Updating an invasive fish and native fish passage model for locks and dams

This project will create an updated version of Computational Fluid Dynamics Agent-Based (CFD-AB) fish passage model using new field data that can better help stop invasive carp while allowing native fish to pass through Mississippi River locks and dams.

The new field data is being generated by an ongoing field study of fish behavior and passage at Lock and Dam 2. Parameters on fish behavior will then be updated in the CFD-AB fish passage model developed earlier by [Zielinski et al., 2018] to improve it. We will use this updated CFD-AB model to predict fish passage for invasive carp (silver carp, common carp) and two native fishes (channel catfish, lake sturgeon) at two model lock and dams (2 and 8). The updated CFD-AB model will allow to determine optimum spillway gate positions to stop invasive carps at these sites. We will share these new data with the U.S. Army Corps of Engineers and the Minnesota DNR.

Progress:

As of January 2019, the code development and validation of the Computational Fluid Dynamics - Agent Based model is complete. The accuracy of the fish swimming calculation was improved. Numerous simulations with common carp, which were trying to pass through Lock and Dam 2, have been performed and provided excellent comparisons between the model and the experimental field data. A modification that considers fish swimming both up and downstream has been finished as well, and includes areas that fish may be attracted to, such as resting, migration, and feeding zones.

Next, this model will be used to create predictions of how both invasive carp and native species pass through locks and dams and suggestions for new gate settings that could stop carp and let native fish pass will be made. This work is ongoing.


Project manager: Anvar Gilmanov   

Funded by: Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources

Project start date: 2018

Estimated project end date: 2019

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