Impacts of Landuse Management and Climate Change on Landslides Susceptibility over the Olympic Peninsula of Washington State Muhammad Barik and Jennifer Adam Washington State University, Department of Civil & Environmental Engineering, Pullman, WA. AGU Fall Meeting 2009 ABSTRACT: The Olympic Experimental State Forest (OESF) is a commercial forest lying between the Pacific coast and the Olympic Mountains. As this area is critical habitat for numerous organisms, including salmon, there is a need to investigate potential management plans to promote the economic viability of timber extraction while protecting the natural habitat, particularly in riparian areas. As clear-cutting reduces the strength of the soil, and as projected climate change may result in storms with higher intensity precipitation, this area may become more susceptible to landslide activity. This may result in potentially severe consequences to riparian habitat due to increased sediment loads. Therefore, this study was performed with the objective to quantify the impacts of land cover and climate changes on slope stability. A physically-based hydrology model, the Distributed Hydrology Soil Vegetation Model (DHSVM) with the sediment module, was used for this analysis. To map areas susceptible for landslides, logging was done for different combinations of soil, vegetation and slope classes. To investigate the impacts of climate change on landslide susceptibility we applied downscaled output from two General Circulation Models (GCMs) with two greenhouse gas emission scenarios. An understanding of the areas most vulnerable to landslides in an altered climate and due to timber harvesting will help in the development of sustainable forest management practices.  The study domain for this research is the Queets basin, located on the Olympic Peninsula in northwest Washington State (see inset at top).  The DHSVM mass sediment module was applied to a select tributary of the Queets basin (shown at left). Area (km 2 )Elevation (m) Average Annual Precipitation (m) Average Annual Flow (m 3 /s) Table: Queets river characteristics Figure: Observed and simulated stream flow between 2001 and Peak flows and dry season flows are underestimated by the model.  For the calibration period ( ),the Nash Sutcliffe coefficient is 0.62 while the volume error is 8%; and for the evaluation period ( ) they are 0.58 and 11%, respectively.  For evaluation of the mass wasting module, we compared observed and simulated landslide area for storms occurring in 1985 and Conclusions We have presented an approach to quantify the effects of logging and future climate on landslide susceptibility, which can be developed as a decision-making tool for forest management. Findings from this analysis include:  Landslide frequency increases substantially in harvested areas and the increment is related to geography, soil and vegetation types.  Certain combinations of soil, vegetation, and slopes are more vulnerable to landslide activity than others.  Mapping of susceptible landslide areas based on the study shows vulnerable areas for timber harvesting. This information can be used for long-term forest management planning.  A more rigorous evaluation should be done with historical landslide data, which is part of the future scope of this study. ACKNOWLEDGEMENTS: Funding for this project is being provided by the State of Washington Water Research Center (SWWRC). 2.Study Domain  Model: DHSVM (Wigmosta et al. 1994) with its mass wasting module (Doten etal.2006)  The key component for this study, the mass wasting module, is stochastic in nature.  Simulations were carried out for year 2045 projected climate and for the historical period of  We applied downscaled climate data from two GCMs (CGCM3.1_t47 and CNRM_cm3) for two emissions scenarios (A1B and B1). The DHSVM results for the two GCMs were averaged.  The results indicate that landslide frequency increases under 2045 projected climate but the rate is not significant. Schematic diagram for DHSVM mass wasting module (Doten et al., 2006) 3.Model Description Q Q sed Slide Year Historic Landslides Total Surface Area (m 2 ) Total Surface Area (m 2 ) of All Cells Factor Safety <1 (From Modeled Run)  The weighting factors (W f ) described in Box 5 are binned into 1 of 7 index ranges (see table at right and figure at left). High index values indicate a high landslide susceptibility.  Logged areas for the year 1985 are detected by a Landsat-5 image (see black polygons at left).  The locations of actual landslides that occurred between were projected onto the map (see red points at left)  Comparison of actual landslide activity and the distribution of index values show a positive relationship (see table at right). abc Figure (a) shows landslide susceptibility associated with timber harvesting in the basin for historic climate. Figures (b) & (c) show susceptibility levels for the B1 & A1B carbon emission scenarios, respectively. From these maps we can observe little change in landslide susceptibility due to projected climate change by the year Index Range Percentage of the Logging Area in the Index Range Number of Historical Landslides > Table: Number of historical landslides in the logged areas of the basin for different index ranges. (The index ranges represent zones of landslide susceptibility). 1.The Goal 4.Model calibration & Evaluation c 7.Results of climate change From the table we can see that the model over-predicts landslide activity for 1990, but performs reasonably for DHSVM Hydrology Model Soil, vegetation, DEM and mask file input at 150m resolution Run Mass Wasting Module for historical case Run Mass Wasting Module for wide-spread logging in different slope, elevation, soil and vegetation classes Weighting factor (W f ) calculated for each cell of the basin and classified Create landslide risk zoning map for timber harvesting Meteorology Input DHSVM Mass Wasting Module for 10m Resolution Compared to historical landslides 5.Methodology for Mapping Landslide Risk due to Timber Harvesting A weighting factor (W f ) is applied to relate landslide vulnerability with timber harvesting for each cell (f) of the basin, according to the following method: W f = ∑ W ij for each I and j, where: W ij = Weighting factor for class j of factor i i = Factor: elevation, slope, vegetation, or soil j = Class for each factor (i.e., elevation, slope, vegetation, and soil are binned into various classes) W ij is calculated as follows: W ij = 1000*F ij /(T ij *T b ) F ij = Increased number of failed cells due to logging for class j of factor i T ij = Number of cells in the logged area for class j of factor i T b = Total number of failed cells failed for the base case 6.Landslides and Harvesting  The objective of this study is to predict the long term effects of timber harvesting under projected climate change on slope stability.  The overarching goal of this approach is to develop a decision making tool that can be used by forest managers to make long-term planning decisions.