Open access peer-reviewed chapter

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

Written By

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

Submitted: 03 February 2020 Reviewed: 05 May 2020 Published: 14 August 2020

DOI: 10.5772/intechopen.92730

From the Edited Volume

Landslides - Investigation and Monitoring

Edited by Ram Ray and Maurizio Lazzari

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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 alarms [6]. In fact, rainfall thresholds approach evaluates only the amount of cumulated rainfall and it neglects the primary role of other vital parameters, such as evapotranspiration, soil moisture, rainfall infiltration, soil porosity, and permeability.

In order to consider predisposing hydrological factors on empirical threshold calculation, recent studies have focused on the role of the antecedent daily rainfall in landslides triggering [7, 8, 9, 10, 11, 12]. These approaches have found a strong relationship between the hourly rainfall data triggering landslides and the initial soil moisture contributing to improving the predictive accuracy of empirical thresholds. Those results have also stimulated a critical revision of the intensity/duration thresholds in the last few years [6, 13, 14, 15]. In particular, Bogaard and Greco [6] introduced the cause-trigger concept for defining hydro-regional thresholds for predicting landslide occurrence, also suggesting taking into consideration the slope water balance. Starting from this new perspective, we aim to contribute to this discussion by evaluating the correlation between antecedent soil moisture conditions and rainfall intensity during shallow landslide events. In particular, we would like to explore better how much the initial saturation degree of soil affects the intensity/duration (I/D) relationships in landslide prediction. For this purpose, it is very important to use reliable databases in the literature or otherwise build a specific one.

There are many soil moisture datasets that have been successfully used to calibrate and validate catchment or watershed scale models of infiltration, soil-moisture storage, and in some ways, even to examine the first landslide trigger [16, 17, 18, 19].

In this chapter, we addressed this issue by reconstructing and leveraging a dataset of 326 landslide events that occurred in the Basilicata region (southern Italy, Figure 1 , and Appendix), from January 2001 to March 2018. For each georeferenced landslide, we derived the rainfall event characteristics and antecedent soil moisture conditions using a parsimonious physically-based distributed model applied at the regional scale with a spatial resolution of 200 m and a daily and hourly time scale. This approach allowed us to reconstruct all of the main forcing factors that may have produced a change in the slope stability and to detect the impact of antecedent soil moisture on the rainfall intensity/duration relationship. The numerical simulation has been needed to reconstruct the antecedent soil moisture values not available for the whole study area.

Figure 1.

Geographical distribution of the weather stations and landslide events for the study area. The graph in the inset shows the monthly distribution of landslides in Basilicata from 2001 to 2018.

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2. Data and methods

This study was carried out within a research agreement with the Civil Protection of the Basilicata region, which supported in part the reconstruction of the list of landslide events used for the analysis. The database was constructed with the primary aim of creating an updated description of the most recent landslides and the associated rainfall events. Therefore, the present section will be devoted to the description of the study area, the methodology adopted to build the database, and the modeling approach used to reconstruct the antecedent soil saturation conditions.

2.1 Study area

Basilicata is a region of southern Italy covering an area of 9.992 km2 characterized by different topographical and geomorphological contexts, landscape types (47% mountains, 45% hillocks, and 8% plains) and geolithological conditions. The north-western and south-western regions are characterized by mountain landscapes (southern Apennines) with significant elevations of the relief (between 1300 and 2000 m of altitude) and steep slopes, particularly where Mesozoic successions (dolomite and siliceous limestones) outcrop. The eastern region shows a hilly landscape characterized by soft shapes or tabular hills (alternating ridges and valleys in conglomeratic sandstone—clayey—marly), usually with low gradients of the slopes, often modeled in foredeep Plio—Pleistocene units with clayey dominant [20, 21].

Precipitation values are typical of the Mediterranean, with distinct dry and wet seasons [22]. Higher precipitation totals occur during the last autumn-winter period when landslides and floods usually take place (more than 70%). A near real-time hydrometeorological network covers the territory uniformly with a density of one station every 80 km2. It has been operating over a time interval of about 70 years, providing temperature and precipitation data at the resolution of 10 minutes.

2.2 Landslide and rainfall data

Based on detailed bibliographical research [23, 24, 25], which explored all available sources including national and local newspapers and journals, Internet blogs, and the scientific and technical literature, we have collected a database of 326 shallow landslide events (landslide event is a single landslide) from January 2001 to March 2018.

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

  • accurate or approximate location of the landslide event;

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

  • rainfall conditions that resulted in slope failures collected from the nearest rain gauge, including the total event rainfall, the rainfall duration, the mean rainfall intensity, and the antecedent rainfall for 2001–2018;

  • landslide type;

  • a generic description of the lithology.

In addition to this data that is also reported in Appendix in a tabular format, meteorological data and the output of the hydrological model have been used for the subsequent elaborations. In particular, hourly rainfall and temperature data were obtained from the rain gauges of the Civil Protection of the region. The hydrological model proposed on a regional scale considers homogeneous soil moisture conditions in the space and the first meters of depth in the areas affected by each landslide identified in our database.

The regional pedological map is depicted in Figure 2 with the spatial distribution of landslides ( Figure 2 ). This maps provides a nested description of soil classes that indentifies four regions at the first level (soil map of Italy, scale 1: 5,000,000), the 15 provinces, and 75 soil units (scale 1: 250,000). Based on the pedological characteristics of the regions, it was observed that the highest number of landslides (56 landslides) occurred in the soil province n.6. It is also worthy to mention a high number of events occurred on the soil unit 12.4 (33 landslides) and 10.2 (21 landslides). These last two soil units correspond to:

  • 12.4—hilly clay soils with steep slopes, badlands, intended for grazing or arable land, with low permeability (Vertic Haploxerepts; Inceptisol);

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

Figure 2.

Pedological regions and shallow landslides distribution over the Basilicata region.

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

Figure 3.

Histogram with the distribution of the number of landslides in the various regional soil units. Red circles, units with multiple landslides; black dotted rectangle, landslides included in unit 6.

2.3 Reconstruction of rainfall events

The rainfall duration (D) was determined by measuring the time between the moment of the beginning of each rainfall event, which triggered a shallow landslide, considered in the database, and rainfalls ending time. The rainfall ending time was taken to coincide with the time of the last rainfall measurement of the day when the landslide occurred. As suggested by Brunetti et al. [26], the starting time was considered a minimum period without rain (a 2-day period without rainfall was selected for late spring and summer, May–September, and a 4-day period without rainfall was selected for the other seasons, October–April) to separate two consecutive rainfall events. Once the duration of the rainfall event was established, the corresponding rainfall mean intensity I (mm h−1) was calculated dividing the cumulated (total) rainfall (mm) in the considered period by the length of the rainfall period (hours). The full list of events is given in the Appendix of the present chapter (Table 1—Appendix). In recent studies, authors [27, 28, 29] have used an approach that provides the seasonality criterion (April-October for the “dry/warm” season and November-March for the “wet/cold” season) to calculate the rainfall events. In this chapter, the proposed method is different from that proposed by Peruccacci et al. [29], because the saturation value and condition are a parameter regardless of seasonality. It provides a more detailed parameter, overcoming the possibility that in the same season, it can have more dry or wet phases.

2.4 Modeling soil water content

Although antecedent soil moisture can be obtained by in-situ measurements at a point scale, measurements on a regional scale are time-consuming and expensive. Recently, more information is available from satellite data, but they are too coarse to provide local estimates of soil water content on a specific landslide [30]. Thus, we used the hydrological model AD2 to describe the temporal evolution of soil water content over the entire Basilicata region using a distributed approach at 240 m spatial resolution.

The AD2 model is a 1D model capable of describing the soil water budget along the vertical direction, but its physically based nature allows to associate physical characteristics such as soil texture, land cover, and mean slope to each pixel/location that affects the model parametrization.

The model parameters were obtained from physical maps such as national and regional pedological maps of Italy and Basilicata [31], the III level of the CORINE Land Cover map [32], and the Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM) extracted from HydroSHEDS (hydrosheds.cr.usgs.gov/index.php).

The model was run at 1 h temporal resolution using rainfall and temperature data derived from the rainfall network of the Civil Protection for the period January 1, 2001 to March 28, 2018 [32].

