We analysed 20 years of satellite data to study changes in surface phytoplankton concentration in Lake Tanganyika. Results reveal strong seasonal patterns linked to known hydrodynamics, with decreasing levels in deep areas and increases in shallow regions. These changes could alter the lake’s ecosystem and threaten the resources that millions of people rely on.

This research investigates how water flows through porous media, focusing on tiny swirling currents called eddies that form around particles. Using experiments and computer, we found that faster flow and larger particles create more eddies, which trap pollutants and slow down their cleanup. A new method is developed to measure these eddies and showed how they can be accurately represented in standard transport models. This study provides a clearer physical basis for predicting pollutant spread.

Two models, AquaCrop (crop model) and Noah-MP (land surface model), were compared estimating irrigation in Italy's Po Valley. Noah-MP simulated higher water use (434 mm/yr) than AquaCrop (268 mm/yr), mainly due to extra water losses like runoff. Once losses were accounted for, both aligned with basin-scale reports of around 500 to 600 mm/yr. The study highlights how complex irrigation modeling is, and the need for better observational data to validate results.

Investigating changing snow in response to global warming can be done with a simple model and only temperature and precipitation data, simplifying snow dynamics with assumptions and parameters. We provide a large-scale and long-term evaluation of this approach and its performance across diverse climates. Temperature thresholds are more robust over cold climates but melt parameters are more robust over warmer climates with deep snow. The model performs well across climates despite its simplicity.