In this work, we apply a physically based model, namely the HIRESSS (HIgh REsolution Slope Stability Simulator) model, to forecast the occurrence of shallow landslides at the regional scale. HIRESSS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions during a rainfall event. The modeling software is made up of two parts: hydrological and geotechnical. The hydrological model is based on an analytical solution from an approximated form of the Richards equation, while the geotechnical stability model is based on an infinite slope model that takes the unsaturated soil condition into account. The test area is a portion of the Aosta Valley region, located in the northwest of the Alpine mountain chain. The geomorphology of the region is characterized by steep slopes with elevations ranging from 400 m a.s.l. on the Dora Baltea River's floodplain to 4810 m a.s.l. at Mont Blanc. In the study area, the mean annual precipitation is about 800–900 mm. These features make the territory very prone to landslides, mainly shallow rapid landslides and rockfalls. In order to apply the model and to increase its reliability, an in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslide formation was conducted. In particular, two campaigns of on site measurements and laboratory experiments were performed using 12 survey points. The data collected contributed to the generation of an input map of parameters for the HIRESSS model. In order to consider the effect of vegetation on slope stability, the soil reinforcement due to the presence of roots was also taken into account; this was done based on vegetation maps and literature values of root cohesion. The model was applied using back analysis for two past events that affected the Aosta Valley region between 2008 and 2009, triggering several fast shallow landslides. The validation of the results, carried out using a database of past landslides, provided good results and a good prediction accuracy for the HIRESSS model from both a temporal and spatial point of view.
Landslide prediction at a regional scale can be performed following two approaches: (a) using rainfall thresholds based on the statistical analysis of rainfall and landslides, and (b) using physically based deterministic models. While the first approach is currently extensively used at regional scales (Aleotti, 2004; Cannon et al., 2011; Martelloni et al., 2012; Rosi et al., 2012; Lagomarsino et al., 2013), the latter is more frequently applied at slope or catchment scales (Dietrich and Montgomery, 1998; Pack et al., 2001; Baum et al., 2002, 2010; Lu and Godt, 2008; Simoni et al., 2008; Ren et al., 2010; Arnone et al., 2011; Salciarini et al., 2012, 2017; Park et al., 2013; Rossi et al., 2013). The poor knowledge of hydrological and geotechnical parameters' spatial distributions, caused by the extreme heterogeneity and inherent variability of soil at large scales (Mercogliano et al., 2013; Tofani et al., 2017), means that the application of physically based models is generally avoided at regional scales. Conversely, physically based models allow for the spatial and temporal prediction of the occurrence of landslides with high accuracy, producing accurate hazard maps that can be of help for landslide risk assessment and management.
In this work, we apply the physically based HIRESSS (HIgh REsolution Slope Stability Simulator) model (Rossi et al., 2013) in the eastern section of the Aosta Valley region (Italy), in the northwest Alpine mountain chain, in order to test the capacity of the model to forecast the occurrence of shallow landslides at the regional scale. In particular, the objectives of this study are as follows: (i) to properly characterize the geotechnical and hydrological parameters of the soil to feed the HIRESSS model, and to spatialize this punctual information in order to have spatially continuous maps of the model input data; and (ii) to test the HIRESSS code for two selected rainfall events, which triggered several shallow landslides, and to validate the model results. HIRESSS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions in real time and over large areas using parallel computational techniques. In the area selected, an in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslides formation was conducted; this involved performing two campaigns (using 12 survey points) of in situ measurements and laboratory tests. Furthermore, the HIRESSS model was modified to take the effect of the root reinforcement to the stability of slopes based on plant species distribution and literature values of root cohesion into account.
The study area, called “alert zone B” by the regional civil protection authorities, is located in the eastern part of the Aosta Valley region, in the northwest Alpine mountain chain (Fig. 1). The area is characterized by three main valleys: Champorcher Valley, Gressoney or Lys Valley, and Ayas Valley. The first is located on the right side of the Dora Baltea catchment, and represents the southern part of the study area. The second and third valleys show a north–south orientation, and are delimited to the north by Monte Rosa Massif (4527 m a.s.l.) and to south by the Dora Baltea River.
