Climate change is making it harder to estimate Sierra snowpack. AI may be able to help - KCRA Sacramento
How AI is used to track California's snowpack
Climate change is making it harder to estimate Sierra snowpack. AI may be able to help
Water managers rely on an accurate snowpack estimate for flood control and annual water supply allocations.
Changes in California's climate patterns are making it more difficult for scientists to accurately estimate the state's snowpack. This could present a problem for water managers, who rely on accurate numbers to plan for spring flood control and annual water supply allocations.
Historically, a large network of automatic sensors called "snow pillows" has been used to take automated measurements of the weight of the snow at a specific location. Knowing the weight allows scientists to calculate the amount of water in the snow.
Decades of that measured data combined with an understanding of historical snow trends have allowed scientists to make informed estimates about the amount of snow in certain areas. As California's climate changes, the relationships informing those estimates may become less useful.
"When we use all of this knowledge that we’ve gained from our really long record of the past and try to apply it in a new situation that doesn’t look quite like the past, we don’t trust that application as well," said Marianne Cowherd.
Cowherd is currently a PhD candidate at the University of California, Berkeley, studying environmental science policy and management. As part of her program, Cowherd is working with UC Berkeley's Central Sierra Snow Lab to help find ways to factor climate change into snowpack estimates.
"We were really interested in situations where the snow pillow itself would be experiencing a decrease [in snowpack]," Cowherd said.
She added that areas at elevations above those snow pillows may also be experiencing a decrease in snowpack, but in some cases, there could be an undetected increase.
"And if we’re just using that snow pillow record and our expectations based on what’s happened in the past, we might miss something that’s happening in a region that we’re not measuring," said Cowherd.
Artificial intelligence could be part of the solution. Cowherd and her colleagues have been exploring different AI algorithms that can learn from past snow pillow data and incorporate climate change models to produce a more accurate picture of future snowpack scenarios.
"AI is definitely a potential helper for solving this problem of wanting to have better understanding of where our snow is based on a sparse number of observations," said Cowherd, adding that AI should be seen as a suite of possible tools. The trick is finding which tool is best for this situation.
That will be part of future research along with getting a better understanding of how different water users rely on snow pillow data.
California currently utilizes several methods to get a picture of the snowpack each winter. They include manual snow surveys which are conducted each month. Images from high-resolution satellites and LIDAR measurements are also being used in addition to the automated snow pillow network.
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Related Knowledge Center: Climate economics
Climate change is now a driver of the world’s economy. The impact is profound:
- Catastrophic weather events — hurricanes, floods, droughts, high temperatures — are more frequent and more extreme. The cost can be measured in both the initial damage caused and the longer-term impact on the economy.
- Vulnerable communities are increasing climate-related investments, hardening infrastructure and taking other protective measures.
- Investment in carbon capture and storage technologies is increasingly rapidly.
- Governments are making investments and crafting regulations to reduce emissions of carbon dioxide and other greenhouse gases.
On top of all of this is the cost of uncertainty. As the Fourth National Climate Assessment, released in 2018 by the U.S. Global Change Research Program, says, “The assumption that current and future climate conditions will resemble the recent past is no longer valid.”