[Objective] This study aims to select appropriate evaluation methods to comprehensively quantify the economic benefits of the Middle Route of South-to-North Water Diversion Project in Henan Province, and provide reference for the construction of a sound comprehensive benefit evaluation index system and the refinement of evaluation methods. [Methods] The evaluation is structured vertically into two phases: the construction phase and the operational phase, aligned with the project’s lifecycle. Horizontally, it distinguishes between macroeconomic benefits and direct water supply benefits. Using 2014 as the base year and 2023 as the evaluation year, the assessment covers macroeconomic benefits including the economic stimulus from construction investments and the macroeconomic support from water supply during operation. Direct water supply benefits encompass urban water supply and irrigation benefits. Methodologies employed include statistical analysis, investment multiplier, and water supply benefit allocation coefficient methods. [Results] The Middle Route of South-to-North Water Diversion Project in Henan Province has generated 150.2 billion yuan in investment-driven benefits during its construction phase, with operational water supply providing 5.493 534 trillion yuan in economic support. In terms of direct water supply benefits, urban areas receiving water from the Middle Route in Henan have achieved cumulative economic returns of 147.704 billion yuan, while irrigation water supply has contributed 16.235 billion yuan in cost-sharing benefits. [Conclusions] The Middle Route of South-North Water Diversion Project has brought significant economic benefits to Henan Province. The factors influencing the cost-sharing coefficient for urban and irrigation water supply are numerous, and further detailed research is recommended for the next phase.

[Objective] Due to regional water shortage and the unreasonable exploitation and utilization of water resources in the Fenhe River Basin, the carrying capacity of water resources is facing serious challenges. Most existing studies on water resources evaluation in the entire Fenhe River Basin use static methods and cannot predict future water resources utilization trends or support rational water resources planning. This study establishes a system dynamics model for water resources carrying capacity to overcome the limitations of static assessment methods, the imbalance between subjective and objective weights in the evaluation indicators, and the multiple-equilibrium problem of single game-theory combined weighting, thereby enabling an accurate analysis of the current status of water resources utilization and the evaluation of water resources carrying capacity under different scenarios in the Fenhe River Basin. [Methods] The cooperative game combined weighting-VIKOR comprehensive evaluation method was adopted to assess the water resources carrying capacity of the Fenhe River Basin during historical periods (2013-2023). Through the system dynamics model, four optimization schemes for water resources utilization were proposed, and the water demand and water resources carrying capacity for the future period (2024-2035) were predicted. [Results] (1) The water resources carrying capacity of Fenhe River Basin generally showed an upward trend from 2013 to 2023. Taiyuan City, benefiting from the Yellow River Water Diversion Project and water-saving and groundwater-extraction restriction policies, had relatively abundant water resources and higher carrying capacity than the other four cities. Because of low irrigation and industrial water-use efficiency and excessive groundwater exploitation, the water resources carrying capacity values of the four areas of Jinzhong City, Lüliang City, Linfen City and Yuncheng City were all between 0.19 and 0.45, belonging to the critical overload and overload states. (2) Among the four prediction schemes, the current continuation scheme had relatively high short-term total water demand in the basin, with a prominent contradiction between water supply and demand, and was in a long-term water shortage state. The supply and demand gap of the water resources conservation scheme shrank year by year and reached a balanced state in 2027, changing from critical overload to a relatively strong carrying capacity state. The water demand and supply of the agriculture-priority scheme increased rapidly year by year and reached a balance in 2031, and the scheme entered a relatively strong carrying capacity state. The comprehensive coordinated development scheme balanced economic and ecological development. The supply and demand gap reached equilibrium in 2028, and the carrying capacity of water resources increased rapidly. It was the optimal scheme. [Conclusion] The research results provide theoretical reference for the sustainable utilization and management of water resources in the Fenhe River Basin.

