Integrated Analysis of Seasonal Swells, Wind-seas and associated Wave Energy along the major Indian Ports

doi:10.21203/rs.3.rs-4277351/v1

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Integrated Analysis of Seasonal Swells, Wind-seas and associated Wave Energy along the major Indian Ports

https://doi.org/10.21203/rs.3.rs-4277351/v1

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An in-depth comprehension and evaluation of the spatio-temporal progression of wind-induced gravity waves encompassing wind-seas and swells in coastal areas are crucial for optimizing the extraction of renewable energy efficiently and identifying ideal locations for planning renewable energy infrastructure. This research offers a thorough examination of the combined potential of average and extreme offshore winds, swells, and wind-seas derived energy along the Indian coastline, utilizing the ERA5 3-hourly reanalysis dataset spanning the past 44 years (1979–2022). The study employs the Generalized Extreme Value (GEV) method for extreme value analysis. Among the six major port locations, the most significant rise in swell and wind-sea wave power is identified at Kandla (1.33 kW/m dec-1) in the Arabian Sea (AS) and Mumbai (0.1 kW/m dec-1) during the June–August (JJA) period, and subsequently during September-November (SON). Likewise, the highest increase in decadal wind energy is observed at Mumbai (2.47 W/m2 dec-1) and Kochi (2.39 W/m2 dec-1). Results indicate that both mean and extreme wave and wind energy exhibit substantial swell wave power at Kandla, averaging around 21.05 kW/m (with peaks up to 66.84 kW/m) during the JJA season. Similarly, the peak mean (extreme) wave power generated from wind-seas per annum is recorded at Kandla and Mumbai, averaging approximately 5.81 kW/m (with peaks reaching 62 kW/m) during JJA. The highest mean (extreme) wind energy is observed at Kandla, averaging about 0.51 kW/m2 (with peaks up to 3.65 kW/m2) during JJA, followed by SON. Across the six principal port locations, the analysis exhibits the JJA season as the prime period for maximum energy production, followed by SON. This scrutiny also underscores the significance of considering seasonal fluctuations and local climatic conditions when developing renewable energy initiatives along the coastal regions of India.

Offshore renewable energy. Combined wind-wave energy. Generalized Extreme Value distribution. North Indian Ocean. Port location

The utilization of renewable energy sources has become a crucial aspect of global energy strategies, promoting sustainability and reducing greenhouse gas emissions. Among renewable energy sources, the wave power and wind energy offer great potential for harnessing clean and renewable electricity. As the global population trend continues to increase, the demand for energy requirements becomes more significant due limited availability of natural resources. In response, alternative and sustainable energy sources and associated technologies have been under progressive development, encompassing hydropower, wind, wave, solar, bioenergy, geothermal, and marine energy. Offshore energy extraction from various renewable energy sources faces high operation and maintenance (O&M) costs, around one-third of total expenses, more than the onshore setups (Scheu et al., 2012). Consequently, generating power from a single marine energy source tends to have higher costs when contrasted with onshore renewable energy alternatives. Beyond the operational and financial challenges, addressing pertinent issues related to periods of no power output and the unpredictable nature of energy generation from these variable sources becomes crucial (Gaughan et al., 2020). However, the simultaneous utilization of various marine energy sources at a single offshore site holds the potential to address challenges posed by individual renewable energy systems. This integrated approach can enhance the overall efficiency and reliability (Astariz et al., 2016).

Assessment of wave energy resources have been conducted for different geographical regions in the world, utilizing wave data sourced from buoys, satellites, and numerical wave hindcasts, either individually or in combination. Regions include the Baltic Sea (Bernhoff et al., 2006), the Black Sea (Akpinar et al., 2013), and the Persian Gulf (Kamranzad et al., 2013) etc. Additionally, wave energy assessments have been conducted for regions such as Indo-Pacific (Kumar et al., 2021; Kaur et al., 2021), Canada (Dunnett et al., 2009), Malaysia (Nik et al., 2011), Portugal (Rusu et al., 2009; Mota et al., 2014), Taiwan (Chiu et al., 2013), and the United Kingdom (Neill et al., 2013). The study by Reguero et al. (2015) conducted an analysis of global wave power and its temporal variability during the period 1948–2008. Utilizing satellite wind data, assessments of wind resources were conducted for Europe (Hasager et al., 2015), the US Atlantic coast (Sheridan et al., 2012), and the South China Sea (Chang et al., 2015). Zheng et al. (2018) also carried out a comprehensive evaluation of global wind energy resources. Additionally, a global review of wind energy assessments was performed by Murthy and Rahi (2016). By using cross-calibrated wind data from various platforms, Zheng et al. (2013) examined the wind energy density across the global ocean. They observed prosperous regions situated in the western belts of the Northern Hemisphere (500–1000 W/m²) and in the Southern Hemisphere (800–1600 W/m²).

