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River deltas are dynamic systems whose channels can widen, narrow, migrate, avulse, and bifurcate to form new channel networks through time. With hundreds of millions of people living on these globally ubiquitous systems, it is critically important to understand and predict how delta channel networks will evolve over time. Although much work has been done to understand drivers of channel migration on the individual channel scale, a global-scale analysis of the current state of delta morphological change has not been attempted. In this study, we present a methodology for the automatic extraction of channel migration vectors from remotely sensed imagery by combining deep learning and principles from particle image velocimetry (PIV). This methodology is implemented on 48 river delta systems to create a global dataset of decadal-scale delta channel migration. By comparing delta channel migration distributions with a variety of known external forcings, we find that global patterns of channel migration can largely be reconciled with the level of fluvial forcing acting on the delta, sediment flux magnitude, and frequency of flood events. An understanding of modern rates and patterns of channel migration in river deltas is critical for successfully predicting future changes to delta systems and for informing decision makers striving for deltaic resilience.

River deltas are complex, dynamic systems whose channel networks evolve in response to internal and external forcings. To capture these changes, methods to extract and analyze deltaic morphodynamics automatically using available remotely sensed imagery and experimental observations are needed. Here, we apply a promising method for the automatic extraction of channel presence called RivaMap, on both synthetic and experimental data sets, to investigate the changes experienced by the system in response to five changes in forcings. RivaMap is an automated method to extract nonbinarized channel locations from imagery based on a singularity index that combines the multiscale first and second derivatives of the image intensity to favor the identification of curvilinear features and suppress edges. We quantify how the channelization varies by computing the channelized response variance (CRV), which we define as the variance of each pixel's singularity index response through time. We find that increasing magnitudes of sediment inflow (Q_s) and water inflow (Q_w) result in corresponding increases in the maximum CRV. We find that increasing the ratio ofQ_stoQ_wresults in increased number of channelized areas. We see that adding cohesion to the exposed sediment surface of the experimental delta results in decreased magnitude and decreased number of channelized areas in the CRV. Finally, by observing changes to the CRV over time, we are able to quantify the timescale of internal channel reorganization events as the experimental delta evolves under constant forcings.