Hydrological models and services are essential for water management, flood and drought
preparedness, and ecosystem protection. However, large-scale process-based models often
struggle with local-scale accuracy due to the complexity of hydrological processes, uncertainties
in meteorological inputs, and human interventions.
This webinar dives into how state-of-the-art artificial intelligence (AI) and machine learning
(ML) can enhance streamflow predictions across Europe’s diverse hydro-climatic and geographic
conditions. We will explore hybrid approaches that integrate traditional hydrological modeling
with ML post-processing techniques, such as Long Short-Term Memory (LSTM) model and
Random Forest, to improve predictive accuracy. Furthermore, we will present AI-driven
regionalization strategies designed to enhance streamflow forecasting in data-scarce and
ungauged regions.
Join us to explore how these innovations are shaping the future of hydro-climate services,
enabling more reliable and accessible water management solutions, and strengthening early
warning capabilities in a changing climate.
