Do reservoir-influenced gauges need explicit consideration in machine learning models? A case study with Hydra-LSTM

Abstract. Reservoirs fundamentally alter downstream river flow regimes, decoupling discharge from natural meteorological forcing and challenging standard hydrological prediction. While data-driven models, such as Long Short-Term Memory (LSTM) networks, show promise in regulated catchments, it remains unclear how training data composition across natural and regulated rivers influences model generalisability and behaviour. In this study, we investigate how the presence or absence of reservoir-influenced catchments in training data impacts model performance across different flow regimes and alters the physical drivers the models learn to rely on. Using carefully matched subsets of the CAMELS-GB dataset, we trained separate specialist LSTMs (reservoir and non-reservoir), a pooled Full LSTM, and a multi-headed Hydra-LSTM to investigate whether explicit architectural specialisation offers any advantage over pooled training alone. Models were evaluated on held-out test gauges using standard performance metrics and gradient importance analysis to interpret feature reliance. Our results demonstrate that exposure to reservoir-influenced catchments during training is essential. Models trained exclusively on natural catchments consistently overestimate the mean and variance of regulated flows. Conversely, training exclusively on reservoir-influenced data degrades performance on non reservoir-influenced rivers (KGE reduction of ≥ 0.1) giving importance primarily to anthropogenic static features, such as abstraction rates, at the expense of precipitation drivers. A single Full LSTM trained on combined data matched the performance of both specialist models in their respective domains, implicitly switching its feature reliance between regimes. The Hydra-LSTM performed comparably to the Full LSTM throughout, indicating that the shared body may act as a regulariser limiting over-specialisation, but that explicit architectural specialisation provides no further benefit under these conditions. We conclude that pooling training data across regimes is a highly effective strategy for general-purpose modelling. However, case studies highlight a fundamental limitation: purely meteorological inputs remain insufficient for predicting flows in heavily managed single-purpose reservoirs, where unobserved human operational decisions dominate the hydrograph.