Landslide Hazard - CERM

Transcription

POLITECNICO DI MILANO
CIVIL ENGINEERING FOR RISK MITIGATION
Valmalenco Geological
Report
Course: Emergency Plans for Hydrogeological Risk
INTRIERI, Sarah - matr. 765913
LEGNANI, Lucia - matr. 764559
MEMISOGLU, Gokay Caglar - matr.763120
INDEX
Introduction ..............................................................................................................................................................3
1. Hazard Scenarios in Valmalenco...........................................................................................................................4
1.1 From Rain to Sediment Transport ..................................................................................................................5
1.2 Spriana Landslide ............................................................................................................................................6
2. Widespread Slope Erosion ....................................................................................................................................8
2.1 Values of Parameters Characterizing The Widespread Erosion .....................................................................9
2.1.1 Vegetation Cover Factor [C] and [Σ] ..................................................................................................... 11
2.1.2 Composition of Superficial Soil ............................................................................................................. 11
2.1.3 Coefficient of Soil Resistance................................................................................................................ 12
2.1.4 Coefficient Type and Extent of Erosion ................................................................................................ 12
2.1.5 Temperature – Mean Temperature [t] ................................................................................................. 12
2.1.6 Precipitation – yearly rainfall [H] .......................................................................................................... 13
2.1.7 Precipitation - rainfall depth for duration of 6 hours and return period 2 years ................................. 13
2.1.8 Water volume of rain for severe event of 100 years return period ..................................................... 14
2.1.9 Peak flow for severe event of 100 years return period........................................................................ 14
2.2 Models Used to Study the Widespread Erosion .......................................................................................... 15
2.2.1 USLE Method ........................................................................................................................................ 15
2.2.2 RUSLE Method ...................................................................................................................................... 16
2.2.3 Gavrilovic Method ................................................................................................................................ 17
2.2.4 MUSLE Method ..................................................................................................................................... 17
2.3 Results of the Model for One Single Intense Event - MUSLE ...................................................................... 18
2.4 Estimation of Material Mobilized During the Event of July 1987 ................................................................ 18
2.5 Estimation of Material Mobilized in One Year ............................................................................................ 22
2.6 Observations about Results and Uncertainties ........................................................................................... 23
2.7 Definition of Two Possible Scenarios........................................................................................................... 26
2.7.1 Normal Rainfall ..................................................................................................................................... 26
2.7.2 Intense Event (Tr=100years and Duration 24hr) .................................................................................. 27
2.7.3 Summary of the two scenarios ............................................................................................................. 29
3. Spriana Landslide ............................................................................................................................................... 30
3.1 History of Landslide ..................................................................................................................................... 30
3.2 Monitoring system ...................................................................................................................................... 30
1
3.2.1 Real-time Warning Instrumentation .................................................................................................... 32
3.2.2 Understanding the Phenomena ........................................................................................................... 34
3.3 Geometry of the Landslide .......................................................................................................................... 35
3.3.1 3D Model .............................................................................................................................................. 35
3.4 Lithology of the Landslide............................................................................................................................ 37
3.5 Behavior of the Landslide ............................................................................................................................ 38
3.6 Modelling of Spriana Landslide ................................................................................................................... 40
3.6.1 Physical Models .................................................................................................................................... 40
3.6.2 Geomechanical Model .......................................................................................................................... 40
3.6.3 Centrifugate Model .............................................................................................................................. 42
3.6.4 Mathematical Models........................................................................................................................... 44
3.6.5 Models to Study the Distribution of Fallen Material ............................................................................ 45
3.7 Definition of Scenario .................................................................................................................................. 46
4. Early Warning System ........................................................................................................................................ 49
4.1 Definitions and Functions ............................................................................................................................ 49
4.2 Monitoring and Forecasting System ............................................................................................................ 51
4.3 Definition of the Levels of Alert................................................................................................................... 53
4.3.1 Monitoring Parameters for Spriana landslide ...................................................................................... 55
4.3.2 Definition of the Levels of Alert ............................................................................................................ 58
4.4 Integration of the EWS for widespread events ........................................................................................... 59
4.5 Case study: Spriana Landslide ..................................................................................................................... 66
4.5.1 Monitoring Parameters ........................................................................................................................ 66
4.5.2 Definition of the Level of Alert ............................................................................................................. 71
Bibliografy .............................................................................................................................................................. 72
2
Introduction
The present report analyzes the main hydro-geological hazard scenarios in Valmalenco. These
hazards directly or indirectly threaten the city of Sondrio and this analysis has been made with the final
aim of giving the technical and theoretical basis for the implementation of an emergency plan for the
city.
Valmalenco is a lateral valley of Valtellina, in northern Italy, which presents a large variety of
slides and instability phenomena currently ongoing. The river that flows in Valmalenco is the Mallero
creek; during its path it is threatened by various active landslides that may supply a very large amount
of debris to the river itself and therefore to the confluence with the much larger Adda river, where the
city of Sondrio is situated. The most dangerous landslide threatening the city is Spriana landslide, the
most important rock avalanche in Valmalenco. Moreover, the whole valley is interested by diffused
erosion and accelerated sediment yield. According to these features of the valley, two analyses have
been performed: the computation of sediments due to widespread erosion and the investigation of the
possible reactivation of Spriana landslide; in the next chapter the two cases of study will be explained
with more details.
3
1. Hazard Scenarios in Valmalenco
For the present analysis, two main probable hazard scenarios for the city of Sondrio have been
considered.
The first one involves the widespread erosion phenomenon in the whole valley which can be
translated in a huge amount of sediment transported to the city by the Mallero creek, causing possible
flooding depending on the severity of the hazard. In particular two events have been considered:
erosion due to a single intense rain event and annual erosion during an year with not exceptional
precipitation.
The second one involves the activation of Spriana landslide, the complete collapse of the slope
with the consequent formation of an earth dam in the valley and, therefore, of a lake in the upstream
part that will over-top the dam causing a severe flood wave with huge sediment transport to threaten
the city of Sondrio.
In the next paragraphs, the two scenarios will be analyzed in detail on the geological side.
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1.1 From Rain to Sediment Transport
In this report the geological problem related to the production of debris and sediment in
Valmalenco will be analyzed; therefore the attention will focus on the red first part of the following
scheme (Figure 1), which describes the complete event scenario in case of water and sediment flooding
in the city of Sondrio.
In this particular case the widespread phenomenon of slope erosion will be taken into account
considering different methods and hypotheses for the computation of the total sediment volume. The
input parameters needed for the calculations are:

The morphology of the area (mean slope, characteristics of the hydrological basin, type of soil
coverage, etc.);

The rainfall data (event based or yearly based depending on the purposes of the analysis);

The already existing mitigation and prevention measures.
The final result of the analysis will be given in terms of volumes of sediment for different sub-
basins in case of different events.
5
Figure 1: Scheme for Hydrogeologic Problem 1
1.2 Spriana Landslide
Another aspect that will be analyzed in this report regards the possible collapse of Spriana
landslide and the consequent formation of an earth dam in Valmalenco that may obstruct the whole
6
valley. The attention will be therefore focused on the first part of the following scheme (red box),
which describes the complete event scenario in case of Spriana landslide collapse due to heavy rainfall
and the consequent water and sediment flooding in the city of Sondrio.
Figure 2: Scheme for Hydrogeologic Problem 2
In this particular case, the possible occurrence of the Spriana landslide collapse has been
analyzed through different models:
7

Geometric model;

Geomechanical model / Centrifuge model;

Empirical model.
The input data required by the models can be briefly summarized as:

Geometry of the landslide (including a 3D model of the landslide body);

Stratification layers and lithology;

