Landslide Hazards in North Alabama: Physical Assessment, Monitoring, and Prediction

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Monday, 3 February 2014
Hall C3 (The Georgia World Congress Center )
Brian Freitag, University of Alabama, Huntsville, AL; and A. Kaulfus, E. Anderson, Y. Wu, K. Srinivasan, U. Nair, K. Keiser, B. Howell, B. Ashmall, and D. Irwin

Landslides are often costly and, at times, deadly disasters. Whereas intense and/or sustained rainfall is often the primary trigger for landslides, soil type and slope are also important factors in determining the location and timing of slope failure. Accounting for the substantial spatial variability of these factors is one of the major difficulties when predicting the timing and location of slope failures. The wireless sensor network (WSN), developed by NASA SERVIR and USRA, with peer-to-peer communication capability and low power consumption, makes it ideal for high spatial in situ monitoring in remote locations. In collaboration with the University of Huntsville at Alabama, the WSN equipped with accelerometers, rainfall and soil moisture sensors is being integrated into an end-to-end landslide warning system. A set of these WSN nodes are undergoing field testing at the Monte Sano State Park site in Huntsville, AL where substantial elevation gradients and periodic high rainfall provide an ideal proving ground. The WSN is being tested to ascertain communication capabilities and the density of nodes required depending upon the nature of terrain and land cover. The performance of a hydrologic-slope stability model, to be utilized in the end-to-end system, is being evaluated by comparing against landslides that occurred during the 6th and 7th of May, 2003 and 20th and 21st of April, 2011. The model provides a deterministic assessment of slope stability by evaluating horizontal and vertical transport of underground water and associated weight bearing capacity. Terrain is specified using high spatial resolution SRTM data and rainfall using high spatial resolution numerical modeling, radar derived rainfall datasets. In the proposed end-to-end system, the model will be coupled to the WSN, and the in situ data collected will be used to drive the model. In turn, the output from the model could be communicated back to the WSN providing the capability for warning at site. The WSN will also be able to monitor slope failure and provide further information for tuning the model. Results from the field tests of the WSN, model evaluation and the performance of the end-to-end system will be presented.