Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides Occurrence

*Maurizio Lazzari, Marco Piccarreta, Ram L. Ray and Salvatore Manfreda*

## **Abstract**

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.

**Keywords:** landslides, soil saturation, geomorphology, hydrogeological risk, Basilicata

## **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

## *Landslides - Investigation and Monitoring*

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.

There are many soil moisture datasets that have been successfully used to calibrate and validate catchment or watershed scale models of infiltration, soil-moisture stor-

age, and in some ways, even to examine the first landslide trigger [16–19].

**Figure 1**, and Appendix), from January 2001 to March 2018. For each

moisture values not available for the whole study area.

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

**2. Data and methods**

**2.1 Study area**

dominant [20, 21].

station every 80 km<sup>2</sup>

2018.

**93**

**2.2 Landslide and rainfall data**

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,

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides…*

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

This study was carried out within a research agreement with the Civil Protection

Basilicata is a region of southern Italy covering an area of 9.992 km<sup>2</sup> 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

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

Based on detailed bibliographical research [23–25], 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

providing temperature and precipitation data at the resolution of 10 minutes.

. It has been operating over a time interval of about 70 years,

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.

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 [7–12]. 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, 13–15]. 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.

#### **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.*

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

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 [16–19].

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.

## **2. Data and methods**

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 [7–12]. 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, 13–15]. 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

*Landslides - Investigation and Monitoring*

use reliable databases in the literature or otherwise build a specific one.

*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.*

**Figure 1.**

**92**

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 km<sup>2</sup> 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 km<sup>2</sup> . 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 [23–25], 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**):


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);

> chapter (**Table 1**—Appendix). In recent studies, authors [27–29] 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

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides…*

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

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

possibility that in the same season, it can have more dry or wet phases.

*Pedological regions and shallow landslides distribution over the Basilicata region.*

**2.4 Modeling soil water content**

**Figure 2.**

**95**

• 10.2—hilly sandy-conglomerate soils, intended for pasture, vineyards or shrubs (Typic Xerorthents; Inceptisol).

The number of event recorded in each soil unit is described in the histogram of **Figure 3**.

#### **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<sup>1</sup> ) 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 *Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

#### **Figure 2.**

The information collected and stored in the inventory includes (**Figure 1**):

• rainfall conditions that resulted in slope failures collected from the nearest rain gauge, including the total event rainfall, the rainfall duration, the mean rainfall

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

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 land-

• 12.4—hilly clay soils with steep slopes, badlands, intended for grazing or arable

The number of event recorded in each soil unit is described in the histogram of

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

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

) was calculated dividing the

• 10.2—hilly sandy-conglomerate soils, intended for pasture, vineyards or

land, with low permeability (Vertic Haploxerepts; Inceptisol);

• accurate or approximate location of the landslide event;

intensity, and the antecedent rainfall for 2001–2018;

• a generic description of the lithology.

*Landslides - Investigation and Monitoring*

slides). These last two soil units correspond to:

shrubs (Typic Xerorthents; Inceptisol).

corresponding rainfall mean intensity I (mm h<sup>1</sup>

**2.3 Reconstruction of rainfall events**

• landslide type;

identified in our database.

**Figure 3**.

**94**

• accurate or approximate time, date, or period of the failures;

*Pedological regions and shallow landslides distribution over the Basilicata region.*

chapter (**Table 1**—Appendix). In recent studies, authors [27–29] 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

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.

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides…*

and the surface runoff, Rt, at time t (mm):

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

8 >>><

>>>:

Rt ¼

generally assumed 0.05.

Lt ¼

mean slope using [37, 40, 41].

**2.5 Rainfall thresholds**

**97**

8 ><

>:

takes into account the potential saturation of the soil:

St

water content above the field capacity reference parameter:

The infiltration is derived from the difference between the rainfall amount, Pt,

Runoff is calculated using the equation proposed by De Smedt et al. [36], which

Smax � �Pt if Pt <sup>≤</sup>Pc <sup>¼</sup> Smaxð Þ Smax � St

Pt � ð Þ Smax � St if Pt <sup>&</sup>gt;Pc <sup>¼</sup> Smaxð Þ Smax � St

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

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

potential evapotranspiration. It may be described by the following equation:

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 Δt:

Smax <sup>þ</sup> St�<sup>1</sup>

Et <sup>¼</sup> max 0, min St

St�<sup>1</sup> � Smax <sup>Δ</sup>tKs

soil permeability at saturation, and β is a dimensionless exponent.

The evapotranspiration is assumed to be a bi-linear function of the soil content and

where EP is the potential evapotranspiration, 0.75Sc is an estimate of the water

Smax � �<sup>1</sup>�<sup>β</sup> � �<sup>1</sup>*=*ð Þ <sup>1</sup>�<sup>β</sup> ! if St <sup>&</sup>gt;Sc

where Lt is the groundwater recharge in Δt, Ks is a parameter that interprets the

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

To determine rainfall thresholds for shallow landslide occurrence, we adopted the Frequentist method [26]. The threshold curve is assumed to follow a power law:

0*:*75Sc

It ¼ Pt � Rt*:* (2)

ð Þ Smax � CSt

Rð Þ out,t ¼ max 0, c S f g ð Þ <sup>t</sup>–Sc , (4)

� �EP, EP � � � � , (5)

0 if St ≤Sc

ð Þ Smax � CSt

(3)

, (6)

#### **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.*

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]:

$$\mathbf{S}\_{\mathbf{t}+\Delta \mathbf{t}} = \mathbf{S}\_{\mathbf{t}} + \mathbf{I}\_{\mathbf{t}} \mathbf{-} \mathbf{R}\_{\text{out},\mathbf{t}} - \mathbf{L}\_{\mathbf{t}} - \mathbf{E}\_{\mathbf{t}},\tag{1}$$

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 *Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

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):

$$\mathbf{I}\_{\mathbf{t}} = \mathbf{P}\_{\mathbf{t}} - \mathbf{R}\_{\mathbf{t}}.\tag{2}$$

Runoff is calculated using the equation proposed by De Smedt et al. [36], which takes into account the potential saturation of the soil:

$$\mathbf{R\_{t}} = \begin{cases} \begin{pmatrix} \mathbf{S\_{t}} \\ \mathbf{S\_{max}} \end{pmatrix} \mathbf{P\_{t}} \text{ if } \mathbf{P\_{t}} \le \mathbf{P\_{c}} = \frac{\mathbf{S\_{max}}(\mathbf{S\_{max}} - \mathbf{S\_{t}})}{(\mathbf{S\_{max}} - \mathbf{C\_{S\_{t}}})} \\\\ \mathbf{P\_{t}} - (\mathbf{S\_{max}} - \mathbf{S\_{t}}) \text{ if } \mathbf{P\_{t}} > \mathbf{P\_{c}} = \frac{\mathbf{S\_{max}}(\mathbf{S\_{max}} - \mathbf{S\_{t}})}{(\mathbf{S\_{max}} - \mathbf{C\_{S\_{t}}})} \end{cases} \tag{3}$$

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:

$$\mathcal{R}\_{\text{(out,t)}} = \max\left\{ \mathbf{0}, \mathbf{c}(\mathbf{S}\_t \mathbf{-S}\_c) \right\}, \tag{4}$$

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:

$$\mathbf{E}\_{\mathrm{t}} = \max\left\{ \mathbf{0}, \min\left\{ \left( \frac{\mathbf{S}\_{\mathrm{t}}}{\mathbf{0}.75 \mathbf{S}\_{\mathrm{c}}} \right) \mathbf{E} \mathbf{P}, \mathbf{E} \mathbf{P} \right\} \right\}, \tag{5}$$

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 Δt:

$$\mathbf{L}\_{t} = \begin{cases} 0 & \text{if } \quad \mathbf{S}\_{t} \le \mathbf{S}\_{\mathbf{c}} \\ \left( \mathbf{S}\_{t-1} - \mathbf{S}\_{\max} \left( \frac{\Delta t \mathbf{K}\_{\mathbf{c}}}{\mathbf{S}\_{\max}} + \left( \frac{\mathbf{S}\_{t-1}}{\mathbf{S}\_{\max}} \right)^{1-\beta} \right)^{1/(1-\beta)} \right) & \text{if } \quad \mathbf{S}\_{t} > \mathbf{S}\_{\mathbf{c}} "\end{cases} \tag{6}$$

where Lt is the groundwater recharge in Δt, 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:

characteristics such as soil texture, land cover, and mean slope to each pixel/loca-

*Histogram with the distribution of the number of landslides in the various regional soil units. Red circles, units*

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

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].

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.

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

StþΔ<sup>t</sup> ¼ St þ It–Rout,t � Lt � Et, (1)

Soil water balance is described by the following Equation [35]:

tion that affects the model parametrization.

*Landslides - Investigation and Monitoring*

*with multiple landslides; black dotted rectangle, landslides included in unit 6.*

1, 2001 to March 28, 2018 [32].

**Figure 3.**

*2.4.1 AD2 model structure*

**96**

$$\mathbf{I} = \mathbf{a} \mathbf{D}^{-\boldsymbol{\beta}} \tag{7}$$

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

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides…*

*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*

*Mean rainfall intensity/duration and the simulated initial degree of saturation for the 326 landslide events in*

to as the starting time of the triggering rainfall event.

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

**Figure 4.**

**Figure 5.**

**99**

*Basilicata region (southern Italy) from 2001 to 2018.*

*landslide events in the monitored areas.*

where, I is the rainfall mean intensity (mm h�**<sup>1</sup>** ), 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.

## **3. Results and discussion**

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.

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 [7–15, 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.

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 4.**

<sup>I</sup> <sup>¼</sup> <sup>α</sup>D�<sup>β</sup> (7)

), D is the rainfall event duration

where, I is the rainfall mean intensity (mm h�**<sup>1</sup>**

*Landslides - Investigation and Monitoring*

**3. Results and discussion**

in the soil profile.

slope stability.

**98**

tion log(I) = log(α) � β log(D), equivalent to that described above.

rainfall events triggering landslides under different initial conditions.

(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 equa-

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

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

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

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

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

landslides. This finding aligns with the previous studies [7–15, 42].