Limited attempts have been made to examine the wind and wave patterns in the Indian Ocean (Sardana et al.,2022; Krishnan et al.,2012; Kumar et al., 2013; Pogarskii et al., 2012). Numerous studies have focused on assessing either offshore wind (Gadad et al., 2016; Nagababu et al., 2017; Kumar et al., 2020) or wave power resources (Kumar et al., 2015; Patel et al., 2020) within the Indian Ocean region. One study investigated the wind-wave field across the Indian Ocean using a 12-year dataset of wind measurements from the National Centers for Environmental Prediction and the National Oceanic and Atmospheric Administration (NCEP/NOAA), along with wave data from the numerical wind-wave model (WAM). This study revealed that the average wave height increases by 1% each year (Pogarskii et al., 2012). Shanas et al. (2015) noted a declining trend in both mean and extreme wind speeds for the Arabian Sea (AS) and the Bay of Bengal (BoB). Interestingly, during this period, a contrasting trend of increasing extreme wave height emerged from a 33-year analysis of wind and wave data (Sadhukhan et al., 2020). In a separate study, it was observed that wind power varies as one goes higher above the surface due to its non-stationary nature (Calif et al., 2012).

In a recent investigation, the reanalysis of wave data unveiled a consistent annual increase of 0.4% in global wave energy since 1948, attributed to ocean warming (Reguero et al., 2019). Gadad et al. (2016) explored offshore wind energy potential at Karnataka, India, utilizing OceanScat (OSCAT) scatterometer wind data. Their findings reveal India's greater capacity for harnessing wind energy from offshore sources (up to 450 W/m²) compared to onshore sources (up to 300 W/m²). Examining wave resources across nineteen distinct locations within the Indian Shelf seas using ERA-Interim data, Kumar et al. (2015) conducted a comprehensive wave energy analysis. Additionally, a meticulous assessment of wave energy exploration along the Indian coastline employing high-resolution hindcast data from the WAVEWATCH III (WWIII) model indicated India's wave potential ranging from low to moderate (up to 12 kW/m). Relative to European nations, where offshore wind potential can reach up to 1500 W/m² and wave potential up to 150 kW/m, India's offshore wind and wave potential is comparatively constrained. Considering this situation, the combined utilization of wind and wave resources emerges as an advantageous strategy, as elaborated in the earlier sections.

This research investigated the combined potential of wind and wave energy at six key port locations situated along the eastern and western coasts of India. It delved into the seasonal climatology and variability patterns of mean significant wave height (SWH) from swells (Hs) and wind-seas (Hw), as well as mean wind speed and wave period across the North Indian Ocean (NIO) region. Additionally, the study explored the seasonal climatology and variability of extreme SWH from swells (Hmaxsw), SWH from wind-seas (Hmaxws), maximum wind speed (Wmax), and wave period (Pmax), along with extreme wave power and wind energy in the NIO region. The substantial impact of long-term resource variations on strategic planning has been well-established in previous studies (Kamranzad et al., 2020; Sreelakshmi et al., 2020). Therefore, leveraging ERA5 reanalysis data, this study analyzed seasonal trends in wind and wave energy within the NIO, aiming to provide essential support for long-term planning initiatives and sustainable development endeavors.

2.1 Datasets and Study area

The mean and extreme wave power and wind energy are examined at six locations along the Indian coastline (Fig. 1), namely Kandla, Mumbai, Kochi, Paradip, Vishakhapatnam, and Chennai. In recent years, drastic improvements in data quality have made it possible to use reanalysis data directly to study the wind resources. The ERA5 is the latest global atmospheric reanalysis product from the ECMWF, and its advantage being high spatial and temporal resolution compared to other reanalysis data (Gualtieri et al., 2021). In addition, previous reanalysis data were limited to a height of only 10 m above the water surface. In this context, the ERA5 has additional advantage that provides wind components (u- and v-components) at 100 m level beneficial for wind energy applications (Camargo et al., 2019).

