Joint operation of reservoirs using forecast information has been demonstrated to enhance floodwater utilization efficiency. However, forecasting uncertainties may lead to flood risk that may propagate across the reservoir systems. Currently, no systematic framework exists to quantitatively assess how forecast uncertainties influence flood risks. This study established a risk-controlled framework to determine the upper bound of pre-fill volume (UBFV) of the reservoirs, ensuring no additional flood risk is introduced. In this model, forecast uncertainty is explicitly linked to flood risk through probabilistic constraints, and the UBFV for reservoir clusters is derived under risk-neutral conditions. Furthermore, inter-reservoir UBFV relationships within cascades are quantified, and the impacts of forecast uncertainty are comprehensively analyzed across three key dimensions: spatial rainfall distribution, forecast lead time, and allowable flood high water level. The Jinxia Reservoir Group and the Three Gorges Cascade Reservoir System are examined as a case study. Results show that: (1) The maximum UBFV is determined to be 52 x 108 m3 and 51 x 108 m3 for the Jinxia and Three Gorges systems, respectively, when utilizing 1-7-day rainfall forecasts. (2) UBFV initially increases and then decreases with longer forecast periods, reaching maximum value at 6-7 days in Jinxia and 3-5 days in Three Gorges. Higher forecast accuracy reduces UBFV. (3) The higher the allowable flood high water level, the higher the UBFV of the reservoir.

Hydro-meteorological variables are essential drivers of the water cycle, and understanding their dynamics is crucial for effective water resources management and disaster mitigation. Conventional trend analysis methods typically assume sequence normality and independent, lacking flexibility to efficiently analyze non-stationary and nonlinear hydrological series. To overcome these limitations, this study proposes a Grey Wolf Optimization-enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method for integrated trend-periodic analysis. Validation using synthetic datasets demonstrates superior performance over the traditional methods, achieving accuracy higher than 85% for sequences with complex autocorrelation and heterogeneous distributions, while identifying periodicities undetectable by other methods. Applied to the Yangtze River Basin, the method reveals (1) warming trend in annual mean temperature, (2) spatially heterogeneous precipitation changes i.e., increases in northwest and northeast, decreases in central and southwestern regions. (3) declining runoff trends in the mainstem and most tributaries, with significant periodicities at 2–3-year and 11-year intervals. With the trend-periodic analysis, the runoff extreme can be forecasted. This method achieves an accuracy rate of 79.31%, surpassing the wavelet decomposition method by 20.67% points (58.64%). Extrapolation indicates potential low-value runoff extremes during 2025–2027, suggesting sustained high frequency or intensity of severe droughts over the coming decade.

As a typical high-dimensional, multiconstraint optimization problem, the joint flood control operation of reservoir groups faces challenges such as the curse of dimensionality and the coupling of multiple constraints. This paper proposes a joint flood control peak-shaving optimization operation method for reservoir groups based on the identification of critical reservoirs. The method begins by optimizing the selection of reservoirs participating in the joint operation process. To enhance computational efficiency and interpretability, critical reservoirs are identified preliminarily using hydrological and structural indicators, providing a structure-aware dimensionality reduction strategy that complements existing systemwide optimization approaches. This method was applied to a seven-reservoir system in the Dawen River basin in China. It achieved flood control and peak-shifting effects comparable to those of full-system optimization, while reducing the number of reservoirs by 40%. The dimensionality and decision-making complexity arise from the interdependencies between reservoirs and operational constraints. Although the system in this study involved only seven reservoirs, the primary goal was to demonstrate the feasibility of the proposed dimensionality-reduction method. This approach is scalable and can be applied to larger, more complex systems, in which its benefits would be more pronounced. The study was implemented in the Dawen River basin of Shandong Province, China, and provides a technical reference for similar research.

