Prediction Of Shoreline Changes Research Articles

A DEEP LEARNING MODEL TO PREDICT SHORELINE CHANGE

As coastal population increases, so does the risk for social and economic losses under a changing climate. To assess future changes, much progress has been made towards developing shoreline numerical models, although producing reliable shoreline change predictions remains a challenge (Montaño 2020). Here we present a Deep Learning (DL) model to predict long-term shoreline evolution due to waves and large-scale atmospheric patterns. The model is based on two types of Artificial Neural Networks: Long-Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN).

Improving multi-decadal coastal shoreline change predictions by including model parameter non-stationarity

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 (RMSEnon-stationary = 11.1 m; RMSEstationary = 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.

GLOBAL WARMING AND ITS IMPACT ON MANGROVE LAND DEGRADATION ON THE NORTH COAST OF BENGKALIS ISLAND, RIAU PROVINCE

Bengkalis Regency is located on the north coast of Riau Province, where the coastal area is very vulnerable to the threat of maritime disasters. Global warming can result in natural disasters, which means catastrophe on earth. Global warming causes an increase in the temperature of the earth's surface. This study aims to analyze the progress of the coastline and sea level rise that occurs and their impact on the degradation of Mangrove land on the north coast of Bengkalis Island, Riau Province. The research method used is the quantitative method through analysis of satellite images to map predictions of abrasion in the future. Data processing is carried out using satellite image data with different temporal, namely 1991, 2002, 2012, and 2021. Analysis of shoreline change predictions is carried out using the Digital Shorelines Analysis System Method. Utilization of GIS data can be used to map areas with the potential for abrasion, and make anticipatory measures against abrasion that may occur. In Bengkalis Regency itself, several efforts have been made by the local government, namely through mangrove planting programs, and increasing public awareness.

UTILIZATION OF GEOGRAPHIC INFORMATION SYSTEMS IN IMPROVING SOCIAL RESILIENCE OF FARMER COMMUNITIES IN DEMAK

Coastal abrasion that occurred in Demak Regency caused the loss of agricultural land due to drowning and brought social changes to the community among farmers. In anticipating this, it is necessary to overcome abrasion and increase the social resilience of the community. The ability of GIS in monitoring and mapping the area can be used as a means of useful information for the community. This study uses a quantitative approach. Data processing is carried out using satellite image data with different temporal, namely 1991, 2002, 2012, and 2021. Analysis of shoreline change predictions is carried out using the Digital Shoorelines Analysis System Method. The results of the analysis using the Digital Shoreline Analysis System (DSAS) method found that changes in the northern coastline of Demak Regency tend to experience significant abrasion from year to year. The coastline prediction obtained from the map is known to predict the coastline in the next 10 years the north coast of Demak Regency will experience abrasion. The highest abrasion occurred in Sayung District. Abrasion has an impact on changes in farmers' economic income which changes the behavior of the farming community so that efforts are needed to increase the resilience of the farming community, efforts that can be made include: 1). Prevention of coastal abrasion in areas that have the potential to experience coastal abrasion. 2). Building synergy to increase social resilience between the Government and the community. 3). Build and revive strategic leadership oriented to farmer welfare. The results of the research can also be used as material for consideration in policy making by the relevant government in disaster management efforts that may occur.

