HS
Hydrological Sciences

Natural Hazard

Bridging the Gap Between Global Tech and Local Expertise: Why We Open-Sourced Google’s Hydrology Framework

and October 8, 2026
Bridging the Gap Between Global Tech and Local Expertise: Why We Open-Sourced Google’s Hydrology Framework

Dear EGU Hydrology community, Over the past few years, machine learning (ML) and artificial intelligence have fundamentally changed what is possible in large-scale hydrological modeling. At Google, our team has worked to develop machine learning river forecast models that operate globally, extending reliable forecasting to ungauged basins and covering almost two billion people through the …[Read More]

What machine learning can and cannot tell us about floods

July 23, 2026
Collage based on a picture by Imad Clicks via Pexel

Floods are shaped by complex and nonlinear interactions between weather patterns, rainfall, soil properties, topography, land cover, and how wet a catchment already is. This complexity is one reason machine learning (ML) is increasingly used in hydrology. ML models can learn patterns from large and complex datasets with many variables.  Early in my PhD, while …[Read More]

HydroTalks: Prof. Thom Bogaard on Water and Landslides, Early Warning Systems, and IAHS-HELPING Decade

and July 9, 2026
HydroTalks: Prof. Thom Bogaard on Water and Landslides, Early Warning Systems, and IAHS-HELPING Decade

For episode 11 of HydroTalks, we welcomed Prof. Thom Bogaard of Delft Technical University and visiting professor at Kasetsart University, Bangkok. His research explores the intersection of hydrology, geomorphology, ecology, and natural hazards. We discussed his work on understanding how water triggers landslides, improving regional early warning systems, and developing practical solutions that reduce disaster …[Read More]