Shoreline Data Research Articles

Integrated Remote Sensing and GIS for Calculating Shoreline Change in Rokan Estuary

This paper presents an application of satellite remote sensing techniques to detect and to<strong><em> </em></strong>analyze the spatial changes as well as quantify the shoreline change in Rokan estuary, Riau Province, Indonesia. Coastal zone of Rokan estuary, a place through which Rokan River flows into Malacca Strait is dynamically changed because of the hydrodynamic nature and high sediment transport in downstream of Rokan River. By integrating modern techniques of remote sensing and GIS (Geographic Information System), the rates of shoreline change would be easily and quickly determined for a regional area. Landsat satellite images were used with a combination of histogram thresholding and band ratio method for shoreline change detection for last 14 years from 2000 to 2014. The shoreline data then were adjusted for serving as an input for GIS tool to estimate the erosion and deposition rates. The statistical method called as LRR (Linear Regression Rate) in DSAS (Digital Shoreline Analysis System) was used in this study. The results of this study present shoreline changes map of Rokan estuary for last 14 years. Quantitatively, the shoreline of Rokan estuary is dynamically changed over a time because accretion rate is very high. The accretion rates in Halang, Barkey, and Serusai Island within 14 years are 67 m/yr, 53 m/yr, and 114 m/yr respectively.This occurs because

Shoreline change analysis using end point rate and net shoreline movement statistics: An application to Elmina, Cape Coast and Moree section of Ghana’s coast

Most coastlines in the world are under the threat of erosion. As such many developed nations have instituted long-term measures to control the rate of change. However, along most developing nation coastlines, little attention is given to coastal erosion management. Ghana like most developing countries has little commitment to ensure the effective monitoring and management of coastal erosion. Consequently, many of its coastal communities and important historical monuments are now under severe risk to sea erosion. This study focuses on the shoreline evolution that occurred along the Elmina, Cape Coast and Moree coast of Ghana during a thirty-eight year period using available datasets that allowed the authors to discern between what happened between 1974 and 2005, and in the most recent years, between 2005 and 2012. Shoreline data from 1974, 2005 and 2012 were incorporated in Geographic Information System (GIS) using ArcGIS for analysis. The net shoreline movement and end point rate statistics were generated by ArcGIS together with Digital Shoreline Analysis System software extension. The study identified that in all the three epochs considered, there were a general erosion trend in the shoreline changes. This study has provided valuable and comprehensive baseline information on the state of the coastline in the Elmina, Cape Coast and Moree area which can serve as a guide for coastal engineers, coastal managers and policy makers in Ghana to manage the risk.

Evaluation of Opportunistic Shoreline Monitoring Capability Utilizing Existing “Surfcam” Infrastructure

ABSTRACT Bracs, M.A.; Turner, I.L.; Splinter, K.D.; Short, A.D.; Lane, C.; Davidson, M.A.; Goodwin, I.D.; Pritchard, T., and Cameron, D., 2016. Evaluation of opportunistic shoreline monitoring capability utilizing existing “surfcam” infrastructure. This paper investigates the opportunistic repurposing of existing low-elevation recreational surf cameras (“surfcams”) to provide quantitative shoreline position data. Shoreline data are of fundamental importance in coastal management; however, intensive effort is required to routinely sample this dynamic environmental parameter. Established (and generally research-oriented) coastal imaging systems provide shoreline data in higher temporal resolution across broader spatial scales than is achievable by traditional survey techniques. The key benefits of adopting surfcams for shoreline monitoring are the wide availability of existing stations and their relatively low cost. In addition to the known challenges of optical remote sensing, there are further constraints...