Triggering factors (that may be the loss of cohesion due to heavy rainfall or the loss of stability
due to an earthquake): in this particular case only rainfall has been considered as triggering
factor since the probability of occurrence of a heavy rainfall event in this area is much larger
than to have an earthquake phenomenon.
The final result of the implementation will be given in terms of volume of the displaced
material of the landslide and in terms of possible shapes of the fallen mass and, consequently, of the
earth dam that may obstruct the valley.
2. Widespread Slope Erosion
The phenomenon of widespread erosion is important in Valmalenco because it’s the source of
many types of sediment that flow in the rivers decreasing the conveyance and increasing the possibility
of flood in the valley.
Two different kind of analysis has been performed to evaluate the sediment volume due both to
the annual erosion for normal precipitation and to the erosion for one single intense event. The first
analysis has been done using three different methods: USLE, RUSLE and Gavrilovic. The analysis of
the intense event, instead, has been performed using the MUSLE method. Finally an effort has been
done in order to try to evaluate which kind of minor rainfall event can cause a smaller sediments’
volume that can be considered as a critical threshold value.
In the following paragraphs first the values of the parameters characterizing the widespread
erosion are reported and then the four methods and the results will be explained. A comparison will be
also done between the results of MUSLE method and the ones of a survey made to estimate the
mobilized volume of sediments during the big event of July 1987. This comparison is useful to perform
8
a back-analysis and check the validity of the implemented MUSLE method.
2.1 Values of Parameters Characterizing The Widespread Erosion
In order to study the phenomena of the widespread erosion it’s necessary to know the values of
several parameters concerning the precipitations, the morphology and the pedology of the basin. In this
paragraph the values of these parameters are reported and in case of lack of data the assumptions that
have been done are explained.
The data used to describe the morphology and the pedology of the basin are the same both in
the analysis of annual erosion and single event erosion, but the data about rainfall and liquid discharge
are different. In the following the data used for all the four methods are described. The parameters that
refers to the same features of the basin are listed together even if utilized in different models, however
when a data is used in some specific models only, and not in others , it’s specified.
The basin of Mallero can be subdivided in 3
sub-basins homogeneous from a hydrologic point
of view (Figure 3):
- A1: from the sources to the inlet of the
Lanterna river (A=91 km2)
- A2:
the basin of the Lanterna river
(A=115 km2)
- A3: from of the inlet of Lanterna river to
the outlet of the Mallero river in the Adda
river (A=123km2)
Figure 3: Sub-basins
The erosion models will be apply separately for each sub-basin, therefore values of the
parameters, when available, are reported divided for each sub-basin.
Table 1: Length of river reaches [L]
Sub-basin Main reach (m) Secondary reaches (m)
A1
50.916
232.445
A2
67.054
172.714
A3
68.039
251.654
9
Tot (m)
283.361
239.769
319.694
The length of the rivers’ reaches is given for each sub-basin and it’s subdivided in main reach’s
contribution and secondary reaches’ contribution.
Table 2: Mean slope [S]
Sub-basin Mean Slope (degree) Mean slope (%)
30,07
A1
63
26,51
A2
55
30,78
A3
63
The following map (Figure 4) represents the slope of the basin expressed in percentage.
Figure 4: Slope of the basin expressed in percentage.
Table 3: Perimeter [0] and mean height [D]
Sub-basin Perimeter [m] Height [m]
A1
42.560
2.321
A2
46.887
2.349
A3
59.063
1.814
10
2.1.1 Vegetation Cover Factor [C] and [Σ]
The vegetation cover factor takes into account the kind of vegetation on the terrain and it
represents the protection of vegetation that prevents rain drop erosion. Values of C can be evaluated
using tables. The values of these coefficients have been chosen considering the different features of
each sub-basin (Table 4).
Table 4: Vegetation cover factor [C ] and [Σ]
Sub-basin C
Σ
A1
0.35 0.68
A2
0.078 0.50
A3
0.050 0.40
A high value has been assigned to the sub-basin A1 because in that region the percentage of
bare soil area is really high, especially in the valley of Sissone River. In the sub-basin A2 the surface is
subdivided in two portions: one composed just by bare soil and another one covered by forest, however
some pasture and riparian area with shrubs and grass are also present. In basin A3 the portion of bare
soil area is smaller than in the two other basins and the percentage of surfaced covered by forest is
bigger, few agricultural fields (3% of surface) are also present.
This subdivision of the area has been done both according to the document “Piano Programma
di Ricostruzione, Riconversione e Sviluppo della Valtellina – prog ASP/4430” and to the observation of
some satellite photos.
The two coefficients C and Σ refer to the same features of the basin but C is used in USLE,
MUSLE and RUSLE methods, Σ, instead, is used in Gavrilovic method.
2.1.2 Composition of Superficial Soil
No specific data about the composition of the shallow strata of soil in Valmalenco are available.
Thus, it has been decided to use some values typical of the area and of glacial valleys. However it is
suggested by this report that; to make some analyses and to build pedological map of the area in order
to have more precise data and to be able to make more reliable analysis.
Table 5: Composition of superficial soil
Debris
f% Silt f% Clay
Lithology
8
24
f% Sand
68
msilt
0.03
mclay
0.002
msand
0.07
fi : % of soil in the i-th class
i =arithmetic mean of soil particles of i-th class in mm
Looking at these properties (Table 5) it’s possible to compute K, the soil erodibility factor that
11
represents the propensity of a soil to be eroded. K is used in USLE; MUSLE and RUSLE methods.
2.1.3 Coefficient of Soil Resistance
According to the composition of soil previously considered, it has been chosen a value of Π
corresponding to sediments, clay and other rock with little erosion resistance (Table 6). Since in the
sub-basin A1 the area characterized by rocks with poor erosion resistance is bigger than in the other
basins it has been decided to use a slightly higher coefficient.
Sub-basin
A1
1,8
A2
1,7
A3
1,7
Table 6: Coefficient of soil resistance
Π is used in Gavrilovic method.
2.1.4 Coefficient Type and Extent of Erosion
For all the three areas it has been chosen to assign values of Φ corresponding to basins
characterized by 50-80% surface affected by erosion and landslides (Table 7). In particular slightly
higher values have been assigned to the sub-basins A1 and A2 because in the areas at higher altitude a
bigger portion of the surface is interested by soil movements.
Sub-basin
A1
0,8
A2
0,8
A3
0,7
Table 7: Φ values
Φ is used in Gavrilovic method.
2.1.5 Temperature – Mean Temperature [t]
The value of the mean temperature over an year couldn’t have been assessed using the values
recorded at the stations located in the basin because the ARPA cannot provide the data. Subsequently
reasonable values have been assigned according to the value of the mean height of each sub-basin
(Table 8).
Sub-basin
A1
A2
A3
0
0
3
Table 8: Mean Temperature
The temperature is used in Gavrilovic method only.
12
2.1.6 Precipitation – yearly rainfall [H]
The values of yearly rainfall depth have been computed using the values recorded at different
rain gauges stations (one located in Chiesa Valmalenco and two located in Sondrio) in the last years.
The data have been taken from the website of ARPA Lombardia.
An average of the three values is taken ( H=958 mm)
H is used in Gavrilovic method.
2.1.7 Precipitation - rainfall depth for duration of 6 hours and return period 2 years
The values of rainfall depth for precipitation with a duration of 6 hours and return period of 2
years have been extrapolated from depth-duration-frequency curves. These graphs has been built doing
a statistical analysis on the rain depths measured at some sections in the basin. Each curve links the rain
depth with the duration of a rainfall event and the return period (the time interval during which the
given value of rain depth is reached one time).
The data available refer to three rain gauges sited at Sondrio , Lanzada and Campo Moro. The
rain gauges used to assess these data are different than the one used for assessing the depth of yearly
rainfall because for the two groups of rain gauges different data are available. These data have been
taken from the document “Piano Programma di Ricostruzione, Riconversione e Sviluppo della
Valtellina – prog ASP/4430”.
P [mm] Sub-basin of location of rain gauge
A3
Sondrio
32,2
A2
Lanzada
29
A2
Campo Moro
28
Table 9
Since the precipitations slightly decrease from South to North it has been decided to use the
value recorded in Sondrio for the sub-basin A3 and an average value between the one of Lanzada and
the one of Campo Moro (28,5 mm) for the sub-basins A1 and A2. This value of rain depth has to be
used to compute the coefficient R, erosive factor, in USLE and RUSLE methods. R=27,38 P 2,17
(To use this formula with SI units it’s necessary to enter the value of rain depth in inches and
then to multiply the obtained value of R by 17,02)
13
2.1.8 Water volume of rain for severe event of 100 years return period
The water volume of rain for severe event can be computed multiplying the value of rain depth
on a specific sub-basin by the value of the area of the sub-basin.
In the following table the values of rain depth for a severe event of 100 years return period are
recorded, for each sub-basin different values of rain depth for different duration of rain event are
reported. These values have been taken from the document “Studi propedeutici alla valutazione
dell'impatto ambientale degli interventi del bacino del torrente Mallero”
Duration (h)
1
3
6
12
24
A1
26
42
57
78
105
A2
26
42
57
78
105
A3
27
44
59
81
109
Table 10
These values are useful to perform the analysis of intense event with MUSLE method. It has
been assumed a severe event of duration 24h to simulate a rainfall similar to the one that occurred in
July 1987.
2.1.9 Peak flow for severe event of 100 years return period
The values of peak flow for a sever event with return period of 100 at the outlet of the subbasin are reported in the Table 11. These values are necessary to implement the MUSLE model.
Qc (m3/s) Qc (m3/s)
A1 outlet
218
230
A2 outlet
287
281
A3 outlet
596
579
Table 11: Peak flow
Since there aren’t discharge gauges along the river it’s not possible to have a direct
measurement of these values, therefore this values have been estimated from the values of precipitation
using a Nash model. The two values refer to slight different formulations of the model. The values are
taken from the document “Studi propedeutici alla valutazione del’impatto ambientale degli interventi
nel bacino del torrente Mallero”.
The values reported in the table are close to the values of Qpeak computed for the strong rain
14
event of 1987 using both a corrivation model and Giandotti formula. The application of this two
methods and the detail of the results are reported in the document “Progetto esecutivo - Idrologia e
trasporto solido” of the year 1993.
2.2 Models Used to Study the Widespread Erosion
In the following the different methods used to study the slope erosion phenomena are briefly
described.
2.2.1 USLE Method
The USLE method computes the soil loss per unitary area of basin in one year [A].
The formula used is simple: A = R K L S C P
Where:
A = soil loss per unitary area per year [t/ha y]
R = erosive factor (computed as explained in par.2.1)
K = soil erodibility factor
L = parameter concerning the length of all the reaches in the basin
S = slope parameter
C = vegetation cover factor (evaluated as explained in par.2.1)
P = conservation techniques factor

The soil erodibility factor K is computed as:
Where:
fi : % of soil in the i-th class
mi =arithmetic mean of soil particles of i-th class

L is computed as:
15
Where: m empirical coefficient = 0,5 when slope is greater than 5% for small basin:
A is the basin area.
Lch is the sum of the length of all the rivers of the basin.

S is computed as
Where: θ is the slope in radians.

The P factor considers the impact of erosion defeating systems. If no actions have been done a
value of 1 is used; in other cases this value should be updated according to interventions done.
2.2.2 RUSLE Method
RUSLE uses the same parameters of USLE with an unique difference: the two parameters L and
S are combined in one parameter called LS.
A = R K LS C P

Where
For slopes where tan θ > 0.09 is possible to apply this formula:
is an empirical coefficient and comes from:
Where f is calculated from:
16
2.2.3 Gavrilovic Method
This method is based on the idea of treating separately the production of sediment and the
routing of sediment.
In the following the equations are described:

G mean annual sediment crossing closing section:

W sediment production due to erosion:
Where:

R routing coefficient:

T temperature coefficient:

Z erosion coefficient:
H: mean yearly rainfall [mm/y]
A: basin area [km2]
O: basin perimeter [km]
D: mean height of basin [km]
l: length of the main river reach
li: length of minor reaches
t: mean yearly temperature
I: mean slope of the basin
Σ: soil cover coefficient
Π: soil resistance coefficient
Φ: kind and extension of erosion
coefficient
2.2.4 MUSLE Method
USLE and RUSLE only allow to estimate the mean sediment yield over a whole year, however
a modification can be done on these two methods to consider the eroded volume for one single event.
The modified method is defined MUSLE (Modified USLE) and it computes
, the sediment yield for
one single event, as:

