Nearshore Waves and Littoral Drift Along a Micro-Tidal Wave-Dominated Coast Having Comparable Wind-Sea and Swell Energy

The nearshore wave characteristics and variations in littoral drift (longshore sediment transport; LST) are estimated based on different approaches for four years along the Vengurla coast, with comparable wind-sea and swell energy assessed. The waverider buoy-measured data at 15 m water depth is utilized as the input wave parameters along with the reanalysis model data, and the numerical wave model Delft-3D is used for estimating the nearshore wave parameters. The relative contribution of wind-seas and swells on LST rates are specifically examined. The clear prevalence of west-southwest waves implies the prevalence of south to north longshore sediment transport with net transport varying from 0.19–0.37 × 105 m3/yr. LST is strongly dependent on the breaker angle and a small change in the wave direction substantially alters the LST, and hence reanalysis/model data with coarse resolutions produce large errors (~38%) in the LST estimate. The annual gross LST rate based on integral wave parameters is only 58% considering the wind-seas and swells separately, since the wind-sea energy is comparable to swell energy, and the direction of these two systems differs significantly.


Introduction
Coastal zones are the most dynamic regions due to the release of energy by the surface gravity waves (hereafter waves). The waves propagating into the shallow water transform due to reflection, refraction, bottom friction, shoaling and breaking. When refracted, oblique waves reach the shore; the alongshore wave energy flux induces a net sediment transport whose gradients are responsible for shoreline evolution [1]. Breaking waves in the surf zone make an additional sediment stirring effect, caused by the breaking-induced flows, enhanced turbulent kinetic energy in the water column, and pore pressure responses in the sediments [2]. Knowledge of the physical processes in the nearshore region that impact sediment transport is essential to understand the erosion/accretion processes that mainly regulate the coastline stability [3]. Substantial morphological changes occur in sandy beaches due to the action of waves, currents, and tides, often controlled by the height and length of the nearshore waves. This morpho-dynamic change can be observed on different timescales and may vary daily, monthly and even yearly. The temporal variations in the littoral system are observed due to variations in the wave climate, and the change in the direction of longshore sediment transport (LST) is mainly due to change in the wave direction [4][5][6]. The wave direction can vary due to the changes in the offshore wave climate or because of the variations in the nearshore bathymetry [7][8][9]. Quantitative processes and coastal evolution through numerical modelling are now possible due to the significant data for more than a year. Thus, the present study aims to understand the changes in the nearshore wave parameters and the LSTR and investigates the influence of wind-seas and swells on the LST.

Study Area
The west coast of India is subjected to high wave activity during the Indian summer, i.e., southwest monsoon (June-September, hereafter monsoon) and during this period most of the sandy beaches undergo erosion [29]. Typically, the beach restores its equilibrium state during the post-monsoon period [28]. The study area is off Vengurla, situated along the central west coast of India (location 15.87 • N to 73.63 • E) ( Figure 1). The east side of this coastal segment is bound by the Western Ghats mountain range, while the Arabian Sea extends along its west side. It is surrounded by a semicircular range of hills and is exposed to open ocean waves from northwest to south. The nearest landmass is ∼1500 km in the northwest, ∼2000 km in the west, ∼4000 km southwest and ∼9000 km in the south [30]. The Vengurla coast is characterized by a tropical climate, and the annual precipitation is~3100 mm [31]. Nature and spatial distribution of geomorphic features along the coast indicate that the study area is a scene of neotectonic activity [32]. The tidal regime of this region is 2.3 m during spring tide and 1.3 m during neap tide. A longshore current velocity of 0.5 m/s was reported [31]. High wave activity (seasonal average H s~2 m) is experienced during monsoon and calm wave conditions (H s < 1 m) during the remaining period of the year [25]. Based on Davies classification [33,34], the wave and tide data together exhibit that the study area is on a microtidal wave-dominated coast and the predominant breaker is a plunging breaker with multiple breakers [35].  [31]. High wave activity (seasonal average Hs ~2 m) is experienced during monsoon and calm wave conditions (Hs < 1 m) during the remaining period of the year [25]. Based on Davies classification [33,34], the wave and tide data together exhibit that the study area is on a microtidal wave-dominated coast and the predominant breaker is a plunging breaker with multiple breakers [35].

