01 Nov 2017
An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation
Scuola Universitaria Superiore IUSS Pavia, Pavia, 27100, Italy
CIMA Research Foundation, Savona, 17100, Italy
Department of Information Engineering, Electronics and
Telecommunications, Sapienza University of Rome, Rome, 00184, Italy
Luca Pulvirenti
CIMA Research Foundation, Savona, 17100, Italy
Giorgio Boni
CIMA Research Foundation, Savona, 17100, Italy
Department of Informatics, Bioengineering, Robotics and Systems
Engineering, University of Genoa, Genoa, 16145, Italy
Marco Chini
Luxembourg Institute of Science and Technology, Belvaux, 4422,
Luxembourg
Patrick Matgen
Luxembourg Institute of Science and Technology, Belvaux, 4422,
Luxembourg
Simone Gabellani
CIMA Research Foundation, Savona, 17100, Italy
Giuseppe Squicciarino
CIMA Research Foundation, Savona, 17100, Italy
Nazzareno Pierdicca
Department of Information Engineering, Electronics and
Telecommunications, Sapienza University of Rome, Rome, 00184, Italy
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J. Zhao, F. Roth, B. Bauer-Marschallinger, W. Wagner, M. Chini, and X. X. Zhu
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Giulia Bruno, Doris Duethmann, Francesco Avanzi, Lorenzo Alfieri, Andrea Libertino, and Simone Gabellani
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Manuscript not accepted for further review
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Theresa C. van Hateren, Marco Chini, Patrick Matgen, and Adriaan J. Teuling
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Manuscript not accepted for further review
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Renaud Hostache, Dominik Rains, Kaniska Mallick, Marco Chini, Ramona Pelich, Hans Lievens, Fabrizio Fenicia, Giovanni Corato, Niko E. C. Verhoest, and Patrick Matgen
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Silvia Terzago, Valentina Andreoli, Gabriele Arduini, Gianpaolo Balsamo, Lorenzo Campo, Claudio Cassardo, Edoardo Cremonese, Daniele Dolia, Simone Gabellani, Jost von Hardenberg, Umberto Morra di Cella, Elisa Palazzi, Gaia Piazzi, Paolo Pogliotti, and Antonello Provenzale
Hydrol. Earth Syst. Sci., 24, 4061–4090, https://doi.org/10.5194/hess-24-4061-2020, https://doi.org/10.5194/hess-24-4061-2020, 2020
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J. Zhao, M. Chini, R. Pelich, P. Matgen, R. Hostache, S. Cao, and W. Wagner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 395–400, https://doi.org/10.5194/isprs-annals-V-1-2020-395-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-395-2020, 2020
W. Wagner, V. Freeman, S. Cao, P. Matgen, M. Chini, P. Salamon, N. McCormick, S. Martinis, B. Bauer-Marschallinger, C. Navacchi, M. Schramm, C. Reimer, and C. Briese
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 641–648, https://doi.org/10.5194/isprs-annals-V-3-2020-641-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-641-2020, 2020
Massimiliano Burlando, Djordje Romanic, Giorgio Boni, Martina Lagasio, and Antonio Parodi
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2019-371, https://doi.org/10.5194/nhess-2019-371, 2019
Manuscript not accepted for further review
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This paper investigates the weather conditions during the collapse of the Morandi bridge, which caused 43 fatalities. Despite a thunderstorm developed over the city at the time of collapse, weather is officially ruled out as a contributing factor for it. A meteorological analysis is relevant to support this hypothesis or possibly reconsider it. The analysis, mainly based on measurements complemented with numerical simulations, reveals that quite strong winds occurred at the time of collapse.
Barbara Glaser, Marta Antonelli, Marco Chini, Laurent Pfister, and Julian Klaus
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We demonstrate how thermal infrared images can be used for mapping the appearance and disappearance of water at the surface. The use of thermal infrared images allows for mapping this appearance and disappearance for various temporal and spatial resolutions, and the images can be understood intuitively. We explain the necessary steps in detail, from image acquisition to final processing, by relying on image examples and experience from an 18-month mapping campaign.
Gaia Piazzi, Guillaume Thirel, Lorenzo Campo, and Simone Gabellani
The Cryosphere, 12, 2287–2306, https://doi.org/10.5194/tc-12-2287-2018, https://doi.org/10.5194/tc-12-2287-2018, 2018
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The study focuses on the development of a multivariate particle filtering data assimilation scheme into a point-scale snow model. One of the main challenging issues concerns the impoverishment of the particle sample, which is addressed by jointly perturbing meteorological data and model parameters. An additional snow density model is introduced to reduce sensitivity to the availability of snow mass-related observations. In this configuration, the system reveals a satisfying performance.
Antonio Parodi, Luca Ferraris, William Gallus, Maurizio Maugeri, Luca Molini, Franco Siccardi, and Giorgio Boni
Clim. Past, 13, 455–472, https://doi.org/10.5194/cp-13-455-2017, https://doi.org/10.5194/cp-13-455-2017, 2017
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Initial and boundary condition data from the 20th Century Reanalysis Project in ensemble mode are used to address the feasibility of performing cloud-resolving simulations with 1 km horizontal grid spacing of a historic extreme event that occurred over Liguria: the 1915 San Fruttuoso case. The proposed approach focuses on the ensemble Weather Research and Forecasting model runs that show strong convergence over the Ligurian Sea, as these runs are the ones most likely to best simulate the event.
Melissa Wood, Renaud Hostache, Jeffrey Neal, Thorsten Wagener, Laura Giustarini, Marco Chini, Giovani Corato, Patrick Matgen, and Paul Bates
Hydrol. Earth Syst. Sci., 20, 4983–4997, https://doi.org/10.5194/hess-20-4983-2016, https://doi.org/10.5194/hess-20-4983-2016, 2016
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We propose a methodology to calibrate the bankfull channel depth and roughness parameters in a 2-D hydraulic model using an archive of medium-resolution SAR satellite-derived flood extent maps. We used an identifiability methodology to locate the parameters and suggest the SAR images which could be optimally used for model calibration. We found that SAR images acquired around the flood peak provide best calibration potential for the depth parameter, improving when SAR images are combined.
Francesco Silvestro, Nicola Rebora, Lauro Rossi, Daniele Dolia, Simone Gabellani, Flavio Pignone, Eva Trasforini, Roberto Rudari, Silvia De Angeli, and Cristiano Masciulli
Nat. Hazards Earth Syst. Sci., 16, 1737–1753, https://doi.org/10.5194/nhess-16-1737-2016, https://doi.org/10.5194/nhess-16-1737-2016, 2016
F. Silvestro, S. Gabellani, R. Rudari, F. Delogu, P. Laiolo, and G. Boni
Hydrol. Earth Syst. Sci., 19, 1727–1751, https://doi.org/10.5194/hess-19-1727-2015, https://doi.org/10.5194/hess-19-1727-2015, 2015
A. Piscini, M. Picchiani, M. Chini, S. Corradini, L. Merucci, F. Del Frate, and S. Stramondo
Atmos. Meas. Tech., 7, 4023–4047, https://doi.org/10.5194/amt-7-4023-2014, https://doi.org/10.5194/amt-7-4023-2014, 2014
F. Silvestro, S. Gabellani, F. Delogu, R. Rudari, and G. Boni
Hydrol. Earth Syst. Sci., 17, 39–62, https://doi.org/10.5194/hess-17-39-2013, https://doi.org/10.5194/hess-17-39-2013, 2013
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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...
