The ability to generate hydrological data using satellites, drones, cell towers, and other measurement systems has never been greater than now. Machine learning and data-driven models are other methods for new data sources that together create entirely new conditions for developing hydrological models with higher resolution in time and space and better descriptions of different hydrological processes. The new models, in turn, create conditions to better utilize various types of data for calibration, assimilation, and uncertainty estimations.
Additionally, SMHI has a strong international position and actively contributes to the Agenda 2030 work for climate transition with service exports to data-poor countries. Here, for example, combinations of satellite and drone data can help improve model results from World-Wide HYPE so they can be fully used in warning services and local community planning.
Research lead: David Gustafsson