Data and Methods
The bathymetry of the study area was obtained by merging the nearshore measured bathymetry data from coast to 5 m water depth with the digitized naval hydrographic chart No. 2043 [36]. During September 2014 to August 2015, the median size of the nearshore sediment (D50) was obtained in a monthly interval. The D50 was extracted by the following geometric (modified) Folk and Ward [37] graphical measures using GRADISTAT: a grain analysis package [38] and the size distribution of sediments was obtained using an electromagnetic sieve shaker. Wave data was collected at 30 min intervals at 15 m from September 2014 to August 2018 and is used as an input for the model. The WAVE module of the Delft3D model [39] was used as the numerical model for nearshore wave transformation to estimate the breaker wave parameters. This numerical model is based on the SWAN model [40], a third-generation phase-averaged wave model based on a fully spectral representation of the action balance equation and it can accurately reproduce the main wave propagation processes in coastal areas, e.g., shoaling, refraction due to bottom and current variations, transmission/blockage through/by obstacles, white capping, bottom friction, depth-induced wave breaking, non-linear wave-wave interactions, and wind effects [41]. This model was previously employed along several locations in the central west coast of India [4,19,26,42] and researchers have

Data and Methods
The bathymetry of the study area was obtained by merging the nearshore measured bathymetry data from coast to 5 m water depth with the digitized naval hydrographic chart No. 2043 [36]. During September 2014 to August 2015, the median size of the nearshore sediment (D 50 ) was obtained in a monthly interval. The D 50 was extracted by the following geometric (modified) Folk and Ward [37] graphical measures using GRADISTAT: a grain analysis package [38] and the size distribution of sediments was obtained using an electromagnetic sieve shaker. Wave data was collected at 30 min intervals at 15 m from September 2014 to August 2018 and is used as an input for the model. The WAVE module of the Delft3D model [39] was used as the numerical model for nearshore wave transformation to estimate the breaker wave parameters. This numerical model is based on the SWAN model [40], a third-generation phase-averaged wave model based on a fully spectral representation of the action balance equation and it can accurately reproduce the main wave propagation processes in coastal areas, e.g., shoaling, refraction due to bottom and current variations, transmission/blockage through/by obstacles, white capping, bottom friction, depth-induced wave breaking, non-linear wave-wave interactions, and wind effects [41]. This model was previously employed along several locations in the central west coast of India [4,19,26,42] and researchers have described in detail the wave transformation model, grid generation, attributes, input parameters etc., which are the same for the present study. Significant wave height (H s ), mean wave direction and mean wave period (T m02 ) are the input wave parameters used in the model. Other input variables used in the model are JONSWAP bottom friction coefficient (0.067 m 2 s −3 [43]), depth-induced breaking (Alpha (1) and Gamma (0.73), [44]), and nonlinear triad interactions (Alpha and Beta being 0.1 and 2.2, respectively) based on Lumped Triad Approximation model [45]. The model simulations were carried out in stationary mode and have three boundaries: (1) open boundary to the west, (2) closed boundary to the east, and (3) lateral boundaries to the north and south. The model has 24 frequencies between 0.05-1 Hz and the directional resolution of the model is 10 degrees. No wind fields were included in the model since the domain is small. Furthermore, sea level changes due to the influence of tide were also not considered. The domain was divided into 200 × 450 grid points with a grid spacing of 20 m each along x and y directions. The method suggested by Portilla et al. [46] was used to separate wind-seas and swells from the measured data. The 1-D separation algorithm was based on the assumption that the energy at the peak frequency of a swell cannot be higher than the value of a Pierson-Moskowitz (PM) spectrum with the same frequency. If the ratio between the peak energy of a wave system and the energy of a PM spectrum at the same frequency is above a threshold value of 1, the system is considered to represent wind-sea, else it is taken as swell. A separation frequency fc is estimated following Portilla et al. [46] and the swell and wind-sea parameters are obtained for frequencies ranging from 0.025 Hz to fc and from fc to 0.58 Hz, respectively. The 4 years of simulations carried out in a workstation (HP Z 800) took a period of 2 months and the output was stored to a 500 GB hard disk. ERA-Interim data [47] from September 2014 to August 2015 of significant wave height (H s ), mean wave period and mean wave direction at six-hour intervals were also obtained with a spatial resolution of 0.5 • × 0.5 • to compare the LSTR estimated with the measured and reanalysis data. For wave transformation based on ERA-I data, the domain was first divided into 400 × 250 points with a grid spacing of 100 m and 75 m respectively. This low-resolution grid was nested to a high-resolution grid (450 × 450) with a grid spacing of 10 m each along x and y direction. The model simulation was carried out for four years from 1 September 2014 to 31 August 2018, and the transformed breaking characteristics were extracted by considering the breaking criteria (H b /h b = 0.78; [15]). These extracted output parameters, breaking wave height (H b ), T m02 and wave breaking angle were used for the calculation of LSTR.
The LST is estimated based on the bulk transport formula developed by Kamphuis [18]. The Kamphuis equation includes the wave period, which influences wave breaking and grain size, which affects the sediment motion. Due to the orientation of the coastline at 22 • with respect to the west, a wave direction less than 248 • generates northerly LST and is taken as positive and wave direction more than 248 • generates southerly LST and is taken as negative.
In Equation (1), Q LST = the longshore sediment transport in volume per unit time, H b = breaking wave height (m), T p = peak wave period (s), m b = slope of the bottom in the surf zone, d 50 = sediment median grain size (mm), and θ = the angle between the incident wave ray and local shoreline (deg).

