Enhancing the Robustness of Deep Learning Hydrological Models in High-Latitude Catchments

As climate change accelerates, hydrological models are increasingly required to predict water resources under climatic conditions they have never seen before. While modern data-driven approaches, such as machine learning models, have shown higher accuracy in reproducing historical streamflow, their ability to generalize to unseen future climates remains a critical concern. These data driven models often learn statistical patterns that maximize performance on training data but fail when facing new weather patterns or extreme events. Current research into improving model robustness has largely focused on conceptual models in temperate, rain-dominated catchments. This leaves the applicability of these techniques unverified in high-latitude, snow-dominated catchments, such as Finland. These regions face distinct challenges, particularly the complex, non-linear processes of snow accumulation and melt. Because these processes are highly sensitive to temperature thresholds, standard data-driven models may struggle to capture them consistently when extrapolating to warmer future conditions. Furthermore, widely used stability techniques have rarely been adapted for the specific architecture of machine learning models. This study proposes to investigate whether integrating residual stability constraints, mathematical penalties that force model errors to remain consistent over time, can improve the transferability of AI models in boreal catchments. Rather than relying solely on minimizing error, we aim to explore training schemes that prioritize time-invariance, ensuring that the model's behavior does not degrade significantly between different climatic periods. We outline a framework to test these stability-based training methods on a large dataset of Finnish catchments. By comparing standard AI training against stability-constrained approaches, this research aims to determine if trading a small amount of historical accuracy can yield models that are more physically plausible and robust for future climate scenarios. This work seeks to bridge the gap between advanced machine learning techniques and the unique hydrological needs of cold-climate regions.