Spatial scaling of snow processes : modelling implications

Abstract

Snow cover affects life on Earth in many important ways. The high reflectivity and high moisture content of snow cover affects the global energy balance, atmospheric circulation, and weather. The characteristic heterogeneity of ephemeral mountain snowpacks exhibits strong controls on hydrological, biological, and ecological processes. Accurately predicting process responses is based on knowing the volume, distribution, and state of the snow cover. Physically-based distributed snow models (DSMs) are capable of explicitly representing these vital heterogeneities and are well suited for predicting future impacts such as those associated with climate change. These models however, are currently limited by high computational demands. This research sought to reduce these computational demands and extend the limits of physically-based DSMs. In many regions, wind plays a dominant role in determining snow accumulation patterns .: New algorithms based on terrain and vegetation structure were developed that capably reproduced observed heterogeneities in mountain winds and wind-affected snow distributions. Characterizing the wind and snow patterns in this simplified manner bypassed the heavy computational demands associated with numerically solving the fluid mechanics of windflow and mass transport. The algorithms were incorporated into a mass and energy balance DSM which accurately depicted the heterogeneous accumulation and melt of the snow cover. The computational efficiency of these new algorithms enabled what was perhaps the first DSM application to include the effects of blowing and drifting snow over this large an area at this fine a temporal resolution. Model scale also plays an important role in determining computation times. It was shown that a 100 metre model scale was sufficient for characterizing mountain snow distributions and melt. Furthermore, it was determined that not all the driving processes required the same level of detail creating the potential for additional cost savings. The presented findings have substantially reduced costs and expanded the capabilities of DSMs.EThOS - Electronic Theses Online ServiceGBUnited Kingdo