This approach is straightforward and can be easily replicated elsewhere after a simple calibration against the local soil moisture and landslide datasets. It must be stated that obtained values can be affected by several errors due to model structure, parametrization, and climatic data, but at present, such an approach offers a realistic description of the expected relative saturation of the soil providing a synthesis of the state of the system according to the available information on soil texture, antecedent rainfall, and evolution of temperatures. Moreover, several evidences suggesting that the use of a physically based approach allows obtaining more robust outputs [33, 34].

2.4.1 AD2 model structure

Model simulations carried out using at least 1 year of rainfall and temperature data recorded before each landslide event to reach a reliable estimate of the relative soil water content at the date of the considered event. AD2 [34] provides a hydrological prediction that considers several hydrological components such as infiltration, surface runoff, sub-surface runoff, deep percolation, and evapotranspiration. Soil water balance is described by the following Equation [35]:

S t + Δt = S t + I t R out , t L t E t , E1

where: St is the basin soil water content at the generic instant of time t, which represents a key variable of the model influencing runoff production, leakage, and evapotranspiration; It is the infiltration; Rout,t is the sub-surface runoff production; Lt is the leakage to the groundwater; and ET is the actual evapotranspiration.

The infiltration is derived from the difference between the rainfall amount, Pt, and the surface runoff, Rt, at time t (mm):

I t = P t R t . E2

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

R t = S t S max P t ifP t P c = S max S max S t S max CS t P t S max S t ifP t > P c = S max S max S t S max CS t E3

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:

R out t = max 0 c S t S c , E4

where Sc is the threshold water content for sub-surface flow production, assumed here equal to 0.6 Smax, and c is the sub-surface coefficient, which is generally assumed 0.05.

The evapotranspiration is assumed to be a bi-linear function of the soil content and potential evapotranspiration. It may be described by the following equation:

E t = max 0 min S t 0.75 S c EP EP , E5

where EP is the potential evapotranspiration, 0.75Sc is an estimate of the water content at which the stomata closure starts to reduce the evapotranspiration.

Leakage is computed using the expression derived by Manfreda et al. [38] integrating the power-law function of leakage by Eagleson [39] over a time-step ∆t:

L t = 0 if S t S c S t 1 S max Δ tK s S max + S t 1 S max 1 β 1 / 1 β if S t > S c , E6

where Lt is the groundwater recharge in ∆t, Ks is a parameter that interprets the soil permeability at saturation, and β is a dimensionless exponent.

It must be clarified that all the parameters mentioned in the model equations reported above can be estimated using the existing literature values that associate this parameter to physical features of the area such as soil texture, land use and mean slope using [37, 40, 41].

2.5 Rainfall thresholds

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

I = α D β E7

where, I is the rainfall mean intensity (mm h1 ), 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.

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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.

Figure 4.

Daily rainfall (blue) and simulated daily soil degree saturation (red) at (a) Lauria, (b) Vietri, and (c) Pisticci from January 1, 2009 to December 31, 2016. Dark stars represent the data of the occurrence of shallow landslide events in the monitored areas.

It is noticed that most of the different landslide events (reported in the graph with a dark star) occurred after significant rainfall amounts and relatively high or moderate soil saturation degree. When the same rainfall amounts occurred in conditions of low antecedent soil moisture content, they have not produced shallow landslides. This finding aligns with the previous studies [7, 8, 9, 10, 11, 12, 13, 14, 15, 42].

This preliminary plot shows that there is an interplay between the antecedent soil moisture conditions and the amounts and the duration of the triggering rainfall. Moreover, it appears how the same degree of soil saturation and rainfall I/D conditions do not necessarily produce the same effects in different geopedological regions.

The role of the antecedent soil moisture condition on the triggering rainfall intensity is clearly shown in Figure 5 , where the rainfall intensity/duration has been plotted against the simulated antecedent soil saturation of each landslide event. This graph was developed following the trigger-cause concept of Bogaard and Greco [6] and highlights the role of both rainfall dynamics and antecedent soil moisture on the slope stability.

Figure 5.

Mean rainfall intensity/duration and the simulated initial degree of saturation for the 326 landslide events in Basilicata region (southern Italy) from 2001 to 2018.

In fact, there is a clear reduction of the rainfall intensity needed to trigger a landslide with the increase of the antecedent soil moisture. In this graph, the data grouped in the function of the rainfall duration trying to explore also the role of this additional parameter on the process. It is observed that the rainfall dynamics also matter, being shorter rainfall events more sensitive to the antecedent soil moisture respect to, while more extended events are less influenced by such parameter.

Similarly, previous studies [7, 8, 12, 43] also found a linearly decreasing trend between the mean rainfall intensity and the initial soil moisture conditions. The slope of the regression functions derived from a different subset of our database changes based on the relative duration of the rainfall events. It is higher for rainfall durations lower than 48 h, while the function becomes almost independent from the relative saturation when rainfall events have longer durations (more than 48 h). This is probably due to the nature of the long-lasting rain events, which are often characterized by a high total amount of rainfall. Results in high values both of the initial saturation degree and of low rainfall intensity that is averaged over longer periods. It must be clarified that the relative degree of saturation has been referred to as the starting time of the triggering rainfall event.

Figure 6 depicts a clear picture of the dependence between mean rainfall intensity of event of different durations and the antecedent soil moisture, where the rainfall intensity values of the 326 investigated events are associated to the simulated soil saturation degree using a color scale (from blue to yellow starting from lower to higher values of degree of saturation). This graph clearly shows that higher amounts of rainfall intensity are observed in correspondence to lower values of soil saturation and vice-versa. This tendency is not always consistent due to the presence of several spurious data relative to the occurrence of extraordinarily wet events, which resulted in both landslides and floods.

Figure 6.

Rainfall intensity as a function of the duration of the triggering rainfall events for the 326 landslide events recorded in Basilicata region (southern Italy) during the period 2001–2018. Each event is associated with a color that represents the simulated antecedent degree of saturation, whose range is given in the color bar on the right (ranging from 0 to 1). We also included the regression lines estimated for the two groups of events selected based on the antecedent soil saturation conditions. The solid line represents the regression function obtained using the observations with soil degree saturation lower than 0.70, while the dotted line represents the regression function obtained using the observations with degree soil of saturation equal or higher than 0.70.

To evaluate the role of degree of soil saturation on the regional mean rainfall intensity/duration function, we have identified two distinguished sub-samples based on the antecedent soil moisture conditions of each event. The two groups were distinguished using a sensitivity analysis, exploiting different antecedent soil saturation values. The selection was made using a subjective selection that tried to identify the most diverse groups of landslides using a given threshold of soil saturation. Therefore, we determined mean rainfall intensity/duration functions (rainfall thresholds) under middle-low antecedent soil moisture conditions that seemed to those that responded better to the data considered (soil degree saturation lower than 0.70), and moderate to high antecedent soil moisture conditions (soil degree saturation equal or higher than 0.70). In this way, it was possible to derive critical rainfall threshold functions conditional on the antecedent soil moisture conditions.

The two functions plotted in the graph ( Figure 6 ), which has significantly different slopes. This implies that they must cross somewhere in the space of rainfall intensities and event duration. In the present case, we observed that they cross in a point corresponding to the duration of about 200 h. At such duration, the impact of antecedent soil water content becomes not relevant, and this part of the curve should not be considered.

The proposed approach allows taking into account both rainfall characteristics (intensity and duration) and the antecedent soil moisture state in a specific study area, contributing to foresee a landslide event.

Of course, a methodology like this should be evaluated widely, also taking into consideration the ability of the method to distinguish between true and false alarms. Unfortunately, this field-test is challenging to be implemented in a region like Basilicata with a low density of population (as single possible observators), where a lot of landslide events are not reported or are missing.

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4. Final remarks

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].

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Acknowledgments

This work was carried out within a scientific agreement between the Civil Protection Department of Basilicata, the Interuniversity Consortium for Hydrology (CINID), and the University of Basilicata to the start-up the Basilicata Hydrologic Risk Center.

