Changing coastlines: data assimilation for morphodynamic prediction and predictability

2005年，卡特里娜飓风过后发生的严重洪灾使全世界的注意力集中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。海底地形一般称为水深，随着沉积物在水的作用下被侵蚀、搬运和沉积而随时间演变。水深的变化本身也会改变水的运动，这也会受到潮汐和天气模式（如风暴潮）的影响。准确、最新的沿海水深测量知识将有助于改进洪水预报。改进对未来水深的预测，并了解这种预测的不确定性，将有助于建造更好的海防设施，更好地管理沿海生境，并更好地了解沿海土地使用变化的影响。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。沿海沉积物迁移模型正变得日益复杂。然而，观测到的测深样本通常只能部分覆盖这种模型的区域。因此，仅使用一组最近的观测结果来初始化这些模型是不可行的。有效和高效地利用有限的数据，如这些，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型预测相结合，以提供更准确和校准良好的预测，并能够计算预测中的不确定性。虽然数据同化已在大气和海洋预测方面使用了一些年，但在沿海沉积物建模方面的使用是新颖的。该项目将使用数据同化技术和沿海沉积物迁移模型，以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，并为沿海监测设计最佳观测战略。已确定三个沿海地点进行数值试验。将使用第一个地点的数据制定和测试方法，并使用其他地点的独立数据验证方法，以证明这些想法的更广泛适用性。数据同化的新用途将允许改进对当前水深的估计，并通过更好的初始化、改进的水深误差估计以及从间接观测估计模型参数的手段来改进对未来水深的预测。环境局直接参与该项目将确保将由此产生的惠益转化为业务实践。

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.