ABSTRACT

     Map data that predict the varying likelihood of landsliding can help public agencies make informed decisions on land use and zoning. This map, prepared in a geographic information system from a statistical model, estimates the relative likelihood of local slopes to fail by two processes common to an area of diverse geology, terrain, and land use centered on metropolitan Oakland. The model combines the following spatial data: (1) 120 bedrock and surficial geologic-map units, (2) ground slope calculated from a 30-m digital elevation model, (3) an inventory of 6,714 old landslide deposits (not distinguished by age or type of movement and excluding debris flows), and (4) the locations of 1,192 post-1970 landslides that damaged the built environment. The resulting index of likelihood, or susceptibility, plotted as a 1:50,000-scale map, is computed as a continuous variable over a large area (872 km²) at a comparatively fine (30 m) resolution. This new model complements landslide inventories by estimating susceptibility between existing landslide deposits, and improves upon prior susceptibility maps by quantifying the degree of susceptibility within those deposits.
     Susceptibility is defined for each geologic-map unit as the spatial frequency (areal percentage) of terrain occupied by old landslide deposits, adjusted locally by steepness of the topography. Susceptibility of terrain between the old landslide deposits is read directly from a slope histogram for each geologic-map unit, as the percentage (0.00 to 0.90) of 30-m cells in each one-degree slope interval that coincides with the deposits. Susceptibility within landslide deposits (0.00 to 1.33) is this same percentage raised by a multiplier (1.33) derived from the comparative frequency of recent failures within and outside the old deposits. Positive results from two evaluations of the model encourage its extension to the 10-county San Francisco Bay region and elsewhere. A similar map could be prepared for any area where the three basic constituents, a geologic map, a landslide inventory, and a slope map, are available in digital form. Added predictive power of the new susceptibility model may reside in attributes that remain to be explored—among them seismic shaking, distance to nearest road, and terrain elevation, aspect, relief, and curvature.