Monitoring changes in shoreline position adjacent to the Hadera power station, Israel

Klein, Micha; Lichter, Michal

doi:10.1016/j.apgeog.2006.01.001

Cited by 17 publications

(7 citation statements)

References 1 publication

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“…Reported works display different approaches to simulate the effect of thermal plumes on the receiving waters: simple models to account for thermal stratification of the water column (Kirillin et al, 2013), mixed approaches using physical and numerical models (El-Ghorab, 2013), schematic studies using 2D ) or 3D numerical models (You-liang & Jing, 2011, and Lagrangian coherent structures (Wei et al, 2013). The Sines power plant is similar to other energy production units with water pumped into the power plant to cool the turbines and then channeled back into the sea, lake or river via an open canal (Klein & Lichter, 2006;Ingleton & McMinn, 2012;Kirillin et al, 2013). In such setting the water discharged at the outlet is similar distinct physical properties (temperature) from the to a discharge from a small river or tributary, having receiving water body.…”

Section: The Choice Of Modelmentioning

confidence: 99%

Modelling the thermal effluent of a near coast power plant (Sines, Portugal)

Salgueiro

Pablo

Neves

et al. 2015

RGCI

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The present work is focused on the dispersion of a thermal effluent, produced by the Sines power plant, Portugal, along coastal waters. This facility intakes a yearly average around 40 m 3 /s of seawater, for the required cooling process, which is subsequently discharged back to the ocean at a 10 ºC increase in temperature. A three-dimensional hydrodynamic local model was nested into a regional model and set up to simulate the transport of the thermal effluent during two distinct periods, August and October 2013, respectively featuring dominant north and south wind. The simulations were performed for both situations, with and without the thermal discharge, where the later provides baseline scenarios. Obtained model results closely followed the existing field data. The temperature increase is shown to decay from 10 ºC near the outlet vicinity to 2 ºC at a distance of 2 km from the outlet for both scenarios. Even though the main driving force of this phenomenon is the wind, tidal conditions also have additional influence on thermal plume dispersion near the discharge area. In the north wind scenario the plume extends away from the coast while under south wind dominance the plume is contained near the coast, extending towards the inlet. As a consequence there is a positive feedback under south wind dominance, which is caused by the intake of already warm water from the thermal plume itself. Consequently, south wind dominance is the most unfavorable scenario for both coastal environment and the operational efficiency of the power plant. RESUMOModelação de um efluente térmico numa zona costeira (central termoelétrica de Sines, Portugal) Este artigo tem como objetivo estudar a dispersão do efluente térmico da central termoelétrica de Sines (Portugal) na zona costeira. Esta central retira em média 40 m 3 /s de água do oceano Atlântico que após o processo de refrigeração é restituída à fonte através de dois canais, com uma temperatura de 10º C acima daquela que tinha na zona de captação. De modo a estudar o transporte deste efluente térmico foi implementado um modelo hidrodinâmico tridimensional acoplado a um modelo regional. Foram simulados e analisados dois cenários de ventos diferentes, vento predominante do quadrante norte e vento predominante do quadrante sul. Para cada tipo de vento são comparados os resultados para a situação com e sem descarga. Os resultados obtidos com o modelo evidenciam a anomalia térmica, observável nos dados de campo, mostrando um aumento variável entre 10º C, na região próxima à descarga, até 2º C a cerca de 2 km da mesma área, para ambos cenários. Contudo, enquanto que no cenário de vento norte se observa uma pluma térmica estreita, ao longo da costa, no caso do vento sul observa-se uma pluma mais confinada à região da saída do efluente. O vento sul é o cenário mais desfavorável à eficiência da This article contains supporting information online at http://www.aprh.pt/rgci/pdf/rgci-577_Salgueiro_Supporting-Information.pdf central uma vez que nesta situação a pluma é direcionada para zona de...

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Section: The Choice Of Modelmentioning

confidence: 99%

Modelling the thermal effluent of a near coast power plant (Sines, Portugal)

Salgueiro

Pablo

Neves

et al. 2015

RGCI

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“…Shoreline position is an expression of sand availability to the beach and it has a distinct seasonal pattern (Klein and Lichter, 2006). It was measured on the aerial photograph as the average distance of the high waterline in a specific observation from the average position of all the high waterlines in the dataset of a specific mouth.…”

Section: Methodsmentioning

confidence: 99%

“…The calculation of shoreline position is presented here in the example of section Q in observation number 40410 of the Alexander mouth. It is based on the methodology suggested by Klein and Lichter (2006): the mean distance from the baseline in each of the sections was calculated by dividing the area of the section by its height.…”

Section: Methodsmentioning

confidence: 99%

Dynamic morphology of small south‐eastern Mediterranean river mouths: a conceptual model

