AI can enhance large-scale hydrological modelling for local applications

In our recent publication in Nature Communications Earth & Environment we show how combining process-based models with Machine Learning and statistical post-processing significantly boosts the accuracy of local streamflow predictions across Europe—even from large-scale models like SMHI's E-HYPE.

🔗 Hybrid approaches enhance hydrological model usability for local streamflow prediction 

What makes this more than just an academic result? 

The methods are designed with operational scalability in mind. This is a step forward for continental- and global-scale services such as in Copernicus EMS and C3S and WMO HydroSOS of the World Meteorological Organization and an example of how hybrid modeling can bridge science and practice in water resource management.

Your thoughts on hybrid modelling or experiences with continental/global forecasting systems?