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Tide-surge interaction (TSI) is a critical factor in assessing flooding in shallow coastal systems, particularly in estuaries and harbours. Non-linear interactions between tides and surges can occur due to the water depth and bed friction. Global investigations have been conducted to examine TSI, but its occurrence and impact on water levels in Aotearoa New Zealand (NZ) have not been extensively studied. Water level observations from 36 tide gauges across the diverse coast of NZ were analysed to determine the occurrence and location of TSI. Statistical analysis and numerical modelling were conducted on data from both inside and outside estuaries, focusing on one estuary (Manukau Harbour) to determine the impact of TSI and estuarine morphology on the co-occurrence rate of extreme events. TSI was found to occur at most sites in NZ and primarily affects the timing of the largest surges relative to high tide. There were no regional patterns associated with the tide, non-tidal residual, or skew-surge regimes. The strongest TSI occurred in inner estuarine locations and was correlated with the intertidal area. The magnitude of the TSI varied depending on the method used, ranging from -16 cm to +27 cm. Co-occurrence rates of extreme water levels outside and inside the same estuary varied from 20% to 84%, with TSI modulating the rate by affecting tidal amplification. The results highlight the importance of investing in a more extensive tide gauge network to provide longer observations in highly populated estuarine coastlines. The incorporation of TSI in flooding hazard projections would benefit from more accurate and detailed observations, particularly in estuaries with high morphological complexity. TSI occurs in most sites along the coast of NZ and has a significant impact on water levels in inner estuarine locations. TSI modulates the co-occurrence rate of extreme water levels in estuaries of NZ by affecting tidal amplification. Therefore, further investment in the tide gauge network is needed to provide more accurate observations to incorporate TSI in flooding hazard projections.

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.

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.

We integrate published data sets of field and laboratory experiments of wave ripples and use genetic programming, a machine learning paradigm, in an attempt to develop a universal equilibrium predictor for ripple wavelength, height, and steepness. We train our genetic programming algorithm with data selected using a maximum dissimilarity selection routine. Thanks to this selection algorithm we use less data to train the genetic programming software, allowing more data to be used as testing (i.e. to compare our predictor vs. common prediction schemes). Our resulting predictor is smooth and physically meaningful, different from other machine learning derived results. Furthermore our predictor incorporates wave orbital ripples that were previously excluded from empirical prediction schemes, notably ripples in coarse sediment and long wavelength, low height ripples (‘hummocks’). This new predictor shows ripple length to be a weakly nonlinear function of both bottom orbital excursion and grain size. Ripple height and steepness are both nonlinear functions of grain size and predicted ripple length (i.e. bottom orbital excursion and grain size). We test this new prediction scheme against common (and recent) predictors and the new predictors yield a lower normalized root mean squared error using the testing data. This study further demonstrates the applicability of machine learning techniques to successfully develop well performing predictors if data sets are large in size, extensive in scope, multidimensional, and nonlinear. © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/