Rainfall-induced shallow landslides are common phenomena in many parts of
the world, affecting cultivation and infrastructure and sometimes causing
human losses. Assessing the triggering zones of shallow landslides is
fundamental for land planning at different scales. This work defines a
reliable methodology to extend a slope stability analysis from the
site-specific to local scale by using a well-established physically based
model (TRIGRS-unsaturated). The model is initially applied to a sample slope
and then to the surrounding 13.4 km
Shallow landslides can be defined as slope movements affecting a small thickness (generally lower than 2 m) of superficial deposits. The failure surface is often located along the interface between the soil and bedrock or between soil levels with differences in permeability. These movements are very hazardous phenomena: although they generally involve small volumes of soil, they can be densely distributed across territories as a consequence of particularly intense and concentrated rainfalls (Howard et al., 1988; Montrasio and Valentino, 2008). Moreover, these phenomena are very common in slopes close to urbanized areas; for this reason, they can cause significant damage to cultivation, structures and infrastructures and sometimes cause human losses.
In this work, a methodology that links long-term field observations on a sample slope with a distributed slope stability analysis at the local scale is presented. To assess the occurrence of rainfall-induced shallow landslides in a certain area, three main aspects can be considered to be of prominent importance: (1) a detailed description of the physical–mechanical triggering mechanism in relation to the site-specific characteristics of the involved soils and stratigraphy; (2) the choice of the more suited slope stability model to be applied at the local scale; and (3) the definition of a reliable methodology to extend the model from the site-specific to local scale, according to the definition of the scale of the analysis proposed by Corominas et al. (2014).
Regarding the first aspect, it is well known that shallow landslide triggering mechanisms are strictly linked to rainfall events with the hydrological and mechanical responses of an usually unsaturated soil. In particular, the quick decrease in negative pore water pressure and the development of positive pressures when a soil approaches saturated conditions could be considered the most important cause of shallow landslides (Lim et al., 1996; Vanapalli et al., 1996). From this perspective, continuously monitoring the climatic and meteorological parameters and the physical and hydrological properties of the unsaturated soil zone is needed to understand the triggering mechanisms of shallow landslides and the main features of these phenomena.
More recently, monitoring techniques have focused not only on the soil's hydrological and mechanical conditions during shallow landslide triggering but also on some unsaturated soil behaviours which could play a primary role in promoting or inhibiting the development of shallow failures. Hydrological monitoring can identify both the predisposing and triggering mechanisms of shallow landslides. Matsushi et al. (2006) analysed rainwater infiltration and groundwater fluxes towards underlying permeable bedrock, which leads to the development of shallow landslides. Some works identified the time changes of the hydrological features of unsaturated soils, which then provoked the triggering of shallow landslides (Matsushi and Matsukura, 2007; Godt et al., 2009; Damiano et al., 2012; Springman et al., 2013). In particular, Godt et al. (2009) were the first to observe the development of a shallow soil failure in unsaturated conditions linked to a rainfall event in a natural setting. Moreover, monitoring systems measured the increase in pore water pressure and the development of a perched water table in the covering soils that could promote shallow landslides (Lim et al., 1996; Godt et al., 2008a, b; Baum et al., 2010, 2011).
The choice of the more suited method to describe the phenomena at the site-specific scale depends on the objectives of the analysis: finite elements methods, for example, can be considered appropriate to analyse an area some hundreds of square metres wide, but they cannot be considered suitable for application at the local or regional scale.
Recently, physically based models proved rather promising in assessing the triggering zones of shallow landslides. Different types of models were developed to analyse the triggering times and locations of shallow landslides according to the following aspects: the development of positive pore water pressures in saturated soils (Montgomery and Dietrich, 1994; Baum et al., 2002), the change in the soil's pore water pressure (Baum et al., 2008; Rossi et al., 2013) or soil saturation (Montrasio and Valentino, 2008) linked to rainfall intensity and duration; the possibility of modelling the size and depth of shallow landslides at the basin and local scale (Alvioli et al., 2014), the connections between different points of a slope or a basin that influence the soil's hydrological behaviour and the development of unstable conditions (Lanni et al., 2012), and the possibility of modelling the triggering conditions of shallow failures based on the natural variability of geotechnical and hydrological soil features through a probabilistic approach (Grelle et al., 2014; Mergili et al., 2014; Raia et al., 2014). Furthermore, physically based models were used to determine the rainfall thresholds for the timing and localization of shallow landslides at the local and regional scale (Salciarini et al., 2006, 2008; Godt et al., 2008a, b; Papa et al., 2013).
At the moment, an important challenge is represented by the possibility of applying a slope stability model at different scales, keeping the same level of reliability both on single slopes and areas some square kilometres wide. The spatial distribution of both geotechnical and hydrological soil properties can be reasonably inferred only from a limited number of field or laboratory tests, taking into account the spatial variability of the parameters through a probabilistic approach (Simoni et al., 2008; Mergili et al., 2014; Raia et al., 2014).
The implementation of physically based models at the local or regional scale with respect to a single slope requires homogenized soil parameters as input data in the mapping of distinct soil units, the boundaries of which can be defined in different ways. In most cases, the units used in distributed slope stability analyses are defined according to the geology of the bedrock (Salciarini et al., 2006; Baum et al., 2010; Sorbino et al., 2010; Rossi et al., 2013; Park et al., 2013; Zizioli et al., 2013). This choice is linked to the hypothesis that geotechnical and hydrological properties have spatial variations due to the spatial distribution of the bedrock materials from which the soils are derived. More rarely, the mapping unit of the soils is considered according to a pedological classification of the soil deposits (Meisina and Scarabelli, 2007), or these units are defined as engineering–geological or litho-technical units based on the main geotechnical and mechanical properties of the soils in an independent way with respect to the geology of the bedrock (Meisina, 2006; Grelle et al., 2014).
Location of the study area.
