Modelling runoff in a glacierized catchment: the role of forcing product and spatial model resolution
Abstract. Glaciers are vital water resources, particularly in alpine regions, sustaining ecosystems and communities during dry summer months. Accurate glacio-hydrological models are essential for understanding water availability under climate change. However, these models face numerous challenges, including limited observations for model forcing, calibration and validation, as well as computational constraints at fine spatial resolutions. This study assesses the reliability of glacio-hydrological simulations in a glacierized catchment (39.4 km2) in Switzerland using the Glacier Evolution Runoff Model (GERM). Two experiments investigate how simulated glacier mass balance and runoff are affected by (1) varying meteorological forcing products, from point data to coarse grids, and (2) spatial model resolution, from 25 m to 3000 m. We find that the forcing from different precipitation data sets has the largest effect on model results. In this study, model resolutions coarser than 1000 m fail to capture essential glaciological and topographic details, affecting the accuracy of small and medium-sized glaciers. Single-data calibration on geodetic glacier ice volume change can accurately reproduce annual glacier mass balance but lead to seasonal biases, driven by underestimating winter precipitation and compensatory parameter adjustments. Calibrating the model on multi-data, including geodetic glacier ice volume change and runoff, improves seasonal accuracy but is limited by constant precipitation adjustments that cannot account for temporal forcing biases. These findings highlight the trade-offs between computational efficiency and model reliability, emphasizing the need for high-resolution forcing data and careful calibration strategies to capture glacio-hydrological processes accurately. While the results are derived for a single, well-instrumented catchment, they hint at broader implications for modelling glacierized catchments under data-scarce conditions.