This study focused on understanding the trends and evaluating wave power in major ports of India. The study considered essential wave characteristics like significant height of total swell (H_s), significant height of wind waves (H_w), mean wave period (MWP) for mean wave power (MWP), and peak wave period (PWP) for extreme wave power calculations. Monthly averages and extreme wind speeds were obtained from the wind data at a height of 100 meters to assess the wind energy. The ERA5 datasets at 3-hourly data was utilized in this study, and downloaded from the ECMWF website (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels). These data sets were obtained with a resolution of 0.5°×0.5° (for parameters: H_s, H_w, PWP, and MWP) and at a finer resolution of 0.25°×0.25° (for wind components) for the period spanning 44 years from 1979 to 2022.

2.2 Offshore wind and wave energy potential

To determine the wind energy W_e (W/m²) the formulation proposed by (Astariz et al., 2017) is used and expressed in the form:

$${W}_{e}= \frac{1}{2} \rho {w}^{3}$$

where, w denotes the velocity of the wind (m/s); $\rho$ represents the density of the air at the air-sea interface; when the altitude is less than 500m, the value of $\rho$ is established as ⁓ 1.225 kg/m³.

The wave power per unit width transmitted by irregular waves can be approximated (Cornett et al., 2008) as:

$$WP= \frac{\rho {g}^{2}}{4\pi } \left(\frac{{T}_{p}{H}_{s}^{2}}{16}\right)$$

Where ${T}_{p}$ represents the wave period; g is the acceleration due to gravity (9.81 m/s²); Hs is the significant wave height (SWH); $\rho$ is the seawater mass density (~ 1025kg/ ${m}^{3}$) (Rusu et al., 2016). The estimation of the wave energy period was derived from the formula T=$\alpha$T_p, where T_p denotes the peak wave period, and the parameter α was assigned a value of 1 as per the calculation (Hagerman et al., 2001).

2.3 Trend analysis and Generalized Extreme Value (GEV)

A simple linear regression technique is implemented to determine the average wave power (both) and wind energy.

$$y=a+bt$$

where, y represents the seasonal wave power (both) and wind energy at time t = 1, 2, 3, ......n;

a and b denote the regression coefficient. Further, a and b are determined by using the least square method (Kumar et al., 2021).

The GEV distribution is implemented for extreme wind-wave power and wind energy analysis. Extreme value theory finds extensive application in various climate-related studies aimed at modeling the climate extremes (Coles and Casson, 1999; Nogaj et al., 2007), assuming that the random variables involved are independent and identically distributed. This theory is rooted in Fisher and Tippet's limit theorem, which is based on the extreme max stability and combines three distinct extreme value distributions: Weibull, Gumbel, and Fréchet. The amalgamation of these three distributions is referred to as the GEV distribution. The cumulative distribution function (CDF) of a GEV distribution is expressed as follows:

$$F\left(x, \mu , \sigma , \xi \right)=\left\{\begin{array}{c}\text{exp}\left[-\text{exp}\left\{-\left(\frac{x-\mu }{\sigma }\right)\right\}\right], \xi =0\\ e\text{xp}\left[-\left\{1+\xi {\left(\frac{x-u}{\sigma }\right)}^{-\frac{1}{\xi }}\right\}\right] , \xi \ne 0, 1+\xi \left(\frac{x-\mu }{\sigma }\right)>0 \end{array}\right\}$$

Where $\mu \in \mathbb{R}$, $\sigma >0$, and $\xi ϵ\mathbb{R}\mathbb{ }$are the respective location, scale, and shape parameters respectively. $\xi =0$,$\xi <0$, $\xi >0$are correspondence to Types I, Types II, and Types III. The location parameter $\mu$ signifies the central position of the GEV distribution and represents the mean intensity of extreme values. Scale parameter characterizes the spread in distribution, influencing changes from year to year. Lastly, the shape parameter provides insights into the tail behavior of the distribution.