[Objective] Although System Dynamics (SD) models comprehensively capture the complex dynamic feedback relationships in water demand forecasting, existing applications lack standardized paradigms for incorporating future socioeconomic pathways, leaving a critical knowledge gap in fine-grained spatio-temporal water balance projections. To bridge this gap, this study aims to quantitatively project multi-sectoral water demands (2024-2050) across Nanchang City and its subordinate districts/counties under five Shared Socioeconomic Pathways (SSPs) and evaluate future water supply-demand balance relationships. The primary innovation lies in integrating the SD framework with localized parameterizations of SSP scenarios, providing a valuable reference for dynamic water resource allocation and adaptive management under multi-scenario socioeconomic futures. [Methods] Nanchang City and its five administrative units (Main Urban Area, Nanchang County, Jinxian County, Xinjian District, and Anyi County) were selected as the empirical study area. Multi-source historical socio-economic, hydro-meteorological, and water quota data spanning from 2000 to 2023 were integrated. A comprehensive SD simulation model was constructed using Vensim, comprising three major demand subsystems (production, domestic, and ecological) and two supply sources (surface water and groundwater). Five distinct future pathways (SSP1: Sustainability, SSP2: Middle of the Road, SSP3: Regional Rivalry, SSP4: Inequality, and SSP5: Fossil-fueled Development) were parameterised into the SD model by adjusting growth rates of water supply, urban green space, and unit GDP water consumption. Historical data from 2015 to 2023 were utilized for model validation via mean absolute percentage error (MAPE), followed by dynamic simulations of sectoral water demand and supply-demand gaps from 2024 to 2050. [Results] Validation confirms high model accuracy, with mean relative errors for key variables remaining well within 10% and a mean relative error of 0.55% for historical total water demand simulation. Future projections reveal a significant upward trajectory in Nanchang’s total water demand across all five SSPs from 2024 to 2050. Growth is fastest under SSP5 and slowest under SSP1. By 2050, total projected water demands for SSP1 through SSP5 reach 3.574 billion m³, 3.830 billion m³, 4.361 billion m³, 4.682 billion m³, and 5.218 billion m³, respectively. Sectorally, production water demand dominates, accounting for over 84% (up to 89.42%) of total demand across all scenarios. Industrial and ecological demands trend upward, whereas domestic demand declines in most scenarios except SSP2 and SSP3. Under SSP5 in 2050, production, domestic, and ecological demands reach 4.710 billion m³, 0.301 billion m³, and 0.207 billion m³, respectively. Spatially, demand is heavily concentrated in the Main Urban Area (exceeding 56% of the total city demand, averaging 2.083 billion m³ under SSP1), exceeding the combined demand of all other counties. Water demand ranked from highest to lowest: Main Urban Area, Nanchang County, Jinxian County, Xinjian District, Anyi County, with southeastern regions consuming significantly more than northwestern areas. Equilibrium analysis demonstrates that no water deficits occur prior to 2033. Post-2035, only the green-growth pathway (SSP1) maintains full water security through 2050. SSP2 exhibits a brief deficit peaking at 0.036 billion m³ in 2043 before stabilizing. Conversely, by 2050, water deficits under SSP3, SSP4, and SSP5 escalate to 0.532 billion m³, 0.340 billion m³, and 0.840 billion m³, respectively. [Conclusions] The coupled SD-SSP modeling framework successfully captures the complex dynamic feedback and spatial heterogeneity of regional water resource systems. The findings demonstrate that sustainable (SSP1) and moderate (SSP2) development pathways effectively satisfy China’s “Three Red Lines” water management regulations, whereas high-carbon and uncoordinated expansion pathways (SSP3, SSP4, SSP5) will trigger severe structural water shortages by 2050 due to unrestrained production demands.

[Objective] In plain regions, the flat terrain and ambiguous flow directions lead to significant accuracy limitations in traditional DEM-based river network extraction methods. To address this technical challenge, this study proposes an improved extraction method integrating DEM elevation perturbation enhancement with hydrological station geographic information correction. [Methods] Firstly, the original DEM data were optimized through preprocessing steps including plain terrain identification, elevation perturbation enhancement, and depression filling. Subsequently, the generated river network underwent flow direction and structural optimization by incorporating the spatial distribution information of hydrological stations. [Results] Comparative experiments in the small Tangbai River Basin area and the large-scale Jilin Province area demonstrated that, compared with the conventional method, the proposed method reduced the river network overlay errors from 2.51% to 0.45% and from 1.82% to 0.67%, respectively, demonstrating its superiority and applicability in river network extraction in regions dominated by plain geomorphology. [Conclusion] (1) Elevation perturbation enhancement improves the identification accuracy of main streams and tributaries as well as river network continuity. (2) The incorporation of hydrological stations effectively corrects channel positions and topological structures, and the number of stations has a significant influence on river network extraction accuracy. (3) Compared with conventional methods, the proposed method shows significant advantages in the overlay error metric, with markedly improved river network extraction accuracy. This study validates the effectiveness and practical value of the method, providing a reliable technical solution for accurate river network extraction in plain regions.