Seasonal shoreline behaviours along the arcuate Niger Delta coast: Complex interaction between fluvial and marine processes

Deltaic coasts are dynamic geomorphic systems where continuous changes occur on diverse spatial and temporal scales, and these changes constitute an important aspect of their evolution. Based on three-year satellite-derived shoreline data coupled with re-analyzed wave data and hydro-meteorological data, a comprehensive analysis of the dominant processes governing the seasonal shoreline changes along the oil-rich arcuate section of the Niger Delta, in the Nigerian Shelf of the North Atlantic Ocean has been undertaken. Shoreline analysis results show that the delta coast is characterized by predominant summer erosion and maximum winter accretion. Between 2010 and 2012, erosion dominated over accretion and a total of 9.1km2 deltaic land was lost to coastline erosion at an annual average erosion rate of 4.55±1.21km2/yr. A greater understanding of the dominant factors responsible for the change is presented. Shoreline change interactions with cross-shore sediment exchange processes are prominent at seasonal timescale (Summer R2=−0.85 and Winter R2=0.7), and interannual timescale (R2=−0.93) with longshore sediment transport processes. Correlation analysis reveals a gradual degeneration of relationship between the suspended sediment flux and coastal hydrodynamics beginning from 2010 to 2012 (cross-shore transport, R=0.68, 0.36 and 0.2 for 2010, 2011 and 2012, respectively; longshore transport R=0.63, 0.44 and 0.2 for 2010, 2011 and 2012, respectively). The study concludes that the effect of fluvial sediment reduction to the delta coast due to capital dredging of the Lower Niger River channels between 2009 and 2012, and periodic fluctuations in the nearshore hydrodynamics processes caused the observed annual shoreline erosion that eventually forced the deltaic coastline toward a state of landward migration during the study period.

Temporal analysis of remotely sensed turbidity in a coastal archipelago

A topographically fragmental archipelago with dynamic waters set the preconditions for assessing coherent remotely sensed information. We generated a turbidity dataset for an archipelago coast in the Baltic Sea from MERIS data (FSG L1b), using CoastColour L1P, L2R and L2W processors. We excluded land and mixed pixels by masking the imagery with accurate (1:10 000) shoreline data. Using temporal linear averaging (TLA), we produced satellite-imagery datasets applicable to temporal composites for the summer seasons of three years. The turbidity assessments and temporally averaged data were compared to in situ observations obtained with coastal monitoring programs. The ability of TLA to estimate missing pixel values was further assessed by cross-validation with the leave-one-out method. The correspondence between L2W turbidity and in situ observations was good (r=0.89), and even after applying TLA the correspondence remained acceptable (r=0.78). The datasets revealed spatially divergent temporal water characteristics, which may be relevant to the management, design of monitoring and habitat models. Monitoring observations may be spatially biased if the temporal succession of water properties is not taken into account in coastal areas with anisotropic dispersion of waters and asynchronous annual cycles. Accordingly, areas of varying turbidity may offer a different habitat for aquatic biota than areas of static turbidity, even though they may appear similar if water properties are measured for short annual periods.

Shoreline Change Rate of the Barrier Islands in Nakdong River Estuary over the Southern Part of Korea

ABSTRACT Jeong, S.H.; Khim, B.K..; Jo, Y.H., Kim, B.O., Lee, S.H. and Park, K.S., 2016. Shoreline change rate of the barrier islands in Nakdong River Estuary over the southern part of Korea. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 248–252. Coconut Creek (Florida), ISSN 0749-0208. Shoreline data of the barrier islands in the Nakdong River Estuary in Korea for the last three decades were assembled using six sets of aerial photographs and seven sets of satellite images. Canny Algorithm was applied to untreated data in order to obtain a wet-dry boundary as a proxy shoreline. Digital Shoreline Analysis System (DSAS 4.0) was used to estimate the rate of shoreline changes in terms of three statistical variables; End Point Rate (EPR), Linear Regression Rate (LRR) and Least Median of Squares (LMS). Based on Digital Shoreline Analysis, shoreline cha...

