Hydrological modeling with LSTMs in glacier and snowmelt fed catchments: the role of catchment attributes and multitask learning

Deep learning models based on Long Short-Term Memory (LSTM) networks are increasingly applied in rainfall runoff modeling, yet their behavior in heavily glacierized catchments remains understudied. We train a regional, lumped LSTM model for 49 glacier and snowmelt influenced catchments in Iceland using the LamaH-Ice dataset, driven by a regional atmospheric reanalysis and informed by static catchment attributes. We assess model performance in these basins, examine whether cryospheric processes are learned implicitly from streamflow alone, evaluate the role of static attributes, and test whether multitask learning with cryospheric targets improves discharge predictions.

We find that the model predicts daily streamflow robustly across most basins, achieving high skill during the test period. Model skill remains largely unchanged when physiographic attributes are randomly shuffled or replaced by simple climate statistics, but declines noticeably when static attributes are omitted. Counterfactual experiments in which glacier fraction is set to zero show summer discharge reductions that increase with the degree of glacier coverage. Using linear probes, we show that the LSTM implicitly learns signals related to remotely sensed snow cover and glacier albedo when trained only on streamflow.

We further explore a multitask learning configuration in which the LSTM is trained to predict both streamflow and satellite derived snow cover. The linear probes reveal that this setup improves the model’s internal representation of cryospheric variables but does not improve discharge predictions compared to a single task streamflow model.

Overall, we demonstrate that LSTM based hydrological models can simulate streamflow skillfully in glacierized catchments, with static catchment attributes supporting physical interpretation of model behavior. We further show that these models can internalize physically meaningful cryospheric information without explicit supervision, while highlighting limitations of multitask learning using remote sensing observations for improving streamflow predictions in glacierized catchments.