MUHAMMAD BARIK & JENNIFER ADAMWASHINGTON STATE UNIVERSITY
LANDSLIDE SUSCEPTIBILITY MAPPING TO INFORM LANDUSE MANAGEMENT DECISIONS IN AN
ALTERED CLIMATE
Steve Burges Retirement SymposiumMarch 26th, 2010
Availability: the open doorInspiration, optimismBalancing direction and self directionThe art of questioning and listeningBeing widely read and widely acceptingThe initial projectLife after scienceCelebration
Mentoring Tips
Land Use Change:Logging has increased landslide frequency by
2-23 times in the Pacific Northwest (Swanson and Dyress 1975, Jakob 2000, Guthrie 2002, Montgomery et al. 2000).
Climate Change: PNW winters are expected to become wetter;
precipitation events are expected to become more extreme (Mote and Salathe 2010).
Impacts on Riparian Health:Resulting sediment negatively affects riparian
ecosystems, i.e., reduced success of spawning and rearing of salmon (Cederholm et al. 1981; Hartman et al. 1996).
Motivation
Forest Management ObjectiveIncreasing economic viability while preserving
the natural environment.“Zoned” management approach
Previous Best Management Practice StudiesImpacts on landslides are site specificNo incorporation of climate change effects into
long term plans
Improved Forest Management Practices
To provide high resolution maps of the susceptibility of landslide activity to timber extraction under historical and future climate conditions.
Objective
How is landslide activity affected by timber extraction and how does this impact vary over a range of topographic, soil, and vegetation conditions?
How will landslide susceptibility to timber extraction respond to projected climate change?
Research Questions
The Olympic Experimental State Forest (OESF)
Source: DNR
“Unzoned” Management Approach
The Queets River Basin
The Distributed Hydrology Soil Vegetation Model (DHSVM) (Wigmosta et al. 1994), with a sediment module (Doten et al. 2006) was used for this study.
DHSVM mass wasting is stochastic in nature.
Model
HILLSLOPE EROSION
Soil Moisture Content
CHANNEL ROUTI NGPrecipitationLeaf Drip
Infiltration and Saturation Excess Runoff
DHSVM
Q
Qsed
Q
Qsed
Sediment
MASS WASTI NG
Erosion
Deposition
ROADEROSION
Sediment
Channel Flow
Infinite Slope Model
Uses Factor of Safety Approach
Doten et al 2006
Hydrologic calibration and evaluation (NS = 0.52, Volume Error = 22%; other studies looking into reasons behind poor model performance)
Evaluation of mass wasting module over sub-basins
Model Calibration and Evaluation
Slide Year Historic Landslides TotalSurface Area(m2)
Total Surface Area(m2) of All Cells Factor Safety <1
(From Modeled Run)
Sub-basin 1 10614 11400Sub-basin 2 15257 13678
1 23
Factors considered: slope, soil, vegetation
* The primary factors triggering harvesting-related shallow landslides (Watson et al. 1999).
Factors affecting landslide susceptibility
Watson et al. 1999
Elevation class (m)
Slope Class (Degree)
Soil Classes Vegetation Classes
0-500 0-10 Sand Deciduous Broadleaf
<500 11-20 Silty Loam Mixed forest
21-30 Loam Coastal conifer
31-40 Silty clay Loam Mesic conifer
40-50 Talus
>50
Logging Scenarios for Model Simulation
Properties changed to simulate logging:
1.Root cohesion 2.Vegetation Surcharge 3.Fractional coverage
Selection of Logging Scenario
Clear-cutting done in20-30 degree sloperange.
Results: Slope Classes
Results: Soil Classes
Results: Vegetation Classes
Results: Elevation Classes
Landslide Susceptibility Map for Historical ClimateWeighted
indices calculated for each category of each class
Used to determine the susceptibility class
Comparison to actual landslides in logged areas
All the polygons are harvested areas processed from 1990 Landsat-TM image. Weights were calculated for each cell on the harvested area and three susceptibility classes are created.
Red marks are all historical landslides between 1990 to 1997, collected from DNR HZP inventories.
Climate change effectsCGCM(B1) 2045
Climate change effectsCGCM(A1B) 2045
Results indicate that 30 to 50 degree slopes range and certain types of soils (e.g. talus, sandy) are most vulnerable for logging-induced landslides.
For 2045 projected climate areas with high landslide risk increased on average 7.1% and 10.7% for B1 and A1B carbon emission scenarios, respectively.
Ongoing Work:Model inputs and calibrationMore extensive model evaluation Isolate effects of soil and terrain factors Isolate effects of precipitation versus temperature
changesMore realistic post-logging effects Impacts on riparian habitat
Summary
Factor of safety calculation
CS = Soil cohesionCr = Root cohesionФ= Angle of internal frictiond= Depth of soilm= Saturated depth of soilS = Surface slopeq0 = Vegetable surcharge
Weight Calculation
Wi= The weight given to the ith class of a particular thematic layer
Npix(Si)=The number of slides pixels in a certain thematic class
Npix(Ni)=The total number of pixels in a certain thematic class.
n= The number of classes in the Thematic map
Yin and Yan (1988), Saha et al. (2005)
Weight for a particular cell W = ƩWi
Susceptibility Class Segmentation
No of landslides cell in the
susceptibility classNo. of total cells in the
susceptibility classPercentage of landslides in a susceptibility class
Low(<.05) 621 28049 2.2
Medium(.05-.79) 617 25099 2.5
High(>0.79) 627 19021 3.3
Index segmentation and evaluation
Frequency of slides in different susceptibility classes.
LSI value had the range from -3.24 to 2.21. This range was divided into three susceptibility classes based on cumulative frequency values of LSI on slide areas ( Saha et al. 2005). The breaks were done at 33 and 67%.
classesCGCM_3.1t47 (A1B)
CGCM_3.1t47 (B1)
CNRM-cm3 (A1B)
CNRM-cm3 (B1)
(a)Elevation(m)0-500 2.3 1.9 2.3 2.6>500 4.6 5.4 3.0 4.4(b)slope(Degree)<10 US* US* US* US*
10-20 8.5 7.1 8.0 8.720-30 6.2 9.1 10.7 6.030-40 2.3 5.0 2.9 2.740-50 1.0 1.0 0.6 2.5>50 0.2 0.1 0.2 0.2(c)Soil Sand 12.3 16.9 8.5 19.2Silty Loam 1.2 1.4 1.5 2.1Loam 4.4 2.8 1.9 3.2silty clay Loam 7.7 9.0 6.0 7.7Clay 3.6 10.9 14.5 10.9Talus 9.2 12.0 11.2 8.5(d) VegetationDeciduous Broadleaf 10.8 12.5 10.9 11.2Mixed forest 1.4 5.1 9.8 4.8Coastal conifer forest 0.2 0.6 0.3 0.5Mesic conifer forest 5.2 6.4 5.1 6.7
Climate change scenarios
Increment of slides in harvested areas for different climate change scenarios
Susceptibility Class
Historical
CGCM_A1B
Percentage change
CGCM_B1
Percentage change
CNRM_A1B
Percentage change
CNRM_B1
Percentage change
Low 4120646 4130782 0.25 4131625 0.27 4130782 0.25 4130782 0.25
Medium 3224217 2783816 -13.66 3078979 -4.50 2750346 -14.70 2772799 -14.00
High 4187537 4617802 10.27 4321796 3.21 4651272 11.07 4628819 10.54
Climate Change Effect
Change in percentage of areas in different susceptibility classes for different climate change scenarios with respect to the historical scenario. For all the future climate change scenarios areas increased under the high susceptibility class.