Data reduction algorithm based on curvature and topology control for radar simulation in maritime simulator

This paper presents a data reduction algorithm to increase the efficiency of radar simulation. The features of artificial and natural shoreline data are analyzed on the basis of original shoreline data, and the characteristic points of shoreline data are extracted on the whole by identifying the direction and curvature of the vector shorelines and using the topology controlling algorithm. Characteristic points of shoreline data are also extracted locally using an optimization algorithm of area loss ratio control. Therefore, all data points that represent shoreline features are extracted as the reduction result. Theoretical analysis of the results indicates that the proposed algorithm has a high data reduction rate. Moreover, the reduced data retains the shoreline's geometric features and fits well with the shoreline. Further, the accuracy analysis results show that the geometric accuracy of the algorithm is sufficiently precise for radar simulation.

Evaluating global paleoshoreline models for the Cretaceous and Cenozoic

Paleoshoreline maps represent the distribution of land and sea through geological time. These compilations provide excellent proxies for evaluating the contributions non-tectonic vertical crustal motions, such as mantle convection-driven dynamic topography, to the flooding histories of continental platforms. Until now, such data have not been available as a globally coherent compilation. Here, we present and evaluate a set of Cretaceous and Cenozoic global shoreline data extracted from two independent published global paleogeographic atlases. We evaluate computed flooding extents derived from the global paleoshoreline models with paleo-environment interpretations from fossils and geological outcrops and compare flooding trends with published eustatic sea-level curves.Although the implied global flooding histories of the two models are similar in the Cenozoic, they differ more substantially in the Cretaceous. This increase in consistency between paleoshoreline maps with the fossil record from the Cretaceous to the Cenozoic likely reflects the increase in the fossil preservation potential in younger geological times. Comparisons between the two models and the Paleogeographic Atlas of Australia on a regional scale in Australia reveal a higher consistency with fossil data for one model over the others in the mid-Cretaceous and suggest that a review of the interpretation of the Late Cretaceous–Cenozoic paleogeography may be necessary. The paleoshoreline maps and associated paleobiology data constraining marine vs terrestrial environments are provided freely as reconstructable GPlates-compatible digital files and form a basis for evaluating the output of geodynamic models predicting regional dynamic surface topography.

Managing coastal erosion hotspots along the Elmina, Cape Coast and Moree area of Ghana

Like most coastlines the world over, Ghana's coast has been receding, requiring management interventions to protect coastal communities and assets of national importance. In order to adopt sound management interventions that would have a long lasting positive impact on the coastal zone, an understanding of the historic pattern of coastline change and coastal dynamics is required. This paper presents an analysis of the historic trends in coastline changes along the Elmina, Cape Coast and Moree area of Ghana using three shoreline data that spans a period of 38 years using ArcGIS and Digital Shoreline Analysis System tools. The study found that the Elmina, Cape Coast and Moree area had been eroding at a rate of 1.22 m/year ± 0.16 m from 1974 to 2012. It was identified that the widespread practice of beach sand mining in the area has significantly contributed to the erosion of several sections of the coastline. The study also identified the lack of an existing coastline management plan for Ghana's coast as the reason for the poor coastal erosion management techniques often used by coastal managers in response to the threat of coastal erosion, which eventually causes an acceleration of local erosion rates. The study finally makes a case for the adoption of a proactive and coordinated coastline management plan for Ghana's coast similar to that of the United Kingdom shoreline management plans because of the numerous known advantages.

