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 P-Sum, 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.

研究水质参数的季节性变化对于了解水生生态系统结构变化的机制及其污染控制至关重要。尽管关于水质参数时空分布特征的科学成果不断增加，例如水库中的总氮 (TN)，但仍缺乏使用已发表数据并将其纳入大规模比较和趋势分析的尝试。在这里，我们提出了一个数据提取、数据分组和统计分析（DDS）的框架，并以 TN 在油藏中的例子说明了这个 DDS 框架的应用。在与储层TN相关的1722篇出版物中，19个储层的58个TN时间序列数据符合分析要求，使用DDS框架提取。我们使用动态时间扭曲 (DTW) 结合凝聚层次聚类以及位置、尺度和形状的广义加性模型 (GAMLSS) 对这些时间序列数据进行了统计分析。确定了三种季节性 TN 动态模式。在V-Sum模式中，TN浓度呈“V”形变化，夏季降至最低值；在模式 P-Sum 中，TN 在夏末/初秋增加，然后再次减少；在 P-Spr 模式中，TN 在春季达到峰值。已识别的模式由浮游植物生长和降水（模式 V-Sum）、硝酸盐湿沉降和农业径流（模式 P-Sum）以及人为排放（模式 P-Spr）驱动。DDS 框架的应用确定了评估 TN 动态的关键瓶颈——低数据可访问性和可用性。提供一个易于访问的数据共享平台并提高研究原始数据的可访问性和可用性将有助于改进和扩展 DDS 框架的适用性。识别水质参数的其他时空模式可以为更全面的污染控制和水生生态系统管理提供新的见解。