Reducing hydrological modelling uncertainty by using MODIS snow cover data and a topography-based distribution function snowmelt model

Abstract This work aims to describe how MODIS snow cover maps can be used to reduce uncertainty in hydrological modelling carried out by means of a topography-based distribution function snowmelt model. The well-known GLUE methodology is applied with a multi-objective approach, combining streamflow observations recorded at the outlet section and satellite-derived snow cover maps, aggregated to fractional value of the catchment area. The hydrological model includes a snowpack routine which exploits a statistical representation of the distribution of clear sky potential solar radiation - a significant advantage when parameter sensitivity and uncertainty estimation procedures are carried out. This study provides an assessment of this technique based on operational quality data from two medium-size mountainous basins (a nested one included in a larger parent basins) located in the eastern Italian Alps. Results show a positive feedback between streamflow and snow cover area likelihoods, highlighted by means of the Pareto plot. Moreover, a better identifiability of the parameter driving snowmelt rate is found and consequently a shrink of the predictive streamflow uncertainty is observed. A containing ratio of 0.54 and a mean sharpness of 0.11 are found at the outlet of the parent basin, while a containing ratio equal to 0.65 and a mean sharpness equal to 0.17 were estimated at the nested upstream hydrometric station, used as a validation test. These results confirm the potential of MODIS snow cover maps as additional data to inform hydrological models leading to more reliable and sharper streamflow estimates. This approach might be also appealing for areas where streamflow data are available only downstream and runoff estimates are required at other ungauged locations across the catchment.