Analyses of Shoreline Retreat by Peak Storms using Hasaki Coast Japan Data

The prediction of shoreline retreat due to storms is important for coastal management. Previous studies have analyzed the relationship between shoreline retreat by storms and offshore energy flux of waves Ef to estimate shoreline position. In the present study, the combined effect of the maximum wave run-up level of sea waves R on beaches and Ef was used to analyze shoreline retreat. Eighteen peak storms were selected from shoreline data for Hasaki Coast, Japan, from 1987 to 2006, and shoreline retreat was analyzed for two phases of erosion: storm phase and post-storm phase. The R.Ef concept was shown to be applicable for estimating shoreline retreat in both phases of erosion. Because berm erosion tends to occur during storm-phase erosion, shoreline retreat is likely to occur during storm-phase rather than during post-storm erosion. Therefore, R.Ef per meter in storm-phase erosion, and particularly for peak storms, is much less than that in post-storm phase. It was also found that R.Ef per meter increases with landward shoreline retreat in both phases of erosion. The maximum annual shoreline position at Hasaki Coast was also analyzed statistically, and returns period (RP) was determined using cumulative frequency techniques. RP versus maximum shoreline position was plotted to determine the probable eroded shoreline position that would likely occur within a given period of time. It was found that the trend of the return period changes at 5 years and that the RP of the maximum shoreline position at a distance of 36 m landward from the foot of Hasaki pier is 24 years.

Trends of Shoreline Position: An Approach to Future Prediction for Balasore Shoreline, Odisha, India

The present study aims to analyze the shift in shoreline due to coastal processes and formulate available for best estimate of future shoreline positions based on precedent shorelines. Information on rates and trends of shoreline change can be used to improve the understanding of the underlying causes and potential effects of coastal erosion which can support informed coastal management decisions. In this paper, researchers go over the changes in the recent positions of the shoreline of the Balasore coast for the 38 years from 1975 through 2013. The study area includes the Balasore coastal region from Rasalpur to Udaypur together with Chandipur, Choumukh, Chandrabali as well as Bichitrapur. Transects wise shoreline data base were developed for approximately 67 kilometers of shoreline and erosional/accretional scenario has also been analysed by delineating the shoreline from Landsat imageries of 1975, 1980, 1990, 1995, 2000, 2005, 2010 and 2013. A simple Linear Regression Model and End Point Rate (EPR) have been adopted to take out the rate of change of shoreline and its future positions, based on empirical observations at 67 transects along the Balasore coast. It is found that the north eastern part of Balasore coast in the vicinity of Subarnarekha estuary and Chandrabali beach undergo high rates of shore line shift. The shoreline data were integrated for long- (about 17 years) and short-term (about 7 years) shift rates analysis to comprehend the shoreline change and prediction. For the prediction of future shoreline, the model has been validated with the present shoreline position (2013). The rate of shoreline movement calculated from the fixed base line to shoreline position of 1975, 1980, 1990, 1995, 2000, 2005 and 2010 and based on this, the estimated shoreline of 2013 was calculated. The estimated shoreline was compared with the actual shoreline delineated from satellite imagery of 2013. The model error or positional shift at each sample point is observed. The positional error varies from ?4.82 m to 212.41 m. It has been found that model prediction error is higher in the left hand side of river Subarnarekha. The overall error for the entire predicted shoreline was found to be 41.88 m by Root Mean Square Error (RMSE). In addition, it was tested by means difference between actual and predicted shoreline positions using “t” test and it has been found that predicted shore line is not significantly different from actual shoreline position at (t132 = 0.278) p < 0.01.

Modeling Uncertainty in Photogrammetry-Derived National Shoreline

Tidally-referenced shoreline data serve a multitude of purposes, ranging from nautical charting, to coastal change analysis, wetland migration studies, coastal planning, resource management and emergency management. To assess the suitability of the shoreline for a particular application, end users require reliable estimates of the uncertainty in the shoreline position. Previous studies on modeling uncertainty in shoreline mapping from remote sensing data have focused on airborne light detection and ranging; to date, these methods have not been extended to aerial imagery and photogrammetric shoreline mapping, which remains the primary shoreline mapping method used by the National Geodetic Survey. The aim of this article is to develop and test a rigorous total propagated uncertainty model for shoreline compiled from both tide-coordinated and non-tide-coordinated aerial imagery using photogrammetric methods. The uncertainty model is developed using data from a study site in northeast Maine. For the study area, the standard uncertainty was found to be ∼3.2–3.3 m, depending on whether the imagery was tide coordinated or not. The uncertainty model developed in this paper can easily be extended from the study area to other areas and may facilitate estimation of uncertainty in inundation models and marsh migration models.

