AIFL: advancing global streamflow prediction with operational machine learning

Summary

Every year, floods affect more people worldwide than any other natural hazard. Accurate streamflow forecasts, predicting how much water will flow through a river in the coming days, are essential for issuing timely warnings, managing reservoirs, and protecting lives. For over 15 years, ECMWF has provided the computational backbone for the European and Global Flood Awareness Systems (EFAS and GloFAS), coupling weather forecasts with process-based hydrological models, which are physics-driven simulations that represent the movement of water through the landscape using mathematical equations. Now, we are exploring how machine learning can complement these capabilities. In a new study, we introduce Artificial Intelligence for Floods (AIFL), a global streamflow forecasting model that learns directly from data and, crucially, learns to cope with the realities of operational weather forecasting. This work contributed to the Destination Earth (DestinE) initiative of the European Commission. As part of DestinE, ECMWF together with many partners is building data-driven models across the Earth system. AIFL targets the hydrology component, complementing parallel efforts on the atmosphere, ocean, sea ice, waves, and land, and helping lay the groundwork for a fully coupled machine learning Earth system model. The challenge: training on the past, forecasting in real time Machine learning models for hydrology are typically trained and forced with atmospheric reanalysis data, which provide gridded reconstructions of past weather that are temporally consistent and grounded in observations via data assimilation. This is a problem as when these models are deployed operationally, they are driven not by reanalysis, but by weather forecasts from systems like ECMWF’s Integrated Forecasting System (IFS). These systems have a different climatology as well as forecast uncertainty. This mismatch, called the reanalysis-to-forecast domain shift, can significantly degrade a model's performance in the real world. A model that looks excellent in research papers may struggle when it matters most. A two-stage learning strategy AIFL addresses the gap between reanalysis data and operational forecasts with a straightforward but effective two-stage training approach. In the first stage, we pre-train a Long Short-Term Memory (LSTM) neural network on 40 years of ERA5-Land reanalysis data (1980–201…