This study integrates an analytical method with realistic heuristic rules for more effective realization of forecast-informed reservoir operation (FIRO). It presents dFLWL-EFO, a new FIRO decision-support framework that combines optimal hedging and risk tolerance-based operating rules, considering flood control and water conservation for the postflood season, which are often inconsistent, especially under increasing inflow variability. Effective use of weather and hydrological forecasts via FIRO is essential in addressing this challenge. dFLWL-EFO adapts ensemble inflow forecasts via the Bayesian model averaging method to quantify forecast uncertainty. It includes a two-stage stochastic optimization model for dynamic control of the flood-limited water level (dFLWL), explicitly incorporating hedging policies to balance flood mitigation and water shortage reduction under low to moderate flood risk; meanwhile, the framework integrates risk tolerance-based release rules from ensemble forecast operations (EFO), an approach that has been validated for the operation of real-world reservoirs, to ensure dam safety during high-risk flood events. Thus, the integrated framework holds the complementariness of dFLWL and EFO. Moreover, dFLWL-EFO provides reservoir operators with the flexibility to incorporate operational priorities, legal constraints, and institutional guidelines into the implementation of FIRO. This FIRO framework is tested on Folsom Lake in California. Results show significant increase in water conservation in the postflood season without increasing flood risks. By introducing operational flexibility enabled by forecast, the proposed dFLWL-EFO presents a realistic, effective, and generalized approach to move research to real-world reservoir operation practices.

Conventional flood-season regulation typically keeps reservoir water levels at or below the planned flood limited water level (FLWL). While this practice safeguards flood-control safety, it can leave flood-control storage unused in small-flood years or when short-term forecasts are available, limiting further gains in reservoir benefits. To address this issue, we propose an FLWL optimization and control framework for the lower Jinsha River cascade-Wudongde (WDD), Baihetan (BHT), Xiluodu (XLD), and Xiangjiaba (XJB) reservoirs (Jinxia Reservoir Group). The framework integrates dynamic control of flood-season operating water levels, formulation and allocation of total pre-storage volume, and risk–benefit analysis. It includes three modules: (1) estimating the cascade-wide total pre-storage volume under multiple constraints using a pre-release capacity constraint method; (2) allocating this volume among alternative schemes to derive reservoir-specific dynamic FLWL control domains; (3) assessing maximum and expected risk rates together with hydropower benefits to identify the optimal pre-storage allocation plan. Application results show a total pre-storage volume of 2.16 billion m³ (13.9

Accurate rainfall forecasting is vital for reservoir operation, shifting focus from minimizing missed detections in flood season to reducing false alarms post-flood. Conventional Multi-Model Ensemble (MME) methods use static weights, ignoring forecast skill variations across rainfall intensities. This study proposes MME-MID, a novel framework using Normalized Average Mutual Information Decomposition to dynamically weight forecasts by categorized rainfall intensities. Two decomposition approaches are applied: (1) Uncertainty decomposition prioritizes higher-accuracy categories to reduce post-flood false alarms; (2) Information decomposition enhances detection of high-intensity events during flood season. Validation in Dahuofang and Huanren basins shows MMEMID reduces miss ratio by 5.5 % in flood season and false alarm ratio by 11 % post-flood versus conventional MME, without increasing overall error, significantly improving decision reliability.

Hydrological regimes in complex river basins are governed by heterogeneous hydrological interactions, including driver-to-response interactions induced by multi-source inflows and response-to-response interactions within basin-scale hydrological processes. These interactions are essential for guiding scientific river-basin regulation, while their systematic quantification remains challenging, constrained by computation-intensive modeling and scenario-limited analysis. To this end, this study develops an Interpretable Spatio-Temporal Hydrological Interaction Learning (InSTIL) framework, which combines LSTM-based temporal encoding, graph-based spatial propagating and Shapley Additive Explanations, to explicitly capture and quantify the hydrological interactions. Applied to the Three Gorges Reservoir (TGR) and Dongting Lake (DTL) system, InSTIL delivers robust simulations of lake water levels with an average RMSE of 0.425 m, consistently outperforming LSTM and GCN benchmarks, particularly under extreme conditions. Furthermore, interaction interpretation reveals that: (1) For driver-toresponse interactions between rivers and the lake, their spatial patterns from different rivers are governed by inflow locations and runoff volumes, with TGR releases exerting the dominant influence (contributing 32.89%48.58% across individual sites) and tributary Yuan River ranking second. Meanwhile, these interactions appear unimodal over time, showing the strongest daily impacts at 1-3 days before lake's responses. Their multi-day cumulative effects inform an effective regulation window of 7-15 days. (2) For response-to-response interactions within the lake, the heterogeneous response timing and magnitude across DTL reveal the weak hydraulic connectivity in the south sub-lake and the presence of a cross-sublake hydraulic pathway. Overall, InSTIL provides a transferable paradigm for robust hydrological simulations and systematic interaction interpretation, offering practical insights for sustainable river-basin management.

