Numerical Simulation on Shoreline Change in Western Region of Badung Regency, Bali, Indonesia

Shoreline change is considered the most dynamic processes in coastal region. Coastal erosion is a global problem where 70% beaches around the world are recessional. Almost all coastal area in Bali is potential to suffer from erosion. Badung Regency in Bali has many beaches that famous as tourism area where from about 64 km shoreline length, 11,5 km were recorded suffered by erosion in 1985 and 12,1 km erosion in 2007. This study aims to determine the value of shoreline changes that occur in western of Badung Regency from 2001 to 2010 based on the predicted wave data using monthly wind data from Ngurah Rai, Tuban, Badung, Bali meteorological station. Shoreline change simulation measured the forward (accretion) or backward (erosion) distance of the shoreline on the East-West direction. Bali has wind patterns that influenced by the Northwest monsoon from November-April and Southeast monsoon from May-October. In 2001-2010, dominant wind in this region was coming from east, southeast, and west. Geographically western coast of Badung influenced by incoming winds from the west, southwest, and south. Wind blow towards the coast in 2001-2010 are dominantly come from the west with wind speed range was about 1,7-4,7 m/s. Simulation indicated that generally shoreline tends to experience accretion in the north and erosion in the south. From 16000 m of study shoreline, along 7100 m of shoreline tend to suffer by erosion. Oppositely, along 8900 m of shoreline tend to have accretion

The Model of Shoreline Change in Estuary of Porong River after Mud Volcano

The basic research of this paper is to produce the result of the model of extended area or/and degradasion area in estuary of Porong river after mud volcano phenomenon. The model is the part of conception of coastal management in the sector of coastal protection. This sector is concerning to the stability of shoreline change. This is obviously the extended area from sedimentation or/and degradasion area due to erosion processes in coastal vicinity.nbsp It means stable from the sedimentation and/or erosion processes that may not be wanted. This research is to create the model of shoreline change, based on the previous years to the recent condition, and then to estimate the feature condition.nbsp This model based on the conception of longshore transport (lonsgshore current) in the certain location of estuary of Porong river. The model works on the two stages.nbsp (1). Using data ofnbsp year 2000 for initial condition, the model produced three difference results for next 14 years from three difference sediment transport formulations.nbsp This is to find the most apropriate result when to be compared to the existing data of 2014 among those formulatios. The formulation of Komar-Inman [6] is the best one due to getting result that have the smallest error ofnbspnbsp 7 % to the existing data 2014.nbsp (2). By using data ofnbsp year 2014 as initial condition, the model have produced thenbsp estimation of shoreline change for the next period of 5, 10, 15 and 20 years. After 20 years implementation, the model gives resultnbsp of extended land area to the offshore direction in around of 1000 meters. The accuration of the result is depend on the accuration of Komar-Inman [6] formulation in the transport sediment conception

Evaluation of shoreline change using optical satellite images, case study of Progreso, Yucatán

A technique to extract the shoreline from optical satellite images has been developed, evaluated and applied to the case study site of Progreso, Yucatán, México. This site was chosen as it is frequently subject to hurricanes, shows shoreline erosion and has a paucity of coastal data. The area under investigation is an 8 km length of shoreline that faces north into the Gulf of México. A novel method to extract satellite-derived shorelines (SDS) was developed ensuring the maximum contrast between sea and land. The SDS was validated using quasisimultaneous in situ shoreline measurements from one day in two different years (2008 and 2010). The in situ shoreline measurements recorded the instantaneous shorewards extent of the wave run-up when walking along the beach. The validation of SDS revealed that the SDS locates consistently seawards of the in situ shoreline, explained by: a) the water depth that an optical satellite image requires to identify a pixel either as sea or land, and b) the shorewards extent of the wave run-up. At Progreso, the overall distance between SDS and in situ shoreline is 5.6 m on average and standard deviation of 1.37 m (in the horizontal) over 8 km of shoreline. For an accurate location of the mean SDS, estimation of the shorewards extent of the wave run-up, tidal level and inter-tidal beach slope were required. In situ measurements regarding the beach profile, shoreline location and water levels were taken into consideration to achieve this. The shoreline change observed over a 6.5 year period allowed the estimation of intraannual and inter-annual shoreline changes and progressive changes in the shoreline location. The intra-annual shoreline change revealed seasonality in the shoreline position. The shoreline position from late winter (March 20, 2004) was landwards (approx. 5 to 9 m) in relation to the earlier winter shoreline position (November 11, 2003). The assessed SDSs from the hurricane season (June to November) are at the landwards envelope limit during the year, between -30 to 15 m in relation to the estimated mean SDS. The largest landward movement (100 m) is related to Hurricane Ivan, detected 13 days after the hurricane passed by Yucatán. The inter-annual shoreline change highlighted that an approximate length of 6 km of shoreline is retreating at a rate between -2.4 and -1.2 m per year. Such estimates of shoreline change would not be possible using other available coastal information at this site. The results of this research show that optical satellite images can be used to study shoreline change over large spatial scales (> 5 km), as well as in short (< 1 yr) and long (> 5 yrs) temporal scales.CONACy

