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Rainfall-triggered shallow landslide events have caused losses of human lives and millions of euros in damage to property in all parts of the world. The need to prevent such hazards combined with the difficulty of describing the geomorphological processes over regional scales led to the adoption of empirical rainfall thresholds derived from records of rainfall events triggering landslides. These rainfall intensity thresholds are generally computed, assuming that all events are not influenced by antecedent soil moisture conditions. Nevertheless, it is expected that antecedent soil moisture conditions may provide critical support for the correct definition of the triggering conditions. Therefore, we explored the role of antecedent soil moisture on critical rainfall intensity-duration thresholds to evaluate the possibility of modifying or improving traditional approaches. The study was carried out using 326 landslide events that occurred in the last 18 years in the Basilicata region (southern Italy). Besides the ordinary data (i.e., rainstorm intensity and duration), we also derived the antecedent soil moisture conditions using a parsimonious hydrological model. These data have been used to derive the rainfall intensity thresholds conditional on the antecedent saturation of soil quantifying the impact of such parameters on rainfall thresholds.
Interuniversity Consortium for Hydrology (CINID), Italy
Ram L. Ray
College of Agriculture and Human Sciences, Prairie View A&M University, USA
Salvatore Manfreda*
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Italy
*Address all correspondence to: salvatore.manfreda@unina.it
1. Introduction
Rainfall-induced shallow landslides are critical issues of scientific and societal interest, causing billions of euros in damages and thousands of deaths every year [1]. A large number of studies investigated the functional relationship between rainfall characteristics and landslide events [2]. One of the main results is the definition of empirical rainfall thresholds associated with the triggering of the shallow landslide, such as total event rainfall, intensity-duration, event-duration, and event-intensity thresholds ([3] and reference therein) [4, 5]. However, these approaches lead to a limited understanding of the geomorphological process and, if used for warning purposes, they can produce a large number of false positives alarms [6]. In fact, rainfall thresholds approach evaluates only the amount of cumulated rainfall and it neglects the primary role of other vital parameters, such as evapotranspiration, soil moisture, rainfall infiltration, soil porosity, and permeability.
In order to consider predisposing hydrological factors on empirical threshold calculation, recent studies have focused on the role of the antecedent daily rainfall in landslides triggering [7, 8, 9, 10, 11, 12]. These approaches have found a strong relationship between the hourly rainfall data triggering landslides and the initial soil moisture contributing to improving the predictive accuracy of empirical thresholds. Those results have also stimulated a critical revision of the intensity/duration thresholds in the last few years [6, 13, 14, 15]. In particular, Bogaard and Greco [6] introduced the cause-trigger concept for defining hydro-regional thresholds for predicting landslide occurrence, also suggesting taking into consideration the slope water balance. Starting from this new perspective, we aim to contribute to this discussion by evaluating the correlation between antecedent soil moisture conditions and rainfall intensity during shallow landslide events. In particular, we would like to explore better how much the initial saturation degree of soil affects the intensity/duration (I/D) relationships in landslide prediction. For this purpose, it is very important to use reliable databases in the literature or otherwise build a specific one.
There are many soil moisture datasets that have been successfully used to calibrate and validate catchment or watershed scale models of infiltration, soil-moisture storage, and in some ways, even to examine the first landslide trigger [16, 17, 18, 19].
In this chapter, we addressed this issue by reconstructing and leveraging a dataset of 326 landslide events that occurred in the Basilicata region (southern Italy,
Figure 1
, and Appendix), from January 2001 to March 2018. For each georeferenced landslide, we derived the rainfall event characteristics and antecedent soil moisture conditions using a parsimonious physically-based distributed model applied at the regional scale with a spatial resolution of 200 m and a daily and hourly time scale. This approach allowed us to reconstruct all of the main forcing factors that may have produced a change in the slope stability and to detect the impact of antecedent soil moisture on the rainfall intensity/duration relationship. The numerical simulation has been needed to reconstruct the antecedent soil moisture values not available for the whole study area.
Figure 1.
Geographical distribution of the weather stations and landslide events for the study area. The graph in the inset shows the monthly distribution of landslides in Basilicata from 2001 to 2018.