[Objective] Research on the discharge regulation and downstream water-level response mechanism of a super-large hydropower station is of great significance for ensuring downstream navigation safety and ecological protection. This study aims to reveal the hydrological variation mechanism in the reservoir tail reach under ultra-high dam operation and to provide a theoretical basis for coordinated cascade reservoir operation and navigation safety assurance. [Methods] Based on hourly discharge and water level observation data at the downstream section below the dam in 2023, the standardized hourly discharge series was analyzed using Morlet continuous wavelet transform. The main periodic components of the time series were identified using wavelet variance analysis, and the regulation cycle characteristics were extracted and the discharge-water level response relationship was quantified to systematically reveal the spatiotemporal response mechanism between discharge variation and downstream water level variation. [Results] (1) The discharge of the hydropower station under study exhibited two dominant periods of 38 h and 19 h, corresponding respectively to a diurnal regulation cycle and a semidiurnal peak-valley rhythm, and the operational intensity increased significantly during the flood season and storage period. (2) The water level variation at the downstream main control section showed significant spatiotemporal heterogeneity. During the main flood season (July-August), jointly driven by flood control operation and diurnal regulation, the peak daily water level variation reached 10.94 m, and the instantaneous variation reached 3.92 m, which was 6-19 times higher than that at the downstream reference section (permanently backwater-influenced reach). (3) A significant positive correlation was observed between discharge variation and water level variation (R²=0.83). An increase of 1 000 m³/s in discharge corresponded to an average water level rise of about 2 m. A discharge fluctuation of 800 m³/s could induce an instantaneous water level variation of [-1,1]m. (4) A rapid attenuation of water level variation along the river was observed. In the near-dam reservoir tail reach, water level response was dominated by unsteady discharge from the studied hydropower station, whereas in the downstream reach, it was controlled by backwater effects from the downstream reservoir, showing a 6-19-fold spatial difference. [Conclusion] Water level variation can be limited to within 1 m by controlling discharge fluctuation to ≤800 m³/h, providing theoretical basis for coordinated cascade hydropower operation and downstream comprehensive risk assessment.

[Objective] Located in the core of the Yangtze River Delta, the Taihu Basin is a typical plain tidal river network region characterized by numerous lakes. As a key watercourse in this basin, the Huangpu River serves as a representative case for studying ecological flow, which is critical for maintaining the stability and health of river-lake ecosystems. [Methods] Utilizing flow monitoring data from the Songpu Bridge section and hydrological records (including typhoons and rainfall) from 2007 to 2023, this study analyzed instances where the daily average flow fell below the sensitive ecological flow threshold of 90 m³/s. The ecological flow compliance rate was calculated as the ratio of days meeting the assessment indicator (≥90 m³/s) to the total days in the evaluation year. To accurately determine compliance, the analysis comprehensively accounted for force majeure events and major natural disasters (e.g., droughts, floods, typhoons, and earthquakes). Furthermore, factor analysis was employed to conduct a comprehensive assessment of the factors contributing to flow deficits relative to the sensitive ecological threshold. [Results] (1) Daily average flows at the Songpu Bridge section dropping below the sensitive ecological threshold occurred most frequently between August and October (Gregorian calendar), peaking in September. These occurrences were also more prevalent prior to the semi-monthly high tides (specifically the 3rd and 18th days of the lunar month). (2) Factor analysis indicated that the primary causes for flows falling below the sensitive threshold were force majeure factors, specifically the backwater effect of astronomical tides and storm surge-induced water level rises caused by typhoons and cold air. (3) During periods of sub-threshold flow, chloride ion concentrations at the section remained significantly lower than 250 mg/L, satisfying the ecological protection targets for tide prevention and saltwater intrusion control. Consequently, after accounting for force majeure and water quality compliance, the ecological flow assessment compliance rate for the Huangpu River from 2007 to 2023 was 100%. [Conclusions] Adopting a basin-scale perspective, this study establishes a comprehensive methodology and mitigation strategies for evaluating ecological flow maintenance in tidal river systems. Given its critical geographical position, the Huangpu River is identified as a representative tidal watercourse within the Taihu Basin. This research elucidates the influence of tidal processes on riverine ecosystems, offering a reference framework for similar estuarine reaches. We recommend integrating digital twin watershed construction to facilitate a closed-loop management system characterized by “technological intelligence, refined dispatching, and diversified objectives”, providing a reference for establishing ecological flow guarantee systems in water-abundant regions.

[Objective] Traditional evaporation models are limited in floating ball coverage scenarios by complex parameterization, reliance on sensible heat flux data, and cumbersome aerodynamic calculations. This study aims to develop a highly accurate evaporation prediction formula with simplified parameters, adaptable to varying coverage ratios, to provide a reference for water resource quantification. [Method] Based on the Priestley-Taylor model, a modified model was developed by introducing correction term g(m) to address evaporation prediction under floating ball coverage. By analyzing the relationship between coverage ratio and latent heat flux, exponential (for high-temperature seasons) and linear (for transition seasons) correction forms were determined. Robust regression was employed to minimize the impact of outliers. The optimal model was selected through model comparison and cross-validation. [Result] (1) The response relationship between latent heat flux and floating ball coverage ratio exhibited significant monthly variations. At high-temperatures during high-radiation season (June-August), the exponential correction yielded the best fit (R²≥0.985), whereas the linear correction performed better during the transition seasons (March-May and September-October) (R²≥0.986). (2) The exponential model demonstrated superior adaptability and predictive advantages for floating ball evaporation. Over the entire experimental period, it achieved a Willmott’s index of agreement (D) of 0.986, a Nash-Sutcliffe efficiency (NSE) of 0.947, a root mean square error (RMSE) of 0.54 mm/d, and a mean absolute error (MAE) of 0.43 mm/d, with strong parameter stability and no systematic bias. (3) The model also showed good adaptability during external validation under high-coverage conditions (NSE=0.734, D=0.890). [Conclusion] The proposed modified model establishes a unified prediction framework based on basic meteorological parameters for varying floating ball coverage ratios. It enables precise estimation of evaporation processes across different coverage levels, providing an efficient and practical computational method for water resource management in arid regions.

