Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models

Abstract. Deep learning-based rainfall-runoff models, in particular long short-term memory networks (LSTM), have been shown to outperform traditional hydrological models at various tasks, both when used as purely data-driven models and when combined with process-based models in a hybrid setting. These tasks include predictions in ungauged basins (PUB) and regions (PUR), tasks which have traditionally been challenging for conceptual hydrological models. While the spatial generalizability of deep learning-based models has received a lot of attention, it is less clear how they generalize to unseen and warmer climate conditions, i.e. how suitable these models are for hydrological climate impact studies. To address this research gap, we assess the ability of three types of models including (1) fully data-driven (LSTMs), (2) conceptual (Hydrologiska Byråns Vattenbalansavdelning; HBV), and (3) hybrid (differentiable hydrologic models) to simulate streamflow under conditions warmer than those used to train the models by running a differential split sample test. That is, we trained the models using data from the historical period 1960–1990 and evaluated them on data of this period as well as of the warmer period 2000–2023. We find that the LSTM is the most accurate model overall (median NSE: 0.74–0.79), followed by the hybrid (median NSE: 0.68–0.71) and HBV model (median NSE: 0.53–0.56). However, the LSTM shows a stronger change in performance between the warm and cold period (NSE: −0.05 to −0.04) compared to the hybrid (NSE: −0.03–0) and HBV model (NSE: −0.03 to −0.02). When comparing observed with simulated streamflow signals, we find a higher sensitivity of the LSTM to changes in meteorological forcings (mainly temperature change) for mean flows (MQ: −8.5 % to −12.6 %) and high flows (Q99: −7.0 % to −13.4 %) than in observations (MQ: −4.6 % to −9.4 %, Q99: −3.6 % to −9.7 %). In contrast, the hybrid and HBV model show good agreement with observed change signals. These findings indicate that the LSTM, while being the most accurate overall, has a lower generalizability to warmer conditions than the other models. However, we also find that the models' capability to generalize can differ between catchment type. While all models generalize better in rainfall-dominated catchments, they all show larger but similar reductions in performance in snow-dominated catchments. This suggests that all models struggle to generalize to warmer conditions in snow-dominated catchments implying that better process-representation in the models might be needed to accurately capture the dynamics of snow-melt and -accumulation processes, which are highly sensitive to changes in temperature. We conclude that hybrid models effectively combine the strengths of LSTMs in having a high accuracy when predicting in ungauged basins, with the good generalizability under changes in climate from the conceptual hydrological models. This makes hybrid models a suitable choice for hydrological climate change impact assessments, particularly in ungauged basins.