The Aosta Valley region in northwest Italy. The study area, alert zone B, is delineated in red.
From a geological point of view, the Aosta Valley is located northwest of the Insubric Line; in particular, there are three systems of Europa chain: the Austroalpine, the Penninic and the Helvetic systems (De Giusti, 2004). Figure 2 shows the lithological map of the study area obtained by reclassifying the geological units according to 11 lithological groups: landslides, calcareous schist, alluvial deposits, glacial deposits, colluvial deposits, glacier, granites, mica schists, green stone, black schists, and serpentinites. The main lithologies outcropping in the study area are metamorphic and intrusive rocks, in particular granites, metagranites, schists, and serpentinite.
Spatial distribution of survey points compared to the geolithology.
The geomorphology of the region is characterized by steep slopes and valleys shaped by glaciers. The glacial modeling is shown in the U-shape of the Lys and Ayas valleys, and the erosive depositional forms found in the Ayas Valley. The three valleys' watercourses, the Lys Creek, the Evançon Creek, and the Dora Baltea River, contributed to the glacial deposits modeling with the formation of alluvial fans. The climate of the region is characterized by high variability that is strongly influenced by altitude (ranging from 400 m a.s.l. of Dora Baltea River's floodplain to 4810 m a.s.l. of Mont Blanc), with a continental climate in the valley floors and an Alpine climate at high altitudes.
The slope steepness and the mean annual precipitation of 800–900 mm are the main landslide triggering factors. These features lead the study area to be prone to landsliding, in particular rockfalls, deep seated gravitational slope deformations (DSGSD), rocks avalanches, debris avalanches, debris flows, and debris slides (Catasto dei Dissesti Regionale – form Val d'Aosta Regional Authorities). In this work we model the triggering conditions of shallow landslides, i.e., soil slips and translational slides, and we do not take other types of movement into account.
The HIRESSS model simulated two past events, one in 2008 and one in 2009, and a validation of the model performance was then carried out comparing the results with the landslide regional database.
The two simulated events are as follows:
24–31 May 2008: on 28 and 29 May 2008 intense and persistent rainfall was
recorded across the Aosta Valley region with a total precipitation in the
study area of about 250 mm causing flooding, debris flows, and rockfalls. 25–28 April 2009: from 26 to 28 April 2009 heavy rainfall affected
the southeastern part of the Aosta Valley region, with the highest
precipitation of about 268 mm recorded at the Lillianes Granges station.
This precipitation triggered several landslides.
The physically based distributed slope stability simulator HIRESSS (Rossi et
al., 2013) is a model developed to analyze shallow landslide triggering
conditions on a large scale at high spatial and temporal resolutions using a
parallel calculation method. The model is composed of a hydrological and a
geotechnical component (Rossi et al., 2013). The hydrological component is based on
dynamic input of the rainfall data, which are used to calculate the
pressure head and provide it to the geotechnical stability model. The
hydrological model is initiated as a modeled form of hydraulic diffusivity
using an analytical solution, which is an approximated form of the Richards
equation under the wet condition (Richards, 1931). The equation solution
allows us to calculate the pressure head variation (
The geotechnical stability model is based on an infinite slope stability
model. The model considers the effect of matric suction in unsaturated soils,
taking the increase in strength and cohesion into account. The stability of
the slope at different depths (
Regarding the geotechnical influence of roots on the soil strength, roots seem to affect the cohesion parameter only, while the friction angle is poorly or not at all impacted by reinforcement (Waldron and Dakessian, 1981; Gray and Ohashi, 1983; Operstein and Frydaman, 2000; Giadrossich et al., 2010). Therefore, it is necessary to consider the root cohesion when calculating the FS and consequently when applying the HIRESSS model.
