摘要
海滩地貌动力学为理解海滩形态和海岸动力环境提供了理论框架,在海滩的保护与开发中有重要的指导意义。基于地质背景、海滩地貌形态、海滩沉积物和海洋动力要素选取7个可以量化的海滩特征指标,采用聚类分析对雷州半岛23个海滩进行分类和地貌动力特征识别,针对海滩状态分类模型Ω-RTR在雷州半岛复杂多变海岸线环境下的适用性进行了分析,结果表明:①雷州半岛海滩可以分为六类:反射组、低潮阶地/反射组(有裂流)、沙坝-槽谷/中间状态组、沙坝消散组、超消散组和低潮阶地/反射组(无裂流);②波浪、潮汐、沉积物特征以及海岸地质构造背景等对雷州半岛海滩状态均有不同程度的影响,在不同岸段各要素的影响各异。地质背景是海滩状态分类不可忽视的因素。③由于Ω-RTR模型在判别海滩状态时更侧重于动力和沉积因素,分类结果与聚类分析结果稍有差异,但总体上可以有效区分海滩状态类型,是理解海滩动力地貌系统有效手段。
Beach morphodynamic provides a theoretical framework for understanding the beach morphology and coastal dynamic environment, and has important guiding significance in beach protection and development. In order to determine the beach classification on the Leizhou Peninsula, 7 quantifiable beach characteristics about geological background, beach morphology, beach sedimentation and coastal dynamic factors were selected. Results show that:(1) beaches on the Leizhou Peninsula coast can be divided into 6 groups: reflective group,low tide terrace/reflective group(with rip current), sand bar-trough/middle state group, barred dissipative group, ultra-dissipative group and low tide terrace/reflective group(without rip current).(2) Wave, tide, sediment characteristics and coastal background of geological structure have different effects on the beach state in the study area. Factors have different influences on different coastal sections on Leizhou Peninsula. Geological background is an important factor in beach classification.(3) Because the Ω-RTR model is more focused on dynamic and sedimentary factors in identifying beach state, classification results derived from Ω-RTR model are slightly different from those from cluster analysis. As a whole, the Ω-RTR model can effectively distinguish beach state type and is an effective means to understand beach morphodynamic system.
作者
朱士兵
李志强
张会领
曾春华
陈海银
ZHU Shibing;LI Zhiqiang;ZHANG Huiling;ZENG Chunhua;CHEN Haiyin(College of Electronic and Information Engineering,Guangdong Ocean University,Zhanjiang 524088,Guangdong,China;College of Ocean Engineering,Guangdong Ocean University,Zhanjiang 524088,Guangdong,China)
出处
《地理研究》
CSSCI
CSCD
北大核心
2020年第6期1269-1282,共14页
Geographical Research
基金
国家自然科学基金项目(41676079)
广东海洋大学创新强校工程项目(Q18307)。