[Objective] Water level variations in tidal river reaches are highly complex and influenced by both upstream runoff and downstream tides. Accurate prediction of these variations is crucial for flood control, disaster reduction and ecological security. Taking the Nanjing Hydrological Experimental Station in the tidal reach of the Yangtze River as a case study, this paper aims to develop a model that can capture the nonlinear, dynamic and complex fluctuation characteristics of water levels and achieve real-time prediction. [Methods] A hybrid CNN-LSTM-KAN model is proposed, which combines a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a Kolmogorov-Arnold Network (KAN). First, the CNN is used to extract spatial features from the water level sequence. Then, the LSTM captures the dynamic temporal dependencies of water level changes. Finally, KAN is introduced to further enhance the model’s ability to represent nonlinear and dynamic characteristics. [Results] The hybrid model shows strong real-time prediction performance on the measured data from the Nanjing station. It outperforms conventional deep learning methods such as optimized LSTM and CNN-LSTM in terms of prediction accuracy, peak capture capability and robustness to input data. On the test set, the model achieves a root mean square error (RMSE) of 0.065 4 m, a mean absolute error (MAE) of 0.042 9 m, a mean absolute percentage error (MAPE) of 0.023 5, and a Nash-Sutcliffe efficiency coefficient (NSE) of 0.995 1. [Conclusions] The proposed CNN-LSTM-KAN hybrid model has a simple structure and is easy to implement. It provides strong technical support for flood and drought disaster prevention and sustainable socio-economic development in the tidal reaches of the lower Yangtze River.

[Objective] There are significant differences between source reservoirs and receiving reservoirs in arid regions of Northwest China in terms of spatial distribution and functional positioning. Traditional single-level operation models are difficult to achieve the global optimal allocation of water resources. To improve the scientific basis of reservoir operation and the efficiency of intensive water resources utilization, this study constructs a joint operation model for reservoir groups based on bi-level optimization. [Methods] A bi-level optimization model was established for the joint operation of reservoir groups, and the particle swarm optimization (PSO) algorithm was used to solve the model. The upper-level model takes the source reservoir as the operation core and aims to minimize water transfer deviation and evaporation-seepage losses. The lower-level model takes the receiving reservoirs as the regulation objects and aims to minimize water shortage in irrigation districts. Hydrological methods were used to quantitatively evaluate ecological flow, which was incorporated into the operation model as a rigid constraint. [Results] The variation trends of water transfer amount and water diversion amount were consistent, and the ratio between them remained within the range of 0.87-1.07. The multi-year monthly average evaporation-seepage loss decreased from 413.14×10⁴ m³ before optimization to 348.46×10⁴ m³ after optimization, with a reduction of 15.66%. The water supply reliability of each sub-irrigation district reached 87.33%-95.63%. [Conclusions] The proposed reservoir group joint operation model based on bi-level optimization can coordinate the relationship between source reservoir regulation and receiving reservoir water demand, reduce evaporation-seepage losses, and improve irrigation water supply reliability under ecological flow constraints. The research results can provide a reference for the joint operation of reservoir groups in similar regions.

