K.J. Bormann, J.P. Evans, M.F. McCabe
Journal of Hydrology, 517,
652-667, (2014)
Snow density, Snow modelling, Melt factor, Degree-day factor, Warm maritime snowpack dynamics, Snow depth
Current snowmelt parameterisation schemes are largely untested in warmer
maritime snowfields, where physical snow properties can differ
substantially from the more common colder snow environments. Physical
properties such as snow density influence the thermal properties of snow
layers and are likely to be important for snowmelt rates. Existing
methods for incorporating physical snow properties into
temperature-index models (TIMs) require frequent snow density
observations. These observations are often unavailable in less monitored
snow environments. In this study, previous techniques for end-of-season
snow density estimation (Bormann et al., 2013) were enhanced and used
as a basis for generating daily snow density data from climate inputs.
When evaluated against 2970 observations, the snow density model
outperforms a regionalised density-time curve reducing biases from
−0.027 g cm−3 to −0.004 g cm−3 (7%). The simulated
daily densities were used at 13 sites in the warmer maritime snowfields
of Australia to parameterise snowmelt estimation. With absolute snow
water equivalent (SWE) errors between 100 and 136 mm, the snow model
performance was generally lower in the study region than that reported
for colder snow environments, which may be attributed to high annual
variability. Model performance was strongly dependent on both
calibration and the adjustment for precipitation undercatch errors,
which influenced model calibration parameters by 150–200%. Comparison of
the density-based snowmelt algorithm against a typical
temperature-index model revealed only minor differences between the two
snowmelt schemes for estimation of SWE. However, when the model was
evaluated against snow depths, the new scheme reduced errors by up to
50%, largely due to improved SWE to depth conversions. While this study
demonstrates the use of simulated snow density in snowmelt
parameterisation, the snow density model may also be of broad interest
for snow depth to SWE conversion. Overall, the study responds to recent
calls for broader testing of TIMs across different snow environments,
improves existing snow modelling in Australia and proposes a new method
for introducing physically-based constraints on snowmelt rates in
data-poor regions.