3.1 Climatology and variability

The climatological patterns of swell generated wave power (P_sw), wave power due to wind-sea (P_w−s) and wind energy (E_wind) are illustrated in Fig. 2corresponding to four distinct seasons: December to February (DJF), March to May (MAM), JJA, and SON. These patterns are derived from a 44-year reanalysis ERA5 dataset spanning from 1979 to 2022, covering the Shelf Sea of the Indian Ocean, which includes the AS and the BoB. The swell patterns in the northern and southern sectors of the AS and BoB exhibit gradual increases in all seasons, with an exception during the JJA season. During JJA, the P_sw is robust when compared to other seasons, and maximum intensity has been observed in central AS and BoB. The seasonal mean of P_sw reaches its maximum value (20–25 kW/m) in the northern AS during JJA, followed by SON (5–15 kW/m). In contrast, during DJF, P_sw registers its lowest values (< 5 kW/m) in both the AS and BoB. It is to be noted that P_w−s, in comparison to P_sw, consistently remains at lower levels across all seasons. The lowest P_w−s values (< 2 kW/m) are consistently observed in all seasons except for JJA. Conversely, the highest P_w−s levels (4–15 kW/m) are recorded during the JJA season, with a pronounced intensity spread over the central AS. The spatial distribution of seasonal average E_wind at a height of 100 m above mean sea level is illustrated in the third column of Fig. 2. Notably, the maximum values of E_wind (400–1000 W/m²) are observed during the JJA seasons, while the lowest E_wind is noticed for Kochi port during DJF and MAM months. Furthermore, it is worth highlighting that both P_w−s and E_wind exhibited similar patterns, as local wind conditions directly influence the Pw-s. During MAM, there is a significant increase in E_wind across the entire coastline of the BoB and the northern part of the AS. In contrast, the patterns of E_wind during SON showed a higher intensity over the central BoB and AS, gradually decreasing along the coastline of AS and in the northern part of BoB.

The variability patterns of wave power, including P_sw, P_w−s, and E_wind are presented in Fig. 3 for four distinct seasons: DJF, MAM, JJA, and SON. Numerous studies have previously explored inter-annual variations in wave climate (Gulev et al., 2004; Shanas et al., 2014). As a result, our investigation delves into the inter-annual fluctuations in wave power and wind energy to ensure the stability of wave power. The variability of P_sw exhibited a gradual increase from the northern to the southern sector of the AS and BoB in all seasons except for JJA in the AS. The most substantial variability in P_sw is observed for the AS during JJA, while the lowest variability occured along the entire Indian coastline in DJF. The standard deviation (SD) ranges from 0.4 to 3.5 kW/m for P_sw, 0 to 3 kW/m for P_w−s, and 0 to 1.8 10^− 3 W/m² for E_wind. The highest SD is recorded in the AS for P_sw (more than 2 kW/m) and P_w−s in the northern AS and central AS during JJA. Similarly, for E_wind, the highest SD (> 1 10^− 3 W/m²) is observed during JJA. On the other hand, the lowest variability in E_wind is observed along the Kerala coast during MAM and along the entire coast during SON, with an exception for the Chennai port.

The seasonal climatological patterns of extreme P_sw, Pw-s, and E_wind are illustrated in Fig. 4. The location parameter (${\mu }_{0}$) of the GEV distribution characterizes the climatology of extreme responses. Among all seasons, the maximum values for Pmax_sw and Pmax_ww (> 0.7 10¹W/m) and the highest Emax_wi (> 3 10^− 3W/m²) are observed in the northern sector of the AS and the BoB during JJA. Conversely, Pmax_sw (< 0.5 10¹W/m) along the eastern coastline, Pmax_ww (< 0.3 10¹W/m) in the southern part, and Emax Emax_wi along the Kerala coastline exhibited weaker values during JJA. During DJF, Pmax_ww (< 0.15 10¹W/m) is at its lowest along the entire Indian coastline and near Chennai port in MAM. In DJF, Pmax_ww (< 0.15 10¹W/m) is lower along the eastern coastline and the southern sector of AS, as well as in the AS and the southern coastline during SON. Emax_wi is weakest during DJF in most of the regions in the NIO, with a slight increase in the BoB during MAM, followed by an increasing trend in the BoB during the SON seasons.