The release rules for large reservoirs are generally graded and judgment-based, allowing for some flexibility in managing uncertainty in forecast information. Ignoring this aspect can introduce bias in flood water resource utilization and flood risk analysis. This study proposes a dynamic control flood limit method for reservoirs, accounting for the uncertainty in forecast errors, to optimize the use of forecast data. Monte Carlo simulations are employed to model and assess uncertainty in the sample, and the error domain is systematically quantified to evaluate the probability that the observed inflow falls within the forecast error range. This determination is pivotal in ascertaining the utilization of forecast data, thereby facilitating the integration of multiple forecast lead times. The proposed method is applied to the Three Gorges Reservoir and compared with a traditional approach using single forecast data. Results show that: (1) the proposed method has been demonstrated to facilitate real-time dynamic decision-making, thereby enabling the selection of forecast data that exhibits both acceptable risk and optimal benefit. (2) during the rising flood stage of 20,190,724, this method has been shown to enhance power generation by 2.83 × 108 kWh while concomitantly reducing flood risk in comparison with conventional methods. Flood risks upstream and downstream are reduced by 0.415 and 0.005, respectively, without compromising the benefits of power generation. This methodology provides a reference for flood water resource management in basin reservoirs, providing decision-makers with a strategy that best aligns with their preferences.

In actual reservoir operations, water levels are often below the flood-limited water level, providing additional flood control storage and enabling the reservoirs to store more floodwater. This allows other reservoirs to reduce flood control storage through storage substitution, raising their water levels to enhance conservation benefits. The key question is how much the water level can raise, i.e., what is the substitution relationship of flood control storages. To address this issue, this study develops a flood control storage substitution model to utilize additional flood control storage without increasing the system's flood control risk. The cascade hydropower system of the Wudongde, Baihetan, Xiluodu, Xiangjiaba, and Three Gorges Project on the Yangtze River, China, is taken as a case study. Results indicate that the water level of the Three Gorges Project can be raised to 153 m when the operating water level of XLD and BHT are at dead water levels. The substituted ratio is less than 1.0 due to the discharge of the Three Gorges Project is reduced by substitution. The substituted ratio and upper bound of the substituted flood control storage are related with the volume of additional flood control storage, flood magnitudes and hydrographs, and the flood composition. In real-time operations, the storage substitution relationship can be used to adjust the operating water levels and increase power generation without increasing flood risk.

Although joint flood control operation models have advanced, practical applications still face key challenges. High-dimensional decision spaces and low efficiency limit their widespread engineering use. This study addresses these problems by proposing a phase-based joint flood operation method. To accommodate the evolving hydrometeorological conditions and real-time flood control demands, the overall scheduling process is divided into distinct operational phases. By optimizing only key flood phases, the number of time steps involved in the optimization process is substantially reduced, thereby enhancing computational efficiency. An optimization model is developed to support this method. On the basis of the constructed optimization model, the rationality of the stage-based operation period division is analyzed, and its impact on overall operation performance is discussed. A case study involving seven reservoirs in the Dawen River Basin is presented. Results show that the method reduces decision variables by 86