*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.*

#### **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.*

**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.

impact of antecedent soil water content becomes not relevant, and this part of the

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides…*

The proposed approach allows taking into account both rainfall characteristics (intensity and duration) and the antecedent soil moisture state in a specific study

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

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 condi-

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

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

root zone soil moisture over large areas [10, 41, 44].

The authors declare no conflict of interest.

curve should not be considered.

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

**4. Final remarks**

tions for landslides better.

**Acknowledgements**

**Conflict of interest**

Risk Center.

**Appendix**

**101**

area, contributing to foresee a landslide event.

lot of landslide events are not reported or are missing.

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

#### **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.*

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

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.

## **4. Final remarks**

**Figure 6** depicts a clear picture of the dependence between mean rainfall inten-

sity 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

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

*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.*

events, which resulted in both landslides and floods.

*Landslides - Investigation and Monitoring*

**Figure 6.**

**100**

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].

## **Acknowledgements**

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.

## **Conflict of interest**

The authors declare no conflict of interest.

## **Appendix**


**103**

22 23 24 25 26 27 28

29 30 31 32

33 34 35

36

37

38

39 40

Barile

29/03/2005

 4,533,072,0

 557,767,6

 25,8

 29,0

 0,89

 0,64

Nemoli

07/03/2005

 4,438,133,6

 570,116,6

 45,8

 69,0

 0,66

 0,93

Pietrapertosa

 02/03/2005

 4,485,954,8

 589,868,4

 44,6

 23,0

 1,94

 0,73

Campomaggiore

Nemoli SAL

Venosa SAL

 SAL

 Laurenzana

 01/03/2005

 4,482,440,8

 578,878,4

 86,6 148,0

 0,59

 0,93

Laurenzana

 SAL

 Gallicchio

 27/02/2005

 4,460,528,9

 596,783,3

 21,0

 12,0

 1,75

 0,83

 Guardia Perticara SAL

Sant'Arcangelo

 26/02/2005

 4,456,637,8

 609,566,5

 18,6

 18,0

 1,03

 0,68

Potenza

26/02/2005

 4,504,521,5

 572,376,6

 64,4

 98,0

 0,66

 0,87

Potenza PC

Roccanova PC

Potenza

26/02/2005

 4,498,664,2

 567,834,7

 64,4

 98,0

 0,66

 0,87

 Rionero in Vulture

 26/02/2005

 4,527,555,0

 558,531,6

 14,2

 6,0

 2,37

 0,72

Calvello

25/02/2005

 4,478,560,2

 573,724,4

 55,4

 56,0

 0,99

 0,94

Laurenzano

Venosa SAL Potenza PC

La Gazzetta del

Mezzogiorno

magazine

Civil Protection

villasmunta.it

Civil Protection Civil Protection Civil Protection

villasmunta.it

*…*

Civil Protection

 PC

Tito

24/02/2005

 4,492,615,5

 557,165,5

 60,8

 55,0

 1,11

 0,86

 Satriano di Lucania SAL

Picerno

23/02/2005

 4,498,702,2

 554,494,8

 64,8

 74,0

 0,88

 0,90

Balvano PC

villasmunta.it

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides*

villasmunta.it

adnkronos.com

villasmunta.it

Castronuovo

 di

23/02/2005

 4,449,641,7

 600,766,2

 26,0

 30,0

 0,87

 0,64

Sant'Andrea

Bella

23/02/2005

 4,512,013,7

 545,332,3

 59,8

 54,0

 1,11

 0,84

San Fele

Nemoli

26/01/2005

22/02/2005

 4,518,910,4

 546,690,8

 77,0

 80,0

 0,96

 0,97

San Fele PC Bella Casalini Roccanova PC

 4,435,028,2

 568,166,9

 76,2

 47,0

 1,62

 0,90

Nemoli SAL

Tito

24/01/2005

 4,493,939,0

 556,898,4

 40,0

 17,0

 2,35

 0,72

 Satriano di Lucania SAL

Tricarico

13/11/2004

 4,498,622,9

 582,231,5

 46,2

 31,0

 1,49

 0,32

 Albano di Lucania PC

 La Gazzetta del

Mezzogiorno

magazine

villasmunta.it

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

Civil Protection Civil Protection

villasmunta.it

Civil Protection

Pisticci

13/11/2004

 4,470,264,9

 643,085,0

 94,6

 34,0

 2,78

 0,25

 Pisticci da Castelluccio

SAL

Civil Protection

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**


### *Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

**ID**

**102**

1 2

3 4 5 6 7 8 9 10 11

12

13

14 15 16

17 18 19 20

21

Montescaglioso

 13/11/2004

 4,491,198,4

 641,477,5

 126,6

 33,0

 3,84

 0,17

Montescaglioso

 SAL

 Montalbano

 Jonico

 13/11/2004

 4,460,236,3

 634,365,1

 67,2

 33,0

 2,04

 0,23

 Montalbano

 SAL

Melfi

20/09/2004

 4,538,361,1

 555,130,7

 119,4

 81,0

 1,47

 0,09

Valsinni

26/07/2004

 4,448,638,8

 624,031,9

 86,6

 6,0

 14,43

 0,13

Nova Siri

 26/07/2004

 4,445,645,0

 631,600,0

 64,0

 4,0

 16,00

 0,13

Montescaglioso

 26/07/2004

 4,490,946,3

 640,686,5

 31,4

 23,0

 1,37

 0,16

Montescaglioso

Nova Siri SAL Nova Siri SAL

Melfi

 SAL

 II Quotidiano Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection

 magazine

Craco

12/12/2003

 4,467,134,0

 623,760,9

 117,6

 78,0

 1,51

 0,24

Venosa

10/09/2003

 4,535,319,9

 569,252,5

 35,0

 8,0

 4,38

 0,28

Montescaglioso

 09/09/2003

 4,490,159,8

 640,165,8

 21,2

 4,0

 5,30

 0,11

Montescaglioso

Venosa SAL

Craco PC

 SAL

 Muro Lucano

 08/02/2003

 4,512,107,1

 540,718,1

 105,6 172,0

 0,61

 0,87

 Muro Lucano PC

Castronuovo

 di

04/02/2003

 4,449,625,0

 600,976,5

 33,4

 34,0

 0,98

 0,90

Sant'Andrea

Pisticci

25/01/2003

 4,472,200,4

 631,782,9

 54,2

 48,0

 1,13

 0,82

 Pisticci Scalo SAL

Roccanova PC

 La Nuova Basilicata magazine

Civil Protection Civil Protection Civil Protection Civil Protection Piccarreta et al., 2004