Shoreline Change Prediction for Integrated Coastal Erosion Management

Kim, Y.-J.; Kim, T.-W.; Yoon, J.-S.; Hur, D.-S, and Kim, M.-K., 2021. Shoreline change prediction for integrated coastal erosion management. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 121–125. Coconut Creek (Florida), ISSN 0749-0208. The mechanism of sediment transport occurring in littoral zone shows complex patterns. Establishing an accurate longshore sediment budget prediction is difficult. Littoral drift flowing into and out of the coast shows sensitive responses to natural and anthropogenic changes in the surrounding area. In particular, at the coast where rivers are located, the littoral drift is significantly affected by the inflow of sediment from the upstream. However, the sediment management at the coast has been conducted by respective measures established in different areas, such as mountains in the upstream region, dams, rivers, and coasts, and thus, measures are established without an appropriate feedback between these different areas. Therefore, determining the exact causes of erosion occurring in the coastal zone is challenging. In addition, for accurate prediction of sediment budget in coastal areas, it is necessary to construct a model with mixed particle size distribution that can consider the properties of sediment inflow from the river and the sediment constituting the sea area. Thus, for integrated coastal erosion management, based on the determination of sediment transport mechanism flowing into the coastal zone, accurate estimation of sediment transport rate and sediment budget prediction according to the shoreline changes affected by waves play an important role. In this study, a series of mechanism of sediment transport flowing into the sea area through the river was analyzed, and the sediment budget according to the shoreline change occurring in the sea area was estimated. The sediment load flowing into the sea area and the representative wave acting on the sea area were calculated to construct the IN-MPS (INje-Mixture Particle Shoreline) model considering the sediment particle size distribution in mixed condition. The accuracy of the model was evaluated by comparing the results obtained from the model with those of long-term observations by calculating the exact sediment budget in the coastal zone, and when the inflow from the river and the mixed particle size distribution of sediment were considered, the accuracy in the shoreline change prediction was improved.

The Application of Ensemble Wave Forcing to Quantify Uncertainty of Shoreline Change Predictions

AbstractReliable predictions and accompanying uncertainty estimates of coastal evolution on decadal to centennial time scales are increasingly sought. So far, most coastal change projections rely on a single, deterministic realization of the unknown future wave climate, often derived from a global climate model. Yet, deterministic projections do not account for the stochastic nature of future wave conditions across a variety of temporal scales (e.g., daily, weekly, seasonally, and interannually). Here, we present an ensemble Kalman filter shoreline change model to predict coastal erosion and uncertainty due to waves at a variety of time scales. We compare shoreline change projections, simulated with and without ensemble wave forcing conditions by applying ensemble wave time series produced by a computationally efficient statistical downscaling method. We demonstrate a sizable (site‐dependent) increase in model uncertainty compared with the unrealistic case of model projections based on a single, deterministic realization (e.g., a single time series) of the wave forcing. We support model‐derived uncertainty estimates with a novel mathematical analysis of ensembles of idealized process models. Here, the developed ensemble modeling approach is applied to a well‐monitored beach in Tairua, New Zealand. However, the model and uncertainty quantification techniques derived here are generally applicable to a variety of coastal settings around the world.

Shoreline Change Analysis along the Udupi-Dakshina Kannada Coastal Stretch in Karnataka, West Coast of India using DSAS

Study of morphological variations and the effects of oceanographic processes such as erosion and accretion at different temporal scales are important to understand the nature of the coast and the cyclic changes occurring during different seasons. The Udupi-Dakshina Kannada coast along the west coast of India exhibits a wide range of changes depending on the interactions of tide and wave energy, sediment supply and more importantly human intervention. In view of this, the present work has been carried out to study the changes in shoreline changes along the Udupi-Dakshina Kannada coast over a period of 29 years from 1990 to 2019. Remote Sensing and GIS techniques have been used to demarcate shorelines and calculate the shoreline change rates. Overall accretion and erosion rates were found to be 1.28 m/year and 0.91 m/year respectively along the coast. Highest accretion and erosion rates of 12.57 m/year and 5.34 m/year was noticed along the Dakshina Kannada coast. The study also suggests that multi-dated satellite data along with statistical techniques can be effectively used for prediction of shoreline changes.
 Keywords: remote sensing, GIS, Dakshina Kannada coast, oceanography, shoreline.

Shoreline change monitoring for coastal zone management using multi-temporal Landsat data in Mahi River estuary, Gujarat State

