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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.


Incorporating topographic variability into a simple regional snowmelt model

Sloan, W. and Kilsby, C. and Lunn, R.J. (2004) Incorporating topographic variability into a simple regional snowmelt model. Hydrological Processes, 18 (17). pp. 3371-3390. ISSN 0885-6087

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General circulation models (GCMs), or stand-alone models that are forced by the output from GCMs, are increasingly being used to simulate the interactions between snow cover, snowmelt, climate and water resources. The variation in snowpack extent, and hence albedo, through time in a cell is likely to be substantial, especially in mid-latitude mountainous regions. As a consequence, the energy budget simulation by a GCM relies on a realistic representation of snowpack extent. Similarly, from a water resource perspective, the spatial extent of the pack is key in predicting meltwater discharges into rivers. In this paper a simple computationally efficient regional snow model has been developed, which is based on a degree-day approach and simulates the fraction of the model domain covered by snow, the spatially averaged melt rate and the mean snowpack depth. Computational efficiency is achieved through a novel spatial averaging procedure, which relies on the assumptions that precipitation and temperature scale linearly with elevation and that the distribution of elevations in the domain can be modelled by a continuous function. The resulting spatially averaged model is compared with both observations of the duration of snow cover throughout Austria and with results from a distributed model based on the same underlying assumptions but applied at a fine spatial resolution. The new spatially averaged model successfully simulated the seasonal snow duration observations and reproduced the daily dynamics of snow cover extent, the spatially averaged melt rate and mean pack depth simulated by the distributed model. It, therefore, offers a computationally efficient and easily applied alternative to the current crop of regional snow models.