[Objective] This study aims to quantitatively assess the contributions of individual meteorological factors to hydrological drought evolution in the Yuanjiang River Basin, a typical humid region in the middle reaches of the Yangtze River, and to reveal the climatic mechanisms. [Methods] We integrated historical hydrometeorological observations (1975-2022) with future climate projections (2026-2050) derived from five CMIP6 global climate models (GCMs) under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP370). To ensure regional accuracy, the future meteorological projections were bias-corrected using the quantile delta mapping method. Subsequently, we developed and validated a monthly streamflow prediction model across six hydrological stations utilizing the XGBoost algorithm. This model incorporated a comprehensive set of predictors, including meteorological variables (precipitation, relative humidity, radiation, and temperature) and human activity indicators (reservoir operation and land use changes). To ensure the interpretability of the machine learning model, the SHAP (SHapley Additive exPlanations) framework was employed to quantify the marginal contributions and unravel the directional influences of each driving factor on streamflow variations. Finally, the validated XGBoost model was driven by the bias-corrected future data to project long-term streamflow dynamics. Based on these projections, hydrological drought events were characterized using the standardized runoff index (SRI) at 3- and 6-month scales, with key drought features (frequency, duration, severity, and intensity) systematically extracted via the run theory. [Results] From 1975 to 2022, the XGBoost model demonstrated excellent performance, achieving average Nash-Sutcliffe efficiency (NSE) values of approximately 0.90 for calibration and 0.80 for validation. SHAP analysis revealed precipitation as the dominant driver of streamflow variability, accounting for 69.8% of the total explained variance, followed by relative humidity (11.9%). Furthermore, analysis of the daily-to-monthly aggregation methods showed that the monthly mean contributed 58.2% of the variance, whereas metrics capturing intra-monthly fluctuations and extreme distributions (std, p90, and p10) cumulatively contributed 41.8%. Specifically, standard deviation (20.8%) and the 90th percentile (14.4%) had high explanatory weights, highlighting the critical importance of intra-monthly meteorological variability. Historically, hydrological droughts exhibited distinct spatiotemporal heterogeneity, with more frequent occurrences in winter and spring and greater frequency and severity at upstream stations (e.g., Taoyi). Under future climate scenarios (2026-2050), precipitation was projected to follow a “drier dry season and wetter wet season” pattern, with reductions of approximately 25 mm from September to November and increases of up to 50 mm from April to August. Concurrently, relative humidity was projected to decline throughout the year, with pronounced decreases of 0.05-0.10 during the dry season. Consequently, hydrological droughts were expected to worsen, with projected increases in frequency (0.04-0.06), duration (0.2-0.4 months), and intensity (0.05). A significant structural shift in drought categories was also anticipated: moderate drought frequency increased markedly (0.04-0.08), while severe drought frequency generally declined, dropping by approximately 0.06 at Taoyi Station. This structural transition from “long-duration, high-intensity” to “high-frequency, moderate-intensity” droughts in the future was driven by seasonal moisture dynamics. Sharp declines in precipitation and relative humidity in June acted as the trigger for drought onset (with the combined SHAP negative contribution reaching approximately -230 m³/s), followed by continuous moisture deficits from July to October that drove the progression into moderate drought. However, steady moisture increases from November to May created a significant compensation effect, interrupting the deep accumulation of streamflow deficits and preventing the evolution into severe droughts. These findings highlighted that future drought mitigation in the Yuanjiang River Basin should prioritize managing seasonal consecutive droughts and the cumulative impacts of moderate droughts. [Conclusion] Precipitation and relative humidity are the dominant meteorological factors controlling hydrological drought in the Yuanjiang Basin. The XGBoost-SHAP framework effectively quantifies their individual contributions and reveals that the seasonal coupling between precipitation reduction and enhanced evapotranspiration due to declining relative humidity is the primary mechanism driving future drought intensification. Future hydrological drought across the basin is projected to intensify, characterized by increased frequency, prolonged duration, and a structural shift toward more frequent moderate drought events. Machine-learning interpretability approach provides a valuable and robust supplement to traditional physical models for understanding and projecting climate change impacts on hydrological drought. This integrated analytical framework offers scientific support for adaptive water resource management and drought mitigation strategies in the Yuanjiang River Basin and similar humid regions facing escalating drought risks under climate change.

[Objective] In 2024， the Xin’anjiang Reservoir experienced the largest inflow flood since its construction due to persistent and intense rainfall during the Meiyu season. The return periods of the maximum 5-day and 7-day inflow volumes both reached the 50-year level. This study conducts a post-event assessment based on the actual forecasting and operation process. [Methods] Multi-source and multi-model quantitative precipitation forecasts were assimilated to analyze the rainfall intensity characteristics of typical historical floods. Representative forecast rainfall time series were optimized under unfavorable， moderate， and favorable conditions with phased temporal distribution， extending forecast lead time while reducing rainfall uncertainty. The Xin’anjiang three-component runoff generation framework was applied to construct a basin-wide flood forecasting model， coupled with reservoir operation rules to form an integrated forecasting-operation modeling scheme. Hourly rolling forecasts were performed based on real-time rainfall and operational information. Considering multiple objectives such as controlling maximum reservoir level， ensuring downstream safety discharge， and managing flood-peak staggering with the mainstream， iterative forward-and-reverse scenario simulations generated 215 forecast scenarios， providing precise support for ten rounds of gate operation decisions. [Results] A comparative assessment of the two major floods in 2024 and 2020 shows that although the 2024 event involved larger rainfall and greater inflow volume， the forecasting was more refined， the regulation was earlier and more proactive， and the peak staggering with the Lan River was more precise. This demonstrates that the integrated forecasting-operation model can effectively support multi-objective risk control， achieving the systemic goal of “zero major disaster and zero casualty”. [Conclusion] The full-process summary highlights that dynamic and accurate forecasting is the key to scientific operation， comparative scenario analysis supports refined decision-making， and the principle of “early action， rapid response， and incremental adjustment” is more conducive to basin-wide flood risk reduction.

