PhD on Hydrological model-data interaction and machine learning for headwater catchment analyses

Your position

This project will advance multi-fidelity modelling approaches tailored to headwater systems and deliver new insights into groundwater-surface water interactions. A key challenge for hydrological headwater catchment analyses lies in characterizing and predicting their hydrological functioning, and this is fundamentally related to the fact that the majority of them are unmonitored and their hydraulic parameters unknown. The objective of this PhD project is bridge this gap and develop data-driven, machine learning-based approaches to identify the governing hydraulic parameters of headwater catchments, hind- and forecast their stream and groundwater outflows, and to understand their susceptibility to extreme hydrometeorological conditions based on storyline approaches.

You will specifically:

Contribute to the development of the multi-fidelity modelling platform HydroModPy,

Implement machine-learning based approaches to estimat...