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ID Municipality Date UTM N UTM E H (mm) D (h) I (mm/h) Antecedent soil saturation Weather station Sources
1 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
2 Montalbano Jonico 20/01/2001 4,461,333,2 632,500,7 66,6 16,0 4,16 0,48 Montalbano SAL Civil Protection
3 Pisticci 20/01/2001 4,472,103,8 633,501,3 83,6 34,0 2,46 0,20 Pisticci Scalo SAL Evalmet web site
4 Rotondella 21/01/2001 4,447,722,3 629,857,3 88,6 52,0 1,70 0,78 Nova Siri Sal Evalmet web site
5 Tursi 21/01/2001 4,456,520,6 624,987,7 86,2 27,0 3,19 0,70 Tursi SI Evalmet web site
6 San Fele 09/03/2002 4,518,711,1 545,705,9 23,6 11,0 2,15 0,59 San Fele PC Civil Protection
7 Lagonegro 12/01/2003 4,441,050,6 565,797,2 148,2 140,0 1,06 0,89 Lagonegro PC Civil Protection
8 Acerenza 25/01/2003 4,516,566,8 579,515,5 43,0 43,0 1,00 0,20 Acerenza SAL Civil Protection
9 Nova Siri 25/01/2003 4,444,982,6 631,308,4 80,0 56,0 1,43 0,91 Nova Siri SAL Civil Protection
10 Pisticci 25/01/2003 4,472,200,4 631,782,9 54,2 48,0 1,13 0,82 Pisticci Scalo SAL La Nuova Basilicata magazine
11 Castronuovo di Sant’Andrea 04/02/2003 4,449,625,0 600,976,5 33,4 34,0 0,98 0,90 Roccanova PC Civil Protection
12 Muro Lucano 08/02/2003 4,512,107,1 540,718,1 105,6 172,0 0,61 0,87 Muro Lucano PC Civil Protection
13 Montescaglioso 09/09/2003 4,490,159,8 640,165,8 21,2 4,0 5,30 0,11 Montescaglioso SAL Civil Protection
14 Venosa 10/09/2003 4,535,319,9 569,252,5 35,0 8,0 4,38 0,28 Venosa SAL Civil Protection
15 Craco 12/12/2003 4,467,134,0 623,760,9 117,6 78,0 1,51 0,24 Craco PC Piccarreta et al., 2004
16 Montescaglioso 26/07/2004 4,490,946,3 640,686,5 31,4 23,0 1,37 0,16 Montescaglioso SAL II Quotidiano magazine
17 Nova Siri 26/07/2004 4,445,645,0 631,600,0 64,0 4,0 16,00 0,13 Nova Siri SAL Civil Protection
18 Valsinni 26/07/2004 4,448,638,8 624,031,9 86,6 6,0 14,43 0,13 Nova Siri SAL Civil Protection
19 Melfi 20/09/2004 4,538,361,1 555,130,7 119,4 81,0 1,47 0,09 Melfi Civil Protection
20 Montalbano Jonico 13/11/2004 4,460,236,3 634,365,1 67,2 33,0 2,04 0,23 Montalbano SAL Civil Protection
21 Montescaglioso 13/11/2004 4,491,198,4 641,477,5 126,6 33,0 3,84 0,17 Montescaglioso SAL Civil Protection
22 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
23 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
24 Tito 24/01/2005 4,493,939,0 556,898,4 40,0 17,0 2,35 0,72 Satriano di Lucania SAL villasmunta.it
25 Nemoli 26/01/2005 4,435,028,2 568,166,9 76,2 47,0 1,62 0,90 Nemoli SAL Civil Protection
26 San Fele 22/02/2005 4,518,910,4 546,690,8 77,0 80,0 0,96 0,97 San Fele PC Civil Protection
27 Bella 23/02/2005 4,512,013,7 545,332,3 59,8 54,0 1,11 0,84 Bella Casalini villasmunta.it
28 Castronuovo di Sant’Andrea 23/02/2005 4,449,641,7 600,766,2 26,0 30,0 0,87 0,64 Roccanova PC Civil Protection
29 Picerno 23/02/2005 4,498,702,2 554,494,8 64,8 74,0 0,88 0,90 Balvano PC villasmunta.it
30 Tito 24/02/2005 4,492,615,5 557,165,5 60,8 55,0 1,11 0,86 Satriano di Lucania SAL villasmunta.it
31 Calvello 25/02/2005 4,478,560,2 573,724,4 55,4 56,0 0,99 0,94 Laurenzano PC adnkronos.com
32 Rionero in Vulture 26/02/2005 4,527,555,0 558,531,6 14,2 6,0 2,37 0,72 Venosa SAL villasmunta.it
33 Potenza 26/02/2005 4,498,664,2 567,834,7 64,4 98,0 0,66 0,87 Potenza PC La Gazzetta del Mezzogiorno magazine
34 Potenza 26/02/2005 4,504,521,5 572,376,6 64,4 98,0 0,66 0,87 Potenza PC Civil Protection
35 Sant’Arcangelo 26/02/2005 4,456,637,8 609,566,5 18,6 18,0 1,03 0,68 Roccanova PC villasmunta.it
36 Gallicchio 27/02/2005 4,460,528,9 596,783,3 21,0 12,0 1,75 0,83 Guardia Perticara SAL Civil Protection
37 Laurenzana 01/03/2005 4,482,440,8 578,878,4 86,6 148,0 0,59 0,93 Laurenzana SAL Civil Protection
38 Pietrapertosa 02/03/2005 4,485,954,8 589,868,4 44,6 23,0 1,94 0,73 Campomaggiore SAL Civil Protection
39 Nemoli 07/03/2005 4,438,133,6 570,116,6 45,8 69,0 0,66 0,93 Nemoli SAL villasmunta.it
40 Barile 29/03/2005 4,533,072,0 557,767,6 25,8 29,0 0,89 0,64 Venosa SAL Civil Protection
41 Accettura 07/06/2005 4,483,267,3 598,029,9 17,4 5,0 3,48 0,23 San Mauro Forte PC Civil Protection
42 Terranova del Pollino 24/02/2006 4,426,066,5 610,796,4 79,6 78,0 1,02 0,72 Terranova del Pollino PC Civil Protection
43 Bernalda 28/02/2006 4,474,473,5 648,787,6 44,0 11,0 4,00 0,72 Bernalda SAL La Gazzetta del Mezzogiorno magazine
44 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
45 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
46 Calvello 12/03/2006 4,479,726,4 575,486,3 50,6 29,0 1,74 0,92 Laurenzano PC Basin Authority of Basilicata (AdB)
47 Montalbano Jonico 12/03/2006 4,472,188,9 640,718,0 37,8 56,0 0,68 0,64 Tursi SAL Evalmet web site
48 Rionero in Vulture 13/03/2006 4,529,892,8 556,341,5 114,7 70,0 1,64 0,91 Melfi Civil Protection
49 Venosa 13/03/2006 4,460,626,0 633,605,6 113,2 62,0 1,83 0,84 Venosa SAL Civil Protection
50 Corleto Perticara 23/03/2006 4,475,673,7 588,722,5 43,6 43,0 1,01 0,83 Guardia Perticara SAL AdB
51 Ripacandida 24/03/2006 4,529,316,2 560,944,2 30,5 58,0 0,53 0,77 Venosa SAL Civil Protection
52 Picerno 27/03/2006 4,499,289,0 554,063,1 32,8 9,0 3,64 0,91 Balvano PC Civil Protection
53 Trecchina 26/09/2006 4,430,915,4 567,168,1 133,0 72,0 1,85 0,63 Trecchina Civil Protection
54 Rivello 23/10/2006 4,533,947,8 566,654,7 140,2 50,0 2,80 0,35 Nemoli SAL IFFI Project ISPRA CNR IBAM
55 Maratea 19/12/2006 4,438,301,0 564,922,4 144,6 55,0 2,63 0,74 Maratea PC infocilento
56 Maratea 04/04/2007 4,473,927,4 630,747,2 31,4 16,0 1,96 0,91 Maratea PC infocilento
