Il documento illustra le attività realizzate nell'ambito dell'Accordo tra l'Agenzia Regionale per la Protezione dell'Ambiente Ligure (ARPAL) e il Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica (CNR-IRPI) per la manutenzione, il mantenimento, l'aggiornamento e la validazione del SARF Liguria, firmato digitalmente il 22 gennaio 2021. In particolare, il documento contiene la validazione dei prodotti previsionali del SARF-Liguria ottenuti attraverso l'analisi della banca dati esistente dei movimenti franosi indotti da piogge in Liguria. Dopo una sezione introduttiva (Capitolo 1), viene descritto il catalogo delle frane indotte da piogge in Liguria utilizzato per la validazione del SARF Liguria (Capitolo 2). Il Capitolo 3 descrive la procedura adottata per la validazione del sistema d'allertamento e il Capitolo 4 riporta i risultati della validazione.

The southwestern foothills of the Himalayan Mountain range have been experiencing a surge of catastrophic landslides in the last two decades, as a tragic result of the adverse effects of climate change. This research is about the landslide risk assessment (LRA) which has not been explored yet in the landslide-prone district Muzaffarabad, Pakistan. Landslide susceptibility (spatial probability) was analyzed using random forest model while landslide hazard (temporal probability) was analyzed using Poisson probability model. A random forest-based landslide susceptibility map depicts an accuracy of 0.90. A landslide hazard map was generated by multiplying the temporal probability with the spatial probability and classified as well. Semi-quantitative danger pixels and a fuzzy set theory approach for LRA have been adopted to estimate future landslide risks in the region. The pixel-based LRA approach indicates that 14, 18 and 20 km(2) area of settlement while, the fuzzy set theory-based approach depicts that 15, 19 and 21 km(2) area of the settlement are under very high landslide risk for 1-, 3-, and 5- year return period respectively. Both approaches produced risk maps that designated various risk zones with almost the same area coverage and results. The LRA maps were classified into five classes including very high (1.99%, 2.33%, 2.80%), high (2.16%, 2.53%, 3.04%), moderate (8.02%, 9.79%, 11.22%), low (17.76%, 22.94%, 23.20%), and very low (70.08%, 62.40%, 59.74%) risk zones for 1, 3 and 5 years return period respectively. This research will assist planners and scientists in developing high-precision management strategies for landslide-affected natural resources, especially in the context of the increasing impact of geomorphic hazards on climate change.

Timely and systematic collection of landslide information after a triggering event is pivotal for the definition of landslide trends in response to climate change. On September 15, 2022, a large part of central Italy, particularly Marche and Umbria regions, was struck by an anomalous rainfall event that showed characteristics of a persistent convective system. An extraordinary cumulated rainfall of 419 mm was recorded by a rain gauge in the area in only 9 h. The rainfall triggered 1687 landslides in the area affected by the peak rainfall intensity and caused widespread flash floods and floods in the central and lower parts of the catchments. In this work, we describe the characteristics of the landslides identified during a field survey started immediately after the event. Most of the mass movements are shallow, and many are rapid (i.e., debris flows, earth flows) and widely affecting the road network. Landslide area spans from a few tens of square meters to 105 m2, with a median value of 87 m2. Field evidence revealed diffuse residual risk conditions, being a large proportion of landslides located in the immediate vicinity of infrastructures. Besides reporting the spatial distribution of landslides triggered by an extreme rainfall event, the data collected on landslides can be used to make comparisons with the distribution of landslides in the past, validation of landslide susceptibility models, and definition of the general interaction between landslides and structures/infrastructures.

