Subgrid variability of snow water equivalent at operational snow stations in the western USA
Article first published online: 24 MAY 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Volume 27, Issue 17, pages 2383–2400, 15 August 2013
How to Cite
Meromy, L., Molotch, N. P., Link, T. E., Fassnacht, S. R. and Rice, R. (2013), Subgrid variability of snow water equivalent at operational snow stations in the western USA. Hydrol. Process., 27: 2383–2400. doi: 10.1002/hyp.9355
- Issue published online: 16 JUL 2013
- Article first published online: 24 MAY 2012
- Accepted manuscript online: 24 APR 2012 07:46PM EST
- Manuscript Accepted: 20 APR 2012
- Manuscript Received: 5 DEC 2011
- snow water equivalent;
- water resources;
The spatial distribution of snow water equivalent (SWE) is a key variable in many regional-scale land surface models. Currently, the assimilation of point-scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid-scale SWE. To improve the understanding of the relationship between point-scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1-, 4- and 16-km2 grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger-scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy. Copyright © 2012 John Wiley & Sons, Ltd.