Nova Siri

Acerenza

25/01/2003

25/01/2003

 4,444,982,6

 631,308,4

 80,0

 56,0

 1,43

 0,91

Nova Siri SAL

 4,516,566,8

 579,515,5

 43,0

 43,0

 1,00

 0,20

Lagonegro

 12/01/2003

 4,441,050,6

 565,797,2

 148,2 140,0

 1,06

 0,89

Lagonegro PC

Acerenza SAL

San Fele

Tursi

21/01/2001

09/03/2002

 4,518,711,1

 545,705,9

 23,6

 11,0

 2,15

 0,59

San Fele PC

 4,456,520,6

 624,987,7

 86,2

 27,0

 3,19

 0,70

Rotondella

 21/01/2001

 4,447,722,3

 629,857,3

 88,6

 52,0

 1,70

 0,78

Pisticci

20/01/2001

 4,472,103,8

 633,501,3

 83,6

 34,0

 2,46

 0,20

 Pisticci Scalo SAL

Nova Siri Sal

Tursi SI

 Montalbano

 Jonico

 20/01/2001

 4,461,333,2

 632,500,7

 66,6

 16,0

 4,16

 0,48

 Montalbano

 SAL

Civil Protection Evalmet web site Evalmet web site

*Landslides - Investigation and Monitoring*

Evalmet web site

Civil Protection Civil Protection Civil Protection Civil Protection

Rotondella

 14/01/2001

 4,447,838,6

 630,012,0

 186,8

 26,0

 7,18

 0,40

Nova Siri SAL

 Evalmet web site web site web site

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**


**105**

60

61 62

63 64 65 66

67

68 69

70 71 72

73

74

75 76

77

78

79

80

Maratea

11/02/2010

 4,427,557,0

 562,256,5

 56,6

 76,0

 0,74

 0,96

 San Chirico Raparo

 San Severino Lucano

 Vietri di Potenza

 San Martino D'Agri

Tricarico

 24/04/2009

 28/04/2009

 22/10/2009

 18/12/2009

 07/02/2010

 4,448,987,6

 591,663,1

 29,0

 5,0

 5,80

 0,83

Castelsaraceno

Maratea PC

 PC

 La Gazzetta del Civil Protection

Mezzogiorno

*…*

 4,430,832,7

 596,722,0

 12,2

 8,0

 1,53

 0,60

Viggianello

 SAL

 4,500,015,6

 596,374,9

 11,6

 9,0

 1,29

 0,68

 4,455,394,2

 587,277,7

 22,6

 21,0

 1,08

 0,85

Sarconi SAL

Vietri

Quotidiano

 del sud, Civil Protection

Metauronews

La Gazzetta del

Mezzogiorno

 4,524,102,2

 555,355,7

 75,0 135,0

 0,56

 0,90

 Albano di Lucania PC

 Ripacandida

 26/03/2009

 4,528,798,8

 561,165,2

 22,8

 36,0

 0,63

 0,79

 Gallicchio

 20/03/2009

 4,528,878,0

 562,300,7

 26,2

 12,0

 2,18

 0,67

 Ripacandida

 07/03/2009

 4,471,046,5

 639,954,0

 93,5

 69,0

 1,36

 0,62

Tursi

06/03/2009

 4,457,119,8

 624,774,7

 44,4

 33,0

 1,35

 0,82

Pisticci

06/03/2009

 4,429,791,9

 561,758,0

 40,6

 34,0

 1,19

 0,85

 Montalbano

 Jonico

 06/03/2009

 4,460,493,9

 632,869,2

 39,2

 34,0

 1,15

 0,74

 Montalbano Torre Accio PC

Tursi SAL Venosa SAL Aliano SAL Venosa SAL

 SAL

Maratea

28/01/2009

 4,441,638,3

 566,470,6

 295,2 185,0

 1,60

 0,93

 Acerenza

 Laurenzana

 14/01/2009 23/01/2009

 4,515,988,0

 578,994,5

 32,0

 28,0

 1,14

 0,58

 4,477,838,6

 584,070,9

 38,8

 35,0

 1,11

 0,95

Potenza

13/01/2009

 4,499,028,2

 571,863,4

 17,6

 20,0

 0,88

 0,94

Pisticci

13/01/2009

 4,495,207,5

 617,127,3

 104,6 113,0

 0,93

 0,62

 Pisticci Scalo SAL

Potenza PC

Laurenzana

Acerenza SAL

Maratea PC

 SAL

 La Gazzetta del Civil Protection Civil Protection Civil Protection

La Siritide website

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides*

Civil Protection Evalmet web site

Civil Protection palazzosangervasio.net

Civil Protection Civil Protection Civil Protection

Mezzogiorno

Pisticci

13/01/2009

 4,487,771,2

 600,527,1

 105,2 114,0

 0,92

 0,62

 Pisticci da Castelluccio

SAL

Montescaglioso

 13/01/2009

 4,489,561,2

 641,250,1

 76,6

 95,0

 0,81

 0,52

Montescaglioso

 SAL

 La Gazzetta del Civil Protection

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

Mezzogiorno

Grottole

13/01/2009

 4,469,982,7

 634,601,8

 87,8

 95,0

 0,92

 0,59

 Grottole da Serre

 Lagonegro

 06/01/2009

 4,502,711,0

 621,802,2

 109,8

 65,0

 1,69

 0,88

Lagonegro PC

lucanianet

II Quotidiano

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**

**soil saturation**

#### *Landslides - Investigation and Monitoring*


### *Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

**ID**

**104**

41

42

43

44 45 46 47

48

49 50

51

52 53

54 55 56 57 58 59

Calvello

05/01/2009

 4,480,624,6

 572,077,3

 35,2

 62,0

 0,57

 0,85

Grassano

11/12/2008

 4,492,851,3

 557,094,7

 79,4

 16,0

 4,96

 0,45

Tito

25/11/2008

 4,429,377,2

 561,850,9

 77,4 120,0

 0,65

 0,43

Maratea

04/04/2007

 4,473,927,4

 630,747,2

 31,4

 16,0

 1,96

 0,91

Maratea

19/12/2006

 4,438,301,0

 564,922,4

 144,6

 55,0

 2,63

 0,74

Rivello

23/10/2006

 4,533,947,8

 566,654,7

 140,2

 50,0

 2,80

 0,35

 Trecchina

 26/09/2006

 4,430,915,4

 567,168,1

 133,0

 72,0

 1,85

 0,63

Picerno

27/03/2006

 4,499,289,0

 554,063,1

 32,8

 9,0

 3,64

 0,91

 Ripacandida

 24/03/2006

 4,529,316,2

 560,944,2

 30,5

 58,0

 0,53

 0,77

Venosa SAL Balvano PC

Trecchina Nemoli SAL

Maratea PC Maratea PC Picerno PC Matera PC

Laurenzano

 PC

Civil Protection

La Gazzetta del

Mezzogiorno

 IFFI Project ISPRA CNR IBAM

infocilento

infocilento

Civil Protection

 Corleto Perticara

 23/03/2006

 4,475,673,7

 588,722,5

 43,6

 43,0

 1,01

 0,83

 Guardia Perticara SAL

Venosa

13/03/2006

 4,460,626,0

 633,605,6

 113,2

 62,0

 1,83

 0,84

 Rionero in Vulture

 Montalbano

 Jonico

 12/03/2006

 13/03/2006

 4,529,892,8

 556,341,5

 114,7

 70,0

 1,64

 0,91

 4,472,188,9

 640,718,0

 37,8

 56,0

 0,68

 0,64

Calvello

12/03/2006

 4,479,726,4

 575,486,3

 50,6

 29,0

 1,74

 0,92

Laurenzano

Tursi SAL

Melfi Venosa SAL

Evalmet web site

Civil Protection Civil Protection

AdB

Civil Protection Civil Protection Civil Protection

 PC

 Basin Authority of Basilicata (AdB)

Pisticci

28/02/2006

 4,533,072,0

 557,767,6

 33,8

 15,0

 2,25

 0,67

Torre Accio PC

 La Gazzetta del

Mezzogiorno

magazine

Grottole

28/02/2006

 4,495,569,9

 617,180,7

 95,4 176,0

 0,54

 0,67

 Grottole da Serre

 La Gazzetta del

Mezzogiorno

magazine

 Bernalda

28/02/2006

 4,474,473,5

 648,787,6

 44,0

 11,0

 4,00

 0,72

 Terranova del Pollino

 Accettura

 07/06/2005

 24/02/2006

 4,426,066,5

 610,796,4

 79,6

 78,0

 1,02

 0,72

 Terranova del Pollino

PC

Bernalda SAL

 La Gazzetta del

Mezzogiorno

*Landslides - Investigation and Monitoring*

magazine

 4,483,267,3

 598,029,9

 17,4

 5,0

 3,48

 0,23

 San Mauro Forte PC

Civil Protection Civil Protection

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**


**107**

100

101

102

103 104

105

106

107

108 109

110

111

112 113

114

115

116

117 118

119

120

Lauria

05/03/2011

 4,432,403,8

 572,113,3

 97,8 114,0

 0,86

 0,84

 Laurenzana

 03/03/2011

 4,478,608,5

 582,713,6

 81,6

 63,0

 1,30

 0,90

 Valsinni

Tursi

02/03/2011

02/03/2011

 4,472,089,5

 632,234,9

 129,2

 18,0

 7,18

 0,65

Nova Siri SAL

Laurenzana

Nemoli SAL

 SAL

 4,460,225,2

 632,086,0

 152,0

 18,0

 8,44

 0,44

Tursi SAL

 Tricarico

 Rotondella

 02/03/2011 02/03/2011

 4,496,864,3

 597,934,1

 31,4

 15,0

 2,09

 0,82

 Albano di Lucania PC

 4,518,058,7

 611,730,4

 126,6

 17,0

 7,45

 0,65

 Pisticci

 Montalbano

 Jonico

 02/03/2011 02/03/2011

 4,449,746,0

 630,633,3

 81,4

 18,0

 4,52

 0,65

 Pisticci Scalo SAL

Nova Siri SAL

 II Quotidiano,

 Evalmet web site

Civil Protection

vigilfuoco.it

Evalmet web site Evalmet web site Evalmet web site

Civil Protection

*…*

 4,471,789,5

 633,568,6

 153,6

 24,0

 6,40

 0,52

Montalbano

 SAL

Irsina

02/03/2011

 4,456,172,0

 625,292,0

 89,4

 21,0

 4,26

 0,19

 Santa Marla d'Irsi SAL

 Grassano

 Colobraro

 02/03/2011 02/03/2011

 4,499,277,9

 609,377,8

 149,7

 19,0

 7,88

 0,66

 4,449,038,6

 620,816,0

 78,0

 18,0

 4,33

 0,44

 Bernalda

Teana

01/03/2011

02/03/2011

 4,474,369,9

 642,163,4

 102,4

 21,0

 4,88

 0,58

 4,442,436,7

 598,621,8

 64,2

 21,0

 3,06

 0,90

Episcopia PC

Bernaida SAL

Tursi SI Grassano SAL

 Matera

 Ferrandina

 01/03/2011 01/03/2011

 4,501,551,8

 634,766,1

 103,0

 23,0

 4,48

 0,58

 Matera Nord SAL

 4,485,235,5

 624,000,0

 99,0

 19,0

 5,21

 0,70

 Cancellara

 01/03/2011

 4,509,339,7

 577,969,2

 42,8

 14,0

 3,06

 0,93

 San Nicola D'Avigliano

PC

Ferrandina

 SAL

Civil Protection Civil Protection Civil Protection

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides*

Civil Protection metapontino.it

Evalmet web site

pisticci.com

Evalmet web site

 Valsinni

Tursi

19/02/2011

20/02/2011

 4,447,737,2

 622,905,0

 22,8

 16,0

 1,43

 0,59

Nova Siri SAL

 4,457,443,9

 626,287,3

 30,8

 24,0

 1,28

 0,39

 Pisticci

 Montalbano

 Jonico

 19/02/2011 19/02/2011

 4,456,984,3

 610,237,7

 29,0

 49,0

 0,59

 0,60

 Pisticci Scalo SAL

Tursi SAL

 4,461,782,0

 633,367,9

 28,4

 17,0

 1,67

 0,48

 Montalbano

 SAL

 Bernalda

19/02/2011

 4,474,641,9

 641,273,3

 20,0

 10,0

 2,00

 0,55

Bernalda SAL

Civil Protection Civil Protection Evalmet web site

Civil Protection Civil Protection

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

Civil Protection

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**


## *Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

**ID**

**106**

81

82

83 84

85 86 87

88 89 90

91 92 93 94 95

96 97

98 99

 Armento

19/02/2011

 4,462,213,4

 588,308,3

 83,4

 52,0

 1,60

 0,83

 Guardia Perticara SAL

Alianello

19/02/2011

 4,437,455,5

 564,407,9

 36,8

 10,0

 3,68

 0,57

 Castelluccio

 Inferiore

 03/01/2011

 4,428,643,1

 583,828,8

 50,8

 41,0

 1,24

 0,86

Rivello

03/12/2010

 4,512,300,7

 536,385,6

 248,2 271,0

 0,92

 0,90

Nemoli SAL

Viggianello

Aliano SAL

ANAS (National Istitution for

Highways)