The TRIGRS-unsaturated model (Baum et al., 2008) was applied to a study area
in the Oltrepò Pavese (northern Italy; Fig. 1) to assess the triggering
zones of shallow landslides, referring to a well-documented case that
occurred on 27–28 April 2009 (Zizioli et al., 2013). The main goals of the
work were as follows: (i) identify the hydrological behaviour of the slope
soils in the study area through continuous field monitoring on a sample
slope; (ii) use field data to calibrate the TRIGRS-unsaturated model;
(iii) evaluate the efficiency of the TRIGRS-unsaturated model on the estimation of
the pore water pressure trend at the site-specific scale; and (iv) compare
the results of the TRIGRS-unsaturated distributed analyses at the local
scale in the study area, taking into account different mapping units of the
slope soils. According to the classification proposed by Corominas et al. (2014),
the analyses of the sample slope have been defined as site-specific
because the extent of the sample slope is less than 10 km
In this way, a methodology linking long-term field observations at the site-specific scale with a distributed slope stability analysis at the local scale has been developed.
The study area is strongly characterized by a traditional viticulture, which represents the most important branch of the local economy, and most shallow failures affect slopes cultivated with vineyards. For this reason, it is fundamental to assess the triggering zones of shallow landslides to correctly plan land use, manage agricultural practices and reduce the economic effects of these landslides. The developed methodology may then be applied in other geological contexts where vineyards are located on slopes affected by shallow landslides (Tiranti and Rabuffetti, 2010; Galve et al., 2015).
The study area is located in the north-eastern sector of Oltrepò Pavese,
which belongs to the north-western Italian Apennines (Fig. 1). The area is
13.4 km
The slopes are characterized by a medium–high gradient with a slope angle
that can reach 35
The climatic regime is temperate/mesothermal according to Köppen's
classification of world climates, with a mean annual temperature of
12
In this area, the bedrock is characterized by a series of formations belonging to the Mio–Pliocenic succession that geologically characterizes this Apennine area and is called “Serie del Margine” (Vercesi and Scagni, 1984). Medium–low permeability arenaceous conglomeratic bedrock (Monte Arzolo Sandstones, M. A. Sand.; Rocca Ticozzi Conglomerates, R. T. Cong.) overlies impermeable silty-sandy marly bedrock and evaporitic chalky marls and gypsum (Sant'Agata Fossili Marls, S. F. Marls; Gessoso–Solfifera Formation, G. F. Form.) (Fig. 2a). The strata are sub-horizontal, dipping east-north-east. The medium–low hydraulic conductivity of the arenaceous–conglomeratic bedrock is linked to its low primary porosity and the limited number of fractures, which cannot cause the development of a high secondary hydraulic conductivity. The deep water circulation is then confined in less cemented or more fractured levels located at different depths in the bedrock and corresponding to horizons of poorly cemented gravels, sands or conglomerates with a limited lateral extension and thickness ranging between 0.2 and 1.0 m. These bodies do not seem to constitute a continuous, more permeable level that can form a deep aquifer. The presence of water in the bedrock can be identified only by considering the more permeable levels as isolated bodies.
Moreover, a limited number of springs are detected in the valley bottom from the contact between the arenaceous conglomeratic bedrock and sandy-silty marls. These springs are detected only during more rainy periods.
Geological
In this sector of the Oltrepò Pavese, the shallow soils are mainly derived from bedrock weathering and have a prevalently clayey-silty or silty-sandy texture. The soil thickness, determined in different points of this area through trench and manual pits (Zizioli et al., 2013), ranges from a few centimetres to 2.5 m and generally increases from the top to the bottom of the slopes due to the presence of landslide accumulation areas.
The soils have also been classified from a pedological point of view, and
this information is available from soil maps at a scale of 1 : 10 000 that
cover the entire study area (ERSAL, 2001). Four pedological units can be
identified (Fig. 2b):
BRS1: Eutric Leptosols characterized by good drainage, thickness between 0.3
and more than 2.0 m, high carbonate content ( FGE1: Calcaric Cambisols characterized by good drainage, thickness that can
reach values higher than 1.5 m, high carbonate content ( ILM1/RUM1: Eutric Leptosols characterized by good well drainage, thickness
lower than 1.5 m, low carbonate content ( MRL1: Calcaric Cambisol characterized by good drainage, thickness higher than
0.8 m, medium carbonate content (between 10 and 20 %) and parent material
composed of sandstone and lenses of conglomerates.
The FGE1 pedological unit seems to be present only where the bedrock
consists of deposits from the Gessoso–Solfifera Formation, while the others
are widespread and come from different geological formations.
The study area is characterized by a high density of landslides: the IFFI (Italian Landslides Inventory) database indicates the presence of several deep landslides with failure surfaces below 2–3 m from ground level. In particular, these phenomena are rotational slides, translational slides and complex landslides (roto-translational slides evolving in earth flows) (Cruden and Varnes, 1996) and do not show evidence of recent movement, so they can be classified as dormant landslides. These phenomena were triggered by prolonged rainfall without significantly high intensity.
Examples of rainfall-induced shallow landslides that occurred in the
study area during the event of 27–28 April 2009, according to the classifications of Cruden
and Varnes (1996) and Campus et al. (1998):
The widespread shallow landslides that occurred on 27–28 April 2009
constituted the first documented case of a rainfall-induced shallow
landslide event to hit the Oltrepò Pavese since the 1950s. Throughout
the Oltrepò Pavese area, this event triggered of more than 1600 shallow
landslides (Zizioli et al., 2013): the highest density was registered in the
study area (491 landslides, approximately 36 landslides per km
Additional shallow landslide events occurred in the study area between March and April 2013 (Zizioli et al., 2014) and between 28 February and 2 March 2014. These events triggered a limited number of shallow landslides (17 and 20 respectively) in the study area.
