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Novel Framework for a Snowpack Decision Support Tool Likely to Help Water Managers

Midwinter “rain-on-snow” events in the Sierra Nevada take place when rain falls on top of the existing snowpack, resulting in some of the region’s largest and most damaging floods.

Novel Framework for a Snowpack Decision Support Tool Likely to Help Water Managers.
In January 2017, a rain-on-snow event caused flooding along the South Fork of the Yuba River in California. Climate change is expected to make such events larger and more frequent. Image Credit: JD Richey.

Rain-on-snow events are expected to grow in size and frequency in the years ahead, but water resource managers have little guidance on how to mitigate flood risk when the snowpack is rapidly changing. During winter storms, their minute-by-minute decisions can have long-term consequences for people, property and water supplies.

Using hourly data from existing snow monitoring stations, a team from DRI, the University of California, Berkeley, the National Weather Service, and the University of Nevada, Reno has developed the first framework for a snowpack decision support tool that could assist water managers to prepare for potential flooding during rain-on-snow events.

During rain-on-snow events, the people managing our water resources always have decisions to make, and it’s really challenging when you’re dealing with people’s lives and property and livelihood.

Anne Heggli MS, Study Lead Author and Graduate Assistant, Desert Research Institute

Anne Heggli added, “With this work, we’re leveraging existing monitoring networks to maximize the investment that has already been made, and give the data new meaning as we work to solve existing problems that will potentially become larger as we confront climate change.”

Heggli and her coworkers utilized hourly soil moisture data from UC Berkeley’s Central Sierra Snow Laboratory from 2006 to 2019 to identify periods of terrestrial water input to create a testable framework for a decision support tool.

In an attempt to optimize model accuracy, they established quality control procedures. They learned lessons about midwinter runoff from their findings, which they can use to develop a framework for a more broadly applicable snowpack runoff decision support tool.

We know the condition (cold content) of the snowpack leading into a rain-on-snow event can either help mitigate or exacerbate flooding concerns. The challenge is that the simplified physics and lumped nature of our current operational river forecast models struggle to provide helpful guidance here. This research and framework aims to help fill that information gap.

Tim Bardsley, Study Co-Author, National Weather Service, Reno

Andrew Schwartz Ph.D., co-author of the study and manager of the snow lab states, “This study and the runoff decision framework that has been built from its data are great examples of the research-to-operations focus that has been so important at the Central Sierra Snow Lab for the past 75 years.

Andrew Schwartz adds, “This work can help inform decisions by water managers as the climate and our water resources change, and that’s the goal—to have better tools available for our water.”

Heggli and her brother were evaluating snow water content sensors in California during the winter of 2017, which sparked the idea for this project. There were several significant rain-on-snow events, including a series of storms in January and February that culminated in the Oroville Dam Spillway Crisis.

I noticed in our sensors that there were these interesting signatures—and I heard a prominent water manager say that they had no idea how the snowpack was going to respond to these rain-on-snow events. After hearing the need of the water manager and seeing the pattern in the data, I wondered if we could use some of that hourly snowpack data to shave off some level of uncertainty about how the snowpack would react to rain.

Anne Heggli MS, Study Lead Author and Graduate Assistant, Desert Research Institute

Heggli is at present enrolled in a Ph.D. program at UNR and has been working on her long-term goal of developing a decision support tool for reservoir operators and flood managers with the help of DRI faculty advisor Benjamin Hatchett Ph.D.

The findings of this research will be used to create basin-specific decision support systems that will offer water resource managers real-time guidance. The observations will be applied to a new project with the Nevada Department of Transportation.

Hatchett, DRI Assistant Research Professor of Atmospheric Science remarks, “Anne’s work, inspired by observation, demonstrates how much we still can learn from creatively analyzing existing data to produce actionable information supporting resource management during high-impact weather events as well as the value of continued investment to maintain and expand our environmental networks.”

The research was financially supported by University Corporation for Atmospheric Research’s COMET Outreach program, Desert Research Institute’s Internal Project Assignment program, and the Nevada Space Grant Consortium Graduate Research Opportunity Fellowship.

The authors of the study include Anne Heggli (DRI), Benjamin Hatchett (DRI), Andrew Schwartz (University of California, Berkeley), Tim Bardsley (National Weather Service, Reno), and Emily Hand (University of Nevada, Reno).

Journal Reference:

Heggli, A., et al. (2022) Toward snowpack runoff decision support. iScience.


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