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| Titel |
Modeling bulk density and snow water equivalent using daily snow depth observations |
| VerfasserIn |
J. L. McCreight, E. E. Small |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1994-0416
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| Digitales Dokument |
URL |
| Erschienen |
In: The Cryosphere ; 8, no. 2 ; Nr. 8, no. 2 (2014-03-27), S.521-536 |
| Datensatznummer |
250116083
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| Publikation (Nr.) |
 copernicus.org/tc-8-521-2014.pdf |
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| Zusammenfassung |
Bulk density is a fundamental property of snow relating its depth and mass.
Previously, two simple models of bulk density (depending on snow depth,
date, and location) have been developed to convert snow depth observations
to snow water equivalent (SWE) estimates. However, these models were not
intended for application at the daily time step. We develop a new model of
bulk density for the daily time step and demonstrate its improved skill over
the existing models.
Snow depth and density are negatively correlated at short (10 days)
timescales while positively correlated at longer (90 days) timescales. We
separate these scales of variability by modeling smoothed, daily snow depth
(long timescales) and the observed positive and negative anomalies from the
smoothed time series (short timescales) as separate terms. A climatology of
fit is also included as a predictor variable.
Over half a million daily observations of depth and SWE at 345 snowpack telemetry (SNOTEL) sites
are used to fit models and evaluate their performance. For each location, we
train the three models to the neighboring stations within 70 km, transfer the
parameters to the location to be modeled, and evaluate modeled time series
against the observations at that site. Our model exhibits improved
statistics and qualitatively more-realistic behavior at the daily time step
when sufficient local training data are available. We reduce density root mean square error (RMSE) by
9.9 and 4.5% compared to previous models while increasing R2
from 0.46 to 0.52 to 0.56 across models. Focusing on the 21-day window around
peak SWE in each water year, our model reduces density RMSE by 24 and
17.4% relative to the previous models, with R2 increasing from 0.55
to 0.58 to 0.71 across models. Removing the challenge of parameter transfer
over the full observational record increases R2 scores for both the
existing and new models, but the gain is greatest for the new model (R2 = 0.75). Our model shows general improvement over existing models when data
are more frequent than once every 5 days and at least 3 stations are
available for training. |
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