Light absorbing impurities in snow and ice (LAISI) originating from atmospheric deposition enhance the snow melt by increasing the absorption of short wave radiation. The consequences are a shortening of the snow duration due to increased snow melt and, on a catchment scale, a temporal shift in the discharge generation during the spring melt season. In this study, we present a newly developed snow algorithm for application in hydrolgical models that allows for an additional class of input variables: the deposition rate of various species of light absorbing aerosols.
5To show the sensitivity of different model parameters, we first use the model as 1-D point model forced with representative synthetic data and investigate the impact of parameters and variables specific to the algorithm determining the effect of LAISI.We then demonstrate the significance of the additional forcing by simulating black carbon deposited on snow of a remote south Norwegian catchment over a six years period, from September 2006 to August 2012. Our simulations suggest a significant impact of BC in snow on the hydrological cycle, with an average increase in discharge of 2.5 %, 9.9 %, and 21.4 % for our 10 minimum, central and maximum effect estimate, respectively, over a two months period during the spring melt season compared to simulations where radiative forcing from LAISI is turned off. The increase in discharge is followed by a decrease caused by melt limitation due to faster decrease of the catchment's snow covered fraction in the scenarios where radiative forcing from LAISI is applied. The central effect estimate produces reasonable surface BC concentrations in snow with a strong annual cycle, showing increasing surface BC concentration during spring melt as consequence of melt amplification. However, we 15 further identify large uncertainties in the representation of the surface BC concentration and the subsequent consequences for the snowpack evolution. and magnitude of the snow melt are major predictors for flood (Berghuijs et al., 2016) and land slide (Kawagoe et al., 2009) forecasts, and important factors in water resource management and operational hydropower forecasting. The extent and the temporal evolution of the snow cover is a controlling factor in the processes determining the growing-season of plants (Jonas et al., 2008). For all these reasons, a good representation of the seasonal snowpack in hydrological models is paramount.However, there are large uncertainties in many variables specifying the temporal evolution of the snowpack, and the snow 5 albedo is one of the most important among those due to the direct effect on the energy input to the snowpack from solar radiation. Fresh snow can have an albedo of over 0.9, reflecting most of the incoming solar radiation. However, the snow albedo undergoes strong variations: as snow ages, the snow grain size increases and albedo will drop as a result of the altered scattering properties of the larger snow grains. Furthermore, ambient conditions also play a large role. The ratio ...