Supplement - Physiographic Variable Raster Data

To link physiographic variables to lithology and trail characteristics (Objective 2), we created process domain maps and used principal component analysis considering physiographic variables such as slope, topographic position index (TPI), and concavity as a function of lithology and trail type. All metrics were calculated using ArcGIS Pro and lidar collected 2013 by McKim & Creed Inc. for City of Boulder. High-resolution lidar data can be useful in determining different types of processes, ranging from slow creep to landslides (Booth et al. 2009; Booth et al. 2013) and variables like slope highlight areas where mass movements are likely to occur. Similarly, concavity, or landscape curvature, indicates where advective versus diffusive processes occur, correlated to convex versus concave landscapes, respectively (Dietrich and Perron 2006; Sweeney et al., 2015). Concave processes are dominated by diffusive movement (slope-dependent transport; Dietrich and Perron 2006). These processes are competing against one another on the hillslope and give rise to diffusion-dominated ridges and advection-dominated valleys (Dietrich and Perron 2006; Sweeney et al., 2015). The TPI is a landform classification used to determine roughness indices like valleys and ridges in the study area. The TPI was calculated at different resolutions (5-m, 10-m, 50-m, 100-m) to see if different hillslope attributes were identifiable at the different scales. To calculate the TPI, the mean for each resolution was subtracted from the Digital Elevation Model (DEM). Derived physiographic variables and GIS data products can be found in this repository.