An empirical modeling of road related and non-road related landslide hazard for a large geographical area using logistic regression in tandem with signal detection theory is presented. This modeling was developed using geographic information system (GIS) and remote sensing data, and was implemented on the Clearwater National Forest in central Idaho. The approach is based on explicit and quantitative environmental correlations between observed landslide occurrences, climate, parent material, and environmental attributes while the receiver operating characteristic (ROC) curves are used as a measure of performance of a predictive rule. The modeling results suggest that development of two independent models for road related and non-road related landslide hazard was necessary because spatial prediction and predictor variables were different for these models. The probabilistic models of landslide potential may be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.

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The Water Erosion Prediction Project (WEPP) is a physically based erosion model for applications to dryland and irrigated agriculture, rangeland, and forests. U.S. Forest Service (USFS) experience showed that WEPP was not being adapted because of the difficulty in building files describing the input conditions in the existing interfaces. To address this difficulty, a suite of Internet interfaces with a database was developed to more easily predict soil erosion for a wide range of climatic and forest conditions, including roads, fires, and timber harvest. The database included a much larger climate database than was previously available for applications in remote forest and rangeland areas. Validation results showed reasonable agreement between erosion values reported in the literature and values predicted by the interfaces to the WEPP model.

Many forests and their associated water resources are at increasing risk from large and severe wildfires due to high fuel accumulations and climate change. Extensive fuel treatments are being proposed, but it is not clear where such treatments should be focussed. The goals of this project were to: (1) predict potential post-fire erosion rates for forests and shrublands in the western United States to help prioritise fuel treatments; and (2) assess model sensitivity and accuracy. Post-fire ground cover was predicted using historical fire weather data and the First Order Fire Effects Model. Parameter files from the Disturbed Water Erosion Prediction Project (WEPP) were combined with GeoWEPP to predict post-fire erosion at the hillslope scale. Predicted median annual erosion rates were 0.1–2 Mg ha–1 year–1 for most of the intermountain west, ~10–40 Mg ha–1 year–1 for wetter areas along the Pacific Coast and up to 100 Mg ha–1 year–1 for north-western California. Sensitivity analyses showed the predicted erosion rates were predominantly controlled by the amount of precipitation rather than surface cover. The limited validation dataset showed a reasonable correlation between predicted and measured erosion rates (R2 = 0.61), although predictions were much less than measured values. Our results demonstrate the feasibility of predicting post-fire erosion rates on a large scale. The validation and sensitivity analysis indicated that the predictions are most useful for prioritising fuel reduction treatments on a local rather than interregional scale, and they also helped identify model improvements and research needs.