A Novel Perspective on Exploring Reservoir Impacts on River Water Temperature Using Machine Learning

The construction of reservoirs has altered river water temperature, consequently impacting aquatic ecosystems. In this study, we investigated the influence of cascade reservoirs on river water temperature, focusing on six cascade reservoirs in the Lancang River Basin. Seasonal and trend decomposition (STL) and the Pettitt test were employed to analyze the characteristics of water temperature changes. Trend analysis and the Pettitt test identified critical water temperature variation points that align with reservoir construction time. By comparing the water temperature data before and after reservoir construction, it is shown that reservoir construction significantly changes the annual process of water temperature, with a significant increase in low-temperature water. Among all reservoirs, Xiaowan (XW) and Nuozhadu (NZD), two reservoirs with high regulation capacity, have a particularly prominent impact on water temperature. Ecological operation is an effective way to improve the outflow water temperature of reservoirs, and it uses accurate outflow water temperature prediction as a basis. Compared to numerical models, machine learning models have the advantages of high efficiency and nonlinear fitting; hence, they can be used to predict the outflow water temperature of reservoirs. However, most machine learning models from previous studies exhibit poor interpretability. To simulate and predict reservoir outflow water temperature, four machine learning algorithms—Support Vector Regression (SVR), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)—were applied to XW and NZD reservoirs. Hyperparameters were tuned using Bayesian optimization. Results indicated that the XGBoost model performed the best, achieving the highest prediction accuracy (RMSE ≤ 0.25°C, R² = 0.98), with a maximum prediction error of less than 1°C. RF and LightGBM also demonstrated strong performance, while SVR showed relatively lower accuracy. In order to improve the interpretability of machine learning models, we use Shapley additive explanations (SHAP) method to reflect the importance of input variable features. SHAP analysis results of the XGBoost model revealed that thermal input factors, such as reservoir inflow temperature (Tin) and inflow discharge (Qin), were the most influential variables affecting outflow water temperature, followed by reservoir operation factors, including outflow discharge (Qout) and water level (WL). Air temperature (Tair) had the least impact. The research frame and results can provide a reference for reservoir ecological regulation and watershed ecological environment management.