Wave Characteristics at 15 m Water Depth
As expected, high waves are observed during the monsoon, since the Arabian Sea is the key region of strong air-sea interactions associated with the monsoon. The highest H s observed (~5 m) is in June 2015 and July 2018, and during the monsoon, H s ranged from 0.5 to 5 m with an average value of 1.8 m (Figure 2a). They were predominantly from the west-southwest direction ( Figure 3) due to the dominance of swells in the west coast of India during this season [21,48]. The T m02 during monsoon varied from 3.4 to 9.7 s with an average value of 6.2 s (Figure 2b  The highest Hs of about 2 m along the study region during non-monsoon months was previously reported by Amrutha et al. [25] and the existence of long-period waves (Tp > 18 s) along the eastern Arabian sea was also previously reported [49,50]. Tm02 values observed off Vengurla are similar to the value (7-8 s) observed off the Goa coast [46]. During the study period, 71% of the waves had a Hs less than 1 m and 2% of the waves had a Hs of more than 3m.   annually averaged values of Tm02 (5.1, 5.3, 5.3 and 5.1 s) and Tp (11.6, 11.8, 11.6 and 11.3 s.) were observed during different annual cycles. The surface height variance at the study area is separated into wind-seas and swells (see data and methodology) and the resulting wave height, mean wave period and wave direction of windseas and swells for different months during different annual cycles are presented in Tables 2 and 3, The highest H s of about 2 m along the study region during non-monsoon months was previously reported by Amrutha et al. [25] and the existence of long-period waves (T p > 18 s) along the eastern Arabian sea was also previously reported [49,50]. T m02 values observed off Vengurla are similar to the value (7-8 s) observed off the Goa coast [46]. During the study period, 71% of the waves had a H s less than 1 m and 2% of the waves had a H s of more than 3m. The annual average values of H s during the four-yearly cycles (2014-2015, 2015-2016, 2016-2017, 2017-2018) are 0.94, 1.02, 0.95 and 1.01 respectively, indicating an inter-annual variation of up to 8%. However, no significant variations in annually averaged values of T m02 (5.1, 5.3, 5.3 and 5.1 s) and T p (11.6, 11.8, 11.6 and 11.3 s.) were observed during different annual cycles.
The surface height variance at the study area is separated into wind-seas and swells (see data and methodology) and the resulting wave height, mean wave period and wave direction of wind-seas and swells for different months during different annual cycles are presented in Tables 2 and 3, respectively. The monthly average swell H s varies from 0.24 to 2.23 m, whereas the monthly averaged wind-sea H s varies from 0.21 to 1.17 m. The wave period and direction for both wind-sea and swells are observed to be somehow consistent, and their variation is observed to be less than 3% annually for different months. Most importantly, the wind-sea H s is comparable to swell H s during non-monsoon seasons (Tables 2 and 3).
During most of the time, the wave spectrum in the study area consisted of multiple peaks, except in the monsoon season which had considerable energy in the secondary peak ( Figure 4). The wave direction corresponding to both the primary and the secondary peak was also different. Hence, the spectral energy density at the primary peak and the secondary peak are compared separately for the monsoon and non-monsoon period ( Figure 5). At both water depths (15 and 5 m) during the monsoon, the spectral energy density at secondary peak is~40% of that in the primary peak. During the non-monsoon period, the energy at the secondary peak is 49% (28%) of that in the primary peak at 15 m (5 m) water depth. There is a significant difference between the direction of the wind-sea and the swells, especially during the non-monsoon months at both 15 m and 5 m water depth (Figure 4).  (Tables 2 and 3). During most of the time, the wave spectrum in the study area consisted of multiple peaks, except in the monsoon season which had considerable energy in the secondary peak ( Figure 4). The wave direction corresponding to both the primary and the secondary peak was also different. Hence, the spectral energy density at the primary peak and the secondary peak are compared separately for the monsoon and non-monsoon period ( Figure 5). At both water depths (15 and 5 m) during the monsoon, the spectral energy density at secondary peak is ~40% of that in the primary peak. During the non-monsoon period, the energy at the secondary peak is 49% (28%) of that in the primary peak at 15 m (5 m) water depth. There is a significant difference between the direction of the wind-sea and the swells, especially during the non-monsoon months at both 15 m and 5 m water depth ( Figure 4).      Table 3. Monthly average of significant wave height, mean wave period and mean wave direction of wind-sea measured at 15 m water depth for four years.

