| NSF Org: |
EAR Division Of Earth Sciences |
| Recipient: |
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| Initial Amendment Date: | March 13, 2018 |
| Latest Amendment Date: | May 21, 2021 |
| Award Number: | 1761441 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Hendratta Ali
heali@nsf.gov (703)292-2648 EAR Division Of Earth Sciences GEO Directorate for Geosciences |
| Start Date: | March 15, 2018 |
| End Date: | February 28, 2023 (Estimated) |
| Total Intended Award Amount: | $531,033.00 |
| Total Awarded Amount to Date: | $617,293.00 |
| Funds Obligated to Date: |
FY 2019 = $280,976.00 FY 2021 = $86,260.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
3100 MARINE ST Boulder CO US 80309-0001 (303)492-6221 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
CO US 80303-1058 |
| 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): |
Hydrologic Sciences, XC-Crosscutting Activities Pro |
| Primary Program Source: |
01001920DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT 01002122DB NSF RESEARCH & RELATED ACTIVIT |
| 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
Seasonal snow in mountains is a critical water resource for billions of people worldwide. The amount of water stored in snow, or snow water equivalent (SWE), depends on its depth and density, both of which vary in space and time. New technologies permit mapping of snow depth across large watersheds, but there is no similar advance for measuring snow density. Instead, snow density models are used, but different models can yield divergent results across mountain landscapes. Therefore, understanding why snow density models differ is essential for reducing uncertainty in SWE. This project will improve knowledge of the physical processes that affect snowpack density, focusing on how forests yield predictable variations in snowpack density across watersheds. The project will advance snow density modeling and thus predictions of SWE, snowmelt, and runoff. Snow density models will be used to transform snow depth datasets into SWE datasets, benefiting hydrologic research. The project will train one graduate student, develop two field classes, and support multiple undergraduate interns at the university and through programs that strives to increase diversity in geoscience students.
Spatial variations in snowpack density are driven primarily by differential compaction due to the mass of overlying snow - density tends to increase with greater snow depth. In contrast, secondary processes lead to increased density as depth decreases (e.g., wind compaction) or decreased density as depth increases (e.g., new snowfall). The guiding hypothesis is that landscape properties and climate govern the relative importance of primary and secondary densification processes. Snow in open areas is typically deeper than in forests, so snowpack density is predicted to be greater in open areas. However, secondary processes can enhance or counteract this effect, depending on environmental factors. Hypotheses that assess the roles of primary and secondary densification effects will be tested using coordinated field investigations and modeling experiments. Field investigations in distinct snow climates with landscape controls (e.g., forest vs. open) will measure differences in snow density, depth, water content, and layer characteristics. Snowpack will be simulated with process-based modular snow models and reference models. Field data will be used to quantify model errors, evaluate model representation of primary and secondary effects, and test model sensitivity to meteorological uncertainty. Models will be further evaluated using data from the NASA SnowEx campaign and the second Snow Model Intercomparison Project. Robust models identified will be applied to produce datasets of density, SWE, and uncertainty for community usage.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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.
The amount of water stored in seasonal snowpack, or snow water equivalent (SWE), depends on its depth and density, both of which vary in space and time due to interacting physical processes. Lidar systems and other technologies provide basin-wide snow depth data at a high spatial resolution. The hydrologic utility of these snow depth observations depends critically on the accuracy of associated snow density data. However, there is no parallel advance in density measurement technology and thus robust density models are essential. Snow models yield a wide range of density estimates in both open and forested areas. As a result, uncertainty in modeled snow density contributes more to total SWE error than uncertainties in snow depth measurement. Our project has impacted the field of hydrology by linking landscape-based process investigations with snow modeling to advance our ability to predict variations in snow density and SWE across forested mountain watersheds.
This project yielded an improved characterization and understanding of how snow densification processes vary across forested mountain landscapes spanning a range of winter climates. In forests, we have identified that the delivery of snow and liquid water from the forest canopy to the ground surface is the primary control on snow density in some environments. Therefore, this process must be more accurately represented in snow models. It has been known for a decade that snow models perform poorly in forested areas, compared to areas without tree canopies. Our results demonstrate that a key contributor to this problem is how models represent the transport of snow and liquid water from the canopy to the snow surface. To improve models, measurements of snow and liquid water storage and release in forest canopies are essential but are difficult to make and thus rarely available. To that end, we also developed and tested a new technique for quantifying the magnitude of snow stored in a tree canopy based on changes in tree motion due to wind. This technique offers some potential but is complicated because other factors (tree freeze-thaw cycles) also influence tree motion due to wind. Future work is needed to advance our measurements of snow and water capture and transfer in forest canopies.
As part of this project, we collected, quality-controlled, and published a dataset including coordinated measurements of snowpack and meteorological conditions from paired forested and open sites from a montane site in Colorado (continental snowpack). Additional snowpack surveys in paired forest and open sites were conducted in Oregon to support investigations in a contrasting maritime snowpack. Near-real time raw data from the Colorado study site were shared with the public through the life of the project with an interactive website, while the quality-control data were archived in a long-term online repository. The coordinated modeling experiments in different snow climates (Colorado, Oregon, Alberta) help pinpoint weaknesses in current snow models and highlight areas where more detailed process knowledge may inform model development.
The project provided hands-on research experience for undergraduate and graduate students, which provided opportunities for advancing critical thinking abilities and technical skills for the next generation of STEM talent. This project brought together research, teaching, training, and learning at two universities. Students gained experience digging snow pits, measuring and recording vertical profiles of snow properties, and operating related sampling tools. Students also gained experience designing, deploying, and maintaining automatic weather stations in remote locations, and experience in processing, analyzing, and applying the data collected at those stations. They conducted statistical analyses while comparing measurements from different parts of the landscape. The project offered exposure to both field and lab experiences, which will help prepare students for a variety of careers in STEM.
Last Modified: 08/29/2023
Modified by: Eric E Small
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