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Rainfall triggered landslides occur in all mountain ranges posing threats to people and the environment. Given the projected climate changes, the risk posed by landslides is expected to increase, and the ability to anticipate their occurrence is key for effective risk reduction. Empirical thresholds and physically-based models are used to anticipate the short-term occurrence of rainfall-induced shallow landslides. But, evidence suggests that they may not be effective for operational forecasting over large areas. We propose a deep-learning based strategy to link rainfall to landslide occurrence. We inform and test the system with rainfall and landslide data available for the last 20 years in Italy. Our results indicate that it is possible to anticipate effectively the occurrence of rainfall-induced landslides over large areas, and that their location and timing are controlled primarily by the precipitation, opening to the possibility of operational landslide forecasting based on rainfall measurements and quantitative meteorological forecasts. How much rain does it take to trigger a landslide? This work shows that deep learning can identify the driving forces that can cause rainfall induced landslides, opening up the possibility of forecasting landslide events over large areas

Recent estimates suggest that landslides occur in about 17.1% of the landmasses, that about 8.2% of the global population live in landslide prone areas, and that population exposure to landslides is expected to increase. It is threfore not surprising that landslide early warning is gaining attention in the scientific and the technical literature, and among decision makers. Thanks to important scientific and technological advancements, landslide prediction and early warning are now possible, and landslide early warning systems (LEWSs) are becoming valuable resources for risk mitigation. A review of geographical LEWSs examined 26 regional, national and global systems in the 44.5-year period from January 1977 to June 2019. The study relevaled that only five nations, 13 regions, and four metropolitan areas benefited from operational LEWSs, and that large areas where landslide risk to the population is high lack LEWS coverage. The review also revealed that the rate of LEWSs deployment has increased in the recent years, but remains low, and that reniewed efforts are needed to accelerate the deployment of LEWSs. Building on the review, recommendations for the further development and improvement of geographical LEWSs are proposed. The recommendations cover six areas, including design, deployment, and operation of LEWS; collection and analysis of landslide and rainfall data used to design, operate, and validate LEWSs; landslide forecast models and advisories used in LEWSs; LEWSs evaluation and performance assessment; operation and management; and communication and dissemination. LEWSs are complex and multi-faceted systems that require care in their design, implementation and operation. To avoid failures that can lead to loss of credibility and liability consequences, it is critical that the community of scientists and professionals who design, implement and operate LEWSs takes all necessary precautions, guided by rigorous scientific practices.

Landslide inventory maps are commonly prepared through the visual interpretation of stereoscopic aerial photographs and field checks. Stereoscopic satellite images can also be interpreted visually to recognize and map landslides. When interpreting stereoscopic imagery, shadows can conceal the photographic elements typical of landslides, hampering the recognition and mapping of the landslides. To mitigate the problem, we propose a method that exploits normalized difference vegetation index (NDVI) images and digital stereoscopy for the 3D visual recognition and mapping of landslides in shadowed areas. We tested the method in the 25 km2 Pogliaschina catchment, northern Italy, where intense rainfall caused abundant landslides on 25 October 2011. Using a PLANAR® StereoMirror™ digital stereoscope, we prepared an event landslide inventory map (E-LIM) through the visual interpretation of a pair of NDVI images obtained from a WorldView-2 stereoscopic multispectral bundle. We compared the event inventory with two independent E-LIMs for the same area and landslide event. The 3D vision of the NDVI stereoscopic image pair maximized the use of the radiometric (color and tone) and the terrain (elevation, slope, relief, and convexity) information captured by the stereoscopic multispectral images, allowing for the recognition of more landslides and more landslide areas than the other E-LIMs in the shadowed areas. Our results confirm that use of NDVI images facilitates the visual recognition and mapping of landslides in terrain affected by shadows. We expect that the proposed method can help trained interpreters to map landslides more accurately in areas affected by shadows.