Lichter

Klein

Zviely

2010

Earth Surf Processes Landf

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River mouths along the Israeli Mediterranean coast are characterized by a dynamic morphology as their channels migrate hundreds of meters along the coast. This study examines the dynamic morphology of seven such river mouths. It offers a conceptual model aimed at generalizing and describing their spatial and temporal morphological patterns, and the environmental factors influencing them.The study methodology comprised a detailed monitoring and mapping by GIS techniques, with quantitative data derived from historic aerial photographs, river discharge records, wave measurements, and a digital elevation model. These data were incorporated into a homogenous database and subsequently applied in the investigation of the morphological patterns of these mouths, and the analysis of their influencing factors.River mouths in this study occur in two distinctive topographic settings. In one setting (here termed barrier topography) the river mouth is deflected alongshore by a sandy barrier. In the second setting (termed funnel topography) the river mouth is confined to a funnel-shaped topographic depression perpendicular to the coast. The behavior of river mouths in these two settings is quite distinctive. Barrier mouths usually migrate over larger distances, as they tend to deflect along a sand barrier and establish semi-permanent channels along the dune toe. This enables the wide range migration of semi-permanent channels over decades. Funnel topography mouths deflect over shorter distances and they rapidly migrate within the funnel boundaries.This study concludes that the topographic setting of the beach, a constant element in the temporal scale of this study, is the primary influencing factor on the morphology of the mouths studied. The influence of other factors on the morphology of these mouths differs in space and time and depends on the topographic settings.

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“…Wu et al [27] took the Shenzhen Special Economic Zone of China as an example to study the changes on coastline in the region from 1988 to 2015, and found that the characteristics of coastline stability were completely different between the eastern and western coastlines of Shenzhen, its regional differences were mainly reflected in the morphological changes and changing laws of the coastlines. Throughout the above studies, the research on coastline changes has been mainly focused on two aspects: firstly, that the characteristics of the spatiotemporal changes to the coastline are described by the rate of coastline change and the changes in the land and sea area caused by the coastline changes [28,29]; secondly, analyzing the characteristics and trends, whilst also exploring the influence of climate, geology, human activities, and other factors on coastline changes [30][31][32][33]. However, existing coastline research has mainly focused on smaller areas, such as bays and estuaries [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Characteristics of Coastline Changes in Indonesia from 1990 to 2018

et al. 2020

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As a valuable resource in coastal areas, coastlines are not only vulnerable to natural processes such as erosion, siltation, and disasters, but are also subjected to strong pressures from human processes such as urban growth, resource development, and pollution discharge. This is especially true for reef nations with rich coastline resources and a large population, like Indonesia. The technical joint of remote sensing (RS) and geographic information system (GIS) has significant advantages for monitoring coastline changes on a large scale and for quantitatively analyzing their change mechanisms. Indonesia was taken as an example in this study because of its abundant coastline resources and large population. First, Landsat images from 1990 to 2018 were used to obtain coastline information. Then, the index of coastline utilization degree (ICUD) method, the changes in land and sea patterns method, and the ICUD at different scales method were used to reveal the spatiotemporal change pattern for the coastline. The results found that: (1) Indonesia’s total coastline length has increased by 777.40 km in the past 28 years, of which the natural coastline decreased by 5995.52 km and the artificial coastline increased by 6771.92 km. (2) From the analysis of the island scale, it was known that the island with the largest increase in ICUD was Kalimantan, at the expense of the mangrove coastline. (3) On the provincial scale, the province with the largest change of ICUD was Sumatera Selatan Province, which increased from 100 in 1900 to 266.43 in 2018. (4) The change trend of the land and sea pattern for the Indonesian coastline was mainly expanded to the sea. The part that eroded to the land was relatively small; among which, Riau Province had the most significant expansion of land area, about 177.73 km2, accounting for 23.08% of the increased national land area. The worst seawater erosion was in the Jawa Barat Province. Based on the analysis of population and economic data during the same period, it was found that the main driving mechanism behind Indonesia’s coastline change was population growth, which outweighed the impact of economic development. However, the main constraint on the Indonesian coastline was the topographic factor. The RS and GIS scheme used in this study can not only provide support for coastline resource development and policy formulation in Indonesia, but also provide a valuable reference for the evolution of coastline resources and environments in other regions around the world.

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Monitoring changes in shoreline position adjacent to the Hadera power station, Israel

Cited by 17 publications

References 1 publication

Modelling the thermal effluent of a near coast power plant (Sines, Portugal)

Modelling the thermal effluent of a near coast power plant (Sines, Portugal)

Dynamic morphology of small south‐eastern Mediterranean river mouths: a conceptual model

Spatial-Temporal Characteristics of Coastline Changes in Indonesia from 1990 to 2018

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