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Light-absorbing impurities in snow and ice (LAISI) lower the snow albedo and cause accelerated snowmelt. The radiative forcing caused by LAISI is in this connection the key variable in understanding LAISI-snowpack dynamics. Here we present an approach combining distributed hydrologic model simulations and remotely sensed radiative forcing from LAISI in order to improve model predictions of radiative forcing impacts. In a case study, we assess the seasonal cycle of instantaneous at-surface clear-sky radiative forcing from LAISI as predicted by model and satellite observations for a river basin located at the southern slope of the Himalayas. By scaling dust depositions, we optimize simulated radiative forcing conditioned on satellite observations. The optimized model predicts that LAISI-induced radiative forcing in snow contributes to 4.1% to 5.8% of the annual discharge. The presented approach has a wide range of applications as it provides a novel method to constrain and evaluate measures of LAISI-induced radiative forcing.

Plain Language Summary Certain particles that have the ability to absorb sunlight deposit onto mountain snow via atmospheric transport mechanisms. The presence of such particles in snow leads to a reduction of the snow's ability to reflect sunlight, which increases snowmelt. The key variable to understand these processes is hereby the additional energy that is taken up by the snow due to the presence of these particles. In this study, we present a method that allows the comparison of this additional energy measured by satellites with those predicted by numerical models. In a case study, we use the satellite data to improve the model representation of this variable by adapting the amount of particles in the snow of an area located at the southern slope of the Himalayas. Using the improved model, we estimate the increase in streamflow resulting from increased snowmelt in the area. We find that in the study region, light-absorbing particles in snow are responsible for 4.1% to 5.8% of the annual streamflow.