Climate controls on longshore sediment transport

Subtle changes in the directional wave climate driven by changes in large scale climate variability have the potential to result in spatial changes (erosion and accretion) of sandy coastlines due to deviations in longshore sediment transport supply. 50+ years of hindcast offshore directional wave conditions for southeast Queensland. Australia were used to derive yearly wave climates that were input into a spectral wave model to estimate nearshore breaking wave conditions and resulting longshore sediment transport along a 35 km stretch of sandy coastline. Resulting temporal deviations in net annual longshore transport were smoothed using a 5-year Hamming filter and compared against two climate indices known to influence the Australian climate: the Southern Oscillation Index (SOI) and the Inter-decadal Pacific Oscillation (IPO). During the negative IPO phase (more El Nino like) deviations in estimated transport were significantly correlated to the SOI index at - 1 year lag and correlated to the IPO index at + 1 year lag. During positive phases of the IPO (more La Nina like) highest correlations were found at 6 year lag (SOI) and -10 year lag (IPO). A linear regression model combining the influence of both indices explained 65% of the predicted variability in longshore transport during negative phases of the IPO and 48% of the variance during positive phases of the IPO. During positive phases of the IPO, the 501 index was shown to dominate model response and may indicate an increase in the influence of the South Pacific Convergence Zone with respect to the wave climate off the east coast of Australia. A model spanning both phases and only considering positive lags (knowledge of past climate indices) explained approximately half of the predicted variability in longshore transport over the entire record length. The results presented here indicate strong links between these large scale climate indices and estimated longshore transport (0.69 <= R <= 0.83), such that a simple model derived from knowledge of these indices could be used as a first pass estimate of spatial and temporal variability in longshore sediment transport and resulting large scale coastal evolution. (C) 2012 Elsevier Ltd. All rights reserved.