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 exploring machine learning models for ...[Read More]
In episode 8 of the Hydrotalks podcast, we hosted four coordinators of working groups of the HELPING hydrological decade. We warmly welcomed Dr. Giulio Castelli (University of Florence) and Dr. Natalie Ceperley (University of Bern), group co-leaders of Co-Creating Water Knowledge working group; Dr. Soham Adla (ING Bank, Netherlands), a coordinator of Science communication, outreach, and promoting ...[Read More]
Picture this: you’re hiking through a dry landscape when suddenly you hear it—the serene sound of a babbling brook. You round a corner and discover a small waterfall cascading into crystal-clear pools, surrounded by lush green ferns and wildflowers attracting buzzing bees. It feels like stumbling upon a secret oasis. These magical streams that appear and disappear with the seasons are called ...[Read More]
