Shorelines as graphs: A spatio-temporal data-driven model for predicting shoreline dynamics - Yongjing Mao; Kristen D. Splinter (Coastal Engineering, 2026)

A mixture of experts approach to sandy shoreline modelling in storm dominated systems - Kit Calcraft; Joshua A. Simmons; Lucy A. Marshall; Kristen D. Splinter (Coastal Engineering, 2025)

Evaluating five shoreline change models against 40 years of field survey data at an embayed sandy beach - Oxana Repina; Rafael C. Carvalho; Giovanni Coco; José A.Á. Antolínez; Iñaki de Santiago; Mitchell D. Harley; Camilo Jaramillo; Kristen D. Splinter; Sean Vitousek; Colin D. Woodroffe (Coastal Engineering, 2025)

Data-driven shoreline modelling at timescales of days to years - Joshua A. Simmons; Kristen D. Splinter (Coastal Engineering, 2025)

Application of SAR-Optical fusion to extract shoreline position from Cloud-Contaminated satellite images - Yongjing Mao; Kristen D. Splinter (ISPRS Journal of Photogrammetry and Remote Sensing, 2025)

Do LSTM memory states reflect the relationships in reduced-complexity sandy shoreline models - Kit Calcraft; Kristen D. Splinter; Joshua A. Simmons; Lucy A. Marshall (Environmental Modelling & Software, 2025)

A framework for national-scale coastal storm hazards early warning - Ian L. Turner; Christopher K. Leaman; Mitchell D. Harley; Mandi C. Thran; Daniel R. David; Kristen D. Splinter; Nashwan Matheen; Jeff E. Hansen; Michael V.W. Cuttler; Diana J.M. Greenslade; Stefan Zieger; Ryan J. Lowe (Coastal Engineering, 2024)

Interannual variability in dominant shoreline behaviour at an embayed beach - Raimundo Ibaceta; Mitchell D. Harley; Ian L. Turner; Kristen D. Splinter (Geomorphology, 2023)

Improving multi-decadal coastal shoreline change predictions by including model parameter non-stationarity - Raimundo Ibaceta; Kristen D. Splinter; Mitchell D. Harley; Ian L. Turner (Frontiers in Marine Science, 2022)
Our ability to predict sandy shoreline evolution resulting from future changes in regional wave climates is critical for the sustainable management of coastlines worldwide. To this end, the present generation of simple and efficient semi-empirical shoreline change models have shown good skill at predicting shoreline changes from seasons up to several years at a number of diverse sites around the world. However, a key limitation of these existing approaches is that they rely on time-invariant model parameters, and assume that beaches will evolve within constrained envelopes of variability based on past observations. This raises an interesting challenge because the expected future variability in key meteocean and hydrodynamic drivers of shoreline change are likely to violate this ‘stationary’ approach to longer-term shoreline change prediction. Using a newly available, multi-decadal (28-year) dataset of satellite-derived shorelines at the Gold Coast, Australia, this contribution presents the first attempt to improve multi-decadal shoreline change predictions by allowing the magnitude of the shoreline model parameters to vary in time. A data assimilation technique (Ensemble Kalman Filter, EnKF) embedded within the well-established ShoreFor shoreline change model is first applied to a 14-year training period of approximately fortnightly shoreline observations, to explore temporal variability in model parameters. Then, the magnitudes of these observed non-stationary parameters are modelled as a function of selected wave climate covariates, representing the underlying seasonal to interannual variability in wave forcing. These modelled time-varying parameters are then incorporated into the shoreline change model and tested over the complete 28-year dataset. This new inclusion of non-stationary model parameters that are directly modelled as a function of the underlying wave forcing and corresponding time scales of beach response, is shown to outperform the multi-decadal predictions obtained by applying the conventional stationary approach (RMSE non-stationary = 11.1 m; RMSE stationary = 254.3 m). Based on these results, it is proposed that a non-stationary approach to shoreline change modelling can reduce the uncertainty associated with the misspecification of physical processes driving shoreline change and should be considered for future shoreline change predictions.

