Linking Water Quality Indicators in Stable Reservoir Ecosystems: Correlation Analysis and Ecohydrological Implications

This research was conducted to determine the connections between dissolved oxygen (DO), chemical oxygen demand (COD), permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), and ammonia nitrogen (NH3-H) across five reservoirs of Yunmeng County, China, from January to November 2022. Each month, water samples were collected and subjected to analysis using standard methods. The samples were collected and analyzed using standard methods: dissolved oxygen was determined using the electrochemical probe method, COD was measured via the rapid digestion spectrophotometric method, CODMn was detected using the potassium permanganate oxidation method, BOD5 was determined using the dilution and inoculation method, and NH3-N was measured by using the Nessler reagent spectrophotometry method. The results confirmed strong positive correlations between COD and CODMn, with different intensities from reservoir to reservoir. More specific and demanding COD parameters were used to estimate the level of oxygen consumption; hence, a more variable correlation strength was observed between BOD5 and the other two parameters. Thus, BOD5 was found to be the main indicator of biodegradable organic matter and bacterial oxygen consumption. However, the results were negative, showing a decreasing trend. This means that the oxygen content was lower in the majority of reservoirs, which is attributed to the decomposition of ammonia nitrogen and the presence of organic matter. These findings significantly contribute to the development of appropriate programs for efficient water quality monitoring and the development of reservoir-specific management strategies. This study suggests that there is a need for continuous monitoring of these parameters, together with the extension of the program to additional reservoirs and water quality indicators, along with the use of advanced modeling techniques to clarify the underlying factors that connect water quality parameters in these complex reservoir ecosystems. © 2024 by the authors.