通过在短时间内熟练预测海岸侵蚀来保护重要的石油和天然气基础设施

E. Steele, Mark S. Davidson, A. Saulter, N. Fournier, J. Upton
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引用次数: 2

摘要

海岸侵蚀的准确预测对于天然气终端和浅埋近岸管道等关键基础设施的有效管理(操作和保护)至关重要,可以防止因风暴破坏或暴露而造成的昂贵的生产损失。传统上,这些预测是对海滩表面三维结构进行计算成本高昂的形态动力学模拟的保留,然而,最近在降低复杂性的“平衡”模型中的发展已经被证明能够在更长的时间尺度上更准确地预测跨海岸和长海岸运输主导环境中的海岸变化。这些模型的简单性和稳定性——表示为入射波能和无量纲下降速度的相对平衡的函数——使它们特别适合以可操作的方式评估当前海岸线的“健康”,同时释放它们在预测模式中的潜在用途。在这里,我们提出了这样一个系统,由英国气象局海浪集合预测系统的数据驱动,能够提供重要海岸指数(如海滩体积和海岸线位置)的实时概率预测,提前七天。该系统使用扩展卡尔曼滤波器进行校准,随着时间的推移,由于吸收了更多的观测数据,该系统变得更加精确。校准后,对2017/18年冬季英国佩兰珀斯普利茅斯大学海岸监测站的未见数据进行测试,证实它可以准确预测极端风暴序列对海岸侵蚀和随后恢复的影响。这有望成为一种新的沿海管理工具,能够应用于其他脆弱地区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Protection of Critical Oil and Gas Infrastructure via the Skilful Prediction of Coastal Erosion at Short Lead Times
Accurate forecasts of coastal erosion are essential for the effective management (operation and protection) of critical infrastructure such as gas terminals and shallow-buried nearshore pipelines, preventing the costly losses of production associated with storm damage or exposure. Traditionally, these predictions were the preserve of computationally-expensive, morphodynamic simulations of the three-dimensional structure of the beach surface, however recent developments in reduced-complexity ‘equilibrium’ models have been shown to skilfully hindcast coastal change in cross-shore and long-shore transport dominated environments more accurately, over much longer time-scales. The simplicity and stability of these models – expressed as a function of the incident wave power and the relative equilibrium in dimensionless fall velocity – make them particularly appropriate for assessing the current ‘health’ of the coastline in actionable terms, while unlocking their potential use in forecast mode. Here, we present such a system, forced by data from the Met Office Wave Ensemble Prediction System, capable of providing real-time probabilistic forecasts of important coastal indices (e.g. beach volume and shoreline position) out to seven days ahead. The system is calibrated using an extended Kalman Filter and becomes more accurate over time as it assimilates more observational measurements. Once calibrated, tests on unseen data from the University of Plymouth coastal monitoring station at Perranporth, UK, during Winter 2017/18 confirm it can accurately predict the impact of an extreme storm sequence on coastal erosion and subsequent recovery. This promises the potential for a new coastal management tool, able to be applied to other vulnerable locations.
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