Snowpack characteristics and modelling in the marginal snowfields of southeast Australia

Abstract

Seasonal snowfields provide water resources to large populations. Snowfield observations may consist of a comprehensive collection of variables at point locations, or a limited set of variables distributed over a spatial domain from remote sensing. Both types of observations have significant limitations when attempting to characterise marginal snowfields that display large spatial heterogeneity and interannual variability. Using both observations and modelling this thesis examines the marginal snowfields of southeast Australia to improve understanding of snow water resources. In this thesis, snow density observations were used to identify the climate influences dominant in variability in physical snow properties in the Australian snowfields compared to northern hemisphere counterparts; satellite data was used to develop robust methods for monitoring snow cover in the marginal snowfields of Australia; and these findings were used to develop a spatially distributed snow model that performed well for the region.Using a large sample of snow density data and climate observations precipitation was found to be the strongest driver of seasonal snow densification rates, with air temperatures and melt-refreeze events also locally significant. Interannual variability in snow density comprised a large proportion of the variance within Australia and the western US. Using daily remote sensing retrievals from NASA's MODIS-Terra sensor, a regionalised snow detection algorithm was developed to build a snow cover dataset for Australia. Significant declines in snow cover, season duration and a shift towards earlier snowmelt date were observed, suggesting a link to observed warming trends in the area. Using a temperature-index snow model, multiple simulations throughout southeast Australia were conducted to examine physical links between melt parameters, site features and climate characteristics. Results show that the choice of snowmelt algorithm was less important for SWE estimation than other factors such as snowfall undercatch in the forcing data. The spatial application of this model highlights considerable pre-spring snowmelt and annual variability in the Australian snow pack. Overall this research highlights challenges encountered in marginal snowfields where current limitations in snow research are exacerbated. The research is novel for the region and makes significant contribution towards water resource planning in a warming climate