Uncertainty in forecast information can introduce flood risk into the process of reservoir flood water resource utilization. However, reservoirs exhibit an accommodative transformation relationship to forecast uncertainty. A flood risk analysis model that ignores this relationship may lead to biased flood risk calculations, which in turn may result in incorrect scheduling decisions. In this study, flood risk in the process of reservoir flood water resource utilization is divided into two components: pre-release volume risk (PVR) and pre-release timing risk (PTR), based on an analysis of the underlying risk mechanism. A flood risk calculation method, based on the conditional value-at-risk (CVaR) theory, is proposed. The accommodative transformation relationship of forecast uncertainty in reservoirs is quantified through numerical simulation, and the factors influencing this relationship are explored. Finally, a forecast information utilization method based on the accommodative transformation relationship is presented. The proposed method is validated using the Three Gorges Reservoir as a case study. The main research findings are as follows: (1) Reservoirs have an accommodation space for forecast uncertainty in flood water resource utilization. Flood risk only becomes significant when this uncertainty exceeds the accommodation space. Neglecting this relationship may lead to an overestimation of flood risk, hindering optimal flood water resource utilization. (2) The faster the inflow rises, the larger the accommodation space for forecast uncertainty in the reservoir, but this also increases the flood risk. (3) The forecast utilization approach based on accommodation space can increase power generation by 2.25 x 108 kWh without raising flood risk during a typical flood rise.

Floodwater utilization is critical for balancing flood control and water conservation in multi-reservoir cascade systems, yet conventional reservoir operations often overlook the potential of storage substitution among different reservoirs. This study develops an extended flood control storage substitution model that accounts for both upstream-downstream interactions and intermediate reservoir regulation, thereby enabling a more precise assessment of flood control tasks substitution across multiple reservoirs. Focusing on the Jinsha River cascade and the Three Gorges Project (TGP), we systematically evaluate how additional flood control storage can be substituted and how to reserve storage to maximize hydropower benefits while maintaining flood safety. Results show that upstream reservoirs can collectively substitute as much as 3.3 billion m3 of TGP's design flood control storage, raising TGP's operating water level by up to 8 m and boosting system-wide power generation by 2.8 billion kWh. Moreover, strategic initial storage reservation-particularly assigning smaller additional storage to Baihetan and larger to Xiluodu-further enhances hydropower output without increasing flood risk. These findings underscore the importance of coordinated multi-reservoir operations that harness the "substitution effect" to optimize water concervation and electricity generation. By offering a generalizable model for quantifying reservoir substitution and guiding initial storage reservation decisions, this study provides valuable insights into floodwater resource utilization, promoting strategies to optimize water conservation, improve hydropower generation in multiple reservoirs systems.

ABSTRACT Severe droughts typically last for extended periods and result in substantial water shortages, posing challenges for water conservancy projects. This study proposed a framework for coordinating drought mitigation operations across projects of various scales. First, the regulation and drought mitigation capacities of each project were analyzed, and thus critical reservoirs was identified. Subsequently, a joint regulation model for water supply, prioritizing projects based on their regulatory capacity from weak to strong, was established. An optimization model is then developed to determine the drought-limited levels for critical reservoirs, aiming to minimize water shortages. This model facilitates temporal coordination of water resources to prevent severe water shortages with frequent mild water shortages. Results in the Chuxionglucheng District of Chuxiong, Yunnan Province, during the severe drought period from 2009 to 2013 demonstrate significant reductions in water shortage. Specifically, the maximum shortage ratio decreased from 59 to 45% for agriculture and from 52 to 8% for industry. Moreover, emergency measures for drought mitigation were compared and recommended for regions with weak projects regulation. Overall, this framework offers a systematic approach to enhancing drought resilience across diverse water conservancy projects in severe drought conditions.

Many studies have evaluated the value of long-term inflow forecast for hydropower operation in high-precision watersheds and explored the impact of forecast uncertainty on hydropower benefits. However, few of them focuses on the low-accuracy regions and the specific impact of forecast errors on hydropower benefits. Taking Hunjiang cascaded system in China as an example, this study investigated the value of low accuracy long-term inflow forecast for hydropower operation, revealed the influence mechanism of forecast errors on hydropower benefits, and determined the critical threshold for accuracy of beneficial forecast. Results show that low accuracy forecast but higher than critical threshold can increase hydropower generation. Hunjiang has a low accuracy long-term forecast with qualified rate being 30-40 %, but annual hydropower generation can be increased by 14.86-29.58 million kWh. The influence mechanism of forecast errors on hydropower operation presents three typical scenarios, two of which are harmless. One is that the error tolerance of operation rules can avoid some decision-making errors; the other one is that when current misreporting is consistent with the inflow in a longer lead time, the misreporting may instead be beneficial. We conclude that low accuracy long-term forecast is valuable but higher accuracy brings higher benefits.