Civil Protection

 SAL

 II Quotidiano

 del sud magazine

Civil Protection

 Muro Lucano

 02/12/2010

 4,440,667,8

 573,120,0

 135,4 250,0

 0,54

 0,79

 Muro Lucano PC

 La Gazzetta del

Mezzogiorno

magazine

Lauria

22/11/2010

 4,504,815,2

 572,162,3

 169,8 140,0

 1,21

 0,87

Nemoli SAL

La Gazzetta del

Mezzogiorno

magazine

Potenza

11/11/2010

 4,539,263,4

 551,961,4

 78,8

 90,0

 0,88

 0,57

Melfi

10/11/2010

 4,491,130,5

 640,684,3

 55,5

 57,0

 0,97

 0,59

Tursi

03/11/2010

 4,456,064,4

 625,339,9

 62,3

 11,0

 5,66

 0,39

Tursi SAL

Melfi Potenza PC

La Gazzetta del

Mezzogiorno

magazine

II Quotidiano

 magazine

 Salandra

Rivello

02/11/2010

02/11/2010

 4,486,232,1

 612,353,1

 108,0

 6,0

 18,00

 0,32

 San Mauro Forte PC

 4,437,163,0

 564,328,3

 38,4

 10,0

 3,84

 0,82

Pisticci

02/11/2010

 4,491,414,3

 544,109,2

 73,4

 10,0

 7,34

 0,44

 Pisticci Scalo SAL

Nemoli SAL

Montescaglioso

 02/11/2010

 4,454,988,6

 623,153,8

 45,2

 12,0

 3,77

 0,37

Montescaglioso

 SAL

 II Quotidiano Civil Protection Civil Protection Civil Protection Civil Protection

 magazine

Matera

02/11/2010

 4,502,417,4

 634,533,0

 61,8

 9,0

 6,87

 0,39

Grottole

02/11/2010

 4,493,102,4

 615,427,5

 115,2

 7,0

 16,46

 0,46

 Grottole da Serre

Matera PC

 Ferrandina

 02/11/2010

 4,485,211,8

 627,576,3

 63,2

 7,0

 9,03

 0,45

Tursi

11/10/2010

 4,494,906,8

 541,098,2

 15,8

 11,0

 1,44

 0,22

 Latronico

 20/02/2010

 4,472,103,8

 633,501,3

 112,2 124,0

 0,90

 0,85

Castelsaraceno

Tursi SI

Ferrandina

 SAL

 PC

II Quotidiano

 magazine

Tursi tani.com Civil Protection

*Landslides - Investigation and Monitoring*

Civil Protection Civil Protection

 Viggianello

 13/02/2010

 4,424,622,3

 591,954,8

 145,8 184,0

 0,79

 0,93

Viggianello

 SAL

Civil Protection

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**


**109**

141

142

143

144

145

146

147

148 149 150

151 152 Castelluccio

153

154

155 156

157

158 159 160

161 162

 Savoia di Lucania

 18/01/2013

 4,491,704,0

 544,971,0

 87,6

 93,0

 0,94

 0,68

Lauria

18/01/2013

 4,434,754,0

 571,329,9

 268,0 180,8

 1,49

 0,79

 San Severino Lucano

 17/01/2013

 4,430,496,6

 596,772,9

 175,6 177,0

 0,99

 0,85

Barile

08/12/2012

 4,532,258,7

 556,671,8

 26,1

 12,0

 2,18

 0,46

Lauria

04/12/2012

 4,472,662,9

 632,178,6

 220,0 152,0

 1,45

 0,80

 Vietri di Potenza

 Roccanova

 20/11/2012

 20/11/2012

 4,494,560,8

 543,083,9

 27,0

 29,0

 0,93

 0,44

 4,453,378,3

 603,847,3

 96,2

 90,0

 1,07

 0,30

Pisticci

20/11/2012

 4,533,376,1

 575,381,2

 73,8

 67,0

 1,10

 0,54

 Pisticci Scalo SAL

Roccanova PC

Vietri Nemoli SAL Venosa SAL

Viggianello

Nemoli SAL

Vietri

 SAL

Quotidiano

 del sud, regione.basilicata.it

Civil Protection Civil Protection Civil Protection

*…*

Civil Protection

Metauronews

Campomaggiore

 20/11/2012

 4,491,247,0

 590,935,1

 66,8 106,0

 0,63

 0,24

Campomaggiore

 SAL

 Rotonda

 Inferiore (3) 03/10/2012

Venosa

02/09/2012

 4,460,329,4

 4,427,492,7

29/10/2012

 4,423,198,3

 588,915,1

 111,2

 28,0

 3,97

 0,27

 587,471,1

 9,0

 2,0

 4,50

 0,12

 633,421,4

 141,0

 32,0

 4,41

 0,08

 Lavello

Teana

23/06/2012

01/09/2012

 4,545,949,2

 568,245,9

 20,8

 4,0

 5,20

 0,02

 4,442,695,5

 598,168,9

 29,6

 3,0

 9,87

 0,30

Episcopia PC

Lavello SAL Venosa SAL

Viggianello

Rotonda SAL

 SAL

Lauria

06/06/2012

 4,432,952,2

 570,399,6

 58,2

 11,0

 5,29

 0,76

 Rivello

 Avigliano

 20/04/2012 21/04/2012

 4,435,386,6

 565,318,9

 256,8 192,0

 1,34

 0,71

 4,509,561,9

 561,009,0

 20,4

 33,0

 0,62

 0,76

Avigliano PC

Nemoli SAL Nemoli SAL

 Rapone

 Rivello

 Avigliano

 09/03/2012 14/04/2012

18/04/2012

 45,873,9

 542,457,0

 50,6

 KO

 0,56

 0,76

San Fele PC

 4,436,142,6

 554,613,5

 118,0

 30,0

 3,93

 0,71

 4,507,355,3

 561,591,1

 31,4

 18,0

 1,74

 0,71

Avigliano PC

Nemoli SAL

 Vietri di Potenza

Pietrapertosa

 24/02/2012

 08/03/2012

 4,495,336,2

 543,776,2

 10,0

 7,0

 1,43

 0,50

 4,440,620,5

 607,006,8

 39,2

 39,0

 1,01

 0,61

Campomaggiore

Vietri

Quotidiano

 del sud, Civil Protection Civil Protection Civil Protection

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides*

Civil Protection Civil Protection Civil Protection Civil Protection

pisticci.com

Civil Protection

Metauronews

 SAL

Civil Protection

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**


## *Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

**ID**

**108**

121

122

123 124

125 126

127

128

129 130

131

132 133

134

135

136

137

138

139

140

Tursi

23/02/2012

 4,456,338,6

 624,326,0

 53,8

 43,0

 1,25

 0,63

 Montalbano

 Jonico

 23/02/2012

 4,461,399,7

 632,113,0

 46,8

 43,0

 1,09

 0,51

Chlaromonte

 23/02/2012

 4,499,219,0

 647,309,0

 100,4

 45,0

 2,23

 0,51

Noepoli PC

Montalbano

Tursi SAL

 SAL

Civil Protection Civil Protection

basilicatanotizie.net

Castronuovo

 di

23/02/2012

 4,448,673,1

 604,075,0

 95,4

 50,0

 1,91

 0,48

Sant'Andrea

 Bernalda

 Avigliano

Montemurro

 11/02/2012 12/02/2012

23/02/2012

 4,475,260,0

 643,993,1

 52,4

 42,0

 1,25

 0,48

 4,509,076,7

 560,722,3

 29,8

 39,0

 0,76

 0,61

Avigliano PC

Bernalda SAL Roccanova PC

 4,461,336,3

 583,827,5

 118,6 254,0

 0,47

 0,54

 Rapone

Craco

08/02/2012

10/02/2012

 4,521,844,9

 542,278,7

 20,1

 17,0

 1,18

 0,53

San Fele PC Grumento Nova

 4,470,541,6

 622,549,9

 37,4

 24,0

 1,56

 0,38

 Savoia di Lucania

 Rivello (2)

 20/01/2012

 04/02/2012

 4,490,321,5

 547,596,1

 57,2

 79,0

 0,72

 0,37

 4,436,157,6

 558,533,0

 16,6

 10,0

 1,66

 0,80

Lauria

06/01/2012

 4,431,454,0

 572,485,4

 41,6

 46,0

 0,90

 0,82

 Stigliano

 Latronico

 San Fele

Matera

06/11/2011

06/12/2011

15/11/2011

25/12/2011

 4,473,739,4

 605,248,1

 46,6

 8,0

 5,83

 0,13

 4,437,998,4

 586,199,8

 65,0

 44,0

 1,48

 0,89

Episcopia PC

Stigliano SAL

Nemoli SAL Nemoli SAL

Vietri Craco PC

 4,515,972,5

 551,039,6

 23,8

 30,0

 0,79

 0,44

San Fele PC

 4,502,097,8

 635,438,5

 34,2

 2,0

 17,10

 0,10

Matera PC

 Muro Lucano

 08/10/2011

 4,511,194,6

 540,805,5

 127,6

 6,0

 21,27

 0,07

 Muro Lucano PC

 La Gazzetta del

Mezzogiorno

*Landslides - Investigation and Monitoring*

magazine

Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection basilicatanotizie.net

Bella

07/10/2011

 4,512,353,3

 546,253,8

 94,2

 5,0

 18,84

 0,17

Bella Casalini

 Laurenzana

 (riatt.)