Model Validation and Comparison
The performance of the Delft-3D WAVE module is assessed by comparing the model results with the measured data at 5 m water depth from September 2014 to June 2015 and August 2015. The output parameters from the numerical wave model which are used as input to the LSTR estimate are significant wave height, peak wave period and wave direction. Hence, these parameters are compared with the measured buoy data at 5 m water depth. The skill of the model is determined based on the amount of deviation which is measured by root-mean-square error (RMSE) and bias between the model results and the observational data and the correlation coefficient (R). The comparison of the measured and simulated wave parameters at 5 m water depth for both nonmonsoon and monsoon seasons are presented in Figure 6. It is observed that both Hs and Tm02 underpredicts by ~18% during monsoon season, and by 16% and 23% during non-monsoon, whereas the direction underpredicts less than 2% during both seasons. During the non-monsoon period, when the windsea is stronger and close to the swell energy, the comparison between the wave direction between the model and the measured data show large scatter. The performance of the model using the separated swell and wind-sea has also been compared with the measured data ( Figure 6) and is presented in Table 4. Kumar et al. [21] observed that the Delft-3D wave module [17] results are comparable (RMSE ~0.067 m, bias ~0.017 m and SI ~0.083) with the measured data at a water depth of 2.5 m.

Model Validation and Comparison
The performance of the Delft-3D WAVE module is assessed by comparing the model results with the measured data at 5 m water depth from September 2014 to June 2015 and August 2015. The output parameters from the numerical wave model which are used as input to the LSTR estimate are significant wave height, peak wave period and wave direction. Hence, these parameters are compared with the measured buoy data at 5 m water depth. The skill of the model is determined based on the amount of deviation which is measured by root-mean-square error (RMSE) and bias between the model results and the observational data and the correlation coefficient (R). The comparison of the measured and simulated wave parameters at 5 m water depth for both non-monsoon and monsoon seasons are presented in Figure 6. It is observed that both H s and T m02 underpredicts by~18% during monsoon season, and by 16% and 23% during non-monsoon, whereas the direction underpredicts less than 2% during both seasons. During the non-monsoon period, when the wind-sea is stronger and close to the swell energy, the comparison between the wave direction between the model and the measured data show large scatter. The performance of the model using the separated swell and wind-sea has also been compared with the measured data ( Figure 6) and is presented in Table 4. Kumar et al. [21] observed that the Delft-3D wave module [17] results are comparable (RMSE~0.067 m, bias~0.017 m and SĨ 0.083) with the measured data at a water depth of 2.5 m.  6. Scatter plot of (a) significant wave height, (b) mean wave period and (c) mean wave direction for integral wave parameters, swell and wind-sea between measured buoy data at 5 m water depth and that based on wave model.