This study was carried out within a research agreement with the Civil Protection of the Basilicata region, which supported in part the reconstruction of the list of landslide events used for the analysis. The database was constructed with the primary aim of creating an updated description of the most recent landslides and the associated rainfall events. Therefore, the present section will be devoted to the description of the study area, the methodology adopted to build the database, and the modeling approach used to reconstruct the antecedent soil saturation conditions.
2.1 Study area
Basilicata is a region of southern Italy covering an area of 9.992 km2 characterized by different topographical and geomorphological contexts, landscape types (47% mountains, 45% hillocks, and 8% plains) and geolithological conditions. The north-western and south-western regions are characterized by mountain landscapes (southern Apennines) with significant elevations of the relief (between 1300 and 2000 m of altitude) and steep slopes, particularly where Mesozoic successions (dolomite and siliceous limestones) outcrop. The eastern region shows a hilly landscape characterized by soft shapes or tabular hills (alternating ridges and valleys in conglomeratic sandstone—clayey—marly), usually with low gradients of the slopes, often modeled in foredeep Plio—Pleistocene units with clayey dominant [20, 21].
Precipitation values are typical of the Mediterranean, with distinct dry and wet seasons [22]. Higher precipitation totals occur during the last autumn-winter period when landslides and floods usually take place (more than 70%). A near real-time hydrometeorological network covers the territory uniformly with a density of one station every 80 km2. It has been operating over a time interval of about 70 years, providing temperature and precipitation data at the resolution of 10 minutes.
2.2 Landslide and rainfall data
Based on detailed bibliographical research [23, 24, 25], which explored all available sources including national and local newspapers and journals, Internet blogs, and the scientific and technical literature, we have collected a database of 326 shallow landslide events (landslide event is a single landslide) from January 2001 to March 2018.
The information collected and stored in the inventory includes (
Figure 1
):
accurate or approximate location of the landslide event;
accurate or approximate time, date, or period of the failures;
rainfall conditions that resulted in slope failures collected from the nearest rain gauge, including the total event rainfall, the rainfall duration, the mean rainfall intensity, and the antecedent rainfall for 2001–2018;
landslide type;
a generic description of the lithology.
In addition to this data that is also reported in Appendix in a tabular format, meteorological data and the output of the hydrological model have been used for the subsequent elaborations. In particular, hourly rainfall and temperature data were obtained from the rain gauges of the Civil Protection of the region. The hydrological model proposed on a regional scale considers homogeneous soil moisture conditions in the space and the first meters of depth in the areas affected by each landslide identified in our database.
The regional pedological map is depicted in
Figure 2
with the spatial distribution of landslides (
Figure 2
). This maps provides a nested description of soil classes that indentifies four regions at the first level (soil map of Italy, scale 1: 5,000,000), the 15 provinces, and 75 soil units (scale 1: 250,000). Based on the pedological characteristics of the regions, it was observed that the highest number of landslides (56 landslides) occurred in the soil province n.6. It is also worthy to mention a high number of events occurred on the soil unit 12.4 (33 landslides) and 10.2 (21 landslides). These last two soil units correspond to:
12.4—hilly clay soils with steep slopes, badlands, intended for grazing or arable land, with low permeability (Vertic Haploxerepts; Inceptisol);
10.2—hilly sandy-conglomerate soils, intended for pasture, vineyards or shrubs (Typic Xerorthents; Inceptisol).
Figure 2.
Pedological regions and shallow landslides distribution over the Basilicata region.
The number of event recorded in each soil unit is described in the histogram of
Figure 3
.
Figure 3.
Histogram with the distribution of the number of landslides in the various regional soil units. Red circles, units with multiple landslides; black dotted rectangle, landslides included in unit 6.
