Landslide hazard and risk mapping at catchment scale in the Arno River basin

Abstract

We present the methodologies adopted and the outcomes obtained in the analysis of landslide risk in the basin of the Arno River (Central Italy) in the framework of a project sponsored by the Basin Authority of the Arno River, started in the year 2002 and completed at the beginning of 2005. In particular, a complete set of methods and applications for the assessment of landslide susceptibility and risk are described and discussed.A new landslide inventory of the whole area was realized, using conventional (aerial-photo interpretation and field surveys) and nonconventional methods (e.g. remote sensing techniques such as DIn-SAR and PS-InSAR).The great majority of the mapped mass movements are rotational slides (75%), solifluctions and other shallow slow movements (17%) and flows (5%), while soil slips, and other rapid landslides, seem less frequent everywhere within the basin. The relationships between landslide characteristics and environmental factors have been assessed through statistical analysis. As expected, the results show a strong control of land cover, lithology and morphology on landslide occurrence. The landslide frequency-size distribution shows a typical scaling behaviour already underlined in other landslide inventories worldwide. The assessment of landslide hazard in terms of probability of occurrence in a given time, based for mapped landslides on direct and indirect observations of the state of activity and recurrence time, has been extended to landslide-free areas through the application of statistical methods implemented in an artificial neural network (ANN). Unique conditions units (UCU) were defined by the map overlay of landslide preparatory factors (lithology, land cover, slope gradient, slope curvature and upslope contributing area) and afterwards used to construct a series of model vectors for the training and test of the ANN. Various different ANNs were selected throughout the basin, until each UCU was assigned a degree of membership to a susceptibility and a hazard class. Model validation confirms that prediction results are very good, with an average percentage of correctly recognized mass movements of about 85%. The analysis also revealed the existence of a large number of unmapped mass movements, thus contributing to the completeness of the final inventory. Temporal hazard was estimated via the translation of state of activity in recurrence time and hence probability of occurrence. The following intersection of hazard values with vulnerability and exposure figures, obtained by reclassification of digital vector mapping at 1:10,000 scale, lead to the definition of risk values for each terrain unit for different periods of time into the future. The final results of the research are now undergoing a process of integration and implementation within land planning and risk prevention policies and practices at local and national level.

Quantitative estimation of landslide risk from rapid debris slides on natural slopes in the Nilgiri hills, India

“…Difficulties also arise if landslides are first-time failures. If the return period of triggering events for reactivated landslides is known, the estimation of the probability of future landslide activity is more straightforward (e.g., Coe et al, 2000;Catani et al, 2005).…”

Shallow landslide's stochastic risk modelling based on the precipitation event of August 2005 in Switzerland: results and implications

“…Examples of quantitative risk analysis (QRA) at regional scale can be found in Cardinali et al (2002), Remondo et al (2005) and Catani et al (2005). However, these studies provide a mean annual risk with no information on the expected distribution of annual costs.…”

“…More recently, applications of regional-scale QRA providing exceedance probabilities were presented in Jaiswal et al (2011) and Ghosh et al (2012). Although most of the QRA methodologies are developed for local or regional scales, some of them, for example Catani et al (2005), might be generalized to a larger area.…”

Satellite remote sensing for global landslide monitoring

“…The most exact method to assess landslide susceptibility is through field surveys. However, performing such surveys in data-sparse or mountainous regions is difficult; in many countries, remote sensing data may be the only infor mation available for this purpose [Catani et al, 2005;Nadim et al, 2006]. Recent advances in remote sensing tech niques contribute to determining landslide susceptibility by providing information on land surface features and characteristics.…”