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Akter, J.; Sarker, M.H.; Popescu, I., and Roelvink, D., 2016. Evolution of the Bengal Delta and its prevailing processes. Bangladesh, occupying low-lying floodplains and tidal plains, has one of the largest and the most disaster-prone populous deltas in the world. The Bengal Delta is a tide-dominated delta, where tides play the key role in the sediment dispersal process and in shaping the delta. There are many studies and reports on river-dominated deltas, but research is sparse on tide-dominated deltas. The Ganges and Brahmaputra Rivers, which combined form one of the three largest riverine sources of water and sediment for the world's oceans, have developed the Bengal Delta to its present form with an aerial extent of 104 km2. About 1012 m3 of water with 109 tonnes of sediment per year make this system morphologically active. In the last five decades, the Bengal Delta has prograded at a rate of 17 km2/y, whereas most large deltas elsewhere in the world suffered from sediment starvation. Delta progradation always makes the river system unstable, and rapid changes cause the delta to become dynamic. Sea level rise induced by unequivocal climate change and subsidence would make the delta more vulnerable in the coming decades. Although some literature is available on the millennium-scale development process of the Bengal Delta, sound knowledge on the decade- to century-scale processes of the delta development for facing the threats of climate change and deltaic subsidence is limited. In addition, there are significant differences in opinions and widely varying findings in the literature to the response of the delta to different natural and human interventions. Against this backdrop, relevant available literature on Bengal Delta and deltas elsewhere in the world, is reviewed and evaluated to provide direction for future research that would help to form a way out of the present situation and a way into sustainable planning for this delta.

With large-scale human interventions and climate change unfolding as they are now, coastal changes at decadal timescales are not limited to incremental modifications of systems that are fixed in their general geometry, but often show significant changes in layout that may be catastrophic for populations living in previously safe areas. This poses severe challenges that are difficult to meet for existing models. A new free-form coastline model, ShorelineS, is presented that is able to describe large coastal transformations based on relatively simple principles of (1) alongshore transport gradient driven changes as a result of coastline curvature and (2) spit formation at high-angle wave incidence. An arbitrary number of coast sections is supported, which can be open or closed and can interact with each other through relatively straightforward merging and splitting mechanisms. Rocky parts or structures may block wave energy and/or longshore sediment transport. These features allow for a rich behavior including shoreline undulations and formation of spits, migrating islands, merging of coastal shapes, salients and tombolos. The main formulations of the (open-source) model are presented. Test cases show the capabilities of the flexible, vector-based model approach, while field validation cases for a large-scale sand nourishment (the Sand Engine; 21 million m3) and an accreting groyne scheme at Al-Gamil (Egypt) show the model’s capability of computing realistic rates of coastline change as well as a good representation of the shoreline shape for real situations.

This article presents a novel approach to explore mangrove dynamics on a prograding delta by integrating unmanned aerial vehicle (UAV) and satellite imagery. The Porong Delta in Indonesia has a unique geographical setting with rapid delta development and expansion of the mangrove belt. This is due to an unprecedented mud load from the LUSI mud volcanic eruption. The mangrove dynamics analysis combines UAV-based Structure from Motion (SfM) photogrammetry and 11 years (2009–2019) satellite imagery cloud computing analysis by Google Earth Engine (GEE). Our analysis shows unique, high-spatiotemporal-resolution mangrove extent maps. The SfM photogrammetry analysis leads to a 3D representation of the mangrove canopy and an estimate of mangrove biophysical properties with accurate height and individual position of the mangroves stand. GEE derived vegetation indices resulted in high (three-monthly) resolution mangrove coverage dynamics over 11 years (2009–2019), yielding a value of more than 98% for the overall, producer and consumer accuracy. Combining the satellite-derived age maps and the UAV-derived spatial tree structure allowed us to monitor the mangrove dynamics on a rapidly prograding delta along with its structural attributes. This analysis is of essential value to ecologists, coastal managers, and policymakers.

Coastal zones are experiencing notable changes attributed to natural and anthropogenic effects. This study investigates the potential of machine learning (ML) in predicting shoreline changes, a developing field still in its early exploration phase. Traditional methods, while insightful, have faced challenges in terms of adaptability, accuracy, and computational demands. ML, as a data-driven approach, potentially offers flexibility, computational efficiency, and can avoid the constraints associated with physics-based models. This study aims to evaluate various machine learning models’ efficacy in predicting shoreline changes using synthetic data. Through comprehensive testing across one complex shoreline evolution scenario, this research identifies the ConvLSTM model—trained on 2D gridded data— as the optimal machine learning approach suited for addressing specific shoreline complexities and evolution patterns. This approach can learn shoreline evolution, predict it, and serve as a foundational component of a proposed method for probabilistic shoreline position prediction. Additionally, the study shows that the choice of ML model depends on the complexity of shoreline evolution and the desired level of accuracy.

A new set of empirical formulations has been derived to predict wave run-up at naturally sloping sandy beaches. They are obtained by fitting the results of hundreds of XBeach-NH+ model simulations. The simulations are carried out over a wide range of offshore wave conditions (wave heights ranging from 1 to 12 m and periods from 6 to 16 s), and surf zone (Dean parameters aD ranging from 0.05 to 0.30) and beach geometries (slopes ranging from 1:100 to 1:5). The empirical formulations provide estimates of wave set-up, incident and infragravity wave run-up, and total run-up R2%. Reduction coefficients are included to account for the effects of incident wave angle and directional spreading. The formulations have been validated against the Stockdon dataset and show better skill at predicting R2% run-up than the widely used Stockdon relationships. Unlike most existing run-up predictors, the relations presented here include the effect of the surf zone slope, which is shown to be an important parameter for predicting wave run-up. Additionally, this study shows a clear relationship between infragravity run-up and beach slope, unlike most existing predictors.