2.3 Reconstruction of rainfall events
The rainfall duration (D) was determined by measuring the time between the moment of the beginning of each rainfall event, which triggered a shallow landslide, considered in the database, and rainfalls ending time. The rainfall ending time was taken to coincide with the time of the last rainfall measurement of the day when the landslide occurred. As suggested by Brunetti et al. [26], the starting time was considered a minimum period without rain (a 2-day period without rainfall was selected for late spring and summer, May–September, and a 4-day period without rainfall was selected for the other seasons, October–April) to separate two consecutive rainfall events. Once the duration of the rainfall event was established, the corresponding rainfall mean intensity I (mm h−1) was calculated dividing the cumulated (total) rainfall (mm) in the considered period by the length of the rainfall period (hours). The full list of events is given in the Appendix of the present chapter (Table 1—Appendix). In recent studies, authors [27, 28, 29] have used an approach that provides the seasonality criterion (April-October for the “dry/warm” season and November-March for the “wet/cold” season) to calculate the rainfall events. In this chapter, the proposed method is different from that proposed by Peruccacci et al. [29], because the saturation value and condition are a parameter regardless of seasonality. It provides a more detailed parameter, overcoming the possibility that in the same season, it can have more dry or wet phases.
2.4 Modeling soil water content
Although antecedent soil moisture can be obtained by in-situ measurements at a point scale, measurements on a regional scale are time-consuming and expensive. Recently, more information is available from satellite data, but they are too coarse to provide local estimates of soil water content on a specific landslide [30]. Thus, we used the hydrological model AD2 to describe the temporal evolution of soil water content over the entire Basilicata region using a distributed approach at 240 m spatial resolution.
The AD2 model is a 1D model capable of describing the soil water budget along the vertical direction, but its physically based nature allows to associate physical characteristics such as soil texture, land cover, and mean slope to each pixel/location that affects the model parametrization.
The model parameters were obtained from physical maps such as national and regional pedological maps of Italy and Basilicata [31], the III level of the CORINE Land Cover map [32], and the Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM) extracted from HydroSHEDS (hydrosheds.cr.usgs.gov/index.php).
The model was run at 1 h temporal resolution using rainfall and temperature data derived from the rainfall network of the Civil Protection for the period January 1, 2001 to March 28, 2018 [32].
This approach is straightforward and can be easily replicated elsewhere after a simple calibration against the local soil moisture and landslide datasets. It must be stated that obtained values can be affected by several errors due to model structure, parametrization, and climatic data, but at present, such an approach offers a realistic description of the expected relative saturation of the soil providing a synthesis of the state of the system according to the available information on soil texture, antecedent rainfall, and evolution of temperatures. Moreover, several evidences suggesting that the use of a physically based approach allows obtaining more robust outputs [33, 34].
2.4.1 AD2 model structure
Model simulations carried out using at least 1 year of rainfall and temperature data recorded before each landslide event to reach a reliable estimate of the relative soil water content at the date of the considered event. AD2 [34] provides a hydrological prediction that considers several hydrological components such as infiltration, surface runoff, sub-surface runoff, deep percolation, and evapotranspiration. Soil water balance is described by the following Equation [35]:
St+Δt=St+It–Rout,t−Lt−Et,E1
where: St is the basin soil water content at the generic instant of time t, which represents a key variable of the model influencing runoff production, leakage, and evapotranspiration; It is the infiltration; Rout,t is the sub-surface runoff production; Lt is the leakage to the groundwater; and ET is the actual evapotranspiration.
The infiltration is derived from the difference between the rainfall amount, Pt, and the surface runoff, Rt, at time t (mm):
It=Pt−Rt.E2
Runoff is calculated using the equation proposed by De Smedt et al. [36], which takes into account the potential saturation of the soil:
where, Smax is the maximum water storage capacity of the bucket, Pc is the critical rainfall producing the surface soil saturation, and C the default runoff coefficient that is parameterized as a function of soil type, soil cover, and slope [37].
The sub-surface runoff production is assumed to be a linear function of the soil water content above the field capacity reference parameter:
Routt=max0cSt–Sc,E4
where Sc is the threshold water content for sub-surface flow production, assumed here equal to 0.6 Smax, and c is the sub-surface coefficient, which is generally assumed 0.05.
The evapotranspiration is assumed to be a bi-linear function of the soil content and potential evapotranspiration. It may be described by the following equation:
Et=max0minSt0.75ScEPEP,E5
where EP is the potential evapotranspiration, 0.75Sc is an estimate of the water content at which the stomata closure starts to reduce the evapotranspiration.
Leakage is computed using the expression derived by Manfreda et al. [38] integrating the power-law function of leakage by Eagleson [39] over a time-step ∆t:
where Lt is the groundwater recharge in ∆t, Ks is a parameter that interprets the soil permeability at saturation, and β is a dimensionless exponent.