Coastline changes are caused by sea-level rise, erosion and anthropogenic activities, and have important consequences on coastal ecosystems and communities. In this study, shore line changes during the last 40 years near the Mahi estuarine belt in the Gulf of Khambhat region have been monitored using multi-temporal Landsat data from 1978 to 2018. Multi-date Landsat digital data with 10-year interval from 1978 to 2018 was downloaded from the website https://earthexplorer.usgs.gov/ . The Landsat data covering the study area with 15-km buffer was extracted and analyzed for monitoring the changes in the shoreline. The Normalized Difference Water Index (NDWI) images were also generated for delineation of the shoreline. The data was analyzed using the Digital Shoreline Analysis System in ArcGIS software, which provides a set of tools permitting transects-based calculation of shoreline displacement. A total number of 10 transects from the centre of the village up to the coast line were marked on the multi-date Landsat digital data. The changes in the shoreline from the centre of the village were calculated using the End Point Rate (EPR) and temporal linear regression model to predict future shoreline position. The decadal shoreline change indicated that shoreline has continuously increased from 113.9 to 831.4 m during the 40-year period from 1978 to 2018, respectively. This indicates that total migration of shoreline towards the land area of the study village during the last 40 years (1978 to 2018) was 1590.5 m with an alarming annual rate of change of 39.76 m year−1, which is very remarkably high and alarming which may lead to any future disaster for the study village. The coastline was not significantly changing until 1988; however, change in coastline is exponentially faster from 1989 to 2018. The linear regression method was adopted for predicting the shoreline change rates over the entire period of the study from 1978 to 2018 and from 2018 to 2028 and 2038. The results of shoreline change prediction for future 10 years (2028) and 20 years (2038) indicate the average predicted transect length of 1908 m and 1543 m, respectively. With the projected estimates of shoreline change during next 20 years at the rate of almost 120 m/year, it appears that the Chokari village may get submerged in the sea water. The analysis of change in the land surface area of the Chokari village indicated that it has decreased from 12.45 to 9.63 km2, which indicates that area reduced by 2.8 km2 may be due to the erosion along the river-bed which is the result of change in the shoreline during the last 40 years. Based on these results of shoreline change analysis, it is recommended that the Government of Gujarat needs to take immediate steps to protect the Chokari village from submergence by adopting Integrated Coastal Zone management strategies.

Artificial neural network for the prediction of shoreline changes in Narrabeen, Australia

Shorelines around the world are of great importance because of their role in people's lives. Therefore, understanding its behavior seems to be vital. Shoreline changes and its realignments are highly nonlinear and finding a method in such a way that it could model its patterns would be very useful. ANN's are popular methods, as they have been applied to numerous problems. In this study, recurrent ANNs such as NARNET and NARXNET are used to model the shoreline changes in Narrabeen Coast, Australia, between 1980 and 2014. Their outputs represent a reliable performance for predicting shoreline changes based on historical data. These results also are compared with other methods, including RBF, GRNN, and TDNN. The NARNET showed most accurate results with MAPE= 17.18%, and the NARXNET had the best correlation with CC=0.26. It has been indicated that the NARNET and NARXNET are better methods since they need less extra data, besides the shoreline position itself.

Mapping the Shoreface of Coastal Sediment Compartments to Improve Shoreline Change Forecasts in New South Wales, Australia

The potential response of shoreface depositional environments to sea level rise over the present century and beyond remains poorly understood. The shoreface is shaped by wave action across a sedimentary seabed and may aggrade or deflate depending on the balance between time-averaged wave energy and the availability and character of sediment, within the context of the inherited geological control. For embayed and accommodation-dominated coastal settings, where shoreline change is particularly sensitive to cross-shore sediment transport, whether the shoreface is a source or sink for coastal sediment during rising sea level may be a crucial determinant of future shoreline change. While simple equilibrium-based models (e.g. the Bruun Rule) are widely used in coastal risk planning practice to predict shoreline change due to sea level rise, the relevance of fundamental model assumptions to the shoreface depositional setting is often overlooked due to limited knowledge about the geomorphology of the nearshore seabed. We present high-resolution mapping of the shoreface-inner shelf in southeastern Australia from airborne lidar and vessel-based multibeam echosounder surveys, which reveals a more complex seabed than was previously known. The mapping data are used to interpret the extent, depositional character and morphodynamic state of the shoreface, by comparing the observed geomorphology to theoretical predictions from wave-driven sediment transport theory. The benefits of high-resolution seabed mapping for improving shoreline change predictions in practice are explored by comparing idealised shoreline change modelling based on our understanding of shoreface geomorphology and morphodynamics before and after the mapping exercise.

