Transcript Document

IMPROVING LANDSLIDE MOVEMENT FORECASTING
USING ASCAT SOIL MOISTURE DATA
Duration
(h)
Pmax-1h
(mm)
Ptot
(mm)
SWI75
(%)
European Geosciences Union
General Assembly 2012
Vienna, Austria, 22 – 27 April 2012
API20
(mm)
http://hydrology.irpi.cnr.it
http://hydrology.irpi.cnr.it
dH
(mm)
Luca Brocca (1), Francesco Ponziani (2), Nicola Berni (2), Florisa Melone (1), Tommaso Moramarco (1), Wolfgang Wagner (3)
Date
07-10-30 17:30
6.5
6.6
14.7
15.3
37.8
0.109
07-11-14 08:00
10.5
4.0
28.8
18.7
68.9
0.000
07-12-07 17:30
7.5
1.8
13.8
22.0
21.4
0.101
07-12-08 04:30
10.0
3.6
21.2
22.8
42.6
0.052
08:00
12.0
1.4
12.4
27.1
22.0
0.025
Predicting the spatial and temporal occurrence of rainfall triggered landslides represents an important scientific and operational08-01-06
issue
due
to
their
high
threats
to
human
lives
and
properties. This study investigates the relationship between rainfall, soil moisture conditions and landslide
08-02-04 23:30
6.5
4.2
18.2
32.9
41.9
0.111
movement by using recorded movements of a rock slope located in central Italy, the Torgiovannetto landslide. This landslide is 08-03-07
a very05:00
large rock
threatening
county
and state
12.0 slide,1.6
20.8
28.8
33.4
0.145 roads. Data acquired by a network of extensometers and a meteorological station clearly indicate that the
08-03-11are
02:00associated
10.0
3.6rainfall
13.0patterns.
29.8 By
54.6
movements of the unstable wedge, firstly detected in 2003, are still proceeding and the alternate phases of quiescence and reactivation
with
using0.109
a multiple linear regression approach, the opening of the tension cracks (as recorded by the extensometers) as
08-03-23 10:00
13.0
4.0
21.2
31.2
97.6
0.520
a function of rainfall and soil moisture conditions prior the occurrence of rainfall, are predicted for the period 2007-2009. Specifically,
soil moisture
indicators
are
through
the Soil Water Index, SWI, product derived by the Advanced SCATterometer (ASCAT) on board the MetOp
08-03-27 17:00
4.5
3.4
10.6 obtained
31.5
90.6
0.413
(Meteorological Operational) satellite and by an Antecedent Precipitation Index, API.
08-04-08 15:00
11.0
6.2
34.0
30.3
105.3
0.573
08-04-22 14:30
3.5
10.8
12.6
30.6
104.6
0.739
08-05-21 01:00
12.5
2.4
21.8
22.4
44.6
0.411
08-05-29 18:30
1.5
9.5
15.1
22.2
73.3
0.492
08-06-13 16:30
3.0
6.6
12.0
23.1
79.8
0.444
08-06-14 10:30
7.0
6.0
17.2
23.3
103.2
0.513
08-07-02 14:30
4.5
3.8
14.4
22.6
60.8
0.090
08-07-14 12:30
2.5
8.4
14.2
21.4
29.0
0.045
08-07-22 00:00
5.5
15.2
21.8
20.3
53.6
0.136
Based on a previous study (Ponziani et al., 2012) indicating the significant
effect of initial
soil moisture
conditions
on landslide
triggering,
the
08-08-23 14:30
3.0
13.4
20.6
16.1
27.6
0.003
relationship between rainfall, ASCAT soil moisture and the slope movement
was investigated
here
a multiple
regression
08-09-01 15:30
6.0
7.6 by applying
17.4
14.8
45.0linear0.032
analysis for a sequence of rainfall events occurred in the period 2007-2009.