Dynamic Motions of Piled Floating Pontoons Due to Boat Wake and Their Impact on Postural Stability and Safety - Elizabeth L. Freeman; Kristen D. Splinter; Ron J. Cox; Francois Flocard (Journal of Marine Science and Engineering, 2022)
Piled floating pontoons are public access structures that provide a link between land and sea. Despite floating pontoons being frequented by the public, there is limited data available to coastal or maritime engineers detailing the dynamic motions (acceleration and rotation) of these structures under wave action and the impact of these motions on public comfort and safety to inform their design. This contribution summarises results from a set of laboratory-scale physical model experiments of two varying beam width piled floating pontoons subjected to boat wake conditions. Observed accelerations and roll angles were dependent on beam-to-wavelength ratio (B/L), with the most adverse motion response observed for B/L ~0.5. Internal mass of the pontoon played a secondary role, with larger mass structures experiencing lower accelerations for similar B/L ratios. Importantly, these new experimental results reveal the complex interaction between the piles and pontoon that result in peak accelerations more than six times the nominated operational safe motion limit of 0.1g. Root mean square (RMS) accelerations were more than three times the nominated comfort limit (0.02g) and angles of rotation more than double what would be perceived as safe (6 degrees) for the boat wake conditions tested. The frequency of acceleration also suggests patrons standing on these platforms are likely to experience discomfort and instability. Laboratory results are compared against a series of field-scale experiments of pontoon motion response and patron feedback. The dynamic motion response of pontoons tested in both field-scale and laboratory experiments compared well.

Challenges and Opportunities in Coastal Shoreline Prediction - Kristen D. Splinter; Giovanni Coco (Frontiers in Marine Science, 2021)
Sandy beaches comprise approximately 31% of the world's ice-free coasts. Sandy coastlines around the world are continuously adjusting in response to changing waves and water levels at both short (storm) and long (climate-driven, from El-Nino Southern Oscillation to sea level rise) timescales. Managing this critical zone requires robust, advanced tools that represent our best understanding of how to abstract and integrate coastal processes. However, this has been hindered by (1) a lack of long-term, large-scale coastal monitoring of sandy beaches and (2) a robust understanding of the key physical processes that drive shoreline change over multiple timescales. This perspectives article aims to summarize the current state of shoreline modeling at the sub-century timescale and provides an outlook on future challenges and opportunities ahead.

Bathymetric Data Requirements for Operational Coastal Erosion Forecasting Using XBeach - Nashwan Matheen; Mitchell D. Harley; Ian L. Turner; Kristen D. Splinter; Joshua A. Simmons; Mandi C. Thran (Journal of Marine Science and Engineering, 2021)
There is an increasing interest in the broad-scale implementation of coastal erosion early warning systems (EWS) with the goal of enhancing community preparedness to extreme coastal storm wave events. These emerging systems typically rely on process-based models to predict the storm-induced morphological change. A key challenge with incorporating these models in EWSs is the need for up-to-date nearshore and surf zone bathymetry data, which is difficult to measure routinely, but potentially important for accurate erosion forecasting. This study evaluates the degree to which up-to-date bathymetry is required for accurate coastal erosion predictions using the morphodynamic model XBeach and, subsequently, whether a range of “representative” and/or “synthetic” bathymetries can be used for the bottom boundary, when a survey of the immediate pre-storm bathymetry is not available. Twelve storm events at two contrasting sites were modelled using six different bathymetry scenarios, including the expected “best case” bathymetry surveyed immediately pre-storm. These results indicate that alternative bathymetries can be used to obtain sub-aerial erosion predictions that are similar (and in some cases better) than those resulting from the use of an immediately pre-storm surveyed bathymetry, provided that rigorous model calibration is undertaken prior. This generalized finding is attributed to specific parametrizations in the XBeach model structure that are optimized during the calibration process to match the particular bottom boundary condition used. This study provides practical guidance for the selection of suitable nearshore bathymetry for use in operational coastal erosion EWSs.