Breaker Characteristics
During the post-monsoon season, breaker heights (Hb) less than 1 m are observed (Figure 7a). Along the study region, Chandramohan et al. [51] observed breaker heights of about 0.7 m and mean Figure 6. Scatter plot of (a) significant wave height, (b) mean wave period and (c) mean wave direction for integral wave parameters, swell and wind-sea between measured buoy data at 5 m water depth and that based on wave model.

Breaker Characteristics
During the post-monsoon season, breaker heights (H b ) less than 1 m are observed (Figure 7a). Along the study region, Chandramohan et al. [51] observed breaker heights of about 0.7 m and mean wave periods of 5-8 s during non-monsoon seasons. There is a considerable increase in the wave height to about 2 m during the monsoon with its peak around 3.8 m during 2015 (Figure 7a). During the study period, about 69% of the wave height is less than or equal to 1 m and during 17% of the time, H b exceeded 1.5 m (Figure 7a). Off the central west coast of India, Shanas and Kumar [4] observed that during an annual cycle, 0.6-1.1 m waves occurred for about 55% of the time. Wave period at the breaker zone generally varies in the range of 2.7-9.6 s with an average value of 4.4 s. (Figure 7b). Also, Figure 7c clearly shows that the breaker angle decreased during the monsoon period and is high during the non-monsoon season. The frequency distribution indicates that around 19% of waves break at angles of more than 20 • and 44% of waves break at an angle less than 10 • . About 74% of the time, breaker angle is directed towards the north. The predominance of northerly directed waves in the eastern Arabian Sea were previously reported by Jesbin et al. [42]. wave periods of 5-8 s during non-monsoon seasons. There is a considerable increase in the wave height to about 2 m during the monsoon with its peak around 3.8 m during 2015 (Figure 7a). During the study period, about 69% of the wave height is less than or equal to 1 m and during 17% of the time, Hb exceeded 1.5 m (Figure 7a). Off the central west coast of India, Shanas and Kumar [4] observed that during an annual cycle, 0.6-1.1 m waves occurred for about 55% of the time. Wave period at the breaker zone generally varies in the range of 2.7-9.6 s with an average value of 4.4 s. (Figure 7b). Also, Figure 7c clearly shows that the breaker angle decreased during the monsoon period and is high during the non-monsoon season. The frequency distribution indicates that around 19% of waves break at angles of more than 20° and 44% of waves break at an angle less than 10°. About 74% of the time, breaker angle is directed towards the north. The predominance of northerly directed waves in the eastern Arabian Sea were previously reported by Jesbin et al. [42].

LSTR Estimate Based on Integral Wave Parameters
LST rates along the Vengurla coast during different months of four annual cycles were estimated using Equation 1. Estimated LSTR is low during the non-monsoon period due to small breaker heights ( Table 5). The annual gross LSTR ranges from 0.59-0.74 × 10 5 m 3 /yr for the study region. The gross LSTR for a region ~140 km north of the study area is 0.4 × 10 5 m 3 /yr [26]. Shanas and Kumar [4] and Kumar et al. [21] along the Kundapura and Malpe coasts estimated LST using the Kamphuis equation and reported an LSTR of 1.63 × 10 5 m 3 /yr and 8.9 × 10 5 m 3 /yr, respectively, indicating that there are large variations in the gross LSTR within a 440 km stretch along the central west coast of