 05/05/2011

 4,481,944,5

 581,225,0

 149,8 210,0

 0,71

 0,78

Laurenzana

 SAL

 Grottole

05/05/2011

 4,447,270,2

 625,680,9

 62,8 102,0

 0,62

 0,94

 Grottole da Castellano

provincia di Matera

ANAS

Civil Protection

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**

**111**

185

186

187

188 Guardia Perticara (3)

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

 Grottole(3)

 02/12/2013

 4,494,492,3

 616,732,9

 186,8

 57,0

 3,28

 0,48

 Grottole da Serre

Garaguso/Grassano

 02/12/2013

 4,545,101,3

 565,137,4

 150,0

 52,0

 2,88

 0,47

 Colobraro

 02/12/2013

 4,461,913,3

 596,895,9

 140,7

 26,0

 5,41

 0,68

 Sinni a Valsinni SI

Grassano SAL

La Gazzetta del Mezz

*…*

Civil Protection

 Cirigliano

 02/12/2013

 4,472,564,9

 599,264,5

 162,0

 56,0

 2,89

 0,59

 San Mauro Forte PC

 Bernalda

 Armento

 Tricarico

 Savoia di Lucania

 01/12/2013 01/12/2013

02/12/2013

02/12/2013

 4,473,912,5

 643,362,6

 221,3

 49,0

 4,52

 0,74

Bernalda SAL

 4,449,866,2

 621,395,1

 120,0

 57,0

 2,11

 0,81

 Guardia Perticara SAL

 4,497,852,6

 597,122,7

 114,8

 32,0

 3,59

 0,55

 Albano di Lucania PC

 4,490,996,4

 546,572,9

 125,6

 51,0

 2,46

 0,63

 Potenza

 Pomarico (4)

 01/12/2013 01/12/2013

 4,496,760,3

 571,166,4

 63,2

 25,0

 2,53

 0,60

 4,486,804,1

 631,164,0

 128,6

 26,0

 4,95

 0,51

 Ginestra

 Gallicchio

 01/12/2013 01/12/2013

 4,531,394,8

 562,130,8

 89,5

 23,0

 3,89

 0,32

 4,489,127,2

 591,325,4

 95,6

 20,0

 4,78

 0,54

 Craco (7)

Chiaromonte

 01/12/2013 01/12/2013

 4,470,341,9

 623,096,0

 156,4

 29,0

 5,39

 0,61

 4,479,622,4

 582,489,7

 65,0

 25,0

 2,60

 0,85

Episcopia PC

Craco PC Aliano SAL Venosa SAL

Ferrandina

Potenza PC

Vietri

 SAL

 Miglionico

 30/11/2013

 4,492,223,8

 627,675,2

 20,0

 8,0

 2,50

 0,48

 Valsinni

 Laurenzana

 26/11/2013 26/11/2013

 4,446,786,7

 622,424,8

 193,0 115,0

 1,68

 0,25

Nova Siri SAL

Ferrandina

 SAL

 4,440,683,6

 606,559,5

 89,0

 94,0

 0,95

 0,61

 San Severino Lucano

 San Fele

 Pisticci

Chiaromonte

 16/11/2013 16/11/2013

22/11/2013

 24/11/2013

 24/11/2013

 4,429,132,5

 594,345,3

 133,8 100,0

 1,34

 0,58

Viggianello

Laurenzana

 SAL

 SAL

 4,468,838,7

 592,161,9

 103,6

 115,9

 0,90

 0,61

 Guardia Perticara SAL

 4,518,881,1

 545,411,7

 109,8

 82,0

 1,34

 0,42

 4,472,993,9

 631,724,0

 157,0 135,0

 1,16

 0,24

 Pisticci Scalo SAL

San Fele PC

 4,433,677,5

 571,261,9

 238,2 132,0

 1,80

 0,22

Noepoli PC

La Siritide Civil Protection Civil Protection Civil Protection

lapretoria.it

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

Civil Protection Civil Protection Civil Protection

Fonti Civil Protection

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides*

Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection

 Fonti Civil Protection Civil Protection

emmenews

Cronachistiche

Cronachistiche

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**

**soil saturation**

#### *Landslides - Investigation and Monitoring*


## *Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

**ID**

**110**

163

164

165

166

167

168

169

170

171

172

173

174 Castelluccio

175

176

177

178

179

180

181

182

183 184

 Bernalda

Lauria

11/10/2013

16/11/2013

 4,472,586,8

 647,193,6

 148,4 136,0

 1,09

 0,37

Metaponto

 4,470,914,4

 645,919,6

 43,4

 51,0

 0,85

 0,39

 Pisticci

Montescaglioso

 07/10/2013 07/10/2013

 4,487,593,1

 594,710,1

 105,6

 57,0

 1,85

 0,11

Torre Accio PC

Nemoli SAL

 4,490,286,3

 641,544,0

 135,2

 35,0

 3,86

 0,07

Montescaglioso

 SAL

 Montalbano

 Jonico

 07/10/2013

 4,461,773,7

 634,506,4

 84,2

 2,90

 2,90

 0,10

 Montalbano

 SAL

 Bernalda

 Atella - Filiano

 Accettura

 21/08/2013

 21/08/2013 07/10/2013

 4,474,073,6

 643,387,8

 190,4

 56,0

 3,40

 0,08

 4,517,964,3

 559,520,9

 43,6

 5,0

 8,72

 0,21

 4,428,008,9

 574,521,3

 27,4

 25,0

 1,10

 0,19

Campomaggiore

Atella PC Bernalda SAL

 SAL

 Vietri di Potenza

 Muro Lucano

 10/07/2013

 21/07/2013

 4,493,924,6

 541,919,0

 13,8

 4,0

 3,45

 0,32

 4,510,677,3

 541,344,5

 59,2

 53,0

 1,12

 0,36

 Muro Lucano PC

Vietri

Quotidiano

 del sud,

accettura online Civil Protection

Quotidiano

 del sud, Evilmet

Civil Protection Civil Protection Evalmet web site

infopinione.it

Gazzettino.it

Metauronews

 Inferiore

 10/04/2013

 4,428,714,4

 584,450,1

 12,2

 3,0

 4,07

 0,83

Lauria

21/03/2013

 4,495,266,6

 544,987,6

 339,2 353,0

 0,96

 0,82

 Castelluccio

 Inferiore

 15/03/2013

 4,428,429,2

 584,348,2

 136,0 214,0

 0,64

 0,84

Viggianello

Nemoli SAL

Viggianello

 SAL

 SAL

 Vietri di Potenza

 Avigliano

 24/02/2013

 14/03/2013

 4,437,619,1

 594,262,1

 44,8

 49,0

 0,91

 0,89

 4,508,495,2

 559,778,8

 18,4

 27,0

 0,68

 0,95

 Balvano

 Armento

 San Severino Lucano

 03/02/2013 13/02/2013

13/02/2013

 4,500,024,3

 543,479,7

 34,4

 31,0

 1,11

 0,74

Balvano PC Avigliano PC

Vietri

Quotidiano

 del sud, Civil Protection

youtube

Civil Protection Civil Protection

Metauronews

 4,462,461,3

 590,367,6

 12,0

 10,0

 1,20

 0,73

 Guardia Perticara SAL

 4,429,936,5

 596,946,0

 28,2

 26,0

 1,08

 0,90

 Episcopia

 Lagonegro

 21/01/2013 25/01/2013

 4,445,478,1

 562,707,3

 366,6 362,0

 1,01

 0,87

 4,488,383,1

 547,262,1

 266,8 244,0

 1,09

 0,80

 Sant'Angelo

 le Fratte

 19/01/2013

 4,494,125,0

 541,429,1

 99,6 101,0

 0,99

 0,63

 Vietri di Potenza

 18/01/2013

 4,432,069,0

 573,009,3

 87,6

 93,0

 0,94

 0,73

Vietri Tito PC Lagonegro PC

Episcopia PC

Viggianello

 SAL

Civil Protection Civil Protection Civil Protection Civil Protection

Quotidiano

 del sud,

Fonti La Siritide basilicatanotizie.net

*Landslides - Investigation and Monitoring*

Cronachistiche

Metauronews

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**


**113**

228 229

230 231

232

233

234

235

236 237

238 Castelluccio

239

240

241

242

243 244

245 Guardia Perticara (2)

246

247

248

249

 Cirigliano

 05/02/2014

 4,476,117,9

 599,525,8

 124,8

 90,0

 1,39

 0,79

 San Mauro Forte PC

Sant'Arcangelo

 (3)

 04/02/2014

 4,454,767,9

 612,151,0

 94,6

 81,0

 1,17

 0,92

 Noepoli

Missanello(2)

