Confluence mixing: near-field hydrodynamic controls of the intermodal behavior and their implications for fish ecology

• Applicant: Alexander Sukhodolov, Ph.D.
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 535183985

River confluences are vital nodal points of fluvial networks and are hotspots of biodiversity in freshwaters ecosystems. Flow structure at confluences is non-uniform and produces complex patterns within mixing interfaces that are characterized by intermodal behavior. The overall mixing rate at confluences is related to the interface pattern, which has a considerable impact on the spread of harmful pollutants, water temperature, sediments and biota. The proposed research aims to improve knowledge of confluence mixing by providing fundamental theory-based insights into the effects of jetting, flow stagnation and river regulation on the intermodal behavior of mixing interfaces and their implications for fish ecology. In this project, complex mixing interfaces generated in shallow, rough-bedded river environments will be assessed directly in the field at a wide range of hydrological conditions using remote sensing and instrumental measurements. Mixing dynamics will be further analyzed by comparing field observations with the theory of intermodal behavior, which will be expanded and validated using results from field-based experiments and numerical simulations. These experiments will also provide information on fish trajectories and fish behavior within mixing interfaces. From these diverse sources of information on mixing, we will develop an agent-based model to simulate fish survival during a passage of harmful toxins. Simulations will be validated using results of a fish survey in a regulated river which ecosystem has recently suffered a mass kill of fish. Theoretical and empirical results of our study will be used to advance predictive methods based on remote sensing of mixing in rivers.

• DFG Programme: Research Grants
• International Connection: Canada
• Co-Investigator: Christian Wolter, Ph.D.
• Cooperation Partner: Professor Quinn Lewis, Ph.D.