LSTR Estimate Based on Integral Wave Parameters
LST rates along the Vengurla coast during different months of four annual cycles were estimated using Equation (1). Estimated LSTR is low during the non-monsoon period due to small breaker heights ( Table 5). The annual gross LSTR ranges from 0.59-0.74 × 10 5 m 3 /yr for the study region. The gross LSTR for a region~140 km north of the study area is 0.4 × 10 5 m 3 /yr [26]. Shanas and Kumar [4] and Kumar et al. [21] along the Kundapura and Malpe coasts estimated LST using the Kamphuis equation and reported an LSTR of 1.63 × 10 5 m 3 /yr and 8.9 × 10 5 m 3 /yr, respectively, indicating that there are large variations in the gross LSTR within a 440 km stretch along the central west coast of India. Chandramohan and Nayak [52] have reported that the south Maharashtra coast has a negligible annual net LSTR. The monthly pattern shows that net LSTR varied from 13 (in February) to 19,993 (in June) m 3 /month (Table 5). Previously, along the study region, based on visual observations of breaker heights and period, Chandramohan et al. [51] estimated a southerly directed net LSTR of 0.53 × 10 5 m 3 /yr. Recently, Noujas et al. [27], using a LITDRIFT model, estimated the LSTR of 1.18 × 10 5 m 3 /yr along Vengurla coast with an annual net value ranging from −7778 to −9015 m 3 /yr. The annual net value for the present study is observed to be ranging from 0.26 to 0.37 × 10 5 m 3 /yr, and the transport is towards the north. The direction of sediment transport is predominantly towards the north from June to September (Figure 7d) which is due to the predominance of southwesterly swell waves (Figure 3) along this region. However, large southerly LST rates were observed during the 2015 monsoon, which was associated with high breaker heights ( Figure 7a) and southerly breaker heights (Figure 7c). During the pre-monsoon, sediment transport was predominantly observed to be southerly with low magnitude. The longshore current is responsible for the directional change during the monsoon, and the seasonal wind pattern is responsible for the change in the direction of LSTR during non-monsoon. Table 5. Monthly net and gross longshore sediment transport rate using the measured buoy data for four years.  Variation of LSTR with the H s indicates that the LST increases with an increase in the wave height and decreases with the reduction of wave height (Figure 8). LST rates are influenced mainly by the breaker parameters [14]. The influence of breaker height on LSTR for different seasons is studied through a scatter plot between these two parameters during the annual cycle 2014-2015 ( Figure 8). The breaker height is high during the monsoon and is in the order of 1 to 2 m; the LSTR is also high during this season. Wave breaker angle is low during the monsoon, whereas the sediment transport is high during this time (Figure 8). Noujas et al. [27] examined the sensitivity analysis of net LSTR by increasing the wave height contribution by 10% and observed an increasing trend in LSTR. Recently, Jesbin et al. [26] investigated the influence on variations in LSTR due to errors in breaking angle and breaker height and observed that they are sensitive to LST rates and could lead to large errors in their estimation. There is a high variability of LSTR during the monsoon season. The annual gross LST for 5%, 10%, 15% and 20% increases in the H s values are 71,933, 78,961, 86,427 and 94,322 m 3 /yr. A 5%, 10%, 15% and 20% increases in the H s value results in 10%, 21%, 32% and 44% increases in the annual LST. Even though the waves are influenced by the sea breeze during the pre-monsoon period, the influence of sea breeze on LST is negligible (figure not shown) since the LST depends on both the wave height and wave period and these two parameters have an inverse relationship during the sea breeze period [49,53]. As discussed in Section 4.1, wind-seas and swells along the study region are comparable during non-monsoonal months. Thus, LSTR based on the separated wind-seas and swells were estimated along the study region from September 2014 to August 2015 (Table 6). Monthly gross LSTR based on wind-sea varies from 244 m 3 /month (in November) to 3232 m 3 /month (in June), whereas that based on swell varies from 1461 m 3 /month (in February) to 23,018 m 3 /month (in June). The wind-sea contribution to the monthly gross LSTR varies from 6% to 25% with the lowest in March and the highest in June. The bulk (75% to 94%) of the gross LSTR is due to swells (Table 6). Based on remote sensing techniques, Kunte and Wagle [54] reported that sediment transport direction along the Maharashtra coast is bidirectional and seasonal-dependent. From Table 6, it is evident that wind-seas generally contribute to the southerly net transport of LST, whereas swells contribute to northerly net LST. The annual gross LSTR due to wind-seas is 0.149 × 10 5 m 3 and that due to swells is 0.987 × 10 5 m 3 . The annual gross LSTR based on bulk wave parameters (0.6534 × 10 5 m 3 /yr) is only 58% of that considering the wind-seas and swells (1.13632 × 10 5 m 3 /yr). The gross transport during the pre-monsoon, monsoon and post-monsoon period is 13%, 70% and 17% of the annual transport. Similarly, the net transport during the pre-monsoon, monsoon and post-monsoon period is 10%, 78% and 12%. The LSTR based on integral wave parameters is less (37% to 47%) based on relative contribution of wind-seas and swells during November to May, when both wind-seas and swells coexist in the area. During monsoon, when swells dominate, the LSTR based on integral wave parameters is 64% to 66% of that based on relative contribution of wind-seas