Where Rd is defined as;
Where:
V: water volume [m3] computed as
where H= rain depth
A= area of the basin
c = 0.8 afflux coefficient
Qp: peak flow at basin outlet [m3/s]
17
2.3 Results of the Model for One Single Intense Event - MUSLE
MUSLE method gives the estimation of sediment yield for a single intense event of return
period 100 years (Table 12). The magnitude of this considered event is comparable with the one of the
strong rainfall of July 1987.
Sub-basin
A1
A2
A3
Total Valmalenco
Volume MUSLE [m3/event]
1.282.257
350.291
308.663
1.941.211
Table 12: Results of MUSLE method - sediment yield
2.4 Estimation of Material Mobilized During the Event of July 1987
In this paragraph it will be introduced a survey done in the 1993 in order to estimate the
volume of material mobilized during the event of 1987. ( ISMES(1993) “Progetto esecutivo - Idrologia
e trasporto solido” )
The estimation has been done on the basis of geomorphologic evidences observed during the
investigations of January-September 1989. In the survey the assessment of total mobilized volume
considers both the contributions due to river bank erosion and to the slope. The transport of solid
discharge along the rivers has been also studied to put in evidence the main areas of erosion and deposit
along the river reaches.
It has been decided to focus the attention only on the estimation of the volume due to slope
erosion in order to compare it with the volume estimated with MUSLE method for an intense event of
return period 100 years. This comparison can be relevant since, as previously observed, the magnitude
of the event considered for MUSLE method is comparable with the one of the strong rainfall of July
1987.
Along some sections of the river the survey considers as slope contribution not only the slope
erosion but also the contribution of some small concentrated and shallow landslides, for this reason in
some areas where the contribution due to the concentrated landslides is quite big the two contributions
are analyzed separately. In particular in Table 13 the only contribution of slope erosion is reported.
The survey gives the results divided for sub-basins; however the division is different than the
one done applying the MUSLE method so some arrangements have to be done in order to compare the
two results.
18
River reaches
Mallero: S.Giuseppe - inlet of Sissone
Sissone
Ventina
Entovasco
Nevasco
Mallero: Chiesa Valmalenco – S.Giuseppe (*)
Furla
Curlo
Lanterna
Mallero: Ponte di Spriana – Chiesa Valmalenco (*)
Sora
Suello
Secchione
Daglia (*)
Torreggio (*)
Marveggio
Venduletto
Mallero: Sondrio – Ponte di Spriana (*)
Valdone (*)
Antognasco (*)
Bedoglio
Volume slope erosion [m3]
108.000
932.000
46.000
5.000
5.000
8.800
2.000
66.000
Tot for sub-basin A1
248.400
203.820
1.500
4.000
4.500
16.500
500
1.500
2.500
3.100
8.000
1.172.800 m3
248.400 m3
245.920 m3
Table 13: Results of the survey: contribution of slope erosion
In the rows where there’s the symbol (*) it means that for that reach it has been considered the
only contribution due to slope erosion, but the survey put in evidence that for the given reach there’s
also a contribution due to concentrated and shallow landslides. This contribution will be considered
after because it’s not relevant now for the comparison with the result of MUSLE method.
Looking at the results it’s evident that the reaches that give a bigger contribution to the
accumulation of eroded material are Sissone and Lanterna. The big contribution of the Lanterna river
can be explained by the fact that its basin is quite big, the big contribution given by Sissone instead is
due to the high erodibility of the higher part of sub-basin A1. However the Sissone sub-basin is quite
small in comparison to the whole basin and, even considering an high erodibility factor, the result of
the survey looks unexpected, thus it’s suggested to make additional analyses on this sub-basin.
.
19
Figure 5:
The results of the survey have been compared with the one computed with MUSLE method
(Table 14).
Sub-basin
A1
A2
A3
Sum
Volume slope erosion [m3]
Survey
MUSLE
1.172.800
1.282.257
248.400
350.291
245.920
308.663
1.667.120
1.941.211
Table 14: Comparision of the results of Survey and MUSLE Method
The comparison reported in the Table 14 give satisfactory results since the contributions of the
tree sub-basins computed by MUSLE method are close to the results estimated by the survey. The
differences can be due to the difficulty in assigning the exact values to some coefficients as the
erodibility factor and the surface coverture factor.
River reaches
Mallero: S.Giuseppe - inlet of Sissone
Sissone
Ventina
Entovasco
Nevasco
Mallero: Chiesa Valmalenco –
S.Giuseppe (slope erosion)
Mallero: Chiesa Valmalenco –
Volume slope
contribution [m3]
108.000
932.000
46.000
5.000
5.000
88.000
27.800
20
Volume bank
erosion [m3]
Deposit along the
reach [m3]
498.000
530.000
42.000
141.800
93.000
Balance
S.Giuseppe (conc./shal. landslide)
Furla
Curlo
Lanterna
Mallero: Ponte di Spriana – Chiesa
Valmalenco (slope erosion)
Mallero: Ponte di Spriana – Chiesa
Valmalenco (conc./shal. landslide)
Sora
Suello
Secchione
Daglia (slope erosion)
Daglia (conc./shal. landslide)
Torreggio (slope erosion)
Torreggio (concentrated)
Marveggio
Venduletto
Mallero: Sondrio – Ponte di Spriana
(slope erosion)
Mallero: Sondrio – Ponte di Spriana
(conc./shal. landslide)
Valdone (slope erosion)
Valdone (conc./shal. landslide)
Antognasco (slope erosion)
Antognasco (conc./shal. landslide)
Bedoglio
2.000
66.000
+1.279.800
248.400
+248.400
203.820
+141.800
-1.163.000
207.400
-207.400
637.000
=258.600
=41.000
126.780
1.500
4.000
4.500
2.100
31.300
16.500
624.250
500
1.500
165.000
606.000
27.000
2.500
82.000
3.100
25.300
8.000
+1.162.500
32.000
+165.000
-1.277.100
=50.400
Tot at Spriana
350.000
Table 15: Results of the survey
To conclude it can be interesting to report the complete results of the survey done about the
event of July 1987. Table 15 specifies for each sub-basin, not only the contribution of the slope erosion
to the solid discharge, but also the estimation of:
- Volume eroded from the rivers’ banks (only along Mallero river)
- Volume of sediments due to a concentrate landslide in Torreggio valley
- Volume of sediments due to some little concentrated shallow landslides
- Volume of sediments deposited along the reaches.
% of tot sediments produced
that arrives at the outlet
258.600
18,26
41.000
16,50
350.000
11,67
tot sediment produced [m3] sediment at output [m3]
A1
A2
A1+A2+A3
1.421.600
248.400
2.997.500
Table 16: Sediments that arrives at the outlet of each sub-basin
21
It can be noticed that the survey put in evidence that not all the eroded material flows along the
river and reaches the city of Sondrio, in fact a big part of it deposits along the reach and only a smaller
volume (350000 m3) reaches the Adda River. Thus it can be considered that only the 11,7% of the total
eroded material reaches the final section of the Mallero basin. The detail of the % of the total produced
sediments that arrives at the outlet of each sub-basin is reported in Table 16.
2.5 Estimation of Material Mobilized in One Year
The results of USLE, RUSLE and Gravilovic are summarized in the following tables.
Table 17 gives the result in m3/year, Table 18 instead divide the contribution in 365 days per year and
give the result in m3/s. This discharge has been computed only to give an idea of the order of
magnitude; however it must taken in account that the precipitations are not equally distributed along all
the months of the year so the given value cannot be considered an exact estimation.
For USLE and RULE it has been used the same value of C utilized in the MUSLE method in
the previous paragraph.
.Sub-basin
Produced in
A1
Produced in
A2
Produced in
A3
Tot
Valmalenco
Volume USLE
[m3/year]
Volume RUSLE
[m3/year]
Volume Gavrilovic
[m3/year]
2.326.806
1.572.067
117.343
648.469
514.284
45.893
676.607
467.928
99.675
3.651.884
2.554.281
262.912
Table 17: Results in m3/year
Sub-basin
Outlet A1
Outlet A2
Sondrio
Qsolid USLE [m3/s
0,073
0,020
0,116
Qsolid RUSLE [m3/s]
0,049
0,016
0,081
Qsolid Gavrilovic [m3/s]
0,0037
0,0014
0,0083
Table 18: Results in m3/s
The results obtained with USLE and RUSLE methods are really bigger than the ones obtained
with Gavrilovic method. The Gravilovic results can be considered more reliable because they are close
to other values estimated in literature (Table 19). In particular in the past the values of annual sediment
yield for normal rain event has been estimated both using empirical formula and mathematical models
and the results are reported in the document “Progetto esecutivo-Idrologia e trasporto solido”
22
.
Computed volume at Sondrio section[m3/year]
262.912
Gavrilovic
206.446
Empirical formula (Prof.Datei)
Mathematical model (Ing.Peviani) 170.000-270.000
Table 19: Comparison of Results
In addition the result that has been found is coherent with the one estimated for the ValTartano.
In fact in Valtartano using Gravilovic methods it can be estimated a sediment yield of 53000 m3/year,
considering that Valmalenco is six times bigger than ValTartano it’s reasonable to expect a volume of
sediment yield equal to 6x53000 m3/year
2.6 Observations about Results and Uncertainties
Some observations about the degree of uncertainties of the results reported in this chapter need
to be done.
First of all it has to be notice that the USLE, RUSLE and MUSLE methods has been originally
created to solve the problem of erosion on agricultural fields, they have been then adapted to mountain
catchments but errors in results can be expected.
Then uncertainties in the results can also arise from the difficulty in assessing the values of
some parameters. In fact the values of the morphologic features as the length of the rivers, the altitude
of the basins and the inclination of the slopes can be easily derived from DTMs and maps, but other
coefficients that refers to the vegetation, the composition and the properties of the soil or to the
mitigation measures implemented in the basin are more difficult to be assessed. In the following it will
be briefly explained in which way the uncertainties on the parameters influence the final results.
In USLE , MUSLE and RUSLE methods the coefficients more difficult to be assessed are:

the composition of the superficial soil, due to uncertainty about the pedology of the basin;

the coverture factor, due to not precise data about the percentage of area covered by different
type of vegetation and to the fact that in literature there’re lots of different tables with different
coefficients;

the precise value of rain depth due to the availability of data only for few rain gauges over the
whole basin.
23
The uncertainty about the composition of the superficial soil doesn’t affect strongly the results since
a variation of 20% of the material present in each class makes the K coefficient vary only in a small
range between values of 0,037-0,043. This variation doesn’t affect the final result in a significant
way since the order of magnitude and the first digit of the total volume of sediments don’t change.
K= 0,037
K= 0,043
USLE [m3/y]
3.108.539 3.651.884
RUSLE [m3/y]
2.174243
2554.281
MUSLE [m3/event] 1.482.433 1.741.549
For what concern the vegetation cover factor C, it can be shown that changing a little the
assumptions about the type of vegetation covering the different parts of the basin or choosing the
values from different tables available in literature the final value of C can change a lot. The change
in the value of C affects directly the final result since in these methods the total volume of
sediments is given by the product of C with the other coefficients.
As example it’s shown how the results can vary assuming a reduction of surface of bare soil (-15%)
and an increase of surface covered by forest (+15%).
No changes -15% bare soil & +15% forest
USLE [m3/y]
3.651.884
1.401.091
RUSLE [m3/y]
2.554.281
991.456
MUSLE [m3/event]
1.941.211
673.876
In USLE and RUSLE methods an error on the value of rain depth (for a precipitation of duration
6hours and return period 2 years) can make the results vary a lot, in fact a variation of the depth of
only 5 mm makes the R coefficient varying of 1/3 and since the final value of sediment is given by
the multiplication of R with the other coefficients also the final results is affected by the same
variability of the R coefficient.
In MUSLE method instead the same variation of 5mm in the input rain depth ( depth for a single
rain event of 24 hours) doesn’t affect the final result.
Rain depth used depht lowered of 5 mm
USLE [m3/y]
3.651.884
2.438.075
RUSLE [m3/y]
2.554.281
1.697.200
MUSLE [m3/event] 1.941.211
1.694.610
24
In Gavrilovic method, for the same reasons mentioned for the other methods, the coefficients
more difficult to be assessed are:

the coefficient of soil resistance and the expected extension of erosion , due to uncertainty about
the composition of superficial soil of the basin;

the vegetation cover factor;

the precise value of yearly rain depth
However, since this method differs from the previous ones because of the formulae and the tables
from which the coefficients are derived, the influence of the variability of the coefficients on the
final result can be different.
In Gavrilovic method the value of soil resistance Π can be assessed looking at a table that define it
according to the composition of the soil. For the basin under investigation a value in the range of
1,7-2 could have been chosen , in the table it’s shown the variability of the final result according to
the different values of Π.
Π= 1,7
Π= 1,8
Π= 1,9
Π= 2
Gavrilovic [m3/y] 253.271 275.944 299 256 323.189
The expected extension of erosion is difficult to be precisely defined, however for the studied basin
it can been roughly estimated that about50- 80 % of the total basin is affected by erosion. For this
range of percentages the value of Φ can be assumed equal to 0.7-0.9, the deriving variability of the
volume of sediment is represented in the table:
Φ = 0,7
Φ = 0,8
Φ = 0,9
Gavrilovic [m3/y] 246.986 273.526 300.954
To analyze the dependency of the final result on the variability of the vegetation cover coefficient
the assumptions about the type of vegetation in each sub-basin have been changed. In the table it’s
shown an example in which it has been reduced the surface of bare soil (-15%) and increased
surface covered by forest (+15%). The same example has been previously done for the three other
models, therefore it can be notice that for Gavrilovic method the final result changes but less
respect the other methods.
No changes -15% bare soil & +15% forest
Gavrilovic [m3/y] 262.912
186.956
25
Finally the variation of rain depth strongly influences the volume of sediments. In fact the final
result is computed multiplying the precipitation depth by the other coefficients so a variation in the
depth directly causes the same variation in the volume of sediments.
To conclude it must be mentioned that the models implemented don’t take in consideration the
possibility of reactivation of both shallow and deep concentrated landslides. In particular in the Mallero
basin significant concentrated landslides can be expected in Torreggio Valley, the reactivation of these
landslides would produce a consistent increase of sediment yield in the Mallero River. For the case of
intense rain event, it can be taken as reference value the estimated volume of sediments due to
concentrated landslides for the event of 1987, reported in survey previously mentioned.
2.7 Definition of Two Possible Scenarios
Considering the problem of widespread erosion, two possible scenario must be considered in
the Mallero basin: one for a condition of normal rainfall and another for an intense event (Tr=100
years).
2.7.1 Normal Rainfall
In this scenario the sediment volumes and the solid discharges that can be expected for a normal
year, during which not exceptional rainfall occurs, are considered. The weather conditions that have
been considered are:

Average yearly temperature t=0◦C in sub-basin A1 and A2 and t=3◦C in sub-basin A3;

Yearly rainfall depth h=958mm.
Sub-basin
Produced in A1
Produced in A2
Produced in A3
Tot produced in Valmalenco
Volume [m3/year]
117.343
45.893
99.675
262.912
Qsolid [m3/s]
0,0037
Outlet A1
0,0014
Outlet A2
0,0083
Sondrio
* considering Q constant during the year
Table 20
The results given in the table refer to the total volume of sediments eroded from the slope that
flows to the Mallero river every year. In order to know if this volume can affect in a relevant way the
conveyance of the river and facilitate the occurrence of flood it should be necessary to know if and
26
where the sediments deposit along the reach of the Mallero river. For this purpose an analysis of the
transport of solid discharge and of morphologic evolution of the basin should be performed.
2.7.2 Intense Event (Tr=100years and Duration 24hr)
For one single severe rain event with return period 100 years and duration 24h, the cumulated
values of rain in each sub-basin are shown in Table 21.
H [mm]
A1
105
A2
105
A3
109
Table 21
The values of peak flow for a sever event with return period of 100 at the outlet of the sub-basin
are reported in Table 22. These values have been estimated from the values of precipitation using a
Nash model.
Qc (m3/s)
A1 outlet
218
A2 outlet
287
A3 outlet
596
Table 22
Using the previous data it has been assessed that the sediment volumes and the solid discharges
that can be expected for the single severe rain event are as reported in Table 23
.
Sub-basin
Produced in A1
Produced in A2
Produced in A3
Tot produced in Valmalenco
Volume [m3/event]
1.282.257
350.291
308.663
1.941.211
Qsolid [m3/s] *
11,88
Outlet A1
4,09
Outlet A2
20,16
Sondrio
* considering Q constant during the 24 hours
Table 23: Sediment volumes and solid dischargesfor slope erosion
As previously discussed, the values refer to total volume of sediments eroded from the slope
without taking in consideration that part of this material deposits along the rivers. In addition it should
be considered that also the erosion of the rivers’ banks and shallow and deep concentrated landslides
give a contribution to the total solid discharge. Taking as reference the assessed values of sediments for
the event of 1987, volumes of sediments are estimated in Table 24.
27
A1
A2
A3
shallow/deep
concentrated
landslide [m3]
27.800
bank
contribution
[m3]
141.800
916.630
165.000
Table 24: Volumes of sediments for concentrated landslides and bank erosion
Using as reference the same survey, it can also be assessed the percentage of total eroded
materials that arrive at the outlet of each sub-basin.
Outlet A1
Outlet A2
Outlet A3
% of tot sediments produced in the upstream
basin that arrives at the outlet section
18,26
16,50
11,67
Table 25: Percentage of total sediments produced in the upstream basin
Finally it’s reported the estimation of the granulometric distribution of the sediments at the
section of Spriana for the event of 1987; this information can be relevant to study the morphologic
evolution of the river sections due to solid transport.
Figure 6: Granulometric distribution at Spriana for the event of 1987 from “ISMES(1993) – Idrologia e trasporto solido”
The potential risk given by this scenario is the deposition of a consistent volume of sediments along the
Mallero reach and the consequent reduction of conveyance capacity that can increase the possibility of
flooding at some sections and in the city of Sondrio as well. In order to study this phenomena it’s
28
necessary to perform and hydraulic analysis that takes in account a morphologic evolution of the river.
It has been decided not to produce a third scenario that considers the combination of normal
year rainfall and single intense event because the production of sediments during the intense event is
really big and the contribution of normal year rainfall can be considered as negligible.
2.7.3 Summary of the two scenarios
In the following table the volumes of sediments mobilized in the two scenarios previously described
are summarized.
Intense event
Produced in A1
Produced in A2
Produced in A3
Produced in the whole basin
Slope erosion
[m3]
1.282.257
350.291
308.663
1.941.211
Yearly precipitation
Bank erosion
[m3]
141.800
Concentrated
landslide [m3]
27.800
165.000
306.800
916.630
944.430
Table 26: description of the two scenarios
29
Slope erosion [m3]
117.343
45.893
99.675
262.912
3. Spriana Landslide
3.1 History of Landslide
The history of the Spriana landslide has been characterized by periodic movements of varying
intensity and duration, alternating with periods of quiescence. The dates of major historical events are:
1878: First movement.
1911: Significant movement due to heavy rainfall.
1916: Other movement made to stop construction of ENEL tunnel.
1919: Reconstruction of ENEL tunnel started again in the rock.
1927: Some displacement after the flood.
1960: Significant movement on the slope above the Erta and after above Cucchi.
1977–1978: Reactivation of the landslide occurred, the crown was visible.
1983: Final movement which had been seen.
3.2 Monitoring system
The Spriana Landslide area is largely furnished with different instrumentation networks for the
real-time and periodic control of the landslide movements and of the hydrological parameters. The
system has been installed at the end of the 1989 and is basically constituted by three different types of
network that give the possibility to measure the following parameters:

Shallow movements (extensometric and topographic networks);

Deep deformations (inclinometric network);

Water table levels (piezometric network);

Meteorological parameters (thermometers, barometers, rain and snow gauges).
The instrumentation that has been set up can be basically divided into two categories, on the
basis of the use that can be made out of the registered data:

Networks for the real-time warning;

Networks for the achievement of a better medium and long term understanding of the
phenomena.
The real-time warning instrumentation has been set up in order to be able to forecast the events
30
that may originate as a consequence of the
evolution of instability phenomena in the
landslide area and of the formation of
natural flood waves.
The
nowadays
configuration,
typology, number and position of the
monitoring instruments are the result of
design analysis carried out in the very first
phase of the approach to the landslide and
they are based on the knowledge of the
phenomenon that was available at that time.
The acquisition of new information through
Figure 7: Monitoring Network
the interpretation of the most recent data
and the production of further studies have
highlighted a substantial deficiency in the
available monitoring instrumentation.
Another weak point that can be highlighted is the fact that, starting from 1993 until 1998, no
normal maintenance program has been activated in the framework of the monitoring system
management. This means that the measurement carried out cannot be considered completely reliable
since there is no evidence of the correct functioning of the whole system. A maintenance and renewal
program should be included in the emergency plan that might consider the possibility of the
implementation of an early warning system for the city of Sondrio.
31
3.2.1 Real-time Warning Instrumentation
3.2.1.1 Automatic Extensometric Network
The opening width of the principal fractures on the landslide is monitored through a network of
automatic measurement reading control bases that are connected with the Local Operative Unit (USP)
through radio stations. In the whole landslide area 12 electric transducers have been placed: 6 of them
(E107 to E112) have been installed inside the exploring tunnel, forming a whole chain of
measurements; the remaining 6 (E101 to E106) have been installed on the two principal crowns. The
scan of all the measurement channels in normal conditions is carried out with a frequency of one
reading every 30 minutes.
3.2.1.2 Manual Extensometric Network
The opening width of the principal fractures on the landslide is also monitored through
extensometric measurements that are manually carried out by in-situ operators. The measuring system
consists in the determination of the variation of the distance between previously defined fixed points
(couples of threaded pins anchored to extensometric bases). These measurements are carried out on a
monthly basis in order to control the automatic extensometric network and, just in case of long
unavailability of the automatic network; the number of the manual measurements can be increased. The
precision of the measurements is of about 0.1 mm.
3.2.1.3 Piezometric Network
The measurement of the water table level inside the landslide body is performed using three
different piezometric sensors with automatic reading system: 2 open tube, 6 automatic Casagrande and
1 electric device. In particular, the stations have been placed in 6 boreholes made specifically for the
registration and acquisition of the data regarding the variations of the water table level. Some of the
piezometers installed in the late 1989 are now out of service and cannot be repaired for different
reasons; in particular, some of them have been permanently deformed by the landslide movements. The
functioning sensors are all cable-connected to the remote units (UR) which are able to ensure the
acquisition and transmission of the data to the Local Operative Unit (USP). The scan of all the
automatic measurement channels is carried out every 30 minutes. In case of long unavailability of the
automatic acquisition network, manual readings can be performed.
32
The type and code of the different devices employed can be found in the Table 26.
Open tube
Casagrande
Electric
PZ101
PZ 102
PZ 107
PZ118
PZ 106
PZ 108
PZ 110
PZ 117
Table 27: The type and code of the different devices
3.2.1.4 Topographic Network
The topographic network has been installed in order to control the movement of some
significant points located on the landslide body. The measurement system is made of 28 stations: 4
stations are located on the slope opposite to the landslide (Master network) and 24 stations are
distributed on the sliding slope (fixed points). Measurements are performed manually starting from the
4 Master stations using a teodolite or other traditional techniques or, more rarely, with GPS (global
positioning system) measuring methods. These last measurements involve just 3 fixed points of the
topographic network (C8, C12, C22), that are representative of three different altimetric positions on
the monitored slope, and 2 other fixed points placed outside the landslide area.
The readings give the possibility to obtain the values in terms of N and E coordinates for every
fixed point. The comparison between two different measurements gives the possibility to individuate
the reactivation of controlled movements and the presence of new instability phenomena on the slope.
33
3.2.1.5 Hydro-Meteorological Network
The control system for the hydro-meteorological conditions requires the acquisition and the
transmission to the Local Operative Unit (USP) of the data registered by the automatic stations
distributed all across the Mallero hydrological basin. In particular, rain gauges are especially relevant
for the control of the hydro-meteorological parameters and of the hydrological mode. The devices
employed in the system are:

12 rain gauges for the rainfall depth measurement;

3 hydrometers for the automatic measurement of the water depth in the Mallero creek (in Curlo,
Ganda di Lanzada and Torre Santa Maria);

10 thermometers for the automatic measurement of the air temperature;

7 ultrasonic snow gauges for the automatic measurement of the snow depth;

2 barometers for the automatic measurement of the atmospheric pressure;

1 wind gauge for the automatic measurement of the wind speed and direction;