It must be clarified that all the parameters mentioned in the model equations reported above can be estimated using the existing literature values that associate this parameter to physical features of the area such as soil texture, land use and mean slope using [37, 40, 41].
2.5 Rainfall thresholds
To determine rainfall thresholds for shallow landslide occurrence, we adopted the Frequentist method [26]. The threshold curve is assumed to follow a power law:
I=αD−βE7
where, I is the rainfall mean intensity (mm h−1), D is the rainfall event duration (h), α is the intercept, and β defines the slope of the power law function. Empirical data were log-transformed to calculate the best-fit line by means of a linear equation log(I) = log(α) − β log(D), equivalent to that described above.
Following the methods adopted in previous studies [9, 12, 15], we identified the rainfall events associated with each landslide event and the corresponding degree of soil saturation at the starting time of each event. Including this additional information in the database, it was possible to explore its role in the general behavior of the rainfall events triggering landslides under different initial conditions.
The comparison between the rainfall intensity and the relative saturation before each event is depicted in
Figure 4
.
Figure 4
provides the temporal evolution of the rainfall and relative soil saturation of three different sites (Lauria, Vietri di Potenza and Pisticci; see Appendix) characterized by different lithological conditions during the period from January 1, 2009 to December 31, 2015. This window was extracted from the model simulation to emphasize the seasonal dynamics of soil moisture over the considered sites. Such a seasonality is clearly one of the motivations to conduct this study because such a dynamic strongly affects the hydraulic processes in the soil profile.
Figure 4.
Daily rainfall (blue) and simulated daily soil degree saturation (red) at (a) Lauria, (b) Vietri, and (c) Pisticci from January 1, 2009 to December 31, 2016. Dark stars represent the data of the occurrence of shallow landslide events in the monitored areas.
It is noticed that most of the different landslide events (reported in the graph with a dark star) occurred after significant rainfall amounts and relatively high or moderate soil saturation degree. When the same rainfall amounts occurred in conditions of low antecedent soil moisture content, they have not produced shallow landslides. This finding aligns with the previous studies [7, 8, 9, 10, 11, 12, 13, 14, 15, 42].
This preliminary plot shows that there is an interplay between the antecedent soil moisture conditions and the amounts and the duration of the triggering rainfall. Moreover, it appears how the same degree of soil saturation and rainfall I/D conditions do not necessarily produce the same effects in different geopedological regions.
The role of the antecedent soil moisture condition on the triggering rainfall intensity is clearly shown in
Figure 5
, where the rainfall intensity/duration has been plotted against the simulated antecedent soil saturation of each landslide event. This graph was developed following the trigger-cause concept of Bogaard and Greco [6] and highlights the role of both rainfall dynamics and antecedent soil moisture on the slope stability.
Figure 5.
Mean rainfall intensity/duration and the simulated initial degree of saturation for the 326 landslide events in Basilicata region (southern Italy) from 2001 to 2018.
In fact, there is a clear reduction of the rainfall intensity needed to trigger a landslide with the increase of the antecedent soil moisture. In this graph, the data grouped in the function of the rainfall duration trying to explore also the role of this additional parameter on the process. It is observed that the rainfall dynamics also matter, being shorter rainfall events more sensitive to the antecedent soil moisture respect to, while more extended events are less influenced by such parameter.
Similarly, previous studies [7, 8, 12, 43] also found a linearly decreasing trend between the mean rainfall intensity and the initial soil moisture conditions. The slope of the regression functions derived from a different subset of our database changes based on the relative duration of the rainfall events. It is higher for rainfall durations lower than 48 h, while the function becomes almost independent from the relative saturation when rainfall events have longer durations (more than 48 h). This is probably due to the nature of the long-lasting rain events, which are often characterized by a high total amount of rainfall. Results in high values both of the initial saturation degree and of low rainfall intensity that is averaged over longer periods. It must be clarified that the relative degree of saturation has been referred to as the starting time of the triggering rainfall event.
