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Abstract

Snowmelt can vary largely across time and space, especially in complex terrain. However, hydrological models often represent snowmelt using a single static degree-day factor that relates the melt runoff with air temperature. Seasonally or spatially varying degree-day factors have been shown to better capture the snowmelt heterogeneity, but still rely on simplified parameterizations. One interesting solution proposed in the literature is to use MODIS satellite imagery to capture the true snowmelt heterogeneity, and use it to inform hydrological models on the temporal and spatial evolution of the degree-day factor on a near-daily basis. However, the limited spatial resolution of MODIS makes this process difficult to apply in complex mountainous terrain. Meanwhile, Landsat or Sentinel 2 satellite imagery could be an interesting alternative as they have a much higher spatial resolution but fall short in temporal resolution. In this study, we overcome both these obstacles with a synthetically generated daily snow cover time series based on Landsat resampling. We use the daily synthetic snow cover maps to derive the snow cover depletion within each coarse resolution hydrological model grid cell, which in turn defines the degree-day factor for each cell using a transfer function. To capture the inherent uncertainty of this methodology, we run an ensemble of models using different meteorological forcings and different stochastic realizations of the synthetic snow cover maps. The resulting degree-day factors are evaluated through the skill of the modeled streamflow and snow water equivalent, using different transfer functions in several snow-influenced catchments in Switzerland.