Patterns of the relationship between the Secchi disk depth and the optical characteristics of water in the Neva Estuary (Baltic Sea): the influence of environmental variables

The Secchi disc depth (Dsd) measurement is widely used to monitor eutrophication and the quality of the aquatic environment. This study aimed to investigate the relationship between Dsd and various factors, including the coefficient of attenuation of photosynthetically active radiation [Kd (PAR)], the depth of the euphotic zone (Deu), PAR at the Secchi disk depth (Esd) and the absorption coefficient of PAR (F) in the Neva Estuary, one of largest estuaries of the Baltic Sea. Environmental variables impacting these indices were identified using data collected from midsummer 2012 to 2020. The Dsd values in the estuary ranged from 0.3 to 4.0 m, with an average value of 1.8 m, while the Deu/Dsd ratio ranged from 1.5 to 6.0 with an average value of 2.8. These values were significantly lower than those observed in the open waters of the Baltic Sea. The highest Deu/Dsd ratio was observed in turbid waters characterized by high Kd(PAR) and low Dsd. Contrary to expectations, Dsd did not exhibit a significant relationship with the concentration of chlorophyll a, raising doubts about the utility of historical Dsd data for reconstructing phytoplankton development in the estuary. Principal component analysis did not identify the primary environmental variables strongly affecting the optical characteristics of water. However, recursive partitioning of the dataset using analysis of variance (CART approach) revealed that the concentration of suspended mineral matter (SMM) was the primary predictor of Deu/Dsd, Kd(PAR), and F. This SMM was associated with the frequent resuspension of bottom sediments during windy weather and construction activities in the estuary. Concentrations of suspended organic matter and the depth of the water area were found to be less significant as environmental variables. Furthermore, the CART approach demonstrated that different combinations of environmental variables in estuarine waters could result in similar optical indicator values. To reliably interpret the data and determine the optical characteristics of water in estuaries from Dsd, more complex models incorporating machine learning and neural connections are required. Additionally, reference determinations of Esd in various regions with specific sets of environmental variables would be valuable for comparative analyses and better understanding of estuarine systems.