57 Tito 25/11/2008 4,429,377,2 561,850,9 77,4 120,0 0,65 0,43 Picerno PC Civil Protection
58 Grassano 11/12/2008 4,492,851,3 557,094,7 79,4 16,0 4,96 0,45 Matera PC La Gazzetta del Mezzogiorno
59 Calvello 05/01/2009 4,480,624,6 572,077,3 35,2 62,0 0,57 0,85 Laurenzano PC Civil Protection
60 Lagonegro 06/01/2009 4,502,711,0 621,802,2 109,8 65,0 1,69 0,88 Lagonegro PC lucanianet
61 Grottole 13/01/2009 4,469,982,7 634,601,8 87,8 95,0 0,92 0,59 Grottole da Serre II Quotidiano
62 Montescaglioso 13/01/2009 4,489,561,2 641,250,1 76,6 95,0 0,81 0,52 Montescaglioso SAL La Gazzetta del Mezzogiorno
63 Pisticci 13/01/2009 4,487,771,2 600,527,1 105,2 114,0 0,92 0,62 Pisticci da Castelluccio SAL Civil Protection
64 Pisticci 13/01/2009 4,495,207,5 617,127,3 104,6 113,0 0,93 0,62 Pisticci Scalo SAL La Gazzetta del Mezzogiorno
65 Potenza 13/01/2009 4,499,028,2 571,863,4 17,6 20,0 0,88 0,94 Potenza PC Civil Protection
66 Laurenzana 14/01/2009 4,477,838,6 584,070,9 38,8 35,0 1,11 0,95 Laurenzana SAL Civil Protection
67 Acerenza 23/01/2009 4,515,988,0 578,994,5 32,0 28,0 1,14 0,58 Acerenza SAL Civil Protection
68 Maratea 28/01/2009 4,441,638,3 566,470,6 295,2 185,0 1,60 0,93 Maratea PC La Siritide website
69 Montalbano Jonico 06/03/2009 4,460,493,9 632,869,2 39,2 34,0 1,15 0,74 Montalbano SAL Civil Protection
70 Pisticci 06/03/2009 4,429,791,9 561,758,0 40,6 34,0 1,19 0,85 Torre Accio PC Evalmet web site
71 Tursi 06/03/2009 4,457,119,8 624,774,7 44,4 33,0 1,35 0,82 Tursi SAL Civil Protection
72 Ripacandida 07/03/2009 4,471,046,5 639,954,0 93,5 69,0 1,36 0,62 Venosa SAL palazzosangervasio.net
73 Gallicchio 20/03/2009 4,528,878,0 562,300,7 26,2 12,0 2,18 0,67 Aliano SAL Civil Protection
74 Ripacandida 26/03/2009 4,528,798,8 561,165,2 22,8 36,0 0,63 0,79 Venosa SAL Civil Protection
75 Tricarico 24/04/2009 4,524,102,2 555,355,7 75,0 135,0 0,56 0,90 Albano di Lucania PC Civil Protection
76 San Martino D’Agri 28/04/2009 4,455,394,2 587,277,7 22,6 21,0 1,08 0,85 Sarconi SAL La Gazzetta del Mezzogiorno
77 Vietri di Potenza 22/10/2009 4,500,015,6 596,374,9 11,6 9,0 1,29 0,68 Vietri Quotidiano del sud, Metauronews
78 San Severino Lucano 18/12/2009 4,430,832,7 596,722,0 12,2 8,0 1,53 0,60 Viggianello SAL Civil Protection
79 San Chirico Raparo 07/02/2010 4,448,987,6 591,663,1 29,0 5,0 5,80 0,83 Castelsaraceno PC La Gazzetta del Mezzogiorno
80 Maratea 11/02/2010 4,427,557,0 562,256,5 56,6 76,0 0,74 0,96 Maratea PC Civil Protection
81 Viggianello 13/02/2010 4,424,622,3 591,954,8 145,8 184,0 0,79 0,93 Viggianello SAL Civil Protection
82 Latronico 20/02/2010 4,472,103,8 633,501,3 112,2 124,0 0,90 0,85 Castelsaraceno PC II Quotidiano magazine
83 Tursi 11/10/2010 4,494,906,8 541,098,2 15,8 11,0 1,44 0,22 Tursi SI Tursi tani.com
84 Ferrandina 02/11/2010 4,485,211,8 627,576,3 63,2 7,0 9,03 0,45 Ferrandina SAL Civil Protection
85 Grottole 02/11/2010 4,493,102,4 615,427,5 115,2 7,0 16,46 0,46 Grottole da Serre Civil Protection
86 Matera 02/11/2010 4,502,417,4 634,533,0 61,8 9,0 6,87 0,39 Matera PC Civil Protection
87 Montescaglioso 02/11/2010 4,454,988,6 623,153,8 45,2 12,0 3,77 0,37 Montescaglioso SAL II Quotidiano magazine
88 Pisticci 02/11/2010 4,491,414,3 544,109,2 73,4 10,0 7,34 0,44 Pisticci Scalo SAL Civil Protection
89 Rivello 02/11/2010 4,437,163,0 564,328,3 38,4 10,0 3,84 0,82 Nemoli SAL Civil Protection
90 Salandra 02/11/2010 4,486,232,1 612,353,1 108,0 6,0 18,00 0,32 San Mauro Forte PC Civil Protection
91 Tursi 03/11/2010 4,456,064,4 625,339,9 62,3 11,0 5,66 0,39 Tursi SAL Civil Protection
92 Melfi 10/11/2010 4,491,130,5 640,684,3 55,5 57,0 0,97 0,59 Melfi II Quotidiano magazine
93 Potenza 11/11/2010 4,539,263,4 551,961,4 78,8 90,0 0,88 0,57 Potenza PC La Gazzetta del Mezzogiorno magazine
94 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
95 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
96 Rivello 03/12/2010 4,512,300,7 536,385,6 248,2 271,0 0,92 0,90 Nemoli SAL II Quotidiano del sud magazine
97 Castelluccio Inferiore 03/01/2011 4,428,643,1 583,828,8 50,8 41,0 1,24 0,86 Viggianello SAL Civil Protection
98 Alianello 19/02/2011 4,437,455,5 564,407,9 36,8 10,0 3,68 0,57 Aliano SAL ANAS (National Istitution for Highways)
99 Armento 19/02/2011 4,462,213,4 588,308,3 83,4 52,0 1,60 0,83 Guardia Perticara SAL Civil Protection
100 Bernalda 19/02/2011 4,474,641,9 641,273,3 20,0 10,0 2,00 0,55 Bernalda SAL Civil Protection
101 Montalbano Jonico 19/02/2011 4,461,782,0 633,367,9 28,4 17,0 1,67 0,48 Montalbano SAL Civil Protection
102 Pisticci 19/02/2011 4,456,984,3 610,237,7 29,0 49,0 0,59 0,60 Pisticci Scalo SAL Evalmet web site
103 Tursi 19/02/2011 4,457,443,9 626,287,3 30,8 24,0 1,28 0,39 Tursi SAL Civil Protection
104 Valsinni 20/02/2011 4,447,737,2 622,905,0 22,8 16,0 1,43 0,59 Nova Siri SAL Civil Protection
105 Cancellara 01/03/2011 4,509,339,7 577,969,2 42,8 14,0 3,06 0,93 San Nicola D’Avigliano PC Civil Protection
106 Ferrandina 01/03/2011 4,485,235,5 624,000,0 99,0 19,0 5,21 0,70 Ferrandina SAL Civil Protection
107 Matera 01/03/2011 4,501,551,8 634,766,1 103,0 23,0 4,48 0,58 Matera Nord SAL Civil Protection
108 Teana 01/03/2011 4,442,436,7 598,621,8 64,2 21,0 3,06 0,90 Episcopia PC Civil Protection
109 Bernalda 02/03/2011 4,474,369,9 642,163,4 102,4 21,0 4,88 0,58 Bernaida SAL Civil Protection
110 Colobraro 02/03/2011 4,449,038,6 620,816,0 78,0 18,0 4,33 0,44 Tursi SI metapontino.it
111 Grassano 02/03/2011 4,499,277,9 609,377,8 149,7 19,0 7,88 0,66 Grassano SAL Evalmet web site