 04/02/2014 04/02/2014

 4,473,633,7

 637,169,5

 101,6

 74,0

 1,37

 0,90

 4,459,645,8

 598,794,7

 96,6

 81,0

 1,19

 0,79

Aliano SAL Noepoli PC

Roccanova PC

 Gorgoglione

 04/02/2014

 04/02/2014

 4,468,759,4

 593,490,1

 91,8

 85,0

 1,08

 0,95

 Guardia Perticara SAL

 4,471,971,5

 597,509,0

 86,2

 74,0

 1,16

 0,95

 Guardia Perticara SAL

Aliano

04/02/2014

 4,462,662,3

 608,499,5

 94,6

 81,0

 1,17

 0,92

Roccanova PC

 San Giorgio Lucano

 03/02/2014

 4,441,730,4

 618,593,4

 60,2

 74,0

 0,81

 0,94

 San Giorgio Lucano SAL

 Pisticci

 Latronico

 03/02/2014 03/02/2014

 4,458,130,1

 631,793,0

 62,4

 68,0

 0,92

 0,87

 4,438,541,9

 586,536,2

 47,6

 68,0

 0,70

 0,94

 Gallicchio

 03/02/2014

 4,460,176,1

 597,304,5

 88,0

 67,0

 1,31

 0,92

 Inferiore

 03/02/2014

 4,428,421,9

 583,511,5

 272,6 381,0

 0,72

 0,78

Viggianello

Roccanova PC

Episcopia PC

Craco PC

Fonti

Cronachistiche

Civil Protection Civil Protection

basilicata24.it

Civil Protection Civil Protection

La Siritide Civil Protection

*…*

sassiland

 magazine

 SAL

castelluccioinferiore.comune.news

Civil Protection Civil Protection

 Accettura

 03/02/2014

 4,482,603,0

 600,241,7

 105,6

 77,0

 1,37

 0,79

 San Mauro Forte PC

Tursi

02/02/2014

 4,436,149,4

 600,183,4

 30,6

 36,0

 0,85

 0,86

 Potenza

 Guardia Perticara

 Francavilla

 in Sinni

 30/01/2014

 02/02/2014 02/02/2014

 4,496,094,5

 568,378,3

 12,4

 7,0

 1,77

 0,91

Potenza PC

Tursi SAL

 4,467,955,0

 593,670,0

 46,4

 46,0

 1,01

 0,95

 Guardia Perticara SAL

 4,433,285,2

 569,276,0

 247,8 238,0

 1,04

 0,86

 Maratea

 Guardia Perticara

 24/01/2014 28/01/2014

 4,427,583,9

 561,128,4

 234,6 218,0

 1,08

 0,93

Maratea PC Episcopia PC

 4,469,389,1

 594,580,2

 103,6 128,0

 0,81

 0,85

 Guardia Perticara SAL

Lauria

22/01/2014

 4,444,641,2

 597,413,0

 267,2

 86,0

 3,11

 0,86

 Latronico

 22/01/2014

 4,436,079,5

 583,979,5

 194,9

 70,0

 2,77

 0,86

Senise

21/01/2014

 4,444,739,2

 610,507,1

 52,0

 50,0

 1,04

 0,84

Senise SAL Episcopia PC

Nemoli SAL

Civil Protection La Siritide website

Fonti Civil Protection Civil Protection

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

regione.basilicata.it

Coldiretti webpage

Civil Protection oltrefreepress.com

accettura online

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides*

Cronachistiche

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**

**soil saturation**

*Landslides - Investigation and Monitoring*


## *Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

**ID**

**112**

207

208

209

210

211

212 213 214 215

216

217

218

219

220

221 222

223

224 225

226

227

Rivello

21/01/2014

 4,436,820,3

 566,903,7

 114,8

 41,0

 2,80

 0,86

Nemoli SAL

Civil Protection

Castronuovo

 di

21/01/2014

 4,450,030,0

 600,486,2

 87,4

 60,0

 1,46

 0,83

Sant'Andrea

 Calvera

Aliano

21/01/2014

21/01/2014

 4,454,657,0

 574,796,0

 193,6

 69,9

 2,81

 0,86

 4,463,968,1

 603,952,3

 88,4

 52,0

 1,70

 0,83

 Sarconi

 Viggianello

 04/12/2013 26/12/2013

 4,457,355,0

 632,845,6

 26,0

 11,0

 2,36

 0,85

 4,427,183,0

 589,852,6

 74,8

 61,0

 1,23

 0,85

Tursi

03/12/2013

 4,472,751,0

 632,461,7

 138,6

 66,0

 2,10

 0,58

Tursi SAL

Viggianello

Sarconi SAL Roccanova PC

Episcopia PC

Roccanova PC

 SAL

 Trivigno (4)

