1. Description of Research Question:

The Oregon Coast Range routinely plays host to disastrous landslides. The primary reason for these landslides is that the range provides a unique combination of high annual precipitation with the presence weak marine sediments (Olsen et al. 2015). During winter storms, it is not uncommon for major transportation corridors to become inoperable, impacting local economies and the livelihoods of residents (The Oregonian 2015a, 2015b). Overall, landslides in Oregon cost an average of $10 million annually, with losses from particularly severe storms having cost more than $100 million (Burns and Madin 2009).

While these rainfall-induced landslides may sometimes be large, deep-seated failures, they most frequently occur in the form of shallow translational failures. These shallow landslides typically occur in the upper few meters of the soil profile, and may result in heavy damage to forest access roads or the temporary closure of major roads.

Recently, I developed a limit equilibrium slope stability model for use in mapping shallow landslides during rainfall. In its current form, the model a deterministic equation that computes a factor of safety against failure for each cell of a digital elevation model (DEM). The problem with this approach is that if fails to account for spatial and temporal variation of input parameters and it only considers a single DEM resolution. My research question is to explore how the incorporation of a probabilistic framework, which expresses the confidence in each input and multiple scales of application, influences the predictive power of the model.

2. Datasets:

The dataset analyzed for this project consists of three parts:

1. Data from Smith et al. (2013), who performed hydrologic monitoring of a clear-cutted hillslope in the Elliot State Forest of southwestern Oregon. Monitoring was performed over a three year period, with measurements of rainfall, volumetric water content, and negative pore water pressure taken at hourly increments. Volumetric water content and negative pore water pressure were measured in eight separate soil pits, with each pit being instrumented three times between 0 and 3.0 meters in depth.
2. Lidar derived DEM from the Oregon Lidar Consortium for the Elk Peak quadrangle in southwestern Oregon.
3. The Statewide Landslide Information Database for Oregon (SLIDO) corresponding to the Elk Peak quadrangle.

3. Hypotheses:

The existing model, despite being insufficient to meet the goals of this project, has provided valuable insight into the influence of rainfall on slope instability. Like other slope stability methods, topography and soil strength will account for most of the stability. These two factors combined are expected to bring soils to a critical state, but not a state of failure. The addition of rainfall will then determine whether slopes fail or not. This approach should be most interesting when using the model to forecast landslide hazards based on predicted weather.

4. Approaches:

I am not clear on exactly what types of analyses need to be undertaken to further my project. My hope is that the advice from peers and assignments associated with this course will help me choose the necessary steps, given my set of goals. I anticipate that most work will be performed in either ArcGIS or Matlab.

5. Expected outcome:

This project is expected to produce a statistical model that estimates the probability of failure for a given set of conditions. The model is intended for use in mapping applications, and the primary outcome will be rainfall-induced landslide hazard maps for the Elk Peak quadrangle.

6. Significance:

Accurate hazard maps allow land managers and homeowners to better understand the risk posed by landslides. This method is expected to go a step forward by using rainfall predictions to produce pre-storm maps, which will provide hazard maps specific to a severe rainfall event. Maps of this nature would be especially important because they would allow agencies like the Oregon Department of Transportation to know where resources might be needed before any damage has actually occurred.

7. Your level of preparation:
   1. I have extensive experience with ArcGIS and model builder from coursework and research during my master’s degree. I have also served as a TA for the OSU CE 202 course (a civil engineering course on GIS), which gave me greater abilities in troubleshooting ArcGIS and working with Modelbuilder.
   2. My experience with GIS programming in Python is moderate, and mainly the resulting of taking GEO 578.
   3. I have no experience with R.

References

Burns, W.J., and Madin, I.P. (2009). “Protocol for Inventory Mapping of Landslide Deposits from Light Detection and Ranging (LIDAR) Imagery.” Oregon Department of Geology and Mineral Industries, Special Paper 42.

Olsen, M.J., Ashford, S.A., Mahlingam, R., Sharifi-Mood, M., O’Banion, M., and Gillins, D.T. (2015). “Impacts of Potential Seismic Landslides on Lifeline Corridors.” Oregon Department of Transportation, Report No. FHWA-OR-RD-15-06.

Smith, J.B., Godt., J.W., Baum, R.L., Coe, J.A., Burns, W.J., Lu, N., Morse, M.M., Sener-Kaya, B., and Kaya, M. (2013). “Hydrologic Monitoring of a Landslide-Prone Hillslope in the Elliot State Forest, Southern Coast Range, Oregon, 2009-2012.” United States Geological Survey, Open File Report 2013-1283.

The Oregonian (2015a). “U.S. 30 closes and reopens in various locations due to landslides, high water.” December 17, 2015. <http://www.oregonlive.com/portland/index.ssf/2015/12/high_water_closes_one_us_30_ea.html>

The Oregonian (2015b). “Landslide buckles Oregon 42, closing it indefinitely,” December 25, 2015. <http://www.oregonlive.com/pacific-northwest-news/index.ssf/2015/12/landslide_buckles_oregon_42_cl.html>