Research focus

My research involves the development and improvement of modelling approaches to achieve a more realistic representation of the water cycle in hydrologic models. Models encapsulate our knowledge about how hydrologic systems function. Therefore, our ability to understand and anticipate changes relies on simulations of the water cycle that are crucial for water resources management under a changing climate.

I am particularly interested in the integration of large-sample hydrologic datasets and satellite Earth observations in hydrologic modelling frameworks. The development of multi-criteria sensitivity analysis methods and multi-objective optimization strategies is a key aspect of my investigation, as well as the application of Machine Learning workflows that can identify new and complex relationships in datasets about the system functioning that otherwise would remain unknown. For this, High-Performance Computing (HPC) environments and parallelization techniques have become an essential part of my scientific work.