modeling landslide recurrence in seattle,...
TRANSCRIPT
MODELING LANDSLIDE
RECURRENCE IN SEATTLE,
WASHINGTON, USA
在西雅圖模擬崩塌的重現期距
7101042025 趙逸幃
指導教授:張光宗
2013/4/29
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1.INTRODUCTION
2.STUDY AREA
3.THEORETICAL BASIS
4.INTENSITY DURATION
FREQUENCY (IDF) CURVES
5.APPLICATION OF THE MODEL
6.CONCLUDING DISCUSSION
2
INTRODUCTION
• For purposes of hazard assessment and mitigation planning, it
is helpful to know the severity of the rainfall events that can
produce landslides and debris flows, along with their
recurrence time.
• Hazard is defined as the probability of occurrence of a
potentially damaging phenomenon within a given area in a
given period of time (Varnes et al., 1984).
• Temporal element
• A hazard map typically includes an evaluation of the probability
of occurrence of future landslides in a given year.
• statistical treatment of rainfall data & modeling
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INTRODUCTION
• When rainfall duration and intensity of triggering events are
known, different landslide hazard scenarios can be modeled,
each one corresponding to a specific return period.
• TRIGRS ,module CRF (Critical RainFall)
• CRF provides the evaluation of deterministic rainfall thresholds
that cause distributed slope failures.
• thresholds>intensity, duration, frequency (IDF
relations)>recurrence intervals
• simulate shallow landslides
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1.INTRODUCTION
2.STUDY AREA
3.THEORETICAL BASIS
4.INTENSITY DURATION
FREQUENCY (IDF) CURVES
5.APPLICATION OF THE MODEL
6.CONCLUDING DISCUSSION
5
STUDY AREA
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•colluvium – loose & highly
permeable.
•Often the very permeable
colluvium overlies a less
permeable material that we will
consider as impervious in the
modeling that follows.
STUDY AREA
• 93%
• fast-moving & potentially destructive debris flows
• 1 and 3 m
• contact between the colluvium and the underlying more
consolidated parent material
• winter-season precipitation & intense rainfall or rapidly
melting snow
• we assume that the hillslope colluvium generally fines
downslope
• This assumption is used to attribute the map of hydraulic
properties of the colluvium and to determine its saturated
thickness described in subsequent sections.
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1.INTRODUCTION
2.STUDY AREA
3.THEORETICAL BASIS
4.INTENSITY DURATION
FREQUENCY (IDF) CURVES
5.APPLICATION OF THE MODEL
6.CONCLUDING DISCUSSION
8
THEORETICAL BASIS
• The potential failure surface typically lies at or near the
contact between the relatively permeable colluvium and
underlying relatively impermeable substrate.
• If the colluvium cover has limited thickness compared
with the length of the slope, an infinite slope stability
hypothesis can be assumed in the analysis.
• The safety factor for an infinite soil slope can be
expressed as:
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THEORETICAL BASIS
• where we assume a water table parallel to the hillslope,
steady seepage in the direction parallel to the slope.
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• FS=1,”limiting equilibrium
condition”-critical pressure
head value, ψcrit
• geometric parameters -critical
pressure head, ψcrit
• linearized form of the
Richards equation
•
THEORETICAL BASIS
• Two major assumptions are made:
a)the rainfall causing instability is spatially homogenous and has uniform distribution over time (constant hyetograph);
b)the failure occurs at the contact between the surficial soil cover and the impervious boundary.
• A number of studies are known in the scientific literature to assess the effect of the hyetograph shape on the hillslope response .
• Experimentally, an input of rainfall represented by a rectangular hyetograph (constant rainfall) generates the most severe condition for slope stability and thus the assumed hypothesis is conservative.
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1.INTRODUCTION
2.STUDY AREA
3.THEORETICAL BASIS
4.INTENSITY DURATION
FREQUENCY (IDF) CURVES
5.APPLICATION OF THE MODEL
6.CONCLUDING DISCUSSION
12
INTENSITY DURATION
FREQUENCY (IDF) CURVES
IDF relationships for the Seattle area
• The climate of the Seattle area is typical of the Pacific
Northwest, with a pronounced seasonal precipitation
regime.
• Precipitation is generated almost entirely during the
wintertime.
• Short duration precipitation with durations of 3-hours or
less.
• One set of Intensity–Duration–Frequency (IDF) curves
were developed for durations from 5 min through 180 min.
