Plain English Summary

We present, and make publicly available, snowpack and vegetation datasets derived from airborne LiDAR at four field Critical Zone Observatory (CZO) sites in the Western U.S. The CZO sites all contain mixed-conifer forested catchments and cover a range of winter hydroclimates and varying forest structure. The high-resolution (1 m^2) snow depths were generally within 20 cm of ground-based measurements, but the accuracy depended on the methods used to extract the snow surface from the raw returns. Locations with steeper slopes and/or denser forest canopies also had lower accuracy of LiDAR-derived snow depths. This dataset will provide a valuable resource for quantifying snow-vegetation interactions and improving water resource management in montane, forested catchments.

Abstract

Airborne-based Light Detection and Ranging (LiDAR) offers the potential to measure snow depth and vegetation structure at high spatial resolution over large extents and thereby increase our ability to quantify snow water resources. Here we present airborne LiDAR data products at four Critical Zone Observatories (CZO) in the Western United States: Jemez River Basin, NM, Boulder Creek Watershed, CO, Kings River Experimental Watershed, CA, and Wolverton Basin, CA. We make publicly available snow depth data products (1 m² resolution) derived from LiDAR with an estimated accuracy of <30 cm compared to limited in situ snow depth observations.

Both snow depth and vegetation datasets are available for the four Western U.S. CZO sites at the website: ftp://snowserver.colorado.edu/pub/WesternCZO_LiDAR_data.

Citation

Harpold, A.A., Q. Guo, N. Molotch, P. D. Brooks, R. Bales, J.C. Fernandez-Diaz, K.N. Musselman, and T.L. Swetnam, P. Kirchner, M. Meadows, J. Flanagan, R. Lucas (2014): LiDAR-derived snowpack data sets from mixed conifer forests across the Western United States. Water Resources Research 50(3): 2749-2755. DOI: 10.1002/2013WR013935

This Paper/Book acknowledges NSF CZO grant support.