The variability patterns of seasonal Pmax_sw, Pmax_ww, and Emax_wi are presented in Fig. 5. The scale parameter (${\sigma }_{0}$) of the GEV distribution characterizes the variability of extreme wave power and wind energy. There is significant interannual variability in Pmax_sw in the AS during JJA and the eastern sector of the BoB in MAM, JJA, and SON, while smaller variability is observed in the entire NIO during DJF and in the AS during MAM. The highest variability in Pmax_ww is observed for the northern part of AS and BoB during JJA, the northeast sector of BoB in MAM, and the northern sector of BoB in SON. In contrast, there is less variability in the entire NIO during DJF and in the AS and the eastern coastline during MAM. Emax_wi is weakest in the AS during DJF among all the seasons, while the maximum variability is observed during JJA and SON, as well as in the BoB during MAM.

3.2 Seasonal trends of swell, wind-sea wave power, and wind energy

The rate of increase in P_sw, P_w−s, and E_wind per decade has been calculated at six locations along the Indian coastline. Further, the seasonal time series analysis of wind-wave and wind energy in both the west and east coast locations of India reveals that the JJA season is the most suitable for extracting wind and wave energy, followed by the SON season. Consequently, we conducted a detailed calculation for both the JJA and SON seasons at all the six locations. We computed the seasonal mean and extreme wave-wave power and wind energy by employing a 2⁰x2⁰ grid size box averaged at each location of the major Indian ports.

3.2.1 Swell generated wave power trends

The mean time series and rate of increase in P_sw per decade are illustrated in Fig. 6 and Table 1(a), respectively, after a comprehensive analysis for all the seasons. It is important to note that a significant trend is observed primarily during certain seasons, most prominently in JJA seasons, followed by the SON season.

Table 1

The seasonal mean rate of increase per decade in wind-wave power and wind energy from 1979 to 2022.
Locations	(a) P_sw (kW/m dec^-1)				(b) P_w-s (kW/m dec^-1)				(c) E_wind (kW/m² dec^-1)
	DJF	MAM	JJA	SON	DJF	MAM	JJA	SON	DJF	MAM	JJA	SON
Kandla	-0.02	0.01	1.33	0.10	0.03	-0.008	-0.25	-0.02	1.95	-2.21	-21.8	-2.91
Mumbai	1	0.03	0.71	0.13	0.009	-0.005	0.10	0.05	0.02	-1.59	-5.78	2.47
Kochi	0.06	0.08	0.75	0.29	0.0002	0.006	-0.008	0.034	1.95	-1.13	-3.58	2.39
Paradip	0.003	0.11	0.23	0.21	0.004	-0.11	-0.03	0.04	-0.20	-11.73	-7.25	-0.39
Visakhapatnam	0.05	0.15	0.38	0.25	-0.009	-0.07	-0.19	0.04	-3.19	-11.22	-20.95	-1.09
Chennai	0.05	0.08	0.21	0.18	-0.005	-0.02	-0.04	0.008	-2.87	-6.99	-9.65	-2.43

Keeping in view the locations along west coast of India, it is noteworthy that while all locations exhibited a significant increase in wave power during JJA seasons, the highest increment is observed at Kandla (1.33 kW/m dec^− 1) during JJA, followed closely by Mumbai (1 kW/m dec^− 1) during DJF. Similarly, for locations along the east coast of India, the maximum rate of increase is observed at Vishakhapatnam (0.38 kW/m dec^− 1), with the lowest rate recorded at Paradip (0.003 kW/m dec^− 1) during DJF. In summary, the analysis reveals that P_sw experiences a significant increase at all locations during the JJA season, followed by the SON season.

Key findings from the study indicate that the average swell wave power (shown in Table 2) during the JJA season, varied from approximately 4.74 to 21.05 kW/m across these locations. For locations along west coast of India, such as Kandla, Mumbai, and Kochi experienced wave powers of 21.05 kW/m, 16.85 kW/m, and 14.81 kW/m, respectively, while Chennai recorded the lowest wave power at 4.74 kW/m. Similarly, during the SON season, we observed the minimum wave power at Chennai port, estimated as 3.69 kW/m, while Paradip port recorded the highest wave power at 6.14 kW/m. We also estimated the maximum achievable wave power using the seasonal maximum for both wave power and wind energy, employing a 2⁰x2⁰ grid size box averaged at all the six locations. This analysis reveals that the maximum attainable swell wave power (Table 3) ranged between 22.13 to 66.97 kW/m during JJA and from 20.36 to 40.69 kW/m during the SON season.