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INTENSITY DURATION
FREQUENCY (IDF) CURVES
The Seattle–Tacoma rain gauge has been considered the
most representative for the analyses in the study area .
We summarize the Intensity–Frequency estimates for
Seattle–Tacoma.
14
1.INTRODUCTION
2.STUDY AREA
3.THEORETICAL BASIS
4.INTENSITY DURATION
FREQUENCY (IDF) CURVES
5.APPLICATION OF THE MODEL
6.CONCLUDING DISCUSSION
15
5.APPLICATION OF THE MODEL
1 INPUT DATA
• topographic slope, depth of the lower impervious
boundary, initial water table depth, material hydraulic and
strength properties.
• For Seattle, topographic slopes were calculated from a 3-
meter DEM.
• lower impervious boundary, dlb, varies systematically
among the three hillslope landforms (escarpment,
midslope, and footslope). Therefore, an empirical relation
was developed using a rule-based scheme and linear
regression of colluvium depth versus individual
topographic variables .
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APPLICATION OF THE MODEL
1 INPUT DATA
• Water-level information during the year from 39 borehole
logs was used to develop the “saturated thickness
model” .
• This is a conceptual model that combines the empirical
based models for colluvium formation and groundwater
occurrence on coastal bluffs.
• The saturated thickness of the colluvium and the colluvial
depth were combined to yield a surface that defines the
depth of the water table, dwt(0-6.8m).
• GIS
• The study area was divided into three zones based on the
results from the colluvial thickness and initial water-table
maps.
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APPLICATION OF THE MODEL
1 INPUT DATA
• Saturated hydraulic conductivity Kz, and diffusivity D0
were assigned to each of the zones based on laboratory
and field tests of colluvial material (Godt et al., in press),
as shown in Table 2 and Fig. 2.
• Material strength values were assumed to be spatially
invariant and were determined from published values
(Savage et al., 2000) and laboratory tests (Table 2).
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APPLICATION OF THE MODEL
1 INPUT DATA
• The CRF module requires input data on rainfall duration T.
• Typical durations for rainfall causing slope failure lie
between 22 and 52 h (Godt et al., 2006).
• We considered a slightly larger range of variability for
rainfall durations and investigated the effect of 1-, 3-, 12-,
24-, 48-, and 72-hour rainfall durations.
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APPLICATION OF THE MODEL
2 RESULTS
• The main results of the CRF module implemented in
TRIGRS are the deterministic rainfall thresholds causing
slope failure.
• We used the results derived from the rainfall analysis for
the Seattle area (MGS Engineering Consultants, 2003) to
assign to each intensity-duration threshold the
corresponding recurrence intervals.
• This is intended as a general guide to landslide
occurrence, not as predictor of landslide hazard at
specific sites.
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APPLICATION OF THE MODEL
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• So far, nothing has been said regarding instabilities for
rainfall durations shorter than 12 h.
• This issue needs a specific discussion, considering the
base hypotheses of the model.
• The limit in the implementation of the CRF module is
related to the simplifying hypothesis that assumes
instabilities occur at the end of an assigned rainfall.
• As shown by Iverson (2000) and D'Odorico et al., 2002 and
D'Odorico et al., 2005, the most severe condition for slope
stability is, in many circumstances, subsequent to the end
of the rainfall.
• Also, they found that this delay is greater for short
duration rainfalls.
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APPLICATION OF THE MODEL
3
Therefore, slope stability analyses should be carried out
referring to the peak pressure head ψp that occurs at the
peak time tp.
Defining T* as the normalized rainfall duration ,it was shown
that the peak behaviour varies as a function of rainfall
duration T*, with a systematic change that occurs between
T*=1 and T*=10 (Iverson, 2000).
The peak time tp is almost constant for T*<1, whereas it
increases about linearly for T*>1.
For rainfall durations T*>1,peak responses occur sooner
after rainfall ceases.
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APPLICATION OF THE MODEL
3
Our assumption that the failure occurs at the end of the
rainfall applies only for normalized rainfall duration T* > 1.
For the study area, D0 varies between 5e−5 and e−4 (Table 2),
and the failures occur typically at depths < 2 m (Troost et al.,
2005).
In the case study, our assumption is essentially valid for
rainfall durations longer than 20 h, because, in these cases,
the pressure head peak occurs at t*p = T*, hence at the end
of the rainfall event.
