A new era of hybrid hydrological forecasting at ECMWF

Hydrological modeling has entered a new era in recent years, largely driven by the curation of extensive datasets and availability of open-source machine learning libraries. While traditional physically based models have been key to improving our understanding of hydrological systems and establishing early warning systems, they often face challenges such as high computational costs and requiring simplifications of complex processes. Conversely, machine learning methods, despite potential pitfalls such as generating unphysical outputs and requiring large volumes of training data, are computationally quick and capable of capturing highly non-linear relationships. Hybrid hydrological modeling bridges these approaches, combining the efficiency and flexibility of machine learning with the proven capabilities and interpretability of traditional physical models.

This talk will provide an overview of the hybrid hydrological modeling research being conducted at the European Centre for Medium-range Weather Forecasts (ECMWF). Using case studies, we will show how machine learning methods could be incorporated into the pre-established physical modeling chain, addressing both scientific and operational challenges. Examples will cover the full modelling chain including the coupling of machine learning and physically based models, the use of emulators of sub-models to reduce computational overhead, and the integration of data-driven techniques to correct model biases in real time. The development of a machine learning forecasting model will also be discussed as a component of a hybrid multi-model system. Attention will be given to the practical aspects of implementation, including computational efficiency both for an operational system and for sensitivity and calibration experiments, scalability to large operational systems, and the potential to incorporate new datasets, such as remote sensing data, into hybrid frameworks. We will also discuss how artificial intelligence can be used to support auxiliary services such as simulation verification.

Finally, we will reflect on the implications of hybrid hydrological modeling for advancing hydrological science and operational forecasting. By combining the strengths of physical and machine learning models, this approach has the potential to improve flood prediction, water resource management, and climate impact assessments. This hybrid approach marks an important step forward in the development of hydrological modeling, enabling more accurate, efficient, and actionable understanding of water systems in a rapidly changing world.