Cumulative versus transient shoreline change : dependencies on temporal and spatial scale

Author Posting. © American Geophysical Union, 2011. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 116 (2011): F02014, doi:10.1029/2010JF001835.Using shoreline change measurements of two oceanside reaches of the North Carolina Outer Banks, USA, we explore an existing premise that shoreline change on a sandy coast is a self-affine signal, wherein patterns of change are scale invariant. Wavelet analysis confirms that the mean variance (spectral power) of shoreline change can be approximated by a power law at alongshore scales from tens of meters up to ∼4–8 km. However, the possibility of a power law relationship does not necessarily reveal a unifying, scale-free, dominant process, and deviations from power law scaling at scales of kilometers to tens of kilometers may suggest further insights into shoreline change processes. Specifically, the maximum of the variance in shoreline change and the scale at which that maximum occurs both increase when shoreline change is measured over longer time scales. This suggests a temporal control on the magnitude of change possible at a given spatial scale and, by extension, that aggregation of shoreline change over time is an important component of large-scale shifts in shoreline position. We also find a consistent difference in variance magnitude between the two survey reaches at large spatial scales, which may be related to differences in oceanographic forcing conditions or may involve hydrodynamic interactions with nearshore geologic bathymetric structures. Overall, the findings suggest that shoreline change at small spatial scales (less than kilometers) does not represent a peak in the shoreline change signal and that change at larger spatial scales dominates the signal, emphasizing the need for studies that target long-term, large-scale shoreline change.Our thanks to the NSF (grant EAR‐04‐ 44792) for funding this researc

Solusi Model Perubahan Garis Pantai dengan Metode Transformasi Elzaki

. Pantai merupakan kawasan yang sering dimanfaatkan untuk berbagai kegiatan manusia, namun seringkali upaya pemanfaatan tersebut menyebabkan permasalahan pantai sehingga garis pantai berubah. Salah satu cara yang dapat digunakan untuk mengetahui perubahan garis pantai yaitu dengan membuat model matematika. Model perubahan garis pantai berbentuk persamaan diferensial parsial dapat diselesaikan secara analitik dengan menggunakan metode transformasi Elazki. Metode transformasi Elzaki merupakan salah satu bentuk transformasi integral yang diperoleh dari integral Fourier sehingga didapatkan transformasi Elzaki dan sifat-sifat dasarnya. Perubahan garis pantai pada penelitian ini dipengaruhi oleh adanya groin. Penyelesaian model perubahan garis pantai dengan metode transformasi Elzaki dilakukan dengan menerapkan transformasi Elzaki pada model perubahan garis pantai untuk memperoleh model perubahan garis pantai yang baru, kemudian menerapkan syarat batas, kemudian menerapkan invers transformasi Elzaki sehingga diperoleh solusi model perubahan garis pantai. Berdasarkan hasil penelitian, diperoleh bahwa terdapat kesamaan antara pola grafik yang dihasilkan dari solusi model perubahan garis pantai dengan metode transformasi Elzaki dan solusi model perubahan garis pantai dengan metode numerik.Kata Kunci: Perubahan garis pantai, Groin, Analitik, Transformasi Elzaki.The beach is a region that is often used for various human activities, however often these utilization efforts cause beach problems so that the shoreline changes. One way that can be used to determine changes in shoreline is to make a mathematical model. The shoreline change model shaped of partial differential equation can be solved analytically by using the Elzaki transform method. The Elzaki transform method is a form of integral transform obtained from the Fourier integral so that the Elzaki transform and its basic properties are obtained. Shoreline change in this research were affected by groyne. Solution of shoreline change model using Elzaki transform method is carried by applying the Elzaki transform to the shoreline change model to obtain a new shoreline change model, then applying the boundary value, then applying the inverse of Elzaki transform so obtained a solution shoreline change model. Based on the research result, it was found that there was a similiarity between the graphic patterns generated from the solution of shoreline change model using Elzaki transform method and the solution of shoreline change model using numerical method.Keywords: Shoreline change, Groyne, Analitic, Elzaki transfor

