Abstract
The general assumptions and the most popular methods used to assess landslide hazard and for risk evaluation have not changed significantly in recent decades. Some of these assumptions have conceptual weakness, and the methods have revealed limitations. In this work, I deal with populations of landslides i.e. numerous landslides caused in an area by a single trigger (e.g. a rainstorm, an earthquake, a rapid snowmelt event), or by multiple events in a short or long period. Following an introduction on what we need to predict to assess landslide hazard and risk, I introduce the strategies and the main methods currently used to detect and map landslides, to predict populations of landslides in space and time, and to anticipate the numerosity and size characteristics of the expected landslides. For landslide detection and mapping, I consider traditional methods based on the visual interpretation of aerial photographs, and modern approaches that exploit the visual, semi-automatic or automatic analysis of remotely sensed images. For landslide spatial prediction, I discuss the results of a global review of statistical, classification-based methods for landslide susceptibility assessment. For the temporal prediction, leveraging on a global analysis of geographical landslide forecasting and early warning systems, I discuss short term forecast capabilities and their limitations. Next, I discuss long term landslide projections considering the impact of climate variations on landslide projections. For landslide numerosity and size characteristics, I discuss existing statistics of landslide area and volume obtained from large populations of event-triggered landslides. This is followed by an analysis of the landslide consequences, with emphasis on a spatial-temporal model of societal landslide risk in Italy. I end offering recommendations on what I think we should do to make significant progress in our collective ability to predict the hazard posed by populations of landslides, and to mitigate their risk.
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Acknowledgements
The paper distils results of research work I have done in the last three decades mainly with colleagues of the Geomorphology Research Group of the Research Institute for Geo-Hydrological Protection (IRPI), of the Italian National Research Council (CNR). Without their enduring efforts, the research I have conducted, and this article, would not have been possible. The article, and the talk on which the article is based, further benefited from comments, suggestions, and discussions with several colleagues at CNR IRPI, and particularly Paolo Allasia, Mauro Cardinali, Stefano L. Gariano, Daniele Giordan, Piernicola Lollino, Lorenzo Marchi, Alessandro C. Mondini, and Paola Reichenbach. I thank all of them for their comments, insight and wisdom.
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Guzzetti, F. (2021). On the Prediction of Landslides and Their Consequences. In: Sassa, K., Mikoš, M., Sassa, S., Bobrowsky, P.T., Takara, K., Dang, K. (eds) Understanding and Reducing Landslide Disaster Risk. WLF 2020. ICL Contribution to Landslide Disaster Risk Reduction. Springer, Cham. https://doi.org/10.1007/978-3-030-60196-6_1
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