Prograded coastal barriers provide paleoenvironmental records of storms and sea level during late Quaternary highstands

Abstract

Records of past shoreline evolution, storm impacts, and variations in sea level are essential to accurately assess the vulnerability of beaches to future climate change. Prograded sandy barriers provide the opportunity to utilize our understanding of coastal dynamics documented over decades to determine storm impacts over centuries and sea‐level change over millennia. Composite coastal barriers that have prograded throughout the Pleistocene and Holocene maintain, within their stratigraphy, a record of shoreline evolution extending back to when sea level was known to be higher than today (Marine Isotope Stage or MIS 5e), including high‐energy events during these late Quaternary highstands. This study utilizes ground‐penetrating radar to image the facies architecture of a composite barrier system in Bream Bay, New Zealand. The resulting stratigraphic record mapped paleo‐beachfaces with decimeter resolution spanning ∼5 kilometers from the active shoreline, bridging timescales of coastal evolution over orders of magnitude. Charting the elevation of the beachfaces provides evidence of overall sea‐level falls from +5 to +2 m above present in the Pleistocene and from a ∼2‐m mid‐Holocene highstand. Sixty paleo‐beachfaces with distinct storm‐related signatures are identified within the Pleistocene morphostratigraphy. In the past ∼6,000 years 25 major storm events were recorded, giving an average period between events of ∼230 years. These unprecedented data provide insight, as well as input to forecasting models, needed to mitigate and manage future impacts of global warming.

Storm response of a mixed sand gravel beach ridge plain under falling relative sea levels: A stratigraphic investigation using ground penetrating radar

“…Progressive sigmoidal laminations of berm deposit are evident in the stratigraphy (Figure ), with a preferential seaward dip and evidence of lower erosive surfaces caused by erosion of the lower beach face after berm deposition (Montes et al ., ). This same cut and fill response has been observed as a driver of beach ridge formation over a large Quaternary sand beach ridge plain (Dougherty, ), whereby storm erosion punctuates the progradation sequence, lowers the elevation of the beach and onshore migration of bars during the recovery stage creates the new beach ridge. The ridge at this location is built to a higher elevation than any other in the profile, which is likely a combination of the large return period of the event, resulting in a large amount of sediment in the nearshore, and a falling relative sea level.…”

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

“…7a. Given that the geometry of the paleo-beachface stratigraphy is similar to that imaged in the modern berms, which by their nature are constructed in fair-weather conditions, the conclusion that only storm-eroded profiles are preserved is called into question (Dougherty, 2018c). Without cores or augers to ground-truth these beachface reflections, it is impossible to definitively determine if the beach facies consist only of storm lag concentrations (e.g.…”

“…Once these reflections are ground-truthed as high-energy lag deposits, these data can be used to construct a storm record. Figure modified from Oliver et al (2017b) and Dougherty (2018c). sisting of storm lag deposits (e.g.…”

“…sisting of storm lag deposits (e.g. Dougherty, 2014Dougherty, , 2018c; and (3) use the GPR data to locate the most prominent eroded paleo-beachface reflections and obtain OSL samples from associated materials (e.g. Buynevich, 2007).…”

“…The existence and size of foredune features deposited during both timeframes indicates that there was a continuous supply of sediment to build them. Additional GPR and OSL data are necessary to confirm these interpretations (Dougherty, 2018b). In the meantime, these serve as examples of how despite obtaining a GOaL dataset, significant features or gaps in data can be overlooked and lead to questionable interpretations.…”