Study develops framework for forecasting contribution of snowpack to
flood risk during winter storms
New research advances effort to create a decision-support tool for
reservoir operators and flood managers

Date:
May 3, 2022
Source:
Desert Research Institute
Summary:
A new study provides a framework for a snowpack decision support
tool that could help water managers prepare for potential flooding
during rain-on-snow events, using hourly data from existing snow
monitoring stations.



FULL STORY ==========================================================================
In the Sierra Nevada, midwinter "rain-on-snow" events occur when rain
falls onto existing snowpack and have resulted in some of the region's
biggest and most damaging floods. Rain-on-snow events are projected to
increase in size and frequency in the coming years, but little guidance
exists for water resource managers on how to mitigate flood risk during
times of rapidly changing snowpack. Their minute-by-minute decisions
during winter storms can have long- lasting impacts to people, property,
and water supplies.


==========================================================================
A new study by a team from DRI, University of California, Berkeley,
the National Weather Service, and University of Nevada, Reno, provides
the first framework for a snowpack decision support tool that could help
water managers prepare for potential flooding during rain-on-snow events,
using hourly data from existing snow monitoring stations.

"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," said DRI
Graduate Assistant and lead author Anne Heggli, M.S. "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." To develop a testable framework for a decision support
tool, Heggli and her colleagues used hourly soil moisture data from UC Berkeley's Central Sierra Snow Laboratory from 2006-2019 to identify
periods of terrestrial water input.

Next, they developed quality control procedures to improve model
accuracy. From their results, they learned lessons about midwinter runoff
that can be used to develop the 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," said study coauthor Tim Bardsley of the National Weather
Service in Reno. "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."
"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," said study coauthor Andrew Schwartz, Ph.D., manager of the snow
lab. "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." The idea for this project was sparked
during the winter of 2017, when Heggli and her brother were testing
snow water content sensors in California. Several large rain-on-snow
events occurred, including a series of January and February storms 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,"
Heggli explained. "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." Heggli is currently enrolled in
a Ph.D. program at UNR, and has been working under the direction of
DRI faculty advisor Benjamin Hatchett, Ph.D., to advance her long-term
goal of creating a decision support tool for reservoir operators and
flood managers.

The results of this study can next be used to develop basin-specific
decision support systems that will provide real-time guidance for water resource managers. The study results will also be used in a new project
with the Nevada Department of Transportation.

"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," said Hatchett, DRI Assistant Research Professor of Atmospheric Science.


========================================================================== Story Source: Materials provided by Desert_Research_Institute. Note:
Content may be edited for style and length.


========================================================================== Related Multimedia:
* Anne_Heggli_digging_through_deep_snow_and_installing_equipment ========================================================================== Journal Reference:
1. Anne Heggli, Benjamin Hatchett, Andrew Schwartz, Tim Bardsley, Emily
Hand. Toward snowpack runoff decision support. iScience, 2022; 25
(5): 104240 DOI: 10.1016/j.isci.2022.104240 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/05/220503190149.htm

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