而，与 TV-MVDA 场景相比，这种改进只是微不足道的。最后，基于灵敏度的 TV-MVDA 场景在水流估计方面表现出可比的性能，同时仅同化了 30% 的观测值。这些结果表明，基于灵敏度的 TV-MVDA 可以以最少的观测要求有效地改进模型。