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Seagrasses are critical coastal habitats that provide numerous ecosystem services. The inherent patchiness within meadows exerts a significant influence on the flow‐vegetation interaction, which, in turn, affects the array of services provided by these environments. We present field observations of vegetation distribution and hydrodynamic measurements within and surrounding an approximately 2 m diameter gap (bare sediment) in a fragmented seagrass meadow. We show that variability in mean flows and turbulence is correlated with meadow structure at small (O (100 m)) and large (O (102 m)) spatial scales. Our observations reveal that bare gaps within seagrass meadows lead to faster flows and lower bed elevations. Despite slower flow speeds above the dense seagrass adjacent to the gap, the rates of dissipation of turbulent energy above the vegetation are typically around an order of magnitude larger than above the bare bed. Associated with this enhanced dissipation of turbulent energy, we observed a dominance of down‐deceleration events promoting fluid exchange and mixing and driving mass flux into the canopy. Analysis of directional variograms demonstrates that the major continuity axis within NDVI (normalized difference vegetation index) imagery (used as a proxy for vegetation biomass) coincides with the major axis of flow on both gap and meadow scales. Conversely, along the axis of minor flow variance, vegetation remains dense and exhibits greater uniformity. These findings indicate a feedback mechanism between seagrass meadow patchiness and spatial structure through flow modification, which may be beneficial for plant development. [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]

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]

Traditional sediment transport equations calculate sediment flux from bed shear stress and the equations predict that transport increases nonlinearly with an increase in flow velocity. In a dune field, the dune geometry affects the flow velocity causing accelerating flow over the dune crest and de- and reattachment of the flow downstream of the dune crest. Sediment flux predicted from the reach-averaged bed shear stress gives fairly good results for dune fields, though their simplification is discordant for the complexity of the processes involved. Measurements of the displacement of sand particles over the dune bed were derived from highfrequency image capturing. The two main methods to measure particle velocities from images are particle tracking velocimetry (PTV) and particle image velocimetry (PIV). We compare individual particle tracking with a PIV-based correlation method. The PIV-based method promises to be a more efficient and effective approach to track particle motion. It is more suitable for the conditions of high bedload transport, as present in our experiments. The PIV-based method is based on using images of difference (IoD) and is fully automated and identifies spatial gradients at a support scale in the order of centimetres. Findings align with our general knowledge of accelerating flow over the dune crest. The mean streamwise particle velocity and activity over a dune stoss slope increase. At the scale of 0.026 m the observed particle velocity variability can be explained in the context of general onset and cessation of sediment transport, the effect of the reattachment zone and observed sweep/burst events. By decreasing the streamwise distance between cross-sections, the variations in mean particle velocity induced by superimposed bed defects are distinguished as well. The maximum particle velocity and activity occurred at the same location and consequently the location of the maximum transport over the dune crest was identified. The measurements bridge the gap between individual particle motion studies and (non-local) sediment transport flux measurements.

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]