This paper describes the event landslide inventory map produced to record the ground effects of the extreme rainfall event that hit Umbria-Marche, central Italy, on 15th September 2022. The rainfall event hit an area of ~5,000 km2, with peak rainfall intensities of 419 mm in 9 hours, an exceptionally intense rainfall for this area. The event generated widespread flooding also outside the area affected by rainfall. The rainfall event produced widespread landslides across an area of nearly 1,000 km2. As a direct consequence of floods and landslides, many roads were interrupted and extensive damages were recorded to structures and infrastructures. The landslide inventory presented is the result of an extensive reconnaissance field survey covering a large neighbourhood of the area affected by the highest rainfall intensity. The inventory covers an area of 550 km2 and includes 1,687 landslides, corresponding to an average density of about 3.1 landslides per square kilometre. Landslide size (Landslide Area, AL in m2) is in the range ~1m2 A polygon shapefile of the AOI A polygon shapefile of the raw inventory A point shapefile of the raw inventory A polygon shapefile of the final inventory A point shapefile of the final inventory This dataset consistently supports the scale of 1:15 000. As a consequence, all polygons smaller than 225 m2 (i.e. a square of 1 mm per side in the map) in the raw data polygon layer were transformed into points because their size was too small to be represented as polygons at 1:15000 scale. Copyright: CC BY 4.0

The increasing availability of long-term observational data can lead to the development of innovative modelling approaches to determine landslide triggering conditions at a regional scale, opening new avenues for landslide prediction and early warning. This research blends the strengths of existing approaches with the capabilities of generalized additive mixed models (GAMMs) to develop an interpretable approach that identifies seasonally dynamic precipitation conditions for shallow landslides. The model builds upon a 21-year record of landslides in South Tyrol (Italy) and separates precipitation that induced landslides from precipitation that did not. The model accounts for effects acting at four temporal scales: short-term "triggering" precipitation, medium-term "preparatory" precipitation, seasonal effects, and across-year data variability. It provides relative landslide probability scores that were used to establish seasonally dynamic thresholds with optimal performance in terms of hit and false-alarm rates, as well as additional thresholds related to user-defined performance scores. The GAMM shows a high predictive performance and indicates that more precipitation is required to induce a landslide in summer than in winter/spring, which can presumably be attributed mainly to vegetation and temperature effects. The discussion illustrates why the quality of input data, study design, and model transparency are crucial for landslide prediction using advanced data-driven techniques.

Il documento illustra le attività realizzate nell'ambito dell'Accordo l'Agenzia Regionale per la Protezione dell'Ambiente Ligure (ARPAL) e il Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica (CNR-IRPI) . In particolare, il documento contiene l'analisi della banca dati esistente e raccolta, organizzazione ed elaborazione di informazioni meteorologiche e geomorfologiche su movimenti franosi indotti da piogge in Liguria.

Il presente documento illustra le attività realizzate nell'ambito dell'Accordo di collaborazione tra il Servizio Protezione Civile - Centro Funzionale Multirischi della Regione Marche (CFM) e l'Istituto di Ricerca per la Protezione Idrogeologica del Consiglio Nazionale delle Ricerche (CNR IRPI) finalizzato allo studio delle relazioni esistenti tra piogge, umidità del suolo e fenomeni franosi volto alla definizione delle condizioni idrologiche responsabili dell'innesco di frane indotte da pioggia nel territorio della regione Marche Tale documento riporta dati e informazioni relativi alla consegna di due prodotti previsti: oanalisi della banca dati e raccolta, organizzazione ed elaborazione di informazioni meteorologiche e geomorfologiche su movimenti franosi indotti da piogge nel territorio della regione Marche; odefinizione e aggiornamento delle soglie pluviometriche empiriche per la previsione del possibile innesco di fenomeni franosi nel territorio della regione Marche. Il presente documento riporta le soglie pluviometriche empiriche di tipo cumulata - durata (CD) per il possibile innesco di fenomeni franosi nelle Marche. Le soglie sono calcolate utilizzando i dati del catalogo di eventi di pioggia con frane nelle Marche costruito dal CNR IRPI in collaborazione col CFM della regione Marche e l'Università Politecnica della Marche. Sono state definite soglie pluviometriche oggettive e riproducibili, per percentuali di non superamento scelte e che includono l'incertezza nei parametri che definiscono le soglie.

