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Dust: missing from water supply forecasting models

October 31, 2023

A study in Environmental Research Letters highlights dust as a variable critical to adapt snowmelt and water supply forecasting models in drought-plagued western states.

University of Utah snow researcher McKenzie Skiles observes, “The current models are based upon statistical relationships that assume the future is going to be like the past. And I think we know now that we can’t rely on that assumption.”

Dust accelerates snowmelt

Skiles et al collected data on dust and snowmelt in the Wasatch Mountains over 2021 and 2022. The time period coincides with record low water levels in the nearby Great Salt Lake. Researchers found that dust “accelerated Wasatch snowmelt by 17 days during the 2022 snowmelt season.”

The findings confirm numerous studies conducted between 2010 and 2018 in the Colorado Rockies. In the San Juan Mountains, dusty gusts from the Colorado Plateau “accelerated snowmelt by 3–5 weeks and were correlated with snowmelt forecasting errors.”

Blown onto the mountains from the lake bed, dark-colored dust “absorbs more energy from the sun.” In turn, the warm dust starts to melt the snowpack below it.

Quick snowmelt risks

Quicker snow melt leaves snowpack ineffective for natural water storage. Moreover, excessive runoff creates flooding risk in the near-term. Water supply runs out during the warmer months, later in the year. Soil also dries out earlier in the season.

Updating river forecast models

Snowmelt predictions inform flood warnings and reservoir management. However, many river forecasting models do not account for dust. Every drop of water is scrutinized in the Colorado River Basin, so forecast improvements — down to the fraction of a percent — is critical for communities to plan and adapt.

Hydrologists at the NWS Colorado Basin River Forecast Center (CBRFC) are in the process of updating models as weather patterns are altered.

Unaffiliated with the University of Utah study, the CBRFC is also “testing a more dynamic physical model that relies on additional inputs, such as solar radiation, wind, humidity, and dust on snow, to better simulate the real-time variability in observed snow conditions,” reports Kara West of Eos Science News.

To sustain drinking source water, we supply snow measurement sensors as well as water quality monitoring instrumentation and environmental data management software to track snow, water and even dust data over time. Analysis, reporting and data visualization options give watershed managers advanced tools to more accurately forecast snowmelt and water supply and meet water demand.