Brief communication "A prototype forecasting chain for rainfall induced shallow landslides"

Abstract

Abstract. Although shallow landslides are a very widespread phenomenon, large area (e.g. thousands of square kilometres) early warning systems are commonly based on statistical rainfall thresholds, while physically based models are more commonly applied to smaller areas. This work provides a contribution towards the filling of this gap: a forecasting chain is designed assembling a numerical weather prediction model, a statistical rainfall downscaling tool and a geotechnical model for the distributed calculation of the factor of safety on a pixel-by-pixel basis. The forecasting chain can be used to forecast the triggering of shallow landslides with a 48 h lead time and was tested on a 3200 km2 wide area.

Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results

“…Previous similar studies, in particular Mercogliano et al (2013b), used statistical techniques to resample, from the meso-γ to the micro-γ scale, rainfall data produced by the NWP model. In general, downscaling statistical methods, by construction, introduce a source of uncertainty because they depend upon the choice of predictors (mainly topographical variables) and they suppose stationarity in the predictor-predictand relationship (Fowler et al, 2007).…”

“…They coupled the NWP regional data, originally produced at 2.8 km of resolution and statistically downscaled to 10 m, with a physically based slope stability simulator (hydrological and geo-technical tool). In Mercogliano et al (2013b), the authors tested the procedure in a pilot site in the northwestern part of Tuscany (Lucca, Pistoia and Prato administrative provinces) for a specific rainfall event. A quantitative validation of the results was not performed, nevertheless the authors concluded that additional well-documented study cases need to be simulated to better understand the spatial organization of the input parameters and improve the quality of the results.…”

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

“…Snow has less impact on animal husbandry in developed countries due to better infrastructure in grassland and livestock industry, in which the studying emphasis has been particularly focused on risk assessment and early warning of traffic, landslides and avalanches related to heavy snowfall events (Jones and Jamieson, 2001;Williamson et al, 2002;Martelloni et al, 2013;Mercogliano et al, 2013;Rossi et al, 2013). As for snow disasters in pastoral areas in China, the emphasis has been placed on monitoring the change of snow distribution and livestock loss evaluation post-disasters.…”

Advanced Technologies for Landslides (WCoE 2014–2017, IPL-196, IPL-198)

“…In WP 3 the research activities include the development of a physically-based model, called HIRESSS Mercogliano et al 2013), for the real time prediction of shallow landslides induced by rain on large areas, predictive statistical models (SIGMA and MACUMBA models) (Martelloni et al 2012;Lagomarsino et al 2013;Segoni et al 2014a, b;Rosi et al 2016) based on the identification of rainfall thresholds for triggering landslides, and a nowcasting system which includes a forecasting and alert system valid at the national level for landslides with fast kinematics (not monitorable by satellite).…”