Timely and systematic collection of landslide information after a triggering event is essential for the definition of landslide trends in response to climate change. On September 15, 2022 Marche and Umbria regions, in Central Italy, were struck by an anomalous rainfall event that showed characteristics of a persistent convective system. An extraordinary cumulated rainfall of 419 mm was recorded by a rain gauge in the area in only 9 hours. It was carried out a systematic reconnaissance field survey to prepare an event landslide inventory map in an area of 550 km2 that includes a large neighbourhood of the area that recorded the highest rainfall intensity. The rainfall triggered 1687 landslides in the area affected by the peak rainfall intensity. Landslide area spans from a few tens of square meters to 105 m2, with a median value of 87 m2. We describe the characteristics of the landslides identified during a field survey conducted immediately after the event. Most of the mass movements are shallow, many are rapid (i.e., debris flows, earth flows) and widely affecting the road network. Many national and local roads were interrupted, mostly by earth and rock slides; national and local railways were interrupted at several points; extensive damage was registered to structures and infrastructures. Furthermore, field evidence revealed that a vast proportion of landslides occurred in the immediate vicinity of roads, mostly affecting road embankments and that a large number of landslides initiated within natural and semi-natural areas and hit the road network and, locally, affected houses and activities. Field surveys also revealed diffuse residual risk conditions, being a large proportion of landslides located in the immediate vicinity of infrastructures. Besides reporting the spatial distribution of landslides triggered by an extreme rainfall event, the data collected on landslides can be used to make comparisons with the distribution of landslides in the past, validation of landslide susceptibility models, definition of the general interaction between landslides and structures/infrastructures.

India is heavily affected by rainfall-induced landslides that cause fatalities and damage. Therefore, the development of effective and reliable models for the landslide forecasting and their possible integration in early warning systems (LEWSs) is necessary to mitigate the risk posed by such phenomena. Within the LANDSLIP (LANDSLIde Multi-Hazard Risk Assessment, Preparedness and Early Warning in South Asia: Integrating Meteorology, Landscape and Society; www.landslip.org) project, we developed threshold-based forecasting models to predict the occurrence of rainfall-induced landslides. The models were calibrated in two Indian pilot areas: the Darjeeling and Nilgiris districts, in the states of West Bengal and Tamil Nadu, respectively. For the purpose, we built two catalogs of 84 and 116 rainfall conditions likely responsible for landslide triggering in Darjeeling and Nilgiris, respectively, and daily rainfall measurements, which were used to define frequentist rainfall thresholds at different non-exceedance probabilities by means of an automatic tool (CTRL-T). A revision of the methodology to identify the rainfall conditions that triggered the failures was necessary due to possible inaccuracies in the landslide occurrence date and the daily temporal resolution of rainfall measurements in India. Triggering rainfall conditions were also related to the different monsoon regimes in the study areas. For a few uncertain events, the rainfall conditions automatically reconstructed by CTRL-T were revised after a consensus among several investigators. In agreement with the rainfall regimes of the two pilot areas, the thresholds for Darjeeling are higher than those for Nilgiris; regardless of the rainfall duration, a larger amount of rainfall is necessary to trigger landslides in the Darjeeling area. Despite some limitations, mostly due to the daily temporal resolution of rainfall data and the spatial and temporal distribution of the reported landslides, the uncertainties of the calculated thresholds were acceptable (also thanks to the double checking) to allow their implementation in the LANDSLIP prototype LEWS. The thresholds require ongoing evaluation and refinement. For the purpose, additional landslide and rainfall data were used to validate thresholds and improve forecasts.