The rainfall-induced shallow landslides identified in the study area tended
to be concentrated in three main geomorphological contexts: (i) at the top of
steep slopes (slope angles
According to the classifications of Cruden and Varnes (1996) and Campus et al. (1998) for rainfall-triggered shallow landslides, four main types of landslides were recognized (Fig. 3): (a) incipient translational slides, where fractures are present but the displaced mass has limited movement with little internal deformation (Fig. 3a); (b) translational soil slides, where the mass has moved, the failure surface is completely exposed and the collapsed materials break into different blocks (Fig. 3b); (c) complex landslides which start as shallow rotational–translational failures and then evolve into earth flows due to the large amount of water and the fabric loss of collapsed materials (Fig. 3c); and (d) disintegrating soil slips, similar to type (c) but in which the accumulation zone is not recognizable because the collapsed materials are completely dispersed along the slope and at its toe (Fig. 3d). Type (c) and type (d) shallow landslides were usually predominant in the events that affected this area.
Shallow landslides mainly affect the superficial soils above weathered or non-weathered bedrock, and the failure surfaces are located along the contact between the soil and bedrock, ranging between 0.5 and 2.0 m from ground level. More rarely, these phenomena have failure surfaces located at the point of contact between soil levels with different permeability. These movements mainly occur in vineyards and uncultivated slopes, where shrubs and grass are prevalent. In contrast, a significant number of landslides involve woodlands formed by trees that had developed in the preceding 30 years on abandoned vineyards.
In the study area, an integrated hydro-meteorological monitoring station was installed on 27 March 2012 in a test-site slope located near the village of Montuè (municipality of Canneto Pavese; Fig. 1).
Selected geotechnical and mechanical features of the monitored
slope soil and weathered bedrock: grain size distribution (gravel, sand,
silt, clay), liquid limit (
The slope is characterized by a medium–high topographic gradient (between
22 and 35 the presence of triggering zones of shallow landslides that occurred in April 2009; its position in areas with medium–high susceptibility to shallow landslides
according to previous studies (Zizioli et al., 2013); its representativeness of the whole study area in terms of the
geomorphological (medium–high topographic gradient) and hydrogeological
features (conglomeratic bedrock levels overlying impermeable marly levels); the presence of access roads to easily reach the slope and install instrumentation; its east-facing orientation, allowing for a good
recharge of the photovoltaic panel of the station that supplies power to the devices.
A multidisciplinary characterization of the monitored test-site slope was
carried out. The representative soil of the slope is 1.3 m thick. The soil
belongs to the ILM1/RUM1 pedological unit. Between 1.1 and 1.3 m from the
ground, the soil is characterized by the presence of a calcic horizon (Cgk),
labelled as G in Fig. 5, is enriched in carbonate concretions and has a
carbonate content of 35.3 %. The soil has a basic pH (approximately
8.3–8.8), a low organic carbon content (less than 3.0 %) and a
steady cationic exchange capacity (12.3–15.9 meq L
The geotechnical characterization of the slope deposits was based on standard soil analyses carried out according to the ASTM (American Society for Testing and Materials) standards. The performed tests included (i) an assessment of the physical parameters of the materials (grain size distribution, bulk density, Atterberg limits) and (ii) triaxial tests which allowed the determination of the shear strength parameters in terms of the effective stresses.
The soil derives from the weathering of sands and poorly cemented
conglomerates belonging to the Rocca Ticozzi Conglomerates. Individual soil
horizons along the slope have a clayey sandy silt texture, with high silt
contents ranging from 51.1 to 65.6 %, clay content between 21.3 and
29.0 % and varying amounts of gravel and sand (Table 1). By analysing the
clay soil fraction (
According to the USCS classification, the soil horizons are prevalently
non-plastic or slightly plastic (CL). The liquid limit (
The peak shear strength parameters were reconstructed at different depths
through triaxial tests. Up to 1.0 m from the ground, the soil horizons have
a friction angle (
Schematic representation of the monitoring station installed in the study area.
Comparison of measured and modelled parameters of Mualem and
Van Genuchten models for some soil samples taken in the study area, obtained
through Rosetta pedotransfer function:
The hydrological properties of the different soil horizons were determined
through a laboratory reconstruction of the soil water characteristic curve
(SWCC) and the hydraulic conductivity function (HCF). These functions were
reconstructed through a combination of the Wind–Schindler method (WSM;
Schindler, 1980; Peters and Durner, 2008) technique (Hyprop, UMS GmbH,
Munich, Germany) with a vapour pressure method (VPM; Rawlins and Campbell,
1986) device (WP4T, Decagon Devices, Pullman, WA) on undisturbed soil
samples. The experimental data were fitted by the Van Genuchten (1980) and
Mualem (1976) models. The parameters of these models (saturated water
content
Hydrological properties of the monitored slope soil and weathered bedrock.
A detailed description of the monitoring station is reported elsewhere
(Bordoni et al., 2014; Fig. 4). In this paper, the necessary information
required for completeness is provided. The station collects data with a time
resolution of 10 min. The following meteorological parameters are measured:
rainfall depth, air temperature, air humidity, atmospheric pressure, net
solar radiation, wind speed and direction. Some probes are installed in the
soil and weathered bedrock at different depths to measure the soil water
content and soil pore water pressure. In particular, six time domain
reflectometer (TDR) probes installed at 0.2, 0.4, 0.6, 1.0, 1.2 and 1.4 m
from the ground level measure the soil water content, while a combination of
three tensiometers and three heat dissipation (HD) sensors installed at
depths of 0.2, 0.6 and 1.2 m measure the soil's pore water pressure. The
HD sensors only allow pore water pressures lower than
The monitoring equipment allowed us to identify the soil's main hydrological behaviours, in particular the soil's response to the different seasonal rainy conditions and various rainfall intensities. The test-site slope can adequately represent the geotechnical and hydrological features of slopes affected by shallow landslides over the entire study area. For this reason, the data from continuous monitoring of the sample slope can be useful to identify the soil's hydrological conditions that can lead to the triggering mechanisms of shallow landslides in similar conditions. The field data can be used to infer the soil conditions for periods without monitoring to evaluate the prediction ability of a physically based model such as TRIGRS-unsaturated, which is used to identify the triggering zones of shallow landslides at the local scale.