Statistical Assessment of Long-term Shoreline Changes along the Odisha Coast

1990-1998Odisha, a coastal state on eastern seaboard of India possesses a ~450 km long coastline vulnerable to a multitude of natural and anthropogenic threats. The present study reports a systematic assessment of rates of shoreline change over a period of 25 years from 1990- 2015, using Landsat 5 and 8 series of (Thematic Mapper and Operational Land Imager) satellite images. An analysis of rate of shoreline change was carried out along select regions of Odisha coast using Digital Shoreline Analysis System (DSAS). Linear Regression Method (LRR) was used to estimate net shoreline change at sub decade time scale and End Point Rate (EPR) to estimate net shoreline change rate in between two consecutive years. The highest erosion with a coastline length of 63 km was observed between Rajnagar (around Satabhaya beach) and Mahakalapara (near to Hukitola beach) block of Kendrapara district and between Ersama (around Paradeep port) and Balikuda blocks (northern parts of Devi River mouth) of Jagatsinghpur coastal district. The result suggest that both EPR and LRR techniques were used to estimate shoreline change rate and the similar result of erosion by both EPR and LRR technique indicated weaker cyclic trend in erosion

Simulation of shoreline change using AIRSAR and POLSAR C-band data

This paper presents a new approach for modeling shoreline change due to wave energy effects from remotely sensed data. The airborne AIRSAR and POLSAR data were employed to extract wave spectra information and integrate them with historical remotely sensed data such as aerial photography data to model the rate of change of the shoreline. A partial differential equation (PDE) of the wave conversion model was applied to investigate the wave refraction patterns. The volume of sediment transport at several locations was estimated based on the wave refraction patterns. The shoreline change model developed was designed to cover a 14-km stretch of shoreline of Kuala Terengganu in Peninsular Malaysia. The model utilized data from aerial photographs, AIRSAR, POLSAR, ERS-2, and in situ wave data. The results show that the shoreline rate of change modeled from the quasi-linear wave spectra algorithm has a significant relationship with one estimated from historical vector layers of aerial photography, AIRSAR, and POLSAR data. With the quasi-linear algorithm, an error of ±0.18 m/year in shoreline rate of change determination was obtained with Cvv band

Drivers of Sub-Seasonal to Interannual Shoreline Change at Sunset State Beach in Monterey Bay, CA

