This study exploited the historical rockfall inventory and the meteorological stations database of Mont Cervin and Mont Emilius Mountain Communities (Aosta Valley, northern Italy) to decipher relationships between climate processes, typical of mountain environments and rockfall phenomena. The period from 1990 to 2018 was selected as reference to perform the analysis. Climate processes were translated into four climate indices, namely short-term rainfall (STR), effective water inputs (EWI, including both rainfall and snow melting), wet and dry episodes (WD) and freeze-thaw cycles (FT). The comparison between climate indices values at each rockfall occurrence and the statistical distributions describing the whole indices dataset allowed to define not ordinary climatic conditions for each index and their influence on rockfall occurrence. Most of the events analysed (>95% out of 136) occurred in correspondence of the defined not ordinary climatic conditions for one or for a combination of the indices. The relationships between rockfalls and climate showed a seasonality. In spring, most of the events resulted to be connected to FT (70%) while in autumn to EWI (49%). The relative seasonal importance of WD reached its maximum in summer with 23% of the events related to this index alone. Based on these results, different strategies to define empirical critical thresholds for each climate index were explored, in order to make them valid for the whole study area. A preliminary exploratory analysis of the influence of high temperatures and temperature gradients was carried out for some summertime rockfalls, not correlated to the other investigated indices. The presented approach is exportable in neighbouring regions, given the availability of a dated rockfall dataset, and could be adapted to include different processes.

The overarching goal of the study was the development of a potentially dynamic rockfall susceptibility model by including climate predictors. The work is based on previously defined critical thresholds relating three climate indices — effective water inputs (EWI), wet-dry cycles (WD) and freeze–thaw cycles (FT) — and rockfall occurrence. The pilot area is located in the Aosta Valley region (Italian Western Alps). The susceptibility model settings were optimized through a stepwise procedure, carried out by means of generalized additive models (GAM). Predictors included topographic, climatic and additional snow-related variables. As climatic predictors, the mean annual threshold exceedance frequency was calculated for each index. All models were developed including an automatic penalization of statistically non-significant variables (i.e. shrinkage). The initial susceptibility model was set without considering potential inventory bias. Secondly, a “visibility mask” was produced to limit the modelling domain according to the rockfall event census procedures. Thirdly, GAMs functional relationships were analysed to verify the physical plausibility of predictors. Finally, to reduce concurvity, a principal component analysis (PCA) including climatic and snow-related predictors was carried out. Key findings were as follows: (i) ignoring inventory bias led to excellent model performance but to physically implausible outputs; (ii) the selection of non-rockfall points inside a “visibility mask” is effective in managing inventory bias influence on outputs; (iii) the inclusion of climate predictors resulted in an improvement of the physical interpretability of the associated models and susceptibility maps, being EWI, WD and the maximum cumulated snow melting the most important physically plausible climate predictors; (iv) the PCA strategy can efficiently reduce model concurvity.

