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Landslide early warning systems (LEWS) can be categorized into two groups: territorial and local systems. Territorial landslide early warning systems (Te-LEWS) deal with the occurrence of several landslides in wide areas: at municipal/regional/national scale. The aim of such systems is to forecast the increased probability of landslide occurrence in a given warning zone. The performance evaluation of such systems is often overlooked, and a standardized procedure is still missing. This paper describes a new Excel user-friendly tool for the application of the EDuMaP method, originally proposed by (Calvello and Piciullo 2016). A description of indicators used for the performance evaluation of different Te-LEWS is provided, and the most useful ones have been selected and implemented into the tool. The EDuMaP tool has been used for the performance evaluation of the "SMART" warning model operating in Piemonte region, Italy. The analysis highlights the warning zones with the highest performance and the ones that need threshold refinement. A comparison of the performance of the SMART model with other models operating in different Te-LEWS has also been carried out, highlighting critical issues and positive aspects. Lastly, the SMART performance has been evaluated with both the EDuMaP and a standard 2 × 2 contingency table for comparison purposes. The result highlights that the latter approach can lead to an imprecise and not detailed assessment of the warning model, because it cannot differentiate among the levels of warning and the variable number of landslides that may occur in a time interval. [ABSTRACT FROM AUTHOR]

In high mountain canyon regions, SAR geometric distortion in imaging side may have an inevitable impact on InSAR deformation information, so the effective deformation information acquisition is critical for landslide identification and deformation mechanisms analysis. The landslide deformation around the reservoir of Gushui Hydropower Station located in upstream of the Lancang River has been focused on in the study. Using SAR satellite parameters and topographic information, the visibility analysis of deformation in radar line-of-sight (LOS) direction has been carried out, and a method to obtain LOS effective deformation information based on the visibility analysis has been proposed. The small baseline subsets (SBAS) technique is used to process the L-band and C-band SAR data, and the area affected by the geometric distortion in the InSAR result is masked to obtain the deformation information of the effective deformation region. The landslide identification analysis in the reservoir area has been carried out based on the effective deformation information in LOS direction. Thirteen landslides have been identified, and ten of them are new ones. A new large unstable area (New Zhenggang landslide) has been found near the Zhenggang landslide. The geological survey and displacement time series of the Zhenggang landslide reveals that it is in pull-type landslide mode, that is, due to the local instability of the leading edge of a landslide, the support of the trailing edge may be weakened, which may result in the landslide gradually developing backwards and upwards, and finally becoming a large landslide. The impact of peak rainfall and cumulative rainfall during the rainy season on landslide deformation has been verified in this paper. It indicates that the cumulative precipitation is the dominant factor causing the deformation of the landslide, and it shows that the landslide begins the deformation acceleration period about 12 days after the peak precipitation. The results have shown that the proposed visibility analysis method for extracting the effective deformation information of InSAR results can significantly improve landslide identification and analysis in complex terrain. [ABSTRACT FROM AUTHOR]

Early warning systems (EWSs) rely on the capacity to forecast a dangerous event with a certain amount of advance by defining warning criteria on which the safety of the population will depend. Monitoring of landslides is facilitated by new technologies, decreasing prices and easier data processing. At the same time, predicting the onset of a rapid failure or the sudden transition from slow to rapid failure and subsequent collapse, and its consequences is challenging for scientists that must deal with uncertainties and have limited tools to do so. Furthermore, EWS and warning criteria are becoming more and more a subject of concern between technical experts, researchers, stakeholders and decision makers responsible for the activation, enforcement and approval of civil protection actions. EWSs imply also a sharing of responsibilities which is often averted by technical staff, managers of technical offices and governing institutions. We organized the First International Workshop on Warning Criteria for Active Slides (IWWCAS) to promote sharing and networking among members from specialized institutions and relevant experts of EWS. In this paper, we summarize the event to stimulate discussion and collaboration between organizations dealing with the complex task of managing hazard and risk related to active slides. [ABSTRACT FROM AUTHOR]

The number of natural disasters induced by rainfall events in Taiwan has soared, with typhoons and torrential rains invariably inducing major landslides. In this study, data on major rainfall-generated landslides (605 in total) which occurred between 2006 and 2014 were used to classify landslides as types: shallow landslides (SL, 495), large landslides (LL, 34), and debris flows (DF, 76). LL were defined as landslides having an area, depth, and volume greater than 10 ha, 2 m and 2 × 105 m3, respectively. These were then analysed for their geometric form, geographic distribution, and scale and volume characteristics through a ternary diagram. A significant linear trend was found between the length (L) and volume (V) of SL, with the trend gradually moderating and converging with LL as length increased. The volume of LL displayed a significant increasing trend with depth (H), while SL and DF had less depth and average distribution. The median landslide length/width (L/W) ratios of SL and LL were quite close, and they had relatively similar morphologies; however, SL tended to occur near the slope toe, while large LL, due to their large volume, originated near the mountain ridges and extended to the nearest streams. The power law scaling components of W (β1) and L (β2) of SL were similar because of their (SL) small size, and they were highly concentrated at the centre of the developed ternary diagram. Through logistic regression, we further validated the exponents in classifying the landslides; β1, β2, and β3 (power law scaling component of H) are used in the ternary diagram. Overall, β1 was found to be the best model for classifying DF, SL, and LL having a correct rate of 0.955 and a lowest Akaike information criterion (AIC), 136.115, and Bayesian information criterion (BIC), 153.736. β3, the depth index, though had a poor AIC, was 100% correct in classifying LL. [ABSTRACT FROM AUTHOR]