RAPID: Hurricane-Driven Beach Sand Sorting from Dune to Shoreline: The Case of Hurricane Harvey

该项目将调查飓风哈维对海滩区域的形态和沉积特征的影响，从沙丘拖到沿着德克萨斯州选定的海滩的海岸线。由于风暴潮和与飓风相关的大波强迫，沉积物大小和分选的变化与海岸线上的形态变化有关。2017年8月25日，飓风哈维在德克萨斯州阿兰萨斯港附近登陆，该地区的海滩系统受到严重干扰。现场实验活动将在覆盖德克萨斯州科珀斯克里斯蒂-阿兰萨斯港-帕德雷岛-野马岛地区的几个受影响的海滩上进行。沉积物样本和地形数据将在重大海滩调整发生之前和自然调整过程的早期阶段收集。该数据集将用于对飓风事件期间的沙子分选特征进行基本了解。研究问题将涉及泥沙大小分布特征、负责任的强迫机制和短期海滩恢复过程。使用该数据集的科学发现将促进对飓风期间和之后的短期海滩动态的理解，这将影响各种相关应用。例如，这种改进的理解可能会转化为与海滩营养项目和沙丘恢复/管理、飓风影响海滩的沉积物运输预测等有关的更有效的决策。此外，开发的数据集对于海岸泥沙输运数值模型的校准/验证非常有价值，并有助于研究生和本科生的培养。

This project will investigate the effect of Hurricane Harvey on the morphology and sedimentary characteristics of the beach area extending from the dune tow to the shoreline along selected beaches in Texas. Changes in sediment size and sorting, in response to storm surge and large wave forcing associated with the hurricane, are related to changes in morphology along the coastline. Hurricane Harvey made a landfall near Port Aransas, TX on August 25th 2017, and beach systems in this region were disturbed significantly. Field experimental campaigns will be conducted at several of the affected beaches that cover the region of Corpus Christi - Port Aransas - Padre Island - Mustang Island, TX. Sediment samples and topographic data will be collected before the occurrence of a significant beach adjustment and during the early phases of the natural adjustment processes. This dataset will be used to develop a fundamental understanding on the sand sorting characteristics during hurricane events. The research questions that will be addressed are related to sediment size distribution characteristics, responsible forcing mechanisms, and short term beach recovery processes. The scientific findings using this dataset will advance understanding on the short-term beach dynamics during and after hurricanes, which will impact various relevant applications. For example, this improved understanding may be translated into more effective decisions related to the beach nourishment projects and dune restoration/management, sediment transport predictions for hurricane-impacted beaches, among others. Moreover, the developed dataset will be veryvaluable for the calibration/validation of the numerical coastal sediment transport models and it will contribute to the training of graduate and undergraduate level students.