Spatial Bayesian Network for predicting sea level rise induced coastal erosion in a small Pacific Island

An integrated approach combining Bayesian Network with GIS was developed for making a probabilistic prediction of sea level rise induced coastal erosion and assessing the implications of adaptation measures. The Bayesian Network integrates extensive qualitative and quantitative information into a single probabilistic model while GIS explicitly deals with spatial data for inputting, storing, analysing and mapping. The integration of the Bayesian Network with GIS using a cell-by-cell comparison technique (aka map algebra) provides a new tool to perform the probabilistic spatial analysis. The spatial Bayesian Network was utilised for predicting coastal erosion scenarios at the case study location of Tanna Island, Vanuatu in the South Pacific. Based on the Bayesian Network model, a rate of the island shoreline change was predicted probabilistically for each shoreline segment, which was transferred into GIS for visualisation purposes. The spatial distribution of shoreline change prediction results for various sea level rise scenarios was mapped. The outcomes of this work support risk-based adaptation planning and will be further developed to enable the incorporation of high resolution coastal process models, thereby supporting localised land use planning decisions.

NUMERICAL MODELS TO PREDICTION OF SHORELINE CHANGES

The prediction of shoreline changes numerically via modern computers for a period of time in question, is more practical and realistic, and much efforts would be saved However, theoretically, factors behind shoreline configuration are including the mechanism of sediment transport and wave deformation process utilized to obtain an accurate wave energy distribution along a coast Applications of numerical models are examined through the comparison of result of real situations conducted by different authors; and in most cases, a satisfactory agreement is found in predicting shoreline changes in the vicinity of different man-made structures

Design of Fishing Harbour Layout in High Littoral Drift Zone

Estimation of littoral drift and direction of net drift are needed for design of harbour projects. Different methods are used to study shoreline changes in the coastal area. Among them, mathematical modelling is considered as an effective technique. The current study addresses this issue through the use of mathematical models viz. spectral wave model to derive nearshore wave climate, Boussinesq wave model for evolving the harbour layout to provide adequate wave tranquillity in the harbour basin and one line model for prediction of shoreline changes in the adjacent shoreline of the project. In the present study, the mathematical models were applied for design of a layout for fishing harbour, on the West Coast of India in Kerala State. Different alternatives of the harbour layout were tested in order to reduce siltation in the harbour and also to achieve the desired tranquillity in the harbour basin. In the first alternative, the southern breakwater was extended by 340 m. However it was observed that after two to three years, the shoreline will advance and the drift will start entering the harbour basin. Therefore, in the second alternative, the mouth of the harbour was further taken into deeper water to minimize the drift entering in the harbour. With this alternative the wave tranquility studies showed that the layout is adequate to provide desired tranquility in the harbour basin and the wave heights will remain within 0.3 m almost round the year. Thus, mathematical modelling technique was used to evolve a harbour layout that satisfies the tranquillity criteria and also ensures minimum siltation in the harbour basin.

Coastal Changes along Gamasa Beach, Egypt

Gamasa Beach is considered a long strip beach of white sand on the Nile Delta, Egypt. Gamasa resort locates on a very active concave shoreline which covering 30 km along Nile Delta Coast. Two techniques were used to monitoring shorelines, multi-spectral imagery from Landsat satellite and Global Positioning System (GPS) surveying technique. This research paper presents shoreline maps illustrating the shoreline erosion accretion pattern along Gamasa Beach by using different sources of remote sensing data. In the present study, Landsat TM (1984, 1987, 1990, 1999, 2000, 2002), Landsat ETM (2001, 2003, 2005, 2010) and Landsat OLI/TIRS (2013, 2014) satellite images were used. Post-Processed Kinematic (PPK) points are measured by the author for extracting of Gamasa shoreline. Occupation times for survey points are on the order of seconds. Data must be post-processed to achieve high-precision results; this requires a processing program Lecia Office Software. Digital Shoreline Analysis System (DSAS) model is used to calculate the annual rate of shoreline change (erosion or accretion) between 1984 and 2014. Rates of shoreline changes are estimated from three statistical approaches of DSAS (End point rate, Linear regression rate, Least median of square). The results were validated with field observations of beach profile survey data at the same corresponding positions and time. Results showed that Gamasa beach had insignificant eroded and lower accretion between 1984 and 2014 with average rates of 5.0 m/year. Finally, shoreline change prediction model for coastal zone at Gamasa beach in years 2020, 2030, 2040, 2050and 2060 is estimated according to DSAS settings and Linear regression rate.

