Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment sca
- PDF / 8,813,342 Bytes
- 21 Pages / 595.276 x 790.866 pts Page_size
- 32 Downloads / 104 Views
M. Bordoni I V. Vivaldi I L. Lucchelli I L. Ciabatta I L. Brocca I J. P. Galve I C. Meisina
Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale
Abstract A combined method was developed to forecast the spatial and the temporal probability of occurrence of rainfall-induced shallow landslides over large areas. The method also allowed to estimate the dynamic change of this probability during a rainfall event. The model, developed through a data-driven approach basing on Multivariate Adaptive Regression Splines technique, was based on a joint probability between the spatial probability of occurrence (susceptibility) and the temporal one. The former was estimated on the basis of geological, geomorphological, and hydrological predictors. The latter was assessed considering short-term cumulative rainfall, antecedent rainfall, soil hydrological conditions, expressed as soil saturation degree, and bedrock geology. The predictive capability of the methodology was tested for past triggering events of shallow landslides occurred in representative catchments of Oltrepò Pavese, in northern Italian Apennines. The method provided excellently to outstanding performance for both the really unstable hillslopes (area under ROC curve until 0.92, true positives until 98.8%, true negatives higher than 80%) and the identification of the triggering time (area under ROC curve of 0.98, true positives of 96.2%, true negatives of 94.6%). The developed methodology allowed us to obtain feasible results using satellite-based rainfall products and data acquired by field rain gauges. Advantages and weak points of the method, in comparison also with traditional approaches for the forecast of shallow landslides, were also provided. Keywords Shallow landslides . Data-driven methods . Rainfall . Soil saturation degree . Remote sensing Introduction Rainfall-induced shallow landslides are slope instabilities of a mass of soil and/or debris, which involve the most superficial layers until around 2.0 m from ground level. Although they involve small volumes (101–105 m3) of soil, they can be densely distributed across small catchments, contributing a lot of sediments to the river network, developing into devastating debris flows, provoking significant damages to cultivations and infrastructures, and, sometimes, causing the loss of human lives (Lacasse et al. 2010). Hence, there is a pressing need for developing and implementing actions of risk mitigation and early-warning system strategies to reduce the negative effects of the occurrence of these slope instabilities at local and regional scales (Segoni et al. 2018a). A preliminary and fundamental step is represented by the reconstruction of a reliable model of assessment of the spatial and temporal probability of occurrence of these slope instabilities (Van Westen et al. 2006; Guzzetti et al. 2020). The spatial component determines the most prone areas according to a set of predisposing factors. The temporal component defines the moment o
Data Loading...