 03/12/2013

 4,490,716,4

 583,663,1

 196,0

 88,0

 2,23

 0,55

 Albano di Lucania PC

Montescaglioso

 03/12/2013

 4,489,336,0

 639,955,0

 224,2

 56,0

 4,00

 0,54

Montescaglioso

 SAL

 Ginestra

 Ferrandina

 03/12/2013 03/12/2013

 4,531,331,9

 561,335,0

 128,5

 56,0

 2,29

 0,32

 4,436,089,0

 624,424,9

 161,0

 59,0

 2,73

 0,51

 Accettura, Salandra

 03/12/2013

 4,481,543,0

 598,406,7

 197,8

 63,0

 3,14

 0,59

 San Mauro Forte PC

Ferrandina

Venosa SAL

 SAL

 Accettura

Senise

02/12/2013

03/12/2013

 4,488,048,3

 593,820,9

 211,4

 58,0

 3,64

 0,57

Campomaggiore

 SAL

 La Gazzetta del

 La Gazzetta del Civil Protection Civil Protection Pellicani et al., 2016

Civil Protection Evalmet web site

Civil Protection Civil Protection

Fonti La Siritide Civil Protection

Cronachistiche

Mezzogiorno

Mezzogiorno

 4,445,683,3

 609,339,4

 83,0

 37,0

 2,24

 0,81

Rivello

02/12/2013

 4,436,644,0

 564,876,7

 42,2

 38,0

 1,11

 088

Pisticci

02/12/2013

 4,466,906,3

 594,785,7

 167,2

 33,0

 5,07

 0,63

 Pisticci Scalo SAL

Nemoli SAL Noepoli PC

Pietrapertosa

 02/12/2013

 4,484,995,6

 589,793,9

 168,8

 54,0

 3v13

 0,57

Campomaggiore

 SAL

 Montalbano

 Jonico

 02/12/2013

 4,457,687,7

 626,816,0

 174,8

 28,0

 6,24

 0,66

 Montalbano

 SAL

 Missanello

 02/12/2013

 4,462,814,9

 590,380,8

 138,6

 55,0

 2,52

 0,54

 Lavello

 Guardia Perticara

 02/12/2013 02/12/2013

 4,459,823,2

 599,087,0

 130,0

 57,0

 2,28

 0,30

Lavello SAL Aliano SAL

 4,494,394,2

 605,039,5

 120,0

 57,0

 2,11

 0,81

 Guardia Perticara SAL

Civil Protection

Fonti Fonti Civil Protection

*Landslides - Investigation and Monitoring*

Civil Protection Civil Protection Civil Protection Civil Protection

Cronachistiche

Cronachistiche

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**


**115**

272 San Costantino Albanese

273

274

275

276

277

278 279

280

281 Terranova del Pollino

282

283

284

285

286

287

288

289

290

 Grottole

28/03/2015

 4,495,416,1

 614,736,1

 107,2 123,0

 0,87

 0,64

 Grottole da Serre

 Colobraro

 28/03/2015

 4,451,661,8

 625,343,5

 78,5

 77,0

 1,02

 0,76

 Sinni a Valsinni SI

 La Gazzetta del

Mezzogiorno

magazine

*…*

Civil Protection

Chiaromonte

 28/03/2015

 4,441,944,5

 603,702,8

 71,6

 78,0

 0,92

 0,73

Noepoli PC

Civil Protection

Castronuovo

 di

28/03/2015

 4,449,432,7

 600,769,2

 83,4

 80,0

 1,04

 0,91

Sant'Andrea

 Calvera

 Ferrandina

 27/03/2015 28/03/2015

 4,445,756,6

 595,749,2

 80,6 128,0

 0,63

 0,90

Episcopia PC

Roccanova PC

 4,472,778,5

 627,699,5

 99,2

 72,0

 1,38

 0,80

 Colobraro

 26/03/2015

 4,449,328,3

 620,218,2

 30,2

 9,0

 3,36

 0,76

 Sinni a Valsinni SI

Ferrandina

 SAL

youreporter

Civil Protection Civil Protection

Montemurro

 25/03/2015

 4,461,303,7

 585,075,4

 31,4

 62,0

 0,51

 0,88

Grumento Nova

 Salandra

18/03/2015

 4485066v9

 613,268,6

 13,8

 20,0

 0,69

 0,72

 San Mauro Forte PC

Civil Protection

rainews

Civil Protection

 Vietri di Potenza

 12/03/2015

 16/03/2015

 4,425,194,1

 607,145,4

 85,2 120,0

 0,71

 0,84

 Terranova del Pollino

PC

 4,494,061,7

 543,185,2

 9,6

 5,0

 1,92

 0,84

Vietri

Quotidiano

 del sud magazine

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides*

basilicatanotizie.net

 Tricarico

Lauria

06/03/2015

06/03/2015

 4,497,116,3

 597,635,0

 40,4

 40,0

 1,01

 0,66

 Albano di Lucania PC

 4,434,254,5

 570,436,9

 99,2

 82,0

 1,21

 0,86

 Trecchina

 08/02/2015

 4,428,416,7

 568,127,6

 344,0 248,0

 1,39

 0,95

 San Severino Lucano

Montemurro

 04/02/2015

 06/02/2015

 4,426,488,0

 595,925,0

 315,8 240,0

 1,32

 0,82

Viggianello

 SAL

Castrocucco Nemoli SAL

Fonti La Siritide website

tricarico news

Cronachistiche

 4,461,149,4

 584,066,8

 144,6 132,0

 1,10

 0,54

 Viggianello

 31/01/2015

 4,447,312,7

 586,781,1

 164,0

 61,0

 2,69

 0,81

 San Martino D'Agri

 31/01/2015

 4,454,871,1

 589,437,1

 73,2

 34,0

 2,15

 0,74

 31/01/2015

 4,432,781,2

 612,691,0

 61,6

 80,0

 0,77

 0,49

 Terranova del Pollino

Civil Protection

PC

Roccanova PC

Episcopia PC

Grumento Nova

 Fonti

Cronachistiche

 magazine

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

regione.basilicata.it

meteoweb.eu

meteoweb.eu

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**

**soil saturation**

*Landslides - Investigation and Monitoring*



**114**

250

251

252

253

254

255

256

257

258

259

260

261

262

263 Rionero in Vulture (2)

264

265

266 267

268

269

270 271

 Nemoli

Lauria

31/01/2015

31/01/2015

 4,427,708,0

 574,027,3

 263,8

 43,0

 6,13

 0,92

 4,448,327,7

 565,843,8

 263,8

 43,0

 6,13

 0,88

 Latronico

 Lagonegro

 31/01/2015 31/01/2015

 4,435,217,2

 590,205,7

 164,9

 61,0

 2,69

 0,78

 4,429,110,9

 594,311,5

 284,9

 53,0

 5,36

 0,87

Lagonegro PC

Episcopia PC

Nemoli SAL Nemoli SAL

Castelsaraceno

 31/01/2015

 4,435,933,3

 568,128,3

 157,6

 36,0

 4,38

 0,81

Castelsaraceno

 SI

Civil Protection Civil Protection Civil Protection

meteoweb.eu

Civil Protection

Senise

30/01/2015

 4,444,603,9

 607,201,7

 36,8

 53,0

 0,69

 0,58

Chiaromonte

 30/01/2015

 4,442,083,4

 603,091,6

 36,8

 53,0

 0,69

 0,58

 Brienza

 Rivello

 Calvello

 San Severino Lucano

Montemurro

 17/04/2014

 30/04/2014 24/07/2014

08/11/2014

 31/12/2014 30/01/2015

 4,481,683,5

 553,437,4

 89,8

 24,0

 3,74

 0,76

 4,530,984,6

 556,617,6

 14,0

 6,0

 2,33

 0,43

 4,435,948,7

 554,641,6

 53,4

 19,0

 2,81

 0,30

 4,427,437,3

 600,212,2

 11,0

 1,0

 11,00

 0,31

 4,439,756,0

 605,195,5

 37,4

 47,0

 0,80

 0,87

Viggianello

Laurenzana

Nemoli SAL

Melfi Brienza PC Senise SAL Senise SAL

 SAL

 SAL

 4,460,278,1

 587,345,4

 13,2

 7,0

 1,89

 0,83

Chiaromonte

 16/04/2014

 4,470,704,8

 595,848,0

 21,0

 13,0

 1,62

 0,87

Noepoli PC

Grumento Nova

 Rionero in Vulture

 San Severino Lucano

 06/04/2014

 12/04/2014

 4,530,903,2

 556,997,1

 29,1

 16,0

 1,82

 0,74

 4,429,371,0

 597,800,0

 68,0

 49,0

 1,39

 0,89

 Stigliano

Montemurro

 27/03/2014 27/03/2014

 4,473,506,8

 604,550,1

 12,0

 4,0

 3,00

 0,68

Stigliano SAL

Viggianello

 SAL

> Melfi

Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection

La Siritide website

 4,462,003,7

 585,867,7

 18,2

 20,0

 0,91

 0,89

 Grumento NOva

 Guardia Perticara

 25/03/2014

 4,455,948,6

 575,716,1

 30,8

 16,0

 1,93

 0,81

 Guardia Perticara SAL

 Armento

 Stigliano

 Sarconi

09/02/2014

09/02/2014

25/03/2014

 4,461,174,2

 591,120,8

 17,4

 14,0

 1,24

 0,81

 Guardia Perticara SAL

 4,438,261,8

 613,374,0

 105,6

 98,0

 1,08

 0,78

 4,473,389,5

 604,347,8

 22,4

 30,0

 0,75

 0,93

Sarconi SAL Stigliano SAL

Civil Protection Civil Protection Civil Protection Civil Protection

*Landslides - Investigation and Monitoring*

Civil Protection regione.basilicata.it

sanseverinolucano.com

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**


**117**

310

311

312

313

314

315

316

317

318

319

320

321 322

323

324

325 326

**Table 1.** *Rainfall-triggered*

 *landslides in Basilicata during last 20 years.*

 Laurenzana

 28/03/2018

 4,479,367,5

 582,543,4

 108,2 192,0

 0,56

 0,83

Rivello

07/03/2018

 4,436,820,1

 565,132,1

 208,0 360,0

 0,58

 0,88

Lagonegro PC

Laurenzana

 Viggianello

 05/03/2018

 4,426,435,3

 589,650,4

 123,0 197,0

 0,62

 0,87

Montemilone

 15/07/2017

 4,542,486,2

 581,337,0

 27,4

 2,0

 13,70

 0,05

Montemilone

 PC

> Rotonda

 Avigliano

 08/03/2017

 4,510,867,0

 560,723,4

 53,6

 31,0

 1,73

 0,86

Senise

25/01/2017

 4,445,316,4

 612,080,5

 113,0

 91,0

 1,24

 0,52

Campomaggiore

 25/01/2017

 4,490,127,7

 591,486,1

 67,2

 67,0

 1,00

 0,67

 Colobraro

 23/01/2017

 4,447,990,5

 622,489,0

 127,2

 68,0

 1,87

 0,57

 Sinni a Valsinni SI

Laurenzano

Noepoli PC

Avigliano PC

 PC

 Maratea

 Genzano

 Lavello

 Miglionico

 26/07/2016 11/09/2016

21/09/2016

10/10/2016

 4,426,721,0

 560,774,8

 113,4

 70,0

 1,62

 0,27

 4,521,410,4

 590,384,9

 19,4

 6,0

 3,23

 0,33

 4,545,245,7

 564,838,8

 31,8

 19,0

 1,67

 0,21

 4,492,210,7

 626,535,0

 13,6

 1,0

 13,60

 0,17

 Sant'Angelo

 le Fratte

 26/03/2016

 4,487,108,9

 547,444,0

 35,8

 33,0

 1,08

 0,68

 Pisticci

 Salandra

 Stigliano

 Pisticci

17/03/2016

17/03/2015

18/03/2016

25/03/2016

 4,471,998,1

 633,051,0

 47,0

 18,0

 2,61

 0,73

Torre Accio PC

Tito PC Ferrandina

Lavello SI Genzano SAL

Maratea PC

 PC

 Quotidiano

 della Basilicata magazine

vulturenews

Civil Protection

Fonti Civil Protection

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides*

Civil Protection

trmtv.it

basilicata24.com

vulture news Civil Protection Civil Protection Civil Protection *…*

Cronachistiche

 4,485,538,3

 613,656,0

 156,6 192,0

 0,82

 0,41

 4,472,596,0

 604,150,0

 238,2 142,0

 1,68

 0,36

Stigliano SAL

Salandra SI

 4,482,354,3

 626,252,6

 72,1

 26,0

 2,77

 0,57

Torre Accio PC

youreporter

Civil Protection Civil Protection Civil Protection Civil Protection

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**

**soil saturation**

*Landslides - Investigation and Monitoring*


*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

> **Table 1.**

*Rainfall-triggered landslides in Basilicata during last 20 years.*

**ID**

**116**

291 Terranova del Pollino

292 293

294

295

296

297 298

299

300

301

302

303

304

305

306 Terranova del Pollino

307

308

309

 Colobraro

 17/03/2016

 4,449,758,6

 620,171,4

 321,4 144,0

 2,23

 0,63

 Sinni a Valsinni SI

Sant'Arcangelo

eventi)

 (2

16/03/2016

 4,452,238,1

 611,364,0

 145,6

 48,0

 3,03

 0,38

 Ferrandina

 16/03/2016

 4,472,610,0

 638,594,0

 101,0 150,0

 0,67

 0,40

 14/03/2016

 4,425,609,1

 607,819,7

 63,4

 46,0

 1,38

 0,79

 Terranova del Pollino

PC

Ferrandina

Aliano SAL

 SAL

youreporter

Civil Protection Civil Protection

Castronuovo

 di

13/03/2016

 4,449,163,2

 601,141,5

 120,6

 50,0

 2,41

 0,87

Sant'Andrea

 Venosa

 Picerno

 Rotonda

 Gallicchio

 06/01/2016 13/02/2016

15/02/2016

18/02/2016

 4,535,187,4

 568,991,4

 10,7

 8,0

 1,34

 0,38

 4,500,954,0

 549,546,0

 93,2 103,0

 0,90

 0,51

 4,423,399,8

 588,125,9

 117,0

 91,0

 1,29

 0,77

 4,460,397,1

 596,927,3

 10,6

 3,0

 3,53

 0,39

Chiaromonte

 31/10/2015

 4,442,678,8

 603,349,5

 108,0

 51,0

 2,12

 0,30

 Venosa

 Salandra

Melfi

11/08/2015

11/08/2015

20/10/2015

 4,537,376,0

 569,867,0

 22,8

 15,0

 1,52

 0,24

 4,487,314,1

 610,376,5

 21,8

 26,0

 0,84

 0,22

 San Mauro Forte PC

Venosa SAL Senise SAL Aliano SAL Rotonda SAL

Balvano PC Venosa SAL Roccanova PC

 4,538,822,6

 554,831,5

 11,8

 2,0

 5,91

 0,04

 Matera

 Grottole

 Grassano

 Sasso di Castalda

 12/06/2015 11/08/2015

11/08/2015

11/08/2015

 4,500,866,1

 633,780,7

 54,3

 51,0

 1,06

 0,11

 4,497,727,3

 612,882,2

 86,0

 35,0

 2,46

 0,15

 Grottole da Serre

Matera PC

Melfi

 4,498,091,9

 608,264,6

 106,2

 28,0

 3,79

 0,15

 4,482,734,2

 556,250,0

 178,8

 76,0

 2,35

 0,59

Anzi

06/04/2015

 4,484,904,3

 579,311,8

 31,6

 66,0

 0,48

 0,89

 28/03/2015

 4,424,299,8

 606,589,5

 142,4 129,0

 1,10

 0,86

 Terranova del Pollino

Civil Protection

PC

Laurenzana

Brienza PC Grassano SAL

 SAL

Civil Protection Civil Protection

*Landslides - Investigation and Monitoring*

Civil Protection

meteoweb.eu

Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection

La Siritide website

Civil Protection Civil Protection Civil Protection Civil Protection Civil Protection

**Municipality**

**Date**

 **UTM N**

 **UTM E**

 **H**

**D**

**I**

**Antecedent**

**Weather station**

**Sources**

**(mm)**

**(h)**

**(mm/h)**

*Landslides - Investigation and Monitoring*

## **Author details**

Maurizio Lazzari<sup>1</sup> , Marco Piccarreta<sup>2</sup> , Ram L. Ray<sup>3</sup> and Salvatore Manfreda<sup>4</sup> \* **References**

[1] Hong Y, Adler R, Huffman G. Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophysical Research Letters. 2006;**33**: L22402. DOI: 10.1029/2006GL028010

*DOI: http://dx.doi.org/10.5772/intechopen.92730*

[8] Glade T, Crozier MJ, Smith P. Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical "antecedent daily rainfall model". Pure and Applied Geophysics. 2000;**157**:1059-1079

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides…*

[9] Godt JW, Baum RL, Chleborad AF. Rainfall characteristics for shallow landsliding in Seattle, Washington, USA. Earth Surface Processes and Landforms. 2006;**31**:97-110

[10] Brocca L, Ponziani F, Moramarco T,

[11] Coe J. Regional moisture balance control of landslide motion: Implications for landslide forecasting in a changing climate. Geology. 2012;**40**(4):323-326.

[12] Ponziani F, Pandolfo C, Stelluti M, Berni N, Brocca L, Moramarco T. Assessment of rainfall thresholds and soil moisture modeling for operational hydrogeological risk prevention in the

[13] Mirus BB, Rachel I, Becker E, Rex I, Baum L, Joel I, et al. Integrating realtime subsurface hydrologic monitoring with empirical rainfall thresholds to improve landslide early warning. Landslides. 2018;**15**:1909-1919

[14] Mirus BB, Morphew MD, Smith JB. Developing hydro-meteorological tfor shallow landslide initiation and early warning. Water. 2018;**10**:1274. DOI:

[15] Valenzuela P, Domínguez-Cuesta MJ, Mora García MA, Jiménez-Sánchez M.