Upstream reservoir impoundment has brought adverse impacts on the downstream river-connected lakes, including lower water levels, frequent droughts, and reduced biodiversity. Existing research on ecological reservoir operation focused more on the effectiveness of operation redesign for ecology improvement, but less discussion on why they are eco-friendly. In this paper, the reservoir operation mechanisms targeting lake ecology are explored from three perspectives: ecological impoundment strategy, dominant characteristics of ecology and conservation tradeoffs, and critical parameters of the rule curves. Therefore, a reservoir operation framework, which couples the lake hydrological response model and multi-objective optimization model, is developed in this study. This framework is to maximize the water conservation and lake ecology benefits, assessing the latter by ecological reliability and ecological water shortage rate. Taking the Three Gorges Reservoir and Dongting Lake as the case study, operation rule curves are optimized for different impoundment strategies. The results indicate that impoundment with early storage time and slow storage rate can improve the ecological reliability and ecological water shortage by 9.90 % and 16.39 % than conventional operation. The ecological objectives are weakly competitive or synergistic when impounding fast, but strong competitive when impounding slow. Meanwhile, as the storage rate declines, the ecological improvement causes a slight decrease in power generation and a significant decrease in water supply. Finally, the crucial parameters of the rule curves are explored and appropriate values are recommended for different ecological preferences.

To timely prompt anticipatory operations and relieve prolonged droughts effectively, multiple hydrological forecasts that provide thorough information about future streamflow need to be employed. However, it is challenging to integrate multiple forecasts in reservoir optimization and obtain a proper trading-off between current and future water supply benefits. This study proposes a novel Model Predictive Control (MPC) that can not only utilize the streamflow forecast but also utilize other two categorical forecasts, including the regime state forecast to capture the long-term persistence of the streamflow process and the annual streamflow volume state forecast. In the novel MPC, a discount factor depending on the annual streamflow volume state forecast weighs up the current deficit cost decided by the streamflow forecast and the future cost function determined by the regime state forecast. Taking the Biliuhe Reservoir in China as a case study, the value of each forecast in drought mitigation is evaluated. Results show that with an appropriate discount factor, using seasonal streamflow forecast can relieve intense deficits for extreme droughts and avoid unnecessary intense limitations for slight droughts. Utilizing the annual streamflow volume state forecast contributes to guaranteeing a reliable water supply. And employing the regime state forecast relieves the intensity of severe water deficit. By incorporating these hydrological forecasts, a 31.86% performance gain can be obtained with only a 5.03% reliability decrease compared to the baseline Stochastic Dynamic Programming informed by no forecast information. Nevertheless, forecast value is beset by forecast uncertainty. For streamflow forecast, forecast value decreases with increasing forecast uncertainty. But for the other two categorical forecasts, their forecast value depends on not only the forecast accuracy but also the hydrologic conditions and forecast bias.

Reservoir operation is an effective measure for flood disaster reduction. Previous studies focused on minimizing flood risk to reduce potential flood damage, however, failure and recovery processes are not considered. To address this shortcoming, this study introduces the concept of resilience to measure the ability of flood control systems to maintain or return to the original states after an extreme flood. The quantitative expression of resilience, resilience metric, is proposed based on the system performance and defined as the summation of system functionality loss throughout the entire flood process. Based on the proposed resilience metric, a multiobjective optimization model with flood risk represented by maximum reservoir water level and downstream peak flow, and flood resilience as objectives, is established. Nierji Reservoir, located in Northeast China is taken as a case study. Results show that the proposed method can improve resilience without increasing flood risks (i. e., maximum reservoir water level and downstream peak flow) compared with the traditional model. Pareto optimates of the optimization model show that there exist tradeoffs between risk and resilience, improving resilience inevitably reducing risk. The resilience improvement is large when the flood risk of the reservoir or downstream is medium and is limited when the flood risk is high. Moreover, the resilience improvement is higher for a smaller flood. The improvement is achieved through pre-releasing at the early stage of the flood and releasing slowly at the recession stage of the flood. Six typical schemes, selected referring to the existing scheme are comprehensively compared, and an operation rule for flood control with the highest resilience but without reducing risks is recommended. This study provides a new way for flood control management.

