Spatial and temporal variations of wave energy in the nearshore waters of the central west coast of India

Amrutha, M. M.; Kumar, V. Sanil

doi:10.5194/angeo-34-1197-2016

Cited by 14 publications

(7 citation statements)

References 32 publications

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“…COV close to zero indicates low variability in wave power and greater than 1.2 is for locations having considerable variability. Minimum values of monthly COV for the locations varied between 0.32 (location 12) and 0.59 (location 13) is observed in January (location 20), February (location 19), April (3)(4)(5)(6)(7)(8)18), June (location 12), and July (location 1,2 and 11). Among 20 locations, the maximum values of COV varied between 0.49 and 1.69.…”

Section: Variations In Monthly Meanmentioning

confidence: 98%

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Changes in Wave Energy in the Shelf Seas of India during the Last 40 Years Based on ERA5 Reanalysis Data

Amrutha

Kumar

2019

Energies

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Ocean wave energy is one of the cleanest renewable energy sources around the globe, but wave energy varies widely from place to place and from time to time. The long-term variability of wave power at 20 locations in the Indian shelf seas from 1979 to 2018 is described here using the European Centre for Medium-Range Weather Forecasts recently released ERA5 reanalysis hourly data. The variability is calculated on a yearly and monthly basis for the locations based on the coefficient of variation. The annual average wave power varied from 2.3 (at location 16 in the western Bay of Bengal) to 11 kW/m (at location 2 in the northeastern Arabian Sea). Along the western shelf seas, the maximum value of wave power is during the southwest monsoon period and along the east coast, it is during the tropical cyclone period. The standard deviation in wave power is more than the mean value at locations along the northern shelf seas of India, indicating a large variability in wave power in an annual cycle. The west coast locations are shown to have a slightly higher increasing trend with an average of 0.024 kW/m per year, while the increasing trend in wave power of east coast locations is with an average of 0.015 kW/m per year. The study also examines the variation in wave power from deep to shallow water at 2 locations using the wave characteristics obtained from the numerical model SWAN. The electric power output from a few wave energy converters are calculated for all the locations and found that the southernmost locations have a steady and higher percentage of power production.

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Section: Variations In Monthly Meanmentioning

confidence: 98%

“…For the BoB locations 13 to 17, low variability in average wave power is observed (small SD~0.19-0.35 kW/m). For the rest of the BoB locations (18)(19)(20), the SD is in the range between 0.51 and 1.81 kW/m. On the other hand, for the AS locations, the SD varied from 0.57 (location 9) to 1.18 kW/m.…”

Section: Variations In Monthly Meanmentioning

confidence: 99%

Changes in Wave Energy in the Shelf Seas of India during the Last 40 Years Based on ERA5 Reanalysis Data

Amrutha

Kumar

2019

Energies

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“…The spatial distribution of total mean wave energy flux and seasonal mean wave energy flux along Indian coast was studied and is found that the west coast of India is more energetic which may be due to the open exposure to swell coming from southern Indian ocean (Patel et al, 2020). Similarly, the variation in wave power off the central west coast of India was examined for different years from 2009 to 2015 at water depths of 30, 9, and 5 m based on the measurement from wave rider buoy (Amrutha and Sanil Kumar, 2016). Wave energy assessment along the Exclusive Economic Zone (EEZ) of India for 39 years using ERA-Interim dataset indicates that the western coast of India adjoining the states of Maharashtra, Goa, Karnataka and Kerala shows an annual average of 10-15 kW/m Wave Power (Haripriya et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Modelling and simulation of extreme wave heights around agatti island of lakshadweep, west coast of India

Dinakaran

Alluri²,

Joseph³

et al. 2022

Front. Built Environ.

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Small islands experience variation in wave energy regime along shorter shoreline due to refraction, diffraction and variability in direction of wave travel. Nearshore wave climate around these islands is quite complex due to wave transformation associated with configuration of islands, and high steepness in the bathymetry. Understanding wave energy will help in identifying suitable locations for coastal structures, wave energy devices and sediment budgeting. Assessment of extreme wave climate around the islands play a crucial role in the design and survival of coastal infrastructure for any developmental activities in islands. This study investigates the extreme wave climate around the Agatti island of Union Territory of Lakshadweep located in west coast of India. The spatial variability of wave power distribution around the Agatti island is analyzed based on 10 years of wave hindcast data from spectral wave modelling for the period between 2011 and 2020 using spectral wave model. In this study, third-generation spectral wave model was used to simulate the wave climate over the Arabian Sea. Regional model was established using ECMWF model winds and the offshore wave parameters were validated with deep water ocean observations AD07 and AD09 deployed in the Arabian Sea. To understand the nearshore wave transformation along the Agatti island, local wave model of fine resolution was established using high resolution bathymetry and is validated using the coastal buoy (CB02) deployed near Agatti. Nearshore wave parameters such as Significant wave height, Peak wave period (Tp) and Mean wave direction (θm) were analyzed. The wave power distribution around the island was quantified and demarcated for siting of offshore structures for developmental activities. The study reveals that the south zone of the island is more energetic with annual mean wave power of 8–12 kW/m and during monsoon, wave power more than 28 kW/m is observed. The extreme value theory based on Generalized Extreme Value distribution is applied to the extreme wave height estimate based on modelled wave data around the Agatti island. The extreme wave heights for different return periods were estimated around the Agatti island for design and analysis of coastal infrastructure.

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“…The authors concluded that site selection for commercial development of wave energy, reduction of grid integration hindrances, and increase of power smoothing are highly dependent on directional exposure of sites. Reference [42] has assessed wave power and its temporal variability for various water depths off the central west coast of India and examined the directional distribution of wave power at shallower water depths. Reference [43] has highlighted the wave (and wind) directionality effects on offshore wind turbines, supported by a jacket structure during hurricane events, while [44,45] have stressed the significance of wave direction, among others, that may lead to larger fatigue damage for certain platforms under specific conditions.…”

Section: Introductionmentioning

confidence: 99%

Joint Modelling of Wave Energy Flux and Wave Direction

Soukissian

Karathanasi

2021

Processes

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In the context of wave resource assessment, the description of wave climate is usually confined to significant wave height and energy period. However, the accurate joint description of both linear and directional wave energy characteristics is essential for the proper and detailed optimization of wave energy converters. In this work, the joint probabilistic description of wave energy flux and wave direction is performed and evaluated. Parametric univariate models are implemented for the description of wave energy flux and wave direction. For wave energy flux, conventional, and mixture distributions are examined while for wave direction proven and efficient finite mixtures of von Mises distributions are used. The bivariate modelling is based on the implementation of the Johnson–Wehrly model. The examined models are applied on long-term measured wave data at three offshore locations in Greece and hindcast numerical wave model data at three locations in the western Mediterranean, the North Sea, and the North Atlantic Ocean. A global criterion that combines five individual goodness-of-fit criteria into a single expression is used to evaluate the performance of bivariate models. From the optimum bivariate model, the expected wave energy flux as function of wave direction and the distribution of wave energy flux for the mean and most probable wave directions are also obtained.

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Spatial and temporal variations of wave energy in the nearshore waters of the central west coast of India

Cited by 14 publications

References 32 publications

Changes in Wave Energy in the Shelf Seas of India during the Last 40 Years Based on ERA5 Reanalysis Data

Changes in Wave Energy in the Shelf Seas of India during the Last 40 Years Based on ERA5 Reanalysis Data

Modelling and simulation of extreme wave heights around agatti island of lakshadweep, west coast of India

Joint Modelling of Wave Energy Flux and Wave Direction

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