There is great potential in Data Assimilation (DA) for the purposes of uncertainty identification, reduction and real-time correction of hydrological models. This paper reviews the latest developments in Kalman filters (KFs), particularly the Extended KF (EKF) and the Ensemble KF (EnKF) in hydrological DA. The hydrological DA targets, methodologies and their applicability are examined. The recent applications of the EKF and EnKF in hydrological DA are summarized and assessed critically. Furthermore, this review highlights the existing challenges in the implementation of the EKF and EnKF, especially error determination and joint parameter estimation. A detailed review of these issues would benefit not only the Kalmantype DA but also provide an important reference to other hydrological DA types.

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Remotely sensed soil moisture products are ideal candidates for initializing soil moisture profiles of land surface models via data assimilation. This paper investigates the possibility of using a calibrated Integral Equation Model coupled with a hyperresolution land surface model, called Soil, Vegetation, and Snow (SVS) to simulate backscatter and compares the results with C-band RADARSAT-2 Synthetic Aperture Radar (SAR) backscatter signals in postharvest season when the field is considered bare soil or sparsely vegetated. Modifications to SVS evaporation scheme are shown to improve the comparison against SAR measurements. An improved effective soil roughness calculation scheme was also proposed to focus on the inversion of the root mean square height (H rms ) only. Soil dielectric constant compensation was suggested to reduce the inversion error and expand the dynamic range of Integral Equation Model. The combination of SVS soil moisture and effective roughness is found superior to the absence of either of them. This method is promising considering that it does not require any in situ measurements, and yet it still outperforms the original IEM model, which uses in situ measured soil moisture and soil roughness at point scale.

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