A comprehensive water quality assessment for a typical river–lake watershed in Northeast China: implications for the water management of boundary lake

• An improved water quality index (WQI) model was established based on machine learning algorithm. • The WQI condition of 60%−70% monitoring stations in the Muling-Xingkai watershed was good. • River water quality was worse than that of reservoirs and lakes, especially in summer-autumn. • Main driving factors for water quality deterioration differ in summer-autumn and winter-spring. • Nitrogen input and endogenous phosphorus release should be curbed to protect Xingkai Lake. Seasonal freezing and a mismatch between river and lake water quality targets have limited the accurate evaluation of water quality in the northern river–lake system. The water quality of the boundary lake poses a threat to aquatic ecological security and may also affect regional geopolitical stability. Therefore, there is an urgent need for a comprehensive water quality evaluation system to effectively manage the water health of boundary lakes. In this study, we aimed to develop a new comprehensive water quality index model to analyze the water quality status and identify the underlying driving mechanisms within the Muling-Xingkai watershed, thereby proposing effective water management strategies. The XGBoost model and the aggregation function of eight sub-indicators were employed to identify the primary control indicators across various seasons. These methods reduced data redundancy and enhanced the sensitivity of the comprehensive water quality index (WQI) model. The weighted harmonic mean model ( R 2 = 0.95, RMSE = 7.1 for summer-autumn; R 2 = 0.96, RMSE = 10.2 for winter-spring) and unweighted Canadian Council of Ministers of the Environment model ( R 2 = 0.94, RMSE = 4.2 for summer-autumn; R 2 = 0.90, RMSE = 4.8 for winter-spring) were identified as the optimal functions for water quality assessment. Based on the WQI assessment of 480 water samples collected during 2022–2023, 60 % to 70 % of the monitoring stations achieved a good water quality status (WQI score > 80) in the Muling-Xingkai watershed. The water quality status within the watershed, as assessed by the WQI model, followed the order: river < reservoir < Xingkai Lake < Xiaoxingkai Lake. In addition, our systematic approach efficiently identified key water quality indicators from 11 types of indicators, including total nitrogen (TN), total phosphorus (TP), and water temperature (Tw) during the summer-autumn period, and TN and dissolved oxygen (DO) in the winter-spring period. Based on structural equation modeling (SEM), human activities (irrigated area, fertilizer application rate) and natural factors (air temperature, precipitation, and flow) were identified as the primary driving forces behind water quality deterioration in the Muling-Xingkai watershed during the summer-autumn and winter-spring seasons, respectively. To safeguard the ecological health of Xingkai Lake, it is imperative to reduce nitrogen inputs from the Muling River and mitigate phosphorus release from lake sediments in response to climate warming and the expansion of irrigation districts.

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