When and where shallow landslides occur depends on an interplay of predisposing, preparatory, and triggering factors. At a regional scale, data-driven analyses are extensively used to assess landslide susceptibility based on "static" maps of predisposing conditions. In contrast, data-driven analyses focusing on landslide triggering factors often rely on non-spatially explicit approaches to derive empirical rainfall thresholds. So far, few attempts have been made to integrate the spatial and temporal analysis domains beyond a posterior combination of separately derived susceptibility models and rainfall thresholds. This work focuses on the mountainous Italian province of South Tyrol (7400 km²) and proposes a novel data-driven landslide prediction model that jointly considers landslide predisposition and dynamic preparatory and triggering factors. The approach builds on a hierarchical generalized additive model, multi-temporal shallow landslide data from 2000 to 2020 and a range of environmental variables (e.g., daily rainfall, topography, lithology, forest cover). The model produces maps that portray the relative probability of landslide occurrence. These spatially explicit predictions change dynamically as a function of local predisposition, seasonality, and observed (or hypothesized) dynamic preparatory and triggering rainfall (i.e. cumulative rainfall amounts based on varying day-windows). Linking the model output to known measures of model performance, such as hit rate and false alarm rate, enables the creation of dynamic classified maps that can be interpreted in analogy to commonly used empirical rainfall thresholds. The approach also accounts for potential spatial and temporal biases in the landslide inventory by restricting the underlying data sampling to effectively surveyed areas and time periods and by including (and averaging out) bias-describing random effect variables. Our validation confirms the model's high generalizability and predictive power while providing insights into the interplay of predisposing, preparatory and triggering factors for shallow landslide occurrence in South Tyrol. Application possibilities of this novel approach are discussed. The research leading to these results is related to the PROSLIDE project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano - Südtirol/Alto Adige.

ITALICA (ITAlian rainfall-induced LandslIdes CAtalogue) currently lists 6312 records with information on rainfall-induced landslides that occurred over the Italian territory between January 1996 and December 2021. ITALICA's information on rainfall-induced landslides has high accuracy on their spatial and temporal location, making the catalog particularly suitable for defining rainfall conditions capable of triggering future landslides on the Italian territory.

In India, rainfall-induced landslides cause a high toll in terms of fatalities and damages. Therefore, the adoption of tools to predict the occurrence of such phenomena is urgent. For the purpose, the LANDSLIP project aimed at developing a landslide early warning system (LEWS) to forecast the occurrence of rainfall-induced landslides in two Indian pilot areas: Darjeeling and Nilgiris. Rainfall thresholds are a widely used tool to define critical probability levels for the possible occurrence of landslides in large areas, and are particularly suitable to be implemented in LEWSs. In this work, we exploited two catalogues of 84 and 116 rainfall conditions likely responsible for landslide triggering in Darjeeling and Nilgiris, respectively. Adopting a frequentist statistical method and using an automatic tool, we determined rainfall thresholds at different non-exceedance probabilities for the two pilot areas. Despite the daily temporal resolution of rainfall data and the spatial and temporal distribution of the documented landslides, the thresholds calculated for the two areas have acceptable uncertainties and were implemented in the LANDSLIP LEWS prototype. We expect that the new thresholds and the whole system will contribute to mitigate the landslide risk in the study areas.

Questo documento descrive le metodologie operative per la realizzazione di zonazioni della suscettibilità da frana in contesti geomorfologici significativi in prossimità dell'infrastruttura ferroviaria di RFI. Il documento si divide in 5 capitoli. All'individuazione del contesto applicativo delle metodologie proposte (Cfr. §1), seguono le definizioni e l'inquadramento scientifico di riferimento (Cfr. §2), la struttura e gli elementi chiave e le descrizioni della modellazione della suscettibilità da frana proposta per tipologia di frana e delle attività a supporto (Cfr. 3). Il documento riporta infine la principale bibliografia di riferimento (Cfr. §4). Tale documento è completato da diversi diagrammi RACI in formato .xlsx identificati nel testo (Cfr. §3.1).

