Using Weather and Climate Using Weather and Climate Information for Landslide Prevention Information for Landslide Prevention

and Mitigationand Mitigation

Professor Roy C. SidleDisaster Prevention Research Institute

Kyoto University, Japan

International Workshop on Climate and Land Degradation, Arusha, Tanzania, 11-15 Dec. 2006

OutlineOutline

• Overview of rainfall- and snowmelt-initiated landslides, including processes

• Available methods to link weather and climate information to landslide initiation– Simple rainfall-landslide relationships– Real-time warning systems– Multi-factor empirical assessment methods– Distributed, physically-based models

• How does land degradation and climate change potentially affect initiation of landslides?– Shallow, rapid landslides– Deep-seated mass movements

• Future Challenges

• Shallow landslides triggered by the rapid accretion of pore water pressure following an extended period of rainfall or a short burst of rainfall (or both)

Rainfall-initiated landslides can occur in a variety of settings and conditions:

• Moderately shallow to moderately deep rapid landslides triggered by the rapid accretion of pore pressures during intense storms facilitated by preferential flow in bedrock

Rainfall-initiated landslides can occur in a variety of settings and conditions:

• Shallow to deep landslides triggered by surcharge due to the combined effects of antecedent moisture and water infiltrating into the soil mantle plus suction reduction

Rainfall-initiated landslides can occur in a variety of settings and conditions:

• Deep-seated landslides (slumps and earthflows) triggered or re-activated by longer term accumulation of infiltrating rainwater and the accretion of a groundwater table in the soil mantle

Rainfall-initiated landslides can occur in a variety of settings and conditions:

2006 Landslide in Layton, Utah, USA –Failure occurred in lacustrine and eoliandeposits; triggered by extended snowmelt and rain.

Iwakuni Roadfill Failure, Japan several deaths

February 2006 landslide on LeyteIsland in the Philippines which killed about 1800 people

Debris slides & avalanches in Nepal, 1993, 58 deaths

Deep-seated landslide that damaged a road & hydropower plant, Sumatra

Simple RainfallSimple Rainfall--Landslide RelationshipsLandslide Relationships

• Useful for developing global and regional thresholds for shallow, rainfall initiated landslides

• Can be modified to account for antecedent rainfall conditions

• Not useful for landslides that are triggered by an accumulation of previous rainfall

• If regionally developed, can be useful for real-time warning systems

• Cannot incorporate land use factors

Simple RainfallSimple Rainfall--Landslide RelationshipsLandslide Relationships

0.1

1

10

100

1000

0.01 0.1 1 10 100 1000 10000

Duration of storm (h)

Rai

nfal

l (m

m)

I = 14.82 D-0.39

Caine’s (1980) threshold of mean storm intensity and duration for initiation of shallow landslides

Some problems related to landslides at both ends of this scale

Based on 73 landslide/debris flow occurrences from around the world

0.1

1.0

10.0

100.0

0.1 1 10 100 1000Duration of storm (h)

Rai

nfal

l (m

m)

wet (> 20 mm during the preceding 2 days)dry ( 20 mm during the preceding 2 days)Caine's thresholdwet thresholddry threshold

<

Using only Caine’s ‘valid’ data which had 2-day antecedent moisture (only 12 points) together with new data (total of 67 points) thresholds were derived for ‘wet’ and ‘drier’ antecedent conditions

Multifactor, empirical landslide hazard assessmentsMultifactor, empirical landslide hazard assessments

• Designed to produce decision tools that relate landslide susceptibility to measurable environmental attributes

• Assume that landslides are more likely to occur under conditions similar to those of previous failures

• Are generally not useful for evaluating the specific effects of land management activities on landslide initiation (although vegetation cover may be included as one factor)

• Rather, such assessments delineate areas of differing landslide susceptibility

• Factor weighting is an important, but often subjective component

LithologyQuartzites (1)Limestones (0)Calcareous shales/siltstones (1)Low grade metamorphics (1) High grade metamorphics (1)

Land UseAgricultural land (1)Sparsely vegetated (2)Deciduous forest (0)Coniferous forest (0)Barren land (2)

Distance from major tectonic features

< 1 km (2), 1-2 km(2),2-3 km (2), 3-4 km (1),4-5 km (0), 5-6 km (0),6-7 km (0), > 7 km(0)

Slope Aspect

N (1), NE (2),E (1), SE (0),S (1), SW (2),W (1), NW (1)

Multifactor analysis for the Ramganga catchment in the Lower Himalayas (Gupta & Joshi, 1990)

‘design’ storm that typically causes landslides

Rainfall

Distance from M ≥ 6.0Earthquake

The procedure could be improved by including:Slope Gradient

Topographic Expression

Vegetation CoverSpecific to Region

Limitations of such multifactor analyses include:Limitations of such multifactor analyses include:

• Lump all landslide types together• Do not different well amongst various

triggering factors (e.g., rainfall and earthquakes)

