HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction

TL;DR

HydroDCM introduces a scalable domain generalization framework for reservoir inflow prediction that leverages reservoir metadata to improve cross-reservoir forecasting accuracy and adaptability.

Contribution

The paper proposes HydroDCM, a novel hydrological DG method that uses spatial metadata for pseudo-domain labeling and adaptive feature conditioning, addressing domain shift in reservoir inflow prediction.

Findings

HydroDCM outperforms state-of-the-art DG methods on 30 reservoirs.

The approach is computationally efficient and scalable.

HydroDCM effectively balances domain invariance with location-specific adaptation.

Abstract

Deep learning models have shown promise in reservoir inflow prediction, yet their performance often deteriorates when applied to different reservoirs due to distributional differences, referred to as the domain shift problem. Domain generalization (DG) solutions aim to address this issue by extracting domain-invariant representations that mitigate errors in unseen domains. However, in hydrological settings, each reservoir exhibits unique inflow patterns, while some metadata beyond observations like spatial information exerts indirect but significant influence. This mismatch limits the applicability of conventional DG techniques to many-domain hydrological systems. To overcome these challenges, we propose HydroDCM, a scalable DG framework for cross-reservoir inflow forecasting. Spatial metadata of reservoirs is used to construct pseudo-domain labels that guide adversarial learning of invariant temporal features. During inference, HydroDCM adapts these features through light-weight conditioning layers informed by the target reservoir's metadata, reconciling DG's invariance with location-specific adaptation. Experiment results on 30 real-world reservoirs in the Upper Colorado River Basin demonstrate that our method substantially outperforms state-of-the-art DG baselines under many-domain conditions and remains computationally efficient.