To identify the triggering zones of shallow landslides using physically
based models, the distributions of the main geotechnical and hydrological
properties are required as input parameters to obtain the trend of the slope
safety factor (
To guess an answer to this question, the TRIGRS model was implemented in the study area using different types of mapping units (geological and pedological), the class distribution of which across the study area is represented in Fig. 3.
Mean and standard deviation (SD) values of geotechnical and mechanical characteristics of the unit mapping of the soils of the study area. The SD values are in parentheses.
Mean and standard deviation (SD) values of hydrological properties of the unit mapping of the soils of the study area. The SD values are in parentheses.
The main geotechnical and mechanical soil properties were assigned to each unit after performing an averaging procedure of the data collected through laboratory tests on 160 soil samples taken from different sites in the study area. In this group, 114 disturbed soil samples were used to determine the Atterberg limits and grain size distribution curves, while 52 undisturbed soil samples were used to measure the soil's unit weight. In the group of undisturbed soil samples, which were taken at a depth where the failure surface of the shallow landslides developed, 18 samples were used to measure the peak shear strength parameters through direct shear tests, and 3 samples were used for triaxial tests. As observed in the monitored slope, no significant changes in the geotechnical properties, particularly for the grain size distribution and Atterberg limits, were identified along the depth in the soil levels.
The main differences between the classes for each mapping unit type were linked to the grain size distribution. In fact, the geological and pedological units could be distinguished based on the sand and clay amounts. The soils derived from the weathering of the Monte Arzolo Sandstones and Rocca Ticozzi Conglomerates were classified as clayey-sandy silt because the amount of sand is generally more than 15 % (Table 3). In contrast, the soils derived from the weathering of the Sant'Agata Fossili Marls and Gessoso–Solfifera Formation were classified as clayey silt due to a sand content generally lower than 10 % and a prevalent silt content (Table 3). Moreover, the soils derived from the weathering of the Gessoso–Solfifera Formation exhibited a significantly higher clay content (37.1 %) than the other units (Table 3). The BRS1 and MRL1 pedological units contain groups of soils with clayey silt texture (Table 3), while the ILM1/RUM1 soils are clayey-sandy silt due to a mean sand content of 19.3 %. In contrast, the FGE1 class contains soils with a clayey-silty texture due to similar mean silt and clay contents (46.9 and 43.3 % respectively; Table 3).
According to the USCS classification, the majority of the classes in all the
mapped units are grouped into non-plastic or slightly plastic soils (CL),
with a mean liquid limit
The unit soil weight
The standard deviation (SD) value of each soil property for each unit has
also been provided. For both geological and pedological units, the highest
values of SD were measured for the liquid limit
A similar procedure was performed to assign the hydrological parameters (in
terms of SWCC) to the different
selected units. In particular, the Rosetta pedotransfer function model
(Schaap et al., 2001) was applied to the grain size distribution of the soil
samples to determine the parameters of the SWCCs and HCFs of the materials
of each identified class according to the models of Mualem and Van Genuchten
(Table 4). The average values of the Mualem and Van Genuchten models'
parameters (
The WRCs and HCFs were also reconstructed for eight undisturbed samples
taken in the study area through the same methods (WSM and VPM) used for the
soil samples from the monitored slope. For these soils, the reconstructed
Mualem and Van Genuchten model parameters are confident with respect to the
values modelled through the Rosetta pedotransfer function for
This aspect confirms the reliability of modelling the soil's hydrological properties in the study area through the Rosetta model, correctly identifying the mean values of these properties to be assigned to the selected mapping unit classes.
The TRIGRS-unsaturated model (Baum et al., 2008) has been used for the analyses to assess the triggering zones of shallow landslides in the study area. A brief description of the main principles of this physically based model is provided in Appendix A.
This model has been applied to different rainfall events measured by the
monitoring station installed in the study area during its activity. The
modelled pore water pressures at two depths, 0.6 and 1.2 m from the ground,
for each considered rainfall event along the monitored slope were then
compared with the values measured during the same rainfall at the monitoring
station. The goodness of the TRIGRS-unsaturated model's fit to the pore
water pressure modelling was evaluated with the root mean square error
(RMSE) statistical index, expressed in Eq. (1) as
Moreover, TRIGRS-unsaturated was applied considering the benchmark rainfall
event on 27–28 April 2009. Referring to a real rainfall event, these
analyses provided the assessment of shallow landslide triggering zones
taking into account the two different types of mapping units, namely
geological and pedological. The choice to also consider the pedological unit
is linked to the distinction of the soils in the study area based on their
pedological features, which can be connected to different pedological
processes that can directly influence the physical and hydrological
behaviour of a soil (Baumhardt and Lascano, 1993). Two indexes from the
receiver operating characteristic analysis (Hosmer and Lemeshow, 2000;
Zizioli et al., 2013) have been used to evaluate the predictive capability
of the reconstructed models: the “true positive rate” (TP) and the “false
positive rate” (FP). The TP is the ratio (in percentage) between the number
of elementary cells computed as unstable (safety factor
The results of the reconstructions through the TRIGRS-unsaturated model were also compared, in terms of the TP and FP, with the results obtained in a previous work through the TRIGRS-saturated (Baum et al., 2002), SINMAP (Pack et al., 1999) and SLIP (Montrasio and Valentino, 2008) models in the same study area and for the same event by Zizioli et al. (2013).
As reported in the introduction, the first step to appropriately model rainfall-induced landslides at both the site-specific and local scales is a detailed description of the physical–mechanical triggering mechanism in relation to the site-specific characteristics of the involved soils and stratigraphy. Data from the monitoring station were used to determine the dynamics of the soil's water content and soil's pore water pressure at the test site in relation to the characteristics of the different soil levels and the weathered bedrock (Fig. 6). The monitored hydrological behaviours can adequately represent the typical conditions that characterize the surrounding study area. In this work, the period between 27 March 2012 and 1 October 2014 was analysed.
