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NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Non-Susceptible Landslide Areas in Italy and in the Mediterranean Region
Massimiliano Alvioli1, Francesca Ardizzone1, Fausto Guzzetti1, Ivan Marchesini1, and Mauro
Rossi1,2
1) Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, I-06128 Perugia, Italy 2) Universita` degli Studi di Perugia, Dipartimento di Scienze della Terra, Piazza Universita`, 1, I-06123, Perugia, Italy
NH 3.8, Vienna, 02-05-2014
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Why this work?
● A large number of methods and techniques were proposed and tested to ascertain landslide susceptibility
● A few attempts were made to define landslide susceptibility at the continental and even at the global scale (e.g. Van Den Eeckhaut et al., 2012; Gunther et al., 2013)
● Little effort was made to define where landslides are not expected, i.e. where landslide susceptibility is null, or negligible (Godt et al. 2012)
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
What we did
● In this work, we discuss some methods for the definition of non-susceptible landslide areas, at the synoptic scale.
● We apply the best method in Italy and to the landmasses surrounding the Mediterranean Sea
10° W 40° E
10° W 40° E
50° N
30° N
50° N
30° N
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Data
● Digital terrain elevation: – 40 SRTM data tiles
covering the Mediterranean area
● Landslide information:– 13 inventories of
polygons including geomorphological, event, and multi-temporal inventory maps in Italy
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
SRTM
● We exploited two morphometric parameters computed from the SRTM DEM:– relative relief R (in meters)
– terrain slope S (in degrees)
● We computed – R using a circular moving window with a diameter of 15 cells
– S in a 3 × 3 - cell square moving window.
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Landslide inventories
● The maps cover 8.9% of the Italian territory
● 93,538 landslides● Mapped area: 2726 km2 ● Landslide area is 10.1% of
the mapped areas– Rotational and translational
slides,
– Earth flows
– Complex and compound movements
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Methods
● We defined the areas that are expected to be non susceptible to landslides in Italy, using two different methods:
1.The first method is derived from the work of Godt et al. (2012) (method I)
2.The second method was developed specifically for this work (method II)
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Method I
● We computed the frequency distribution of the relative relief R and of the terrain slope S for all the grid cells in each single landslide in each inventory.
0 10 20 30 40 50 60Local Terrain Slope, S [°]
Em
piric
al C
umul
ativ
e P
roba
bilit
y
0.2
0.4
0.6
0.8
1.0
0.0
0 20 40 60Local Terrain Slope, S [°]
0.2
0.4
0.6
0.8
1.0
0.0
● For each inventory, we prepared the Empirical Cumulative Distribution Functions (ECDFs) for the 50th percentile of the two terrain variables, R and S, in all the mapped landslides.
● Next we arbitrary chose the 5% cumulative frequency of both slope and relief of the ECDFs of the different inventories
0.0
50.
05
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Method I
● Plot of the 5th percentile pairs (R50, S50).
● Data fitting with a Linear Regression model (LR):
Non susceptibleSusceptib
le
S50 = 3.448 + 0.040 R50
A-M: landslide inventories
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Method I
● We used the Linear Regression model (LR) to prepare the binary zonation of the Italian territory.
● The orange color shows areas where landslide susceptibility is expected to be null or negligible.
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Method II
● 354,406 (R,S) pairs of slope and relief values
● Corresponding to all the cells inside the landslide polygons
● We searched for a lower threshold to the cloud of points.
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Method II
● Quantile regression ● We tested
– A Quantile Linear Regression model (QLR)
– A Quantile Non-Linear regression (exponential) model (QNL)
● We instructed the quantile regression to model the 5th percentiles i.e., to search for a regression line that would leave, below the line, 5% of the empirical data points.
95% of data points
5% of data points
?
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Method II
● Quantile Linear Regression model (QLR) resulted in:
S = 0.245 + 0.032 R
● Quantile Non-Linear regression model (QNL) resulted in the exponential function:
S = 3.539*e(0.0028 × R)
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
QLR
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
QLR
Non susceptible
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
QLR, QNL
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
QLR, QNL
Non susceptible
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
LR, QLR, QNL
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
LR, QLR, QNL
Non susceptible
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
QLR zonation
● We prepared a zonation, showing non-susceptible areas in Italy, both using the quantile linear model QLR ...
