Using Weather and Climate Information for Landslide Prevention and Mitigation Professor Roy C. Sidle Disaster Prevention Research Institute Kyoto University, Japan International Workshop on Climate and Land Degradation, Arusha, Tanzania, 11-15 Dec. 2006
Outline 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
Rainfall-initiated landslides can occur in a variety of settings and conditions: 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
2006 Landslide in Layton, Utah, USA Failure occurred in lacustrine and eolian deposits; triggered by extended snowmelt and rain.
Debris slides & avalanches in Nepal, 1993, 58 deaths Iwakuni Roadfill Failure, Japan several deaths Deep-seated landslide that damaged a road & hydropower plant, Sumatra February 2006 landslide on Leyte Island in the Philippines which killed about 1800 people
Simple Rainfall-Landslide 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 Rainfall-Landslide Relationships 1000 100 Caine s (1980) threshold of mean storm intensity and duration for initiation of shallow landslides Rainfall (mm) 10 1 0.1 I = 14.82 D -0.39 0.01 0.1 1 10 100 1000 10000 Duration of storm (h) Based on 73 landslide/debris flow occurrences from around the world Some problems related to landslides at both ends of this scale
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 100.0 Rainfall (mm) 10.0 1.0 0.1 wet (> 20 mm during the preceding 2 days) < dry ( 20 mm during the preceding 2 days) Caine's threshold wet threshold dry threshold 0.1 1 10 100 1000 Duration of storm (h)
Multifactor, 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
Lithology Quartzites (1) Limestones (0) Calcareous shales/siltstones (1) Low grade metamorphics (1) High grade metamorphics (1) Land Use Agricultural 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) Multifactor analysis for the Ramganga catchment in the Lower Himalayas (Gupta & Joshi, 1990) Slope Gradient Topographic Expression Slope Aspect N (1), NE (2), E (1), SE (0), S (1), SW (2), W (1), NW (1) Vegetation Cover Specific to Region The procedure could be improved by including: Rainfall design storm that typically causes landslides Distance from M 6.0 Earthquake
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 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, physically-based 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 pore water pressure response to rain vegetation effects? Dynamic geomorphic processes, e.g., infilling & weathering Tree root strength and dynamics Other dynamic factors Seismic factors Distributed modeling of shallow, rapid landslides
Several process-based landslide models exist that are able to incorporate effects of timber harvesting into predictions of landslide erosion and sediment routing Carnation Creek subcatchment British Columbia Channels < 1 1-1.1 1.1-1.2 Factor of Safety 1.2-1.3 1.3-1.5 1.5-3.0 Dhakal and Sidle, 2003 (Earth Surface Processes & Landforms) > 3
Safety factor 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Storm 67 25 20 15 10 5 0 Rainfall intensity (mm/hr) 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 0 10 20 30 40 50 Time step 349 2779 708 533 434 559 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 subsequent forest regeneration Net root strength Maximum susceptibility to landslides Root Strength Root strength regrowth R = ( a + b e kt) 1 + c Root strength decay - harvested D e kt n = 0 2 4 6 8 10 12 14 Years after clearcutting
Hypothetical examples of the effects of forest conversion on rooting strength Timber harvest followed by forest regeneration Relative root strength Timber harvest and conversion to weaker rooted plantation or agroforestry Conversion from forest to grassland 0 15 30 Time since harvesting (years)
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 storm intensities increase
Future 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.