Anna Johnson / AP
The first snow of the season is seen near Tioga Pass Thursday, Sept. 21, 2017 in Yosemite National Park, Calif. Snow fell in the Sierra Nevada on the last day of summer, giving the towering mountain range shared by California and Nevada a wintry look in September and making travel hazardous. Snow dusted peaks in Yosemite National Park and temporarily closed Tioga Pass road, the soaring eastern entry to the park that typically doesn’t become impassable until mid-November.
Wednesday, Jan. 24, 2018 | 2 a.m.
Farmers, ecologists, water managers, even those tasked with fighting backcountry wildfires could greatly benefit from knowing months in advance how much water will be available from melting mountain snow every spring.
For decades predicting snowpack accumulation has been largely unreliable, based solely on historical data and capricious El Nino weather patterns. But that could be about to change.
A study published Monday in the Proceedings of the National Academy of Sciences found that for most mountains in the Western United States, snowpack over the last 30 years could be accurately predicted as much as eight months in advance using climate models.
"It's a really exciting time," said Sarah Kapnick, a scientist at the National Oceanic and Atmospheric Administration and lead author on the report. "Our grandparents didn't have weather forecasts. Now, we're used to weather models, and wouldn't it be great if the next generation had seasonal forecasts."
The research used one of the most powerful super computers in the world, located in Oak Ridge, Tennessee, to run a climate model crunching data on everything from ocean temperatures, currents and salinity to a wide variety of weather patterns.
The modeling predicted with statistical accuracy March snowpack conditions from 1981 through 2016 using only the climate data available as of the previous July in any given year. The recreated forecasts were tested on the scale of individual mountain ranges.
Climate models have predicted long-term declines in snowpack by end of the century, but getting reliable forecasts for an upcoming winter could revolutionize water management.
"If it were reliable, it would be a huge asset." said Frank Gehrke, chief of the California Cooperative Snow Survey Program in the state's Department of Water Resources. "It's like you've got a new reservoir."
Right now, those managing water in California must be very conservative with how much they use in any given year because scientists have had little ability to predict when drought could strike.
With a better ability to predict how much freshwater will run down rivers each spring, more water could be allocated for agriculture, urban use and to benefit aquatic environments. Firefighters could also preparing earlier for drier, more flammable forest conditions.
The modeling wasn't able to predict with accuracy snowpack in the Sierra Nevada mountains south of Stockton. The area which receives erratic and infrequent precipitation has proved complicated for researchers, especially because there are snow sensors high in the mountains.
"It's a really intriguing paper," said Jeff Dozier, a scientist at the Bren School of Environmental Science & Management at UC Santa Barbara. "I will be interested to see how well it stands the test of time."
Scientists at NOAA are planning to incorporate into the modeling laser mapping technology, called light detection and ranging, or LIDAR, to create three dimensional images of the snowpack. Such data, along with traditional snow sensors and increasingly sophisticated satellite imaging are expected to increase the accuracy of the forecasting.
"We have a lot of pressure on us to make these operational as fast as we can, but at the same time, we also want to improve the modeling." Kapnick said. "We have now found areas that we know that we can improve."
Kapnick said she thinks officials throughout the Western United States will be able to rely on the forecasting technology within the next decade.
