Cameselle Sara, Velo Antón, Doval María Dolores, Broullón Daniel, Pérez Fiz F
Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello 6, 36208, Vigo, Spain.
Facultad de Ciencias del Mar, Universidade de Vigo, 36310, Vigo, Spain.
Sci Rep. 2024 Aug 2;14(1):17929. doi: 10.1038/s41598-024-68694-z.
The present study focuses on the Ría de Vigo (NW Spain), a coastal embayment influenced by the Canary Current Upwelling System, which is among the world's significant Eastern Boundary Upwelling Ecosystems. The research assesses historical changes in the marine carbonate system by generating 25-year weekly time series at six stations . Assessing ocean acidification in the region is complex due to diverse factors influencing coastal carbon dynamics, making predictions more challenging. To capture the specific dynamics in Ría de Vigo, ensembles of Neural Networks were applied. These networks were trained with a data set obtained in several oceanographic cruises, in order to retrieve pH, hydrogen ion concentration and alkalinity, achieving a root mean square error of 0.0272 pH units, 0.588 nmol , and 10.6 mol , respectively. Subsequently, time series of the selected variables were generated, applying data of predictors measured at the aforementioned stations . An increase in normalized alkalinity was observed for all stations, except in the surface layer at the innermost location. A decrease in pH and an increase in hydrogen ion concentration were observed for all points, with trends that exceed reported rates of ocean acidification in the open ocean.
本研究聚焦于西班牙西北部的维哥湾,这是一个受加那利洋流上升流系统影响的沿海港湾,该系统是世界上重要的东边界上升流生态系统之一。该研究通过在六个站点生成25年的每周时间序列来评估海洋碳酸盐系统的历史变化。由于影响沿海碳动态的因素多种多样,评估该地区的海洋酸化很复杂,这使得预测更具挑战性。为了捕捉维哥湾的特定动态,应用了神经网络集合。这些网络使用在几次海洋学巡航中获得的数据集进行训练,以检索pH值、氢离子浓度和碱度,其均方根误差分别为0.0272个pH单位、0.588纳摩尔 以及10.6 摩尔 。随后,利用在上述站点测量的预测变量数据生成所选变量的时间序列。除了最内侧位置的表层外,所有站点的归一化碱度均有所增加。所有点的pH值均下降,氢离子浓度增加,其趋势超过了公海报告的海洋酸化速率。