Weather factor landslide hazard


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Preliminary investigation on landaslide risk assesment by using weather derivative and geographical layer
Poster EGU 2014
Massimo Perna
Valerio Capecchi
Alfonso Crisci
Manuela Corongiu
Manetti Fabrizio

28 Apr all'EGU near Natural Hazard,
session Advanced methods in landslides research
II: modelling

Published in: Environment
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Weather factor landslide hazard

  1. 1. Weather factors sensitivity in shallow landslide hazard: preliminary results Perna(1) M., Capecchi(1) V., Crisci(2) A., Corongiu(3) M., Manetti(3) F. (1) CNR IBIMET - Consorzio LaMMA, Sesto Fiorentino, Italy, (2) CNR IBIMET, Firenze, Italy, (3) Consorzio LaMMA, Sesto Fiorentino, Italy Data and methodsGoal Header [1] Mercogliano et al: A prototype forecasting chain for rainfall induced shallow landslides, Natural Hazards & Earth System Sciences, 13,2013 [2] Segoni et al: Towards a definition of a real-time forecasting network for rainfall induced shallow lanslides., Natural Hazards & Earth System Sciences, 9, 2009 [3] Catani et al: Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues., Natural Hazards & Earth System Sciences, 990 13, 2013. The study and the poster received the support of the Results – Variable importance Input static thematic predictors (30 m spatial resolution): ● Geomorphological: elevation, slope, altitude above channel network, etc... ● Geological: distance from main tectonic features, soil permeability, Slope Structural Setting, etc... ● Hydrological: Time of concentration, Topographic Wetness Index, convergence/divergence to overland flow, etc... ● Climate: mm of rainfall for an event with 100 years as returning period Input dynamical WRF numerical weather predictions (3 km spatial resolution)  Rainfall amounts (mm/24h) and intensities (mm/h)  Soil moisture Results - 25OCT2011 Might rainfall-induced shallow landsliding prediction benefit from the information provided by the WRF numerical weather predictions? (see ref. [1],[2]) Two heavy rainfall events occurred on 25OCT2011 (Lunigiana) and on 18MAR2013 (Garfagnana) triggered a great number of shallow landslides, causing damages, injuries and human losses. Conclusions Data and methods We developed a quantitative indirect statistical modeling. Two statistical techniques are considered: Generalized Linear Model (GLM) and Breiman's Random Forest (RF, ref [3]). Results - 18MAR2013 (I) Results found are in good agreement with observations (see ROC curves and AUC values) (II) 18MAR2013: GLM model performs better than RF (III)25OCT2011: RF performs slightly better than GLM (IV)WRF data are important predictors for 25OCT2011 event, whereas static predictors prevail for 18MAR2013 FINDINGS (I) The statistical model is simple, efficient and provides reliable results even if NWP data are a not downscaled towards higher spatial resolution (II) NWP hourly precipitation intensities and soil moisture should be considered as input predictors since they are classified “important” when deep convection occurs (25OCT2011 rainfall event) DRAWBACKS/LIMITS (I) The statistical model is used as a “black-box”, no tuning performed so far (II) the statistical model is data-driven. It might performs differently in different areas or for different rainfall events VariableimportanceisprovidedbytheRandomForestalgorithm Conclusions Bridge the gap between micro-γ scale (≤ 20-30 m) typical scale of landslide occurring at basin scale, with the meso-γ scale (2-20 km) which is the typical scale of the NWP forecast. LIFE/12/ENV/IT/001054