Future wave-climate driven longshore sediment transport along the Indian coast

Abstract

Longshore sediment transport is an important nearshore process that governs coastal erosion/accretion and in turn, defines the orientation of coastlines. In this study, we assess the changes in longshore transport rates along the Indian coast due to the potential changes in wave parameters under the RCP4.5 climate scenario. The projected wave climate for two time slices, "near-term/present" and "mid-term/future" (2041-2070) were used to investigate changes in the corresponding sediment transport rates. An empirical model accounting for major wave parameters, longshore current, resulting sediment transport, and shoreline evolution, was used. It was found that most of the Indian coast exhibited the same drift direction in both time slices, although changes in transport magnitude were present. To give a broadbrush characterisation of the coastline, the shoreline elements were classified as erosive, accretive or stable based on the comparative longshore transport rates of neighbouring elements. Similar characterisations, carried out for both time slices showed that about 35% of the total coastline would remain unaffected due to the changing wave climate in future (i.e. there is little to no change); about 20% is expected to "worsen" (i.e. expected to undergo higher magnitudes of erosion wrt present rate); and 45% to "improve" (i.e. expected to accrete/reach stability). It was also observed that the net annual transport rates pertaining to the future period are not expected to change significantly with respect to the current scenario. This indicates that the change in longshore transport rates arising from future changes in wave climate as represented by the RCP4.5 climate change scenario will have a broadly neutral effect.

Uncertainties in the Projected Patterns of Wave-Driven Longshore Sediment Transport Along a Non-straight Coastline

“…As illustrated in Figure 9, the use of some GCM-Ws (e.g., MIROC5 and GFDL-CM3) under different emission scenarios can even alter the sign (i.e., increase or decrease) of the projected changes in LST. Such interactions can challenge the reliability of any coastal sediment transport projections, conducted by arbitrarily choosing the GCM-Ws forcing datasets and emission scenarios (e.g., Dastgheib et al, 2016;O'Grady et al, 2019;Chowdhury et al, 2020). Apart from the aforementioned points, it seems that applying bias corrections could sometimes slightly manipulate the patterns of the LST projections under different emission scenarios, compared to those of the original forcing datasets (see Figure 9, the projections associated with BCC-CSM1.1 and ACCESS1.0 forcing conditions at site G).…”

“…Nevertheless, mainly due to the large computational costs of wave transformation through the state-ofart spectral wave models, usually only a limited number of GCMs (an ensemble of them) under one or two emission scenarios were considered for coastal sediment transport studies (e.g., Bonaldo et al, 2015;Dastgheib et al, 2016). In some cases, to decrease the computational costs of the studies, projected offshore waves were transferred to the nearshore zone through a simplified wave transformation method (e.g., Zacharioudaki and Reeve, 2011;Almar et al, 2015;Casas-Prat et al, 2016;Chowdhury et al, 2020). However, it is possible to significantly decrease the computational costs of spectral wave transformation by using hybrid methods (i.e., a combination of spectral models and machine learning techniques), retaining the required accuracy for sediment transport studies (e.g., Camus et al, 2011;Antolínez et al, 2016;Cagigal et al, 2020).…”

“…However, it is possible to significantly decrease the computational costs of spectral wave transformation by using hybrid methods (i.e., a combination of spectral models and machine learning techniques), retaining the required accuracy for sediment transport studies (e.g., Camus et al, 2011;Antolínez et al, 2016;Cagigal et al, 2020). Commonly, to project future LST patterns, the transformed waves (i.e., projected nearshore waves) are introduced to bulk transport formulae (e.g., Casas-Prat et al, 2016;Dastgheib et al, 2016;Chowdhury et al, 2020;Alvarez-Cuesta et al, 2021). In this way, the computational costs of sediment transport simulations are decreased.…”

Uncertainties in wave-driven longshore sediment transport projections presented by a dynamic CMIP6-based ensemble

“…Compared to previous findings for CMIP5 (~35% for GCM-Ws uncertainty; Zarifsanayei et al, 2022b), slight decrease (~5% decrease) in contribution of GCM-Ws to total uncertainty, is observed. Nonlinear interaction of GCM-Ws and scenario is also significant (24%, see Figures 9C, D), questioning the reliability of LST projections based on a single emission scenario (e.g., Chowdhury et al, 2020;Fernańdez-Fernańdez et al, 2020).…”

Long-Term Prediction of Longshore Sediment Transport in the Context of Climate Change

“…There have been some efforts to estimate future LST patterns for different case studies around the world (e.g., West Africa (Almar et al, 2015), Vietnam (Dastgheib et al, 2016), India (Chowdhury et al, 2020)). Nonetheless, they mainly addressed the uncertainty associated with global circulation models and/or emission scenarios, and they overlooked other sources of uncertainty.…”