资源环境科技发展态势分析平台

The mutual interaction between surface gravity waves and vegetation such as mangroves using numerical wave models can provide better location specific wave forecasts having practical implications in the overall development of better near-shore wave climatology. Sundarban delta bordering the head Bay of Bengal is the largest mangrove forest in the world, and the present study reports on inter-seasonal variability of wave damping in presence of mangroves that thrive on a region dominated by seasonal reversing wind field. It aims to understand relative wave attenuation characteristics during a period of one year over this region that experiences a seasonal reversal of wind system. The study considers a multi-scale nested modelling approach to account for distant swells that propagate from Southern Ocean reaching various destinations in the North Indian Ocean region. The model was forced using 0.25 degrees x0.25 degrees spatial resolution 6-hourly ECMWF ERA-Interim winds for coarse grid WAM and SWAN runs, and NCDC blended winds with a similar spatial and temporal resolution was used to force SWAN model executed in a flexible unstructured mesh. To avoid model uncertainties of providing wind from two different sources, the wind data of same temporal and spatial resolution has been adopted. The present study quantifies relative rate of wave energy dissipation for monthly and seasonal scales such as pre-monsoon, monsoon and post-monsoon periods by analysing characteristic variation of wave parameters and their spectral energy in presence of mangroves. The study has put forth an effort to validate model results against altimeter measurements and ERA-Interim data, as no actual measurements are available to validate model results simulated in the presence of mangroves. This study solely emphasizes on role of reversing wind system on relative wave attenuation characteristics and highlights the importance of a more detailed investigation to ascertain impact of varying vegetation parameters on wave attenuation characteristics.