Changing coastlines: data assimilation for morphodynamic prediction and predictability

不断变化的海岸线：形态动力学预测和可预测性的数据同化

• 批准号: NE/E002048/1
• 负责人: Sarah Dance
• 金额: $ 41.89万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FE002048%2F1
• 关键词: Changing; coastlines; data; assimilation; morphodynamic

In 2005, severe flooding in the aftermath of Hurricane Katrina focussed the world's attention on the importance of accurate knowledge of the topography of the coastal zone in natural disaster management and prediction. The topography of the sea floor, generally known as the bathymetry, evolves over time as sediment is eroded, transported and deposited by water action. The change in bathymetry itself changes the motion of the water, which is also influenced by tides and weather patterns, such as storm surges. An accurate, up-to-date knowledge of coastal bathymetry would allow improved flood forecasting. Improved prediction of future bathymetry, and knowledge of the uncertainty in that prediction, would allow construction of better sea defences, better management of coastal habitats, and better understanding of the effects of changes in land use near the coast. It may also provide better understanding of the effects of climate change (e.g. sea level rise, and increased numbers of extreme storm events) on the longer-term evolution of an estuary. Coastal sediment transport models are becoming increasingly sophisticated. However, observed bathymetric samples typically only provide partial coverage of the domain of such a model. Hence, initialisation of such models using only a set of recent observations is not feasible. The effective and efficient use of limited data, such as these, requires state-of-the-art mathematical, statistical and computational methods, known as data assimilation techniques. Data assimilation combines empirical observations with model predictions to give more accurate and well-calibrated forecasts and enables the uncertainties in the forecasts to be calculated. Whilst data assimilation has been in use in the context of atmospheric and oceanic prediction for some years, its use in the context of coastal sediment modelling is novel. This project will use data assimilation techniques with a coastal sediment transport model to maintain up-to-date near-shore bathymetry, predict future bathymetry, answer statistical questions regarding uncertainty and predictability, gain insight into physical processes taking place during intense storm events and to design an optimal observation strategy for coastal monitoring. Three coastal sites have been identified for numerical experiments. Methodologies will be developed and tested using data from the first site and validated using independent data from the other sites, demonstrating the wider applicability of ideas. The novel use of data assimilation will allow improved estimates of the current bathymetry, and improved predictions of future bathymetry via better initialisation, error estimates for the improved bathymetry, and a means to estimate model parameters from indirect observations. The direct involvement of the Environment Agency in the project will ensure that the resulting benefits are transferred into operational practice.

2005年，卡特里娜飓风过后的严重洪灾使世界关注到准确了解沿海地区地形在自然灾害管理和预测中的重要性。海床的地形，通常被称为测深，随着时间的推移，沉积物在水的作用下被侵蚀、运输和沉积。水深测量本身的变化改变了海水的运动，这也受到潮汐和天气模式（如风暴潮）的影响。准确的、最新的海岸测深知识将有助于改进洪水预报。改进对未来水深测量的预测，以及对预测不确定性的认识，将有助于建设更好的海防，更好地管理沿海栖息地，并更好地了解海岸附近土地利用变化的影响。它还可以更好地了解气候变化（例如海平面上升和极端风暴事件数量增加）对河口长期演变的影响。沿海沉积物输运模型正变得越来越复杂。然而，观察到的水深样本通常只提供这种模型域的部分覆盖。因此，仅使用一组最近的观测值来初始化这种模型是不可行的。有效和高效率地使用诸如这些有限的数据，需要最先进的数学、统计和计算方法，称为数据同化技术。数据同化将经验观测与模式预测结合起来，提供更准确和校准良好的预测，并使预测中的不确定性得以计算。虽然数据同化已经在大气和海洋预报中使用了几年，但它在沿海沉积物模拟中的应用是新颖的。该项目将使用数据同化技术和海岸沉积物运输模型来保持最新的近岸水深测量，预测未来的水深测量，回答有关不确定性和可预测性的统计问题，深入了解强风暴事件期间发生的物理过程，并为海岸监测设计最佳观测策略。已经确定了三个沿海地点进行数值试验。将使用来自第一个站点的数据开发和测试方法，并使用来自其他站点的独立数据进行验证，以证明思想的更广泛适用性。数据同化的新应用将改进当前水深测量的估计，并通过更好的初始化、改进水深测量的误差估计以及从间接观测中估计模型参数的方法来改进未来水深测量的预测。环境署对该项目的直接参与将确保由此产生的效益转化为实际操作。

项目成果

Use of fused airborne scanning laser altimetry and digital map data for urban flood modelling

• DOI: 10.1002/hyp.6343
• 发表时间: 2007-05
• 期刊: Hydrological Processes
• 影响因子: 3.2
• 作者: D. Mason;M. Horritt;N. Hunter;P. Bates

Modelling of forecast errors in geophysical fluid flows

地球物理流体流动预测误差建模

• DOI: 10.1002/fld.1618
• 发表时间: 2007
• 期刊: International Journal for Numerical Methods in Fluids
• 影响因子: 1.8
• 作者: Bannister R

Benchmarking 2D hydraulic models for urban flooding

• DOI: 10.1680/wama.2008.161.1.13
• 发表时间: 2008-02-01
• 期刊: PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-WATER MANAGEMENT
• 影响因子: 1.1
• 作者: Hunter, N. M.;Bates, P. D.;Mason, D. C.
• 通讯作者: Mason, D. C.

Approximate Gauss-Newton methods for nonlinear least squares problems

• DOI: 10.1137/050624935
• 发表时间: 2007-01-01
• 期刊: SIAM JOURNAL ON OPTIMIZATION
• 影响因子: 3.1
• 作者: Gratton, S.;Lawless, A. S.;Nichols, N. K.
• 通讯作者: Nichols, N. K.

Approximate Gauss-Newton methods for optimal state estimation using reduced-order models

使用降阶模型进行最优状态估计的近似高斯-牛顿方法

• DOI: 10.1002/fld.1629
• 发表时间: 2007
• 期刊: International Journal for Numerical Methods in Fluids
• 影响因子: 1.8
• 作者: Lawless A