112 Irsina 02/03/2011 4,456,172,0 625,292,0 89,4 21,0 4,26 0,19 Santa Marla d’Irsi SAL pisticci.com
113 Montalbano Jonico 02/03/2011 4,471,789,5 633,568,6 153,6 24,0 6,40 0,52 Montalbano SAL Evalmet web site
114 Pisticci 02/03/2011 4,449,746,0 630,633,3 81,4 18,0 4,52 0,65 Pisticci Scalo SAL II Quotidiano, Evalmet web site
115 Rotondella 02/03/2011 4,518,058,7 611,730,4 126,6 17,0 7,45 0,65 Nova Siri SAL Civil Protection
116 Tricarico 02/03/2011 4,496,864,3 597,934,1 31,4 15,0 2,09 0,82 Albano di Lucania PC vigilfuoco.it
117 Tursi 02/03/2011 4,460,225,2 632,086,0 152,0 18,0 8,44 0,44 Tursi SAL Evalmet web site
118 Valsinni 02/03/2011 4,472,089,5 632,234,9 129,2 18,0 7,18 0,65 Nova Siri SAL Evalmet web site
119 Laurenzana 03/03/2011 4,478,608,5 582,713,6 81,6 63,0 1,30 0,90 Laurenzana SAL Evalmet web site
120 Lauria 05/03/2011 4,432,403,8 572,113,3 97,8 114,0 0,86 0,84 Nemoli SAL Civil Protection
121 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
122 Laurenzana (riatt.) 05/05/2011 4,481,944,5 581,225,0 149,8 210,0 0,71 0,78 Laurenzana SAL ANAS
123 Bella 07/10/2011 4,512,353,3 546,253,8 94,2 5,0 18,84 0,17 Bella Casalini Civil Protection
124 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 magazine
125 Matera 06/11/2011 4,502,097,8 635,438,5 34,2 2,0 17,10 0,10 Matera PC Civil Protection
126 San Fele 06/12/2011 4,515,972,5 551,039,6 23,8 30,0 0,79 0,44 San Fele PC Civil Protection
127 Latronico 15/11/2011 4,437,998,4 586,199,8 65,0 44,0 1,48 0,89 Episcopia PC Civil Protection
128 Stigliano 25/12/2011 4,473,739,4 605,248,1 46,6 8,0 5,83 0,13 Stigliano SAL Civil Protection
129 Lauria 06/01/2012 4,431,454,0 572,485,4 41,6 46,0 0,90 0,82 Nemoli SAL Civil Protection
130 Rivello (2) 20/01/2012 4,436,157,6 558,533,0 16,6 10,0 1,66 0,80 Nemoli SAL Civil Protection
131 Savoia di Lucania 04/02/2012 4,490,321,5 547,596,1 57,2 79,0 0,72 0,37 Vietri Civil Protection
132 Craco 08/02/2012 4,470,541,6 622,549,9 37,4 24,0 1,56 0,38 Craco PC Civil Protection
133 Rapone 10/02/2012 4,521,844,9 542,278,7 20,1 17,0 1,18 0,53 San Fele PC Civil Protection
134 Montemurro 11/02/2012 4,461,336,3 583,827,5 118,6 254,0 0,47 0,54 Grumento Nova Civil Protection
135 Avigliano 12/02/2012 4,509,076,7 560,722,3 29,8 39,0 0,76 0,61 Avigliano PC Civil Protection
136 Bernalda 23/02/2012 4,475,260,0 643,993,1 52,4 42,0 1,25 0,48 Bernalda SAL Civil Protection
137 Castronuovo di Sant’Andrea 23/02/2012 4,448,673,1 604,075,0 95,4 50,0 1,91 0,48 Roccanova PC basilicatanotizie.net
138 Chlaromonte 23/02/2012 4,499,219,0 647,309,0 100,4 45,0 2,23 0,51 Noepoli PC basilicatanotizie.net
139 Montalbano Jonico 23/02/2012 4,461,399,7 632,113,0 46,8 43,0 1,09 0,51 Montalbano SAL Civil Protection
140 Tursi 23/02/2012 4,456,338,6 624,326,0 53,8 43,0 1,25 0,63 Tursi SAL Civil Protection
141 Pietrapertosa 24/02/2012 4,440,620,5 607,006,8 39,2 39,0 1,01 0,61 Campomaggiore SAL Civil Protection
142 Vietri di Potenza 08/03/2012 4,495,336,2 543,776,2 10,0 7,0 1,43 0,50 Vietri Quotidiano del sud, Metauronews
143 Avigliano 09/03/2012 4,507,355,3 561,591,1 31,4 18,0 1,74 0,71 Avigliano PC Civil Protection
144 Rivello 14/04/2012 4,436,142,6 554,613,5 118,0 30,0 3,93 0,71 Nemoli SAL Civil Protection
145 Rapone 18/04/2012 45,873,9 542,457,0 50,6 KO 0,56 0,76 San Fele PC Civil Protection
146 Avigliano 20/04/2012 4,509,561,9 561,009,0 20,4 33,0 0,62 0,76 Avigliano PC Civil Protection
147 Rivello 21/04/2012 4,435,386,6 565,318,9 256,8 192,0 1,34 0,71 Nemoli SAL Civil Protection
148 Lauria 06/06/2012 4,432,952,2 570,399,6 58,2 11,0 5,29 0,76 Nemoli SAL Civil Protection
149 Teana 23/06/2012 4,442,695,5 598,168,9 29,6 3,0 9,87 0,30 Episcopia PC Civil Protection
150 Lavello 01/09/2012 4,545,949,2 568,245,9 20,8 4,0 5,20 0,02 Lavello SAL Civil Protection
151 Venosa 02/09/2012 4,460,329,4 633,421,4 141,0 32,0 4,41 0,08 Venosa SAL Civil Protection
152 Castelluccio Inferiore (3) 03/10/2012 4,427,492,7 587,471,1 9,0 2,0 4,50 0,12 Viggianello SAL Civil Protection
153 Rotonda 29/10/2012 4,423,198,3 588,915,1 111,2 28,0 3,97 0,27 Rotonda SAL Civil Protection
154 Campomaggiore 20/11/2012 4,491,247,0 590,935,1 66,8 106,0 0,63 0,24 Campomaggiore SAL Civil Protection
155 Pisticci 20/11/2012 4,533,376,1 575,381,2 73,8 67,0 1,10 0,54 Pisticci Scalo SAL pisticci.com
156 Roccanova 20/11/2012 4,453,378,3 603,847,3 96,2 90,0 1,07 0,30 Roccanova PC Civil Protection
157 Vietri di Potenza 20/11/2012 4,494,560,8 543,083,9 27,0 29,0 0,93 0,44 Vietri Quotidiano del sud, Metauronews
158 Lauria 04/12/2012 4,472,662,9 632,178,6 220,0 152,0 1,45 0,80 Nemoli SAL regione.basilicata.it
159 Barile 08/12/2012 4,532,258,7 556,671,8 26,1 12,0 2,18 0,46 Venosa SAL Civil Protection
160 San Severino Lucano 17/01/2013 4,430,496,6 596,772,9 175,6 177,0 0,99 0,85 Viggianello SAL Civil Protection
161 Lauria 18/01/2013 4,434,754,0 571,329,9 268,0 180,8 1,49 0,79 Nemoli SAL Civil Protection
162 Savoia di Lucania 18/01/2013 4,491,704,0 544,971,0 87,6 93,0 0,94 0,68 Vietri Civil Protection
163 Vietri di Potenza 18/01/2013 4,432,069,0 573,009,3 87,6 93,0 0,94 0,73 Vietri Quotidiano del sud, Metauronews
164 Sant’Angelo le Fratte 19/01/2013 4,494,125,0 541,429,1 99,6 101,0 0,99 0,63 Tito PC Fonti Cronachistiche
165 Lagonegro 21/01/2013 4,488,383,1 547,262,1 266,8 244,0 1,09 0,80 Lagonegro PC La Siritide
166 Episcopia 25/01/2013 4,445,478,1 562,707,3 366,6 362,0 1,01 0,87 Episcopia PC basilicatanotizie.net
167 San Severino Lucano 03/02/2013 4,429,936,5 596,946,0 28,2 26,0 1,08 0,90 Viggianello SAL Civil Protection
