To promote ecosystem protection in the Wujiang River, this paper proposes a practical approach for calculating the environmental flow. The proposed approach combines the idea of the “guarantee rate” of the flow duration curve (FDC) method and the grading idea of the Tennant method. A daily flow series of the Wujiang River was compiled from 1956 to 2019 and used to compare the effect of the proposed approach versus the traditional approaches in four selected sections along the river. The results show that the environmental flow of the Wujiang River can be divided into five levels by the T-FDC method, with a level-by-level disparity, and all levels can capture the temporal and spatial variability of river flow. Additionally, the calculated basic environmental flow process ranges between the historical minimum and second minimum monthly average flow, and the threshold width of the optimal flow is more reasonable than the Tennant method. The T-FDC method can provide technical support for Wujiang River ecosystem management and sustainable development.

Data-driven models have shown remarkable achievements in runoff prediction, but their simulation results can be overly homogenized due to the distillation of all simulation aspects into the loss function. This can make the models unreliable for predicting extreme events, leading to issues subsequent reservoir operations. This paper proposes a novel data-driven hybrid machine-learning model called the multi-objective optimized extreme learning machine (MOELM) to provide an accurate runoff forecasting for reservoirs. The objective is to minimize simulation error, with an additional focus on flood deviation. The results show that: (1) MOELM can improve flood events prediction, reducing the root mean square error (RMSE) for flood series by 5.27% without increasing the overall prediction error at Longtan reservoir. Compared to hydrological models, MOELM can reduce operational risk with lower reservoir maximum outflow and water level during typical flood events, and it can potentially increase hydropower generation at Longtan reservoir by 130 million kW·h. (2) MOELM can be transferred to other cross-sections with excellent performances, demonstrating hydrological transferability from fluctuation to flatness in regime. (3) Partial mutual information is introduced for input variable selection, with discharge at lag times t-4, t-1, t-8, and t-2 being vital to the prediction model. Our model is practical, requiring no additional input, fitting the hydrological runoff holistically, and capable of providing accurate flood forecasts.

Climate change has far-reaching impacts that have created new challenges for water resource management. As an important measure to coordinate the relationship between society, economy, and environment, reservoir scheduling can reduce the future impact of climate change. It is, therefore, important to investigate the impacts of scheduling on reservoir operation. In this study, a reservoir system in the Pearl River Basin was selected to explore these impacts. Results show that the basin temperature significantly and abruptly increased in 2000, and precipitation and streamflow changed abruptly in 1983 and 1992. Historically, climate change has increased power generation, increased the risks to water supply security and ecological protection, and altered the relationship between power generation and ecological protection objectives. Based on 28 global climate models, the rank scoring method, Delta statistical downscaling, and the SWAT model, three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) in CMCC-CM were evaluated to assess the climate change impact. In the future, the temperature will continue to exhibit an increasing trend, and the amount and distribution of streamflow will be altered. Although climate change will increase power generation in the dry season, it will also bring about new challenges for ecological protection and water supply security. Accordingly, the Datengxia Reservoir operating rules may require significant amendments.

This study investigated the combined impacts of the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on streamflow under four scenarios: neutral, pure ENSO, pure IOD, and a combination of ENSO and IOD. The Jinsha River Basin (JRB), at the head of the Yangtze River, was used as a case study. By using statistical methods such as coherent wavelet analysis (WTC), we are committed to studying what kind of impact the IOD will have, the difference in impact between ENSO and IOD at different stages, and the difference in impact between ENSO and IOD on the mean and extreme values of runoff, compared with traditional single ENSO event, to provide support for water resource management, especially for reservoir operation. The key results are as follows. (a) Both ENSO and IOD events affect annual and seasonal streamflow in the JRB. (b) The impact of pure IOD events on annual streamflow in the JRB was twice as great as that of pure ENSO events in developing years, whereas the opposite was true in decaying years. (c) The combined impact of ENSO and IOD led to a higher streamflow maximum than the annual or seasonal average streamflow. Conversely, their impact on the streamflow minima was less than 10% during both developing and decaying years, except at Zhimenda Station. (d) Overall, water shortages could be more serious in developing years than in neutral years, and much more attention should be given to flooding control in decaying years. These results can be used as a reference for water resource management concerning agricultural planning and ecological protection in the JRB.

It is important to comprehend the evolution of drought characteristics and the relationships between different kinds of droughts for effective drought mitigation and early warnings. The study area was the Pearl River Basin, where spatiotemporal changes in the multiscale water balance and soil moisture at various depths were analyzed. The meteorological data used in this study were derived from the China Meteorological Forcing Dataset, while the soil moisture data were obtained from the ECMWF ERA5-Land reanalysis dataset. The Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSI) were applied to represent meteorological and agricultural droughts, respectively. By using the run theory for drought event identification, the characteristic values of drought events were analyzed. The correlation between the multiscale SPEI and SSI was examined to represent the propagation time from meteorological drought to agricultural drought. This study indicated that while the western part of the Pearl River Basin experienced a worsening atmospheric moisture deficit and the southern part had intensifying dry conditions for soil moisture, the rest of the basin remained relatively moist and stable. Soil conditions were moister in the deeper soil layers. The durations of agricultural droughts have generally been shorter than those of meteorological droughts over the past 40 years. Within the top three soil layers, the severity, duration, and frequency of drought events progressively increased, increased, and decreased, respectively, as soil depth increased. The propagation time scale from a meteorological drought to a four-layer agricultural drought was typically within 1–5 months. This study advanced existing research by systematically analyzing drought propagation times across soil depths and seasons in the Pearl River Basin. The methodology in this study is applicable to other basins to analyze drought complexities under climate change, contributing to global drought resilience strategies. Understanding the spatiotemporal characteristics of meteorological and agricultural droughts and the propagation time between them can help farmers and agricultural departments predict droughts and take appropriate drought-resistant measures to alleviate the damage of droughts on agricultural production.

With the continuous improvement of reservoir projects and the advancement of digital twin basin initiatives in China, rapidly and accurately generating long-term practical reservoir operation schedules has become a key priority for stakeholders. This study proposes a Theory-guided Long Short-Term Memory (TgLSTM) model to extract optimal reservoir operation rules accurately and reliably. Concretely, TgLSTM integrates data-fitting accuracy with the physical constraints of an operation, e.g., water level constraints and minimal discharge constraints, to address the low credibility often observed in conventional LSTM networks. Using the Three Gorges Reservoir during the dry season as a case study, a multi-year hydrological series optimized by particle swarm optimization (PSO) was used to train the TgLSTM network and derive optimized operation rules. Results show that TgLSTM efficiently generates operation schemes close to the theoretical optimum, achieving power generations of 4.27 × 10¹⁰ kW·h and 4.19 × 10¹⁰ kW·h in two test years, with deviations of only 4.20% and 2.33%, respectively. Compared to traditional LSTM models, TgLSTM is more reliable as it captures key operational characteristics such as terminal water levels and water level fluctuations, maintaining an average ten-day drawdown depth below 1.5 m—significantly lower than the 7 m fluctuations observed with conventional LSTM. Furthermore, comparative analyses against SwR, BP–ANN, and SVM confirm that TgLSTM offers a moderate performance in absolute metrics but is the best to simulate the constrained reservoir operation.