Shallow landslides are frequently caused by an interplay of static predisposing factors (e.g., topography), mid-term preparatory factors (e.g., prolonged rainfall, seasonal changes of vegetation, snow melt), and short-term triggers (e.g., heavy rainfall). For large-area assessments, statistical analyses and data-driven approaches are often used to model landslide susceptibility based on spatial environmental variables or to derive critical landslide-triggering rainfall conditions through the definition of empirical rainfall thresholds. Attempts to integrate the spatial and temporal domains in the context of quantitative regional-scale landslide prediction are still rare. This contribution focuses on the landslide-prone area of South Tyrol, northern Italy (7,400 km²) and presents a novel data-driven modelling procedure that integrates spatial predisposing factors and dynamic preparatory and triggering factors to predict the probability of landslide occurrence in space and time. The approach is based on time-stamped landslide inventory data from 2000 to 2021, high-resolution gridded daily precipitation observations for the same period, and a set of relevant static environmental variables (e.g., including topographic indices, lithology). Data preparation included an initial filtering of rainfall-induced landslide presence observations and a rule-based stratified sampling of landslide absence observations at landslide locations and at non-landslide locations. Cross-validation was implemented in the model developing stage to select optimal time windows to represent pre-landslide preparatory and triggering cumulative rainfall conditions. Modelling was based on a binomial Hierarchical Generalized Additive Model (HGAM) that considered the non-linear influence and interactions (i.e., via tensor products) of static and dynamic environmental variables on landslide occurrence (presence vs. absence) while simultaneously accounting for the nested data structure (i.e., multiple considerations of each location) and seasonal effects. The study also considered different biases inherent in the input data, such as the known underrepresentation of landslide data in locations far from infrastructure or potential reporting biases across years, by averaging-out associated random effect variables. The results were validated quantitatively (spatial and temporal cross-validation) and qualitatively (geomorphic plausibility) and visualized as maps and probability surface plots. The assessment and validation confirmed the high generalizability and predictive performance of the model. A closer look at the derived relationships allowed to uncover the effects of predisposing, preparatory and triggering factors on landslide occurrence as well as the associated season-dependent variations. From our perspective, this new approach represents a good compromise between the high model flexibility for landslide prediction purposes and the high interpretability for understanding the underlying relationships. The research leading to these results is related to the PROSLIDE project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano - Südtirol/Alto Adige.

Manuale di utilizzo dell'interfaccia del sistema di allertamento per frane pluvio-indotte SARF Sardegna

Manuale di utilizzo dell'interfaccia del sistema di allertamento per frane pluvio-indotte SARF Liguria

Il documento illustra la procedura e le regole di utilizzo degli output previsionali del SARF Liguria per la definizione dei possibili scenari di occorrenza di frana identificata nell'ambito dell'Accordo l'Agenzia Regionale per la Protezione dell'Ambiente Ligure (ARPAL) e il Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica (CNR-IRPI) per la manutenzione, il mantenimento, l'aggiornamento e la validazione del SARF Liguria, firmato digitalmente il 22 gennaio 2021". Dopo una sezione introduttiva (Capitolo 1), viene descritta la nomenclatura delle variabili calcolate nel sistema (Capitolo 2). Nel Capitolo 3 sono descritte la procedura, le regole di utilizzo degli output previsionali e la scelta dei possibili scenari.

Il documento illustra la procedura e le regole di utilizzo degli output previsionali del SARF Sardegna per la definizione dei possibili scenari di occorrenza di frana identificata nell'ambito dell'Accordo tra la Direzione Generale della Protezione Civile della Regione Sardegna, Servizio previsione rischi e dei sistemi informativi, infrastrutture e reti e il Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, per la manutenzione e aggiornamento del SARF Sardegna, l'aggiornamento delle soglie regionali, la definizione dei livelli di allertamento, l'implementazione delle previsioni da radar e la stima dell'umidità del suolo, firmato digitalmente il 7 dicembre 2021. Dopo una sezione introduttiva (Capitolo 1), viene descritta la nomenclatura delle variabili calcolate nel sistema (Capitolo 2). Nel Capitolo 3 sono descritte la procedura, le regole di utilizzo degli output previsionali e la scelta dei possibili scenari.