A generalized equilibrium model for predicting daily to interannual shoreline response

AbstractCoastal zone management requires the ability to predict coastline response to storms and longer‐term seasonal to interannual variability in regional wave climate. Shoreline models typically rely on extensive historical observations to derive site‐specific calibration. To circumvent the challenge that suitable data sets are rarely available, this contribution utilizes twelve 5+ year shoreline data sets from around the world to develop a generalized model for shoreline response. The shared dependency of model coefficients on local wave and sediment characteristics is investigated, enabling the model to be recast in terms of these more readily measurable quantities. Study sites range from microtidal to macrotidal coastlines, spanning moderate‐ to high‐energy beaches. The equilibrium model adopted here includes time varying terms describing both the magnitude and direction of shoreline response as a result of onshore/offshore sediment transport between the surf zone and the beach face. The model contains two coefficients linked to wave‐driven processes: (1) the response factor (φ) that describes the “memory” of a beach to antecedent conditions and (2) the rate parameter (c) that describes the efficiency with which sand is transported between the beach face and surf zone. Across all study sites these coefficients are shown to depend in a predictable manner on the dimensionless fall velocity (Ω), that in turn is a simple function of local wave conditions and sediment grain size. When tested on an unseen data set, the new equilibrium model with generalized forms ofφandcexhibited high skill (Brier Skills Score, BSS = 0.85).

Shoreline change assessment using remote sensing and GIS techniques: a case study of the Medjerda delta coast, Tunisia

Eight scenes of Landsat Multispectral Scanner, Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager sensors, covering the period between 1972 and 2013, were used to demarcate shoreline positions and estimate shoreline change rates of the Medjerda delta coast, northeastern Tunisia. The method relies on image processing techniques using the IDRISI software, and the Digital Shoreline Analysis System, a free extension for ArcGIS software, which provides a set of tools permitting transects-based calculation of shoreline displacement. First, the Landsat images were radiometrically and geometrically corrected. Second, band ratioing, reclassification, raster to vector conversion, and smoothing techniques are applied successively to detect and extract the multi-temporal shoreline data. Third, these data are overlaid and the changes are calculated using the end points and linear regression methods. The results indicate significant shoreline changes ranging from 8.6 to −42.6 m/year, while some parts remained unchanged. The estimated shoreline change rates are comparable with those obtained through in situ measurements and from the analysis of multi-date aerial photos and toposheets. The main causes of erosion in particular are related to the natural shifting of the Medjerda River course and mouth, damming of Medjerda and its tributaries, construction of Ghar El Melh port, and the destruction of the small bordering dunes in addition to the wave-induced longshore currents, relative sea level rise due mainly to accelerated coastal subsidence, and sand mining.

Tactical Predictions of Shoreline Oiling Probability via Machine Learning Models and Satellite-Derived Surface Oil Analysis Products

ABSTRACT For significant oil spills in remote areas with complex shoreline geometry, apportioning Shoreline Cleanup Assessment Technique (SCAT) survey effort is a complicated and difficult task. Aerial surveys are often used to select shoreline areas for ground survey after an initial prioritization based upon anecdotal reports or trajectory models, but aerial observers may have difficulty locating cryptic surface shoreline oiling in vegetated or other complex environments. In dynamic beach environments, stranded shoreline oiling may be rapidly buried, making aerial observation difficult. A machine learning-based model is presented for estimating shoreline oiling probabilities via satellite-derived surface oil analysis products, wind summary data, and shoreline habitat type and geometry data. These inputs are increasingly available at spatial and temporal scales sufficient for tactical use, enabling model predictions to be generated within hours after satellite remote sensing products are available. The model was constructed using SCAT data from the Deepwater Horizon oil spill, satellite-derived surface oil analysis products generated during the spill by NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) using a variety of satellite platforms of opportunity, and available shoreline geometry, character, and other preexisting data. The model involves the generation of set of spatial indices of relative over-water proximity of surface oil slicks based upon the satellite-derived analysis products. The model then uses boosted regression trees (BRT), a flexible and relatively recently developed modeling methodology, to generate calibrated estimates of probability of subsequent shoreline oiling based upon these indices, wind climatological data over the time period of interest, and other shoreline data. The model can be implemented via data preparation in any Geographic Information System (GIS) software coupled with the open-source statistical computing language, R. The model is entirely probabilistic and makes no attempt to reproduce the physics of oil moving through the environment, as do trajectory models. It is best used in concert with such models to make estimates at different spatial scales, or when time and data requirements make implementation of fine-scale trajectory modeling impractical for tactical use. The details of model development implementation and assessments of model performance and limitations are presented.