Landslides often happen where they have already occurred in the past. The potential of landslides to reduce or enhance conditions for further landsliding has long been recognized and has often been reported, but the mechanisms and spatial and temporal scales of these processes have previously received little specific attention. Despite a preponderance of qualitative and anecdotal evidence, analysis has been limited. As a result, there is little consensus on the meaning of terms such as landslide repetition, recurrence, and reactivation. This source of confusion is evident when such terms are also used to describe systems where landsliding is prevalent but unrelated to landslide history. Recent findings, partly based on a rare multi-temporal landslide inventory for an area in Italy, show that the impacts of earlier landslides affect a substantial fraction of landslides, that landslides following earlier landslides differ from those that do not, and that accounting for the effect of previous landslides can improve susceptibility assessments. These findings await confirmation in other landslide-prone landscapes but show that consecutive landsliding deserves more attention, which requires consistent terminology. No such terminology is presently available, and we therefore propose it in this manuscript. We use the term “uncorrelated landsliding” to describe situations where landslides are common, but where a correlation with environmental variables such as terrain steepness is not implied. We propose “correlated landsliding” to describe situations where landslides are common and correlations with environmental variables exist, and “path-dependent landsliding” to describe situations where causal relations exist between consecutive landslides, for instance, when landslides occur at the scarp of previous landslides. These are situations where past landslides impact future landslides. Within the path-dependent category, we distinguish three subcategories based on the spatial distance between earlier and later landslides: “reactivation” or “continuation” if essentially the same material recommences or continues to slide, “local activation” if an earlier slide causes changes in a local hillslope that cause a later slide, and “remote activation” if an earlier slide causes changes elsewhere in the landscape that cause a later landslide. We use this proposed set of terms to outline some prominent knowledge gaps and potential research questions.

In this report, we summarise the activities of the Research Institute for Geo-Hydrological Protection (IRPI, http://www.irpi.cnr.it/en/), of the Italian National Research Council (CNR, http://www.cnr.it/en/), as a World Centre of Excellence (WCoE) on Landslide Risk, of the International Programme on Landslides (IPL, http://iplhq.org). The report is organised as follows. After a brief description of CNR IRPI (“The Research Institute for Geo-Hydrological Protection”), we present recent developments related to landslide early warning systems (“Landslide early warning systems”), climate change and landslides interactions (“Landslides in a changing climate”), and landslide risk assessment, mitigation and dissemination efforts (“Landslide risk assessment, mitigation and dissemination efforts”). We conclude listing relevant papers published between 2017 and 2018 by CNR IRPI scientists (“References”).

A global database of 2,626 rainfall events that have resulted in shallow landslides and debris flows was compiled through a thorough literature search. The rainfall and landslide information was used to update the dependency of the minimum level of rainfall duration and intensity likely to result in shallow landslides and debris flows established by Nel Caine in 1980. The rainfall intensity–duration (ID) values were plotted in logarithmic coordinates, and it was established that with increased rainfall duration, the minimum average intensity likely to trigger shallow slope failures decreases linearly, in the range of durations from 10 min to 35 days. The minimum ID for the possible initiation of shallow landslides and debris flows was determined. The threshold curve was obtained from the rainfall data using an objective statistical technique. To cope with differences in the intensity and duration of rainfall likely to result in shallow slope failures in different climatic regions, the rainfall information was normalized to the mean annual precipitation and the rainy-day normal. Climate information was obtained from the global climate dataset compiled by the Climate Research Unit of the East Anglia University. The obtained global ID thresholds are significantly lower than the threshold proposed by Caine (Geogr Ann A 62:23–27, 1980 ), and lower than other global thresholds proposed in the literature. The new global ID thresholds can be used in a worldwide operational landslide warning system based on global precipitation measurements where local and regional thresholds are not available..

A process chain for the definition and the performance assessment of an operational regional warning model for rainfall-induced landslides, based on rainfall thresholds, is proposed and tested in a landslide-prone area in the Campania region, southern Italy. A database of 96 shallow landslides triggered by rainfall in the period 2003–2010 and rainfall data gathered from 58 rain gauges are used. First, a set of rainfall threshold equations are defined applying a well-known frequentist method to all the reconstructed rainfall conditions responsible for the documented landslides in the area of analysis. Several thresholds at different exceedance probabilities (percentiles) are evaluated, and nine different percentile combinations are selected for the activation of three warning levels. Subsequently, for each combination, the issuing of warning levels is computed by comparing, over time, the measured rainfall with the pre-defined warning level thresholds. Finally, the optimal percentile combination to be employed in the regional early warning system, i.e. the one providing the best model performance in terms of success and error indicators, is selected employing the “event, duration matrix, performance” (EDuMaP) method.