13 remote units for the real-time collection and transmission of the hydro-meteorological data.
The scan of all the automatic measurement channels is performed every 30 minutes in normal
conditions. The data collected through this network represent the computational basis for the
implementation of the PREVIS hydrological forecasting model, which simulates the discharges of the
Mallero creek at the Eiffel bridge section in Sondrio.
3.2.2 Understanding the Phenomena
3.2.2.1 Inclinometric Network
An automatic inclinometric network has been installed in order to measure the deep
deformations in the landslide body. This network is constituted by sensors placed at standardized
depths. In particular, 6 inclinometric tubes have been installed: I103, I109, I111, I113, I114, and I115.
Furthermore, manual readings are scheduled on a yearly base; these manual readings require the
extraction of the entire inclinometric column. The methods followed in order to obtain the final data
and the time required to perform them make it impossible to implement a warning procedure using
these instruments.
34
3.2.2.2 Piezometric Network
Manual readings are performed on three different types of piezometers: open tube, Casagrande
and 3 Westbay Multipoint (W119, W120, and W121) devices. Anyway, the reading frequency, the
methods followed in order to obtain the final data and the time required to perform them make it
impossible either to implement a warning procedure using these instruments or to use them in the
geotechnical model. In normal conditions, the reading frequency is usually equal to one reading per
month but, in case significant variations are registered in the pore water pressure values, it is possible
to increase it within the automatic system.
3.3 Geometry of the Landslide
The landslide is located at the left side (eastern part) of Mallero River in the Valmalenco Valley
where it is bounded from northwest to Ballone di
Badoglio and from southwest to Val Calchera. The
foot of the landslide is estimated at 700 m ASML,
since there both underground water sources and
reliable bed rock on this section.
Two visible
crowns are observed at 1100 m and 1400 m;
however, by the help of the analysis of
morphological evidence and site investigations as
well as collection of movement data, occurrence of
a new crown at the level of 1700 m should be
considered (Figure 8).
Figure 8 : Boundary and visible crown of the Spriana
Landslide
3.3.1 3D Model
Hypothesis of a surface movement in rock is taking place on two different planes. The most
likely scenario regarding the slide in rock is rock avalanche and the potential of having two separate
surfaces of movement.
There are two different surfaces of movements with different characteristics, which are divided
the landslide vertically into two parts. Table 27 summarizes the characteristics of the two sides.
35
Left Part
Right Part
Glacier Fracture
Fault
Creep with slow velocity
respect to right part
Snow melt
Right part movement
Creep sliding with more
velocity respect to left part
Rainfall
Snow melt
Glacier History
Geomorphological Evidence
Geomorphology
Velocity of the movement
Triggering Factors
Predisposing Factors
Table 268: characteristics of the two sides
The surface of rupture in the right part slides on the fault with a slip direction of 260°/35°-25°. It is
known that the fault is quite dangerous since its inclination is lower than the slope and the direction is
the same. Furthermore, the fault intercepts the slope at an altitude of 700 m which is the same level of
the landslide foot. Some outcrop fractured materials
were observed in Val Calchera which could indeed
imply the existence of an outcropping fault.
The drill hole S112, which is on the external
right side of the landslide at an elevation of 1000 m,
leads to the hypothesis that the drill hole has
intercepted the fault. The reason behind this
situation is; the existence of a medium or high
Figure 9: Definition of the surface of movement for
the Spriana landslide
fractured rock in the first meters of the hole. All these evidences could be explicated by the presence of
a fault with a slip direction of 260°/35°-25° that outcrops in the Val Calchera area and the gain depth
towards the centre of the landslide body. The plane representing this fault is marked in Figure 9 with
the red color.
The left side rupture surface is less obvious due to lack of data on this section of landslide.
Furthermore, it is not a clear rupture surface like the other one. Glacial origins of the valley explicate
the weakness surface. The plane representing this fault is marked in Figure 9 with the yellow color.
Two different ways of movements have characterized those two parts. Right part movement was
characterized by the rainfall which is the most critical triggering factor for the movement where the
sliding creep is expected with high velocity. On the other hand, the left part movement would be
triggered by the right part movement. However, the slide speed of the left part would be considerably
lower than the one observed for the right part because of the unclear rupture surface.
36
Regarding the speed and the acceleration of the slide, the role of the rock bridges that tight the
stable rock with the unstable part should be considered. Some continuity is available in the rupture
surface inside the rock, which can be named as bridge rock. As the slide begins, the bridge rocks starts
to break up which increases the slide speed to a critical value. This situation leads the breakup of more
rock bridges and then a faster slide which will continue by the help of a constantly increasing speed.
Figure 10: The slide of the Spriana landslide
Consequently, this cascade effects are the reason behind for the rock avalanches to be the quickest type
of landslide. Figure 10 represents all those movements step by step.
3.4 Lithology of the Landslide
Lithology is divided into three main parts as debris, fractured rock and hard bedrock; according
to field investigation reports, drill holes results, geophysics part, exploration tunnel and monitoring
system. The materials involved in the landslide mainly belong to the colluvial and moraine deposits.
The debris and the rock are composed by mica schists of the Monte Canale gneiss formation.
Although the depth of debris material varies from 20 m to 40 m with continuity up to the
elevation of 1100 m, the presence of depth is lower starting from this elevation. Furthermore, some
rock boulders are visible at the elevations between 1100 m and 1400 m. The foot of the slope has the
maximum thickness of debris. Presence of blocks and shingle - as well as limited amount of gravel,
sand and silt with different percentages according to depth- exist in the debris material. Pebbles, gravel
and rare large blocks, immersed in full matrix of natural sandy-silt, and some amount clay are
composed in the deeper part of this layer. The instability of debris increases since this part of debris
layer has a high percentage of fine material. (PAPINI, MANNUCCI, LONGONI, CAIMI 2005).
Composition of the second layer which is located under the debris layer is fractured and altered
rock. This layer is characterized by a pronounced weakness shown by RQD results near zero.
37
Thickness of the layer varies between 60 m and 120 m. Finally, the third layer contains good quality
rock with high RQD results.
The results of monitoring tests about surface movements are coherent with the lithology. As
regards to the deep movements, the inclinometer network confirms the presence of a slow and gradual
process of deformation of slope up to a depth of 80 m. The altitudes range between 700 m and 1400 m.
These movements are along the discontinuity between the layer of fractured and weathered rocks and
the layer of good quality rocks. The measurements indicate there is not any movement below the
altitude of 700 m.
Figure 11: The result of the monioring tunnel
Furthermore, it is feasible to use the monitoring tests to obtain information about the water level
table. The level of the groundwater is estimated between an altitude of 900 m and 700 m where the
piezometers are located. It varies between 3 m and 4 m. Groundwater can sustain at a depth of 100 m
from the surface due to some infiltration in the upper part of the landslide slope. Finally, Figure 11
shows the results of monitoring tunnel and it can be taken as an example of the lithology of the slope.
3.5 Behavior of the Landslide
There are two different hypothesis scenarios; debris flow and avalanche rock. The surface
movement in debris flow is the contact with debris and the rock below. Although the debris material is
present up to 1400 m, debris flow is expected only up to the altitude of 1100 m, since the material
beyond this limit is not continuous and it is deposited in a thin layer. In addition, the slope beyond 1100
m is gentler than the one below. Performing drill holes, seismic refraction surveys and by the use of an
38
exploration tunnel it has been possible to assess that the surface of movement between the debris layer
and rock layer is continuous. Therefore, the movement of material is constant through the whole depth
of the debris layer. The predisposing causes of this case can be classified as; high gradient of the slope
and the high percentage of fine material in the debris layer near the contact debris-rock.
Reasons behind expecting a landslide are; the movement in the rock, the high degree of
fracturing in the place and the foot of the landslide which does not reach the valley floor (HENDRON
and PATTON 1989). The surface of movement is in the fractured rock with an average depth varying
between 70-80 m from the ground surface. Moreover, it can reach 120 m at peak movements. However,
it is not known if the surface of movement is continuous surface or not. Some peak displacements,
which indicate the presence of a high fractured rock layer with more shift possibilities, can be found in
the rock layer. The possibility of a surface of movement in the fractured rock layer is lead by the
presence of movements in the weak fractured rock. Furthermore, two different intensities of
movements have been recorded in both left and right parts of the landslide. Two different surfaces of
movements in fractured rock can be assumed as a consequence of this fact.
Figure 12: Spriana Landslide
The main triggering factor in the first studies was the variation of the ground water level.
39
Increase of this level in the lower part of the landslide causes a change in the deformation field in the
foot. As a consequence of comparing the results of the studies between 1977 and 2001, it is revealed
that the landslide has slowed down. This is not justified by this model behavior.
The authors Papini, Manucci, Longoni and Caimi responded the need to comprehend in a better
way the rupture model of the landslide in 2005 by making the hypothesis of a surface of movement in
rock taking place on two different planes. The planes divided the landslide vertically; right and left
parts. The right part of the landslide slides on a fault while the left part slides on a glacier rupture
surface. These two parts have different velocity of movements.
3.6 Modelling of Spriana Landslide
The models used to analysis Spriana landslide have two different aims: check the stability of the
slope computing FS and study, in case of slope failure, the size of the detached volume and its
distribution.
Taking in account a volume of soil on a slope, when the equilibrium between driving forces and
resistance forces is reached the safety factor FS assumes value equal to 1 and the mass of soil flows
downhill. According to geometry of the analyzed slope and of the valley and the geotechnical
properties of the materials the mass can accelerate and lift up on the opposite flank of the valley until
the situation of FS>1 is reached again.
To reproduce this behavior by means of numerical models only leads to big approximations,
that’s why physical models can be implemented to simulate the landslide movements. The physical
models are used to calibrate mathematical models that can evaluate the value of FS.
3.6.1 Physical Models
In the physical methods the landslide behavior is reproduced in scale, in particular in the
analysis of Spriana landslide two physical models have been used: the geomechanical model and the
centrifugal model.
3.6.2 Geomechanical Model
The geomechanical model of Sprina landslide has been built in order to analyze the falling
behavior of an unstable mass of soil, in particular the specific goals of the model are:
a) Reproduce the fall of the entire mass of the landslide;
b) Give an estimation of the heights of the cumulate volume of the fallen material in the valley;
40
c) Determine the lateral distribution of the fallen volume.
It has been analyzed the behavior of a landslide with crown at altitude 1400m and foot at
altitude 700m. The sliding surface is set at depth 50-70 m with inclination 35-37 degree.
The area that contains the sliding movements has a length of 2000m and a width of 800m.
Figure 13
41
Two models have been built in scale 1:250. A two dimensional model has been built to
determine some parameters, such the angle of sliding and the characteristic of failure of the materials,
in order to calibrate the three dimensional model. The three-dimensional model, then, analyses the
global stability of the landslide and the accumulation of material at the foot of the slope in case of
partial or global sliding.
The models are built on a mechanism that can make varying the inclination of the slope, during
the test the inclination can be gradually increased until the limit equilibrium condition is reached (
angle=35-37degree). The test is video recorded and the images are digitalized and memorized in a
computer, then, during the elaboration phase, the coordinates of different points at different stages can
be compared to evaluate the deformations and the velocities.
The input data required are:
- Topography of the area in scale 1:2000
- Shape of sliding surface
- Physical and mechanical properties of the materials.
The model has put in evidence that as soon as the value of FS becomes smaller than one the
entire mass moves and this movement create cracks similar to the ones observed on site. Increasing
more the inclination, bigger movements can be detected, in particular it can be observed that:
- The material that deposits in the Mallero River tends to flow downhill thanks to the natural
slope of the reach;
- There’s a formation of a natural dam having a height of 80m in the area of Badoglio and about
40 m in the remaining parts.
Due to the almost static conditions used to perform this kind of the test, the results refer to a
partial sliding of the material (less than half of the volume that could potentially fall), if the falling
velocity was greater the dimension of the dam would have been greater as well.
3.6.3 Centrifugate Model
In order to overcome the limits of the geomechanical model that considers almost static
condition a centrifugate model has been performed in order to:
a) Reproduce the falling behavior of the total mass due to different falling velocities. The
different velocities reproduce the decreasing of the FS for different causes that can be determined
analysisng geognostic and geologic survey.
42
b) Give an estimation of the heights of the cumulate volume of the fallen material in the valley;
c) Determine the lateral distribution of the fallen volume;
d) Study particular behavior of the potential falling mass in case of solifluction, produced by an
excess of pore pressure, or earthquake.
The geometrical features of the analyzed landslide are the same considered in the
geomechanical model.
The input data required are:
- Geometry of landslide body
- Geometry of the river bed and of the opposite flank of the valley
- Geometry and depth of the failure surface
- Granulometric composition and mechanical properties of the landslide material.
The geometry of the failure surface must be set as input since the models analyze only the postfailure behavior of the landslide. Two different three-dimensional models have been built to make two
simulations that differ for level of detail and portion of the area involved in the sliding.
Performing the centrifugate test
its’
possible to vary two parameters: the
centrifugate’s velocity and the inclination
of the slope.
The test is video recorded and the
images are digitalized and memorized in a
computer in order to analyze the errors in
the falling trajectory due to Coriolis
acceleration. At the end of the test the
geometry of the fallen volume is measured
by means of laboratory instruments.
The test gives results as:
- The falling velocity of the
landslide in function of the
Figure 14
friction angle
- The geometry of the fallen material in function of the falling velocity.
43
The first result can be used to calibrate the mathematical model; the second result is used to
study the behavior the wave of submersion.
3.6.4 Mathematical Models
The aim of mathematical methods is to define an algorithm that can describe the “model of
behavior of a landslide”. Once the algorithm is found, the “model of behavior of a landslide” can be
connected to the monitoring network to compute the safety factor in real time and to indicate conditions
of possible risk.
The mathematical models are calibrated using the results of the physical models.
Different kind of numerical analysis can be carried out:
- Limit equilibrium
- Finite elements FE
- Statistical methods for data processing
The input data that can be required by the models are:
- Topography
- Movement of a set of control points
- Data measured by pieziometers and inclinometers
- Stratigraphy
- Data from geognostic analysis and laboratory test
The results of the models are:
-Safety factors and falling velocities for limit equilibrium methods
-State of stress (from FEM)
- Link cause/effect between the environmental parameters, piezometric levels and velocities of
movement
- Definition of interpretative model of the landslide’s behavior for real time forecast
The limit equilibrium analysis has been applied on the case of Spriana landslide by a group of
authors.
Using the failure criterion of Mohr-Coulomb, they have analyzed different surfaces of rupture:
the surface of contact between debris and fractured rocks and two surfaces of discontinuities within the
fractured rocks.
44
The results show that along the surface between debris material and rocks the situation is very
near to the critical one, along the two other surfaces, instead, the safety factor is less close to one than
in the first scenario but the situation remains critical.
3.6.5 Models to Study the Distribution of Fallen Material
In literature both empirical and numerical methods to study the distribution of fallen material
are available. The empirical methods are based on formulae and diagrams derived from real case that
correlates different parameters (Volume, height of falling, gravity acceleration…) involved in the
landslide phenomena.
The numerical methods usually are one-dimensional models that give information about the
distribution of the landslide only along the direction of motion of the falling material. In these models
it’s possible to define the geometry of the slope, but huge uncertainties of the results are due to the
difficulty of defining the values of the constitutive properties of the materials.
Both the empirical models and the numerical models don’t give accurate results, however to
have an idea of the size of the distribution of fallen material for Spriana landslide in the following two
empirical formula will be applied.
The formula of Scheidegger allows
estimating
the
axial
extension
X
of
accumulated material according to the
volume of the landslide and the gravity
acceleration:
Figure 15
Another empirical formula allows to
find out the value of lateral spread S of the
landslide in the valley:
Figure 16
45
3.7 Definition of Scenario
According to the information obtained from the monitoring networks and the maps it has been
decided to consider three possible scenario of hazard. It’s assumed that potential future landslides will
occur under the same geological, geomorphological and climate condition as in the past.
The “Scenario1” takes in account a debris flow of depth 20-30m. It has been chosen to set the
crown of this landslide at 1100m (visible lower crown) because, also if debris material is present at
higher altitude, above 1100m its distribution is not constant and the thickness is small. In addition the
slope above 1100m is less steep than below.
Both “Scenario2” and “Scenario3” describe a possible rock avalanche, however the two
scenarios differ from each other because of the different elevations of the crowns and different depths.
In “Scenario2” the crown is set at the elevation of 1550 m and the average depth is at 70-80 m
from the ground surface. This elevation has been chosen according to the longitudinal section B-B that
shows that a visible crown exists at the elevation of 1400 m, detachment of material around 1400m can
cause instability in the upper part made of outcrop rocks (between 1400 m to 1550m), for this reason it
has been decided to set the crown at 1550 m ASML.
The “Scenario 3” considers a catastrophic rock avalanche with the crown at the elevation 1700
m and average depth of 90 m.
Figure 17
46
In all the three scenarios the landslide can cause an earth dam in Valmalenco stopping the river
Mallero; the difference between the three scenarios is the height and extension of the dam in the valley.
Consequently to the formation of the dam,the water coming from the river can accumulate and create a
lake. Thus a dam break can occur due either to water pressure or to erosion on top of the natural dam.
The fall down of the landslide involved different municipality with different risks. The
municipalities of Spriana, Cagnoletti e Arquino, that are close to the instable slope, can be affected by
landslide material during the fall. Instead the municipalities upstream the dam as Spriana, Torre di
Santa Maria, Marveggia and Sant Anna can be flooded in case of lake formation. The road SP 15, the
only one along Valmalenco, can be interrupted by the landslide material, if it happens the valley would
be divided in two parts and it would be impossible for the rescue teams to reach the upper one. Finally
the risk for the city of Sondrio can be a flash flood due to the dam break, this scenario would be really
catastrophic due to the high number of people involved and the velocity of the flood.
In Table 28, the characteristics of each scenario and the possible triggering factors and
predisposing causes are summarized.
Type of
failure
Geometry
Triggering
factors
Predisposing
Causes
Scenario 1
Debris flow
Scenario 2
Rock avalanche
Scenario 3
Rock avalanche
Crown: 1100m
Foot: 700m
Depht: 20-30m
-Cumulative rainfall
-Snow melt
-Seismic movement
-Water flow
-Erosion or debris
active
part (under the foot)
-Gravity
Crown: 1550m
Foot: 700m
Depht: 70-80m
-Snow melt
-Seismic movement
-Fault creeps
-Movements due to
scenario1
-Gravity
Crown: 1700m
Foot: 700m
Depht: 90m
-Snow melt
-Seismic movement
-Fault creeps
-Movements due to
scenario1or scenario2
-Gravity
- Geomorphological
evidence (steep slope)
- Fine material near the
rupture surface
- Presence of two different
surface of movement
(3Dmodel)
- Geomorphological
evidence (steep slope, deep
seated landslide)
- Same orientation of slope
and rock layer
-Presence of two different
surface of movement
(3Dmodel)
- Geomorphological
evidence (steep slope, deep
seated landslide)
- Same orientation of slope
and rock layer
Table 279: Characteristics of each scenario and the possible triggering factors and predisposing
The magnitude of the landslide for the different scenario can be computed defining its volume;
47
this can be done using a surfer program. The volume of a landslide is defined as the net volume
between the current topographic surface and surface of rupture.
The shape and dimension of the dam can be estimate using the formulae introduced in the
previous paragraph.
In particular the formula of Scheidegger allows to estimate the axial extension X of
accumulated material according to the volume of the landslide and the gravity acceleration:
The lateral spread S of the landslide in the valley can also be evaluated as:
The values of volume, axial extension and lateral spread for each scenario are reported in
Table29.
Scenario 1
Scenario2
Scenario 3
Volume [10^6 m3]
10
40
100
Axial extension X [m]
50
400
600
Lateral spread S [m]
1000
1500
2700
Table 3028: The values of volume, axial extension and lateral spread for each scenario
The geomorphologic model can give the measure of the possible height of the dam. In particular
the model has been implemented for a landslide with crown at altitude 1400m, foot at altitude 700m
and sliding surface at depth 50-70. Under these conditions, considering a partial failure of the slope
(less than half of the volume), the resulting height of the dam is 80m in the area of Badoglio and about
40 m in the remaining parts. This result can give an idea of the order of magnitude of the height of the
dam also for different conditions of the landslide.
48
4. Early Warning System
Since this is a purely technical report regarding the geological hazards in Valmalenco, the aim
of this paragraph is simply to give some specific information that can be useful for the future
implementation of a complete Early Warning System. In particular, the hazardous events have been
analyzed in their technical characteristics and some thresholds have been suggested for the monitoring
procedures.
4.1 Definitions and Functions
When dealing with hazardous natural events and hydro-geological risks, it is always important to
keep in mind that, no matter how many risk reduction and prevention measures are taken, there will
always be a so called residual risk that cannot be dealt with using a mitigation approach. On this side,
the implementation of an Early Warning System (EWS) may help in managing this residual risk within
a wider and more comprehensive risk policy.
There are many ideas and definitions of what an EWS should comprehend and of what should its
functions be. By combining all these aspects and definitions it is possible to obtain a multilevel
structure system based on four main sub-systems:

