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Abstract We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open‐forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap‐filled. These data are available to download from Bonner, Smyth, et al. (2022) athttps://doi.org/10.5281/zenodo.6618553, at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process‐based snow model. We demonstrate the utility of these data with a modeling experiment that explores open‐forest differences and identifies opportunities for improvements in model representation.
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Converting snow depth to snow water equivalent using climatological variables
Abstract. We present a simple method that allows snow depth measurements tobe converted to snow water equivalent (SWE) estimates. These estimates areuseful to individuals interested in water resources, ecological function,and avalanche forecasting. They can also be assimilated into models to helpimprove predictions of total water volumes over large regions. Theconversion of depth to SWE is particularly valuable since snow depthmeasurements are far more numerous than costlier and more complex SWEmeasurements. Our model regresses SWE against snow depth (h), day of wateryear (DOY) and climatological (30-year normal) values for winter (December,January, February) precipitation (PPTWT), and the difference (TD) between meantemperature of the warmest month and mean temperature of the coldest month,producing a power-law relationship. Relying on climatological normals ratherthan weather data for a given year allows our model to be applied atmeasurement sites lacking a weather station. Separate equations are obtainedfor the accumulation and the ablation phases of the snowpack. The model isvalidated against a large database of snow pillow measurements and yields abias in SWE of less than 2 mm and a root-mean-squared error (RMSE) in SWE ofless than 60 mm. The model is additionally validated against two completelyindependent sets of data: one from western North America and one from thenortheastern United States. Finally, the results are compared with three othermodels for bulk density that have varying degrees of complexity and thatwere built in multiple geographic regions. The results show that the modeldescribed in this paper has the best performance for the validation datasets.
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- Award ID(s):
- 1802726
- PAR ID:
- 10122062
- Date Published:
- Journal Name:
- The Cryosphere
- Volume:
- 13
- Issue:
- 7
- ISSN:
- 1994-0424
- Page Range / eLocation ID:
- 1767 to 1784
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Intermittent snow depth observations can be leveraged with data assimilation (DA) to improve model estimates of snow water equivalent (SWE) at the point scale. A key consideration for scaling a DA system to the basin scale is its performance at locations with forest cover – where canopy-snow interactions affect snow accumulation and melt, yet are difficult to model and parameterize. We implement a particle filter (PF) assimilation technique to assimilate intermittent depth observations into the Flexible Snow Model (FSM2), and validate the output against snow density and SWE measurements across paired forest and open sites, at two locations with different climates and forest structures. Assimilation reduces depth error by 70-90%, density error by 5-30%, and SWE error by 50-70% at forest locations (relative to control model runs) and brings errors in-line with adjacent open sites. The PF correctly simulates the seasonal evolution of the snowpack under forest canopy, including cases where interception lowers SWE in the forest during accumulation, and shading reduces melt during the ablation season (relative to open sites). The snow model outputs are sensitive to canopy-related parameters, but DA reduces the range in depth and SWE estimates resulting from spatial variations or uncertainties in these parameters by more than 50%. The results demonstrate that the challenge of accurately measuring, estimating, or calibrating canopy-related parameters is reduced when snow depth observations are assimilated to improve SWE estimates.</p>more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/tc-13-1767-2019
