Stochastic properties of coastal flooding events – Part 2: Probabilistic analysis

IF 2.8 2区 地球科学 Q2 GEOGRAPHY, PHYSICAL Earth Surface Dynamics Pub Date : 2024-01-10 DOI:10.5194/esurf-12-105-2024
Byungho Kang, Rusty A. Feagin, Thomas Huff, Orencio Durán Vinent
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Abstract

Abstract. Low-intensity but high-frequency coastal flooding, also known as nuisance flooding, can negatively affect low-lying coastal communities with potentially large socioeconomic effects. Partially driven by wave runup, this type of flooding is difficult to predict due to the complexity of the processes involved. Here, we present the results of a probabilistic analysis of flooding events measured on an eroded beach at the Texas coast. A high-resolution time series of the flooded area was obtained from pictures using convolutional neural network (CNN)-based semantic segmentation methods, as described in the first part of this contribution. After defining flooding events using a peak-over-threshold method, we found that their size follows an exponential distribution. Furthermore, consecutive flooding events were uncorrelated at daily timescales but correlated at hourly timescales, as expected from tidal and day–night cycles. Our measurements confirm the broader findings of a recent multi-site investigation of the probabilistic structure of high-water events that used a semi-empirical formulation for wave runup. Indeed, we found a relatively good statistical agreement between our CNN-based empirical flooding data and predictions using total-water-level estimations. As a consequence, our work supports the validity of a relatively simple probabilistic model of high-frequency coastal flooding driven by wave runup that can be used in coastal risk management and landscape evolution models.
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沿海洪水事件的随机特性--第 2 部分:概率分析
摘要。低强度但高频率的沿岸洪水,也称为骚扰性洪水,会对低洼的沿岸社区造成负面影响,并 可能产生巨大的社会经济效应。这种类型的洪水部分是由波浪上升驱动的,由于涉及的过程非常复杂,因此很难预测。在此,我们介绍了对德克萨斯州海岸侵蚀海滩上测量到的洪水事件进行概率分析的结果。如本文第一部分所述,我们使用基于卷积神经网络(CNN)的语义分割方法从图片中获取了洪水泛滥区域的高分辨率时间序列。使用峰值超过阈值的方法定义洪水事件后,我们发现洪水事件的规模呈指数分布。此外,连续的洪水事件在日时间尺度上是不相关的,但在小时时间尺度上是相关的,这是潮汐和昼夜周期所预期的。我们的测量结果证实了最近对高水位事件的概率结构进行的多站点调查所得出的广泛结论,该调查使用了波浪上升的半经验公式。事实上,我们发现基于 CNN 的经验洪水数据与使用总水位估计值进行的预测之间具有相对较好的统计一致性。因此,我们的工作证明了波浪上升驱动的相对简单的高频沿海洪水概率模型的有效性,该模型可用于沿海风险管理和景观演变模型。
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来源期刊
Earth Surface Dynamics
Earth Surface Dynamics GEOGRAPHY, PHYSICALGEOSCIENCES, MULTIDISCI-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
5.40
自引率
5.90%
发文量
56
审稿时长
20 weeks
期刊介绍: Earth Surface Dynamics (ESurf) is an international scientific journal dedicated to the publication and discussion of high-quality research on the physical, chemical, and biological processes shaping Earth''s surface and their interactions on all scales.
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