The average hourly values of the water content and pore water pressure were
considered. Because the tensiometer at 0.2 m from the ground level broke,
the pore water pressure in the range between 0 and
Monitored soil and weathered bedrock water content
Monitored soil and weathered bedrock water content
In the analysis period, the water content ranged between 10 and 45 % in
the topsoil and between 15 and 38 % in the weathered bedrock. In
contrast, the pore water pressure ranged from positive values to 12.7 kPa in
the G horizon to values on the order of
The installed tensiometers required a correction for the measured values due to the height of the water present in the column of the instrument, with an increase of 1 kPa for each 0.1 m of depth in the soil. For this reason, it is possible to measure positive pore water pressure values, as already shown in previous works (Zhan et al., 2006).
By analysing the data acquired over 2 years of monitoring, it is immediately clear that the water content and pore water pressure dynamics are strictly connected to rainfall trends and different hydrological behaviours can be identified in the soil profile (Fig. 6).
The soil horizons within 0.6–0.7 m of ground level had a faster response than the deepest soil horizons to long, dry or long, wet periods. In the summer months, the water content and pore water pressure decreased faster in the most shallow soil horizons than in the deeper ones (Fig. 6) due to evapotranspiration effects and water uptake from the roots of grass and shrubs. Changes in the hydrological parameters are less rapid in soil levels deeper than 0.6–0.7 m below the surface and in weathered bedrock (Fig. 6); this different behaviour is linked to the fact that these levels are less affected by evapotranspiration and root zone effects. The shallowest soil horizons quickly became wet (Fig. 6) during rather prolonged rainy periods following dry periods, such as in autumn months, and after rainfall events characterized by low duration and low cumulative rainfall (e.g. 34.8 mm in 21 h on 31 October–1 November 2012 and 42.2 mm in 34 h on 6–7 October 2013). In contrast, the re-wetting of soil horizons deeper than 0.6–0.7 m below the surface and the weathered bedrock is not so fast, and only prolonged rainy periods with many rainfall events in a few days or weeks can provoke an increase in the pore water pressure and water content at this level (Fig. 6).
The rapid re-wetting as a consequence of early autumn rainfalls of the soil horizons within 0.6–0.7 m and the abrupt increase in pore water pressure in a time span of 5–10 h after the start of the rain during summer concentrated events, as it occurred on 27 June 2013 (13.3 mm in 2 h), on 26 August 2013 (16.5 mm in 3 h) and on 11 September 2013 (9.1 mm in 3 h), may also be due to the presence of desiccation cracks and other macro-voids all along the soil profile where rainwater may flow preferentially. This fact could promote a quick development towards near saturated conditions in the cracks and macro-voids (Bittelli et al., 2012; Smethurst et al., 2012).
In the winter and spring months, especially between December and May,
frequent precipitation can increase the soil wetness until it approaches or
reaches saturated conditions (Figs. 6 and 7). The soil water content ranges
between 38 and 45 %. In contrast, the pore water pressure remains between
During wet periods, the water content in the weathered bedrock at 1.4 m below the surface was lower than in the overlying G horizon (Fig. 6a).
According to the monitored data, it might be hypothesized that during winter and spring months a perched water table can form in the test-site slope soils due to the contact between the soil and weathered bedrock and remain steady at 1.2 m below the surface until the end of the spring. The thickness of this water table is approximately 0.1 m over the contact between the soil and the weathered bedrock. Additionally, when no rain falls for many days, as between 5 and 21 March 2014 (Fig. 7), the pore water pressure remains steady within a positive value range at this depth, thus confirming the presence of a perched water table. In contrast, following particularly intense rainfalls, water table can grow up to 0.8–1.0 m as testified by the significant increase in water content at 1.0 m from ground until conditions of complete saturation are attained (water content between 39 and 42 %).
This condition was not observed following other rainfall events in wetting periods during the monitored time span. In this situation, a shallow landslide affected the test-site slope (Fig. 7). For this reason, the triggering mechanism of rainfall-induced shallow landslides in the study area could be due to the emergence of a thin (0.1–0.2 m) perched water table present in winter and spring months in the slope soils of the study area following particularly intense rainfalls. Thus, it is fundamental to take into account this mechanism on modelling shallow landslides triggering zones through physically based models.
Monitored soil and weathered bedrock water electrical conductivity dynamics at the monitored test-site slope in the study area.
The particular hydrogeological setting of the bedrock levels does not allow
for the formation of a deep groundwater level in the slope, but the water
table develops only at the interface between soil and weathered bedrock.
Other evidence demonstrates the absence of a deep groundwater level.
Figure 8 shows the trends of water electrical conductivity measured through the TDR
probes of the monitoring station at different depths in soil and weathered
bedrock levels. The electrical conductivity values range between 15 and
40
Based on monitoring observations, the development of a thin perched water table above the soil–bedrock contact seems to be the most reasonable hydrological mechanism for shallow landslide triggers in this area, even if other mechanisms (e.g. bedrock exfiltration; Brönnimann et al., 2013) cannot be completely excluded. Moreover, the soil–atmosphere interaction phenomena observed during wet periods were considered a good benchmark for the application of the TRIGRS-unsaturated model. In fact, the model allows for the modelling of shallow landslide triggers due to the emergence of a perched water table (Baum et al., 2008), as we can observe in the monitored test site. This fact implies that the lowest computed safety factors correspond to areas characterized by the presence of a natural permeability barrier, such as the soil-weathered bedrock contact where shallow landslide sliding surfaces develop, as can be observed in the study area (Zizioli et al., 2013).
A DEM acquired before the April 2009 event with a grid size of
10 m
TRIGRS-unsaturated was implemented with both DEMs to evaluate the differences in the models when passing from the site-specific scale and high resolution to a local scale with lower resolution.
