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Exploring the sensitivities of crenulate-bay shorelines to wave climates using a new vector-based one-line model

机译:利用基于矢量的单线模型探索波纹湾海岸线对波浪气候的敏感性

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摘要

We use a new exploratory model that simulates the evolution of sandy coastlines over decadal to centennial timescales to examine the behavior of crenulate-shaped bays forced by differing directional wave climates. The model represents the coastline as a vector in a Cartesian reference frame, and the shoreface evolves relative to its local orientation, allowing simulation of coasts with high planform-curvature. Shoreline change is driven by gradients in alongshore transport following newly developed algorithms that facilitate dealing with high planform-curvature coastlines. We simulated the evolution of bays from a straight coast between two fixed headlands with no external sediment inputs to an equilibrium condition (zero net alongshore sediment flux) under an ensemble of directional wave climate conditions. We find that planform bay relief increases with obliquity of the mean wave direction, and decreases with the spread of wave directions. Varying bay size over 2 orders of magnitude (0.1–16 km), the model predicts bay shape to be independent of bay size. The time taken for modeled bays to attain equilibrium was found to scale with the square of the distance between headlands, so that, all else being equal, small bays are likely to respond to and recover from perturbations more rapidly (over just a few years) compared to large bays (hundreds of years). Empirical expressions predicting bay shape may be misleading if used to predict their behavior over planning timescales.
机译:我们使用一种新的探索性模型来模拟沙质海岸线从十年到百年时间尺度的演变,以研究由不同方向波气候强迫的圆柱状海湾的行为。该模型将海岸线表示为笛卡尔参考系中的矢量,并且岸面相对于其局部方向进行演化,从而可以模拟具有高平面曲率的海岸。沿岸运输中的坡度遵循新开发的算法来推动海岸线变化,这些算法有助于处理高平面曲率的海岸线。我们模拟了在定向波气候条件下,海湾从两个没有外部沉积物输入的固定岬角之间的笔直海岸到平衡条件(沿岸沉积物净流量为零)的演变。我们发现,平面形海湾起伏随着平均波向的倾斜而增加,并随着波向的传播而减小。该模型预测海湾大小在2个数量级(0.1–16 km)内变化,从而预测海湾形状与海湾大小无关。发现建模海湾达到平衡所需的时间与岬角之间的距离的平方成正比,因此,在所有其他条件相同的情况下,小海湾很可能会更快地响应并从摄动中恢复(短短几年)与大型海湾相比(数百年)。如果用于预测海湾形状的经验表达式用于预测其在计划时间范围内的行为,则可能会产生误导。

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