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Parallel tidal channel systems, characterized by commonly cross-shore orientation and regular spacing, represent a distinct class of tidal channel networks in coastal environments worldwide. Intriguingly, these cross-shore oriented channel systems can develop in environments dominated by alongshore tidal currents, for which the mechanisms remain elusive. Here, we combine remote sensing imagery analysis and morphodynamic simulations to demonstrate that the deflection of alongshore tidal currents at transitions in bed elevation determines the characteristic orientation of the parallel tidal channels. Numerical results reveal that sharp changes in bed elevation lead to nearly 90-degree intersection angles, while smoother transitions in bed profiles result in less perpendicular channel alignments. These findings shed light on the potential manipulation of tidal channel patterns in coastal wetlands, thus equipping coastal managers with a broader range of strategies for the sustainable management of these vital ecosystems in the face of climate change and sea level rise. Cross-shore channels can counterintuitively form at coasts dominated by alongshore currents. Sharp changes in bed elevation around the mean sea level lead to nearly 90-degree intersection angles between the channel and the shoreline. [ABSTRACT FROM AUTHOR]

Wave climatologies from historical and projected simulations of the ACCESS1.0, MIROC5 and CNRM-CM5 Global Circulation Models (GCM) were sourced from the Coordinated Ocean Wave Climate Project (COWCLIP) and downscaled using the SWAN wave model. Biases between GCM's historical simulations and a regional hindcast were assessed, and the two best-performing models (ACCESS1.0, MIROC5) had their projections analysed. A general increase in wave height and period was observed along the south/west, together with a decrease in $H_s$ H s along the north/east coasts. The projected near-term (NEA21C) period shows mostly a $H_s$ H s increase, whilst for the long-term (END21C) period, increased and decreased $H_s$ H s are present. The areas of statistically significant changes are larger in the END21C than in the NEA21C period. The wave direction change is counter-clockwise along the west and clockwise along the east coasts. This study is a first assessment of historical and projected GCM-forced waves along New Zealand and the database we generated can be of great value for renewable energy research, risk assessment and the mitigation of future coastal hazards. [ABSTRACT FROM AUTHOR]

Topographic and bathymetric data are essential for accurate predictions of flooding in estuaries because water depth and elevation data are fundamental components of the shallow-water hydrodynamic equations used in models for storm surges and tides. Where lidar or in situ acoustic surveys are unavailable, recent efforts have centred on using satellite-derived bathymetry (SDB) and satellite-derived topography (SDT). This work is aimed at (1) determining the accuracy of SDT and (2) assessing the suitability of the SDT and SDB for extreme water level modelling of estuaries. The SDT was created by extracting the waterline as it tracks over the topography with changing tides. The method was applied to four different estuaries in Aotearoa / New Zealand: Whitianga, Maketū, Ōhiwa and Tauranga harbours. Results show that the waterline method provides similar topography to the lidar with a root-mean-square error equal to 0.2 m, and it is slightly improved when two correction methods are applied to the topography derivations: the removal of statistical bias (0.02 m improvement) and hydrodynamic modelling correction of waterline elevation (0.01 m improvement). The use of SDT in numerical simulations of surge levels was assessed for Tauranga Harbour in eight different simulation scenarios. Each scenario explored different ways of incorporating the SDT to replace the topographic data collected using non-satellite survey methods. In addition, one of these scenarios combined SDT (for intertidal zones) and SDB (for subtidal bathymetry), so only satellite information is used in surge modelling. The latter SDB is derived using the well-known ratio–log method. For Tauranga Harbour, using SDT and SDB in hydrodynamic models does not result in significant differences in predicting high water levels when compared with the scenario modelled using surveyed bathymetry. [ABSTRACT FROM AUTHOR]

The process of shoreline change is inherently complex and reliable predictions of shoreline position remain a key challenge in coastal research. Predicting shoreline evolution could potentially benefit from Deep Learning (DL), which is a recently developed and widely successful data-driven methodology. However, so far its implementation for shoreline time series data has been limited. The aim of this contribution is to investigate the potential of DL algorithms to predict interannual shoreline position derived from camera system observations at a New Zealand study site. We investigate the application of Convolutional Neural Networks (CNNs) and hybrid CNN - Long Short-Term Memory networks. We compare our results with two established models, a shoreline equilibrium model, and a model that addresses time scales in shoreline drivers. Using a systematic search and different measures of fitness we found DL models that outperformed the reference models when simulating the variability and distribution of the observations. Overall, these results indicate that DL models have potential to improve accuracy and reliability over current models. [ABSTRACT FROM AUTHOR]

We applied machine learning to improve the accuracy of present predictors of wave setup. Namely, we used an evolutionary-based genetic programming model and a previously published dataset, which includes various beach and wave conditions. Here, we present two new wave setup predictors: a simple predictor, which is a function of wave height, wavelength, and foreshore beach slope, and a fitter, but more complex predictor, which is also a function of sediment diameter. The results show that the new predictors outperform existing formulas. We conclude that machine learning models are capable of improving predictive capability (when compared to existing predictors) and also of providing a physically sound description of wave setup. [ABSTRACT FROM AUTHOR]