I cambiamenti climatici (CC) rappresentano una delle più gravi minacce che i governi e i cittadini devono affrontare, e già da tempo influenzano negativamente diverse dimensioni della nostra esistenza, dalla salute pubblica alla disponibilità di risorse. Uno dei punti cardine delle iniziative europee volte a mitigare l'impatto dei CC (come l'European Green Deal) è la consapevolezza che il coinvolgimento dei cittadini rappresenti un elemento fondamentale per favorire la sostenibilità ambientale e l'adattamento ai CC. Tra le azioni da considerare ai fini dell'adattamento ai CC nel settore dei rischi geo-idrologici, il Piano Nazionale di Adattamento ai Cambiamenti Climatici (PNACC) include espressamente attività volte al miglioramento dell'addestramento (preparedness) delle persone e mira a "coltivare una vera e propria cultura del rischio [..] sul territorio e delle sue possibili conseguenze" oltre che a favorire la percezione del rischio da parte dei cittadini. Nell'ambito del progetto I-CHANGE (Individual Change of HAbits Needed for Green European transition) alcuni istituti CNR stanno collaborando ad una attività che si allinea a quanto richiesto dal PNACC. Più in particolare, nel Living Lab (LL) di Genova città metropolitana, verranno sperimentate attività di coinvolgimento di cittadini e volontari nella segnalazione, geo-localizzazione e riconoscimento di frane e inondazioni e dei relativi impatti. Le attività non si limiteranno ad una passiva compilazione di questionari ma saranno integrate da due azioni di supporto: (i) cittadini e studenti saranno coinvolti nella costruzione di form, che saranno realizzati mediante applicazioni web-based e che potranno poi essere resi disponibili per la raccolta dati su dispositivi mobili. Questo favorirà una maggiore partecipazione, consapevolezza e comprensione, da parte dei cittadini, dei fenomeni e dei loro effetti. (ii) Il CNR sta sviluppando un sistema per la detection automatica di possibili cambiamenti al suolo legati a fenomeni franosi. Il sistema, basato sull'analisi automatica di immagini satellitari, sarà di supporto ai cittadini che potranno avvalersene al fine di indirizzare la ricerca e che verranno quindi direttamente coinvolti nel miglioramento e nella validazione di un algoritmo di apprendimento basato su reti neurali. Il presente contributo è volto a illustrare lo stato dell'arte delle attività di progetto e in particolare lo stadio a cui si è arrivati nella progettazione/implementazione di form e procedure per l'analisi delle immagini satellitari.

Several devastating landslides have occurred in the NW Himalayas, which has prompted several researchers to strive for improvement in landslide susceptibility modelling (LSM) methodologies. This research analyzes the effectiveness of alternative landslide partitioning techniques on LSM in the landslide-prone district, Muzaffarabad, Pakistan. We developed a landslide inventory of 961 landslides and then traditionally divided it into training (672; 70%) and testing (289; 30%) samples. These training samples (672) are processed by the Average Nearest Neighbour Index (ANNI) method to estimatethe spatial pattern of landslides in nature. The results provide an ANNI ratio of 0.672 confirming that the landslides distribution pattern is cluster in the complex Himalayan terrain of Muzaffarabad. Among 672, the majority of landslides (529; 79%) depict cluster behaviour, while 189 landslides (21%) depict random behaviour. To evaluate the effectiveness of landslide cluster samples in prediction, five machine learning algorithms (MLAs), that is, K-Nearest Neighbour (KNN), Na¨?ve Bayes (NB), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR) using proposed cluster (529) and traditional (672) random training samples along with 17 geo-environmental factors are executed. However, testing samples (289; 30%) separated at the initial stage remained the same to check the model's effectiveness. The areas under the curve (AUC-ROC), sensitivity, specificity, Kappa index and accuracy (ACC) have been used to evaluate the MLA's performances. An alternative partitioning technique (cluster) shows the highest predictive power with AUC-ROC values ranging from 0.96 to 0.86, Kappa index ranges from 0.76 to 0.60 and ACC ranges from 0.90 to 0.83. Conversely, the random partitioning approach performs less well with AUC-ROC values ranging from 0.95 to 0.83, Kappa index ranges from 0.70 to 0.49 and ACC ranges from 0.87 to 0.80. In comparison, the RF cluster sampling-based model outperforms the other models and their counterparts. The RF model achieved the highest accuracy (0.902), highest AUC (0.962) and highest Kappa index (0.755) followed by XGboost having ACC (0.885), AUC (0.95) and Kappa index (0.724) employing proposed cluster training samples. However, traditional random training samples yield comparatively low ACC of RF (0.868) and XGboost (0.862). These results confirm that cluster training sampling performs well in obtaining reliable and precise LSMs for complex Himalayan terrain. Although randomlandslide partitioning for training datasets is seldom utilized in LSM, this study highlights that cluster partitioning for landslide training datasets might be a realistic and reliable approach.