Figure 6
depicts a clear picture of the dependence between mean rainfall intensity of event of different durations and the antecedent soil moisture, where the rainfall intensity values of the 326 investigated events are associated to the simulated soil saturation degree using a color scale (from blue to yellow starting from lower to higher values of degree of saturation). This graph clearly shows that higher amounts of rainfall intensity are observed in correspondence to lower values of soil saturation and vice-versa. This tendency is not always consistent due to the presence of several spurious data relative to the occurrence of extraordinarily wet events, which resulted in both landslides and floods.
Figure 6.
Rainfall intensity as a function of the duration of the triggering rainfall events for the 326 landslide events recorded in Basilicata region (southern Italy) during the period 2001–2018. Each event is associated with a color that represents the simulated antecedent degree of saturation, whose range is given in the color bar on the right (ranging from 0 to 1). We also included the regression lines estimated for the two groups of events selected based on the antecedent soil saturation conditions. The solid line represents the regression function obtained using the observations with soil degree saturation lower than 0.70, while the dotted line represents the regression function obtained using the observations with degree soil of saturation equal or higher than 0.70.
To evaluate the role of degree of soil saturation on the regional mean rainfall intensity/duration function, we have identified two distinguished sub-samples based on the antecedent soil moisture conditions of each event. The two groups were distinguished using a sensitivity analysis, exploiting different antecedent soil saturation values. The selection was made using a subjective selection that tried to identify the most diverse groups of landslides using a given threshold of soil saturation. Therefore, we determined mean rainfall intensity/duration functions (rainfall thresholds) under middle-low antecedent soil moisture conditions that seemed to those that responded better to the data considered (soil degree saturation lower than 0.70), and moderate to high antecedent soil moisture conditions (soil degree saturation equal or higher than 0.70). In this way, it was possible to derive critical rainfall threshold functions conditional on the antecedent soil moisture conditions.
The two functions plotted in the graph (
Figure 6
), which has significantly different slopes. This implies that they must cross somewhere in the space of rainfall intensities and event duration. In the present case, we observed that they cross in a point corresponding to the duration of about 200 h. At such duration, the impact of antecedent soil water content becomes not relevant, and this part of the curve should not be considered.
The proposed approach allows taking into account both rainfall characteristics (intensity and duration) and the antecedent soil moisture state in a specific study area, contributing to foresee a landslide event.
Of course, a methodology like this should be evaluated widely, also taking into consideration the ability of the method to distinguish between true and false alarms. Unfortunately, this field-test is challenging to be implemented in a region like Basilicata with a low density of population (as single possible observators), where a lot of landslide events are not reported or are missing.
We have explored the role and effects of antecedent soil moisture conditions on rainfall I/D thresholds triggering shallow landslides by using a dataset built for a region of southern Italy and a distributed modeling approach. By combining rainfall events data with the simulated antecedent soil moisture conditions, it was possible to derive I/D relationships, which can be used to discriminate the triggering conditions for landslides better.
Two distinct degree of soil saturation values [S < 0.7 and S ≥ 0.7] were identified to distinguish different classes of events. Such soil moisture conditions led to two distinct populations of events that identified statistically significantly different rainfall threshold functions. Our results are consistent with those found in the most recent studies on this topic, reinforcing the idea that simulated soil moisture provides better metrics than antecedent rainfall for the predisposing factors of landslide initiation.
Finally, a forthcoming extension of this research will aim to carry out a local downscaling to define the relations between I/D and the degree of soil saturation in the smallest territorial contexts characterized by the same climatic and lithotechnical conditions, in which the landslides inserted in our database have developed.
Moreover, it is also important to note that the proposed description of the landslide event may undoubtedly support the development of further studies and models for landslide prediction. In fact, the main results obtained in the present study are the fact that the information about the antecedent relative saturation of the soil may help to distinguish the dynamics of the process better. Therefore, it would be a good practice to include such parameters in all landslide database. This can happen with the support of remote sensing techniques that also allow deriving root zone soil moisture over large areas [10, 41, 44].
This work was carried out within a scientific agreement between the Civil Protection Department of Basilicata, the Interuniversity Consortium for Hydrology (CINID), and the University of Basilicata to the start-up the Basilicata Hydrologic Risk Center.
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Written By
Maurizio Lazzari, Marco Piccarreta, Ram L. Ray and Salvatore Manfreda
Submitted: 03 February 2020Reviewed: 05 May 2020Published: 14 August 2020
Open access peer-reviewed chapter
