Decoding meandering river bends
Rivers tend to flow through bends of different shapes — some symmetric like bell curves, others skewed like bananas. These shapes are widely cited in theory and practice, but because river bends are usually mapped and classified by eye, the nature of these patterns remains elusive. We beginning a 5-year project to adapt advanced computing methods to decode the shapes of meandering rivers. Alongside this research, we are partnering with Matthew Burtner (UVA Dept. of Music) and and Jennifer Chiu (UVA School of Education and Human Development) to translate the geometry of rivers to music. In the coming years we will share this fusion of art and science with the local community in Charlottesville, VA and nationally through teaching materials for geoscience educators. This work is supported by an NSF-CAREER Award through the Geomorphology and Land-use Dynamics Program.
Publications and press
Reading the Rivers and Making Them Sing (UVA Today, 09/2023)
Limaye (in review), Journal of Geophysical Research: Earth Surface
From turbulent flow to landscape dynamics
Setting up for physical experiments at the Outdoor StreamLab, University of Minnesota.
River evolve through the interaction of turbulent flow, sediment transport, and topography. Yet in general, predictions over geologic timeframes cannot account for turbulent flow and its impact on channel morphodynamics. In a recent three-year project, we leveraged recent advances in computational fluid dynamics modeling, together with physical experiments and comparison to geologic datasets, to generate and test new predictions for river behavior over decades. This work was an interdisciplinary collaboration with colleagues at the Univ. of Minnesota-Twin Cities, Stony Brook Univ. and Salish Kootenai College. This work was supported through the NSF Geomorphology and Land-use Dynamics Program and Hydrology Program.
Publications
Li and Limaye (2024), Timescale of the morphodynamic feedback between planform geometry and lateral migration of meandering rivers, Journal of Geophysical Research: Earth Surface.
Khosronejad et al. (2023), On the morphodynamics of a wide class of large-scale meandering rivers: Insights gained by coupling LES with sediment-dynamics, Journal of Advances in Modeling Earth Systems.
Li and Limaye (2022), Testing predictions for migration of meandering rivers: Fit for a curvature-based model depends on streamwise location and timescale, Journal of Geophysical Research: Earth Surface.
Kozarek et al. (2023), Linking turbulent flow and bank erosion with controlled experiments in a field-scale meandering channel, Geological Society of London Special Publication.
Zhang et al. (2022), Data-driven prediction of turbulent flow statistics past bridge piers in large-scale rivers using convolutional neural networks, Water Resources Research.
Zhang et al. (2022), Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithm, Computers and Fluids.
Limaye et al. (2021), River sinuosity describes a continuum between randomness and ordered growth, Geology.