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Keywords:

  • snow;
  • modelling;
  • snow water equivalent;
  • water resources;
  • SNOTEL

Abstract

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.