The root reinforcement (or root cohesion) can be considered equal to (Eq. 2):
Therefore, the new equation for FS at unsaturated conditions is as follows:
One of the major problems, associated with the deterministic approach
employed on a large scale, is the uncertainty of the static input parameters
or geotechnical parameters of the soil. The method used for the estimation
of parameters' spatial variability is the Monte Carlo simulation. The Monte
Carlo simulation achieves a probability distribution of input parameters,
providing results in terms of slope failure probability (Rossi et al.,
2013). The developed software uses the computational power offered by
multicore and multiprocessor hardware, from modern workstations to
supercomputing facilities (HPC), to achieve the simulation in a reasonable
runtime and is compatible with civil protection real-time monitoring (Rossi et
al., 2013). The HIRESSS model loads spatially distributed data arranged as
12 input raster maps and the maps of rainfall intensity. These input raster
maps are slope gradient, effective cohesion (
The input parameters can be divided in two classes: the static data and the dynamical data. Static data are geotechnical and morphological parameters while dynamical data are represented by the hourly rainfall intensity. Static data are read only once at the beginning of the simulation while dynamical inputs are continuously updated.
The HIRESSS input is in raster, which means that point data and parameters have to be adequately spatially distributed. In this application the spatial resolution was 10 m.
The slope gradient was calculated from the DEM (digital elevation model). The DEM has a resolution of 10 m and is dated 2006. Effective cohesion, friction angle, hydraulic conductivity, effective porosity, and dry unit weight were obtained and spatialized according to lithology. The soil punctual parameters were derived from the in situ and laboratory geotechnical tests and analysis.
In particular, the properties of slope deposits were determined by in situ and laboratory measurements (Bicocchi et al., 2016; Tofani et al., 2017) at 12 survey points. To carry out the in situ tests the survey points were selected using the following characteristics: (i) physiography, (ii) landslides occurrence, and (iii) geolithology (Fig. 2). Regarding the first point, a high-resolution DEM (from Val d'Aosta Regional Authorities) and careful first surveys were used to identify the most suitable slopes. The surveys took place in two sessions, the first in August 2016, and the second in September 2016. The following analyses were conducted:
Registration of the geographical position was undertaken using a GPS and photographic
documentation of the site characteristics (morphology and vegetation). The in situ measurement of the saturated hydraulic conductivity ( Sampling of an aliquot (
The permeability in situ measurements and the soil samplings were made at
depths ranging from 0.4 to 0.6 m below the ground level. The evaluation of
the
In addition, the samples collected in situ were examined in the laboratory to define a wide range of parameters to more extensively characterize the deposits. In particular, the following tests were performed in order to classify the analyzed soils:
grain size distribution (determination of granulometric curve for sieving
and settling following ASTM recommendations), and classification of soils
(according to AGI and USCS classification, Wagner, 1957); determination of the main index properties (porosity, relationships of
phases, natural water content (w determination of Atterberg limits (liquid limit (LL), plastic limit (PL), and
plasticity index (PI)); direct shear test on selected samples.
Soil thickness was calculated by the GIST model (Catani et al., 2010; Del Soldato et al., 2016). Soil characteristic curves parameters (pore size index, bubbling pressure, and residual water content) were derived from literature values (Rawls et al., 1982).
Root cohesion variations in the area (at the soil depth chosen for the
physical modeling with HIRESSS) were first obtained, identifying the
plant species and determining their distribution from in situ observations and
vegetational maps (Carta delle serie di vegetazione d'Italia, Italian
Ministry of the Environment and Protection of Land and Sea). Then, the
measure of cohesion due to the presence of roots was assigned to each
subarea according to the dominant plant species and literature root cohesion
value for that species (Bischetti, 2009; Burylo et al., 2010; Vergani et al.,
2013, 2017), which were calculated considering the fiber bundle model (Pollen
et al., 2004). The measure of
The last static input data, in this study, are the exposure rock mask. These data were defined considering the lithological and land use maps, so that the HIRESSS model avoided simulation on steep slopes made of bare rocks.
The geotechnical properties and root cohesion of the soils have been spatialized with respect to a lithological classification.
For each lithological class and plant species the mean value has been selected in order to obtain the HIRESSS input raster parameters.
In the study area, the rainfall hourly data from 27 pluviometers were
available; therefore, it was necessary to spatially distribute them to
generate a 10
Comparison of Thiessen polygons methodology;
The results of the geotechnical and hydrological characterization of the soils of the 12 survey points are shown in Table 1 for all survey sites.
