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Nowadays, the availability of soil moisture estimates from satellite sensors offers a great chance to improve real-time flood forecasting through data assimilation. In this paper, two real data and two synthetic experiments have been carried out to assess the effects of assimilating soil moisture estimates into a two-layer rainfall-runoff model. By using the ensemble Kalman filter, both the surface- and root-zone soil moisture (RZSM) products derived by the Advanced SCATterometer (ASCAT) have been assimilated and the model performance on flood estimation is analyzed. RZSM estimates are obtained through the application of an exponential filter. Hourly rainfall-runoff observations for the period 1994-2010 collected in the Niccone catchment (137 km2), Central Italy, are employed as case study. The ASCAT soil moisture products are found to be in good agreement with the modeled soil moisture data for both the surface layer (correlation coefficient (R) of 0.78) and the root zone (R = 0.94). In the real data experiment, the assimilation of the RZSM product has a significant impact on runoff simulation that provides a clear improvement in the discharge modeling performance. On the other hand, the assimilation of the surface soil moisture product has a small effect. The same findings are also confirmed by the synthetic twin experiments. Even though the obtained results are model dependent and site specific, the possibility to efficiently employ coarse resolution satellite soil moisture products for improving flood prediction is proven, mainly if RZSM data are assimilated into the hydrological model. [ABSTRACT FROM AUTHOR]

The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2, 2009. Providing accurate soil moisture (SM) estimation is one of its main scientific objectives. Since the end of the commissioning phase, preliminary global SMOS SM data [Level 2 (L2) product] are distributed to users. In this paper, we carried out a first assessment of the reliability of this product through a comparison with in situ observed and modeled SM over three different sites: One is located in Luxemburg, and two are located in Italy. The period from August 1, 2010, to July 1, 2011, has been analyzed, giving us the opportunity to evaluate the satellite response to different SM states. The selected period is important for hydrological predictions as it is typically characterized by a sequence of transitions from dry to wet and from wet to dry conditions. In order to compare SMOS and ground SM measurements, a two-step approach has been applied. First, an exponential filter has been applied to approximate root-zone SM, and second, a cumulative distribution function matching has been employed to remove systematic differences between satellite and in situ observations and model simulations of SM. Our results indicate rather good reliability of the filtered and bias-corrected SM estimates derived from the first SMOS L2 products. Bearing in mind that an updated/advanced version of the SMOS SM product has been recently produced, our preliminary results already seem to confirm the potential of SMOS for monitoring of water in soils. [ABSTRACT FROM AUTHOR]