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Developing Real-Time Nowcasting System for Regional Landslide Hazard Assessment under Extreme Rainfall Events
Open AccessArticle

Learning Case Study of a Shallow-Water Model to Assess an Early-Warning System for Fast Alpine Muddy-Debris-Flow

1
Department of Engineering and Geology, University of “G. D’Annunzio”, Chieti-Pescara, 66013 Chieti, Italy
2
INDAM Research Group GNCS, National Institute of Advanced Mathematics, National Group of Scientific Computing, University of “G. D’Annunzio”, Chieti-Pescara, 66013 Chieti, Italy
3
National Research Council (CNR), Research Institute for the Hydrogeological Protection (IRPI), 10135 Torino, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Pantaleone De Vita and Claudia Meisina
Water 2021, 13(6), 750; https://doi.org/10.3390/w13060750
Received: 31 December 2020 / Revised: 20 February 2021 / Accepted: 3 March 2021 / Published: 10 March 2021
(This article belongs to the Special Issue Rainfall-Induced Shallow Landslides Modeling and Warning)
The current climate change could lead to an intensification of extreme weather events, such as sudden floods and fast flowing debris flows. Accordingly, the availability of an early-warning device system, based on hydrological data and on both accurate and very fast running mathematical-numerical models, would be not only desirable, but also necessary in areas of particular hazard. To this purpose, the 2D Riemann–Godunov shallow-water approach, solved in parallel on a Graphical-Processing-Unit (GPU) (able to drastically reduce calculation time) and implemented with the RiverFlow2D code (version 2017), was selected as a possible tool to be applied within the Alpine contexts. Moreover, it was also necessary to identify a prototype of an actual rainfall monitoring network and an actual debris-flow event, beside the acquisition of an accurate numerical description of the topography. The Marderello’s basin (Alps, Turin, Italy), described by a 5 × 5 m Digital Terrain Model (DTM), equipped with five rain-gauges and one hydrometer and the muddy debris flow event that was monitored on 22 July 2016, were identified as a typical test case, well representative of mountain contexts and the phenomena under study. Several parametric analyses, also including selected infiltration modelling, were carried out in order to individuate the best numerical values fitting the measured data. Different rheological options, such as Coulomb-Turbulent-Yield and others, were tested. Moreover, some useful general suggestions, regarding the improvement of the adopted mathematical modelling, were acquired. The rapidity of the computational time due to the application of the GPU and the comparison between experimental data and numerical results, regarding both the arrival time and the height of the debris wave, clearly show that the selected approaches and methodology can be considered suitable and accurate tools to be included in an early-warning system, based at least on simple acoustic and/or light alarms that can allow rapid evacuation, for fast flowing debris flows. View Full-Text
Keywords: numerical modelling; muddy flow; shallow water; parametric sensitivity analyses; rheological laws; GPU approach numerical modelling; muddy flow; shallow water; parametric sensitivity analyses; rheological laws; GPU approach
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MDPI and ACS Style

Pasculli, A.; Cinosi, J.; Turconi, L.; Sciarra, N. Learning Case Study of a Shallow-Water Model to Assess an Early-Warning System for Fast Alpine Muddy-Debris-Flow. Water 2021, 13, 750. https://doi.org/10.3390/w13060750

AMA Style

Pasculli A, Cinosi J, Turconi L, Sciarra N. Learning Case Study of a Shallow-Water Model to Assess an Early-Warning System for Fast Alpine Muddy-Debris-Flow. Water. 2021; 13(6):750. https://doi.org/10.3390/w13060750

Chicago/Turabian Style

Pasculli, Antonio; Cinosi, Jacopo; Turconi, Laura; Sciarra, Nicola. 2021. "Learning Case Study of a Shallow-Water Model to Assess an Early-Warning System for Fast Alpine Muddy-Debris-Flow" Water 13, no. 6: 750. https://doi.org/10.3390/w13060750

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