Geotechnical properties of survey points (grain size distribution, Atterberg limits, index properties, permeability, and shear strength parameters).
The results of granulometric tests show that the analyzed soils are
predominantly sands with silty gravel (Fig. 4 and Table 1). Regarding the
index properties, the natural soil water content values were predominantly
about 20 % by weight, with maximum and minimum values of 5.1 and
26.2 %, respectively. These values reflect their different ability to hold
water in their voids. The measured natural unit weight (
Grain size distributions of soil samples (F is fine, M is medium and C is coarse).
The Atterberg limits (LL and PL) were measured on samples with a sufficient
passing fraction (
The effective friction angle varied between a minimum of 25.6 and
a maximum of 34.3
The additional cohesion induced by roots assumes different values not only depending on plant species and environmental characteristics but also on depth of soil, as roots' diameter and density vary with latter. Because of such evidence, studies on root cohesion in different species report values as a function of soil depth. In this study region, soils were thinner than in areas where previous studies have been carried out. In such thin soils, root systems organize their growth depending on available space and do not reach the same depth as roots in thick soils. Consequently, in this context root cohesion of species at different depths was dissimilar to the literature values. Considering this, the map regarding the variation of root cohesion was processed taking the minimum cohesion value for each species (among those specified for each species at the different depth) reported in literature. By doing this, the contribution of vegetation to the stability of slopes is considered in the FS calculation, whilst an overestimate of root cohesion is avoided.
In the study area, root cohesion, defined as mentioned above, ranged from a minimum of 0.0 kPa (mainly in the outcrop area) to a maximum of 8.9 kPa (in areas occupied by mountain maple on the left bank of Dora Baltea River).
In Table 2, the mean values of each of the input parameters are reported with respect to lithological class.
Spatialized geotechnical parameters of each lithological class as input for the HIRESSS model.
The pore size index, bubbling pressure, and residual water content were
constant in the study area, measuring 0.322 (
The distributed soil parameter maps are shown in Fig. 5. The results of rainfall data, elaborated using Thiessen polygon methodology, are 192 and 96 rainfall hourly maps for the 2008 and 2009 events, respectively. In Fig. 6 the cumulative maps of each event are shown.
Static input
parameters for HIRESSS model:
Cumulated rainfall maps for the two events.
Example of the numerical mask used to remove the false positives for the
first event simulated (24–31 May 2008).
The HIRESSS model was used to simulate two past events; one in 2008 (24–31 May) and one in 2009 (25–28 April), both of which triggered several landslides in the study area.
The HIRESSS input data were entered into the HIRESSS model to obtain day-by-day maps of the landslide occurrence probability. The main characteristics of the simulation are shown in Table 3.
The results of the simulations for both events on the first day of the simulation showed pixels with a high landslide probability occurrence in absence of rainfall. These pixels were false positives (i.e., pixels identified unstable by the model but were not really unstable) and occurred due to morphometric reasons, predominantly high slope angles. To remove these false positives, a numeric mask was applied. Using GIS software commands, it was possible to calculate the number of pixels on the first simulation day with a trigger probability value greater than 80 % and delete them (Fig. 7). The mask was then applied to the rest of landslide occurrence probability maps. The resulting maps for each days of the simulated events are shown in the Figs. 8 and 9.
Main characteristics of the simulation.
HIRESSS landslide probability maps of the simulated event from 24 to 31 May 2008 and the reported landslides during this event, with on the four critical
days:
HIRESSS landslide probability maps of the simulated event between 25 and 28 April 2009 and the reported landslides during this event,
The results of the first simulated event (24–31 May 2008) are shown in Fig. 8. The failure probability in the whole area was negligible for the first four days (from 24 to 27 May 2008) (Fig. 8a). The rainfall intensity then increased from 27 May and reached its highest value on 29 May, when the precipitation value was around 100 mm in the eastern sector of study area. The HIRESSS model simulate this passage well, with the 28 and 29 May 2008 landslide occurrence probability maps showing a considerable increase in the probability of failure with maximum values around 90 % in the east of alert zone B (Fig. 8b, c). In the following days rainfall intensity decreases, and the probability also slowly decreases, although it is still high on 30 May 2008.