Landslides leave discernible signs on the land surface, most of which can be captured in remote sensing images. Trained geomorphologists analyse remote sensing images and map landslides through heuristic interpretation of photographic and morphological characteristics. Despite a wide use of remote sensing images for landslide mapping, no attempt to evaluate how the image characteristics influence landslide identification and mapping exists. This paper presents an experiment to determine the effects of optical image characteristics, such as spatial resolution, spectral content and image type (monoscopic or stereoscopic), on landslide mapping. We considered eight maps of the same landslide in central Italy: (i) six maps obtained through expert heuristic visual interpretation of remote sensing images, (ii) one map through a reconnaissance field survey, and (iii) one map obtained through a real-time kinematic (RTK) differential global positioning system (dGPS) survey, which served as a benchmark. The eight maps were compared pairwise and to a benchmark. The mismatch between each map pair was quantified by the error index, E. Results show that the map closest to the benchmark delineation of the landslide was obtained using the higher resolution image, where the landslide signature was primarily photographical (in the landslide source and transport area). Conversely, where the landslide signature was mainly morphological (in the landslide deposit) the best mapping result was obtained using the stereoscopic images. Albeit conducted on a single landslide, the experiment results are general, and provide useful information to decide on the optimal imagery for the production of event, seasonal and multi-temporal landslide inventory maps.

In Italy, rainfall-induced landslides are recurrent phenomena that cause societal and economic damage. Thus, assessing the rainfall conditions responsible for landslides is important and may contribute to reducing risk. The prediction of rainfall-induced landslides relies primarily on empirical rainfall thresholds. However, the thresholds are affected by uncertainties that limit their use in operational warning systems. A source of uncertainty lies in the characterization of the rainfall events responsible for landslides. Objective criteria for the definition of rainfall events are lacking. To overcome the problem, we propose an algorithm that reconstructs the rainfall events, identifies the rainfall conditions that have resulted in landslides, and measures the duration and the cumulated rainfall for the events. The algorithm is independent from the local settings and uses a reduced set of parameters to account for different physical settings and operational conditions. We tested the algorithm in Sicily, Italy, with rainfall and landslide information between January 2002 and December 2012. The rainfall conditions responsible for landslides identified by the algorithm were compared against results obtained manually. The algorithm was proven capable of accurately reconstructing most (87.7 %) of the rainfall events. For each landslide, the algorithm identified a variable number of rainfall conditions responsible for the failures, which are equally likely triggers of the landslide. This opens the possibility of evaluating the uncertainty introduced by different criteria to determine the rainfall events responsible for landslides. Use of the algorithm shall contribute to reducing the uncertainty in the definition of landslide-triggering rainfall events, to compiling large catalogues of rainfall events with landslides and to determining reliable rainfall thresholds for possible landslide occurrence.

Despite abundant information on landslides, and on landslide hazard and risk, in Italy, little is known on the direct impact of event landslides on road networks and on the related economic costs. We investigated the physical and economic damage caused by two rainfall-induced landslide events in Central and Southern Italy, to obtain road restoration cost statistics. Using a GIS-based method, we exploited road maps and landslide event inventory maps to compute different metrics that quantify the impact of the landslide events on the natural landscape and on the road networks, by road type. The maps were used with cost data obtained from multiple sources, including local authorities, and specific legislation, to evaluate statistically the unit cost per metre of damaged road and the unit cost per square metre of damaging landslide, separately for main and secondary roads. The obtained unit costs showed large variations which we attribute to the different road types in the two study areas and to the different abundance of landslides. Our work confirms the long-standing conundrum of obtaining accurate landslide damage data and outlines the need for reliable, standardized methods to evaluate landslide damage and associated restoration costs that regional and local administrations can use rapidly in the aftermath of a landslide event. We conclude recommending that common standardized procedures to collect landslide cost data following each landslide event are established, in Italy and elsewhere. This will allow for more accurate and reliable evaluations of the economic costs of landslide events.