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

被引:0
作者
Monia Elisa Molinari
Massimiliano Cannata
Claudia Meisina
机构
[1] University of Pavia,Department of Earth and Environmental Sciences
[2] SUPSI,Institute of Earth Sciences
来源
Natural Hazards | 2014年 / 70卷
关键词
Modeling; Landslide; GIS; Calibration; Sensitivity analysis; Multi-spatial resolution; UCODE; GRASS;
D O I
暂无
中图分类号
学科分类号
摘要
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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页码:1153 / 1179
页数:26
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