Survey Finds Sierra Snowpack Well Above Average
ECHO SUMMIT (CBS SF) – The first snow survey of the year took place Wednesday morning at four locations near Echo Summit in the Sierra Nevada mountain range and indicates that the state’s snowpack is wetter than average.
“Last year was dry, so this is particularly good news,” said Ted Thomas, a spokesman for California’s Department of Water Resources.
The mountain snow will melt into reservoirs, streams and aquifers in the spring and summer, providing about one-third of the water for the state’s households, farms and industries, according to Thomas.
At the first count, which will continue to be held each month through May, the water content in the northern mountains was measured at 133 percent of normal for the date and 50 percent of the April 1 seasonal average.
Similar readings were found in the central and southern Sierra mountains.
The survey measured snowpacks at elevations of 6,500, 6,700, 6,800 and 7,600 feet.
Despite the good news, state water managers cannot relax yet, according to Thomas.
A wet snowpack can turn dry in the coming months before April when the snowpack is at its peak, Thomas said.
“We’ve seen dry years start out wet,” he said. “But we’re off to a good start.”
As of today, the department estimates it will be able to deliver 40 percent of the more than 4 million acre-feet of water requested for the year to the 29 public agencies that supply more than 25 million Californians and nearly a million acres of irrigated farmland.
An acre-foot is 325,851 gallons.
Last year at this time the snowpack was one of the driest on record, so this year’s higher water content is welcome news.
“We do not want consecutive dry years,” Thomas said.
Last year the snowpack was at 19 percent of the annual average and only 3 percent of the average April 1 measurement.
According to department officials, in addition to the above-average water content, early storms this season have replenished California’s reservoirs.
Wednesday’s manual survey and upcoming checks supplement the accuracy of real-time electronic readings from sensors in the state.
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