Concerning the second event (25–28 April 2009), the landslide occurrence probability was negligible for the first two days (25 and 26 April 2009) over the whole area (Fig. 9a, b), due to the low rainfall intensity. From 27 April 2009 rainfall became more intense, especially in the southeast sector of the region where the cumulated rainfall average was about 151 mm. The probability maps show high values during these days (Fig. 9c, d). This event led to many landslides being triggered (as reported in the database).
In order to validate the HIRESSS simulations the database of landslides triggered during the two events were compared with the models results.
In general, for both events temporal validation showed that the daily highest probability of occurrence, computed by HIRESSS, correspond with days with landslide occurrences and the most intense precipitation.
For the first simulated event, landslides reported in the database are dated 30 and 31 May 2008 (Fig. 8d) which correspond to the days with highest probability of occurrence. The same can be seen for the second event, with many landslides being triggered between 27 and 28 April 2009 (as reported in the database).
In Table 4 the results with over a 75 % slope failure probability for both
events are highlighted and confirm the correct temporal occurrence of
landslides. In particular, we notice that for the first event (2008) the
number of unstable pixels (failure probability
Temporal validation was also carried out, considering daily cumulative rainfall compared to the landslide failure probability. In particular, a median of the landslide occurrence probability was calculated for four pluviometric areas identified by Thiessen polygons methodology, modified according to limits of river basins; this was undertaken both for the event in May 2008 and for the event in April 2009 (Fig. 10a, b). As could be expected, the results showed that when the highest rainfall intensity was measured, the highest probability of occurrence was computed for all areas and for both events.
Correlation graphs between the daily cumulative rainfall and the median of the landslide occurrence probability for both events.
Spatial validation was performed following a pixel-by-pixel method, which is the
most complex method and consists of comparing the probability of the
instability of each pixel with the pixels involved in the actual event.
This validation implies a great deal of uncertainty in the results,
since the reports of landslide events may have errors regarding the precise spatial
location and the size of the phenomenon. To overcome this problem and to
take probable errors caused by the actual spatial location in
the database into account, an area of 1 km
Figure 11 shows an example of a landslide event that occurred in the Arnad
municipality on 30 May 2008. The model computes a low failure probability on
24 May 2008 and an increase in the failure probability on 30 May 2008.
In Fig. 11a and b it is possible to note that, inside the red circle, the red and
yellow areas have increased on 30 May with respect to 24 May. In this case, the
model was able to correctly identify such movement. To better highlight this
validation, Fig. 11c shows the number of pixels above 75 %
probability that were calculated by the model within a ca. 1 km
An example of a landslide event that occurred in the Arnad municipality
compared to a landslide occurrence probability map,
HIRESSS results for both the 2008 and 2009 events.
“No. pixel” represents the number of pixels with a slope failure probability over 75 %;
“Total %” represents the percentage of pixels with slope failure probability over
75 %; and “Pixel area (km
The application of the HIRESSS model to a portion of the Aosta Valley region provided good results in terms of the spatial and temporal accuracy of the model as highlighted in Sect. 4.2. The advantage of the regional physically based model, with respect to rainfall thresholds, is that it is possible to predict the occurrence of shallow landslides with metric spatial resolution and hourly temporal resolution.
Conversely, the application of the HIRESSS model has highlighted some important drawbacks, mainly related to (i) the validation of the models results and (ii) the uncertainty of the input parameters.
To perform a solid validation it is necessary to have information regarding the spatial location and temporal occurrence of landslides. In particular, the time of occurrence is very rarely known with hourly precision; this is due to the fact that landslides are usually related to a rainstorm, without any other precise information on time of occurrence (Rossi et al., 2013). Concerning the spatial landslides locations, in many cases they are only included in the database as points without any information on the area involved. In our database, provided by the local authorities, landslides are points with information on the day of occurrence.
