MSGID: 1:317/3 627201d8   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    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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