Abstract
Abstract. Records of past sea levels, storms, and their impacts on coastlines are crucial for forecasting and managing future changes resulting from anthropogenic global warming. Coastal barriers that have prograded over the Holocene preserve within their accreting sands a history of storm erosion and changes in sea level. High-resolution geophysics, geochronology, and remote sensing techniques offer an optimal way to extract these records and decipher shoreline evolution. These methods include light detection and ranging (lidar) to image the lateral extent of relict shoreline dune morphology in 3-D, ground-penetrating radar (GPR) to record paleo-dune, beach, and nearshore stratigraphy, and optically stimulated luminescence (OSL) to date the deposition of sand grains along these shorelines. Utilization of these technological advances has recently become more prevalent in coastal research. The resolution and sensitivity of these methods offer unique insights on coastal environments and their relationship to past climate change. However, discrepancies in the analysis and presentation of the data can result in erroneous interpretations. When utilized correctly on prograded barriers these methods (independently or in various combinations) have produced storm records, constructed sea-level curves, quantified sediment budgets, and deciphered coastal evolution. Therefore, combining the application of GPR, OSL, and Lidar (GOaL) on one prograded barrier has the potential to generate three detailed records of (1) storms, (2) sea level, and (3) sediment supply for that coastline. Obtaining all three for one barrier (a GOaL hat-trick) can provide valuable insights into how these factors influenced past and future barrier evolution. Here we argue that systematically achieving GOaL hat-tricks on some of the 300+ prograded barriers worldwide would allow us to disentangle local patterns of sediment supply from the regional effects of storms or global changes in sea level, providing for a direct comparison to climate proxy records. Fully realizing this aim requires standardization of methods to optimize results. The impetus for this initiative is to establish a framework for consistent data collection and analysis that maximizes the potential of GOaL to contribute to climate change research that can assist coastal communities in mitigating future impacts of global warming.
Highlights
Global warming is a major driver of sea-level rise and is projected to increase the frequency and magnitude of storms, but the extent of these changes and their impacts on vulnerable sandy coastlines is uncertain (IPCC, 2013)
Combining ground-penetrating radar (GPR), optically stimulated luminescence (OSL), and Lidar (GOaL) on certain systems offers the possibility to determine a history of storms, sea level, sediment supply, and their impact on shoreline evolution all at once
Because this study focused on comparing chronologies, lidar and GPR data were not presented in Oliver et al (2015), but both techniques were used to target specific stratigraphic layers prior to OSL sampling (Fig. 1c)
Summary
Global warming is a major driver of sea-level rise and is projected to increase the frequency and magnitude of storms, but the extent of these changes and their impacts on vulnerable sandy coastlines is uncertain (IPCC, 2013). As Christopher Hein (personal communication, 19 March 2018) succinctly highlighted, some tools like GPR or pre-processed lidar data are perhaps easy to use, but not easy to use well These techniques are all specialty fields of science in their own right and collaboration between experts in these different disciplines can avoid common pitfalls. Combining GPR, OSL, and Lidar (GOaL) on certain systems offers the possibility to determine a history of storms, sea level, sediment supply, and their impact on shoreline evolution all at once. Given the increased prevalence of these techniques and the existence of 300+ prograded barriers located around the world (Scheffers et al, 2012), a systematic application of GOaL to decipher coastal evolution can detect local patterns of sediment supply, regional records of storms, or global changes in sea level. The aim of this article is threefold: (1) present a basic introduction to the capabilities of GOaL individually, (2) provide a simple strategy that logically utilizes information from each technique to optimize the resulting GOaL dataset, and (3) highlight the possibilities and pitfalls associated with the data to maximize the combination of GOaL on prograded systems
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