Tropical cyclones are associated with extreme winds, waves, and storm surge, being among most destructive natural phenomena. Developing capability for a rapid impact estimate is crucial for coastal applications and risk preparedness. When predicting waves characteristics associated to tropical cyclones, the traditional approach involves a two‐step procedure (a) a Holland‐type wind vortex model and (b) numerical simulations using a wave generation model, using buoy and satellite measurements for validation. In this work, we take advantage of the increasing amount of remote sensing observational data and propose a new empirical model to estimate the wind wave footprint of tropical cyclones. For this purpose, we construct a dataset with over a million satellite observations of waves triggered by tropical cyclones assuming a circular shape of the TC influence area and defining composites of significant wave height as a function of representative parameters of the track characteristics like the minimum pressure, its forward velocity, and its latitude. The validation against buoy data confirms the usefulness of the model for a first and rapid estimation of the wave footprint, although an underestimation of the most extreme events is observed due to the relatively small number of observations recorded. Due to its efficiency, the model can be applied for rapid estimations of wave footprints in operational systems, reconstruction of historical or synthetic events and risk assessments. [ABSTRACT FROM AUTHOR]

We applied machine learning to improve the accuracy of present predictors of wave setup. Namely, we used an evolutionary-based genetic programming model and a previously published dataset, which includes various beach and wave conditions. Here, we present two new wave setup predictors, a simple predictor, which is a function of wave height, wavelength, and beach slope, and a fitter, but more complex predictor, which is also a function of sediment diameter. The results show that the new predictors outperform existing formulas. Therefore, we conclude that machine learning models are capable of not only improving prediction capability (when compared to classical predictors) but also of providing physically sound descriptions of the processes modelled. [ABSTRACT FROM AUTHOR]

Bank retreat plays a fundamental role in fluvial and estuarine dynamics. It affects the cross-sectional evolution of channels, provides a source of sediment, and modulates the diversity of habitats. Understanding and predicting the geomorphological response of fluvial/tidal channels to external driving forces underpins the robust management of water courses and the protection of wetlands. Here, we review bank retreat with respect to mechanisms, observations, and modeling, covering both rivers and (previously neglected) tidal channels. Our review encompasses both experimental and in situ observations of failure mechanisms and bank retreat rates, modeling approaches and numerical methods to simulate bank erosion. We identify that external forces, despite their distinct characteristics, may have similar effects on bank stability in both river and tidal channels, leading to the same failure mode. We review existing data and empirical functions for bank retreat rate across a range of spatial and temporal scales, and highlight the necessity to account for both hydraulic and geotechnical controls. Based on time scale considerations, we propose a new hierarchy of modeling styles that accounts for bank retreat, leading to clear recommendations for enhancing existing modeling approaches. Finally, we discuss systematically the feedbacks between bank retreat and morphodynamics, and suggest that to move this agenda forward will require a better understanding of multifactor-driven bank retreat across a range of temporal scales, with particular attention to the differences (and similarities) between riverine and estuarine environments, and the role of feedbacks exerted by the collapsed bank soil. [ABSTRACT FROM AUTHOR]

Bathymetric data are essential for accurate predictions of flooding in estuaries, because water depth is a fundamental component in the shallow-water hydrodynamic equations used in numerical models. Where LiDAR or acoustic in-situ surveys are unavailable, recent efforts have centred on the use of satellite images to estimate bathymetry (SDB). This work is aimed at (1) determining the accuracy of SDB, and (2) assessing the suitability of the SDB for surge/tidal modelling of estuaries. The SDB is created by extracting the waterline as it tracks over the bathymetry with changing tides, and is applied to 4 different estuaries in New Zealand: Whitianga, Maketū, Ōhiwa and Tauranga Harbour. Results show that the waterline method provides similar bathymetries to the LiDAR with root-mean squared error equal to 0.2 m, and it is slightly improved when two proposed correction methods are applied to the bathymetry derivations: the removing of statistical bias (by 2 cm) and hydrodynamic modelling correction (by 1 cm). Finally, the use of SDB in numerical simulations of surge levels is assessed for Tauranga Harbour with 4 different scenarios that explore the use of SDB in comparison to bathymetry data collected using non-satellite survey methods. One of these includes the well-known Stumpf-ratio method to extract the SDB of subtidal regions (so that only satellite information is used). The use of the satellite derived bathymetry in hydrodynamic models does not result in significant differences in terms of water levels, when compared with the scenario modelled using surveyed bathymetry. [ABSTRACT FROM AUTHOR]

Understanding sediment sorting and bedding dynamics has high value to unravelling the mechanisms underlying geomorphological, geological, ecological and environmental imprints of tidal wetlands and hence to predicting their future changes. Using the Nanhui tidal flat on the Changjiang (Yangtze) Delta, China, as a reference site, this study establishes a schematized morphodynamic model coupling flow, sediment dynamics and bed level change to explore the processes that govern sediment sorting and bedding phenomena. Model results indicate an overall agreement with field data in terms of tidal current velocities, suspended sediment concentrations (SSCs), deposition thicknesses and sedimentary structures. Depending on the variation of tidal current strength, sand‐dominated layers (SDLs) and mud‐dominated layers (MDLs) tend to form during spring and neap tides, respectively. Thinner tidal couplets are developed during daily scale flood–ebb variations. A larger tidal level variation during a spring–neap tidal cycle, associated with a stronger tidal current variation, favours the formation of SDLs and tidal couplets. A larger boundary sediment supply generally promotes the formation of tidal bedding, though the bedding detail is partially dependent on the SSC composition of different sediment types. Sediment properties, including for example grain size and settling velocity, are also found to influence sediment sorting and bedding characteristics. In particular, finer and coarser sediment respond differently to spring and neap tides. During neap tides, relatively small flow velocities favour the deposition of finer sediment, with limited coarser sediment being transported to the upper tidal flat because of the larger settling velocity. During spring tides, larger flow velocities transport more coarser sediment to the upper tidal flat, accounting for distinct lamination formation. Model results are qualitatively consistent with field observations, but the role of waves, biological processes and alongshore currents needs to be included in further studies to establish a more complete understanding. [ABSTRACT FROM AUTHOR]