Search

Summary Sediment‐stabilizing and ‐destabilizing organisms, i.e. microphytobenthos (biofilms) and macrozoobenthos (bioturbators), affect the erodibility of muddy sediments, potentially altering large‐scale estuarine morphology. Using a novel eco‐morphodynamic model of an idealized estuary, we investigate eco‐engineering effects of microphytobenthos and two macrozoobenthic bioturbators. Local mud erodibility is based on species pattern predicted through hydrodynamics, soil mud content, competition and grazing. Mud resuspension and export is enhanced under bioturbation and prevented under biostabilization through respective exposure and protection of the supra‐ and intertidal. Bioturbation decreases mud thickness and bed elevations, which increases net mud fluxes. Microphytobenthos reduces erosion, leading to a local mud increase of intertidal sediments. In multi‐species scenarios, an effective mud‐prone bioturbator strongly alters morphology, exceeding that of a more abundant sand‐prone moderate species, showing that morphological change depends on species traits as opposed to abundance. Altering their habitat, the effective mud‐prone bioturbator facilitates expansion of the sand‐prone moderate bioturbator. Grazing and species competition favor species distributions of dominant bioturbators. Consequently, eco‐engineering affects habitat conditions while species interactions determine species dominance. Our results show that eco‐engineering species determine the mud content of the estuary, which suggests large effects on the morphology of estuaries with aggravating habitat degradation., Sediment‐stabilizing and ‐destabilizing organisms alter large‐scale estuarine morphology in a novel eco‐morphodynamic model of an idealized estuary. Mud resuspension and export are enhanced under bioturbation and prevented under biostabilization through respective exposure and protection of the supra‐ and intertidal. This leads to reduced mud content and widening of the estuary under bioturbation and muddy confined morphologies under biostabilization. In multi‐species scenarios, eco‐engineering affects habitat conditions which reorganizes species distribution, while species interactions determine species dominance.

Double sandbar systems often characterize the surf zone of wave-dominated beaches and display a variety of poorly explained spatial configurations. Here, we explore the morphodynamic stability of double-barred beaches using a model based on linear stability analysis. Surf zone hydrodynamics are described by coupling depth and wave-averaged conservation of mass and momentum with the wave energy and phase equations, including roller dynamics. A simplified sediment transport formulation relates flow to seabed changes. Under normal wave incidence, an alongshore uniform coast with a cross-shore profile characterized by the presence of two sandbars can be unstable, thereby resulting in the development of crescentic and/or rip channel patterns. Our study demonstrates that sandbar coupling can be either in phase (highs and lows of both sandbars are at the same alongshore position) or out of phase (highs and lows of one sandbar correspond to lows and highs of the other sandbar). In line with observations, results of simulations show a large variability in the possible emerging bottom patterns. Our analysis indicates that modes of which the amplitude of the inner sandbar perturbation is larger than that of the outer sandbar are dominant for large height–depth differences between the two sandbar crests and small offshore wave heights. Patterns related to the outer sandbar dominate for small values of the difference in sandbar depth. For intermediate differences between the two sandbar depths, patterns on both longshore bars appear to be fully coupled (similar growth rates and strongly correlated pattern shapes). For relatively large waves and large depth over the outer sandbar, patterns can also develop close to the shoreline and/or inner surf zone together with patterns in the inner or outer sandbar.

It has been asserted by Vousdoukas et al., that climate change, in particular global sea-level rise (SLR), poses a threat to the existence of sandy beaches. The authors used global data bases of sandy beaches, bathymetry, wave conditions and SLR to drive a simple model based on the ‘Bruun Rule’ to quantitatively evaluate shoreline retreat. To this modelled retreat, they add a background ambient trend in shoreline dynamics and the modelled response of an extreme storm, that together contribute c. 20% of the shoreline retreat. When retreat was more than 100 m by 2100, they declared those beaches extinct. This is an incorrect and potentially damaging finding that must be repudiated along with the associated implications.

Timely monitoring and modeling of landuse change is vital to manage land resources effectively and to rectify defective land use policies, especially in rapidly urbanizing areas. Our research is applied to an annual land use dataset in order to simulate urban expansion in a fast developing area, through a hybrid model coupling Artificial Neural Networks (ANNs), cellular automata (CA), and Markov Chain (MC). The ANNs were optimized to create the urban suitability index (USI) map that was then integrated with CA-MC to spatially allocate urban expansion cells. Two ANNs, multiple-layer perceptron (MLP) and long short-term memory network (LSTM), were implemented comparatively. Since LSTM is able to take into account more temporal information, it outperformed MLP in modelling urban expansion process over a short temporal interval. The results, validated using kappa and fuzzy kappa simulation, indicate that the integration of ANNs with CA-MC can capture the possible nonlinear relationship between urban expansion and its drivers, hence it can accurately simulate and predict urban expansion in the study area.

Timely monitoring and modeling of landuse change is vital to manage land resources effectively and to rectify defective land use policies, especially in rapidly urbanizing areas. Our research is applied to an annual land use dataset in order to simulate urban expansion in a fast developing area, through a hybrid model coupling Artificial Neural Networks (ANNs), cellular automata (CA), and Markov Chain (MC). The ANNs were optimized to create the urban suitability index (USI) map that was then integrated with CA-MC to spatially allocate urban expansion cells. Two ANNs, multiple-layer perceptron (MLP) and long short-term memory network (LSTM), were implemented comparatively. Since LSTM is able to take into account more temporal information, it outperformed MLP in modelling urban expansion process over a short temporal interval. The results, validated using kappa and fuzzy kappa simulation, indicate that the integration of ANNs with CA-MC can capture the possible nonlinear relationship between urban expansion and its drivers, hence it can accurately simulate and predict urban expansion in the study area.