[Objective] This study aims to analyze the spatiotemporal distribution and probabilistic characteristics of multi-level meteorological droughts at the seasonal scale in Guangxi， China， with a focus on seasonal continuous drought events. The study quantitatively analyzes drought frequency trends， identifies the optimal probability distribution function for seasonal precipitation， and assesses the joint probability of consecutive seasonal droughts with the goal of providing more scientific basis for drought risk management in Guangxi. [Methods] Using daily precipitation data from 18 meteorological stations （1960-2020）， we calculated the Standardized Precipitation Index （SPI） for four seasonal scales and classified drought thresholds into four levels （mild， moderate， severe， and extreme）. The GAMLSS （Generalized Additive Models for Location， Scale and Shape） model compared six probability distributions （Gamma， Normal， Lognormal， Gumbel， Weibull， and Logistic） to fit precipitation sequences， and the Gamma function was selected as the optimal model based on the AIC （Akaike Information Criterion）. Five Copula functions （Clayton， Frank， Gaussian， Gumbel， and t-Copula） were used to construct the joint distribution of continuous seasonal drought， and the optimal Copula function was determined using the squared Euclidean distance （OSL）. Spatial interpolation techniques were applied to visualize regional drought probabilities. [Results] The SPI in spring showed a decreasing trend （-0.005 8/a）， indicating an increase in drought frequency， while the SPI in summer （0.007 6/a）， autumn （0.002 1/a）， and winter （0.015 3/a） showed an increasing trend， indicating a decrease in drought frequency. Based on the fitting results of the GAMLSS model using six probability distribution functions for the seasonal precipitation series in Guangxi， the gamma function appeared most frequently in the optimal and suboptimal distributions， indicating that the gamma function can effectively describe the characteristics of precipitation changes. The seasonal frequencies of different drought levels at various stations show that mild droughts （30%-48%） are more common in summer， while moderate droughts （20%-37%）， severe droughts （10%-27%）， and extreme droughts （5%-17%） are more likely to occur in autumn. The gamma distribution performs exceptionally well， with the difference between theoretical and empirical frequencies not exceeding 11%. Different regions in Guangxi exhibit significant differences in consecutive drought characteristics. Among the four consecutive seasons of spring-summer， summer-autumn， autumn-winter， and winter-spring， the probability of consecutive droughts， moderate droughts， severe droughts， and extreme droughts is higher and more widespread during the winter-spring season. The probability range for consecutive winter-spring droughts is 13.1%-20.9%， primarily distributed in the southern coastal areas of Guangxi， central Guangxi， and northeastern Guangxi， with the highest probability of drought occurring in the northwestern part of Wuzhou City. Regions with a higher probability of severe consecutive droughts are distributed in Liuzhou， Laibin， Qinzhou， and Baise. Regions with a higher probability of severe and extreme consecutive droughts are distributed in Liuzhou， Laibin， Qinzhou， and Baise， among which Liuzhou has the highest probability of severe and extreme consecutive droughts during the spring-summer season， with probabilities of 7.1% and 3.9%， respectively. [Conclusions] The study revealed differences in seasonal drought trends in Guangxi， with spring becoming increasingly dry， while other seasons exhibit a trend toward greater moisture. The robustness of the gamma function in precipitation modeling highlights its practicality in drought frequency analysis. Crucially， the Copula-based joint probability analysis identified winter and spring as the most susceptible periods for consecutive droughts， particularly in regions dominated by karst topography. These findings provide scientific basis for adaptive drought management strategies， emphasizing the need to prioritize addressing composite drought risks in regional water resource planning.

[Objective] The urban agglomeration in the middle reaches of Yangtze River Basin is a key region in the “Rise of Central China” strategy. Dense population and high-level industrialization have led to severe contradictions between water supply and demand. By calculating the connection values of regional water resources carrying capacity （WRCC）， this research aims to provide scientific references and empirical support for optimizing regional water resource allocation， promoting sustainable socioeconomic development， and informing policy practices for high-quality water conservancy development within the Yangtze River Economic Belt. [Methods] Based on multi-year statistical data from Hubei， Hunan， and Jiangxi provinces， we applied the fuzzy analytic hierarchy process （FAHP） to calculate indicator weights， set pair analysis （SPA） to evaluate the WRCC by quantifying the connection numbers between the system and carrying grades， and the standard deviational ellipse （SDE） method to analyze the spatial distribution pattern and evolution trajectory of WRCC. [Results] （1） The WRCC of the urban agglomeration in the middle reaches of Yangtze River were increasing， with the average connection number increasing by 0.20； （2） Water allocation and pollution control have been optimized， with connection numbers for water use per GDP unit and urban sewage treatment rate increasing by more than 0.70； （3） Northern cities exhibit lower WRCC， and the SDE centroid has shifted from the east to the northwest， indicating improved spatial balance in WRCC. [Conclusions] Although the WRCC of the study region has been continuously improving， there are still spatial differences. It is necessary to strengthen water resources regulation and control capabilities in the high-consumption industrial areas and high-population density areas in the northern part of the urban agglomeration. The findings provide scientific evidence and decision-making support for water resource optimization and regional coordinated management in the middle Yangtze River region.