168 Armento 13/02/2013 4,462,461,3 590,367,6 12,0 10,0 1,20 0,73 Guardia Perticara SAL Civil Protection
169 Balvano 13/02/2013 4,500,024,3 543,479,7 34,4 31,0 1,11 0,74 Balvano PC Civil Protection
170 Avigliano 24/02/2013 4,508,495,2 559,778,8 18,4 27,0 0,68 0,95 Avigliano PC Civil Protection
171 Vietri di Potenza 14/03/2013 4,437,619,1 594,262,1 44,8 49,0 0,91 0,89 Vietri Quotidiano del sud, Metauronews
172 Castelluccio Inferiore 15/03/2013 4,428,429,2 584,348,2 136,0 214,0 0,64 0,84 Viggianello SAL Civil Protection
173 Lauria 21/03/2013 4,495,266,6 544,987,6 339,2 353,0 0,96 0,82 Nemoli SAL youtube
174 Castelluccio Inferiore 10/04/2013 4,428,714,4 584,450,1 12,2 3,0 4,07 0,83 Viggianello SAL Civil Protection
175 Muro Lucano 10/07/2013 4,510,677,3 541,344,5 59,2 53,0 1,12 0,36 Muro Lucano PC Civil Protection
176 Vietri di Potenza 21/07/2013 4,493,924,6 541,919,0 13,8 4,0 3,45 0,32 Vietri Quotidiano del sud, Metauronews
177 Accettura 21/08/2013 4,428,008,9 574,521,3 27,4 25,0 1,10 0,19 Campomaggiore SAL accettura online
178 Atella - Filiano 21/08/2013 4,517,964,3 559,520,9 43,6 5,0 8,72 0,21 Atella PC Civil Protection
179 Bernalda 07/10/2013 4,474,073,6 643,387,8 190,4 56,0 3,40 0,08 Bernalda SAL Quotidiano del sud, Evilmet
180 Montalbano Jonico 07/10/2013 4,461,773,7 634,506,4 84,2 2,90 2,90 0,10 Montalbano SAL Civil Protection
181 Montescaglioso 07/10/2013 4,490,286,3 641,544,0 135,2 35,0 3,86 0,07 Montescaglioso SAL Civil Protection
182 Pisticci 07/10/2013 4,487,593,1 594,710,1 105,6 57,0 1,85 0,11 Torre Accio PC Evalmet web site
183 Lauria 11/10/2013 4,470,914,4 645,919,6 43,4 51,0 0,85 0,39 Nemoli SAL infopinione.it
184 Bernalda 16/11/2013 4,472,586,8 647,193,6 148,4 136,0 1,09 0,37 Metaponto Gazzettino.it
185 Chiaromonte 16/11/2013 4,433,677,5 571,261,9 238,2 132,0 1,80 0,22 Noepoli PC La Siritide
186 Pisticci 16/11/2013 4,472,993,9 631,724,0 157,0 135,0 1,16 0,24 Pisticci Scalo SAL Civil Protection
187 San Fele 22/11/2013 4,518,881,1 545,411,7 109,8 82,0 1,34 0,42 San Fele PC Civil Protection
188 Guardia Perticara (3) 24/11/2013 4,468,838,7 592,161,9 103,6 115,9 0,90 0,61 Guardia Perticara SAL Civil Protection
189 San Severino Lucano 24/11/2013 4,429,132,5 594,345,3 133,8 100,0 1,34 0,58 Viggianello SAL lapretoria.it
190 Laurenzana 26/11/2013 4,440,683,6 606,559,5 89,0 94,0 0,95 0,61 Laurenzana SAL Civil Protection
191 Valsinni 26/11/2013 4,446,786,7 622,424,8 193,0 115,0 1,68 0,25 Nova Siri SAL Civil Protection
192 Miglionico 30/11/2013 4,492,223,8 627,675,2 20,0 8,0 2,50 0,48 Ferrandina SAL Civil Protection
193 Chiaromonte 01/12/2013 4,479,622,4 582,489,7 65,0 25,0 2,60 0,85 Episcopia PC Fonti Cronachistiche
194 Craco (7) 01/12/2013 4,470,341,9 623,096,0 156,4 29,0 5,39 0,61 Craco PC Civil Protection
195 Gallicchio 01/12/2013 4,489,127,2 591,325,4 95,6 20,0 4,78 0,54 Aliano SAL Civil Protection
196 Ginestra 01/12/2013 4,531,394,8 562,130,8 89,5 23,0 3,89 0,32 Venosa SAL Civil Protection
197 Pomarico (4) 01/12/2013 4,486,804,1 631,164,0 128,6 26,0 4,95 0,51 Ferrandina SAL Civil Protection
198 Potenza 01/12/2013 4,496,760,3 571,166,4 63,2 25,0 2,53 0,60 Potenza PC Civil Protection
199 Savoia di Lucania 01/12/2013 4,490,996,4 546,572,9 125,6 51,0 2,46 0,63 Vietri Civil Protection
200 Tricarico 01/12/2013 4,497,852,6 597,122,7 114,8 32,0 3,59 0,55 Albano di Lucania PC Civil Protection
201 Armento 02/12/2013 4,449,866,2 621,395,1 120,0 57,0 2,11 0,81 Guardia Perticara SAL Fonti Cronachistiche
202 Bernalda 02/12/2013 4,473,912,5 643,362,6 221,3 49,0 4,52 0,74 Bernalda SAL Civil Protection
203 Cirigliano 02/12/2013 4,472,564,9 599,264,5 162,0 56,0 2,89 0,59 San Mauro Forte PC Civil Protection
204 Colobraro 02/12/2013 4,461,913,3 596,895,9 140,7 26,0 5,41 0,68 Sinni a Valsinni SI emmenews
205 Garaguso/Grassano 02/12/2013 4,545,101,3 565,137,4 150,0 52,0 2,88 0,47 Grassano SAL La Gazzetta del Mezz
206 Grottole(3) 02/12/2013 4,494,492,3 616,732,9 186,8 57,0 3,28 0,48 Grottole da Serre Civil Protection
207 Guardia Perticara 02/12/2013 4,494,394,2 605,039,5 120,0 57,0 2,11 0,81 Guardia Perticara SAL Civil Protection
208 Lavello 02/12/2013 4,459,823,2 599,087,0 130,0 57,0 2,28 0,30 Lavello SAL Fonti Cronachistiche
209 Missanello 02/12/2013 4,462,814,9 590,380,8 138,6 55,0 2,52 0,54 Aliano SAL Fonti Cronachistiche
210 Montalbano Jonico 02/12/2013 4,457,687,7 626,816,0 174,8 28,0 6,24 0,66 Montalbano SAL Civil Protection
211 Pietrapertosa 02/12/2013 4,484,995,6 589,793,9 168,8 54,0 3v13 0,57 Campomaggiore SAL Civil Protection
212 Pisticci 02/12/2013 4,466,906,3 594,785,7 167,2 33,0 5,07 0,63 Pisticci Scalo SAL Civil Protection
213 Rivello 02/12/2013 4,436,644,0 564,876,7 42,2 38,0 1,11 088 Nemoli SAL Civil Protection
214 Senise 02/12/2013 4,445,683,3 609,339,4 83,0 37,0 2,24 0,81 Noepoli PC Civil Protection
215 Accettura 03/12/2013 4,488,048,3 593,820,9 211,4 58,0 3,64 0,57 Campomaggiore SAL La Gazzetta del Mezzogiorno
216 Accettura, Salandra 03/12/2013 4,481,543,0 598,406,7 197,8 63,0 3,14 0,59 San Mauro Forte PC La Gazzetta del Mezzogiorno
217 Ferrandina 03/12/2013 4,436,089,0 624,424,9 161,0 59,0 2,73 0,51 Ferrandina SAL Civil Protection
218 Ginestra 03/12/2013 4,531,331,9 561,335,0 128,5 56,0 2,29 0,32 Venosa SAL Civil Protection
219 Montescaglioso 03/12/2013 4,489,336,0 639,955,0 224,2 56,0 4,00 0,54 Montescaglioso SAL Pellicani et al., 2016
220 Trivigno (4) 03/12/2013 4,490,716,4 583,663,1 196,0 88,0 2,23 0,55 Albano di Lucania PC Civil Protection
