Cle Elum Ridge Forest Treatment Region Dataset

This Cle Elum Ridge (CER) Forest Treatment Region Dataset contains geospatial, field-based, and lidar-derived snow and forest structure observations collected to evaluate how experimental forest thinning treatments influence snowpack storage on Cle Elum Ridge in the headwaters of the Yakima River Basin, Washington, USA. The dataset includes (1) 2023 snow depth time series from a network of field sites across a range of thinning intensities, (2) snow pit measurements collected on 6 March 2023 during the post-treatment lidar flight, (3) geospatial layers defining treatment units, site locations, and ancillary spatial context, and (4) pre-treatment (2021) and post-treatment (2023) snow-on lidar datasets processed into unified DEM, DSM, and numerous canopy cover and snow depth products (see subdirectory ReadMe.txt for the full list of variables). All lidar products were reprojected, gridded, and converted from either raw point clouds or GeoTIFFs to NetCDF formats using consistent units and spatial extents. The raw lidar datasets can be found in their corresponding data repositories (see Related Resources). Time series observations include processed datasets used for analysis, example timelapse images, and selected raw and intermediate files that document field data processing steps. Together, these datasets support the analysis of snow depth, snow storage, canopy openness, and forest structural changes associated with prescribed thinning treatments. They provide a reproducible foundation for evaluating forest-snow interactions and for assessing the hydrologic co-benefits of fuels reduction strategies in mountain forests. This dataset complements the manuscript Lumbrazo et al. (2025), “Can we maximize snow storage through fire-resilient forest treatments? Insights from experimental forest treatments in the Eastern Cascades, WA, USA,” accepted in Frontiers in Forests and Global Change, Forest Hydrology section (doi:10.3389/ffgc.2025.1707812).