Quantifying Glacier Melt Contribution to Streamflow in Switzerland with CAMELS-CH using Deep Learning

Meltwater from glaciers is an essential resource of water in Alpine catchments that is however subject to large inter-annual fluctuations due to varying meteorological conditions. To optimize energy production and support water resource management, it is crucial to understand the expected range of arriving glacier melt volumes during the ablation season. Glacier contribution is generally estimated through dynamic glacier models, in-situ isotope measurements or a data-driven approach using hydrological modeling. We present a simplified data-driven approach to indicate the potential of Deep Learning models for the determination of streamflow contributions. A regional LSTM was set up on the recently released CAMELS-CH dataset that includes over 20 catchments with substantial glacier cover (>10% catchment area coverage according to the Swiss glacier inventory GLAMOS). The LSTM was trained on all river catchments with dynamic meteorological forcings and static catchment attributes, including the glacier cover percentage, as inputs. Thereby reaching a median KGE of 0.83 over the test period. In a second test run, the glacier cover percentage was set to zero. Glacier melt time series were then interpreted as the difference in simulated streamflow with respect to the test simulation with the actual glacier cover percentage. The resulting discharge time series exhibits the expected seasonal pattern for glacier-influenced catchments. Removing the glacier cover reduces the simulated streamflow during summer months, with the largest reduction observed around August/September. Simultaneously, an increase in streamflow is observed during spring months, which might be associated with the melt from excess snow that is not assimilated to the glacier during winter. We find relative glacier melt contributions to streamflow of 0% to 60% for the period of 2006-2020, with an average of 5% over all catchments, which are generally higher than contributions reported in literature determined using estimates from commonly used conceptual hydrologic models.This hints at the potential of DL models as an additional method to quantify glacier melt volumes and streamflow contributions. Further, this enables us to investigate glacier melt and to study glacier dynamics in a data-driven way with a minimum set of assumptions in our modeling approach.