221 Tursi 03/12/2013 4,472,751,0 632,461,7 138,6 66,0 2,10 0,58 Tursi SAL Evalmet web site
222 Viggianello 04/12/2013 4,427,183,0 589,852,6 74,8 61,0 1,23 0,85 Viggianello SAL Civil Protection
223 Sarconi 26/12/2013 4,457,355,0 632,845,6 26,0 11,0 2,36 0,85 Sarconi SAL Civil Protection
224 Aliano 21/01/2014 4,463,968,1 603,952,3 88,4 52,0 1,70 0,83 Roccanova PC Fonti Cronachistiche
225 Calvera 21/01/2014 4,454,657,0 574,796,0 193,6 69,9 2,81 0,86 Episcopia PC La Siritide
226 Castronuovo di Sant’Andrea 21/01/2014 4,450,030,0 600,486,2 87,4 60,0 1,46 0,83 Roccanova PC Civil Protection
227 Rivello 21/01/2014 4,436,820,3 566,903,7 114,8 41,0 2,80 0,86 Nemoli SAL Civil Protection
228 Senise 21/01/2014 4,444,739,2 610,507,1 52,0 50,0 1,04 0,84 Senise SAL Civil Protection
229 Latronico 22/01/2014 4,436,079,5 583,979,5 194,9 70,0 2,77 0,86 Episcopia PC La Siritide website
230 Lauria 22/01/2014 4,444,641,2 597,413,0 267,2 86,0 3,11 0,86 Nemoli SAL Fonti Cronachistiche
231 Guardia Perticara 24/01/2014 4,469,389,1 594,580,2 103,6 128,0 0,81 0,85 Guardia Perticara SAL Civil Protection
232 Maratea 28/01/2014 4,427,583,9 561,128,4 234,6 218,0 1,08 0,93 Maratea PC Civil Protection
233 Francavilla in Sinni 30/01/2014 4,433,285,2 569,276,0 247,8 238,0 1,04 0,86 Episcopia PC regione.basilicata.it
234 Guardia Perticara 02/02/2014 4,467,955,0 593,670,0 46,4 46,0 1,01 0,95 Guardia Perticara SAL Coldiretti webpage
235 Potenza 02/02/2014 4,496,094,5 568,378,3 12,4 7,0 1,77 0,91 Potenza PC Civil Protection
236 Tursi 02/02/2014 4,436,149,4 600,183,4 30,6 36,0 0,85 0,86 Tursi SAL oltrefreepress.com
237 Accettura 03/02/2014 4,482,603,0 600,241,7 105,6 77,0 1,37 0,79 San Mauro Forte PC accettura online
238 Castelluccio Inferiore 03/02/2014 4,428,421,9 583,511,5 272,6 381,0 0,72 0,78 Viggianello SAL castelluccioinferiore.comune.news
239 Gallicchio 03/02/2014 4,460,176,1 597,304,5 88,0 67,0 1,31 0,92 Roccanova PC Civil Protection
240 Latronico 03/02/2014 4,438,541,9 586,536,2 47,6 68,0 0,70 0,94 Episcopia PC Civil Protection
241 Pisticci 03/02/2014 4,458,130,1 631,793,0 62,4 68,0 0,92 0,87 Craco PC Fonti Cronachistiche magazine
242 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 Civil Protection
243 Aliano 04/02/2014 4,462,662,3 608,499,5 94,6 81,0 1,17 0,92 Roccanova PC Civil Protection
244 Gorgoglione 04/02/2014 4,471,971,5 597,509,0 86,2 74,0 1,16 0,95 Guardia Perticara SAL basilicata24.it
245 Guardia Perticara (2) 04/02/2014 4,468,759,4 593,490,1 91,8 85,0 1,08 0,95 Guardia Perticara SAL Civil Protection
246 Missanello(2) 04/02/2014 4,459,645,8 598,794,7 96,6 81,0 1,19 0,79 Aliano SAL Civil Protection
247 Noepoli 04/02/2014 4,473,633,7 637,169,5 101,6 74,0 1,37 0,90 Noepoli PC La Siritide
248 Sant’Arcangelo (3) 04/02/2014 4,454,767,9 612,151,0 94,6 81,0 1,17 0,92 Roccanova PC Civil Protection
249 Cirigliano 05/02/2014 4,476,117,9 599,525,8 124,8 90,0 1,39 0,79 San Mauro Forte PC sassiland
250 Sarconi 09/02/2014 4,473,389,5 604,347,8 22,4 30,0 0,75 0,93 Sarconi SAL Civil Protection
251 Stigliano 09/02/2014 4,438,261,8 613,374,0 105,6 98,0 1,08 0,78 Stigliano SAL Civil Protection
252 Armento 25/03/2014 4,461,174,2 591,120,8 17,4 14,0 1,24 0,81 Guardia Perticara SAL Civil Protection
253 Guardia Perticara 25/03/2014 4,455,948,6 575,716,1 30,8 16,0 1,93 0,81 Guardia Perticara SAL Civil Protection
254 Montemurro 27/03/2014 4,462,003,7 585,867,7 18,2 20,0 0,91 0,89 Grumento NOva Civil Protection
255 Stigliano 27/03/2014 4,473,506,8 604,550,1 12,0 4,0 3,00 0,68 Stigliano SAL regione.basilicata.it
256 San Severino Lucano 06/04/2014 4,429,371,0 597,800,0 68,0 49,0 1,39 0,89 Viggianello SAL sanseverinolucano.com
257 Rionero in Vulture 12/04/2014 4,530,903,2 556,997,1 29,1 16,0 1,82 0,74 Melfi Civil Protection
258 Chiaromonte 16/04/2014 4,470,704,8 595,848,0 21,0 13,0 1,62 0,87 Noepoli PC Civil Protection
259 Montemurro 17/04/2014 4,460,278,1 587,345,4 13,2 7,0 1,89 0,83 Grumento Nova Civil Protection
260 San Severino Lucano 30/04/2014 4,439,756,0 605,195,5 37,4 47,0 0,80 0,87 Viggianello SAL Civil Protection
261 Calvello 24/07/2014 4,427,437,3 600,212,2 11,0 1,0 11,00 0,31 Laurenzana SAL Civil Protection
262 Rivello 08/11/2014 4,435,948,7 554,641,6 53,4 19,0 2,81 0,30 Nemoli SAL Civil Protection
263 Rionero in Vulture (2) 31/12/2014 4,530,984,6 556,617,6 14,0 6,0 2,33 0,43 Melfi Civil Protection
264 Brienza 30/01/2015 4,481,683,5 553,437,4 89,8 24,0 3,74 0,76 Brienza PC Civil Protection
265 Chiaromonte 30/01/2015 4,442,083,4 603,091,6 36,8 53,0 0,69 0,58 Senise SAL Civil Protection
266 Senise 30/01/2015 4,444,603,9 607,201,7 36,8 53,0 0,69 0,58 Senise SAL La Siritide website
267 Castelsaraceno 31/01/2015 4,435,933,3 568,128,3 157,6 36,0 4,38 0,81 Castelsaraceno SI Civil Protection
268 Lagonegro 31/01/2015 4,429,110,9 594,311,5 284,9 53,0 5,36 0,87 Lagonegro PC Civil Protection
269 Latronico 31/01/2015 4,435,217,2 590,205,7 164,9 61,0 2,69 0,78 Episcopia PC Civil Protection
270 Lauria 31/01/2015 4,448,327,7 565,843,8 263,8 43,0 6,13 0,88 Nemoli SAL meteoweb.eu
271 Nemoli 31/01/2015 4,427,708,0 574,027,3 263,8 43,0 6,13 0,92 Nemoli SAL Civil Protection
272 San Costantino Albanese 31/01/2015 4,432,781,2 612,691,0 61,6 80,0 0,77 0,49 Terranova del Pollino PC Civil Protection
273 San Martino D’Agri 31/01/2015 4,454,871,1 589,437,1 73,2 34,0 2,15 0,74 Roccanova PC meteoweb.eu
274 Viggianello 31/01/2015 4,447,312,7 586,781,1 164,0 61,0 2,69 0,81 Episcopia PC meteoweb.eu
275 Montemurro 04/02/2015 4,461,149,4 584,066,8 144,6 132,0 1,10 0,54 Grumento Nova Fonti Cronachistiche magazine
