Morphodynamic equilibrium is a widely adopted yet elusive concept in the in the setting) so that the concept of morphodynamic equilibrium should be 13 * Corresponding
Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer timescales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for tairua beach, new Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. in general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. this was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models. Quantitative prediction of beach erosion and recovery is essential to planning resilient coastal communities with robust strategies to adapt to erosion hazards. Over the last decades, research efforts to understand and predict shoreline evolution have intensified as coastal erosion is likely to be exacerbated by climatic changes 1-5. The social and economic burden of changes in shoreline position are vast, which has inspired development of a growing variety of models based on different approaches and techniques; yet current models can fail (e.g. predicting erosion in accreting conditions). The challenge for shoreline models is, therefore, to provide reliable, robust and realistic predictions of change, with a reasonable computational cost, applicability to a broad variety of systems, and some quantifiable assessment of the uncertainties.
Extracellular polymeric substances (EPS) are ubiquitous on tidal flats but their impact on sediment erosion has not been fully understood. Laboratory-controlled sediment beds were incubated with Bacillus subtilis for 5, 10, 16, and 22 days before the erosion experiments, to study the temporal and spatial variations in sediment stability caused by the bacterial secreted EPS. We found the biosedimentary systems showed different erosional behavior related to biofilm maturity and EPS distribution. In the first stage (5 days), the biosedimentary bed was more easily eroded than the clean sediment. With increasing growth period, bound EPS became more widely distributed over the vertical profile resulting in bed stabilization. After 22 days, the bound EPS was highly concentrated within a surface biofilm, but a relatively high content also extended to a depth of 5 mm and then decayed sharply with depth. The biofilm increased the critical shear stress of the bed and furthermore, it enabled the bed to withstand threshold conditions for an increased period of time as the biofilm degraded before eroding. After the loss of biofilm protection, the high EPS content in the sublayers continued to stabilize the sediment (hindered erosion) by binding individual grains, as visualized by electron microscopy. Consequently, the bed strength did not immediately revert to the abiotic condition but progressively adjusted, reflecting the depth profile of the EPS. Our experiments highlight the need to treat the EPS-sediment conditioning as a bed-age associated and depth-dependent variable that should be included in the next generation of sediment transport models. Plain Language Summary Sedimentology and geomorphology have traditionally been seen as fields in which physical and chemical processes dominate. However, microbial communities should never be bystanders, because they suffuse all sedimentary environments on earth. Under hydrodynamic forces, they take part in an impressive range of sediment processes and thus exercising a formative influence on coastal evolutions. Bio-sediments exhibit more complex characteristics than abiotic systems, and lead to different modelling methods compared to those in traditional settings. For instance, the thresholds for sediment initiation and subsequent erosion rates are no longer solely related to particle properties (e.g., particle size, the most widely used), but mediated by glue-like extracellular polymeric substances (EPS) secreted by microbes. From this point of view, it is easy to understand why sediments in field observations behave differently from predictions, usually appearing considerably strengthened. Our results indicate that the EPS mediation in sediment stability may vary with the rhythms of microbial growth, and re-profile the sediment stability during different stages of cementing processes. A conceptual framework for sediment erosion is hence put forward to transform traditional sediment system to EPS-sediment system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.