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The displacement of a river to a new position within its adjacent floodplain is called avulsion, and here we examine how a newly recognized style, called retrogradational avulsion, affects the surrounding floodplain in tropical rainforests using remote sensing. Retrogradational avulsions begin with a channel blockage that causes self‐propagating upstream dechannelization and flooding. While this flooding results in vegetation die‐off and floodplain sedimentation, few quantitative measurements of disturbance by retrogradational avulsions exist. Here, we first focus on land‐cover change following a single retrogradational avulsion in Papua New Guinea from 2012 to 2021. During the avulsion, the river dechannelized 892 m upstream, and the parent channel width doubled. Using maximum likelihood image classification, we observed healthy vegetation fluctuated around 4.3 km², vegetation regrowth peaked in 2017 at 3.2 km², dead vegetation peaked in 2013 at 2.1 km², and visible extent of deposited sediment was greatest in 2015 at 0.44 km². We also examined 19 other retrogradational avulsions in Papua New Guinea and South America using NDVI. The area of floodplain disturbance (i.e., vegetation die‐off and possible sedimentation) for each avulsion ranged from <1 to >13 km²and scaled with the dechannelization area. Comparing our plan‐view disturbance results with FABDEM digital‐elevation data and ICESat‐2 surface elevation measurements, we hypothesize floodplain disturbance extent is a function of topographic relief. Our results also suggest that retrogradational avulsions, on average, perturb larger areas of forest compared to blowdowns, suggesting this might be an important disturbance regime that influences gap‐filling regeneration in tropical rainforests.

 

Given the inevitability of sea-level rise, investigating processes of human-altered coastlines at the intermediate timescales of years to decades can sometimes feel like an exercise in futility. Returning to the big picture and long view of feedbacks, emergent dynamics, and wider context, here we offer 10 existential questions for research into human–coastal coupled systems.

 

Overwash is the cross‐shore transport of water and sediment from a waterbody over the crest of a sand or gravel barrier beach, and washover is the resulting sedimentary deposit. Washover volume, and alongshore patterns of washover distribution, are fundamental components of sediment budgets for low‐lying coastal barrier systems. Accurate sediment budgets are essential to forecasting barrier system sustainability under future climate‐driven forcing. However, comprehensive surveys of three‐dimensional washover morphology are challenging to deliver. Here, we use the results of a physical experiment, analysis of lidar data, and examples of washover characteristics reported in the literature to develop scaling relationships for washover morphometry that demonstrate volume can be reasonably inferred from planform measurements, for washover in natural (non‐built) and built barrier settings. Gaining three‐dimensional insight into washover deposits from two‐dimensional information unlocks the ability to analyze past aerial imagery and estimate contributions from washover flux to sediment budgets for past storms. 

Abstract River channels are among the most common landscape features on Earth. An essential characteristic of channels is sinuosity: their tendency to take a circuitous path, which is quantified as along-stream length divided by straight-line length. River sinuosity is interpreted as a characteristic that either forms randomly at channel inception or develops over time as meander bends migrate. Studies tend to assume the latter and thus have used river sinuosity as a proxy for both modern and ancient environmental factors including climate, tectonics, vegetation, and geologic structure. But no quantitative criterion for planform expression has distinguished between random, initial sinuosity and that developed by ordered growth through channel migration. This ambiguity calls into question the utility of river sinuosity for understanding Earth's history. We propose a quantitative framework to reconcile these competing explanations for river sinuosity. Using a coupled analysis of modeled and natural channels, we show that while a majority of observed sinuosity is consistent with randomness and limited channel migration, rivers with sinuosity ≥1.5 likely formed their geometry through sustained, ordered growth due to channel migration. This criterion frames a null hypothesis for river sinuosity that can be applied to evaluate the significance of environmental interpretations in landscapes shaped by rivers. The quantitative link between sinuosity and channel migration further informs strategies for preservation and restoration of riparian habitat and guides predictions of fluvial deposits in the rock record and in remotely sensed environments from the seafloor to planetary surfaces. 

Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data‐driven models are only as good as the data used for training, and this points to the importance of high‐quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time‐consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.