CAMPUS (Combining Autonomous observations and Models for Predicting and Understanding Shelf seas)

CAMPUS（结合自主观测和模型来预测和理解陆架海）

• 批准号: NE/R00675X/1
• 负责人: Keith Davidson
• 金额: $ 29.17万
• 依托单位: Scottish Association For Marine Science
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FR00675X%2F1
• 关键词: CAMPUS; Combining; Autonomous; observations; Models

Shelf seas are of major societal importance providing a diverse range of goods (e.g. fisheries, renewable energy, transport) and services (e.g. carbon and nutrient cycling and biodiversity). Managing UK seas to maintain clean, healthy, safe, productive and biologically diverse oceans and seas is a key governmental objective, as evidenced by the obligations to obtain Good Environmental Status (GES) under the UK Marine Strategy Framework, the Convention on Biological Diversity and ratification of the Oslo-Paris Convention (OSPAR) .. The delivery of these obligations requires comprehensive information about the state of our seas which in turn requires a combination of numerical models and observational programs. Computer modelling of marine ecosystems allows us to explore the recent past and predict future states of physical, chemical and biological properties of the sea, and how they vary in 3D space and time. In an analogous manner to the weather forecast, the Met Office runs a marine operational forecast system providing both short term forecast and multi-decadal historical data products. The quality of these forecasts is improved by using data assimilation; the process of predicting the most accurate ocean state using observations to nudge model simulations, producing a combined observation and model product. Marine autonomous vehicles (MAVs) are a rapidly maturing technology and are now routinely deployed both in support of research and as a component of an ocean observing system. When used in conjunction with fixed point observatories, ships of opportunity and satellite remote sensing, the strategic deployment of MAVs offers the prospect of substantial improvement in our observing network. Marine Gliders in particular have the capability to provide depth resolved data sets of high resolution from deployments that can endure several months and cover 100s kms, allowing the collection of sufficient information to be useful for assimilation into models. We will improve the exchange of data between model systems and observational networks to inform an improved strategy for the deployment of the UK's high-cost marine observing capability. In particular we will utilise mathematical and statistical models to develop and test "smart" autonomy - autonomous systems that are enabled to selectively search and monitor explicit features within the marine system. By developing data assimilation techniques to utilise autonomous data, our model systems will be able to better characterise episodic events such as the spring bloom, harmful algal blooms and oxygen depletion, which are currently not well captured and are key to understanding ecosystem variability and therefore quantifying GES.In doing so CAMPUS will provide a step change in the combined use of observation and modelling technologies, delivered through a combination of autonomous technologies (gliders), other observations and shelf-wide numerical models. This will provide improved analysis of key ocean variables, better predictions of episodic events, and 'smart' observing systems in order to improve the evidence base for compliance with European directives and support the UK industrial strategy.

陆架海具有重要的社会意义，提供各种各样的商品（如渔业、可再生能源、运输）和服务（如碳和养分循环以及生物多样性）。管理英国海洋以保持清洁、健康、安全、富有生产力和生物多样性的海洋是政府的一项关键目标，根据《英国海洋战略框架》、《生物多样性公约》和批准《奥斯陆-巴黎公约》获得良好环境地位的义务证明了这一点。履行这些义务需要关于我们海洋状况的全面信息，而这又需要数值模式和观测方案的结合。海洋生态系统的计算机建模使我们能够探索最近的过去，并预测海洋的物理、化学和生物特性的未来状态，以及它们在三维空间和时间中的变化。与天气预报类似，英国气象局有一套海洋预报系统，提供短期预报和多年代际历史数据产品。利用数据同化技术提高了预报的质量；利用观测来推动模型模拟，从而产生观测和模型产品相结合的最准确的海洋状态预测过程。海洋自动驾驶车辆（MAVs）是一项快速成熟的技术，现在经常用于支持研究和作为海洋观测系统的组成部分。当与定点观测站、机遇船和卫星遥感一起使用时，mav的战略部署为我们的观测网络提供了实质性改善的前景。特别是海洋滑翔机能够提供深度分辨率高的数据集，这些数据集可以持续几个月，覆盖100公里，允许收集足够的信息，用于同化到模型中。我们将改进模型系统和观测网络之间的数据交换，为英国高成本海洋观测能力的部署提供改进的战略信息。特别是，我们将利用数学和统计模型来开发和测试“智能”自主-自主系统，能够有选择地搜索和监测海洋系统内的明确特征。通过开发数据同化技术来利用自主数据，我们的模型系统将能够更好地描述诸如春季水华、有害藻华和氧气消耗等偶发事件，这些事件目前还没有得到很好的捕捉，是理解生态系统变化并因此量化GES的关键。为此，CAMPUS将通过自主技术（滑翔机）、其他观测和大陆架范围数值模型的结合，在观测和建模技术的结合使用方面提供一个跨越式的变化。这将提供对关键海洋变量的改进分析，对偶发事件的更好预测，以及“智能”观测系统，以改善遵守欧洲指令的证据基础，并支持英国的工业战略。

项目成果

17

• DOI: 10.7591/9781501728983-026
• 发表时间: 1995-07
• 期刊: The Hatak Witches
• 作者: 강희정;손수연;김소희;정희정

GlobalHAB. Evaluating, Reducing and Mitigating the Cost of Harmful Algal Blooms: A Compendium of Case Studies

全球HAB。

• 发表时间: 2020
• 作者: Davidson K

An approach for evaluating the economic impacts of harmful algal blooms: The effects of blooms of toxic Dinophysis spp. on the productivity of Scottish shellfish farms.

评估有害藻华经济影响的方法：有毒藻华的影响。

• DOI: 10.1016/j.hal.2020.101912
• 发表时间: 2020
• 期刊: Harmful algae
• 影响因子: 6.6
• 作者: Martino S

Temporal and Spatial Patterns of Harmful Algae Affecting Scottish Shellfish Aquaculture

• DOI: 10.3389/fmars.2021.785174
• 发表时间: 2021-12-22
• 期刊: FRONTIERS IN MARINE SCIENCE
• 影响因子: 3.7
• 作者: Gianella, Fatima;Burrows, Michael T.;Davidson, Keith
• 通讯作者: Davidson, Keith

Global Blue Economy - Analysis, Developments, and Challenges

全球蓝色经济——分析、发展与挑战

• DOI: 10.1201/9781003184287-3
• 发表时间: 2022
• 作者: Aleynik D