The morphometry of 432 glacial cirques in the Maritime Alps (Western French‐Italian Alps), studied over several years of fieldwork, was analysed with the use of a geographical information system. Some of the parameters automatically evaluated from digital elevation models required an objective and relatively new definition. In particular, cirque length was measured along a line that, from the threshold midpoint, splits the cirque into two equivalent surfaces; cirque width was automatically drawn as the longest line inscribed in the cirque and perpendicular to the length line. Significant correlations were found among the different factors and parameters analysed. In particular, cirque shape analysis showed that cirques develop allometrically in the three dimensions, i.e. more in length and width than in altitudinal range. Nevertheless cirques of the Maritime Alps have a regular, almost circular shape (mean L/W value = 1.07). The correlations among length, width and area are all very high (r2= 0.8–0.9). In terms of size, cirques show a wide range in area from 0.06 to 5.2 km2 with a mean value of 0.4 km2. The largest cirques are found on SSW‐facing slopes and at high elevations. Small cirques can be found at all altitudes but all those at high elevation are part of compound cirques at the main head valleys. Most cirques (37%) are characterized by a northern aspect; NE and SW are also frequent directions.

A complete sequence of glacial deposits and moraines within the same valley system in\ud the Maritime Alps, spanning from the Last Glacial Maximum (LGM) to the Little Ice Age is presented.\ud The sequence is geomorphologically and morphostratigraphically coherent and most stadials\ud have been chronologically constrained by their cosmogenic exposure ages, lichenometry and by\ud correlation with radiocarbon-dated moraines in neighbouring valleys. The shape, extent and thickness\ud of the palaeoglaciers at each stadial have also been reconstructed and their equilibrium line\ud altitude calculated. The LGM moraine of the Gesso Basin bears a similar equilibrium line altitude\ud and age to that of other LGM moraines across the Alps. The recognized Late-glacial stadials show\ud strong similarities with the corresponding stadials of the central–eastern Alpine valleys, such as\ud Gschnitz, Bu¨hl, Daun and Egesen. The recalculation of the exposure ages of moraine boulders\ud with a new production rate better defines the LGM (24.0 ka) and the Egesen Stadial (13.0 ka),\ud while the Bu¨hl Stadial (18.5 ka) is dated for the first time in the Alps. Three early Holocene glacial\ud advances are defined and correlated to the Kartell, Kromer and Go¨schenen I stadials, widely recognized\ud in other Alpine sectors. Lichenometric dates indicate a three-fold oscillation during the\ud Little Ice Age (thirteenth, seventeenth and nineteenth centuries)

2008 (May): Exposure age dating and Equilibrium Line Altitude reconstruction of an Egesen moraine in the Maritime Alps, Italy.

Two glacial deposits in the Gesso valley (Maritime, Alps) have been 10Be‐dated at 20 140±1080 (weighted mean±SD) and 16 590±970 years, respectively, thus constraining the Last Glacial Maximum (LGM) and Gschnitz stadials in the southwestern part of the Alps. The LGM age is chronologically coherent with MIS 2 and synchronous with most other LGM moraines in the Alps. The Gschnitz stadial also appears to be in agreement with the ages obtained from other Alpine sites and with Heinrich Event I. This suggests that the Alpine glaciers reacted simultaneously and essentially synchronously with the climate change associated with Heinrich Event 1. The Equilibrium Line Altitudes (ELAs) of the LGM and Gschnitz reconstructed palaeoglaciers are 1850 and 1910 m a.s.l., respectively. The ELA comparison across the Alps indicates that the palaeoclimate of the Maritime Alps during the LGM was rather different from that of other Alpine sectors. However, the similar Gschnitz ELA value between the Gesso valley and other sites across the mountain chain indicates that Alpine glaciers responded with the same intensity to the climate change associated with Heinrich Event I. Overall, these results suggest that the interaction between the atmospheric circulation of air masses and local Alpine orography was more complex than has previously been argued.

This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instability developed through statistical models (conditional analysis and logistic regression), and neural network application, in order to better understand the relationship between the geological/geomorphological landforms and processes and landslide occurrence, and to increase the performance of landslide susceptibility models. The proposed experimental study concerns with a wide research project, promoted by the Tuscany Region Administration and APAT-Italian Geological Survey, aimed at defining the landslide hazard in the area of the Sheet 250 ‘‘Castelnuovo di Garfagnana’’ (1:50,000 scale). The study area is located in the middle part of the Serchio River basin and is characterized by high landslide susceptibility due to its geological, geomorphological, and climatic features, among the most severe in Italy. Terrain susceptibility to slope failure has been approached by means of indirect-quantitative statistical methods and neural network software application. Experimental results from different methods and the potentials and pitfalls of this methodological approach have been presented and discussed. Applying multivariate statistical analyses made it possible a better understanding of the phenomena and quantification of the relationship between the instability factors and landslide occurrence. In particular, the application of a multilayer neural network, equipped for supervised learning and error control, has improved the performance of the model. Finally, a first attempt to evaluate the classification efficiency of the multivariate models has been performed by means of the receiver operating characteristic (ROC) curves analysis approach