This proposed research is aimed to investigate the impact of current and future changes of precipitation regimes and land use on runoff and flow propagation processes at large scale. This broad topic requires investigations on the state of the art of statistical hydrology for extreme events but also how it can be coupled with the current physically based (or physics-informed machine learning) methods for rainfall-runoff and flow propagation models and how these frameworks can be enhanced by the synergic adoption of in situ measurements and remotely sensed data for large scale (e.g. regional or at multi-basin scale) assessments. The research topic will also adopt a probabilistic framework to investigate on how uncertainties related to statistical hydrology (e.g. synthetic rainfall events changing through time), as respect to other uncertainties related to measurements of the input variables (e.g. uncertainties in rainfall/flow measurements), model parameters and model structure (e.g. simplification of the physical processes) can impact on prediction of variables of interests such as water levels, water extensions, erosion/deposition processes and potential risks and damages on population and assets.

The candidate should have a basic background on hydrology, statistics, environmental fluid dynamics, climate change, remote sensing as well a basic knowledge of any programming environment for data analysis and environmental modelling. The candidate should also have soft skills such as flexibility and adaptability in the workplace, ability to address work challenges, interacting with others, working in teams, working in open, multicultural and flexible environments, mastering the tools for communication, dissemination and public speaking.

The candidate will work with a research team from the Department of Civil, Environmental And Architectural Engineering (ICEA) and the Department of Land, Environment, Agriculture And Forestry (TESAF) of University of Padua, in which full professors with strong competence in the topic such as proff. Marco Marani , Marco Borga and other researchers, such as Antonio Annis (the reference person), Maria Francesca Caruso, Pietro Devò will support the candidate.