• Do not assess specific land disturbances or land use dynamics

• In this case, uses subjective criteria for factor weighting

However, multifactor analyses can be very However, multifactor analyses can be very useful planning tools when:useful planning tools when:

• Conducted at detailed spatial scales, including the incorporation of high quality landslide hazard mapping data

• Use factors that are related to underlying slope failure processes, including climate data

• Incorporate follow-up geotechnical field investigations• Used as a broad planning tool in developing nations

coupled with aerial photo and field investigations – but again, should be linked to climate triggering factors

Distributed, physicallyDistributed, physically--based landslide modelsbased landslide models

• Generally restricted to shallow, rainfall-initiated landslides• Are able to estimate slope failure probability throughout small

catchments – somewhat spatially limited• Require measurements of estimates of distributed site variables• Address the physical processes inherent in landslide initiation• Some models can respond dynamically to rainfall inputs and thus can

provide real-time estimate of landslides for a given input scenario• Utilize and depend on GIS and DEM technology• Some have the potential to assess specific long-term land use impacts

“Dynamic” factors related to physically-based landslide modeling in managed forests:

Hydrology and porewater pressureresponse to rain

vegetation effects?

Tree root strengthand dynamics

Dynamic geomorphicprocesses, e.g., infilling

& weathering

Distributed modeling of

shallow, rapid landslidesSeismic factors

Other dynamic factors

Several process-based landslide models exist that are able to incorporate effects of timber harvesting into predictions of landslide erosion and sediment routing

< 11-1.11.1 - 1.2

1.2 - 1.31.3 - 1.51.5 - 3.0

> 3

ChannelsDhakal and Sidle, 2003 (Earth Surface Processes & Landforms)

Factor of Safety

Carnation Creek subcatchmentBritish Columbia

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 10 20 30 40 50

Time step

Safe

ty fa

ctor

0

5

10

15

20

25

Rai

nfal

l int

ensi

ty (m

m/h

r)349 2779708 533434 559

Storm 67 An example of how safety factor (FS) gradually decreases during the storm as pore water pressure builds up, and then suddenly drops following high intensity inputs of rain

The six elements (pixels) that failed in simulations

Rainfall hyetographs for the 6 rainstorms (excluding uniform storms) that caused the most landslides - Carnation Creek

Root strength changes following clearcutting with subsequentforest regeneration

Root

Stre

ngth

0Years after clearcutting

2 4 6 8 10 12 14

Net root strengthNet root strength

Root strength regrowthRoot strength regrowth

Maximum susceptibilityMaximum susceptibilityto landslidesto landslides

Root strength decay Root strength decay -- harvestedharvestednktD e−=

1( )k tR a b e c− −= + +

Timber harvest followed by forest regeneration

Timber harvest and conversion to weaker rooted plantation or agroforestry

Conversion from forest to grassland

Time since harvesting (years)

15 300

Rel

ativ

e ro

ot s

tren

gth

→

Hypothetical examples of the effects of forest conversion on rooting strength

The largest producer of landslide sediment per unit area in mountainous terrain

About 30-1000 times more landslide erosion than undisturbed forests

Important aspects of land degradation and climate change related to landslides

• Truths– Conversion of forest vegetation to weaker-rooted vegetation

will increase the susceptibility of shallow landslides– Effects of land cover change on the soil moisture regime

related to slope failure depend on what time of the year landslide producing storms occur

– Mountain roads exert the greatest impact on landslides per unit area of the landscape affected

– Many practices typically implemented to control surface erosion (e.g., water drainage, terraces) may concentrate water on unstable slope sections and promote landslides

– Predicting impacts of potential climate change on slope stability is complex – impacts could be both negative and positive for warming climate scenarios, depending on storm pattern changes (which are uncertain)

Deep-seated landslides:• Are not so much affected by vegetation management except for the timing of activation and cessation of movement related to ET effects

• Movement may be reduced by loss of water from snow intercepted and evaporated from tree canopies

• Movement may be reduced in warmer climate scenarios with lower overall rainfall inputs

Shallow, rapid landslides:• Are greatly affected by vegetation management due to changes in root strength; ET effects are usually minimal

• Frequency may be reduced by loss of water from snow intercepted and evaporated from tree canopies

• Movement may increase even in warmer climate scenarios if stormintensities increase

Future ChallengesFuture Challenges• Better methods for assessing effects of both land degradation

and climate on deep-seated landslide movement and initiation• Development of real-time landslide warning systems for less

developed and very vulnerable areas of the world• Linking real-time climate data with physically-based,

distributed landslide models• ‘Upgrading’ empirical landslide assessment methods to

include relevant climate triggering mechanisms• Development of better information on land degradation effects

(not timber harvesting) related to various types of landslides

So it is my hope that with the implementation of some of this knowledge, we can avoid many of the disasters and costly nuisances that have occurred in many parts of the world.