To analyse the role played by the types of mapping units, the study area was divided into different regions according to each zoning. All input data were acquired from a GIS database in a “raster” form. For each mapping unit, a map was generated at the same spatial resolution as the DEM.
The soil's geotechnical and hydrological parameters are required as input
data by TRIGRS-unsaturated and are summarized in Table 5. To take into
account the uncertainties of the soil input data, the triggering zones of
the shallow landslides were modelled by considering either the mean value or
the value obtained by subtracting or adding the standard deviation of each
parameter. A sensitivity analysis was performed through different
simulations by changing only one parameter in each simulation and keeping
the others constant. The considered variables were the unit weight
Mean and standard deviation (SD) values of the soil parameters used as input data in TRIGRS-unsaturated. The SD values are in parentheses.
Comparison of measured and estimated TRIGRS-unsaturated
pore water pressure trends at 0.6 m from ground corresponding to the
monitoring station for selected rainfall events:
Selected rainfall events for the implementation of TRIGRS-unsaturated features.
Comparison of measured and estimated TRIGRS-unsaturated
pore water pressure trends at 1.2 m from ground in correspondence of the
monitoring station for selected rainfall events:
The parameter indicated as
To create a continuous map of topsoil thickness, a geomorphologically indexed model based on the local slope angle, the elevation and the topographic position was used (Zizioli et al., 2013).
An analysis of the reliability of the pore water pressure at different depths as modelled by TRIGRS-unsaturated was performed. For the monitoring station and the most intense rainfall events in the wet periods of the monitored time span (23–25 March 2013, 30 March 2013, 4–5 April 2013, 20–22 April 2013, 18–20 January 2014 and 28 February–2 March 2014; Table 6), the measured values of the pore water pressure measured between 0.6 and 1.2 m from the ground level were compared to the modelled pore water pressure values using TRIGRS-unsaturated.
For these analyses, the initial water table depth across the study area was
chosen according to the information obtained during the monitoring time span
in the test-site slope. The most superficial measured pore water pressure
values (within 0.6 m of the surface) were considered representative of the
hydrological conditions in the selected area and were used to estimate the
water table depth according to Eq. (3) (Comegna, 2008):
Hourly values of the data related to the rainfall events from the data logger of the monitoring station were included in the model (Table 6).
The modelling of the pore water pressure for the monitored rainfalls in 2013–2014 was made considering the soil's geotechnical and hydrological data reported in Table 5. In particular, the geological class for the monitoring station area is the Rocca Ticozzi Conglomerates, and the pedological class is ILM1/RUM1.
Moreover, the hourly rainfall intensities recorded by the Cigognola rain gauge during the event that occurred on 27–28 April 2009 were assumed as rainfall input data (Table 6). For these analyses, it was assumed that a thin perched water table was already present in the extended study area before the beginning of the event based on what was observed at the monitoring station during the winters and springs. It was also assumed that this water table had an upper limit located at approximately 0.1 m above the soil–bedrock contact and a parallel trend with respect to this contact.
Figures 9 and 10 show the comparison between the measured and modelled pore water pressure trends at 0.6 and 1.2 m respectively for the considered rainfall events reported in Table 6. The modelled trends for the test-site soil are the same even when considering the two different mapping unit types because the soil's hydrological properties remain constant (Table 5). Moreover, the same estimated pore water pressure trends were found for the analyses with two different DEMs.
The graphs related to the pore pressure trends show how TRIGRS-unsaturated is able to adequately model the increase in pore pressure during a rainfall event.
Measured initial–final pore water pressure values versus those computed by TRIGRS-unsaturated for the selected rainfall events at 0.6 m from ground corresponding to the monitoring station in the study area.
Measured initial–final pore water pressure values versus those computed by TRIGRS-unsaturated for the selected rainfall events at 1.2 m from ground corresponding to the monitoring station in the study area.
In particular, differences greater than 2 kPa between the measured and
estimated values have never been found at both depths, except for the final
phase of the 28 February–2 March 2014 event. This result can
be deemed a very positive result, especially considering that the
tensiometers used in the monitoring are characterized by an accuracy equal
to
The lowest RMSEs were found for shorter events and depths of 1.2 m compared to those at shallow depths (Table 8). Generally, the RMSEs values are lower than 1.5 kPa for all the considered events and are always lower at 1.2 m from the ground than at 0.6 m (Tables 7 and 8). The differences between the modelled trends at different depths can be linked to the different hydrological properties of the soil horizons. Overall, the increasing pressure trends agree with each other. The authors of the original code (Baum et al., 2002, 2008) noted that TRIGRS was developed to model intense weather events and not long periods with low levels of precipitation.
Note that a sudden increase in the pore water pressure during the first stages of the rainfall events that occurred on 23–25 March 2013, 30 March 2013 and 4–5 April 2013, as modelled by TRIGRS-unsaturated, is clearly visible at 1.2 m from the ground (Fig. 10a–c). In particular, the field measurements show a quite negative trend in the pore water pressure, while the model shows a slightly positive trend.
Furthermore, TRIGRS-unsaturated models the highest pore water pressure value
at the end of each rainfall event, while in many cases (in particular for
the event that occurred on 28 February–2 March 2014) the field measurements
show that the highest pore water pressure values are not reached at the end
of the event (Figs. 9f and 10f). It is important to note that the
The modelling errors for the pore water pressure trends at the monitoring
station can be linked to the simplification provided by the model. The layers
within 0.6 m of the surface in the test-site slope soil are somewhat
different in terms of the hydrological features with respect to those of the
levels between 0.6 and 1.2 m (Table 2). TRIGRS-unsaturated does not consider a
layered soil; thus, it is appropriate to simulate the pore water pressure by
assuming the mean values of the hydrologic parameters of different layers.
