The overarching effects and benefits of land management decisions, such as through watershed restoration, are often not fully understood due to a lacking control within an experimental design. This can be addressed through the application of a paired watershed approach, allowing for comparison between treatment and control watersheds. We developed and applied a statistic-based hierarchical clustering analysis for watershed pairing within an experimental landscape consisting of numerous superficially structurally-similar sub-basins to address this concern. Our three-step research approach follows: 1) We construct a comprehensive spatial database consisting of various biophysical, structural, and modeled hydrologic data for each watershed. 2) We apply a correlation analysis to reduce the dimensionality of the spatial datasets and select specific spatial variables using a mixed quantitative and qualitative approach. 3) We complete hierarchal clustering analyses to group watersheds based on their spatial properties. This data release consists of three primary products, 1) a vector shapefile, 2) an R software script, and 3) a Google Earth Engine (GEE) script. The vector shapefile displays the selected study sub-basins present within Smith Canyon Watershed. Within the vector shapefile, we included attribute information for each of the spatial variables included in the spatial database as well as the hierarchical cluster designation for the primary and secondary clusters. The R software script was used to complete the correlation analysis and hierarchical clustering. The Google Earth Engine (GEE) script was used to produce the mean Normalized Difference Vegetation Index (NDVI) image product.