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Advances in Geosciences An open-access journal for refereed proceedings and special publications

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Adv. Geosci., 44, 89-100, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
01 Nov 2017
An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation
Luca Cenci1,2,3, Luca Pulvirenti2, Giorgio Boni2,4, Marco Chini5, Patrick Matgen5, Simone Gabellani2, Giuseppe Squicciarino2, and Nazzareno Pierdicca3 1Scuola Universitaria Superiore IUSS Pavia, Pavia, 27100, Italy
2CIMA Research Foundation, Savona, 17100, Italy
3Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, 00184, Italy
4Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, 16145, Italy
5Luxembourg Institute of Science and Technology, Belvaux, 4422, Luxembourg
Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

Citation: Cenci, L., Pulvirenti, L., Boni, G., Chini, M., Matgen, P., Gabellani, S., Squicciarino, G., and Pierdicca, N.: An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation, Adv. Geosci., 44, 89-100,, 2017.
Short summary
This research aims at improving hydrological modelling skills of flash flood prediction by exploiting earth observation data. To this aim, high spatial/moderate temporal resolution soil moisture maps, derived from Sentinel 1 acquisitions, were used in a data assimilation framework. Findings revealed the potential of Sentinel 1-based soil moisture data assimilation for flash flood risk reduction and improved our understanding of the capabilities of the aforementioned satellite-derived product.
This research aims at improving hydrological modelling skills of flash flood prediction by...