Table 2

Annual average wind-wave power and wind energy (mean) from 1979 to 2022.
Locations	P_sw (kW/m)		P_w−s (kW/m)		E_wind (kW/m²)
Locations	JJA	SON	JJA	SON	JJA	SON
Kandla	21.05	4.15	5.81	0.60	0.51	0.07
Mumbai	16.85	4.54	5.68	0.53	0.48	0.05
Kochi	14.81	5.55	1.53	0.26	0.20	0.03
Paradip	12.08	6.14	3.21	0.64	0.26	0.03
Vishakhapatnam	8.76	5.57	5.30	1.21	0.59	0.09
Chennai	4.74	3.69	1.91	0.81	0.27	0.07

Table 3

Annual average wind-wave power and wind energy (max) from 1979 to 2022.
Locations	P_sw (kW/m)		P_w−s (kW/m)		E_wind (kW/m²)
Locations	JJA	SON	JJA	SON	JJA	SON
Kandla	66.84	23.11	62.03	18.03	3.65	0.79
Mumbai	66.56	24.84	66.87	22.72	3.72	1.03
Kochi	51.26	23.91	26.72	11.91	1.42	0.73
Paradip	66.97	40.69	79.84	41.25	4.74	1.85
Vishakhapatnam	44.11	35.81	80.31	46.18	5.06	2.14
Chennai	22.13	20.36	24.81	24.21	3.35	1.77

3.2.2 Wind-sea generate trends

The rate of increase per decade and seasonal mean time series in swell-generated wave power are illustrated in Table 1(b) and Fig. 7, respectively. The long-term trend in P_w−s at most locations showed a negative trend across all seasons. However, a positive trend can be observed in locations along the west coast including the Paradip port during DJF, and at all locations except Kandla during SON. Furthermore, the highest increase is observed in Mumbai (0.1 kW/m dec^− 1) during JJA.

Wind-generated wave power is lower when compared to swell wave power. During the JJA season, the wind power ranged between 1.53 to 5.81 kW/m (Table 2), whereas, during SON, it ranges between 0.26 to 1.21 kW/m. The highest wind power is observed at Kandla (5.81 kW/m) and Kochi (1.53 kW/m) during JJA, while during SON, Vishakhapatnam recorded the highest wind power (1.21 kW/m) and Kochi the lowest (0.26 kW/m). Likewise, the maximum attainable wind-sea wave power (Table 3) ranged between 24.81 to 80.31 kW/m during JJA season and between 11.91 to 46.18 kW/m during the SON months.

3.2.3. Wind-generated trends

The seasonal mean wind energy decadal trends and mean wind energy time series, computed for six locations at 100 m above mean sea level over 44 years, are shown in Table 1(c) and Fig. 8. These decadal trends varied between − 21.8 to 2.47 W/m² dec^− 1. Trend analysis indicate a negative trend observed at all locations during MAM and JJA. However, positive trends are observed at Kandla and Kochi (1.95 W/m² dec^− 1) and Mumbai (0.02 W/m² dec^− 1) during DJF. Moreover, it is also noticed that increasing positive trends are at Mumbai (2.47 W/m² dec^− 1) and Kochi (2.39 W/m² dec^− 1) during SON, while the maximum negative trends are observed in Kandla (-21.8 W/m² dec^− 1) and Vishakhapatnam (-20.95 W/m² dec^− 1). In summary, the increasing trend is evident along the locations in west coast during DJF, and a similar positive trend is observed in SON, with exception for Kandla.

The mean wind energy (Table 2) varied between 0.19 to 0.59 kW/m² during the JJA season, and between 0.03 to 0.08 kW/m² during the SON season. Notably, the highest wind energy is observed at Vishakhapatnam, with 0.59 kW/m² during JJA and 0.08 kW/m² during SON, while the lowest wind energy is seen at Kochi, with 0.19 kW/m² during JJA and 0.03 kW/m² during SON. Similarly, the maximum achievable wind energy (Table 3) during JJA varied between 1.42 to 5.06 kW/m², while during SON, it ranged between 0.72 kW/m² to 2.13 kW/m².

