| NSF Org: |
EAR Division Of Earth Sciences |
| Recipient: |
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| Initial Amendment Date: | September 26, 2012 |
| Latest Amendment Date: | September 26, 2012 |
| Award Number: | 1215771 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Thomas Torgersen
EAR Division Of Earth Sciences GEO Directorate for Geosciences |
| Start Date: | October 1, 2012 |
| End Date: | September 30, 2016 (Estimated) |
| Total Intended Award Amount: | $310,125.00 |
| Total Awarded Amount to Date: | $310,125.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
4333 BROOKLYN AVE NE SEATTLE WA US 98195-1016 (206)543-4043 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
University of Washington Seattle WA US 98195-2700 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
Geography and Spatial Sciences, Hydrologic Sciences, Climate & Large-Scale Dynamics |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.050 |
ABSTRACT
Intermittent snow, which appears and disappears multiple times over the course of a winter, provides critical feedbacks to the fields of atmospheric science (by altering surface albedo and temperature), hydrology (through melt contributions to rain-on-snow floods), soil science (through insulating the land surface), and ecology (through insulation and water supply). Despite its scientific and societal importance, intermittent snow is a modeling challenge. Focusing on intermittent snow in both the Washington Cascades and the plains of Colorado, this research project will use a mix of observations and physically-based modeling to improve understanding of how different snow processes combine to influence snowpack evolution. Controlled numerical experiments will examine (1) multiple methods to estimate fluxes at the snow-atmosphere interface, including approaches used to estimate the surface albedo, the turbulent fluxes of sensible and latent heat, and the partitioning of precipitation between rain and snow; (2) multiple methods to simulate internal processes within the snowpack, including heat conduction, penetration of shortwave radiation, vertical drainage of liquid water, and compaction of the snowpack associated with metamorphism of the snow crystals; and (3) multiple methods to simulate fluxes at the lower boundary associated with heat transfer in the soil. Model simulations, isolating one process at a time, will be compared with detailed measurements, both at point locations and distributed across the landscape. This research aims to provide a better understanding of dominant processes in the intermittent snow zone, a better understanding of major modeling uncertainties, and a path forward towards an improved, coupled atmosphere-hydro model. Because intermittent snow is almost always ripe to melt, it responds immediately to energy inputs, resulting in a change in snow water equivalent (SWE) rather than just a change in internal snowpack temperature. This readiness-to-melt makes intermittent snow an extra sensitive indicator of snow model performance. Therefore, any model improvements vetted in this area will translate into better snow modeling everywhere, including the seasonal snow zone (where snow lasts all winter).
Although intermittent snow is only present part of the winter, it has important impacts on the atmosphere, the land surface, and society. Snow increases the reflectivity of the Earth?s surface and lowers the temperature, and it also insulates the soil, protecting the ground surface from potentially damaging frost. During rain-on-snow storms, this lower-elevation snow melts and contributes to flooding (a hazard), but at other times, melting snow from this zone contributes to summer water supplies (a resource). Intermittent snow in cities and along major highways hinders transportation and city operations. The intermittent snow zone has been clearly identified as the most sensitive region to climate change, and many areas that currently have seasonal snow are predicted to shift to an intermittent snow regime. For all of these reasons, it is important to model intermittent snow correctly. This project will improve the next generation of snow models used for hydrologic and climate prediction.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Most prior snow research has focused on cold snow environments. Through developing a new modeling framework and taking new observations, our work has revealed uniquely important processes in maritime intermittent snow environments. In these environments the air is moist, and often cloudy, with rain falling about as frequently as snow. While conventional knowledge would suggest that solar radiation (sunshine) drives snowmelt, in these environments, longwave radiation from clouds and water vapor in the atmosphere critically contribute to midwinter snowmelt. Longwave radiation has been less well studied than solar radiation and is an uncertain but important value in how we model snowmelt. While longwave radiation is very seldom measured (our site was the first in the Pacific Northwest), we also demonstrated ways to better represent the longwave energy balance through careful modeling and measurement of the snow surface temperature (a less-expensive measurement option).
The largest floods in the western United States are characterized by warm storms that rain on snow at high elevations. The maximum contribution of snowmelt to streamflow during a rain on snow event is between 7 and 29% of the total storm precipitation across select western U.S. basins. Snowmelt rates increase with decreased forest-cover and/or increased wind speeds but still generally do not exceed 29% of storm totals. These careful calculations have helped reservoir operators determine potential uncertainty in streamflow due to snowmelt during rain-on-snow flooding situations in the western United States. They are also currently being considered by the Washington State Government’s Department of Ecology to make long-term management decisions related to how floods relate to snow now and under future climate scenarios. We also created a classroom science module for high school teachers to use. The module explains the energy budget, using a rain-on-snow event as a case study. Throughout, the module determines how various energy sources, such as longwave radiation, contribute to melting snow. The module, which was presented at a workshop for high school teachers, includes PowerPoint slides, a YouTube video of the lecture, an Excel-based snow melt model, and instructions for using it.
Finally, our modular snow-hydrology model (Structure for Understanding Multiple Modeling Alternatives, SUMMA) has enabled us to study the role of individual model processes in model performance and to identify which specific model aspects work well and which need further study. Current hydrologic model parameterizations have trouble with the partitioning of rain vs. snow at temperatures near 0°C, but high-resolution atmospheric models, such as the Weather Research Forecasting (WRF) model offer improvement. We made all of our model code and observational data publicly available and offered a snow modeling class at NCAR to teach students and other researchers how to use it. We also annually teach students at the University of Washington both modeling and measurements, including a field trip to our Snoqualmie Pass Snow Energy Balance Station (see photographs), which is regularly used by the Washington Department of Transportation for Avalanche Forecasts.
Last Modified: 12/02/2016
Modified by: Jessica D Lundquist
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