Indeed, the mismatch is always lower than 2 kPa, although the mismatch
attained a value of 2.8 kPa for the rainfall event on 28 February 2014. It is
true that, in general, the variation could have the same range for the
mismatch, and then the error could be high with respect to the variation, but
we must at least consider the following aspects:
the reliability of these results should be evaluated not in an absolute
sense, with respect to only isolated rainfall events, but in relation to
long-period analyses; during the considered rainfall events, the modelled pore water pressure value
is always higher than the measured one; in turn, overestimated pore pressure
causes an underestimation of the safety factor of the slope, thus ensuring
precautionary conditions; although the results could be considered unsatisfactory at the local scale,
the intrinsic limitations of the TRIGRS model (such as the use of a
homogeneous soil) together with the extreme potential variation in the
hydrologic parameters at the regional scale would make a sensitivity analysis
at the local scale inconsistent.
Pore water pressure trends were also modelled for the April 2009 event (Fig. 11),
when field measurements were not available and many shallow landslides
occurred in the area. For the test-site slope, TRIGRS-unsaturated registered
the greatest increase in pore water pressure at 0.6 and 1.2 m around the
peak intensities of the event. In particular, the pore water pressure
reached approximately 6 kPa at 02:00 LT on 28 April 2009 at 1.2 m and remained
constant until the end of the event (Fig. 11). Most landslides were
triggered between the late evening of 27 April and the early hours of
28 April (hourly interval 32–48 in the plot of Fig. 11).
Pore water pressure modelled by TRIGRS-unsaturated for 27–28 April 2009 event corresponding to the monitoring station in the study area.
Modelled scenarios for 27–28 April 2009 event for the monitored
slope using different DEMs with grid size of 2 m
Shallow landslide susceptibility scenarios corresponding to the
27–28 April 2009 event for the study area by using TRIGRS-unsaturated
model:
Although a comparison with field measurements is not possible in this case due to the lack of field data, this result can be considered reliable, also accounting for the presence of a perched water table 1.2 m from the ground (positive pore water pressure; Fig. 11) at the beginning of the event, which is also the typical condition observed in the test-site slope during wet months.
Figure 12 shows the April 2009 scenario at the monitored test-site slope
considering DEMs with grid sizes of 2 m
The results were the same when mapping either a geological or pedological
zone. In both cases, shallow landslide scarps fell in areas modelled by
TRIGRS-unsaturated as unstable (Fig. 12). There is an overestimation of the
unstable areas in both the reconstructions because the slope angle is quite
steady along the hillslope (between 25 and 35
Figure 13 shows the shallow landslide scenarios for the April 2009 event for
the whole study area by taking into account the mean values of the input
soil features from the selected mapping unit types. To quantify the
differences between the model results based on different mapping units,
which also consider the sensitivity analysis, the TP and FP indexes were
computed from the
Considering the mean values of the input data, the scenarios based on either geological or pedological units did not provide a significant overestimation of the unstable areas: the maps have similar FP values, ranging between 10.1 and 10.2 % (Fig. 14). The differences between the obtained maps become evident when considering the TP values: the best prediction of the effective unstable areas was obtained considering a pedological mapping unit (78.9 %) with respect to the scenarios obtained through a geological mapping unit, which has a TP index of 73.3 % (Fig. 14).
By analysing the results of the sensitivity analyses, negligible variations
in both the TP and FP are linked to the role played by the
Greater variations affect both the TP and FP indexes by changing
Effects of different soil input data on FP and TP indexes
obtained by modelling with TRIGRS-unsaturated, considering the values in Table 5
for geological and pedological zoning:
In this case, a nil cohesion value for both pedological mapping units
(Fig. 14b and c) has been assumed. The highest values of
The results obtained through the TRIGRS-unsaturated model have also been compared to the results obtained by Zizioli et al. (2013) for the same area using the TRIGRS-saturated, SINMAP and SLIP models (Fig. 15). These three models were applied considering different initial hydrological conditions with respect to those considered in this work (Zizioli et al., 2013). Based on the same mapping unit (geological zoning) and considering the mean values of the input soil features, the TRIGRS-unsaturated model provided a better assessment in terms of the ratio between the TP and FP with respect to the other models. In fact, the SLIP and SINMAP models have similar TP (71.7–72.4 %) and FP (30.3–32.2 %), which indicates a significant overestimation of the unstable areas, while TRIGRS-saturated has a lower FP ranging between 10.0 and 10.2 %. The TRIGRS-unsaturated scenario models the effective unstable areas slightly better, as demonstrated by the highest TP value (73.3 %; Fig. 15).
True positive rate (TP) and false positive rate (FP) obtained with the TRIGRS-unsaturated, TRIGRS-saturated, SINMAP and SLIP models for the study area considering a geological unit mapping and mean values of soil input data.
In this work, the implementation of a slope stability model for the assessment of the triggering areas of rainfall-induced shallow landslides has been presented. The followed work procedure allowed us to keep the same level of reliability at different scales, both on site-specific slopes and at the local scale for an area some square kilometres wide in the Oltrepò Pavese (northern Italy).
The work procedure consists of the identification of a sample slope in an area of some square kilometres, which can be assumed as representative of the whole area.
Field measurements from a monitoring station installed on the sample slope have previously been analysed. Through monitoring the soil's hydrological conditions, a detailed description of the physical–mechanical triggering mechanisms of landslides and the hydrological conditions leading to failure in relation to the site-specific characteristics of the involved soils and stratigraphy has been obtained. In particular, the temporal field measurements of some unsaturated soil hydrological parameters, such as soil water content and pore water pressure, proved of paramount importance. In the study area, the triggering mechanism of rainfall-induced shallow landslides is linked to the emergence of a perched water table present in wet months (winter and spring months) in the slope soils caused by particularly intense rainfalls.
The field measurements also allowed us to adequately verify the reliability of a physically based model for the identification of shallow landslide triggering zones, which takes into account unsaturated soil conditions such as the TRIGRS-unsaturated model. The assumption according to which the modelled hydrological parameter trends over wide areas should correspond to the real physical processes without an experimental confirmation can cause errors in the assessment of shallow landslide initiation. For this reason, it is necessary to verify the reliability of the model results, even if it necessarily involves only few sample points.