The present study performed a comprehensive assessment on the combined offshore wind and wave resources along the Indian coast using the ERA5 reanalysis datasets, which provide wind data at a height of 100 meters. Furthermore, the study examined the seasonal climatology and variability of wave power and wind energy, both in terms of mean and extreme values, along the Indian coastline. Notably, swell contribution surpasses wind-seas wave height in terms of wave power potential. The seasonal mean of P_sw attained its maximum, ranging between 20 to 25 kW/m, in the northern sector of the Arabian Sea during JJA, and it remained consistently between 5 to 25 kW/m in SON. Additionally, the maximum values of E_wind (ranging between 400 to 1000 W/m²) are observed during the JJA season. Higher wave power and wind energy variability are noticed during JJA, while the lowest during DJF.

The trend analysis reveals that JJA exhibited the highest increasing trend, followed by SON. The maximum rate of increase in swell wave power is observed at Mumbai (1 kW/m dec^− 1) during DJF, and for Kandla (1.33 kW/m dec^− 1) during JJA. Similarly, along the east coast, Vishakhapatnam records the maximum rate of increase (0.38 kW/m dec^− 1) during both JJA and SON months. For locations along the west coast of India, the maximum rate of increase in wind energy is found at Mumbai (2.47 W/m² dec^− 1) and Kochi (2.39 W/m² dec^− 1) during SON, while Kandla (1.95 W/m² dec^− 1) and Kochi (1.95 W/m² dec^− 1) exhibited similar rates during DJF.

The seasonal mean time series analysis reveals that Kandla, Mumbai, and Kochi experienced swell wave powers of 21.05 kW/m, 16.85 kW/m, and 14.81 kW/m, respectively. In comparison, Chennai recorded the lowest wave power at 4.74 kW/m in comparison to other locations. Similarly, during the SON season, we observed the minimum wave power at Chennai, estimated as 3.69 kW/m, while Paradip recorded the highest wave power at 6.14 kW/m. Furthermore, the highest wind-sea power is observed at Kandla (5.81 kW/m) and Kochi (1.53 kW/m) during JJA, while during SON, Vishakhapatnam recorded the highest wind power (1.21 kW/m) and Kochi the lowest (0.26 kW/m). Notably, the highest wind energy is observed at Vishakhapatnam, with 0.59 kW/m² during JJA and 0.08 kW/m² during SON, while the lowest wind energy is noticed for Kochi, with 0.19 kW/m² during JJA and 0.03 kW/m² during SON.

Ethics approval and consent to participate (kindly mention the name of the Ethics Committee and the Ethical Approval Number)

Not Applicable.

Consent for publication

I, the undersigned, give my consent for the publication of identifiable details, which can include photograph(s) and/or videos and/or case history and/or details within the text (“Material”) to be published in the above Journal and Article.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no known competing financial interests.

Funding

The present study is supported by Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India under core research grant project file no. (CRG/2021/003654).

Authors' contributions (credit statement)

Kamlesh Kumar Saha: Data curation, Investigation, Methodology, Writing- Original draft preparation, Prashant Kumar: Conceptualization, Visualization, Methodology, Supervision, Writing-Reviewing and Editing, Anurag Singh: Conceptualization, Supervision, Writing-Reviewing and Editing, Prasad Kumar Bhaskaran: Data curation, Visualization, Investigation, Validation, Writing-Reviewing and Editing TM Balakrishnan: Conceptualization, Investigation, Writing- Original draft preparation, Yukiharu Hisaki: Conceptualization, Visualization, Writing-Reviewing and Editing, Rajni: Data curation, Visualization, Writing- Reviewing and Editing;

Acknowledgment

This work is supported by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India, under the core research grant (Grant no: CRG/2021/003654).

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No competing interests reported.

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Integrated Analysis of Seasonal Swells, Wind-seas and associated Wave Energy along the major Indian Ports

Status:

Version 1

Abstract

Figures

1 Introduction

2 Data and Methodology

2.1 Datasets and Study area

2.2 Offshore wind and wave energy potential

2.3 Trend analysis and Generalized Extreme Value (GEV)

3 Results and Discussion

3.1 Climatology and variability

3.2 Seasonal trends of swell, wind-sea wave power, and wind energy

3.2.1 Swell generated wave power trends

3.2.2 Wind-sea generate trends

3.2.3. Wind-generated trends

4 Summary and Conclusion

Declarations

References

Additional Declarations

Status:

Version 1