Steven Fassnacht
CIRA Fellow
Job Title:
Professor, Colorado State University
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About Me:
Research Interests:
Prof. Fassnacht’s research interests include improving our understanding of snow and cold land hydrological processes, especially considering different complexities of models. These models are used for water resources forecasting, hydrological simulations, and climate modelling. My focuses are improving the data used for modelling and understanding the sensitivity of process formulations. We have tried to improve weather radar estimation of snowfall for hydrological modelling. We have created a time series of snow water equivalent maps for the Western United States that together with fractional snow covered area data have been assimilated into a hydrological model. We have examined how wind influences precipitation measurements (as snow) compared to snowpack losses via sublimation and blowing snow.
Hydrological and meteorological data exist at various spatial and temporal scales that can influence our analysis. Use of specific data can tell us a specific story that may not be relevant in the bigger picture. For example, a series of large snow storms in January of 1999 created havoc for transportation in Southern Ontario and early indications were that severe flooding was likely. However, due to several preceeding dry years and a dry spring, the lowest peak flows on record were observed.
We have examined the scales for snow depth sampling using fractal analysis, looked at averaging and sub-setting of large datasets, and investigated the interannual consistency of snowpack distribution patterns. Together with Nolan Doesken at the Colorado Climate Center, we have examined the measurement of snow depth using Ultra Sonic Snow Depth Sensors.
Recent work on snow crystals has translated into investigating snow surface roughness. We have developed a method to use digital photography to analysis snow surface roughness and have compared different evaluation metrics. We have shown how the snowpack surface roughness changes over time, has directionality, scales over space, and is influenced by dust on snow.