Framework for proper beach nourishment as an adaptation to beach erosion due to sea level rise

Yoshida, J., Udo, K., Takeda, Y., Mano, A., 2014. Framework for proper beach nourishment as adaptation to beach erosion due to sea level rise. In: Green, A.N. and Cooper, J.A.G. (eds.), Proceedings 13th International Coastal Symposium (Durban, South Africa), Journal of Coastal Research, Special Issue No. 70, pp. 467–472, ISSN 0749-0208.Beach erosion caused by sea level rise is a serious problem for people over the world. Beaches play important roles in disaster prevention, recreational use, and nurturing unique ecosystems. Beach nourishment is capable of maintaining the position of the shoreline and the natural environment. However, applying artificial nourishment to the whole beach area is not a practical method due to high costs and the large quantity sand required. There has been no framework for effective adaptation of beach nourishment to solving coastal erosion issues. In this study, we focus on the beach nourishment as an adaptation to beach erosion due to sea level rise and attempt to construct a framework for proper beach nourishment. The framework for the adaptation proposal is as follows: (i) Prediction of shoreline changes and future beach width due to sea level rise; (ii) Determination of beach width to be protected in terms of disaster prevention, ecosystem conservation and recreation respectively; (iii) Specifying vulnerable areas where the area of and width of the beach Step; (iv) Estimation of sand volume and its cost applied only to the vulnerable area. This framework was applied to Japanese beaches where the determine beach width indicated that when a beach width of more than 10 m is needed for prevention against disasters, more than 20 m for ecosystem conservation, and more than 30 m for recreation use. The volume of sand required to maintain the beach width along the whole Japanese beach varies from 61× 106 to 2,300× 106 m3, and by the use of this framework, it is possible to estimate practical nourishment volume and its associated costs for disaster prevention, ecosystem conservation and recreation use.

Toward Parsimony in Shoreline Change Prediction (III): B-Splines and Noise Handling

ABSTRACT Anderson, T.R. and Frazer, L.N., 2014. Toward parsimony in shoreline change prediction (III): B-splines and noise handling. The traditional single-transect method for predicting long-term shoreline change uses far more parameters than necessary because it assumes that erosion/accretion (change) rates at adjacent alongshore positions (transects) are independent. Such overfitting can cause poor predictions of future shoreline location, so recent work has modeled change rates as linear sums of polynomials, or linear sums of principal components. Here we introduce an alternative method that uses linear sums of B-splines. As in earlier work, an information criterion is used to identify the optimal number of basis functions. The local nature of B-spline models makes them less susceptible to the Gibbs effect than polynomial models, and their smoothness makes them more robust to noise than principal components regression. We also compare three noise-handling techniques by examining their effects on the p...

A Bayesian network to predict coastal vulnerability to sea level rise

Sea level rise during the 21st century will have a wide range of effects on coastal environments, human development, and infrastructure in coastal areas. The broad range of complex factors influencing coastal systems contributes to large uncertainties in predicting long‐term sea level rise impacts. Here we explore and demonstrate the capabilities of a Bayesian network (BN) to predict long‐term shoreline change associated with sea level rise and make quantitative assessments of prediction uncertainty. A BN is used to define relationships between driving forces, geologic constraints, and coastal response for the U.S. Atlantic coast that include observations of local rates of relative sea level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline change rate. The BN is used to make probabilistic predictions of shoreline retreat in response to different future sea level rise rates. Results demonstrate that the probability of shoreline retreat increases with higher rates of sea level rise. Where more specific information is included, the probability of shoreline change increases in a number of cases, indicating more confident predictions. A hindcast evaluation of the BN indicates that the network correctly predicts 71% of the cases. Evaluation of the results using Brier skill and log likelihood ratio scores indicates that the network provides shoreline change predictions that are better than the prior probability. Shoreline change outcomes indicating stability (−1 < rate < 1 m/yr) or erosion (rate < −1 m/yr) tend to occur for two sets of input scenarios. Stable shoreline change rates occur mainly for low rates of relative sea level rise and occur in low‐vulnerability geomorphic settings. Rates indicating erosion result for cases where the rate of relative sea level rise is high and moderate‐to‐high vulnerability geomorphic settings occur. In contrast, accretion (rate > 1 m/yr) was not well predicted. We find that BNs can assimilate important factors contributing to coastal change in response to sea level rise and can make quantitative, probabilistic predictions that can be applied to coastal management decisions.