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
QNL zonation
… and using the quantile non linear model QNL
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Models performance
● Percentage of non-susceptible Italian territory:– LR: 62%,
– QLR: 22%,
– QNL: 42%,
● Quantile Linear model (QLR) is very conservative respect to the other two models
LR QLR QNL
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Models performance
● Test dataset: IFFI, Italian Landslide Inventory (Trigila et al., 2010).
● Obtained though the IFFI WMS service and setting ground resolution at 5 m × 5 m
● The dataset contains: – falls and/or topples, – slow flows, – rapid flows, – complex movements,– rotational/translational slides, – lateral spreads, – sinkholes, – undefined slope movements.
From Trigila et al., 2010.
Progetto IFFI - ISPRA - Dipartimento Difesa del Suolo-Servizio Geologico d'Italia -
www.sinanet.isprambiente.it/progettoiffi
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Models performance
LR QLR QNL
FPR [%] 43.6 6.06 6.33
● We searched the proportion of landslide cells that overlaid non-susceptible areas: the False Positive Rate: – FPR = FP / (FP+TN)
● The more the FPR get close to 5% the better is the model performance
● The QLR and QNL models performed significantly better than the LR model
● QLR model is conservative, and so we concluded that QNL is the best QNL is the best model.model.
FP TNNon
sus
cept
ible
are
a
Landslide
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
QNL model performance
The QNL model performed better for translational and rotational slides
Results are not far from the expected 5% for slow flows, complex and undefined movements
These landslide types represent 92% of the IFFI landslides (in terms of covered area)
The QNL model failed to detect non-susceptible areas for lateral spreads, sinkholes, rapid flows and for falls and topples.
Landslide types FPR [%]
Rotational, translational slides 5.3
Undefined 7.2
Slow flows 7.2
Complex movements 7.4
Falls and topples 8.3
Rapid flows 11.6
Sinkholes 13.8
Lateral spreads 20.9
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
QNL model performance
We further investigated the performance of the QNL model in the 20 administrative regions in Italy.● Size and shape deformations
depend on the IFFI landslide density
● Orange and red colors show high values of False Positive Rate.
● High values of FPR are frequently associated with scarce density of the inventory
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
QNL model performance
● We applied the non linear model QNL to the landmasses surrounding the Mediterranean Sea
● Non-susceptible cells cover 3,652,683 km2, 63% of the area.
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
QNL model performance
We tested the synoptic-scale terrain zonation using independent landslide information in Spain:
● Three inventories: – Pyrenees, Murcia, and the Tramuntana range in Majorca,
– total of 521 landslides,
– total landslide area 27.24 km2.
● The resulting False Positive Rate (FPR) was: 6.11%
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Conclusions
● Exploiting accurate landslide information for 13 study areas in Italy we identified areas non-susceptible to landslides.
● We tested the Italian landslide non-susceptibility map against independent landslide information (IFFI) and we obtained promising results.
● We extended the application of the non-susceptibility model to landmasses surrounding the Mediterranean Sea, and we successfully tested the synoptic subdivision using independent landslide information for three areas in Spain.
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Conclusions
● Our work showed the importance of landslide information for the production of maps of non-susceptible landslide areas, and confirmed the importance of preparing accurate landslide inventory maps.
● We expect that our synoptic-scale zonation for Italy and for the landmasses surrounding the Mediterranean Sea can be used for insurance and re-insurance purposes, for large areas land planning, and in operational landslide warning systems.
NH 3.8, Vienna, 02-05-2014, Ivan Marchesini
Thank you for your attention
NHESSD Open Discussion:
I. Marchesini, F. Ardizzone, M. Alvioli, M. Rossi, and F. Guzzetti, (2014). Non-susceptible landslide areas in Italy and in the Mediterranean region. Nat. Hazards Earth Syst. Sci. Discuss., 2, 2813-2849, 2014