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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.

 

Deltas exhibit spatially and temporally variable subsidence, including vertical displacement due to movement along fault planes. Faulting‐induced subsidence perturbs delta‐surface gradients, potentially causing distributary networks to shift sediment dispersal within the landscape. Sediment dispersal restricted to part of the landscape could hinder billion‐dollar investments aiming to restore delta land, making faulting‐induced subsidence a potentially significant, yet unconstrained hazard to these projects. In this study, we modeled a range of displacement events in disparate deltaic environments, and observe that a channelized connection with the displaced area determines whether a distributary network reorganizes. When this connection exists, the magnitude of distributary network reorganization is predicted by a ratio relating dimensions of faulting‐induced subsidence and channel geometry. We use this ratio to extend results to real‐world deltas and assess hazards to deltaic‐land building projects.

 

Understanding the way fluvially transported materials are partitioned in river deltas is essential for predicting their morphological change and the fate of environmental constituents and contaminants. Translating water‐based partitioning estimates into fluxes of nonwater materials is often difficult to constrain because most materials are not uniformly distributed in the water column and may have characteristic transport pathways that differ from the mean flow. Here, we present a novel reduced‐complexity modeling approach for simulating the patterns of transport of a diverse range of suspended fluvial inputs influenced by vertical stratification and topographic steering. We utilize a mixed Eulerian‐Lagrangian modeling approach to estimate the patterns of nourishment and connectivity in the Wax Lake and Atchafalaya Deltas in coastal Louisiana. Using the reduced‐complexity particle routing modeldorado, in conjunction with a calibratedANUGAhydrodynamic model, we quantify how transport patterns in each system change as a function of a material's Rouse number and environmental conditions. We find that even small changes to local topographic steering lead to emergent system‐scale changes in patterns of fluvial nourishment, with greater channel‐island connectivity for positively buoyant materials than negatively buoyant materials, hydraulically sorting different materials in space. We also find that the nourishment patterns of some materials are more sensitive than others to changes in discharge, tidal conditions, and anthropogenic dredging. Our results have important implications for understanding the eco‐geomorphic evolution of deltas, and our modeling framework could have interdisciplinary implications for studying the transport of materials in other systems, including sediments, nutrients, wood, plastics, and biotic materials.

 

Abstract. The morphology of deltas is determined by the spatial extent and variability of the geomorphic processes that shape them. While in some cases resilient, deltas are increasingly threatened by natural and anthropogenic forces, such as sea level rise and land use change, which can drastically alter the rates and patterns of sediment transport. Quantifying process patterns can improve our predictive understanding of how different zones within delta systems will respond to future change. Available remotely sensed imagery can help, but appropriate tools are needed for pattern extraction and analysis. We present a method for extracting information about the nature and spatial extent of active geomorphic processes across deltas with 10 parameters quantifying the geometry of each of 1239 islands and the channels around them using machine learning. The method consists of a two-step unsupervised machine learning algorithm that clusters islands into spatially continuous zones based on the 10 morphological metrics extracted from remotely sensed imagery. By applying this method to the Ganges–Brahmaputra–Meghna Delta, we find that the system can be divided into six major zones. Classification results show that active fluvial island construction and bar migration processes are limited to relatively narrow zones along the main Ganges River and Brahmaputra and Meghna corridors, whereas zones in the mature upper delta plain with smaller fluvial distributary channels stand out as their own morphometric class. The classification also shows good correspondence with known gradients in the influence of tidal energy with distinct classes for islands in the backwater zone and in the purely tidally controlled region of the delta. Islands at the delta front under the mixed influence of tides, fluvial–estuarine construction, and local wave reworking have their own characteristic shape and channel configuration. The method is not able to distinguish between islands with embankments (polders) and natural islands in the nearby mangrove forest (Sundarbans), suggesting that human modifications have not yet altered the gross geometry of the islands beyond their previous “natural” morphology or that the input data (time, resolution) used in this study are preventing the identification of a human signature. These results demonstrate that machine learning and remotely sensed imagery are useful tools for identifying the spatial patterns of geomorphic processes across delta systems.