Snow and glacier meltwater are critical water resources for mountain regions, yet their accurate representation in hydrological models remains challenging. As climate change alters the timing and magnitude of melt contributions, accurate partitioning between snow and glacier sources becomes increasingly important. To address this challenge, this study couples the hydrological model HBV (as implemented in the Raven modeling framework) with the Global Glacier Evolution Model GloGEM and integrates a snow redistribution scheme for application across 14 glaciated headwater catchments in Switzerland. At this local catchment scale, we investigate whether glacier constraints and the addition of snow redistribution reduce parameter equifinality and increase the reliability of melt contribution estimates. Simulations are evaluated against observed streamflow, gridded snow water equivalent data, and glacier melt data based on observed mass balance. The gravitational snow redistribution algorithm successfully prevents unrealistic high-elevation snow accumulation and improves catchment average SWE simulation performance. Uncoupled HBV configurations outperform coupled HBV-GloGEM setups according to streamflow metrics. However, this superior performance is achieved by simulating glacier melt rates exceeding GloGEM estimates and glacier storage change data by factors of 2–3 in some catchments, effectively using glacier ice to offset precipitation biases in forcing data, which can be critical for climate change impact studies. Glacier melt contributions can vary by nearly an order of magnitude among best-performing parameter sets, highlighting severe parameter equifinality. Coupling with GloGEM, calibrated using glacier-specific geodetic mass balance, produces glacier melt consistent with observations and substantially improves identifiability of both glacier and snowmelt contributions. Despite lower streamflow performance metrics, glacier melt constraints prevent compensatory errors that would compromise projections as melt dynamics shift under climate change. Applying this framework to future climate scenarios and integrating additional constraints such as snow observations may further improve the reliability of melt contribution projections.