Filippo Catani

Recommendations for the quantitative analysis of landslide risk

This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts.

Statistical analysis of drainage density from digital terrain data

Drainage density (D-d), defined as the total length of channels per unit area, is a fundamental property of natural terrain that reflects local climate, relief, geology, and other factors. Accurate measurement of D-d is important for numerous geomorphic and hydrologic applications, yet it is a surprisingly difficult quantity to measure, particularly over large areas. Here, we develop a consistent and efficient method for generating maps of D-d using digital terrain data. The method relies on (i) measuring hillslope flow path distance at every unchanneled site within a basin, and (ii) analyzing this field as a random space function. As a consequence, we measure not only its mean (which is half the inverse of the traditional definition of drainage density) but also its variance, higher moments, and spatial correlation structure. This yields a theoretically sound tool for estimating spatial variability of drainage density. Averaging length-to-channel over an appropriate spatial scale also makes it possible to derive continuous maps of D-d and its spatial variations. We show that the autocorrelation length scale provides a natural and objective choice for spatial averaging. This mapping technique is applied to a region of highly variable D-d in the northern Apennines, Italy. We show that the method is capable of revealing large-scale patterns of variation in D-d that are correlated with lithology and relief. The method provides a new and more general way to quantitatively define and measure D-d to test geomorphic models, and to incorporate D-d variations into regional-scale hydrologic models

Rainfall thresholds for the forecasting of landslide occurrence at regional scale

This paper concerns a regional scale warning system for landslides that relies on a decisional algorithm based on the comparison between rainfall recordings and statistically defined thresholds. The latter were based on the total amount of rainfall, which was cumulated considering different time intervals: 1-, 2- and 3-day cumulates took into account the critical rainfall influencing shallow movements, whilst a variable time interval cumulate (up to 240 days) was used to consider the triggering of deep-seated landslides in low permeability terrains. A prototypal version of the model was initially set up to define statistical thresholds. Then, thresholds were calibrated using a database of past georegistered and dated landslides. A validation procedure showed that the calibration highly improves the results and therefore the model was integrated in the regional warning system of Emilia Romagna (Italy) for civil protection purposes. The proposed methodology could be easily implemented in other similar regions and countries where a sufficiently organised meteorological network is present.

Persistent Scatterer Interferometry (PSI) Technique for Landslide Characterization and Monitoring

: The measurement of landslide superficial displacement often represents the most effective method for defining its behavior, allowing one to observe the relationship with triggering factors and to assess the effectiveness of the mitigation measures. Persistent Scatterer Interferometry (PSI) represents a powerful tool to measure landslide displacement, as it offers a synoptic view that can be repeated at different time intervals and at various scales. In many cases, PSI data are integrated with in situ monitoring instrumentation, since the joint use of satellite and ground-based data facilitates the geological interpretation of a landslide and allows a better understanding of landslide geometry and kinematics. In this work, PSI interferometry and conventional ground-based monitoring techniques have been used to characterize and to monitor the Santo Stefano d’Aveto landslide located in the Northern Apennines, Italy. This landslide can be defined as an earth rotational slide. PSI analysis has contributed to a more in-depth investigation of the phenomenon. In particular, PSI measurements have allowed better redefining of the boundaries of the landslide and the state of activity, while the time series analysis has permitted better understanding of the deformation pattern and its relation with the causes of the landslide itself. The integration of ground-based monitoring data and PSI data have provided sound results for landslide characterization. The punctual information deriving from inclinometers can help in defining the actual location of the sliding surface and the involved volumes, while the measuring of pore water pressure conditions or water table level can suggest a correlation between the deformation patterns and the triggering factors.

Persistent Scatterers Interferometry Hotspot and Cluster Analysis PSI-HCA for detection of extremely slow-moving landslides

The synthetic aperture radar (SAR) interferometry (InSAR) technique has already shown its importance in landslide mapping and monitoring applications. However, the usefulness of traditional differential InSAR applications is limited by disturbing factors such as temporal decorrelation and atmospheric disturbances. The Persistent Scatterers Interferometry (PSI) technique is a recently developed InSAR approach. It generates stable radar benchmarks (namely persistent scatterers, PSI point targets) using a multi-interferogram analysis of SAR images. The PSI technique has the advantage of reducing temporal decorrelation and atmospheric artefacts. The PSI technique is suitable for the investigation of extremely slow-moving landslides due to its capability to detect ground displacements with millimetre precision. However, the interpretation of PSI outputs is sometimes difficult for the large number of possible persistent scatterers (PSs). A new approach of PSI Hotspot and Cluster Analysis (PSI-HCA) is introduced here in order to develop a procedure for mapping landslides efficiently and automatically. This analysis has been performed on PSs in hilly and mountainous areas within the Arno river basin (Italy). The aim is to use PSs processed from 4 years (2003–2006) of Radarsat images for identifying areas preferentially affected by extremely slow-moving landslides. The Getis–Ord Gi * statistic is applied in the study for the PSI-HCA approach. The velocity of PSs is used as weighting factor and the Gi * index is calculated for each single point target. The results indicate that both high positive and low negative Gi * values imply the clustering of potential mass movements. High positive values suggest the moving direction towards the sensor along the satellite line-of-sight (LOS), whereas low negative values imply the movement away from the sensor. Furthermore, the kernel function is used to estimate PS density based on these derived Gi * values. The output is a hotspot map which highlights active mass movements. This spatial statistic approach of PSI-HCA is considered an effective way to extract useful information from PSs at a regional scale, thus providing an innovative approach for rapid mapping of extremely slow-moving landslides over large areas.

