Abstract. Global hydrological models have become a valuable tool for a range of global impact studies related to water resources. However, glacier parameterization is often simplistic or non-existent in global hydrological models. By contrast, global glacier models do represent complex glacier dynamics and glacier evolution, and as such, they hold the promise of better resolving glacier runoff estimates. In this study, we test the hypothesis that coupling a global glacier model with a global hydrological model leads to a more realistic glacier representation and, consequently, to improved runoff predictions in the global hydrological model. To this end, the Global Glacier Evolution Model (GloGEM) is coupled with the PCRaster GLOBal Water Balance model, version 2.0 (PCR-GLOBWB 2), using the eWaterCycle platform. For the period 2001–2012, the coupled model is evaluated against the uncoupled PCR-GLOBWB 2 in 25 large-scale (>50 000 km2), glacierized basins. The coupled model produces higher runoff estimates across all basins and throughout the melt season. In summer, the runoff differences range from 0.07 % for weakly glacier-influenced basins to 252 % for strongly glacier-influenced basins. The difference can primarily be explained by PCR-GLOBWB 2 not accounting for glacier flow and glacier mass loss, thereby causing an underestimation of glacier runoff. The coupled model performs better in reproducing basin runoff observations mostly in strongly glacier-influenced basins, which is where the coupling has the most impact. This study underlines the importance of glacier representation in global hydrological models and demonstrates the potential of coupling a global hydrological model with a global glacier model for better glacier representation and runoff predictions in glacierized basins.

<p>Global hydrological models (GHMs) have become an increasingly valuable tool in a range of global impact studies related to water resources. However, the parameterization of glaciers is often overly simplistic or non-existent in GHMs. The representation of glacier dynamics and evolution, including related products such as glacier runoff, can be improved by relying on dedicated global glacier models (GGMs). Coupling a GGM to a GHM could consequently lead to increased GHM predictive skills, decreased GHM uncertainty, and an increased understanding of the contribution of glaciers to catchment hydrology, particularly in light of climate change.</p><p>To test this hypothesis, the GHM PCR-GLOBWB 2 (Sutanudjaja et al., 2018) is coupled with the GGM GloGEM (Huss and Hock, 2015) using eWaterCycle (Hut et al., 2018). For the years 2001-2012, the coupled model is evaluated against the uncoupled benchmark in 25 large (>50.000 km2) glacierized basins using GRDC streamflow observations. Across all basins, the coupled model produces higher runoff throughout the melt season, which can principally be attributed to the underrepresentation of glacial melt in PCR-GLOBWB 2. In highly glaciated basins this difference is pivotal, while in lowly glaciated basins it is negligible. In the evaluation against the GRDC observations, the performance increment of the coupled model at the peak of the melt season in highly glaciated basins stands out.</p><p>This study underlines the importance of glacier representation in GHMs and demonstrates the potential of coupling a GHM with a GGM for better glacier representation and runoff predictions in glaciated basins.</p><p> </p>

<p>To study long-term changes in the hydrology of snow-fed catchments, there is a need for long-term time series of snow cover data. Satellite imagery and climate reanalysis can both be used to quantify past snow cover, but they lack in baseline period length and spatial resolution respectively. In this study we apply statistical methods to generate synthetic high-resolution daily snow cover maps, and consequently use these maps as forcing in long-term hydrological modeling. The results are benchmarked against a case without synthetic snow cover forcing. Multiple hydrological models are used to reduce the uncertainty related to the model choice. The study is performed on the Thur catchment in Eastern Switzerland, a meso-scale catchment covering a wide elevation range and experiencing multiple periods of intermittent snow cover annually. We expect the synthetic snow cover maps to provide added value through the high-resolution spatial information on snow appearance and disappearance, leading to better estimates of snow melt runoff. However, we also expect them to show some physical inconsistencies, particularly after periods of high snow accumulation. Should it prove promising, this approach can be used to study both past and future hydrological changes in any snow-fed catchment using ERA5 and CMIP6 climate data, potentially spanning the entire period 1950-2100. Additionally, this framework of synthetic satellite data generation could be expanded to other hydrological variables or to environmental image time series in other fields than hydrology.</p>

<p>Snowmelt can vary largely across time and space, especially in complex terrain. However, hydrological models often represent snowmelt using a single static degree-day factor that relates the melt runoff with air temperature. Seasonally or spatially varying degree-day factors have been shown to better capture the snowmelt heterogeneity, but still rely on simplified parameterizations.<em> </em>One interesting solution proposed in the literature is to use MODIS satellite imagery to capture the true snowmelt heterogeneity, and use it to inform hydrological models on the temporal and spatial evolution of the degree-day factor on a near-daily basis. However, the limited spatial resolution of MODIS makes this process difficult to apply in complex mountainous terrain. Meanwhile, Landsat or Sentinel 2 satellite imagery could be an interesting alternative as they have a much higher spatial resolution but fall short in terms of temporal resolution. In this study, we overcome both these obstacles with a synthetically generated daily snow cover time series based on Landsat resampling. We use the daily synthetic snow cover maps to derive the snow cover depletion in each coarse resolution hydrological model grid cell, which in turn defines the degree-day factor for each cell using a transfer function. To capture the inherent uncertainty of this methodology, we run an ensemble of models using different meteorological forcings and different stochastic realizations of the synthetic snow cover maps. The resulting degree-day factors are evaluated through the skill of the modeled streamflow and snow water equivalent, using different transfer functions in several snow-influenced catchments in Switzerland. </p>