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

该项目将调查哈维飓风对德克萨斯州沿着选定海滩从沙丘拖到海岸线的海滩区域的形态和沉积特征的影响。沉积物的大小和分类的变化，以应对风暴潮和大浪强迫与飓风，是有关的形态变化沿着海岸线。 飓风哈维于2017年8月25日在德克萨斯州阿兰瑟斯港附近登陆，该地区的海滩系统受到严重干扰。现场实验活动将在几个受影响的海滩进行，覆盖科珀斯克里斯蒂-阿兰瑟斯港-帕德雷岛-野马岛，得克萨斯州的地区。将在海滩发生重大调整之前和自然调整过程的早期阶段收集沉积物样本和地形数据。该数据集将用于对飓风事件期间的沙子分选特性进行基本了解。将解决的研究问题是有关沉积物粒度分布特征，负责任的强迫机制，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短期海滩动态的理解，这将影响各种相关应用。例如，这一更好的理解可以转化为与海滩营养项目和沙丘恢复/管理、受飓风影响海滩的沉积物迁移预测等有关的更有效的决策。此外，开发的数据集将是非常有价值的数值海岸泥沙输运模型的校准/验证，它将有助于研究生和本科生的培训。

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.