[Objective] This study aims to reveal the seasonal concentration characteristics and spatial differentiation patterns of precipitation in Tianjin from 1980 to 2023 by quantifying long-term seasonal trends， characterizing the spatial patterns of Precipitation Concentration Degree （PCD） and Precipitation Concentration Period （PCP）， and establishing a bivariate risk assessment method based on PCD-PCP joint return periods using Copula functions. The innovation lies in the first application of Copula-based PCD-PCP coupled analysis to the hydrometeorological field of Tianjin， providing support for extreme flood prevention. [Methods] Daily precipitation data from 13 meteorological stations in Tianjin during 1980-2023 were employed. The Mann-Kendall trend test and Sen’s slope estimator were used to detect monotonic trends in seasonal and annual precipitation. The vector-based PCD and PCP indices were calculated to quantify precipitation concentration uniformity and peak timing. Inverse distance weighting （IDW） was applied to visualize spatial patterns. The distribution characteristics of PCD-PCP under specific scenarios were analyzed based on copula joint distribution. [Results] Seasonal precipitation in Tianjin is highly uneven. Summer dominates with a mean of 383.6 mm （70.1% of annual total）， while winter precipitation is extremely low （11.5 mm） but shows the highest interannual variability （extreme value ratio： 180.50）. Summer precipitation exhibits a significant increasing trend， which may elevate the risk of extreme precipitation events， whereas the increased variability of autumn precipitation could prolong the urban waterlogging risk window. Spatially， annual precipitation exhibits a stable “higher in the north and lower in the south” pattern across the 1980s-2010s. The spatial pattern of PCD shows limited variability， with values predominantly ranging from 0.68 to 0.73. A high-value zone is identified in the Binhai New Area （PCD≈0.73）， reflecting strong precipitation concentration within a short annual window， whereas lower values in Jizhou and Wuqing suggest a more even precipitation regime. In contrast， PCP exhibits a west-east gradient， increasing from approximately 201 in the west to 207 in the east （mid-to-late July）， indicating spatial asynchrony in precipitation concentration timing. Temporally， PCD shows substantial interannual fluctuations （0.50-0.85）， yet its long-term trend remains stable， as evidenced by decadal means ranging from 0.69 to 0.71. PCP also exhibits notable interannual variability （180-220） but demonstrates a significant decadal delay， progressing from 198.5 in the 1980s to 203.7 in the 2010s， a trend intrinsically linked to the phenomenon of summer rainfall shifting to autumn in the Beijing-Tianjin-Hebei region. The copula-based analysis reveals a non-independent relationship between PCD and PCP， whereby higher PCD values tend to coincide with PCP falling within the annual peak precipitation period. The 50-year precipitation event corresponds to the combination of high PCD and a specific PCP， indicating that this extreme scenario arises from the simultaneous deviation of both variables from their normal states. Under this scenario， PCD is significantly above the multi-year average， and PCP falls within the main summer flood season. This concurrence of high precipitation concentration and flood season timing will substantially elevate the risk of urban waterlogging and basin flooding. Therefore， responding to extreme precipitation events requires attention not only to increases in total precipitation but also to high temporal concentration and its coincidence with the main flood season. [Conclusion] This study systematically quantifies the seasonal concentration characteristics and spatial differentiation of precipitation across Tianjin. Summer dominates both the total amount and long-term trends. PCD is spatially homogeneous but interannually variable， whereas PCP shows a clear west-east gradient. Under the 50-year precipitation event， Tianjin faces credible extreme flood hazards. Future work should incorporate climate model projections to assess non-stationarity in the PCD-PCP dependence structure under warming scenarios.

[Objective] Current research on water demand prediction primarily focuses on improving prediction accuracy， while studies on interval prediction of water demand remain limited. This study aims to further improve prediction accuracy， reduce error interval and reflect the actual regional water demand by incorporating the Kolmogorov-Smirnov （K-S） normal interval estimation into BP （back propagation） neural network model. [Methods] In view of a wide range of water demand influencing factors， limited sample series， and significant demand fluctuations in Jiangsu Province， we coupled the K-S normal interval estimation with particle swarm optimization （PSO） and BP neural network. Specific processes are as follows： the degree of influence and the number of key factors were preliminarily identified through principal component analysis （PCA）， followed by the calculation of grey relational grades （GRG） to determine the final water demand influencing factor index. This index was utilized as input for both BP and PSO-BP neural networks. The final model was then selected by comparing performance indicators， including relative error， Nash-Sutcliffe efficiency （NSE）， and coefficient of determination （R²）. Using multiple fitting iterations of the PSO-BP neural network， the sample size was expanded， and the K-S normality test and normal interval estimation were performed to further narrow the relative error intervals of the water demand prediction results. [Results] 1） Eight primary influencing factors of water demand were identified for Jiangsu Province： population， GDP， added value of secondary industry， added value of tertiary industry， per capita urban domestic water consumption， per capita rural domestic water consumption， irrigation water quota per mu （1 mu≈666.7 m²）， and water consumption per 10 000 yuan of industrial added value. 2） Both BP and PSO-BP neural networks exhibited strong performance in fitting historical water demand data， demonstrating their feasibility for future water demand prediction. Specifically， the PSO-BP neural network outperformed the standard BP network in the relative error of water demand prediction. The maximum relative errors for the training， validation， and testing samples were 2.23%， 0.88%， and 1.19%， respectively， with an average training error of 1.03%. The NSE and R² reached 0.98 and 0.99， respectively. 3） The selection of sample size significantly influenced the prediction results of water demand. With the increase of the sample size， the average value of samples was closer to the real value， and the overall prediction results of samples were more stable. The increase of sample size would reduce the benefit of improving the accuracy of the overall prediction results. 4） The K-S normality test and normal interval estimation stabilized the prediction results， substantially narrowed the error intervals， and better reflected the actual water demand. At a 95% confidence level， the relative error intervals of water demand prediction in 2021， 2022， and 2023 in Jiangsu Province were reduced to -0.23%-0.09%， -0.28%-0.01%， and -0.28%-0.02%， respectively. [Conclusion] The KS-PSO-BP neural network coupled model significantly reduces the error range and provides more stable and accurate prediction results that closely align with actual water demand in Jiangsu Province. The model serves as an effective method for regional water demand prediction and offers valuable guidance for future water resources planning in Jiangsu Province.

