Testing a distributed snowpack simulation model against spatial observations

Abstract

Summary A snowpack simulation model was developed for high-resolution spatial prediction of wildlife habitat over large areas (100–1000 km 2 ). The model simulates a single layer water and energy balance, based on the Utah Energy Balance (UEB) model. It is driven by daily precipitation and temperature data, and 28.5-m maps of mean annual precipitation, terrain, vegetation, and geothermal heat flux. Parameters were calibrated against daily snow water equivalent (SWE) data at SNOTEL sites. The model was tested against spatial SWE data collected throughout the landscape, as well as snowpack temperature data, and photographic data. Spatial SWE data were supplied as a set of 40 high-accuracy means for different landscape strata, based on 1058 snow cores. At the time of peak snowpack accumulation in 2002, differences between modeled and measured SWE at the pixel scale were apportioned into three potential components: field sampling error (∼22%), parameter mapping error (∼27%), and actual model functional error (∼51%). Since the present study controlled for sampling and mapping error, clear priorities could be identified for reducing model functional error, including improvement of sub-models for snow interception, and snowpack thermal dynamics. The utility and detectability of improvements along these lines is contingent on controlling and quantifying both sampling and mapping error. The model strikes a unique balance between physical detail and spatial resolution and extent that is well suited for use in wildlife studies.