08-09-12 19:30
5.5
5.2
21.8
14.1
42.5
0.001
Specifically, 46 rainfall events were extracted and for each of them the main
characteristic
of rainfall (total
, and maximum
rainfall over
08-09-13
09:30
3.0
9.2 rainfall,
19.6 Ptot15.8
62.1
0.097
17:00
1.0 75, and15.1
25.6
16.2
89.9precipitation
0.212
a duration of 1h, Pmax-1h), the initial Soil Water Index obtained by ASCAT08-09-14
with T=75
days, SWI
the cumulated
antecedent
over
08-09-19
2.6
10.8
17.4
107.3 displacement
0.190
20 days, API20, were computed. Finally, the extensometer crack aperture,
dH, 08:00
computed9.0
as the difference
between
the recorded
08-10-03
21:30
6.0
7.6
18.8
16.9
62.7
0.324
between the end and the start of each rainfall event, was considered as the
predictand.
08-10-17 18:00
2.5
4.8
12.6
17.1
32.1
0.090
08-11-01 00:30
15.0
5.0
20.8
19.9
58.2
0.147
Pmax-1h
Ptot
SWI
API
dH
08-11-04
17:30 Duration
6.5
6.2
14.8
21.975
75.820
0.000
Date
Pmax-1h
22-December-2008
(h)
(mm)
(mm)
(%)
(mm)
(mm)
08-11-13 12:30
5.5
4.2
23.0
26.4
92.5
0.146
08-11-24
21.0
4.0
39.2
28.8
75.2
0.084
07-10-30 12:30
17:30
6.5
6.6
14.7
15.3
37.8
0.109
5
5.8
07-11-14
10.5
4.0
28.8
18.7
68.9
0.000
08-11-30 08:00
02:00
5.5
5.8
11.0
31.7
105.6
0.374
rainfall
crack aperture
4.5
5.6
07-12-07 12:30
17:30
7.5
1.8
13.8
22.0
21.4
0.101
08-12-05
6.0
8.2
24.4
34.5
129.6
0.491
4
5.4
3.5
07-12-08 02:30
04:30
10.0
3.6
21.2
22.8
42.6
0.052
08-12-10
11.5
2.8
27.6
36.6
164.6
0.933
5.2
3
08-01-06 22:30
08:00
12.0
1.4
12.4
27.1
22.0
0.025
08-12-10
13.0
4.2
43.8
39.2
208.4
1.369
5
2.5
08-02-04 20:30
23:30
6.5
4.2
18.2
32.9
41.9
0.111
09-01-24
13.5
1.2
15.4
43.8
228.2
1.102
4.8
2
4.6
08-03-07
12.0
1.6
20.8
28.8
33.4
0.145
09-02-04 05:00
16:00
13.0
1.8
11.6
46.8
40.8
1.054
1.5
4.4
08-03-11 13:30
02:00
10.0
3.6
13.0
29.8
54.6
0.109
09-02-07
6.5
2.2
13.2
46.2
61.8
1.213
1
4.2
0.5
08-03-23 05:00
10:00
13.0
4.0
21.2
31.2
97.6
0.520
09-03-02
9.0
1.8
10.8
41.3
17.2
0.477
0
4
08-03-27 06:30
17:00
4.5
3.4
10.6
31.5
90.6
0.413
09-03-05
8.0
2.6
10.2
42.5
42.4
0.881
10 Dec 08
11 Dec 08
11 Dec 08
11 Dec 08
08-04-08 20:30
15:00
11.0
6.2
34.0
30.3
105.3
0.573
09-03-30
1.5
5.0
11.8
40.1
46.0
0.710
18:00
00:00
06:00
12:00
................
