Tools for more reliable regional landslide risk reduction products

Regional-scale (>500 km2) landslide risk reduction models often suffer from their reliance on difficult-to-obtain data, such as inventories of past landslide locations or geomechanical properties of the hillslopes. However, over regional scales, these data are often sparse, inconsistent, and nonrepresentative, which can lead to biased model outputs. In this talk, I will present two recent studies that help overcome these challenges. First, I will share a method of assessing shallow landslide susceptibility based on a morphometric analysis of the landscape’s topography. Despite having no knowledge of past landslide locations, the morphometric approach can better characterize landslide potential over contrasting data-driven models that are generally used for assessing regional-scale landslide susceptibility. Second, I will present a Bayesian statistical comparison of common metrics used to predict rainfall-triggered landsliding across disparate regions of the United States. I will show that parsimonious leaky-bucket models, whose only inputs are a drainage constant and estimates of precipitation, can better distinguish landslide-triggering rainfall from non-landslide-triggering rainfall compared to traditional rainfall metrics or more complex models. The leaky-bucket model had the best performance across vastly different environments within the United States, suggesting that it may be used universally to enhance regional-scale landslide risk reduction efforts.