Abstract. Short and long-term variability of seawater carbon dioxide (CO₂) system shows large differences between different ecosystems which are derived from the characteristic processes of each area. The high variability of coastal ecosystems, their ecological and economic significance, the anthropogenic influence on them and their behavior as sources or sinks of atmospheric CO₂, highlight the relevance to better understand the processes that underlie the variability and the alterations of the CO₂ system at different spatiotemporal scales. To confidently achieve this purpose, it is necessary to have high-frequency data sustained over several years in different regions. In this work, we contribute to this need by configuring and training two neural networks with the capacity to model the weekly variability of pH and total alkalinity (A_T) in the upper 50 m of the water column of the Ría de Vigo (NW Spain), with an error of 0.031 pH units and 10.9 µmol kg⁻¹ respectively. With these networks, we generated weekly time series of pH and A_T in seven locations of the Ría de Vigo in three depth ranges (0–5 m, 5–10 m and 10–15 m), which adequately represent independent discrete measurements. In a first analysis of the time series, a high short-term variability is observed, being larger for the inner stations of the Ría de Vigo. The lowest values of pH and A_T were obtained for the inner zone, showing a progressive increase towards the outer/middle zone of the ría. The mean seasonal cycle also reflects the gradient between both zones, with a larger amplitude and variability for both variables in the inner zone. On the other hand, the long-term trends derived from the time series of pH show a higher acidification than that obtained for the open ocean, with surface trends ranging from −0.020 pH units per year in the outer/middle zone to −0.032 pH units per year in the inner zone. In addition, positive long-term trends of A_T were obtained ranging from 0.39 µmol kg⁻¹ per year in the outer/middle zone to 2.86 µmol kg⁻¹ per year in the inner zone. The results presented in this study show the changing conditions both in the short and long-term variability as well as the spatial differentiation between the inner and outer/middle zone to which the organisms of the Ría de Vigo are subjected. The neural networks and the database provided in this study offer the opportunity to evaluate the CO₂ system in an environment of high ecological and economic relevance, to validate high-resolution regional biogeochemical models and to evaluate the impacts on organisms of the Ría de Vigo by refining the ranges of the biogeochemical variables included in experiments.

中文翻译：

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通过神经网络（A _T pH-NN）每周重建一个以上升流为主的沿海生态系统的pH和总碱度：以1992年至2019年之间Ríade Vigo（西班牙西北）为例

摘要。海水二氧化碳（CO ₂）系统的短期和长期变异性表明，不同生态系统之间的差异很大，这些差异源于每个地区的特征过程。沿海生态系统的高度可变性，其生态和经济意义，人为影响及其作为大气CO _2的源或汇的行为，凸显了更好地了解构成CO ₂可变性和变化基础的过程的相关性。系统在不同的时空尺度上。为了有信心地实现这一目的，有必要在不同地区保持数年的高频数据。在这项工作中，我们通过配置和训练两个神经网络来满足这一需求，这些神经网络能够对Ríade Vigo（西班牙西北）水柱上部50 m的pH和总碱度（A _T）的每周变化进行建模。）的误差分别为0.031 pH单位和10.9 µmol kg ^-1。借助这些网络，我们生成了pH和A _T的每周时间序列在维加河的七个位置的三个深度范围（0-5 m，5-10 m和10-15 m）中，足以代表独立的离散测量结果。在时间序列的首次分析中，观察到较高的短期变异性，维加河内站的内部变异性较大。pH和A _T的最小值从内部区域获得，表明向里亚的外部/中间区域逐渐增加。平均季节性周期还反映了两个区域之间的梯度，内区域的两个变量的幅度和可变性都较大。另一方面，从pH值时间序列得出的长期趋势显示酸化程度比在公海中获得的酸化程度更高，其表面趋势范围从外/中区每年-0.020 pH单位到-0.032 pH每年在内部区域的广告单元数。此外，获得了A _T的长期正趋势，范围从外/中区每年0.39 µmol kg ^-1到每年2.86 µmol kg ^-1每年在内陆地区。这项研究提出的结果表明，短期和长期可变性的变化情况以及维加河微生物所处的内，外/中区之间的空间差异。这项研究中提供的神经网络和数据库为评估具有高度生态和经济意义的环境中的CO ₂系统，验证高分辨率的区域生物地球化学模型以及评估Ríade Vigo对生物的影响提供了机会。完善实验中包含的生物地球化学变量的范围。