Using regional-scale LiDAR surveys to validate operational snow models

Wednesday, 17 December 2014: 11:35 AM
Andrew R Hedrick1, Hans-Peter Marshall2, Adam H Winstral1, Kelly Elder3, Simon H Yueh4 and Donald W Cline5, (1)USDA-ARS, Boise, ID, United States, (2)Boise State University, Boise, ID, United States, (3)USDA Forest Service, Fort Collins, CO, United States, (4)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (5)Office of Hydrologic Development, Hydrology Laboratory, NOAA-NWS, Silver Spring, MD, United States
As survey costs continue to plummet and storage capabilities soar, large-scale multitemporal airborne Light Detection and Ranging (LiDAR) surveys for high-resolution snow depth measurements are becoming commonplace in mountain research watersheds. Though there are disadvantages to the technique (e.g. poor temporal representation and high uncertainty in steep terrain and dense vegetation), the wealth of information with regard to previously unknown spatial snow depth distributions can be an valuable tool for assessing spatially distributed operational snow models. As a portion of NASA’s second Cold Lands Processes Experiment (CLPX-2), two 750-km2 LiDAR surveys were conducted over Northern Colorado in December and February of the 2006/2007 winter season. The resulting 5-m gridded changes in snow depth overlay 980 individual pixels of the SNOw Data Assimilation System (SNODAS) spatial framework. As an important operational snow model developed by NOAA’s National Operational Hydrologic Remote Sensing Center (NOHRSC), SNODAS generally lacks independent validation datasets due to the data assimilation step critical for adjusting the energy balance and downscaled Numerical Weather Prediction (NWP) model components. The influence of sub-grid variability on SNODAS performance is assessed using the independent high resolution CLPX-2 LiDAR changes in snow depth. This method provides a foundation for further studies to quantitatively address the affect of small-scale physiographic variables on various large-scale operational snow models by making use of forthcoming large-scale LiDAR datasets.