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| Type: | Resource | |
| Storage: | The size of this resource is 10.3 GB | |
| Created: | Dec 04, 2025 at 6 p.m. (UTC) | |
| Last updated: | Dec 17, 2025 at 9:09 a.m. (UTC) (Metadata update) | |
| Published date: | Dec 17, 2025 at 9:09 a.m. (UTC) | |
| DOI: | 10.4211/hs.96f4199c0e4c48e6bc0ea7f9251b16dd | |
| Citation: | See how to cite this resource | |
| Content types: | Multidimensional Content Geographic Raster Content CSV Content |
| Sharing Status: | Published |
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| Views: | 32 |
| Downloads: | 4 |
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Abstract
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).
Subject Keywords
Coverage
Spatial
Temporal
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Content
README.txt
Name: CER Treatment Region Datasets Date: December 2025 Author: Cassie Lumbrazo Contact: cassielumbrazo@gmail.com Purpose: Publish on HydroShare Folder: \cer_treatment_datasets\ All processing and analysis code associated with this dataset and the accepted manuscript is openly available at: https://github.com/cassielumbrazo/CER_treatment_manuscript _________________________________________________________________________________________________________ Folder Overview: _________________________________________________________________________________________________________ --------------------------------------------------------------------------------------------------------- \1_gis_field\ --------------------------------------------------------------------------------------------------------- This folder contains GIS related files such as the 2023 timeseries field site locations, and other useful shapefiles. Inside this folder is also another folder named, \snow_pit_data_6March2023\: which contains all the snow pit data from the 6 March 2023 CER field day --------------------------------------------------------------------------------------------------------- \2_timeseries\ --------------------------------------------------------------------------------------------------------- This folder contains all the 2023 timeseries field site data. A majority of the contents in this folder were produced by John Cramblitt. The subfolders are, \1_final_data\: The final datasets used for all analysis are in this folder. \2_timelapse_data\: Example timelapse images for each site. \3_raw_other_data\: Raw and working files created by John Cramblitt during data processing. --------------------------------------------------------------------------------------------------------- \3_lidar\ --------------------------------------------------------------------------------------------------------- This lidar folder contains all the CER Treatment Domain Lidar Data (not the entire NCALM domain). Within this folder are the following subfolders, \1_raw_lidar\: The raw lidar datasets for the CER Treatment Region. \2_raw_tifs\: The raw tifs created from the raw lidar for the CER Treatment Region. \3_netcdfs\: The netcdfs created in python, which combine all the 2_raw_tif files, project them together, match the gridcells, ensure all the units are the same, and clip data where needed (e.g., the ridge road) \4_processed_tifs\: The tifs exported from the processed netcdfs with all corrected units, projections, etc.
Data Services
Related Resources
| This resource has a related resource in another format | Dickerson-Lange, S. E., J. Lundquist, R. Gersonde, E. Howe, K. Patrick (2023). Eastern Cascades Forest-Snow Observations 2019-2021, HydroShare, https://doi.org/10.4211/hs.6127902b82674b8097ec3c372f14514f |
| The content of this resource is derived from | Lumbrazo, C., K. Dedinsky, A. Lyda, M. Grilliot, J. Zdebski (2025). "Cle Elum Snow Pack Study", in Cle Elum Ridge Snow-On Lidar for Forest Management. DesignSafe-CI. https://doi.org/10.17603/ds2-8hz4-zb88 |
| The content of this resource is derived from | Lumbrazo, C. (2021). Hydrologic Effects of Forest Restoration, WA 2021. National Center for Airborne Laser Mapping (NCALM). Distributed by OpenTopography. https://doi.org/10.5069/G989142F.. Accessed 2025-12-04 |
| The content of this resource can be executed by | https://github.com/cassielumbrazo/CER_treatment_manuscript |
| This resource is described by | Lumbrazo, C., Howe, E. R., Dickerson-Lange, S. E., Pestana, S., Cramblitt, J., Dedinsky, K., Smith, K., & Lundquist., J. D. (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. doi:10.3389/ffgc.2025.1707812 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| The Washington State Department of Natural Resources (DNR) | 93-104079 | |
| The Nature Conservancy (TNC) | TNC WA-G-220620-026 |
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
| Name | Organization | Address | Phone | Author Identifiers |
|---|---|---|---|---|
| Herman Flamenco | The Nature Conservancy | |||
| Rob Deter | Iron Mountain Lumber Co | |||
| Connor Craig | Wildfire Home Protection |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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