One of the most dramatic, yet rarely witnessed, events is when a river avulses (or moves) to a new spot in a floodplain. River avulsions can be hazardous because they move incredible volumes of sediment and cause extensive flooding. Despite this, it is not known why some avulsions are hazardous and others are not. This research will develop new remote sensing methods to detect avulsions and test theories using numerical modeling for why some avulsions are large and create significant landscape change and floods, and others are small. The team will engage in training undergraduate and graduate students in cross-disciplinary methods, and also use the research results to develop new science curriculum for children in upper elementary to middle school grades that is aimed at increasing likelihood of pursuing Earth Science at advanced levels.

The proposed research aims to advance the science of river avulsions by using the remote sensing record and numerical simulations to test models for how avulsions move across the floodplain. The team will test if avulsion style changes moving downstream; preliminary data suggests that near the mountain front avulsions tend to reoccupy older channels, while those farther away tend to create their own channels. This arises because sediment size decreases downstream, and finer sediment is more easily transported overbank into the floodplain, filling abandoned channels, and forcing avulsions to create new channels. The hypothesis will be tested by 1) detecting avulsions in modern foreland basins by using big data remote sensing tools, e.g., Google Earth Engine, to search through Landsat data, 2) quantifying avulsion tendency to reoccupy or create their own channel with our fingerprinting algorithm that isolates the area disturbed by the avulsion, and 3) building a cellular avulsion model that includes downstream fining and overbank sedimentation. The model will be used to test the connection between downstream fining and avulsion behavior. This award is cofunded by the Geomorphology and Land-use Dynamics (GLD) and Prediction of and Resilience against Extreme Events (PREEVENTS) programs.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Earth Sciences (EAR)
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Justin Lawrence
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Indiana University
United States
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