Long‐Term Variations in Nearshore Wave Climate and Littoral Drift at Ganpatipule Coast, Eastern Arabian Sea and its Link to Pacific Climate Variability

Abstract

Nearshore wave characteristics and its variations at Ganpatipule coast, eastern Arabian Sea, for the past 4 decades (1979–2016) have been examined. The study is based on European Centre for Medium‐Range Weather Forecasts Re‐Analysis‐Interim wave data at ~50 m water depth and the nearshore wave parameters estimated using the numerical model Simulating WAves Nearshore, executed from the Delft3D dashboard considering shallow water wave propagation, wave setup, and breaking. The littoral drift is estimated based on bulk transport formula of the Coastal Engineering Research Centre with the input from the hydrodynamic module. The model shows good agreement with wave rider buoy measured wave parameters at 14‐m water depth. Anomalies of breaker height, period, and direction vary in the range of −0.35–0.4 m, −0.80–1 s, and − 16 to 12°, respectively during the study period. A detailed analysis of monthly variations of breaker height along the study region suggests a significant increasing trend during the Asian summer monsoon, whereas a decreasing trend is identified during the nonmonsoon period. The study region accounts for a northerly net transport of 3.9 × 105 m3 yr−1 with a significant increasing trend of 1,840 m3 yr−1. The strong wind field region along the central Arabian Sea (65–70°E and 15–20°N) gives a strengthened positive correlation (r > 0.7) with the littoral drift. Impacts of Pacific climate variability on interannual and decadal timescales are observed in the littoral drift rates.

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

“…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].…”

“…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.…”

“…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].…”

“…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.…”

“…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.…”