Matteo Berti

Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach

[1]xa0Various methods have been proposed in the literature to predict the rainfall conditions that are likely to trigger landslides in a given area. Most of these methods, however, only consider the rainfall events that resulted in landslides and provide deterministic thresholds with a single possible output (landslide or no-landslide) for a given input (rainfall conditions). Such a deterministic view is not always suited to landslides. Slope stability, in fact, is not ruled by rainfall alone and failure conditions are commonly achieved with a combination of numerous relevant factors. When different outputs (landslide or no-landslide) can be obtained for the same input a probabilistic approach is preferable. In this work we propose a new method for evaluating rainfall thresholds based on Bayesian probability. The method is simple, statistically rigorous, and returns a value of landslide probability (from 0 to 1) for each combination of the selected rainfall variables. The proposed approach was applied to the Emilia-Romagna Region of Italy taking advantage of the historical landslide archive, which includes more than 4000 events for which the date of occurrence is known with daily accuracy. The results show that landsliding in the study area is strongly related to rainfall event parameters (duration, intensity, total rainfall) while antecedent rainfall seems to be less important. The distribution of landslide probability in the rainfall duration-intensity shows an abrupt increase at certain duration-intensity values which indicates a radical change of state of the system and suggests the existence of a real physical threshold.

Landslide monitoring with sensor networks: experiences and lessons learnt from a real-world deployment

Wireless sensor networks have the potentials to be a very useful technology for fine-grained monitoring in remote and hostile environments. This paper reports on the implementation and deployment of a system for landslide monitoring in the Northern Italy Apennines and analyses the positive results we have achieved with it. Yet, the paper also critically analyses the problems and the inherent limitations/difficulties we had to face in developing and deploying such a system, challenging many of the big claims that are often heard around wireless sensor networks.

A User-Oriented Methodology for DInSAR Time Series Analysis and Interpretation: Landslides and Subsidence Case Studies

Recent advances in multi-temporal Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) have greatly improved our capability to monitor geological processes. Ground motion studies using DInSAR require both the availability of good quality input data and rigorous approaches to exploit the retrieved Time Series (TS) at their full potential. In this work we present a methodology for DInSAR TS analysis, with particular focus on landslides and subsidence phenomena. The proposed methodology consists of three main steps: (1) pre-processing, i.e., assessment of a SAR Dataset Quality Index (SDQI) (2) post-processing, i.e., application of empirical/stochastic methods to improve the TS quality, and (3) trend analysis, i.e., comparative implementation of methodologies for automatic TS analysis. Tests were carried out on TS datasets retrieved from processing of SAR imagery acquired by different radar sensors (i.e., ERS-1/2 SAR, RADARSAT-1, ENVISAT ASAR, ALOS PALSAR, TerraSAR-X, COSMO-SkyMed) using advanced DInSAR techniques (i.e., SqueeSAR™, PSInSAR™, SPN and SBAS). The obtained values of SDQI are discussed against the technical parameters of each data stack (e.g., radar band, number of SAR scenes, temporal coverage, revisiting time), the retrieved coverage of the DInSAR results, and the constraints related to the characterization of the investigated geological processes. Empirical and stochastic approaches were used to demonstrate how the quality of the TS can be improved after the SAR processing, and examples are discussed to mitigate phase unwrapping errors, and remove regional trends, noise and anomalies. Performance assessment of recently developed methods of trend analysis (i.e., PS-Time, Deviation Index and velocity TS) was conducted on two selected study areas in Northern Italy affected by land subsidence and landslides. Results show that the automatic detection of motion trends enhances the interpretation of DInSAR data, since it provides an objective picture of the deformation behaviour recorded through TS and therefore contributes to the understanding of the on-going geological processes.

Runoff of small rocky headwater catchments: Field observations and hydrological modeling.

In dolomitic headwater catchments, intense rainstorms of short duration produce runoff discharges that often trigger debris flows on the scree slopes at the base of rock cliffs. In order to measure these discharges, we placed a measuring facility at the outlet (elevation 1770 m a.s.l.) of a small, rocky headwater catchment (area ∼ 0.032 km2, average slope ∼ 320%) located in the Venetian Dolomites (North Eastern Italian Alps). The facility consists of an approximately rectangular basin, ending with a sharp-crested weir. Six runoff events were recorded in the period 2011-2014, providing a unique opportunity for characterizing the hydrological response of the catchment. The measured hydrographs display impulsive shapes, with an abrupt raise up to the peak, followed by a rapidly decreasing tail, until a nearly constant plateau is eventually reached. This behavior can be simulated by means of a distributed hydrological model if the excess rainfall is determined accurately. We show that using the Soil Conservation Service Curve-Number (SCS-CN) method and assuming a constant routing velocity invariably results in an underestimated peak flow and a delayed peak time. A satisfactory prediction of the impulsive hydrograph shape including peak value and timing is obtained only by combining the SCS-CN procedure with a simplified version of the Horton equation, and simulating runoff routing along the channel network through a matched diffusivity kinematic wave model. The robustness of the proposed methodology is tested through a comparison between simulated and observed timings of runoff or debris flow occurrence in two neighboring alpine basins. This article is protected by copyright. All rights reserved.

DFLOWZ: A free program to evaluate the area potentially inundated by a debris flow

Abstract The transport and deposition mechanisms of debris flows are still poorly understood due to the complexity of the interactions governing the behavior of water–sediment mixtures. Empirical–statistical methods can therefore be used, instead of more sophisticated numerical methods, to predict the depositional behavior of these highly dangerous gravitational movements. We use widely accepted semi-empirical scaling relations and propose an automated procedure (DFLOWZ) to estimate the area potentially inundated by a debris flow event. Beside a digital elevation model (DEM), the procedure has only two input requirements: the debris flow volume and the possible flow-path. The procedure is implemented in Matlab and a Graphical User Interface helps to visualize initial conditions, flow propagation and final results. Different hypothesis about the depositional behavior of an event can be tested together with the possible effect of simple remedial measures. Uncertainties associated to scaling relations can be treated and their impact on results evaluated. Our freeware application aims to facilitate and speed up the process of susceptibility mapping. We discuss limits and advantages of the method in order to inform inexperienced users.