Thermal-Process-Informed Input-Variable Selection for Multi-Site Short-Term River Water-Temperature Forecasting in the Upper and Middle Reaches of the Yangtze River

River water temperature connects hydrodynamic processes, air–water heat exchange, and aquatic ecological responses. Although data-driven models are increasingly used for short-term water-temperature forecasting, input-variable choice still influences both predictive skill and the interpretation of model errors. This study examined daily water-temperature forecasting at nine hydrological stations in the upper and middle reaches of the Yangtze River. The stations were grouped according to natural hydro-meteorological background, reservoir regulation, and compound disturbance. Based on surface-water heat balance and order-of-magnitude analysis, antecedent water temperature, air temperature, and discharge were selected as process-related candidate inputs and tested using LSTM and xLSTM models. The experiments considered input-window length, learning rate, batch size, and the inclusion of discharge. Under the no-discharge scheme, learning rate had the clearest effect on the predicted water-temperature series. For LSTM, the median predicted-temperature differences caused by changes in window length, learning rate, and batch size were 0.055, 0.077, and 0.056 °C, respectively; the corresponding values for xLSTM were 0.089, 0.102, and 0.073 °C. One-day-ahead forecasts for the selected representative dates produced mean RMSE values of 0.160 °C for LSTM and 0.165 °C for xLSTM, compared with 0.183 °C for a persistence baseline. The reservoir regulation impact group showed the lowest errors, whereas the compound disturbance impact group had higher errors and clear within-group differences. The contribution of discharge varied among stations and models: for LSTM, RMSE decreased at Batang, Panzhihua, and Huanglingmiao, but increased or changed little at Gangtuo, Yichang, and Cuntan; for xLSTM, the average RMSE did not decrease after discharge was added at the seven stations with discharge data. xLSTM showed local advantages at Huanglingmiao and Cuntan. These findings show that process-informed input selection offers a consistent basis for comparing multi-site water-temperature forecasts and for interpreting error differences among stations and input schemes.