Weekly reconstruction of pH and total alkalinity in an upwelling-dominated coastal ecosystem through neural networks (ATpH-NN): The case of Ría de Vigo (NW Spain) between 1992 and 2019

Abstract

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 (AT) in the upper 50 m of the water column of the Ria 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 AT in seven locations of the Ria 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 Ria de Vigo. The lowest values of pH and AT were obtained for the inner zone, showing a progressive increase towards the outer/middle zone of the ria. 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 AT 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 Ria 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 Ria de Vigo by refining the ranges of the biogeochemical variables included in experiments.