Cryosphere Data and Its Value for Deep Learning Hydrological Simulations

Deep learning models have been successfully applied to simulate streamflow in mountain catchments. While these mostly lumped models have demonstrated the ability to learn processes such as snow accumulation and melt that are crucial for streamflow generation in these regions, they still show deficiencies in simulating streamflow during the melting period. This suggests a misrepresentation of melting dynamics encoded within these models. We hypothesize that the sets of lumped meteorological variables (such as air temperature, precipitation, PET) and static attributes currently used to train and drive these models are not sufficient to describe the melting processes. 

To enhance the representation of snow and ice-related processes, we thus propose to incorporate additional data on snow and ice cover, such as Snow Covered Area, Snow Water Equivalent, and glacier mass within the respective basin. We assess (1) how much additional value can be extracted from cryosphere data to improve the representation of cryosphere related processes and (2) how the added value varies across different geographies and catchment types. In a lumped Long Short-Term Memory (LSTM) setup covering a large sample of catchments in different European mountainous regions, we compare different data integration methods with respect to their uncertainty reduction for streamflow simulation and their limitations for model applications.
Our findings provide insights into optimizing model configurations and data usage and offer practical guidance for ultimately improving the accuracy of streamflow simulations in mountainous, snow-influenced regions.