In synthesis the main problems encountered during the model validation are as follows:
Another important limitation related to the application and accuracy of the physically based model is the availability of detailed databases of the physical and mechanical properties of soils in the study areas. The performance of a model can be strongly influenced by the errors or uncertainties in such input data (Segoni et al., 2009; Jiang et al., 2013). Furthermore, the punctual information regarding soil properties has to be spatialized and, in general, is characterized by high spatial variability. The measurement of these parameters is also difficult, time-consuming and expensive, especially when working on large, geologically complex areas (Carrara et al., 2008; Baroni et al., 2010; Park et al., 2013; Bicocchi et al., 2016; Tofani et al., 2017).
In order to prepare raster maps of the input data and feed the physically based models, we adopted a set of constant values for the parameters for distinct lithological units; these values were derived from direct measurements. In particular, we measured soils parameters at 12 survey points (Table 1, Fig. 2) and then spatialized the punctual data according to different lithologies (Table 2). Within the HIRESSS model the soil parameters were then treated with the Monte Carlo simulation, using a equiprobable distribution for each of them.
The HIRESSS model, fed with these parameters provided good results (Sect. 4.2), although the limitations of the validation process are described above.
Nevertheless, further analysis needs to be carried out in the study area in order to define the impact of the uncertainties of the input parameters on model results and to set up the correct approach to increase the efficiency of the model. In particular the following should be considered:
Increase the number of survey points in order to a obtain a sufficient number of points
for each lithology. Use the normal Gaussian frequency model instead of the equiprobable model in the
Monte Carlo simulation for some soil parameters. The normal
distribution model, when applicable, obtains more accurate results
than using an equiprobable distribution model. This is due to the fact that given a mean value and a standard deviation
obtained from the analysis of normally distributed samples, extremely low or
high values are associated with low probability of occurrence; therefore, the
simulation time is dramatically reduced (Bicocchi et al., 2016, Tofani et
al., 2017). To test another approach to spatialize the soil parameters based, for
example, on the soil parameter values as random variables using a probabilistic or
stochastic approach as proposed by Fanelli et al. (2016) and Salciarini et al. (2017).
The HIRESSS code (a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions in real time and over large areas) was applied to the eastern sector of the Aosta Valley region in order to test its capability to forecast shallow landslides at the regional scale. The model was applied in back analysis to two past rainfall events that triggered several shallow landslides in the study areas between 2008 and 2009. In order to run the model and to increase its reliability, an in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslides formation was conducted. In particular, two campaigns of on site measurements and laboratory experiments were performed at 12 survey points. The data collected contributed to the generation of an input map of parameters for the HIRESSS model according to lithological classes. The effect of vegetation on slope stability in terms of root reinforcement was also taken into account, based on the plant species distribution and literature values of root cohesion, to product a map of root reinforcement of the study area. The outcomes of the model are daily failure probability maps with a spatial resolution of 10 m. To evaluate the model performance both temporal and spatial validation were carried out. In general, for both the simulated events, the computed highest daily probability of occurrence correspond to the days and the areas of real landslides.
The application has also highlighted some drawbacks that are mainly related to the validation of the model performance and to the uncertainty of the model input parameters. In particular, a satisfactory validation of the model is only possible if a complete event database of landslides with spatial and temporal resolution equal to the HIRESSS model resolutions is available. Furthermore, a correct geotechnical and hydrological characterization of the soil parameters as input data for the model, as well as a correct approach to spatializing the data are both fundamental to applying the model and obtaining sound result at the regional scale.
The data used in this paper can be requested from the corresponding author.
TS has developed the model code, performed the simulations and wrote the paper. VT coordinated and conceived the work and wrote the paper. GR developed the model code and performed the simulation. MA, CTS, EBM, AR, VP, PV, MP carried out the in situ and laboratory tests and prepared the model input data. SR and HS provided the data for the simulation and relevant information related to the landslides in Aosta Valley. FC and NC supervised the work.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception”. It is not associated with a conference.
This research has been carried out in the framework of a research agreement between the Department of Earth Sciences of the University of Firenze, and the Centro funzionale, Regione Autonoma Valle d'Aosta. We would like to thank the editor and three anonymous referees for their suggestions and careful revisions. Edited by: Luca Piciullo Reviewed by: three anonymous referees