Premium
LiDAR‐derived snowpack data sets from mixed conifer forests across the Western United States
Author(s) -
Harpold A. A.,
Guo Q.,
Molotch N.,
Brooks P. D.,
Bales R.,
FernandezDiaz J. C.,
Musselman K. N.,
Swetnam T. L.,
Kirchner P.,
Meadows M. W.,
Flanagan J.,
Lucas R.
Publication year - 2014
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2013wr013935
Subject(s) - snowpack , lidar , snow , watershed , environmental science , vegetation (pathology) , drainage basin , structural basin , remote sensing , hydrology (agriculture) , physical geography , geology , geography , geomorphology , cartography , machine learning , computer science , medicine , geotechnical engineering , pathology
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 m 2 resolution) derived from LiDAR with an estimated accuracy of <30 cm compared to limited in situ snow depth observations.