Impact of High Temporal Resolution Data on Water Quality Modeling: Insights from Erhai Case Study

Lake monitoring is essential for sustaining aquatic ecosystems, and accurate estimation/prediction of water quality parameters is crucial to this effort. Despite its importance, the performance of predictive models built on varying temporal resolutions remains underexplored systematically. This study used daily and 4 h high temporal resolution (HTR) datasets to assess the performance of multiple machine learning models—namely, Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks—under consistent data scales. The results indicate that dissolved oxygen (DO) exhibits pronounced sensitivity to temporal resolution, while total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH3-N) show distinct, parameter-specific response patterns that align with the temporal characteristics of their underlying biogeochemical processes. This research helps to deepen the understanding of how temporal data resolution influences model performance in water quality prediction, offering valuable insights for selecting optimal data resolutions and modeling techniques to enhance lake monitoring and protection strategies.