Due to climate change and the strong relationships between landslides and atmospheric variables, the concept of a stationary landslide susceptibility appears limited. However, relating landslides with climatic predisposing and triggering factors is challenging, due to the lack of multitemporal event datasets. Rockfalls are even more challenging in this context, as their reaction to meteorological events is connected to widely variable characteristics (e.g. rock type, in situ stress, fracture network).By exploiting and homogenizing a multitemporal rockfall inventory and meteorological datasets of the Aosta Valley Region (Western Italian Alps), the general goal of our study was to develop a procedure to decipher the effects of both the short- and long-term action of rainfall and freeze-thaw cycles on rockfalls occurrence, recognized as main forcing climatic variables in the classic literature. Our specific objective was to define synthetic and effective meteorological variables that can act as predictors in statistical landslide susceptibility models.We analysed 168 rockfall events and meteorological data from 17 stations from 1990 to 2018 (reference period) distributed on an area 670 km2. The analysis was performed considering:Short term (hourly) precipitation expressed both by the intensity-duration characteristics of the single rockfall associated rainfall(1) and by the maximum cumulated rainfall in time intervals from 0.5 to 24 hours before the event(2); Long term precipitation (multiple episodes) expressed both by cumulated rainfall in time interval of 1 day to 60 days (3) and by the number of rainfall episodes occurred in 1- to 12-month time intervals before the event(4); Number of Freeze-thaw cycles in the year before the event, identified as temperature variation crossing the 0°C value(5).   By comparing the statistical distribution, for the whole reference period, of the above mentioned climatic variables and the meteorological conditions before each rockfall event, we recognized four types of not ordinary climatic conditions. All conditions resulted to be associated to long term conditions of any time interval, while hourly intervals did not result significant. Type-a is associated to cumulated rainfall overcoming the 90th percentile of the historical time series(69 out of 168); Type-b to a number of rainfall episodes higher than the 75th percentile value(70 rockfalls out of 168); Type–c to a number to a number of freeze-thaw cycles higher than the 75th percentile value(66 out of 168); Type-d to a combination of these factors (47 out of 168). Only 5 rockfalls occurred during ordinary meteorological conditions, whereas the remaining 37 rockfalls could not being analysed due to the absence of complete meteorological data.Based on these results, we defined a long term Intensity-duration and two episode-duration thresholds, each expressed by a power law equation. The number of times, in the reference period, of exceedance of the selected thresholds represent the synthetic variables to be spatialized by means of geostatistical techniques and tested within a statistical landslide susceptibility model.

The overarching goal of the study was to test the influence of climate-related spatially distributed predictors on rockfall susceptibility in an Alpine environment. The study focused over the central part of Aosta Valley (Western Italian Alps), where a large historical rockfall inventory and an extensive, multi-variable meteorological dataset are available for the period 1990-2020. The first part of the study regarded the definition of process-based climate predictors for the susceptibility model. Based on previous studies (Bajni et al., 2021), three climate indices were known to influence rockfall occurrence in the area: effective water inputs (EWI, including rainfall and snow melting), wet-dry episodes (WD), and freeze-thaw cycles (FT). For each index, the spatially-distributed predictor for the susceptibility analysis was calculated as the mean annual exceedance frequency of previously defined thresholds. Such predictors were produced both starting from a station-based hourly dataset, and consequent regionalization, and a grid-based hourly dataset.The second part of the study comprised the set-up of a rockfall susceptibility model by means of Generalized Additive Models (GAM), including topographic, climatic and two additional snow-related predictors (derived from a Snow Water Equivalent weekly gridded dataset, Camera et al., 2021). The validation of the produced models was carried out through a k-fold cross-validation (CV), while the evaluation of its performance was expressed in terms of area under the receiver operating characteristic curve (AUROC). Variable importance was assessed through the decrease in explained deviance (mDD%).To improve and optimize the model, stepwise modifications of its setup were carried out:a visibility mask related to roads and main infrastructures was introduced to reduce the rockfall inventory bias. Models including alternatively the station-based and grid-based climatic predictors were compared. The evaluation was based both on the physical plausibility of the smoothing functions describing predictors behaviour, and in terms of quantitative performance. For the grid-based model, performance and predictors transferability were evaluated comparing a random CV, a spatial CV and a holdout CV. Concurvity among predictors was reduced through the implementation of a Principal Component Analysis. The key results were: (i) the use of climate predictors (both station-derived and gridded-derived) resulted in an improvement of the model performance (AUROC up to 3%) in comparison to a topographic-only model; (ii) the climate predictors with the strongest physical significance were EWI and WD, with a mDD%= 5-10% each, followed by the maximum cumulated snow melting over a 32-day period (mDD%= 3-5%); (iii) the effect of FT was masked by elevation; (iv) the station-based models were more strongly affected by concurvity issues; (v) the PCA derived predictors maintained explainable physical meanings while consistently decreasing concurvity.The presented procedure is reproducible in other environmental and climatic conditions and allows to implement process-related non-stationary susceptibility models, making them adaptable for future climate change scenarios.ReferencesBajni et al., 2021. Landslides 18, 3279–3298. https://doi.org/10.1007/s10346-021-01697-3Camera et al., 2021. Science of The Total Environment 147360. https://doi.org/10.1016/j.scitotenv.2021.147360AcknowledgementThis study was supported by the RISK-Gest project operating under the INTERREG ALCOTRA 14/20 Programme.