MONITORING and WARNING → hazards monitoring and forecasting to produce information
about impending hazardous events;

RISK KNOWLEDGE → risk scenarios development to figure out the potential impact of an
event (focusing on specific vulnerable groups and sectors of the society);

RESPONSE CAPABILITY → development of strategies and actions required to reduce the
losses and the damage expected from an impending hazardous event;

DISSEMINATION and COMMUNICATION → communication of timely information on an
impending event, potential risk scenarios and preparedness strategies.
49
Figure 18
In this report, only the monitoring and warning sub-system will be taken into account. The basic
function of this sub-system is to generate adequate information on a given impending hazard. This can
be achieved in two ways:

MONITORING → it consists in the observation of all those triggering and alarming
phenomena which usually come before an hazardous event and which may be its cause or the
best conditions for its occurrence;

FORECASTING → it is the process of predicting the possibility that a hazardous event will
occur with certain characteristics (e.g. time, intensity, place) on the basis of observations and by
means of suitable models,
50
Figure 19
4.2 Monitoring and Forecasting System
As regards the monitoring system for the case under analysis, it has already been described in
the related chapter.
For what concerns forecasting, the only instruments currently available are the rainfall forecasts
coupled with hydrological-hydraulic models (for example the CERM and the PREVIS models) that
give the possibility to forecast the expected values of discharges in the Mallero creek at given sections.
No landslide evolution forecast method is available at the present state and therefore the only
possibility to assess the evolution of the landslide is to analyze the interaction between the water table
variation due to certain rainfall events and the landslide movements.
In particular, the hydro-meteorologic monitoring network is connected to the Functional Centre
of the Lombardia Region through modem and radio transmission systems. The Functional Centre
provides different services:

collecting the climatic data;
51

elaborating the collected data;

making them available to the local authorities for civil protection purposes in emergency time.
The Centre currently collects real-time data from the rain gauge stations widely spread on the
territory even in case these are managed by different local authorities; for what concerns the area
coverage in terms of rain gauge stations, it can be considered to be satisfactory and properly
distributed.
Using a decision-making support system, the collected data are constantly analyzed and
elaborated by the Centre through mathematical meteorological and hydrological models, in order to
make the experts able to produce previsions and forecasts regarding the evolution of the ongoing
events. The data collected by the system are:

meteorological data for now-casting and predicting;

meteo-radar rainfall surveys;

meteosat images.
The decision-making support systems comprehend:

the statistical and geo-statistical analysis of actual and historical data;

the extraction of descriptive, anagraphic and managerial information;

risk evaluation and prevision models;

