Technical note: Optimizing the utility of combined GPR, OSL, and Lidar (GOaL) to extract paleoenvironmental records and decipher shoreline evolution

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 valu-able 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.

Holocene Sea-Level Changes in Southern Brazil Based on High-Resolution Radar Stratigraphy

“…With the assumption that this vertical distance has remained almost the same during the Holocene evolution of prograded barriers (meaning relatively constant wave energy and tidal range), it is possible to access past sea-level positions by subtracting the vertical distance from the heights of the contact of relict foredune and backshore deposits identified in GPR records [53]. Rdf I presents irregular, non-continuous, and sub-parallel reflectors in a general segmented undulating pattern, commonly found at the upper shoreface.…”

Recurrence of Extreme Coastal Erosion in SE Australia Beyond Historical Timescales Inferred From Beach Ridge Morphostratigraphy

“…Gaps in individual and/or modeled OSL ages between 20 and 30 m on the WS and MN profiles are thus considered to represent the beach scarp resulting from the erosion prior to the rapid recovery inferred between 1650 and 1700. These gaps in the OSL ages do not necessarily correspond directly to prominent GPR reflections in all cases, which generally result from higher concentrations of heavy minerals or coarser‐grained sediments (Buynevich et al, ; Dougherty, ; Dougherty et al, ; Tamura, ). For instance, in the WS profile no heavy‐mineral layer was observed during augering around the 1970s scarp despite knowledge of its exact position.…”

Late Pleistocene Climate Variation On The Khorat Plateau, Northeastern Thailand Inferred From The Remnants Of Sand Dunes

“…OSL dating is beneficial for late Quaternary dating with the reliability of age ranging from about months to 150,000 years old with an error ranged around 5-10 % (Murray & Wintle, 2000;Murray & Olley, 2002). Calculating the age when the grain was last exposed to sunlight is based on quantifying both the radiation dose received by a sample since its zeroing event and the dose rate which it has experienced during the burial period (Dougherty et al, 2019). In this work, all ages are quoted in years before A.D. 1950.…”