A New Water Temperature Simulation Method Based on Air Temperature–Surface Solar Radiation–Recurrent Neural Network Coupling

Water temperature (WT) is a vital parameter influencing river ecosystems. Air temperature (AT) is usually regarded as a major input in WT simulation, but surface solar radiation (SSR) is often overlooked in current statistical methods. In this study, a new WT simulation method is proposed based on a Recurrent Neural Network (RNN) that integrates AT and SSR. The AT-SSR-RNN coupling model is applied in the lower reaches of the Yangtze River. The results show the following: (i) The annual mean WT in the lower reaches of the Yangtze River is 18.8 °C, and the peak WT usually occurs in August. (ii) The proposed model demonstrates robust simulation performance, yielding a Nash–Sutcliffe Efficiency (NSE) of 0.9100, Root Mean Square Error (RMSE) of 2.08 °C, Mean Absolute Error (MAE) of 1.65 °C, and Symmetric Mean Absolute Percentage Error (SMAPE) of 9.61%. (iii) Incorporation of SSR substantially enhances simulation accuracy, with NSE increasing by 8.2% and RMSE decreasing by 24.6%, MAE by 30.4%, and SMAPE by 33.1% compared to the AT-only model. (iv) Compared with the Back-Propagation Neural Network (BPNN) and Random Forest (RF), the RNN achieves superior performance with the highest NSE (0.9100) and lowest error indicators (RMSE: 2.08 °C, SMAPE: 9.61%).