EGU24-13035, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Probabilistic Estimates of Fractional Snow Cover Area in Mountainous Terrain

David R. Casson1, Andrew W. Wood2,3, Guoqiang Tang2, Karl Rittger4, and Martyn Clark1
David R. Casson et al.
  • 1Department of Civil Engineering, University of Calgary, Calgary, Canada
  • 2Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, United States
  • 3Civil and Environmental Engineering, Colorado School of Mines, Golden, United States
  • 4Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado United States

This study investigates probabilistic estimates of fractional Snow Cover Area (fSCA) in mountainous terrain, aiming to bridge the gap between mechanistic hydrological models and operational remote sensing measurements. To capture the spatial variability of snow cover, we generate high-resolution ensemble meteorological forcing datasets from in-situ measurements, employing locally weighted regression and random forest methods. We then discretize a physically-based hydrological model tailored for mountainous terrain, incorporating the dominant factors influencing snow cover. Subsequently, fluxes from an intermediate complexity snowpack model are utilized to simulate fSCA, which we evaluate against an operational data source. This research is a progressive step toward integrating ensemble data assimilation techniques, with the goal of improving hydrological forecast performance.

How to cite: Casson, D. R., Wood, A. W., Tang, G., Rittger, K., and Clark, M.: Probabilistic Estimates of Fractional Snow Cover Area in Mountainous Terrain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13035,, 2024.