LSTR Estimate Based on Wind-Sea and Swells
As discussed in Section 4.1, wind-seas and swells along the study region are comparable during non-monsoonal months. Thus, LSTR based on the separated wind-seas and swells were estimated along the study region from September 2014 to August 2015 (Table 6). Monthly gross LSTR based on wind-sea varies from 244 m 3 /month (in November) to 3232 m 3 /month (in June), whereas that based on swell varies from 1461 m 3 /month (in February) to 23,018 m 3 /month (in June). The wind-sea contribution to the monthly gross LSTR varies from 6% to 25% with the lowest in March and the highest in June. The bulk (75% to 94%) of the gross LSTR is due to swells (Table 6). Based on remote sensing From Table 6, it is evident that wind-seas generally contribute to the southerly net transport of LST, whereas swells contribute to northerly net LST. The annual gross LSTR due to wind-seas is 0.149 × 10 5 m 3 and that due to swells is 0.987 × 10 5 m 3 . The annual gross LSTR based on bulk wave parameters (0.6534 × 10 5 m 3 /yr) is only 58% of that considering the wind-seas and swells (1.13632 × 10 5 m 3 /yr). The gross transport during the pre-monsoon, monsoon and post-monsoon period is 13%, 70% and 17% of the annual transport. Similarly, the net transport during the pre-monsoon, monsoon and post-monsoon period is 10%, 78% and 12%. The LSTR based on integral wave parameters is less (37% to 47%) based on relative contribution of wind-seas and swells during November to May, when both wind-seas and swells coexist in the area. During monsoon, when swells dominate, the LSTR based on integral wave parameters is 64% to 66% of that based on relative contribution of wind-seas and swells. For incoming west-southwesterly wave conditions, which are predominantly swells, the LSTR is generally higher due to higher spectral peak periods and higher significant wave height (Figure 8e). For incoming north-westerly waves, which are predominantly wind-seas, the LSTR is lower due to lower wave periods and lower significant wave heights. The study shows that in regions where there is a large difference in the direction of the wind-seas and swells, considering the integral parameters will lead to errors in the LSTR estimate. Studies of this type, involving model calibration based on field measurements, considering relative contribution of wind-seas and swells, are deemed to provide a useful perspective on the LSTR to be applied for coastal engineering applications and morphological studies.

LSTR Estimate Based on ERA-Interim Data
In most of the locations, the measured wave data is not available. Thus, the researchers use reanalysis data, such as ERA-Interim or ERA-40 [55] produced by the European Centre for Medium Weather Forecasts, for estimation of the LSTR. Many researchers across the globe have used these datasets to estimate LST rates [42,56] and to understand the physical mechanism driving the wave climate [57]. For example, temporal variations in annual net LST rates were examined using ERA datasets in southeast Queensland, Australia and were linked to climate indices [56]. Along the west coast of India, Jesbin et al. [42] and Chowdhury et al. [58] estimated temporal variations in LST using ERA datasets and established a link with pacific climate variability and the latitudinal position of the inter-tropical convergence zone (ITCZ), respectively. Hence, we have compared the LSTR estimate based on ERA-Interim with the buoy-measured wave data. The monthly gross and net LSTR shows a large difference in the LSTR estimate based on ERA-Interim data compared (Table 6) to that based on measured buoy data ( Table 5).
The large difference in the LSTR between the two estimations is due to the large difference in the wave period and wave direction between the two datasets ( Figure 9). Breaker height from ERA-Interim H s can be estimated with an RMSE of 0.29 m for the study area, but the wave direction shows large scatter (up to 17 • ) (Figure 9). Even a 20% increase in the H s value results in a 44% increase in the annual LST [24]. An earlier study [59] showed that the ERA-Interim wave period is close to the energy wave period (T e = m -1 /m 0 ) and not the mean wave period of the buoy data and hence, there is large scatter between the mean wave period of the buoy-measured data and the ERA-Interim data ( Figure 9). The large difference in the wave direction is due to the fact that ERA-Interim data is based on a low-resolution global model (0.70 • for the atmospheric model and 1 • for the ocean model [43]) and also the measurements are made in the nearshore environment. Another reason for the substantial difference between the wave direction of the ERA-Interim and the buoy data is due to the predominance of wind-seas and swells in the study area. During the non-monsoon period, in some of the instances, the spectral energy of the wind-sea peak and the swell peak have comparable energy, and during this period, the direction of the wind-sea and swell have a large difference ( Figure 5). In some cases, the ERA-Interim data shows the swell direction, whereas the measured data represent the wind-sea direction, whereas, in other instances, vice versa is observed ( Figure 10). Also, the short-term sub-synoptic scale wind pulses and fluctuations are not likely to be captured by a lower resolution ERA-Interim data [42,59]. Hence, datasets like ERA-Interim cannot be solely used for studying the nearshore processes, especially for estimating the LSTR without validation.  Figure 9.
(a-f) Scatter plot between the breaker parameters from buoy-measured data and ERA-Interim data. (g-l) Scatter plot between buoy-measured data and ERA-Interim data at 5 m water depth.  Figure 9.
(a-f) Scatter plot between the breaker parameters from buoy-measured data and ERA-Interim data. (g-l) Scatter plot between buoy-measured data and ERA-Interim data at 5 m water depth.