276 San Severino Lucano 06/02/2015 4,426,488,0 595,925,0 315,8 240,0 1,32 0,82 Viggianello SAL regione.basilicata.it
277 Trecchina 08/02/2015 4,428,416,7 568,127,6 344,0 248,0 1,39 0,95 Castrocucco Fonti Cronachistiche
278 Lauria 06/03/2015 4,434,254,5 570,436,9 99,2 82,0 1,21 0,86 Nemoli SAL La Siritide website
279 Tricarico 06/03/2015 4,497,116,3 597,635,0 40,4 40,0 1,01 0,66 Albano di Lucania PC tricarico news
280 Vietri di Potenza 12/03/2015 4,494,061,7 543,185,2 9,6 5,0 1,92 0,84 Vietri Quotidiano del sud magazine
281 Terranova del Pollino 16/03/2015 4,425,194,1 607,145,4 85,2 120,0 0,71 0,84 Terranova del Pollino PC basilicatanotizie.net
282 Salandra 18/03/2015 4485066v9 613,268,6 13,8 20,0 0,69 0,72 San Mauro Forte PC Civil Protection
283 Montemurro 25/03/2015 4,461,303,7 585,075,4 31,4 62,0 0,51 0,88 Grumento Nova rainews
284 Colobraro 26/03/2015 4,449,328,3 620,218,2 30,2 9,0 3,36 0,76 Sinni a Valsinni SI Civil Protection
285 Ferrandina 27/03/2015 4,472,778,5 627,699,5 99,2 72,0 1,38 0,80 Ferrandina SAL youreporter
286 Calvera 28/03/2015 4,445,756,6 595,749,2 80,6 128,0 0,63 0,90 Episcopia PC Civil Protection
287 Castronuovo di Sant’Andrea 28/03/2015 4,449,432,7 600,769,2 83,4 80,0 1,04 0,91 Roccanova PC Civil Protection
288 Chiaromonte 28/03/2015 4,441,944,5 603,702,8 71,6 78,0 0,92 0,73 Noepoli PC Civil Protection
289 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
290 Grottole 28/03/2015 4,495,416,1 614,736,1 107,2 123,0 0,87 0,64 Grottole da Serre Civil Protection
291 Terranova del Pollino 28/03/2015 4,424,299,8 606,589,5 142,4 129,0 1,10 0,86 Terranova del Pollino PC Civil Protection
292 Anzi 06/04/2015 4,484,904,3 579,311,8 31,6 66,0 0,48 0,89 Laurenzana SAL Civil Protection
293 Sasso di Castalda 12/06/2015 4,482,734,2 556,250,0 178,8 76,0 2,35 0,59 Brienza PC Civil Protection
294 Grassano 11/08/2015 4,498,091,9 608,264,6 106,2 28,0 3,79 0,15 Grassano SAL Civil Protection
295 Grottole 11/08/2015 4,497,727,3 612,882,2 86,0 35,0 2,46 0,15 Grottole da Serre meteoweb.eu
296 Matera 11/08/2015 4,500,866,1 633,780,7 54,3 51,0 1,06 0,11 Matera PC Civil Protection
297 Melfi 11/08/2015 4,538,822,6 554,831,5 11,8 2,0 5,91 0,04 Melfi Civil Protection
298 Salandra 11/08/2015 4,487,314,1 610,376,5 21,8 26,0 0,84 0,22 San Mauro Forte PC Civil Protection
299 Venosa 20/10/2015 4,537,376,0 569,867,0 22,8 15,0 1,52 0,24 Venosa SAL Civil Protection
300 Chiaromonte 31/10/2015 4,442,678,8 603,349,5 108,0 51,0 2,12 0,30 Senise SAL Civil Protection
301 Gallicchio 06/01/2016 4,460,397,1 596,927,3 10,6 3,0 3,53 0,39 Aliano SAL La Siritide website
302 Rotonda 13/02/2016 4,423,399,8 588,125,9 117,0 91,0 1,29 0,77 Rotonda SAL Civil Protection
303 Picerno 15/02/2016 4,500,954,0 549,546,0 93,2 103,0 0,90 0,51 Balvano PC Civil Protection
304 Venosa 18/02/2016 4,535,187,4 568,991,4 10,7 8,0 1,34 0,38 Venosa SAL Civil Protection
305 Castronuovo di Sant’Andrea 13/03/2016 4,449,163,2 601,141,5 120,6 50,0 2,41 0,87 Roccanova PC Civil Protection
306 Terranova del Pollino 14/03/2016 4,425,609,1 607,819,7 63,4 46,0 1,38 0,79 Terranova del Pollino PC Civil Protection
307 Ferrandina 16/03/2016 4,472,610,0 638,594,0 101,0 150,0 0,67 0,40 Ferrandina SAL youreporter
308 Sant’Arcangelo (2 eventi) 16/03/2016 4,452,238,1 611,364,0 145,6 48,0 3,03 0,38 Aliano SAL Civil Protection
309 Colobraro 17/03/2016 4,449,758,6 620,171,4 321,4 144,0 2,23 0,63 Sinni a Valsinni SI Civil Protection
310 Pisticci 17/03/2016 4,482,354,3 626,252,6 72,1 26,0 2,77 0,57 Torre Accio PC youreporter
311 Stigliano 17/03/2015 4,472,596,0 604,150,0 238,2 142,0 1,68 0,36 Stigliano SAL Civil Protection
312 Salandra 18/03/2016 4,485,538,3 613,656,0 156,6 192,0 0,82 0,41 Salandra SI Civil Protection
313 Pisticci 25/03/2016 4,471,998,1 633,051,0 47,0 18,0 2,61 0,73 Torre Accio PC Civil Protection
314 Sant’Angelo le Fratte 26/03/2016 4,487,108,9 547,444,0 35,8 33,0 1,08 0,68 Tito PC Civil Protection
315 Miglionico 26/07/2016 4,492,210,7 626,535,0 13,6 1,0 13,60 0,17 Ferrandina PC Quotidiano della Basilicata magazine
316 Lavello 11/09/2016 4,545,245,7 564,838,8 31,8 19,0 1,67 0,21 Lavello SI vulturenews
317 Genzano 21/09/2016 4,521,410,4 590,384,9 19,4 6,0 3,23 0,33 Genzano SAL Civil Protection
318 Maratea 10/10/2016 4,426,721,0 560,774,8 113,4 70,0 1,62 0,27 Maratea PC Fonti Cronachistiche
319 Colobraro 23/01/2017 4,447,990,5 622,489,0 127,2 68,0 1,87 0,57 Sinni a Valsinni SI Civil Protection
320 Campomaggiore 25/01/2017 4,490,127,7 591,486,1 67,2 67,0 1,00 0,67 Laurenzano PC Civil Protection
321 Senise 25/01/2017 4,445,316,4 612,080,5 113,0 91,0 1,24 0,52 Noepoli PC trmtv.it
322 Avigliano 08/03/2017 4,510,867,0 560,723,4 53,6 31,0 1,73 0,86 Avigliano PC basilicata24.com
323 Montemilone 15/07/2017 4,542,486,2 581,337,0 27,4 2,0 13,70 0,05 Montemilone PC vulture news
324 Viggianello 05/03/2018 4,426,435,3 589,650,4 123,0 197,0 0,62 0,87 Rotonda Civil Protection
325 Rivello 07/03/2018 4,436,820,1 565,132,1 208,0 360,0 0,58 0,88 Lagonegro PC Civil Protection
326 Laurenzana 28/03/2018 4,479,367,5 582,543,4 108,2 192,0 0,56 0,83 Laurenzana Civil Protection

Table 1.

Rainfall-triggered landslides in Basilicata during last 20 years.

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Conflict of interest

The authors declare no conflict of interest.

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Written By

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

Submitted: 03 February 2020 Reviewed: 05 May 2020 Published: 14 August 2020