Multidecadal Shoreline Changes in Denmark

Kabuth, A.K.; Kroon, A., and Pedersen, J.B.T., 2014. Multidecadal shoreline changes in Denmark. Multidecadal shoreline changes along ca. 7000 km coastline around Denmark were computed for the time interval between 1862 AD and 2005 AD and were connected with a geomorphological coastal classification. The shoreline data set was based on shoreline positions from historical and modern topographic maps. Coastal landforms were identified on a digital terrain model in combination with aerial photographs. Two shoreline-change computation methods were evaluated at a test site, aiming for optimized time efficiency and accuracy of the countrywide application: a Nearest Neighbor search and a cross-shore transect method based on the ArcGIS-based Digital Shoreline Analysis System (DSAS). The cross-shore transect method was more robust and performed better in the detection of local extremes in shoreline changes, which was crucial for the scope of the mapping. Countrywide shoreline-change distances and rates were, therefore, computed with the DSAS method. Patterns in coastline dynamics were identified through the connection of shoreline-change rates with the occurrence of coastal landforms. Short-term changes and alterations of shoreline evolution through coastal structures were not resolved in this study. Because of the long time span covered, the relative errors originating from data and method are acceptable. The scope of the mapping was to provide a coastal management tool that allows screening for critical sites with respect to coastal erosion. As the first countrywide quantification of historical shoreline changes around Denmark, the mapping can contribute to enhanced adaptation and mitigation strategies in response to increased risks of erosion and flooding under a changing climate.

Are beach erosion rates and sea-level rise related in Hawaii?

The islands of Oahu and Maui, Hawaii, with significantly different rates of localized sea-level rise (SLR, approximately 65% higher rate on Maui) over the past century due to lithospheric flexure and/or variations in upper ocean water masses, provide a unique setting to investigate possible relations between historical shoreline changes and SLR. Island-wide and regional historical shoreline trends are calculated for the islands using shoreline positions measured from aerial photographs and survey charts. Historical shoreline data are optimized to reduce anthropogenic influences on shoreline change measurements. Shoreline change trends are checked for consistency using two weighted regression methods and by systematic exclusion of coastal regions based on coastal aspect (wave exposure) and coastal geomorphology. Maui experienced the greatest extent of beach erosion over the past century with 78% percent of beaches eroding compared to 52% on Oahu. Maui also had a significantly higher island-wide average shoreline change rate at −0.13±0.05m/yr compared to Oahu at −0.03±0.03m/yr (at the 95% Confidence Interval). Differing rates of relative SLR around Oahu and Maui remain as the best explanation for the difference in overall shoreline trends after examining other influences on shoreline change including waves, sediment supply and littoral processes, and anthropogenic changes; though, these other influences certainly remain important to shoreline change in Hawaii. The results of this study show that SLR is an important factor in historical shoreline change in Hawaii and that historical rates of shoreline change are about two orders of magnitude greater than SLR.

