Assimilation of Surface- and Root-Zone ASCAT Soil Moisture Products Into Rainfall–Runoff Modeling

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.

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