The aim of this study is to contribute to the introduction of a benchmark dataset for landslide susceptibility. The contribution consists in the application of Generalized Additive Models (GAMs) on the test area proposed by Alvioli et al. (2022), located in Central Italy (Umbria Region, 4095 km2), and over the Mountain Communities of Mont Cervin and Mont Emilius (670 km2), located in the central part of Valle d’Aosta Region. In the latter, previous studies regarding landslide susceptibility were carried out by Camera et al. (2021) and Bajni (2022). The susceptibility analysis is based on slope units for both areas and it uses the open-source dataset available for Italy (https://geomorphology.irpi.cnr.it/tools/slope-units, Alvioli et al., 2020). For Central Italy, predictors and response variable are those made available by Alvioli et al. (2022). For consistency, for Valle d’Aosta morphometric variables were calculated from the EUDEM digital elevation model (Copernicus Land Monitoring Service, 25 m horizontal resolution), while soil-related variables – namely soil depth, soil bulk density and particle size fractions - were derived from the SoilGrid global dataset (Hengl et al. 2017). In addition, coherently with Alvioli et al. (2022), two presence/absence landslide response variables (‘1’/’0’) were defined. For the first one, ‘presence1’, a slope unit was considered impacted by landslides (‘1’) if at least an event was recorded within its limits. For the second one, ‘presence2’, a slope unit was considered impacted by landslides (‘1’) if two or more landslides occurred within its limits. For Valle d’Aosta, landslide events were accessed through the regional inventory (http://catastodissesti.partout.it/), which is updated continuously by the Regional Civil Protection Department and the Forest Corps through regular surveys or following warnings from citizens. Two landslide susceptibility maps were calculated for each area (‘presence1’, ‘presence2’). GAMs were applied through the mgcv library of R, with and without the option of variable selection through shrinkage. In addition, predictors behavior was analyzed through the associated Component Smoothing Functions (CSF) to check for physical plausibility. Finally, to evaluate uncertainties, a non-spatial k-fold cross-validation was carried out and a model evaluation was performed based on contingency tables, area under the receiver operating characteristic curve (AUROC) and variable importance (decrease in explained variance). By the application of the same modelling algorithm (GAM) with an input dataset derived from the same data sources, the study is expected to verify the consistency of the obtained landslide susceptibility results in terms of both model performance and main driving processes (predictors). References Alvioli et al., 2020. Parameter-free delineation of slope units and terrain subdivision of Italy. Geomorphology 258, 107124. https://doi.org/10.1016/j.geomorph.2020.107124 Alvioli et al., 2022. Call for collaboration: Benchmark datasets for landslide susceptibility zonation. https://doi.org/10.31223/X52S9C Bajni, 2022. Statistical methods to assess rockfall susceotibility in an Alpine environment: a focus on climatic forcing and geomechanical variables. https://doi.org/10.13130/bajni-greta_phd2022-03-23 Camera et al., 2021. Introducing intense rainfall and snowmelt variables to implement a process-related non-stationary shallow landslide susceptibility analysis. Science of The Total Environment 147360. https://doi.org/10.1016/j.scitotenv.2021.147360 Hengl et al., 2017. SoilGrids250m: Global gridded soil information based on machine learning. PLoS one 12, e0169748. https://doi.org/10.1371/journal.pone.0169748