2009 Portland GSA Annual Meeting (18-21 October 2009)

Paper No. 2
Presentation Time: 8:15 AM

DATA WRANGLING IN EARLY STAGES OF LANDSLIDE INVENTORY: HOW TO PRIORITIZE WHAT YOU HAVE


CRAWFORD, Matthew M., Kentucky Geological Survey, University of Kentucky, 228 Mining and Mineral Resources Building, Lexington, KY 40506, mcrawford@uky.edu

Steep topography, local geology, varying thicknesses of surficial materials, coal mining, and many other factors can contribute to high landslide susceptibility in many parts of Kentucky. Heavy precipitation, drought, artificial slope disturbance, or other triggers can initiate landslides in various forms, which can damage critical infrastructure, businesses, and homes. Landslides are commonly viewed as unpredictable, but knowledge of ground conditions (topography, geology, drainage) combined with well-planned construction can reduce exposure to the hazard and help reduce landslide-related losses. The Kentucky Transportation Cabinet has spent $44 million on landslide remediation since 1998, and has nearly $70 million in projects planned over the next 6 years.

Early stages of inventory development include data collection, field verification, and organization of a landslide database. Sources of landslide data are preexisting maps, the Kentucky Transportation Cabinet, the Division of Natural Resources, Division of Abandoned Mine Lands, and anecdotal information. All mass-wasting processes, consisting of variable failure types and geologic attributes, occur across the state. A cumulative inventory of landslides with such geologic variety and different hazard impacts is difficult. How to prioritize these locations and choosing which ones to focus attention on are also challenging. How reliable are the locations? What source allows the most field verification?

A landslide inventory that can address costs and ultimately reduce the risk of landslides will be the ideal goal, informing State agencies and the public of the risk involved with all types of development. Incorporating vast amounts of variable landslide data in an organized, effective way is the challenge.