Statistical and environmental analyses for the definition of a regional rainfall threshold system for landslide triggering in Tuscany (Italy)

The aim of this work is the determination of regional-scale rainfall thresholds for the triggering of landslides in the Tuscany Region (Italy). The critical rainfall events related to the occurrence of 593 past landslides were characterized in terms of duration (D) and intensity (I). I and D values were plotted in a log-log diagram and a lower boundary was clearly noticeable: it was interpreted as a threshold representing the rainfall conditions associated to landsliding. That was also confirmed by a comparison with many literature thresholds, but at the same time it was clear that a similar threshold would be affected by a too large approximation to be effectively used for a regional warning system. Therefore, further analyses were performed differentiating the events on the basis of seasonality, magnitude, location, land use and lithology. None of these criteria led to discriminate among all the events different groups to be characterized by a specific and more effective threshold. This outcome could be interpreted as the demonstration that at regional scale the best results are obtained by the simplest approach, in our case an empirical black box model which accounts only for two rainfall parameters (I and D). So a set of thresholds could be conveniently defined using a statistical approach: four thresholds corresponding to four severity levels were defined by means of the prediction interval technique and we developed a prototype warning system based on rainfall recordings or weather forecasts.

Landslides triggered by rainfall: A semi-automated procedure to define consistent intensity-duration thresholds

In this paper, a methodology to automate and standardize the identification of rainfall intensity-duration thresholds for landslides triggering is presented. A newly developed software called MaCumBA (MAssive CUMulative Brisk Analyzer) can be used to analyze rain-gauge records, extract the intensities (I) and durations (D) of the rainstorms associated with the initiation of landslides, plot these values on a diagram and identify thresholds that define the lower bounds of the aforementioned I-D values. Because the methodology is automated, it is possible to process a relevant amount of data in short times, while allowing for user decision input. A back analysis using data from past events that did not trigger landslides can be used to identify the threshold conditions associated with the least amount of false alarms. We applied the methodology in two test sites. A validation procedure returned satisfactory results, demonstrating the potential utility of the proposed methodology in the development of landslide warning systems.

Updating and tuning a regional-scale landslide early warning system

This work presents the last improvements of an operative regional-scale warning system developed for the management of the risk related to rainfall-induced landslides (both shallow and deep seated). The warning system is named Sistema Integrato Gestione Monitoraggio Allerta, and it is based on a set of spatially variable statistical rainfall thresholds (Martelloni et al. Landslides 9(4): 485–495, 2012b). The performance of the warning system was enhanced using a larger landslide dataset for the calibration of thresholds and readjusting the boundaries of the territorial units (TUs, the basic spatial unit of application of the warning system). Our tuning leads to define a larger number of TUs and to change some of the previous reference rain gauges. In particular, a statistical analysis highlighted that the spatial organization of missed and correctly predicted landslides does not depend on lithology, land use, and morphometric attributes; therefore, the redefinition of TUs was based on the administrative borders between municipalities. This allowed combining the TU outputs into a decisional procedure which, in a completely automated way, is able to forecast the warning levels based on objective and quantitative criteria (the number of expected landslides), in full accordance with the regional civil protection guidelines. The implementation of these updates was straightforward and could be conveniently applied to similar warning systems based on rainfall thresholds.

Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry

Preparation of reliable landslide hazard and risk maps is crucial for hazard mitigation and risk management. In recent years, various approaches have been developed for quantitative assessment of landslide hazard and risk. However, possibly due to the lack of new data, very few of these hazard and risk maps were updated after their first generation. In this study, aiming at an ongoing assessment, a novel approach for updating landslide hazard and risk maps based on Persistent Scatterer Interferometry (PSI) is introduced. The study was performed in the Arno River basin (central Italy) where most mass movements are slow-moving landslides which are properly within the detection precision of PSI point targets. In the Arno River basin, the preliminary hazard and risk assessment was performed by Catani et al. (Landslides 2:329–342, 2005) using datasets prior to 2002. In this study, the previous hazard and risk maps were updated using PSI point targets processed from 4 years (2003–2006) of RADARSAT images. Landslide hazard and risk maps for five temporal predictions of 2, 5, 10, 20 and 30 years were updated with the exposure of losses estimated in Euro (€). In particular, the result shows that in 30 years a potential loss of approximate €3.22 billion is expected due to these slow-moving landslides detected by PSI point targets.

Improving basin scale shallow landslide modelling using reliable soil thickness maps

Soil thickness is a well-known factor controlling shallow landsliding. Notwithstanding, its spatial organisation over large areas is poorly understood, and in basin scale slope analyses it is often established using simple methods. In this paper, we apply five different soil thickness models in two test sites, and we use the obtained soil thickness maps to feed a slope stability model. Validation quantifies how errors in soil thickness influence the resulting factor of safety and points out which method grants the best results. In particular, in our cases, slope-derived soil thickness patterns produced the worst slope stability assessment, while the use of reliable soil thickness maps obtained by means of a more complex geomorphologically indexed model improved shallow landslides modelling.