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National Water Model simulated snow water equivalent data for the Upper Colorado River Basin SNOTEL sites
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| Type: | Resource | |
| Storage: | The size of this resource is 49.1 MB | |
| Created: | Apr 01, 2024 at 8:44 p.m. | |
| Last updated: | Jul 13, 2024 at 2:07 a.m. | |
| Citation: | See how to cite this resource | |
| Content types: | Multidimensional Content |
| Sharing Status: | Public |
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| Views: | 284 |
| Downloads: | 7 |
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Abstract
This study assesses snow water equivalent (SWE) simulation uncertainty in the National Water Model (NWM) due to forcing and model parameterization, using data from 46 Snow Telemetry (SNOTEL) sites in the Upper Colorado River Basin (UCRB). We evaluated the newly developed Analysis of Record for Calibration (AORC) forcing data for SWE simulation and examined the impact of bias correction applied to AORC precipitation and temperature. Additionally, we investigated the sensitivity of SWE simulations to choices of physical parameterization schemes through 72 ensemble experiments. Results showed that NWM driven by AORC forcings captured the overall temporal variation of SWE but underestimated its amount. Adjusting AORC precipitation with SNOTEL observations reduced SWE root-mean-square error (RMSE) by 66%, adjusting temperature trimmed it by 10%, and adjusting both decreased it by 69%. Among the physical processes, the snow/soil temperature time scheme (STC) demonstrated the highest sensitivity, followed by the surface exchange coefficient for heat (SFC), snow surface albedo (ALB), and rainfall and snowfall partitioning (SNF), while the lower boundary of soil temperature (TBOT) proved to be insensitive. Further optimization of the parameterization combination resulted in a 12% SWE RMSE reduction. When combined with the bias-corrected AORC precipitation and temperature, this optimization led to a remarkable 78% SWE RMSE reduction. Despite these enhancements, a persistent slow and late spring ablation suggests model deficiencies in snow ablation physics. The study emphasizes the critical need to enhance the accuracy of forcing data in mountainous regions and address model parameterization uncertainty through optimization efforts.
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Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| National Oceanic and Atmospheric Administration | #NA18OAR4590410 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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