The establishment and operation of cascade reservoirs in the mainstream of the upper and middle reaches of the Yangtze River have changed the river’s thermal regimes. This study analyzed the correlation between water temperature and its influencing factors and employed various evaluation indexes—including $∆T$ (the temperature-increasing index, °C/100 km), $I_{EC}$ (the extreme fluctuation index), $I_{BD}$ (the baseline deviation index), and $I_{PO}$ (the phase offset time index). The aim was to uncover the variation characteristics and influencing factors of water temperature and quantify the impact of cascade reservoir construction on annual and seasonal water temperature rhythms. Our findings show that the construction and operation of cascade reservoirs weaken the synchronization of water temperature and air temperature downstream. The construction and operation of cascade reservoirs in the middle and lower reaches of the Jinsha River led to obvious homogenization, baseline deviation, and lagging effects on water temperature downstream, which intensified with the increase in storage capacity. These effects were more pronounced in colder months compared to warmer months. Additionally, the influence of tributaries and water–air heat exchange on these effects is alleviated to different degrees. These results are significant for assessing river ecological health in the context of cascade hydropower development.

The Three Gorges Reservoir (TGR), a landmark of human engineering, has significantly altered the hydrodynamics and ecology of its surrounding environment. Our research explores the hydrodynamic and ecological changes in the TGR, focusing on their implications for reservoir-induced water quality and water resource issues. We designed a 2D hydrodynamic and water quality model and implemented 15 operational scenarios with an advanced dynamic storage capacity method for the TGR during flood season, drawdown and impoundment periods. Our simulations well reproduced and predicted water levels, discharge rates, and thermal conditions of the TGR, providing critical insights. The dynamic storage capacity method significantly improved the precision of water level simulations. This approach achieved modeling errors below 0.2 m when compared to real measurements from seven stations. We performed a detailed analysis of the sensitive, sub-sensitive, and insensitive areas during three reservoir operation periods. The drawdown period showed the most extensive impact range (468 km river channel), while the impoundment period had the least impact range (76 km river channel). Furthermore, we quantified the delay of temperature waves during these periods, observing a maximum delay of approximately 120 km and a minimum delay of less than 10 km, which underscores the variability in hydrodynamic responses under different operational scenarios. Our findings reveal the complex sensitivities of the TGR to varied operational modes, aiding in the development of eutrophication and water resources control strategies. Our modeling application provides different operational scenarios and insights for ecological management strategies in large dam systems globally, informing future water resource management and policy-making, ensuring sustainable and effective management of large reservoir systems.

We review the research on L. fortunei over the past 22 years, systematically elucidating its impacts on ecological environments and water engineering structures. We explored the effects of external factors on the invasion and spread of L. fortunei, as well as the internal factors that impact the ecological environment and water engineering structures. We also provide new perspectives and directions for L. fortunei control. The major research findings include the following: (1) L. fortunei negatively impacts hydraulic structures, being hard to remove and capable of damaging them, disrupting normal operations. (2) L. fortunei’s ecological impact is multifaceted: it reduces water cloudiness and organic matter by filtering suspended particles and depositing feces, but its decay after death consumes dissolved oxygen, increasing chemical oxygen demand and lowering water quality. (3) L. fortunei control techniques are effective for localized use in small bodies of water and aqueducts, but their control in large open reservoirs is difficult to achieve with one method. Existing control methodologies for L. fortunei were systematically evaluated across multiple dimensions, including engineering applicability and feasibility, technical advantages and limitations, and economic cost-effectiveness. This comprehensive analysis establishes a decision-support framework for optimizing control strategy selection in diverse engineering scenarios and application contexts.

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.