TY - JOUR AU - Pavlova, Irina AU - Jomelli, Vincent AU - Grancher, Delphine AU - Brunstein, Daniel AU - Vrac, Mathieu PY - 2011/11/30 Y2 - 2024/03/29 TI - Debris flow occurrence and meteorological factors in the French Alps: a regional investigation JF - Italian journal of engineering geology and environment JA - IJEGE VL - IS - SE - Articles DO - 10.4408/IJEGE.2011-03.B-015 UR - https://rosa.uniroma1.it/rosa02/engineering_geology_environment/article/view/1115 SP - 128-134 AB - <p>Debris flow (DF) phenomena is at the top of the list of dangerous natural hazards in the mountains areas all over the world. Among factors resulting in a DF triggering, meteorological conditions are considered to be the most relevant. The general objective of this study was to identify meteorological parameters controlling the triggering of DF in one part of the French Alps over the last 50 years. Major factors are quite well explored at the global scale or contrariwise in very precise territory in particular catchment areas. However for now we have a poor knowledge of those factors at the scale of a medium- sized region (including catchments with different geomorphic characteristics over several km2) especially in the French Alps. In addition in this region only a few studies focused on relationships with climate. To understand DF activity and link it with meteorological parameters in the north region of the French Alps, we used a multivariate statistical approach. Regional meteorological parameters (such as mean monthly temperature and precipitation) were first computed from a Principal Component Analysis of observed meteorological data from four weather stations. A binomial monthly logistic regression (LR) probability model was then tted between the main principal components and DF data base composed of 298 debris flow events triggered between 1971 and 2008. Results revealed that the most successful model including two meteorological predictors (minimal monthly temperature and the number of rainy days between May and September) correctly explains more than 60% of the DF events.</p> ER -