runoutSIM v1.0: an R package for regionally simulating landslide runout and connectivity using random walks

Abstract. Regional-scale runout modelling for landslide hazard assessment and land-use planning helps us understand not only the general likelihood of being impacted by their runout, but also how runout paths and distances vary under different environmental conditions. While R is widely used in geosciences for spatial prediction and susceptibility modelling, most existing runout models are not implemented directly in R, often requiring coupling with external software. This creates barriers for model development, modification, and integration with other geospatial and statistical tools. To address this, runoutSIM is presented, an open-source R package for simulating the spatial extent, velocity, and connectivity of landslide runout at a regional scale. The model combines random walks to represent flow paths with a process-based approach to control runout distance and includes functionality to estimate the connectivity probability of runout from source areas intersecting with downslope features. In this model, the runout path and connectivity probabilities can also be adjusted by using spatial likelihoods of source cell predictions, such as those derived from statistical or machine learning models. In addition, runoutSIM provides an interactive map viewing environment within R that allows users to explore and query simulation results and related spatial data. By implementing these algorithms natively in R, runoutSIM lowers technical barriers, supports flexible model development, and enables integration with data-driven approaches. We demonstrate the package in the Río Olivares basin, Chile, where a regional runout model optimized using a random grid search, machine-learning prediction of source areas, and simulation of runout connectivity help identify areas most susceptible to hazardous runout and potential source locations. runoutSIM provides a transparent and reproducible framework for regional runout modelling, supporting hazard assessment and enabling further development within R, a widely used geoscientific environment.