On the contrary, the predicted instabilities for shorter
rainfalls (particularly for rainfall durations < 12 h) are likely
underestimated.
For Seattle this is appropriate since storms greater than 24 h
have been shown to cause slides (Godt et al., 2006). 25
APPLICATION OF THE MODEL
3
In the future we will complete the implementation of the
model by inserting the theoretical equation to determine the
peak pressure head response at the peak time tP; the
pressure head that poses the most severe threat to hillslope
stability.
In such a way the model will have a general validity, for any
duration.
However, this first work provides an insight on the temporal
scales of the hydrological control on landscape evolution.
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APPLICATION OF THE MODEL
3
1.INTRODUCTION
2.STUDY AREA
3.THEORETICAL BASIS
4.INTENSITY DURATION
FREQUENCY (IDF) CURVES
5.APPLICATION OF THE MODEL
6.CONCLUDING DISCUSSION
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6.CONCLUDING DISCUSSION
• We present an approach to assess rainfall magnitude
causing slope instability in southwest Seattle, Washington.
• The module CRF, implemented in TRIGRS, is able to
provide the rainfall intensity that leads to regional slope
failure for a given rainfall duration.
• temporal parameter in a landslide hazard assessment
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CONCLUDING DISCUSSION
• hybrid approach
• linearized of the Richards equation
• the temporal assessment of a landslide event is expressed
in terms of probability of occurrence
• the model is feasible only for study cases where failures
occur after prolonged rainfall
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CONCLUDING DISCUSSION
The principal products of the study are maps showing the potential for landslide occurrence from hillslope source areas with the recurrence time information.
In addition, relationships between rainfall duration, recurrence time and landslide initiation have been discussed.
In general, the steeper the hillslope, the lower the rainfall recurrence time required to cause instability and that there is a significant influence of the rainfall duration on the hillslope response.
In particular, the results show that the number of simulated landslides increases significantly with rainfall durations between 12 and 48 h.
However a rainfall duration of 72 h yields relatively few additional landslides.
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CONCLUDING DISCUSSION
In order to perform a verification of our model predictions, we compared our results with those of a previous work by Coe et al. (2004) that proposed a probabilistic approach for assessing future landslide occurrence using historical records.
The database that they used contained a record of precipitation-triggered landslides that occurred during the period 1909 to 1999.
The database also includes information regarding location and date of occurrence as well as other landslides characteristics. Sixty-eight percent of the landslides in the database are shallow landslides in colluvium (Laprade et al., 2000).
The limitation of the landslide database is related to the fact that the database contains mostly landslides that caused damage. 3
1
CONCLUDING DISCUSSION
Starting from the historical records, Coe et al. (2000)
provided maps showing the landslide density in the study
area.
Landslide densities were determined based on the number of
landslides occurring within a moving count circle (Campbell,
1973) as follows.
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CONCLUDING DISCUSSION
As already pointed out, the comparison between the results from TRIGRS-CRF approach and the probabilistic approach by Coe et al. (2000) is not completely consistent, since the results from Coe et al. (2000) are rainfall duration independent.
Therefore, to overcome this non-uniformity, we consider the comparison only for the results obtained for a rainfall duration of 24 h. As found by Godt (2004) this is the rainfall duration that typically causes instability on the steep slopes in the Seattle area.
We overlay the results from the deterministic and probabilistic assessment of the occurrence of future landslides. Fig. 10 shows an enlargement of the results.
Although a direct comparison of the TRIGRS results with the empirical model is hindered by the differences in grid resolution, in both approaches, the number of instabilities increases with increasing recurrence intervals.
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CONCLUDING DISCUSSION
However, despite the difficulties in making the comparison, it helps to illustrate an important point about the CRF approach and rainfall thresholds in general.
This approach is more relevant to determining rainfall thresholds for the large, long-return-period storms than for the less intense, short-return-period storms.
Previous works on empirical thresholds for Seattle has shown that they have a similar limitation (i.e. Baum et al., 2005 and Chleborad et al., 2006).
Thresholds that predict the occurrence of landslides from storms that have short-return periods are exceeded by many storms that do not produce landslides.
In other words, thresholds for short-return-period storms overpredict, or, exceedence of such thresholds corresponds to a low probability of landslide occurrence.
Thresholds for long-return-period storms are exceeded by fewer storms and a larger percentage of those storms produce landslides (Baum et al., 2005 and Chleborad et al., 2006). 3
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THANKS FOR LISTENING.
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