Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho

This study utilizes GIS modeling to determine if the location of 559 landsl ides in the 875 km(2) catchment of the Middle Fork of the Payette River, Id aho can be predicted based on topographic attributes and a wetness index ge nerated by the DYNWET model. Slope and elevation were significantly related to landslide occurrence at this landscape scale. Aspect was also retained as a variable for further analysis because, despite a non-significant chi-s quare relation to landslide occurrence, graphical analysis suggested a rela tion between aspect and mass wasting. Chi-square analysis indicated that pl an and profile curvature, flow path length, upslope contributing area. and the DYNWET-based moisture index were not significantly related to landslidi ng. A Bayesian probability model based on combinations of elevation, slope, aspect, and wetness indicates that elevation exhibits the closest relation to landsliding. followed by slope; but that neither aspect nor wetness ind ex values help in prediction. The Bayesian probability model using elevatio n and slope generates a map of relative landslide risk that can be used to direct activities away from mass wasting prone areas, The association betwe en elevation and landslides is perplexing but is perhaps due to the locatio n of logging road at specific elevations (roads could not be included in th e input data for analysis because they have not been adequately mapped). Th e lack of explanation provided by the DYNWET wetness index was also surpris ing and may be due to the 30-m digital elevation model (DEM) and the soils data having resolutions too coarse to adequately portray local variations k ey to mass wasting. We believe the inadequacy of data to drive the models i s typical of the majority of catchment scale setting. For now, the ability of researchers to effectively model landscape scale landsliding is more lim ited by the type, resolution, and quality of available data than by the qua lity of the landslide models. (C) 2001 Elsevier Science B.V. All rights res erved.