08-04-22
3.5
10.8
12.6
30.6
104.6
0.739
09-04-01 14:30
21:30
5.0
2.6
10.2
40.3
60.0
0.773
dH
08-05-21 14:00
01:00
12.5
2.4
21.8
22.4
44.6
0.411
09-04-29
5.5
5.4
11.6
34.5
49.2
0.543
SWI
Ptot
08-05-29
1.5
9.5
15.1
22.2
73.3
0.492
09-05-31 18:30
10:00
2.5
8.4
13.0
26.0
18.0
0.183
08-06-13 15:30
16:30
3.0
6.6
12.0
23.1
79.8
0.444
09-05-31
10.5
2.2
13.2
26.0
31.2
0.239
08-06-14 04:30
10:30
7.0
6.0
17.2
23.3
103.2
0.513
09-06-01
13.5
2.0
17.0
26.5
48.6
0.260
08-07-02 14:30
4.5
3.8
14.4
22.6
60.8
0.090
The multiple linear regression equation was written
as:
1
08-07-14 12:30
2.5
8.4
14.2
21.4
29.0
0.045
08-07-22 00:00
5.5
15.2
21.8
20.3
53.6
0.136
The Torgiovannetto rock slope is located in an abandoned stone quarry
08-08-23 14:30
3.0
13.4
20.6
16.1
27.6
0.003
max-1h
tot
7517.4 14.8 45.0 0.032
08-09-01 15:30 20 6.0
7.6
close to Assisi town in central Italy. The main front of the quarry, oriented
08-09-12
5.5
42.5
0.001
approximately along the SE-NW direction, has an average dip of about 38°.
Successively, different configurations of the model are analysed with the
aim of19:30
understanding
the 5.2
different 21.8
impact 14.1
of rainfall
and soil
moisture
08-09-13 09:30
9.2
15.8
62.1
0.097
In this area, the rock mass is composed of regular stratifications of
conditions on the landslide movement. The first configuration uses as predictors
only the 3.0
rainfall variables
(P19.6
and
P
),
the
second
and the
max-1h 16.2 tot 89.9
08-09-14 17:00
1.0
15.1
25.6
0.212
limestone, with intercalations of thin, weak clay layers. Due to the
third configurations the rainfall variables together with the API20 and the SWI
all the predictors.
75, respectively;
08-09-19
08:00
9.0and the last
2.6 configuration
10.8
17.4
107.3
0.190
orientation of the bedding planes and to the presence of the weak clay
08-10-03 21:30
6.0
7.6
18.8
16.9
62.7
0.324
layers between the hard calcareous strata, the upper part of the slope is in
08-10-17 18:00
2.5
4.8
12.6
17.1
32.1
0.090
The obtained results can be summarized as follows:
marginal stability conditions. A limited number of slope failures have been
08-11-01 00:30
15.0
5.0
20.8
19.9
58.2
0.147
1. for the 1st configuration (only rainfall), the estimated crack aperture poorly
follows
08-11-04
17:30 the observations
6.5
6.2(r=0.219)
14.8
21.9
75.8
0.000
reported on several occasions, and several tension cracks running parallel
08-11-13
12:30 with5.5a better reproduction
4.2
23.0 of 26.4
92.5 magnitude
0.146
2. for the 2nd configuration (rainfall + API20) results significantly improve
(r=0.635)
the seasonal
of crack
to the quarry face have been observed on the upper part of the slope.
08-11-24 12:30
21.0
4.0
39.2
28.8
75.2
0.084
aperture.
The monitoring network is composed of 13 extensometers, 5 inclinometers
08-11-30 02:00(r=0.821)
5.5
5.8
11.0
31.7
105.6
0.374
3. the 3rd configuration (rainfall + SWI75) provides a further significant improvement
and one meteorological station. The data used for this study covers the
08-12-05
6.0 period8.2
34.5
129.6
0.491
4. for the 4th configuration (all the predictors) the agreement is reasonably
good12:30
for the whole
resulting24.4
in r=0.877.
period 2007-2009
08-12-10 02:30
11.5
2.8
27.6
36.6
164.6
0.933
08-12-10 22:30
13.0
4.2
43.8
39.2
208.4
1.369
Moreover, the relative weight of each predictor is quantitatively determined
by 20:30
analysing13.5
the standardized
coefficients
linear 1.102
regression. In the
09-01-24
1.2
15.4
43.8of the
228.2
configuration 4 the coefficients are found to be equal to 0.13, 0.04, 0.36
and16:00
0.74 for P13.0
API20 and
SWI7546.8
, respectively.
Thus,
09-02-04
11.6
40.8
1.054 the SWI75 is
max-1h, Ptot; 1.8
the most significant predictor with a weight ~50% and ~80% higher
than the
API20 and
respectively.