Modeling the effects of wave climate and sediment supply variability on large-scale shoreline change

The application of an integrated data analysis and modeling scheme reveals that decadal-scale shoreline evolution along a U.S. Pacific Northwest littoral cell is highly dependent on both sediment supply and wave climate variability. In particular, accurate estimates of (Columbia River) sediment supply and sediment feeding from the lower shoreface are critical components of balancing the barrier beach sediment budget and are therefore essential to making sensible shoreline change hindcasts and forecasts. A simple deterministic one-line shoreline change model, applied in a quasi-probabilistic manner, enables evaluation of the influence of sediment supply and wave climate variability through simulation of historical shoreline change. Through iteration, a range of realistic scenarios are developed to constrain decadal-scale shoreline change predictions. Modeled shoreline changes are significantly sensitive to directional changes in the incident waves, and therefore sensitive to the occurrence of interannual climatic fluctuations such as major El Niño events. A predicted increase in the intensity of the east Pacific wave climate (1.0 m increase in significant wave height in 20 yr) affects forecast shoreline positions only when this increase occurs during the winter storm season. However, the effect of this increase in storm power during any given year is small relative to the impact of major El Niño events. The model has significant skill in decadal-scale hindcasts suggesting that alongshore gradients in sediment transport dominate coastal change at this scale at this site. However, both data and model results suggest that net onshore feeding from the lower shoreface is responsible for approximately 20% of the decadal-scale coastal change. Field measurements and poor model skill at annual scale indicate that cross-shore processes likely dominate coastal change at shorter time scales.

Toward Parsimony in Shoreline Change Prediction (I): Basis Function Methods

Single-transect methods of shoreline change prediction are unparsimonious, i.e., they tend to overfit data by using more parameters than necessary because they assume that both signal and noise at adjacent transects are independent. Here we introduce some new methods that reduce overfitting by expressing change rate as a linear sum of basis functions. In the method of IC-binning, the basis functions are boxcars—an information criterion is used to assign contiguous alongshore locations into bins within which change rate is constant; the resulting rate is discontinuous but may be useful for beach management. In the polynomial method, the basis functions are polynomials in alongshore distance, and the change rate varies continuously along the beach. In the eigenbeaches method, the basis functions are the principal components of the matrix of shorelines. To choose the number of basis functions in each method, and to compare methods with each other, we use an information criterion. We apply these new methods to shoreline change on Maui Island, Hawaii, briefly here, and in more detail in a companion paper. The polynomial method works best for short beaches with rates that vary slowly in the alongshore direction while eigenbeaches works best for shorelines that are long, or have rates that vary rapidly in the alongshore direction. The Schwarz information criterion and the AICu version of the Akaike information criterion performed well in tests on real data and noisy synthetic data.

Toward Parsimony in Shoreline Change Prediction (II): Applying Basis Function Methods to Real and Synthetic Data

Abstract There is a need to supply coastal managers with statistically defensible hazard predictions that can be used to implement coastal setbacks and other management policies. The goal of this article is to evaluate the widely used single-transect method, as well as several new methods: t-binning, IC-binning, polynomial methods, and eigenbeaches, to identify which method(s) best predicts a 50-year eroded shoreline position. The polynomial and eigenbeach methods allow for acceleration (the rates vary with time). The methods are compared using data from nine beaches on Maui, Hawaii, and four sets of synthetic data. Evaluations of the methods are based on an information criterion, color maps of residuals, long-term (50 year) predictions, and cross-validating the most recent shoreline, which has a short-term span of 5–9 years. The newer methods identified significant rates at 74% of the transects, vs. 0% for single-transect on beaches in Maui, Hawaii. The cross-validation results showed that the polynomial...