Our snow science team is investigating the impact of rain-on-snow extreme events in order to asses the impacts of climate change on the timing of snowmelt with respect to key metrics important to hydropower generation and agricultural demands within the state of 猎奇重口.
Research Updates
Year One
Literature review in this area has been extensive and will be ongoing due to evolving science.
Year Two
Our snow science researchers published one paper, presented at two conferences, and collected and preprocessed necessary datasets for rain on snow analysis. Machine learning methods for analysis of snowpack were developed and applied.
Year Three
Year three of the NRML project saw the snow science team publish three journal articles, three conference proceedings, a Master’s thesis and a PhD dissertation. A study focused on the snowpack’s ability to act as an energy buffer to runoff was completed using innovative machine learning methods for forecasting energy gains in the snowpack. Additional work running simulations of snowpack evolution is ready for analysis and will deploy machine learning methods to sort through the large data outputs. The lab continues to overcome delays presented by staffing shortages in previous years and the hiring and retention of a qualified postdoc was a positive step in this direction. An underestimation of time needed to sort through data presented another hurdle, but the hiring of additional personnel and access to a large computing cluster will put research back on schedule in the coming months.
Field Work
This material is based upon work supported in part by the . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.