Coastal upwelling is an oceanographic process that brings cold, nutrient-rich waters to the ocean surface from depth. These nutrient-rich waters help drive primary productivity which forms the foundation of ecological systems and the fisheries dependent on them. Although coastal upwelling systems of the Western Indian Ocean (WIO) are seasonal (i.e., only present for part of the year) with large variability driving strong fluctuations in fish catch, they sustain food security and livelihoods for millions of people via small-scale (subsistence and artisanal) fisheries. Due to the socio-economic importance of these systems, an "Upwelling Watch" analysis is proposed, for producing updates/alerts on upwelling presence and extremes. We propose a methodology for the detection of coastal upwelling using remotely-sensed daily chlorophyll-a and Sea Surface Temperature (SST) data. An unsupervised machine learning approach, K-means clustering, is used to detect upwelling areas off the Somali coast (WIO), where the Somali upwelling – regarded as the largest in the WIO and the fifth most important upwelling system globally – takes place. This automatic detection approach successfully delineates the upwelling core and surrounds, as well as non-upwelling ocean regions. The technique is shown to be robust with accurate classification of out-of-sample data (i.e., data not used for training the detection model). Once upwelling regions have been identified, the classification of extreme upwelling events was performed using confidence intervals derived from the full remote sensing record. This work has shown promise within the Somali upwelling system with aims to expand it to the rest of the WIO upwellings. This upwelling detection and classification method can aid fisheries management and also provide broader scientific insights into the functioning of these important oceanographic features.

沿海上升流是一个海洋学过程，它将寒冷、营养丰富的海水从深处带到海洋表面。这些营养丰富的水域有助于推动初级生产力，而初级生产力构成了生态系统和依赖它们的渔业的基础。尽管西印度洋 (WIO) 的沿海上升流系统是季节性的（即仅在一年中的部分时间出现），其变化很大，导致渔获量大幅波动，但它们维持着数百万人的粮食安全和生计通过小规模（生计和手工）渔业。由于这些系统的社会经济重要性，建议进行“上升流观察”分析，以生成有关上升流存在和极端事件的更新/警报。我们提出了一种使用遥感的每日叶绿素-a 和海面温度 (SST) 数据检测沿海上升流的方法。一种无监督机器学习方法，K-means 聚类，用于检测索马里海岸 (WIO) 的上升流区域，索马里上升流被认为是 WIO 中最大的上升流系统，也是全球第五大最重要的上升流系统。这种自动检测方法成功地描绘了上升流核心和周围，以及非上升流海洋区域。该技术被证明具有鲁棒性，可以对样本外数据进行准确分类（即，未用于训练检测模型的数据）。一旦确定了上升流区域，就使用从完整遥感记录得出的置信区间对极端上升流事件进行分类。这项工作在索马里上升流系统中显示出前景，旨在将其扩展到 WIO 上升流的其余部分。这种上升流检测和分类方法可以帮助渔业管理，还可以为这些重要海洋学特征的功能提供更广泛的科学见解。这项工作在索马里上升流系统中显示出前景，旨在将其扩展到 WIO 上升流的其余部分。这种上升流检测和分类方法可以帮助渔业管理，还可以为这些重要海洋学特征的功能提供更广泛的科学见解。这项工作在索马里上升流系统中显示出前景，旨在将其扩展到 WIO 上升流的其余部分。这种上升流检测和分类方法可以帮助渔业管理，还可以为这些重要海洋学特征的功能提供更广泛的科学见解。