CONSTRAINING MEAN LANDSLIDE OCCURRENCE RATES FOR NON-TEMPORAL LANDSLIDE INVENTORIES USING HIGH-RESOLUTION ELEVATION DATA

Constraining landslide occurrence rates is important for landscape evolution models and generating landslide hazard models that predict the spatial and temporal occurrence of landslides. However, most landslide inventories available across the world do not include any temporal data due to the large resources required for dating landslide deposits from pre-satellite era landslide events. We introduce a method for estimating the mean landslide occurrence rate of rotational and translational slides derived solely from high-resolution ( 3m) elevation data. The method first estimates representative roughness values of fresh landslide deposits and non-landslide locations within a spatial domain of interest. Non-landslide locations are slope units within the domain that do not contain a landslide. It then applies a linear diffusion model to the fresh landslide deposits until they reach the representative roughness value of the non-landslide locations. This replicates the time for a landslide deposit to be unrecognizable in high-resolution digital elevation data. We term this time the mean lifetime of the landslides. The diffusion coefficient required by the linear diffusion model is estimated from previous work relating globally available datasets of mean annual precipitation to the mean coefficient value. Using the mean lifetime and density of landslides within the spatial domain of interest, we can estimate the mean occurrence rate of landslides over that domain. We validate this approach using a comprehensive temporal inventory of landslides in western Oregon created using age-roughness curves that are calibrated with high-resolution elevation data and radiocarbon dates. We find good agreement between our method and the age-roughness-derived estimates. Preliminary results produce a mean lifetime of 5300 and 6200 years for the age-roughness-derived estimates and our method, respectively. Due to the relative abundance of high-resolution elevation data compared to age-dated landslides, our method could help constrain landslide occurrence rates in areas where it was previously unfeasible.