Unveiling Drivers of Catchment Water Balance Partitioning from Stable Water Isotopes, hydrological modeling, and Machine Learning across landscapes

• Applicant: Professor Dr. Julian Klaus
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management ; Physical Geography
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 545346931

Understanding the partitioning of precipitation (P) into streamflow (Q), transpiration (T), and evaporation (E) across various hydro-climatological regions is crucial for predicting the impact of land cover and climate change on the hydrological and biogeochemical cycle. Despite its significance, this understanding and predictive capability are limited due to challenges in partitioning evapotranspiration (ET) into T and E, primarily due to the lack of measurement approaches and data to validate models at catchment scale. This project aims to address this limitation by innovatively studying T/ET ratios in catchments across different scales. Dominate water loss mechanisms (T, E, Q) will be derived for catchments and related to catchment physiography (i.e., climate, land cover, geology, soil) using machine learning to detect patterns and correlations. The study will encompass catchments worldwide with available stable isotope data (18O and 2H of the water molecule). The partitioning will be estimate for multi-annual time scales for several hundred catchments with available low-frequency isotopic data employing a combination of the isotope and water mass balance. Additionally, a subset of eleven densely monitored catchments in Germany and Canada will undergo partitioning through the isotope-enabled hydrological model isoWATFLOOD with finer temporal resolution. Finally, the project will leverage machine-learning approaches to predict how geographic characteristics influence the dominant water loss mechanisms in catchments.

• DFG Programme: Research Grants
• International Connection: Canada
• Cooperation Partner: Professorin Dr. Tricia Stadnyk