MODELING LANDSLIDE RECURRENCE IN SEATTLE, …swcdis.nchu.edu.tw/AllDataPos/AdvancePos/7101042025/20130429.pdf · is helpful to know the severity of the rainfall events that can produce

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

1

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

3

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

4

1.INTRODUCTION

2.STUDY AREA

3.THEORETICAL BASIS





5

STUDY AREA

6

•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.

7

1.INTRODUCTION

2.STUDY AREA

3.THEORETICAL BASIS





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:

9

THEORETICAL BASIS

• where we assume a water table parallel to the hillslope,

steady seepage in the direction parallel to the slope.

10

• 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.

11

1.INTRODUCTION

2.STUDY AREA

3.THEORETICAL BASIS





12

INTENSITY DURATION


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.

13

INTENSITY DURATION


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





15


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 .

16

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.

17


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).

18


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.

19


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.

20

21

22


3

• 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.

23


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.

24


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


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.

26


3

1.INTRODUCTION

2.STUDY AREA

3.THEORETICAL BASIS





27


• 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

28

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

29


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.

30


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


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.

32


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.

33

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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

5

THANKS FOR LISTENING.

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Documents

MODELING LANDSLIDE RECURRENCE IN SEATTLE, …swcdis.nchu.edu.tw/AllDataPos/AdvancePos/7101042025/20130429.pdf · is helpful to know the severity of the rainfall events that can produce