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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.