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Remote Sens. 2015, 7(9), 11403-11433;

Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe?

Research Institute for Geo-Hydrological Protection, National Research Council CNR, Via di Madonna Alta 126, Perugia 06128, Italy
Author to whom correspondence should be addressed.
Academic Editors: Guy J-P. Schumann, Richard Gloaguen and Prasad S. Thenkabail
Received: 30 June 2015 / Revised: 28 August 2015 / Accepted: 1 September 2015 / Published: 8 September 2015
(This article belongs to the Special Issue Remote Sensing in Flood Monitoring and Management)
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Data assimilation (DA) of satellite soil moisture (SM) observations represents a great opportunity for improving the ability of rainfall-runoff models in predicting river discharges. Many studies have been carried out so far demonstrating the possibility to reduce model prediction uncertainty by incorporating satellite SM observations. However, large discrepancies can be perceived between these studies with the result that successful DA is not only related to the quality of the satellite observations but can be significantly controlled by many methodological and morphoclimatic factors. In this article, through an experimental study carried out on the Tiber River basin in Central Italy, we explore how the catchment area, soil type, climatology, rescaling technique, observation and model error selection may affect the results of the assimilation and can be the causes of the apparent discrepancies obtained in the literature. The results show that: (i) DA of SM generally improves discharge predictions (with a mean efficiency of about 30%); (ii) unlike catchment area, the soil type and the catchment specific characteristics might have a remarkable influence on the results; (iii) simple rescaling techniques may perform equally well to more complex ones; (iv) an accurate quantification of the model error is paramount for a correct choice of the observation error and, (v) SM temporal variability has a stronger influence than the season itself. On this basis, we advise that DA of SM may be not a simple task and one should carefully test the optimality of the assimilation experiment prior to drawing any general conclusions. View Full-Text
Keywords: data assimilation; satellite soil moisture; rescaling technique; observation error data assimilation; satellite soil moisture; rescaling technique; observation error

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Massari, C.; Brocca, L.; Tarpanelli, A.; Moramarco, T. Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe? Remote Sens. 2015, 7, 11403-11433.

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