Intelligent sampling of hydrological events (ISHE)
Streamflow isotope samples taken during rainfall-runoff events are very useful for multi-criteria model calibration because they can help decrease parameter uncertainty and improve internal model consistency. However, the number of samples that can be collected and analysed is often restricted by practical and financial constraints. It is, therefore, important to choose an appropriate sampling strategy and to obtain samples that have the highest information content for model calibration.
Our project aims at designing intelligent sampling strategies for event-based model calibration. The study process and results will contribute to the dialog between experimentalist and modeler in catchment hydrology.
Contact: Ling Wang