instruments for the internal and external diffusion of descriptive and synthetic information.
The collection of the data required for the territorial control and for the risk evaluation is of
paramount importance since it makes the understanding of the evolution of the phenomena simpler and
more reliable. The activities of the Functional Centre of the Lombardia Region can be also found in
details on the website http://www.protezionecivile.regione.lombardia.it/.
For the geological hazard analysis, object of this technical report, an integration between the
monitoring system (already established in the area) and the forecasting system will be implemented in
order to give as outputs different levels of alert, each of which corresponds to a certain operational
procedure to be followed (whose definition will be the object of the emergency plan development).
In this framework, it is important to establish some 'standards' for an implemented monitoring
and forecasting system to give an effective warning. In particular it is of paramount importance to
52
name:

reliability of the instruments → they should always work before and during the event in order to
be able to constantly control the situation; redundancy may be a safe approach;

suitability of the instruments and of their connection network → they must provide the type and
quality of data required by the model and, therefore, if the model needs to be updated or even
changed, there is the necessity to update or change the instruments;

quality of the collected data → it is of great importance to verify the data before inserting them
in the model (rubbish in, rubbish out);

uncertainties → they are unavoidable due to the fact that it is not possible to collect
deterministic data and by the fact that the data need to be processed to run the model.
As regards the first point (reliability of the instruments), a further comment must be made: it is
of paramount importance to underline once more the fact that the present state of the monitoring system
seems to be inadequate and it should be improved. As it has already been said, a maintenance program
should be also included in the management plan of the system. In any case, the present analysis will be
dealt with considering the available instruments and networks.
4.3 Definition of the Levels of Alert
The definition of the levels of alert and the possibility of passing from one level to another
cannot be based on data and parameters rigidly defined. It requires, instead, good interpretative skills
regarding the ongoing phenomena and their possible evolution. A proper level of flexibility can be
achieved thanks to:

an increasing development of the various competences and of the information-sharing skills
among the different local authorities;

a faster, more codified and complete flow of information between the territorial stations and the
Operational Control Rooms;

a real-time online sharing of the available monitoring data coupled with an improvement
program of the already existing hydro-meteorological stations.
What has been done for the present case is an integration among the different aspects that
characterize the hydro-geological risk for this specific area, in order to have a complete and
53
comprehensive approach for the evaluation of the levels of alert.
As a first step, it has been decided to pose the geological problem, and in particular the
landslide hazard, as core example for the definition of a framework useful to set up the thresholds
between the different levels. This analysis leads, then, to the identification of different cases that can be
grouped into four main alert levels: WHITE, GREEN, YELLOW and RED. The different levels are
separated by three alarm thresholds: PRE-ALARM, ALARM and EMERGENCY.
54
4.3.1 Monitoring Parameters for Spriana landslide
It is necessary to implement a succession of thresholds regarding the different parameters that
can be monitored, thus to be able to set up a framework for the identification of the levels of alert
related to the given event. The present scheme has been produced on the basis of the relevant
parameters and of the current monitoring system related to the Spriana landslide but they can be easily
connected and converted to other events and settings by simply changing the available parameters and
the relative thresholds.
Figure 20
55
Rainfall
As it is well known, rainfall represents one of the two main triggering factors for landslides. In
this work, the thresholds regarding the adverse meteorological conditions have been taken from the
Regione Lombardia levels:
Level
Rainfall depth (mm/24h)
Normal
< 50
Pre-alarm
50 – 80
Alarm
>80
Table 29
It is anyway important to remember that:

the rainfall values are expected (forecasted) values and not necessarily happening;

the forecasts are referred to a particularly wide area (almost all the Bergamo mountain area).
Water table level
As it is well known, the position and shape of the water table level in a certain slope exerts a
great influence on the stability of the landslide. In particular it can be established a correlation between
the water table level and the factor of safety (Fs) of a given landslide. Common software like Geoslope
can compute the influence of this parameter on the stability of the slope by means of a simple limit
equilibrium analysis. In particular, different water table levels have been imposed in the simulation and
their relative factors of safety have been computed for Spriana landslide. The details concerning the
followed procedure can be found in paragraph 4.4.4.
The water table level is, furthermore, related to the rainfall intensity. A hydrological model can
be set up in order to understand the effect of a 24h rainfall on the water table level by analyzing the
infiltration phenomenon and the underground water flow mechanisms.
On the basis of these considerations, three threshold levels have been identified for the factor of
safety:
56
Level
Factor of safety (Fs)
Water table depht [m] Water table depht [m]
Pz A
Pz B
PIEZO1
> 1,3
> 53
> 55
PIEZO2
1,1 – 1,3
36 - 53
22 - 55
PIEZO3
< 1,1
< 36
< 22
Table 30
Displacements
Another parameter that is very important to take into account is the magnitude of the
displacements that have already taken place within the phenomenon. It is of great importance to
underline that at the present state of the art it is not possible to implement a forecasting model for the
evolution of the displacement field in a landslide body. What it is possible to do is to take into account
the measurements related to the previous months in order to understand something more about the
general stability of the slope. On this side, each phenomenon has its own displacement thresholds that
need to be evaluated asking to an expert who is able to study the failure mechanisms in detail and to
give some limit values for the levels of alert. In any case, three classes have been identified for the
present EWS implementation.
Level
Recent activity
EXT0
0 mm (Stable)
EXT1
Active
EXT2
Very active
Table 31
57
4.3.2 Definition of the Levels of Alert
The procedure followed till now is able to assess the level of alert whose particular procedure
(which will be defined in the Emergency Planning phase) must be implemented in case of an event
happening. In this framework it has been decided to consider four different levels separated by three
different thresholds. The levels are hereafter described:

WHITE → no impending critical conditions, normality → classes 0 – 1 – 2;
◦ PRE-ALARM threshold:
▪ adverse meteorological conditions forecasted;
▪ pre-alarm thresholds reached in the monitoring instrumentation;

GREEN → watch out, moderate level of alert → classes 3 – 4 – 5;
◦ ALARM threshold:
▪ adverse meteorological conditions ongoing;
▪ diffused small instability events in a large area;
▪ premonitory events related to landslides and instabilities;
58
▪ alarm thresholds reached in the monitoring instrumentation;

YELLOW → high level of alert → classes 6 – 7 – 8;
◦ EMERGENCY threshold:
▪ landslides activation, urban centers and infrastructures involved;
▪ deformation limits reached in the monitoring instrumentation.

RED → very high level of alert → classes 9 – 10 – 11.
4.4 Integration of the EWS for widespread events
The early warning system that has been so far presented is suitable for concentrated events
(such as the Spriana landslide). Anyway, the possibility to integrate this framework in order to make it
available also for the analysis of widespread events, such as erosion or diffused minor landslides, has
been taken into account and is hereafter presented.
Diffused minor landslides
As regards the diffused minor landslides that threaten the greater part of the Mallero basin, in
most cases there is no specific monitoring system available and therefore it can be useful to integrate
the same EWS that has been designed for the particular case of Spriana so that it is possible to give a
certain level of alert to the local authorities on the basis of the available measurements. What is
generally available in every basin is certainly a rain gauges network; therefore in this analysis only this
type of data will be used.
In order to try to make provisions regarding the possibility of occurrence of a landslides it is
therefore necessary to first obtain all those data that can be helpful to understand the evolution of the
phenomenon in time. Studies about landslides hazard usually make previsions in terms of yearly
probability of occurrence or, alternatively, they consider nominal scales (very probable, probable and
less probable events). Anyway, for what concerns the Emergency Planning activity, it is necessary to go
deeper in the understanding of the phenomena in order to gather all those series of information that can
be helpful for the alerting procedures. This can be achieved either by analyzing the temporal series of
the events or by analyzing the temporal series of the triggering factors. Since it is very difficult to
gather proper historical information regarding the instability phenomena, it is usually simpler to
59
proceed on a much more approximated basis that takes into account the triggering causes.
The most significant triggering factors can be briefly summarized as follows:

RAINFALL → it increases the volume weight of soils, the porewater pressure and the level of
saturation; it might also cause piping and filtration phenomena. All these factors result in a loss
of cohesion of the material.

EROSION → it modifies the equilibrium profile of the slope (erosion at the foot, loading of the
slope, etc).