10.3390/w10091274

Umbria region (Central Italy). Landslides. 2012;**9**:229-237. DOI: 10.1007/s10346-011-0287-3

DOI: 10.1130/G32897.1

Melone F, Berni N, Wagner W. Improving landslide forecasting using ASCAT-derived soil moisture data: A case study of the Torgiovannetto landslide in Central Italy. Remote Sensing. 2012;**4**: 1232-1244. DOI: 10.3390/rs4051232

[2] Marc O, Stumpf A, Malet JP, Gosset M, Uchida T, Chiang SH. Initial insights from a global database of rainfall-induced landslide inventories: The weak influence of slope and strong influence of total storm rainfall. Earth Surface Dynamics. 2018;**6**:903-922

[3] Guzzetti F, Peruccacci S, Rossi M, Stark CP. The rainfall intensity-duration control of shallow landslides and debris flows: An update. Landslides. 2008;**5**:3-17

[4] Lazzari M, Piccarreta M, Capolongo D. Landslide triggering and local rainfall thresholds in Bradanic Foredeep, Basilicata region (southern Italy). Landslide Science and Practice. Vol. 2. Early Warning, Instrumentation and Modeling. Springer Series. Margottini C, Canuti P, Sassa K, et al, editors. In: Proceedings of the Second World Landslide Forum; Rome (ITALY); 3–9 October 2011; 2013. pp. 671-678

[5] Segoni S, Piciullo L, Gariano SL. A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides. 2018;**15**:1483- 1501. DOI: 10.1007/s10346-018-0966-4

[6] Bogaard T, Greco R. Invited

perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: Proposing hydro-meteorological thresholds. Natural Hazards and Earth System Sciences. 2018;**18**:31-39

[7] Crozier MJ. Prediction of rainfalltriggered landslides: A test of the antecedent water status model. Earth Surface Processes and Landforms. 1999;

**24**:825-833

**119**

1 CNR-ISPC, Potenza (PZ), Tito Scalo, Italy

2 Interuniversity Consortium for Hydrology (CINID), Potenza, Italy

3 College of Agriculture and Human Sciences, Prairie View A&M University, Prairie View, Texas, USA

4 Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Naples, Italy

\*Address all correspondence to: salvatore.manfreda@unina.it

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

## **References**

[1] Hong Y, Adler R, Huffman G. Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophysical Research Letters. 2006;**33**: L22402. DOI: 10.1029/2006GL028010

[2] Marc O, Stumpf A, Malet JP, Gosset M, Uchida T, Chiang SH. Initial insights from a global database of rainfall-induced landslide inventories: The weak influence of slope and strong influence of total storm rainfall. Earth Surface Dynamics. 2018;**6**:903-922

[3] Guzzetti F, Peruccacci S, Rossi M, Stark CP. The rainfall intensity-duration control of shallow landslides and debris flows: An update. Landslides. 2008;**5**:3-17

[4] Lazzari M, Piccarreta M, Capolongo D. Landslide triggering and local rainfall thresholds in Bradanic Foredeep, Basilicata region (southern Italy). Landslide Science and Practice. Vol. 2. Early Warning, Instrumentation and Modeling. Springer Series. Margottini C, Canuti P, Sassa K, et al, editors. In: Proceedings of the Second World Landslide Forum; Rome (ITALY); 3–9 October 2011; 2013. pp. 671-678

[5] Segoni S, Piciullo L, Gariano SL. A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides. 2018;**15**:1483- 1501. DOI: 10.1007/s10346-018-0966-4

[6] Bogaard T, Greco R. Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: Proposing hydro-meteorological thresholds. Natural Hazards and Earth System Sciences. 2018;**18**:31-39

[7] Crozier MJ. Prediction of rainfalltriggered landslides: A test of the antecedent water status model. Earth Surface Processes and Landforms. 1999; **24**:825-833

[8] Glade T, Crozier MJ, Smith P. Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical "antecedent daily rainfall model". Pure and Applied Geophysics. 2000;**157**:1059-1079

[9] Godt JW, Baum RL, Chleborad AF. Rainfall characteristics for shallow landsliding in Seattle, Washington, USA. Earth Surface Processes and Landforms. 2006;**31**:97-110

[10] Brocca L, Ponziani F, Moramarco T, Melone F, Berni N, Wagner W. Improving landslide forecasting using ASCAT-derived soil moisture data: A case study of the Torgiovannetto landslide in Central Italy. Remote Sensing. 2012;**4**: 1232-1244. DOI: 10.3390/rs4051232

[11] Coe J. Regional moisture balance control of landslide motion: Implications for landslide forecasting in a changing climate. Geology. 2012;**40**(4):323-326. DOI: 10.1130/G32897.1

[12] Ponziani F, Pandolfo C, Stelluti M, Berni N, Brocca L, Moramarco T. Assessment of rainfall thresholds and soil moisture modeling for operational hydrogeological risk prevention in the Umbria region (Central Italy). Landslides. 2012;**9**:229-237. DOI: 10.1007/s10346-011-0287-3

[13] Mirus BB, Rachel I, Becker E, Rex I, Baum L, Joel I, et al. Integrating realtime subsurface hydrologic monitoring with empirical rainfall thresholds to improve landslide early warning. Landslides. 2018;**15**:1909-1919

[14] Mirus BB, Morphew MD, Smith JB. Developing hydro-meteorological tfor shallow landslide initiation and early warning. Water. 2018;**10**:1274. DOI: 10.3390/w10091274

[15] Valenzuela P, Domínguez-Cuesta MJ, Mora García MA, Jiménez-Sánchez M.

**Author details**

Maurizio Lazzari<sup>1</sup>

Prairie View, Texas, USA

**118**

Naples Federico II, Naples, Italy

provided the original work is properly cited.

, Marco Piccarreta<sup>2</sup>

2 Interuniversity Consortium for Hydrology (CINID), Potenza, Italy

\*Address all correspondence to: salvatore.manfreda@unina.it

3 College of Agriculture and Human Sciences, Prairie View A&M University,

4 Department of Civil, Architectural and Environmental Engineering, University of

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

1 CNR-ISPC, Potenza (PZ), Tito Scalo, Italy

*Landslides - Investigation and Monitoring*

, Ram L. Ray<sup>3</sup> and Salvatore Manfreda<sup>4</sup>

\*

Rainfall thresholds for the triggering of landslides considering previous soil moisture conditions (Asturias, NW Spain). Landslides. 2018;**15**:273-282. DOI: 10.1007/s10346-017-0878-8

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[26] Brunetti MT, Peruccacci S, Rossi M, Luciani S, Valigi D, Guzzetti F. Rainfall thresholds for the possible occurrence of landslides in Italy. Natural Hazards and Earth System Sciences. 2010;**10**:447-458

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*Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides… DOI: http://dx.doi.org/10.5772/intechopen.92730*

[30] Ray RL, Jacobs JM. Relationships among remotely sensed soil moisture, precipitation and landslide events. Natural Hazards. 2007;**43**:211-222

Rainfall thresholds for the triggering of landslides considering previous soil moisture conditions (Asturias, NW Spain). Landslides. 2018;**15**:273-282. DOI:

*Landslides - Investigation and Monitoring*

[22] Piccarreta M, Pasini A, Capolongo D, Lazzari M. Changes in daily precipitation extremes in the Mediterranean from 1951 to 2010: The Basilicata region, southern

[23] Lazzari M. Note illustrative della Carta Inventario delle Frane della Basilicata centroccidentale. Lagonegro: Editore Grafiche Zaccara; 2011. p. 136

[24] Lazzari M, Gioia D. Regional-scale landslide inventory, central-western sector of the Basilicata region (southern Apennines, Italy). Journal of Maps. Published Online. 2015;**12**(5):852-859. DOI: 10.1080/17445647.2015.1091749

[25] Lazzari M, Gioia D, Anzidei B. Landslide inventory of the Basilicata region (southern Italy). Journal of Maps. 2018;**14**(2):348-356. DOI: 10.1080/

[26] Brunetti MT, Peruccacci S, Rossi M, Luciani S, Valigi D, Guzzetti F. Rainfall thresholds for the possible occurrence of landslides in Italy. Natural Hazards and Earth System Sciences. 2010;**10**:447-458

[27] Vennari C, Gariano SL, Antronico L, Brunetti MT, Iovine G, Peruccacci S, et al. Rainfall thresholds for shallow landslide occurrence in Calabria, southern Italy. Natural Hazards and Earth System Sciences. 2014;**14**:317-330.

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**123**

**Chapter 6**

*and Jun-Yang Chen*

ment in landslide areas.

**1. Introduction**

**Abstract**

Long-Term Monitoring of Slope

Movements with Time-Domain

Reflectometry Technology in

*Miau-Bin Su, I-Hui Chen, Shei-Chen Ho, Yu-Shu Lin* 

The study employs time-domain reflectometry (TDR) technology for landslide

monitoring to explore rock deformation mechanism and to estimate locations of potential sliding surfaces in several landslide areas, Taiwan, over ten years. Comparing to laboratory and field testing, sliding surfaces in landslide areas occurred mainly at two types, namely shear and extension failure. The TDR technology is used for field monitoring to analyze locations of sliding surfaces and to quantify the magnitude of the sliding through laboratory shear and extension tests. There are several TDR-monitoring stations in six alpine landslide areas in the middle of Taiwan for long-term monitoring. A relation between TDR reflection coefficients and shear displacements was employed for a localized shear deformation in the field. Furthermore, the type of a cable rupture for the TDR monitoring in landslides can be determined as shear, extension, or compound failure through the field TDR waveforms. Overall, the TDR technology is practically used for a long-term monitoring system to detect the location and magnitude of slope move-

**Keywords:** time domain reflectometry (TDR), landslide monitoring, slope movement, shear and extension testing, deformation quantification

Determining the location and magnitude of sliding surfaces is a vital measure for landslide monitoring. For conventional slope monitoring, drill-log reports can illustrate in situ presence of weak rock or location of weak rock masses [1, 2]. Furthermore, inclinometers are used for landslide monitoring to detect sliding zones and to measure the subsurface lateral displacement of soil or rock [3–5]. However, it is time-consuming and difficult by these traditional methods to interpret an accurate location of sliding surface in a landslide area [6, 7]. In recent years, time-domain reflectometry (TDR) is employed for the monitoring of slope movement to locate depths of slope failures [8, 9]. The TDR technology uses a cable tester to detect a coaxial cable grouted in a borehole. While the cable broken or ruptured, a TDR signal

Landslide Areas, Taiwan

## **Chapter 6**
