SUPERVISOR: Christine STUMPP

PROJECT ASSIGNED TO: Karl KNAEBEL

Water transit time distributions (TTDs) are profound descriptors of catchment behavior (Benettin et al. 2022). TTDs characterize the time a parcel of water spends from entering a catchment as precipitation to leaving as streamflow or evaporation. As a metric for physical transport, TTDs are considered the link between hydrology and water quality (Hrachowitz et al. 2016). They describe how conservative solutes and pollutants are stored and released through various flow paths from the catchment. Stable isotopes (i.e., 2H and 18O) and radioactive isotopes such as 3H are commonly used tracers to derive TTDs via tracer-aided hydrological modelling (McGuire and McDonnell 2006; McDonnell et al. 2010; Benettin et al. 2022). 

Studies investigating the link between TTDs and water quality have been conducted at small to meso-scale catchments (e.g. Benettin et al. 2017); however the challenge remains open for larger-scale catchments. Furthermore, little is known about how TTDs change under extreme flow conditions and how this, in turn, might affect the water quality of riverine systems, which is especially crucial in the context of climate change. 

This project therefore aims to:
1. Investigate the alteration of time-variable TTDs derived indices in relation to extreme flows i.e., high and low flows. 
2. Statistically, investigate the relationship between time-variable TTDs indices and the chemical and ecological state of the riverine ecosystems. 
3. Develop TTDs derived indices as vulnerability indicators of riverine ecosystems to pollutants.

To achieve these aims, tracer-aided hydrological modelling using the DYNAMIT modelling framework (Hrachowitz et al. 2013; Hrachowitz et al. 2021) extended with SAS functions (Rinaldo et al. 2015) will be implemented at multiple large European river basins such as the Danube. The research will be conducted using long-term stable and radioactive isotope time-series data. Within HR21, the project contributes to the research topic of extreme events and serves the research cluster focused on connectivity, vulnerability, and metabolism.

 

Publication bibliography

Benettin, Paolo; Bailey, Scott W.; Rinaldo, Andrea; Likens, Gene E.; McGuire, Kevin J.; Botter, Gianluca (2017): Young runoff fractions control streamwater age and solute concentration dynamics. In Hydrological Processes 31 (16), pp. 2982–2986. DOI: 10.1002/hyp.11243.

Benettin, Paolo; Rodriguez, Nicolas B.; Sprenger, Matthias; Kim, Minseok; Klaus, Julian; Harman, Ciaran J. et al. (2022): Transit Time Estimation in Catchments: Recent Developments and Future Directions. In Water Resources Research 58 (11), Article e2022WR033096, e2022WR033096. DOI: 10.1029/2022WR033096.

Hrachowitz, M.; Savenije, H.; Bogaard, T. A.; Tetzlaff, D.; Soulsby, C. (2013): What can flux tracking teach us about water age distribution patterns and their temporal dynamics? In Hydrol. Earth Syst. Sci. 17 (2), pp. 533–564. DOI: 10.5194/hess-17-533-2013.

Hrachowitz, Markus; Benettin, Paolo; van Breukelen, Boris M.; Fovet, Ophelie; Howden, Nicholas J.K.; Ruiz, Laurent et al. (2016): Transit times—the link between hydrology and water quality at the catchment scale. In Wiley Interdisciplinary Reviews: Water 3 (5), pp. 629–657. DOI: 10.1002/wat2.1155.

Hrachowitz, Markus; Stockinger, Michael; Coenders-Gerrits, Miriam; van der Ent, Ruud; Bogena, Heye; Lücke, Andreas; Stumpp, Christine (2021): Reduction of vegetation-accessible water storage capacity after deforestation affects catchment travel time distributions and increases young water fractions in a headwater catchment. In Hydrol. Earth Syst. Sci. 25 (9), pp. 4887–4915. DOI: 10.5194/hess-25-4887-2021.

McDonnell, J. J.; McGuire, K.; Aggarwal, P.; Beven, K. J.; Biondi, D.; Destouni, G. et al. (2010): How old is streamwater? Open questions in catchment transit time conceptualization, modelling and analysis. In Hydrological Processes 24 (12), pp. 1745–1754. DOI: 10.1002/hyp.7796.