Expectations of future change call for a thorough understanding of short- and long-time scale processes that impact sandy beaches, as well as tests of coastal change models in a variety of coastal settings. However, existing shoreline change models have primarily been developed and tested in open coast environments. Therefore, this study takes place in the northern Monterey Bay where we investigate the effects of headland sheltering and complex inner shelf bathymetry on shoreline change at a sandy dune-backed beach, fronted by a submarine canyon system. Twenty months of half-hourly video imagery were used to build a high-resolution time series of shoreline and sandbar positions at Sunset State Beach from September 2017 to May 2019. Past studies have shown that high magnitudes of winter shoreline erosion in the Monterey Bay occur during El Niño periods, when storm tracks over the northeast Pacific Ocean shift southward. This motivated the assessment of interannual shoreline variability by extending the shoreline time series back to September 2014 with biannual in-situ surveys. According to the video derived observations, the shoreline varied by approximately 60 meters while the sandbar varied by approximately 100 meters in the cross-shore direction. Winter shoreline erosion began when nearshore significant wave heights exceeded the 95th percentile (1.7m), and a greater magnitude of shoreline erosion occurred with higher average winter wave energy. Shoreline accretion appeared to be aided by the sandbar, which acted as a source of sediment in the early summer months of 2018. The influence of wave energy and direction on shoreline change was tested using an equilibrium shoreline change model and an alongshore sediment transport model. Shoreline change at Sunset State Beach depended primarily on wave energy, the root-mean-squared error (RMSE) of the equilibrium model alone was 6.4m. The addition of alongshore sediment transport to overall shoreline change resulted in a modest RMSE reduction to 5.6m, but equilibrium model parameters did not change significantly. According to the biannual time series of shoreline observations, high magnitudes of shoreline erosion can also occur during non- El Niño periods, due to westerly waves that bypass the Santa Cruz headlands and expose the northern Monterey Bay to wave attack. The accuracy of the shoreline change models used in this study was limited by annual variability in the summer shoreline position, motivating future investigations of temporally variable alongshore sediment supply. The results suggest that rather than relying on predictions of an El Niño index to predict shoreline change, predictions of the direction of storm tracks over the northeast Pacific Ocean could more accurately inform shoreline change predictions at the study site and in similar environments

Coupling centennial-scale shoreline change to sea-level rise and coastal morphology in the Gulf of Mexico using a Bayesian network

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Earth's Future 4 (2016): 143–158, doi:10.1002/2015EF000331.Predictions of coastal evolution driven by episodic and persistent processes associated with storms and relative sea-level rise (SLR) are required to test our understanding, evaluate our predictive capability, and to provide guidance for coastal management decisions. Previous work demonstrated that the spatial variability of long-term shoreline change can be predicted using observed SLR rates, tide range, wave height, coastal slope, and a characterization of the geomorphic setting. The shoreline is not sufficient to indicate which processes are important in causing shoreline change, such as overwash that depends on coastal dune elevations. Predicting dune height is intrinsically important to assess future storm vulnerability. Here, we enhance shoreline-change predictions by including dune height as a variable in a statistical modeling approach. Dune height can also be used as an input variable, but it does not improve the shoreline-change prediction skill. Dune-height input does help to reduce prediction uncertainty. That is, by including dune height, the prediction is more precise but not more accurate. Comparing hindcast evaluations, better predictive skill was found when predicting dune height (0.8) compared with shoreline change (0.6). The skill depends on the level of detail of the model and we identify an optimized model that has high skill and minimal overfitting. The predictive model can be implemented with a range of forecast scenarios, and we illustrate the impacts of a higher future sea-level. This scenario shows that the shoreline change becomes increasingly erosional and more uncertain. Predicted dune heights are lower and the dune height uncertainty decreases.This work was supported by the USGS Coastal and Marine Geology Program and the USGS Southeast Regional Assessment Project

Physical and Social Factors of Shoreline Change in Gebang, Cirebon Regency 1915 – 2019

Shoreline changes are the main concern for coastal management. In Indonesia coastal zone is the populated region for marine and fishery economic sectors. Dynamic of the region shown by shoreline change. This study aims to explain the dynamics of shoreline change in Gebang, Cirebon Regency from 1915 to 2019, and several factors that influence. This research using overlay intersections to know shoreline change from 1915-2019 and multiple linear regression to determine several factors that influence the shoreline change. The shoreline increased 992.99 meters caused by accretion. Physical factors that influence shoreline changes include total suspended solids, bathymetry, wind, and tides, whereas social factors include the presence of beach building, population density, building density, and distance from the built-up area. The most influential factor in increased shoreline is bathymetry. Based on the results of statistical tests known that physical and social factors are influence significantly the dynamics of shoreline changes. The correlation between the actual and the predicted value reached 0.97 with p-value 0.001