By applying the typical soil hydrological conditions in terms of the perched water table in wetting periods per the field observations, the pore water pressure evaluated by the TRIGRS-unsaturated model essentially agrees with the measurements acquired at the monitored test-site slope for different selected rainfall events.
The correct evaluation of the temporal pore water pressure trend during a rainfall event is fundamental to identify the shallow landslide triggering zones of an area in which the slope soils have incomplete saturation conditions. Based on the good comparison between the measured and modelled pore water pressures for the monitored rainfall events, it was possible to consider the pore water pressure modelled for the 27–28 April 2009 event to be reliable, particularly in terms of the correct discrimination of the increase in pore water pressure due to the intensity peaks during the rainfall.
The site-specific analysis carried out at the test-site slope shows the correct assessment of the shallow landslide scarps from the April 2009 event. Considering DEMs with different resolutions at the scale of the test-site slope, it seems that a higher resolution DEM does not improve the identification of the stable and unstable areas in the sample slope.
After this phase, the same model was applied at the local scale on the
surrounding area (13.4 km
Considering the mean values of the input data for each unit, the results
obtained at the local scale by comparing the real and predicted unstable
areas are quite satisfactory in terms of both the true and false positive
indexes. The main differences between the models were linked to the TP
values of the
Moreover, the results obtained by the TRIGRS-unsaturated model based on the geological units and for the mean values of the input soil data were compared with the results obtained through other physically based models in the same area (Zizioli et al., 2013). The research demonstrates how implementing reliable hydrological conditions derived from the analysis of the typical soil hydrological behaviour and continuous field monitoring improves the prediction of shallow landslide source areas. In fact, based on the same zoning (geological units) of the soil in the study area, the TRIGRS-unsaturated model has a quite higher degree of success with respect to the models (TRIGRS-saturated, SINMAP, SLIP) applied in the past for the same area, which considered different hydrological models and different hydrological input data (Zizioli et al., 2013). The improvement is testified by a significant reduction in the FP on the order of 20 %, which is linked to a lower overestimation of the computed unstable areas. Furthermore, the FP value of the TRIGRS-unsaturated model is higher than that of the other models, thus testifying a better assessment of the shallow landslide triggering areas.
The TRIGRS-unsaturated model results are different, although very close to those previously obtained through the TRIGRS-saturated model (Zizioli et al., 2013). This difference was already shown in other case studies (Sorbino et al., 2010), even if neglecting the effects of the incompletely saturated conditions of the soils can be quite costly on the assessment of shallow landslide triggering zones (Sorbino et al., 2010).
The proposed working scheme for the implementation of the well-established physically based model TRIGRS-unsaturated aims to find a potential methodology for extending the analysis from site-specific to a wider area. An improvement in the results of the model application can be obtained through the use of hydrological monitoring data from a slope, which can be considered representative of the geomorphological, geological and physical conditions of the surrounding area. In particular, the monitoring approach allows us to identify the typical soil behaviour across different seasons, revealing the response of the slopes to different rainfalls. The monitoring approach is also fundamental for verifying the reliability of a physically based model in terms of the soil's hydrological responses before assessing the triggering zones of shallow landslides. Furthermore, this work gives some indications about the influence of the type of mapping unit chosen for the homogenization of the soil parameters, which are required as input data for the model's application at the local or regional scale. The analysis of the role played by mapping units described in this paper aims to identify which zoning can be more representative of the distribution of the soil's properties over the study area and evaluate the differences in the assessment of triggering areas considering different types of soil mapping units.
The scheme followed in this work can also be applied in other similar contexts to identify slopes prone to shallow landslides and better plan land use. In particular, future developments will be the application of the TRIGRS-unsaturated model in areas affected by shallow landslides and characterized by the extensive presence of slopes cultivated with vineyards, as in Oltrepò Pavese. Furthermore, the role played by wine plant roots on soil reinforcement will be considered, allowing for the development of optimal agricultural management practices to prevent instability phenomena.
The TRIGRS-unsaturated model (Baum et al., 2008) is a Fortran program
designed to model the timing and distribution of rainfall-induced shallow
landslides (Baum et al., 2010; Braga et al., 1985; Sorbino et al., 2010; Park
et al., 2013; Zizioli et al., 2013). This physically based model considers
the method outlined by Iverson (2000) to explain shallow landslide
triggering in relation to rainwater infiltration with the implementation of
complex storm histories, assuming an impermeable basal boundary at a finite
depth and a simple runoff-routing scheme. This model computes the pore water
pressure and
The model can consider the unsaturated conditions of the soils at the initial stage through the simple analytic solution for transient unsaturated infiltration proposed by Srivastava and Yeh (1991). The program model's pore water pressure changes using analytical solutions for partial differential equations that represent one-dimensional vertical flow, considering the propagation of the flow into an isotropic, homogeneous material for either saturated or unsaturated conditions according to Iverson's model (Baum et al., 2002, 2008).
To model the soil hydrological pattern for rainwater infiltration,
TRIGRS-unsaturated requires soil hydrological properties as input
parameters. In particular, in addition to the saturated hydraulic
conductivity
The use of step-function series allows TRIGRS-unsaturated to represent pore water pressure changes due to variable rainfall intensity input during the considered event, and a simple runoff-routing model allows for the diversion of excess water from impervious areas to more permeable downslope areas.
An infinite slope model is coupled with the hydrological model to compute
the
We thank M. A. Leoni of the Riccagioia S. C. P. A. – Centro di Ricerca Formazione e Servizi della Vite e del Vino for the pedological analyses of the soil on the monitored slope. We also thank M. Setti of the Department of Earth and Environmental Sciences at the University of Pavia for the X-Ray diffraction analyses of the soil on the monitored slope.
The authors wish to thank the anonymous reviewers for their suggestions and contributions to the work. Edited by: F. Guzzetti Reviewed by: two anonymous referees