MUHAMMAD BARIK & JENNIFER ADAM WASHINGTON STATE UNIVERSITY

Steve Burges Retirement Symposium March 26

th

, 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

Soil Moisture Content MASS WASTING Channel Flow Sediment Q sed 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

Slide Year

Sub-basin 1 Sub-basin 2 2 3 1

Historic Landslides Total Surface Area(m 2 )

10614 15257

Total Surface Area(m 2 ) of All Cells Factor Safety <1 (From Modeled Run)

11400 13678

 Factors considered: slope, soil, vegetation * The primary factors triggering harvesting related shallow landslides (Watson et al. 1999).

Watson et al. 1999

Logging Scenarios for Model Simulation

Elevation class (m) 0-500 Slope Class (Degree) 0-10 <500 11-20 Soil Classes Sand Silty Loam Vegetation Classes Deciduous Broadleaf Mixed forest 21-30 31-40 40-50 >50 Loam Silty clay Loam Talus Coastal conifer Mesic conifer



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

C S C r = Soil cohesion = Root cohesion Ф = Angle of internal friction d= Depth of soil m= Saturated depth of soil S = Surface slope q 0 = Vegetable surcharge

Yin and Yan (1988), Saha et al. (2005) 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 Weight for a particular cell W = ƩW i

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

Low(<.05) Medium(.05-.79) High(>0.79)

No of landslides cell in the susceptibility class

621 617 627

No. of total cells in the susceptibility class

28049 25099 19021

Percentage of landslides in a susceptibility class

2.2

2.5

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

CGCM_3.1t47

(A1B)

2.3

4.6

US * 8.5

6.2

2.3

1.0

0.2

12.3

1.2

4.4

CGCM_3.1t47 (B1)

1.9

5.4

US * 7.1

9.1

5.0

1.0

0.1

16.9

1.4

2.8

CNRM-cm3 (A1B)

2.3

3.0

US * 8.0

10.7

2.9

0.6

0.2

8.5

1.5

1.9

CNRM-cm3 (B1)

2.6

4.4

US * 8.7

6.0

2.7

2.5

0.2

19.2

2.1

3.2

silty clay Loam Clay Talus (d) Vegetation Deciduous Broadleaf Mixed forest Coastal conifer forest 7.7

3.6

9.2

10.8

1.4

9.0

10.9

12.0

12.5

5.1

6.0

14.5

11.2

10.9

9.8

7.7

10.9

8.5

11.2

4.8

0.2

0.6

0.3

0.5

Mesic conifer forest 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 High 3224217 4187537 2783816 4617802 -13.66

10.27

3078979 4321796 -4.50

3.21

2750346 4651272 -14.70

11.07

2772799 4628819 -14.00

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.