Coastal modelling with a GIS bathymetric module

A 3D hydrodynamic/pollutant transport model was used to simulate the currents and pollutant transport in coastal areas. The bathymetric and shoreline data was provided to the model via a GIS module that operates in the ArcView GIS environment. The module is efficient and capable of generating bathymetric rectangular grids and shorelines of different resolution for open and closed boundary scenarios that can automatically be read by the coastal model. This ability could improve the basic patterns and relationships of the model such as grid dependency. The functionality of the 3D model with the GIS module is illustrated in a number of coastal areas in Greece.

항공사진과 SPOT-5 위성영상을 이용한 낙동강 하구역 울타리섬들의 해안선 변화율

Shoreline data of the barrier islands in Nakdong River Estuary for the last three decades were assembled using six sets of aerial photographs and seven sets of satellite images. Canny Algorithm was applied to untreated data in order to obtain a wet-dry boundary as a proxy shoreline. Digital Shoreline Analysis System (DSAS 4.0) was used to estimate the rate of shoreline changes in terms of five statistical variables; SCE (Shoreline Change Envelope), NSM (Net Shoreline Movement), EPR(End Point Rate), LRR (Linear Regression Rate), and LMS (Least Median of Squares). The shoreline in Jinwoodo varied differently from one place to another during the last three decades; the west tail has advanced (i.e., seaward or southward), the west part has regressed, the south part has advanced, and the east part has regressed. After the 2000s, the rate of shoreline changes (-2.5~6.7 m/yr) increased and the east advanced. The shoreline in Shinjado shows a counterclockwise movement; the west part has advanced, but the east part has retreated. Since Shinjado was built in its present form, the west part became stable, but the east part has regressed faster. The rate of shoreline changes (-16.0~12.0 m/yr) in Shinjado is greater than that of Jinwoodo. The shoreline in Doyodeung has advanced at a rate of 31.5 m/yr. Since Doyodeung was built in its present form, the south part has regressed at the rate of -18.2 m/yr, but the east and west parts have advanced at the rate of 13.5~14.3 m/yr. Based on Digital Shoreline Analysis, shoreline changes in the barrier islands in the Nakdong River Estuary have varied both temporally and spatially, although the exact reason for the shoreline changes requires more investigation.

Monitoring data requirements for shoreline prediction: How much, how long, and how often?

Splinter, K.D., Davidson, M.A., and Turner, I.L. Monitoring data requirements for shoreline prediction: How much, how long, and how often?Predicting future shoreline position and the subsequent vulnerability of coastal communities is of great interest to coastal managers and engineers. A number of empirically-based equilibrium shoreline models have been proposed, but all have been formulated on unique and relatively short-term datasets. As such, the transferability and calibration sensitivity of these models is unknown. Here we use a newly developed shoreline model, ShoreFor, driven by changes in offshore wave steepness and wave power, to determine model calibration coefficient sensitivity based on two key constraints in any monitoring program: 1) the duration of data collection of necessary shoreline and wave data (N); and, 2) the shoreline sampling interval (dt). The analysis was undertaken at two distinct sites: a 5-year data set from Gold Coast, QLD, Australia, a long, straight, open coast comprising medium grain sand with a strong annual cycle in the shoreline; and a 6-year data set from Narrabeen-Collaroy, NSW, Australia, which is a coarser, swash-aligned embayed beach whose shoreline variability is driven predominately by storms. In both cases, shoreline data were extracted weekly from video imagery and hourly offshore wave data were obtained from buoy measurements. Shoreline calibration datasets were then sub-sampled in both time (N) and frequency (dt). Results presented in this paper suggest calibration dataset requirements are beach-type dependent and linked to the rates of sediment exchange between the nearshore and shoreline. The storm-dominated site, Narrabeen-Collaroy, required more frequently sampled shoreline data to converge on model free parameters and maintain model hindcast normalized mean square error (NMSE) below 0.5. As a general ‘rule of thumb' a minimum of two years, sampled at dt ≤30 days was required to consistently resolve model coefficients at both sites, while calibration datasets longer than three years (roughly 50% of the data set) resulted in minimal improvement in model hindcast skill at the two sites examined.