09-02-07
13:30
6.5 Pmax-1h, 2.2
13.2
46.2
61.8
1.213
09-03-02 05:00
9.0
1.8
10.8
41.3
17.2
0.477
09-03-05 06:30
8.0
2.6
10.2
42.5
42.4
0.881
ASCAT is a C-band scatterometer on-board METOP
09-03-30 20:30
1.5
5.0
11.8
40.1
46.0
0.710
satellite and operating since 2006. The spatial
09-04-01 21:30
5.0
2.6
10.2
40.3
60.0
0.773
resolution is 50/25 km and a daily coverage for the
09-04-29 14:00
5.5
5.4
11.6
34.5
49.2
0.543
80% of the Earth is provided.
The influence of rainfall and soil moisture conditions in09-05-31
the estimation
movements
of a well
slope located in central Italy, the Torgiovannetto landslide, was here investigated. The results of
10:00
2.5 of the 8.4
13.0
26.0
18.0 monitored
0.183
the multiple regression analysis clearly indicate that ASCAT-derived
estimates
can be0.239
effectively used to predict the crack aperture of the slope with reasonable accuracy (r=0.821). The
09-05-31 15:30
10.5soil moisture
2.2
13.2
26.0
31.2
TU-Wien algorithm (Wagner et al., 1999)
09-06-01
04:30
13.5precipitation
2.0
17.0
26.5 can
48.6be used
0.260 to predict the crack aperture of the Torgiovannetto slope in an operational context even though it is
regression model implemented in this study, coupled with
quantitative
forecasts,
INTRODUCTION
ARE COARSE-RESOLUTION SATELLITE SOIL MOISTURE DATA USEFUL FOR THE PREDICTION OF THE MOVEMENT OF SMALL-SCALE LANDSLIDES?
MULTIPLE REGRESSION ANALYSIS
rainfall (mm/h)
cumulative displacement (mm)
TORGIOVANNETTO LANDSLIDE
dĤ      P
RESULTS
+
   P    API    SWI
+
+
+
ASCAT Soil Water Index (SWI)
CONCLUSIONS
Surface soil moisture (SSM) is retrieved from
the ASCAT backscatter measurements using a
time series-based change detection approach. The surface roughness is
assumed to have a constant contribution in time, and by knowing the typical
yearly vegetation cycle and how it influences the backscatter-incidence
angle relationship, the vegetation effects are removed revealing the soil
moisture variations. The Soil Water Index (SWI) is then obtained by applying
an exponential filter to the SSM data and depends on a single parameter T
(characteristic time length). For more details see Brocca et al. (2011).
1
only based on a simple empirical relationship.
On the basis of these encouraging results, the use of more complex physically-based models linking the rainfall and soil moisture conditions with the landslide movement will be the object of future
investigations. Moreover, the availability of ASCAT satellite soil moisture data at global scale present new opportunities for the integration of this data set in landslide forecasting systems worldwide.
References
Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J., Martin, C., Bittelli, M. (2011). Soil moisture estimation through ASCAT and AMSR-E sensors: an intercomparison and validation study across Europe.
Remote Sensing of Environment, 115, 3390-3408, doi:10.1016/j.rse.2011.08.003.
Brocca L., Ponziani, F., Moramarco, T., Melone, F., Berni, N., Wagner, W. (2012). Improving landslide forecasting using ASCAT-derived soil moisture data: a case study of the Torgiovannetto landslide in central Italy. Remote Sensing, in press.
Ponziani, F., Pandolfo, C., Stelluti, M., Berni, N., Brocca, L., Moramarco, T. (2012). Assessment of rainfall thresholds and soil moisture modelling for operational hydrogeological risk prevention in the Umbria region (central Italy). Landslides, in press, doi:10.1007/s10346-011-0287-3.
Wagner, W., Lemoine, G., Rott, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70, 191-207, doi:10.1016/S0034-4257(99)00036-X.
Contact
E-MAIL: [email protected]
URL: http://hydrology.irpi.cnr.it/people/l.brocca
(1) Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
(2) Umbria Region Functional Centre, Foligno (Perugia), Italy
(3) Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Vienna, Austria