The long - term morphological development of kakinada bay

Abstract

The evolution of Kakinada Bay with Coringa Mangroves at its southern shore and Kakinada Spit and Hope Island at its eastern side has occurred over the past century. This morphological development of the bay is attributed to the biophysical interactions, hydrodynamic forcing like waves, winds, tides, currents and sediment dynamics occurring inside the bay. The processes governing the short-term (decadal) and long-term (century) morphology of the bay need to be studied to develop sustainable coastal management plan for the intermediate time-scale. This study is an effort to extend the use of process based models to longer time scales to provide better understanding of the morphological development by the action of various physical processes governing alone and in combination. This study answers the question if these long-term morphological modeling can produce the reliable results by creating nexus of two techniques „Remote Sensing and Numerical Modeling‟. The numerical modeling hindcast results are validated using remote sensing images. This study quantifies the rate of change of the shoreline of the bay using remote sensing images in the Digital Shoreline Analysis System (DSAS). The trend of erosion and accretion occurring inside the bay was obtained using indices End Point Rate (EPR), Net Shoreline Movement (NSM) and Linear Regression Rate (LRR). The rate and trend of sedimentation and erosion obtained with the satellite imageries are further used to statistically compare the transect–wise hindcast and forecast results. Thus this study demonstrates the model‟s ability to reproduce the long-term morphodynamic development of the bay. This study attempts to investigate the action of physical processes on the morphological changes of the bay over a period of 100 years. For long term morphological modeling various approaches are followed like Input Reduction, Model Reduction and Acceleration techniques. Input reduction simulates the long term morphological modeling using schematized input data like morphological tide, schematized wave which are representative sets of the entire data. Model Reduction follows the approach of giving only the most important processes in the model input. Acceleration technique approach uses the morphological acceleration factor which accelerates the morphological development by the assigned factor. Available variants of the morphological predictions have been considered for the study. The study attempts to answer the hypothesis made to choose the appropriate approach between the two statements issued by Lesser (2009) and Roelvink (1999). The approach for adopting model reduction following the correct use of acceleration techniques as stated by Lesser (2009): “In order to use a morphological acceleration technique in a coastal situation it is essential to identify which coastal processes play a significant role in (residual) sediment transport patterns over the space and time scales of interest”. The second approach following the statement given by Roelvink (1999) and quoted by Dastgheib A. (2012) as: “If you put enough of the essential physics into the model, the most important features of the morphological behavior will come out, even at the longer time scales”. The exercise was varied with different environmental forcing with three scenarios: a) Tide only following Model Reduction, b) Tide and Wave Combined, c) Tide and Wave combined action with decadal MSL changes. The planimetric and decadal volumetric changes, shoreline changes have been compared for all the three scenarios. The outcome of the morphodynamic modeling from the different sets of physical processes will help to isolate the role of each physical process that are making difference in the overall morphological changes of the bay. It aims to isolate the effect of waves by comparing two simulations one with only tide and other with both wave and tidal forcing. The study with obtained forecast results will identify the areas under erosion and accretion and quantify the rate of shoreline changes. These results can help further in taking steps for coastal management. Thus this study gives an exemplary integration of the available techniques that can be helpful for coastal development modeling.