Revisiting seasonal dynamics of total nitrogen in reservoirs with a systematic framework for mining data from existing publications

Investigation of seasonal variations of water quality parameters is essential for understanding the mechanisms of structural changes in aquatic ecosystems and their pollution control. Despite the ongoing rise in scientific production on spatiotemporal distribution characteristics of water quality parameters, such as total nitrogen (TN) in reservoirs, attempts to use published data and incorporate them into a large-scale comparison and trends analyses are lacking. Here, we propose a framework of Data extraction, Data grouping and Statistical analysis (DDS) and illustrate application of this DDS framework with the example of TN in reservoirs. Among 1722 publications related to TN in reservoirs, 58 TN time-series data from 19 reservoirs met the analysis requirements and were extracted using the DDS framework. We performed statistical analysis on these time-series data using Dynamic Time Warping (DTW) combined with agglomerative hierarchical clustering as well as Generalized Additive Models for Location, Scale, and Shape (GAMLSS). Three patterns of seasonal TN dynamics were identified. In Pattern V-Sum, TN concentrations change in a V shape, dropping to its lowest value in summer; in Pattern PSum, TN increases in late summer/early fall before decreasing again; and in Pattern P-Spr, TN peaks in spring. Identified patterns were driven by phytoplankton growth and precipitation (Pattern V-Sum), nitrate wet deposition and agricultural runoff (Pattern P-Sum), and anthropogenic discharges (Pattern P-Spr). Application of the DDS framework has identified a key bottleneck in assessing the dynamics of TN - low data accessibility and availability. Providing an easily accessible data sharing platform and increasing the accessibility and availability of raw data for research will facilitate improvements and expand the applicability of the DDS framework. Identification of additional spatiotemporal patterns of water quality parameters can provide new insights for more comprehensive pollution control and management of aquatic ecosystems.

Automated summary

The study introduces a DDS framework for analyzing seasonal TN dynamics in 19 reservoirs, identifying three distinct patterns driven by various factors. The research highlights the importance of improving data accessibility and availability for enhancing the applicability of the DDS framework. Identifying additional spatiotemporal patterns of water quality parameters can provide new insights for comprehensive pollution control and management of aquatic ecosystems.