A high-resolution spatiotemporal morphological dataset: Port Aransas beach, Texas

Abstract

The study of beach morphology holds significant importance in coastal management, offering insights into coastal and environmental processes. It involves analyzing physical characteristics and beach features such as profile shape, slope, sediment composition, and grain size, as well as changes in elevation due to both erosion and accretion over time. Furthermore, studying changes in beach morphology is essential in predicting and monitoring coastal inundation events, especially in the context of rising sea levels and subsidence in some areas. However, having access to high-frequency oblique imagery and beach elevation datasets to document and confirm coastal forcing events and understand their impact on beach morphology is a notable challenge. This paper describes a one-year dataset comprising bi-monthly topographic surveys and imagery collected daily at 30 min increments at the beach adjacent to Horace Caldwell Pier in Port Aransas, Texas. The data collection started in February 2023 and ended in January 2024. The dataset includes 18 topographic surveys, 6879 beach images, and ocean/wave videos that can be combined with colocated National Oceanic and Atmospheric Administration metocean measurements. The one-year temporal span of the dataset allows for the observation and analysis of seasonal variations, contributing to a deeper understanding of coastal dynamics in the study area. Furthermore, a study that combines survey measurements with camera imagery is rare and provides valuable information on conditions before, after, and between surveys and periods of inundation. The imagery enables monitoring of inundation events, while the topographic surveys facilitate the analysis of their impact on beach morphology, including beach erosion and accretion. Various products, including beach profiles, contours, slope maps, triangular irregular networks, and digital elevation models, were derived from the topographic dataset, allowing in depth analysis of beach morphology. Additionally, the dataset contains a time series of four wet/dry shoreline delineations per day and their corresponding elevation extracted by combining the imagery with the digital elevation models. Thus, this paper provides a high-frequency morphological dataset and a machine learning-ready dataset suitable for predicting coastal inundation.