Utilizing the spatial heterogeneity and climate periodicity of various available renewable energy sources can enhance the multienergy complementarity, which will further reduce the energy storage demand and contributes to the "virtual energy storage gain." In this paper, we propose a spatiotemporal coordination method based on spectral analysis for a wind-PV-hydropower system that targets the maximum virtual energy storage gain. The complementary effect of hydropower on wind and PV power can be seen as changes in the regulation ability resulting from the hydropower construction development as well as a decreased variance in the total system production output. This method is used to determine the optimal coordination distance of multiple energy sources that are matched over different periods representing typical climate variation. A case study in the Yalong River basin in China obtains the best range of hydropower bundling surrounding wind power and PV power stations under different hydropower construction planning scenarios, and it reveals that increased regulation capacity of hydropower will reduce the optimal coordination distance but can achieve more energy balance if both actual storage and virtual storage is accounted for. This approach can realize energy delivery with the optimal coordination distance to meet intensive and efficient development needs, which can provide guidance and support for the planning and construction of wind-PV-hydropower storage systems.

为提高流域中期径流预报精度,提出了一种基于机器学习的多模型融合的中期径流预报方法,并应用于桓仁水库流域.首先采用BP神经网络(BP)、多元线性回归(MLR)、支持向量机(SVM)构建旬尺度的单一径流预报模型;再基于信息熵和机器学习方法对上述单一模型的结果进行融合,分别建立基于信息熵、BP神经网络、SVM的信息融合预报模型;进一步考虑融雪影响,构建春汛期旬径流预报模型.引入平均绝对误差(MAE)、均方根误差(RMSE)和预报合格率(QR)三个误差评价指标,综合评定各模型在汛期和非汛期的径流预报精度.结果表明:①所有模型对径流变化趋势的模拟效果相对较好,单一模型对峰值的模拟表现较差;②基于机器学习算法的融合模型能很好结合不同预报模型的优势,模拟精度优于各单一预报模型和基于信息熵的融合模型,共提高汛期10个旬的径流预报精度,且将6个旬的预报合格率提升至100％,预报合格率的最大提升率达到24％;③考虑融雪影响的旬径流预报模型在3月和4月的预报合格率均在90％以上,提高了流域的非汛期径流预报能力.研究提出的基于机器学习的信息融合预报方法可得到准确性和可靠性较高的径流预报模型,为桓仁水库径流预报工作和水资源高效管理提供数据支持和理论支撑.

Water use was impacted significantly by the COVID-19 pandemic. Although previous studies quantitatively investigated the effects of COVID-19 on water use, the relationship between water-use variation and COVID-19 dynamics (i.e., the spatial-temporal characteristics of COVID-19 cases) has received less attention. This study developed a two-step methodology to unravel the impact of COVID-19 pandemic dynamics on water-use variation. First, using a water-use prediction model, the water-use change percentage (WUCP) indicator, which was calculated as the relative difference between modeled and observed water use, i.e., water-use variation, was used to quantify the COVID-19 effects on water use. Second, two indicators, i.e., the number of existing confirmed cases (NECC) and the spatial risk index (SRI), were applied to characterize pandemic dynamics, and the quantitative relationship between WUCP and pandemic dynamics was examined by means of regression analysis. We collected and analyzed 6-year commercial water-use data from smart meters of Zhongshan District in Dalian City, Northeast China. The results indicate that commercial water use decreased significantly, with an average WUCP of 59.4%, 54.4%, and 45.7%during the three pandemic waves, respectively, in Dalian. Regression analysis showed that there was a positive linear relationship between water-use changes (i.e., WUCP) and pandemic dynamics (i.e., NECC and SRI). Both the number of COVID-19 cases and their spatial distribution impacted commercial water use, and the effects were weakened by restriction strategy improvement, and the accumulation of experience and knowledge about COVID-19. This study provides an in-depth understanding of the impact of COVID-19 dynamics on commercial water use. The results can be used to help predict water demand under during future pandemic periods or other types of natural and human-made disturbance.