[1] The spatial distribution of riparian vegetation can strongly influence the geomorphic evolution of dryland rivers during large floods. We present the results of an airborne lidar differencing study that quantifies the topographic change that occurred along a 12 km reach of the Lower Rio Puerco, New Mexico, during an extreme event in 2006. Extensive erosion of the channel banks took place immediately upstream of the study area, where tamarisk and sandbar willow had been removed. Within the densely vegetated study reach, we measure a net volumetric change of 578,050˙ 490,000 m 3 , with 88.3% of the total aggradation occurring along the floodplain and channel and 76.7% of the erosion focusing on the vertical valley walls. The sediment derived from the devegetated reach deposited within the first 3.6 km of the study area, with depth decaying exponentially with distance downstream. Elsewhere, floodplain sediments were primarily sourced from the erosion of valley walls. Superimposed on this pattern are the effects of vegetation and valley morphology on sediment transport. Sediment thickness is seen to be uniform among sandbar willows and highly variable within tamarisk groves. These reach-scale patterns of sedimentation observed in the lidar differencing likely reflect complex interactions of vegetation, flow, and sediment at the scale of patches to individual plants.

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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 from ten 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 ten 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 does not 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. These results demonstrate that machine learning and remotely sensed imagery are useful tools for identifying the spatial patterns of geomorphic processes across delta systems.

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.