Conclusions
The present study examines the wave characteristics and longshore sediment transport rate (LSTR) for four different annual cycles along the Vengurla coast. The wave data collected at a 15 m water depth at 1 h intervals is used, and the nearshore wave parameters are estimated using a numerical model Delft 3D Wave model. The results have been compared with field-measured data from a buoy, and thus derived parameters are employed for estimating the LSTR. The model results show good agreement with the measured data. Annually, Hs is observed to be varying up to 8%, whereas the mean wave period and peak wave period show no significant inter-annual variations. The study area has three wave regimes: swells from the southwest, wind-seas from the northwest and westerly waves during the monsoon, with comparable wind-seas and swells. Gross LSTR along the region is high during monsoon and non-monsoon periods, LSTR is of low magnitude and varies annually in the range of 0.59-0.74 × 10 5 m 3 /yr. LST direction depends on the breaker direction and is predominantly northward, and hence the monthly net LSTR is towards the north in all months, but the estimate based on ERA-Interim reanalysis data produce a southerly LST in January, July and August. A small error in the breaker angle induces larger errors in the LSTR and, hence, for studies based on LSTR estimation, lower-resolution datasets like ERA-Interim should be used only after validation with measured data, especially in the regions where comparable wind-seas and swells exist. The studies indicate that there is a large variation in the gross LSTR within a 440 km stretch along the central west coast of India. In this study, the relative contribution of wind-seas and swells on LSTR are specifically examined. All previous studies have focused on the integral wave

Conclusions
The present study examines the wave characteristics and longshore sediment transport rate (LSTR) for four different annual cycles along the Vengurla coast. The wave data collected at a 15 m water depth at 1 h intervals is used, and the nearshore wave parameters are estimated using a numerical model Delft 3D Wave model. The results have been compared with field-measured data from a buoy, and thus derived parameters are employed for estimating the LSTR. The model results show good agreement with the measured data. Annually, Hs is observed to be varying up to 8%, whereas the mean wave period and peak wave period show no significant inter-annual variations. The study area has three wave regimes: swells from the southwest, wind-seas from the northwest and westerly waves during the monsoon, with comparable wind-seas and swells. Gross LSTR along the region is high during monsoon and non-monsoon periods, LSTR is of low magnitude and varies annually in the range of 0.59-0.74 × 10 5 m 3 /yr. LST direction depends on the breaker direction and is predominantly northward, and hence the monthly net LSTR is towards the north in all months, but the estimate based on ERA-Interim reanalysis data produce a southerly LST in January, July and August. A small error in the breaker angle induces larger errors in the LSTR and, hence, for studies based on LSTR estimation, lower-resolution datasets like ERA-Interim should be used only after validation with measured data, especially in the regions where comparable wind-seas and swells exist. The studies indicate that there is a large variation in the gross LSTR within a 440 km stretch along the central west coast of India. In this study, the relative contribution of wind-seas and swells on LSTR are specifically examined. All previous studies have focused on the integral wave parameters of the wind-seas and swells. The annual gross LSTR based on integral wave parameters is only 58% of that considering the wind-seas and swells separately. In regions where there is large difference in the direction of the wind-seas and swells, considering the integral parameters will lead to errors in the LSTR estimate.