EARTHQUAKES → seismic phenomena can trigger instabilities by directly moving the
already unstable slopes but they may also provoke the liquefaction of soils.
As regards the particular case study this work is dealing with, it can be stated that the most valid
and proper approach for the definition of the previsions of occurrence is based on the evaluation of the
triggering thresholds related to rainfall events. Of course this method presents a series of limitations.
 The experimental equations are referred to correlations computed for shallow landslides and in
particular soil slips, debris flows, mud flows, mass sediment transport, debris torrents. Although
some graphs individuate some specific portions related to the reactivation of paleolandslides
and large-scale landslides, it must be specified that this particular type of prevision can be
applied only to shallow flow-like phenomena.
 The model does not take into account the geotechical properties of the materials involves, in
particular it disregards the presence and the type of soil coverage, the granulometry, the
thickness of the layers and the geometrical characteristics of the slope. Possible mistakes or
generalizations can be therefore due to this lack of input data.
 The model doesn't give any kind of indication regarding the localization of the single
phenomena but it considers only a critical rainfall regime for the triggering of shallow
landslides and debris flows in a widespread area where soil movements are possible.
The following graph shows the relationship that can be highlighted between the rainfall regime
and the activation of instability events.
60
Figure 21
The use of climatic data for these purposes, at a local scale and at the present state of the art,
gives the possibility to individuate the rainfall thresholds for the triggering of shallow landslides and
debris flows. For the Lombardia region (where the specific case study of this work is located) the limit
values are computed applying proper equations to the mean annual values. These equation have been
developed by the Servizio Geologico della Regione Lombardia, in particular by Ceriani, Lauzi &
Padovan (1992,1994).
One of the most used methods is the one developed by the Servizio Geologico della Regione
Lombardia (Ceriani, 1992) for the individuation of the triggering curves for shallow landslides. The
normalized rainfall intensity curve that sets up the initial stage (less that 10 landslides per km2) has
been computed using the equation
, the intermediate stage curve (10-20
landslides per km2) has been computed using the equation
and the catastrophic
stage curve (more than 20 landslides per km2) has been computed using the equation
. The normalized hourly intensity (IN) can be obtained by multiplying by 100 the hourly rainfall and
mean yearly rainfall ratio. D is the duration of the event (in hours).
The general procedure that must be activated even during an ongoing emergency can be
developed in the following way:
(a) Acquisition of rainfall data from the nearest rain gauge (from Centro Funzionale Regionale).
61
(b) Elaboration of the rainfall intensity normalized with respect to the mean annual values for the
time intervals of the most severe events (or ongoing events).
(c) Superposition of the values found at points (b) and (d) and individuation of the intersections
between the considered curves and the triggering thresholds.
(d) Analysis of the triggering thresholds and hazard assessment.
Erosion
As regards the problem of erosion, it must be highlighted that the spatial scale considered is
much different from the one that characterizes the problems of the major concentrated landslides and of
the minor diffused landslides. The area that must be taken into account is much wider and it generally
coincides with the whole hydrological basin of the considered river or creek.
Much more integrated and specifically set up monitoring system can be used; in order to better
forecast the actual and future risk scenarios, for this purpose it would be necessary an effective
monitoring system characterized by real time processing, remote observation capabilities and an high
grade of flexibility. Ground-based SAR (GB-SAR) interferometric systems, are able to provide multitemporal deformation maps of areas prone to erosion at time intervals that range from few hours up to
many days, using pure remote observations. Anyway, there is no evidence of the presence of such
instrumentation in the considered area and, in any case, it seems too expensive to be installed for the
given purposes.
Since the main triggering factor of erosion is the rain in order to monitor the production of
sediment an effort can be done to determine which rainfall event can cause a sediments’ volume that
can be considered as a critical threshold value.
Definition of thresholds for erosion
In the previous chapter it has been computed the volume of sediments Ws87 eroded from the
slope for an event of Tr=100 years and duration 24 hours, this event is comparable to the one that
occurred in July 1987. In that occasion the huge amount of sediments that flowed through the Mallero
river
caused
terrible
consequences
in
term
of
flooding
in
the
city
of
Sondrio.
In order to develop an emergency plan for Sondrio, it has been thought that it can be interesting to
62
evaluate which kind of minor rainfall event can cause a smaller sediments’ volume that can be
considered as a critical threshold value. In particular it has been decided to assume such threshold
value equal to
and it has been evaluated the kind of precipitation that can generate it. Then the
procedure has been repeated to determine the rainfall events that can produce a volume of
Ws87 to
determine an intermediate threshold between dangerous event and really dangerous event.
For this purpose the MUSLE method, previously described, has been applied in a reverse way:
the volume of sediments has been defined and the amount of rain that generates it has been computed.
In particular the MUSLE method computes the amount of eroded sediments in a catchment on the basis
of morphologic data, information about the shallow strata of soil and the vegetation covering it,
cumulated rainfall depth and peak flow liquid discharge at the outlet section of the considered basin.
The data concerning the basin have been kept equal to the one used to run the MUSLE model for the
1987 event, the rainfall depth has been made varied to obtain the target sediments’ volume and the peak
flow discharge has been estimated using the corrivation model (or linear kinematic model). That model
is based on the concept of “corrivation time” of the net rainfall from the point where the raindrop
reaches the soil to the downstream section of the basin. (For the previous analysis of the event of 1987
it hasn’t been necessary to use such formula because the values of peak flow were available in
literature).
The corrivation time To has been computed as:
in hours
Where:
L= length of the main reach in Km
H=mean height of the basin in m
Z= height of the outlet section
S= area of the basin
Solving the previous formula it results:
To= 3,8 hr for the sub-basin A1
To= 4,7 hr for the sub-basin A2
Since the corrivation times are different for the two sub-basins the analysis of the thresholds has been
carried on separately for A1 and A2.
63
The basins have been considered linear thus the area-time curve is represented by a line
The discharge at the outlet sections of the two sub-basins A1 and A2 can be computed as
,
where is the intensity of rain equal to the ratio between rain depth(h) and rain duration (θ).
If the duration of the rain is longer than the corrivation time (θ>To) the Qpeak is reached at time equal to
, if , instead, the duration of the rain is shorter than the
the corrivation time To and it results
corrivation time (θ<To) the Qpeak is reached at time θ and it results
.
In particular four rainfall events of different duration (3hr , 6hr ,12 hr and 24 hr) have been considered
and for each of them it has been computed the depth of rain that generates the volume of sediments
equal to Ws87 ,
Ws87 and Ws87. The results of the analysis are:
sed. of 1987 event
h [mm]
in 3
hours
h [mm]
in 6
hours
h [mm]
in 12
hours
h [mm]
in 24
hours
Volume
sediments
[m3/event]
72
90
127
180
1282257
66
75
106
149
350291
2/3 sed. of 1987 event
h [mm]
in 3
hours
h [mm]
in 6
hours
h [mm]
in 12
hours
h [mm]
in 24
hours
Volume
sediments
[m3/event]
50
63
88
125
854838
46
52
73
104
233527
1/3 sed. of 1987 event
h [mm]
in 3
hours
h [mm]
in 6
hours
h [mm]
in 12
hours
h [mm]
in 24
hours
Volume
sediments
[m3/event]
27
34
48
67
427419
25
28
40
56
116764
The thresholds can also be plotted in depth-duration curves and intensity-duration curves.
64
It must be noticed that the values of volume of sediments refers to the total volume of sediments
produced in each basin from the erosion of the slope but it doesn’t take in account the river bank
erosion, the shallow/concentrated landslides and the fact that part of these sediments deposit along the
river before reaching the final section.
As discussed in the paragraph 2.4 , for the event of 1987 the amount of the contribution due to
bank erosion and shallow/concentrated landslides and the quantity of deposit can be found in literature.
For the two other scenario characterized by lower rainfall the values reported in the previous
tables cannot be evaluated, anyway it can be chosen to consider the same percentages as estimation.
However that hypothesis is a really strong assumption because if the precipitation varies, even the
liquid discharge vary and thus the amount of erosion/deposition along the river banks varies as well.
To conclude it’s worthy to notice that as mentioned in the paragraph 2.6 the uncertainties linked
to MUSLE methods are high. With the purpose of reducing this uncertainty the parameters of the
model have been calibrated with observed data of 1987, thus the results can be considered quite precise
for the 1987 event but the uncertainty increases a lot for the two other scenarios. In order to try to
decrease this uncertainty it can be suggested to set some flow gauges in the river and to increase the
65
number of rain gauges in order to have a more precise correlation between rain and Q in some sections
of the river.
4.5 Case study: Spriana Landslide
The core phenomenon that has been considered in this work can also be analyzed in order to
understand the effects of the 1987 event on the stability of the Spriana landslide body. As it is known,
this event caused several floods and debris transport problems in the whole Torreggio and Mallero
basins up to the confluence with the Adda River. The event did not trigger any failure in the Spriana
landslide and therefore it is interesting to analyze how a rainfall event, that caused so many harmful
consequences in terms of erosion and sediment transport, could possibly influence the stability of a
large landslide like Spriana.
The particular scenario that has been considered is the worst case regarding the major failure of
the slope with surface of rupture at 90 m of depth and almost 108 m3 of rocks and debris in movement.
The same procedure that has been described until now, will be used for the evaluation of the
level of alert for the Spriana case during an event with the same characteristics of the one that took
place in July 1987.
4.5.1 Monitoring Parameters
Rainfall
As it has already been said, the rainfall event that has been considered is the July 1987 one that
was characterized by an average rainfall depth of 106 mm in 24 hours over the whole considered basin.
Therefore, the class that has been applied is the Alarm one.
Water table level
As regards the water table level, as it has already been said, the limit thresholds should be
individually analyzed for each particular event. For this case study, the software Geoslope has been
used in order to set up the water table levels referred to the threshold values of the factor of safety. The
software uses the limit analysis in order to establish the Fs of the slope once the surface of rupture, the
slope profile, the geometry of the layers, the soil properties and the water table surface have been
66
defined.
For what concerns the geometry of the slope profile and of the different layers, the model that
has been implemented can be found in the following picture with the position of the piezometers that
we suggest to place in order to collect the measurements useful for the implementation of the EWS.
Figure 22
It has been decided to define only two layers, one made of debris and the other one made of
fractured rock.
As regards the geometry of the layers it has been set up as follows.
Figure 23
67
For both layers, the properties that have been used are:
Layer
Type of rock/soil
RMR Q
Debris
Limestone, Marl, Dolomites
3
A
B
T
0.01 0.042 0.534 0
Fractured rock Amphilobites, Gneiss, Granites 23
0.1 0.203 0.686 - 0.0001
Table 32
And the results found for different water table surfaces are:
Fs = 1.52
Figure 24: Fs = 1.52
68

Fs = 1.34

Fs = 1.2
Figure 26: Fs = 1.2
69

Fs = 1.12

Fs = 1.00
70
In the particular case study of 1987 that has been considered in this study, even if it is difficult
to recover precise data regarding the water table depth, the level can be set as not critical and so lower
than the one that gives a factor of safety equal to 1.1 , therefore, the slope can be considered stable.
The class that has been applied is therefore the PIEZO2.
Displacement
Since there is no evidence of any relevant displacement during the event of July 1987, it has
been decided to apply the EXT0 class to the phenomenon (stable slope).
Final monitoring parameters assessment
Intersecting these three values, it has been possible to evaluate the monitoring parameter class
for the considered event. In particular, a class 7 has been applied to the phenomenon.
4.5.2 Definition of the Level of Alert
Using the framework that has been implemented in this analysis, it has been possible to assess
the level of alert that should have been issued in case the present EWS had been activated during the
1987 event. In particular, it has been assessed that a class 7 should have been applied, which
corresponds to a YELLOW level of alert.
This means that even if there have been clear evidences of adverse meteorological conditions
ongoing, of diffused small instability phenomena in a large area (erosion as well) and of monitoring
thresholds trespassing, the stability of the landslide is not completely compromised. In fact the slope
did not collapse during the 1987 event and this can be considered as a confirmation of the validity of
the framework that has been implemented in this work for the design of a proper early warning system
working on the Spriana area.
71
Bibliografy
ISMES (1989) – Piano Programma di Ricostruzione,Riconversione e Sviluppo della Valtellina e delle
zone adiacenti delle provincie di Como, Bergamo e Brescia colpite dalle avversità atmosferiche dei
mesi di Luglio ed Agosto 1987. Prog Asp/4430
ISMES (1990) – Modelli comportamento frana. Modello geomeccanico. Prog Asp/4914
ISMES (1990) – Modelli comportamento frana. Modello geomeccanico. Rapporto finale. Prog
Asp/4914
ISMES (1990) – Modelli comportamento frana. Modello in centrifuga. Prog Asp/4914
ISMES – Modelli comportamento frana. Modello in centrifuga. Disegni
ISMES (1990) – Modelli comportamento frana. Modello matematico. Prog Asp/4914
ISMES (1990) – Modelli comportamento frana. Modello matematico. Disegni. Prog Asp/4914
ISMES (1990) – Indagine geofisica mediante sismica a rifrazione eseguita in località Case Cucchi nel
comune di Spriana (So)
ISMES (1990) – Indagini ed ulteriori studi nel versante in frana. Cunicolo esplorativo. Prog. Asp4914.
L.NOE, R. LANCELLOTTA, F. LIMONETTI, A. MARCELLO - Val Malneco : inquadramento
geologico, problemi di stabilità , criteri di intervento , Milano,1990
ISMES (1991) – Modelli matematici di comportamento della frana. Aggiornamento al 30.04.1991.
Centro di monitoraggio, Sondrio
ISMES (1992) – Idrologia e trasporto solido. Prog Asp/4914
ISMES (1993) – Frana di Spriana. Piano protezione civile Rapporto finale.. Prog Asp/4914
ISMES– Rapporti complementari; Aree di invasione della frana. Rapporto finale.. Prog Asp/4914
ISMES – Disegni. Centro di monitoraggio, Sondrio
RADAELLI, CASTELLOTTI – Indagini geognostiche. Centro di monitoraggio, Sondrio
ISMES – Schemi interpretativi del rischio geologico e idrogeologico, definizione dei livelli di soglia e
delle procedure di analisi dei dati strumentali, nell’area della frana di Spriana. Prog. Asp/4914.
ISMES – Considerazioni sull’evoluzione del versante durante il periodo di monitoraggio:1990-1992
CENTRO MONITORAGGIO GEOLOGICO – Sintesi dei dati registrati nel periodo 1990-1998
QUADRO DI RIFERIMENTO AMBIENTALE - Studi propedeutici alla valutazione dell'impatto
ambientale degli interventi del bacino del torrente Mallero
Piano comunale per la protezione civile (2000)
Piano di emergenza intercomunale
Piano comunale per il coordinamento delle operazioni di evacuazione e di soccorso della popolazione
in caso di eventi calamitosi concernenti il torrente Mallero (1987)
Sito web ARPA Lombardia:
www.arpalombardia.it
Sito web ARPA Lombardia – centro monitoraggio geologico:
Http://89.118.97.243/webcmgfrontend/default.asp
72

Landslide Hazard - CERM

Transcription

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