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r.massmov: an open-source landslide model for dynamic early warning systems

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Abstract

This paper illustrates the main characteristics of the newly developed landslide model r.massmov, which is based on the shallow water equations, and is capable of simulating the landslide propagation over complex topographies. The model is the result of the reimplementation of the MassMov2D into the free and open-source GRASS GIS with a series of enhancements aiming at allowing its possible integration into innovative early warning monitoring systems and specifically into Web processing services. These improvements, finalized at significantly reducing computational times, include the introduction of a new automatic stopping criterion, fluidization process algorithm, and the parallel computing. Moreover, the results of multi-spatial resolution analysis conducted on a real case study located in the southern Switzerland are presented. In particular, this analysis, composed by a sensitivity analysis and calibration process, allowed to evaluate the model capabilities in simulating the phenomenon at different input data resolution. The results illustrate that the introduced modifications lead to important reductions in the computational time (more than 90 % faster) and that, using the lower dataset resolution capable of guaranteeing reliable results, the model can be run in about 1 s instead of the 3.5 h required by previous model with not optimized dataset resolution. Aside, the results of the research are a series of new GRASS GIS modules for conducting sensitivity analysis and for calibration. The latter integrates the automated calibration program “UCODE” with any GRASS raster module. Finally, the research workflow presented in this paper illustrates a best practice in applying r.massmov in real case applications.

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Acknowledgments

The authors wish to acknowledge Dr. Santiago Begueria (Estacion Experimental de Aula Dei—Consejo Superior de Investigaciones Cientifica) for his support in the completion of this study. A special thank to Land Department of the Canton Ticino for partially funding this research, to Canton Ticino and Geofoto S.A. for Lidar data.

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Authors and Affiliations

  1. Department of Earth and Environmental Sciences, University of Pavia, Via Ferrata 1, 27100, Pavia, Italy

    Monia Elisa Molinari & Claudia Meisina

  2. Institute of Earth Sciences, SUPSI, Campus Trevano, 6952, Canobbio, Switzerland

    Massimiliano Cannata

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  1. Monia Elisa Molinari
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  2. Massimiliano Cannata
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  3. Claudia Meisina
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Correspondence to Monia Elisa Molinari.

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Molinari, M.E., Cannata, M. & Meisina, C. r.massmov: an open-source landslide model for dynamic early warning systems. Nat Hazards 70, 1153–1179 (2014). https://doi.org/10.1007/s11069-013-0867-8

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  • DOI: https://doi.org/10.1007/s11069-013-0867-8

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