Understanding snowpack variability is an important goal of water management, in particular, in the arid west where snow represents a major water storage feature. Snowpack observations in the Intermountain West are sparse and short, making them difficult for use in depicting past variability and extremes. Utah’s rapidly growing population coupled with its pronounced diverse topography makes it more vulnerable to the effects of a potential decrease in snowpack and consequentially, water resources. Research suggests that the snowpack in the state of Utah is declining.
Nearly 70 percent of mountain precipitation arrives as snow during October thru March and peaks near April 1. This snowpack is a critical form of water storage for the state. As it melts gradually over the summer, it becomes an important source of runoff. Understanding the climate controls on past snowpack will help water managers to plan for the future needs and demands of this resource.
Scientists developed an April 1 snow water equivalent (SWE) reconstruction over a 139-year period, from 1850–1989, using increment [tree] cores combined with available tree-ring chronologies in the region collected by the U.S. Forest Service, Interior West Forest Inventory and Analysis program. In the state of Utah, the SWE was reconstructed for 38 snow course locations.
These individual reconstructions were then interpolated to a 4-kilometer grid, with elevation correction, to create an SWE product that can be used for water management. Results showed a significant correlation with observed SWE as well as good correspondence to regional tree-ring-based drought reconstructions. The SWE reconstruction represents a useful proxy of past mountain snowpack at the watershed level.
Results suggested statewide coherent climate variability on inter-annual and decadal time-scales
The gridded reconstruction has added geographical details that would not be possible using courser pre-instrumental proxy datasets.
This SWE reconstruction provides water resource managers and forecasters with better spatial resolution to examine past variability in snowpack, which will be important as future hydroclimatic variability is amplified by climate change.