<p><strong>Abstract.</strong> Combining multiple data sources with multi-physics simulation frameworks offers new potential to extend snow model inter-comparison efforts to the Himalaya. This study evaluates the importance and performance of different snowpack process representations for simulating snow cover and runoff dynamics in the region. Focusing on the Astore catchment in the upper Indus basin, a spatially distributed version of the Factorial Snowpack Model (FSM) was driven by climate

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... lds from the High Asia Refined Analysis (HAR) dynamical downscaling product. Ensemble performance was evaluated using observed runoff and MODIS remote sensing of snow-covered area, albedo and land surface temperature. The results show that FSM ensemble spread depends primarily on the interactions between parameterisations of albedo and snowpack hydrology when applied in the western Himalaya. These interactions incur variation in the importance of other model choices, most notably the atmospheric stability adjustment. Although no single FSM configuration performs best in all years, applying the prognostic albedo parameterisation while accounting for liquid water retention, refreezing and drainage leads to the highest overall performance. Years when this is not the case tend to coincide with probable inaccuracies in HAR climate inputs. While the results indicate that ensemble spread and errors may be notably lower in anomaly space, FSM configurations show substantial differences in their absolute sensitivity to climate variation. Therefore, a subset of the ensemble should be retained for climate change projections, namely those members including both prognostic albedo and snowpack hydrology, while additional stability adjustment options should be tested.</p>