Transcript Slide 1

MUHAMMAD BARIK & JENNIFER ADAM
WASHINGTON STATE UNIVERSITY
Steve Burges Retirement Symposium
March 26th, 2010
 Availability: the open door
 Inspiration, optimism
 Balancing direction and self direction
 The art of questioning and listening
 Being widely read and widely accepting
 The initial project
 Life after science
 Celebration
 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).
 Forest Management Objective
 Increasing economic viability while preserving the
natural environment.
 “Zoned” management approach
 Previous Best Management Practice Studies
 Impacts on landslides are site specific
 No incorporation of climate change effects into long
term plans
 To provide high resolution maps of the susceptibility
of landslide activity to timber extraction under
historical and future climate conditions.
 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?
 “Unzoned”
Management
Approach
Source: DNR
 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.
 Infinite Slope
Model
 Uses Factor of
Safety
Approach
MASS WASTING
Soil Moisture
Content
Q
Sediment
Qsed
Channel Flow
Sediment
DHSVM
Precipitation
Leaf Drip
Infiltration and Saturation
Excess Runoff
CHANNEL ROUTING
Erosion
Deposition
Doten et al 2006
HILLSLOPE
EROSION
ROAD
EROSION
 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
3
1
2
Historic Landslides Total
Surface Area(m2)
Total Surface Area(m2) of
All Cells Factor Safety <1
(From Modeled Run)
Sub-basin 1
10614
11400
Sub-basin 2
15257
13678
Slide Year
 Factors considered:
slope, soil,
vegetation
* The primary factors
triggering harvestingrelated shallow
landslides (Watson et
al. 1999).
Watson et al. 1999
Logging Scenarios for Model Simulation
Elevation class
(m)
Slope Class
(Degree)
Soil Classes
Vegetation
Classes
0-500
0-10
Sand
<500
11-20
Silty Loam
Deciduous
Broadleaf
Mixed forest
21-30
Loam
Coastal conifer
31-40
Silty clay Loam
Mesic conifer
40-50
Talus
>50
 Properties changed to simulate
logging:
1.Root cohesion
2.Vegetation Surcharge
3.Fractional coverage
Clear-cutting done in
20-30 degree slope
range.
 Weighted
indices
calculated for
each category
of each class
 Used to
determine
the
susceptibility
class
Red marks are
all historical
landslides
between
1990 to 1997,
collected from
DNR HZP
inventories.
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.
CGCM(B1) 2045
CGCM(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 calibration
 More extensive model evaluation
 Isolate effects of soil and terrain factors
 Isolate effects of precipitation versus temperature changes
 More realistic post-logging effects
 Impacts on riparian habitat
CS = Soil cohesion
Cr = Root cohesion
Ф= Angle of internal friction
d= Depth of soil
m= Saturated depth of soil
S = Surface slope
q0 = Vegetable surcharge
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
Yin and Yan (1988), Saha et al. (2005)
Weight for a particular cell W = ƩWi
Npix(Ni)=The total number of pixels
in a certain thematic class.
n= The number of classes in the
Thematic map
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%.
Susceptibility Class
Segmentation
No of landslides cell in
the susceptibility class
No. of total cells in the
susceptibility class
Percentage 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
Frequency of slides in different susceptibility classes.
classes
(a)Elevation(m)
0-500
>500
(b)slope(Degree)
<10
10-20
20-30
30-40
40-50
>50
(c)Soil
Sand
Silty Loam
Loam
silty clay Loam
Clay
Talus
(d) Vegetation
Deciduous
Broadleaf
Mixed forest
Coastal conifer
forest
Mesic conifer
forest
CGCM_3.1t47
(A1B)
CGCM_3.1t47 (B1)
CNRM-cm3 (A1B)
CNRM-cm3 (B1)
2.3
4.6
1.9
5.4
2.3
3.0
2.6
4.4
US*
8.5
6.2
2.3
1.0
0.2
US*
7.1
9.1
5.0
1.0
0.1
US*
8.0
10.7
2.9
0.6
0.2
US*
8.7
6.0
2.7
2.5
0.2
12.3
1.2
4.4
16.9
1.4
2.8
8.5
1.5
1.9
19.2
2.1
3.2
7.7
3.6
9.2
9.0
10.9
12.0
6.0
14.5
11.2
7.7
10.9
8.5
10.8
1.4
12.5
5.1
10.9
9.8
11.2
4.8
0.2
0.6
0.3
0.5
5.2
6.4
5.1
6.7
Increment of slides in harvested areas for different climate change scenarios
Susceptibi
lity Class
Historical
CGCM_A1
B
Percentag
e change
CGCM_B1
Percentag
e change
CNRM_A1
B
Percentag
e change
CNRM_B1
Percentag
e 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
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.