Short-term beach morphodynamics are typically modelled solely through storm-induced erosion, disregarding post-storm recovery. Yet, the full cycle of beach profile response is critical to simulating and understanding morphodynamics over longer temporal scales. The XBeach model is calibrated using topographic profiles from a reflective beach (Faro Beach, in S. Portugal) during and after the incidence of a fierce storm (Emma) that impacted the area in early 2018. Recovery in all three profiles showed rapid steepening of the beachface and significant recovery of eroded volumes (68–92%) within 45 days after the storm, while berm heights reached 4.5–5 m. Two calibration parameters were used (facua and bermslope), considering two sets of values, one for erosive (Hm0 ≥ 3 m) and one for accretive (Hm0 < 3 m) conditions. A correction of the runup height underestimation by the model in surfbeat mode was necessary to reproduce the measured berm elevation and morphology during recovery. Simulated profiles effectively capture storm erosion, but also berm growth and gradual recovery of the profiles, showing good skill in all three profiles and recovery phases. These experiments will be the basis to formulate event-scale simulations using schematized wave forcing that will allow to calibrate the model for longer-term changes.

In this study, we estimate the shoreline retreat, the vulnerability and the erosion rates of an open beach-dune system under projected sea level rise and the action of wind-waves (separately and in combination). The methodology is based on the combination of two state-of-the-art numerical models (XBeach and Q2D-morfo) applied in a probabilistic framework and it is implemented in an open sandy beach in Menorca Island (Western Mediterranean). We compute the shoreline response to sea level rise during the 21st century and we assess the changing impacts of storm waves on the aerial beach-dune system. Results demonstrate the relevant role that the beach backshore features, such as the berm, play as coastal defence, reducing the shoreline retreat and dune vulnerability rates in the near-term (a few decades ahead) and highlighting the importance of simulating the beach morphodynamic processes in coastal impacts assessments. Our findings point at sea level rise as the major driver of the projected impacts over the beach-dune system, leading to an increase of ~25% of the volume eroded due to storm waves by the end of the century with respect to present-day conditions.

Mangrove forests’ restoration has gained traction as a sustainable solution to mitigate the effects of greenhouse gas emissions and to provide ecosystem services, such as coastal protection. Restoration projects are often informed by expert judgment rather than a quantitative understanding and have a high failure rate. Monitoring mangrove restoration performance may take decades and has a strong case study dependency. To optimise restoration strategies, we developed an individual-based mangrove and process-based hydro-morphodynamic model to simulate multi-species mangrove forest trajectories, including the physical environment’s feedback. We find a significant impact of planting zonation on the mudflat behaviour, with seaward erosion and in-forest-landward deposition. Planting mangroves close to mean sea level decreases carbon storage potential due to increased mudflat erosion. Configuring planting in multiple patches proves beneficial to mangrove biomass development, expansion, and sediment accumulation. Combined with sound monitoring, the developed tool can potentially optimize planned mangrove restoration strategies. Planting mangroves close to high water level and configuring plantation in multiple patches along with strong monitoring measures is beneficial for strategic mangrove restoration, according to an individual-based mangrove and process-based hydro-morphodynamic model simulation.

Coastal mangroves, thriving at the interface between land and sea, provide robust flood risk reduction. Projected increases in the frequency and magnitude of climate impact drivers such as sea level rise and wind and wave climatology reinforce the need to optimize the design and functionality of coastal protection works to increase resilience. Doing so effectively requires a sound understanding of the local coastal system. However, data availability particularly at muddy coasts remains a pronounced problem. As such, this paper captures a unique dataset for the Guyana coastline and focuses on relations between vegetation (mangrove) density, wave attenuation rates and sediment characteristics. These processes were studied along a cross-shore transect with mangroves fringing the coastline of Guyana. The data are publicly available at the 4TU Centre for Research Data (4TU.ResearchData) via https://doi.org/10.4121/c.5715269 (Best et al., 2022) where the collection Advancing Resilience Measures for Vegetated Coastline (ARM4VEG), Guyana, comprises of six key datasets. Suspended sediment concentrations typically exceeded 1 g L−1 with a maximum of 60 g L−1, implying that we measured merely fluid-mud conditions across a 1 m depth. Time series of wind waves and fluid-mud density variations, recorded simultaneously with tide elevation and suspended sediment data, indicate that wave–fluid-mud interactions in the nearshore may be largely responsible for the accumulation of fine, muddy sediment along the coast. Sediment properties reveal a consolidated underlying bed layer. Vegetation coverage densities in the Avicennia-dominated forest were determined across the vertical with maximum values over the first 20 cm from the bed due to the roots and pneumatophores. Generalized total wave attenuation rates in the forest and along the mudflat were between 0.002–0.0032 m−1 and 0.0003–0.0004 m−1 respectively. Both the mangroves and the mudflats have a high wave-damping capacity. The wave attenuation in the mangroves is presumably dominated by energy losses due to vegetation drag, since wave attenuation due to bottom friction and viscous dissipation on the bare mudflats is significantly lower than wave dissipation inside the mangrove vegetation. Data collected corroborate the coastal defence function of mangroves by quantifying their contribution to wave attenuation and sediment trapping. The explicit linking of these properties to vegetation structure facilitates modelling studies investigating the mechanisms determining the coastal defence capacities of mangroves.