[Objective] This study aims to clarify the driving mechanisms and spatiotemporal propagation characteristics of water level fluctuations in the river section about 40 km downstream of the Wudongde hydropower station during its operation. The primary objectives are to： （1） quantify the dual impacts of unsteady flow from the hydropower station and the backwater effect from the downstream Baihetan Reservoir； （2） reveal the spatial attenuation patterns of water level variation； and （3） propose safety thresholds for water level variation in navigable waters. [Methods] Twelve high-frequency water level monitoring stations （SD1-SD12） were deployed along the river section 40 km downstream of the dam， and water level data were collected at 5-minute intervals. Four intensive monitoring activities were conducted， covering key operational periods including the impoundment period， drawdown period， flood season， and low-water level operation of the Baihetan Reservoir. The correlations among flow variation from the Wudongde hydropower station， water level of the Baihetan Reservoir， and downstream water level variation were quantified using statistical methods， including linear regression and correlation analysis. In addition， the spatial gradients and temporal attenuation of daily and hourly water level variation were calculated and analyzed. [Results] 1） The water level fluctuations downstream of the Wudongde Dam exhibited a bidirectional coupled driving mechanism， consisting of unsteady flow and reservoir backwater effects. In the near-dam reach （0-15 km）， the station’s flow was the primary driver of water level variation， with the water level response coefficient remaining stable at 0.12-0.15 m per 100 m³/s （R²>0.99）. Conversely， the far-dam reach （> 35 km） was dominated by the water level of the Baihetan Reservoir， exhibiting slow-varying， reservoir-controlled behavior with relatively stable water levels. 2） Downstream water level fluctuations exhibited a three-stage attenuation pattern. In the near-dam reach （0-15 km）， water level variation decreased rapidly， with a maximum daily attenuation rate of 0.41‰， and was strongly correlated with flow fluctuations （R²=0.85）. In the transition zone （15-35 km）， the interaction of nonlinear waves increased the variability of the attenuation rate （0.02‰-0.31‰ per day）， leading to a sharp weakening or disappearance of the correlation with flow. In the far-dam reach （>35 km）， the backwater effects of the Baihetan Reservoir stabilized water levels. 3） Water level regulation at the Baihetan Reservoir exerted a dampening effect on wave peak propagation. For every 10 m decrease in reservoir water level， the propagation distance of the flow peak increased by an average of 2.3 km （R²=0.96）. The nighttime flow peak at the Wudongde Dam occurred between 16：00 and 24：00， and flow levels exceeded daytime peaks by more than 40%. When the daily and hourly flow variations at Wudongde exceeded 5 000 m³/s and 1 500 m³/s， respectively， the resulting water level variation （3.2 m/d and 1.2 m/h） exceeded the shipping safety thresholds （3.0 m/d and 1.0 m/h）. [Conclusion] Downstream water level fluctuations are governed by a bidirectional coupled mechanism of unsteady flow and reservoir backwater effects， and exhibit a distinct three-stage attenuation pattern. This study identified the nonlinear superposition phenomenon in the transition zone （15-35 km） for the first time based on prototype observations， revealing that opposing wave phases （flow waves and backwater waves） generated complex peak interference effects. Furthermore， navigation safety thresholds are determined， and a “spatiotemporal peak-staggering” management strategy is proposed. This strategy involves dynamic zoning control， such as restricting navigation in the high-risk near-dam reach （0-15 km， SD1-SD4） during the peak period of 16：00-24：00.

[Objective] This study aims to investigate the differentiated effects of various driving factors on the stage-specific characteristics of hydrological drought in the Jitai Basin under the influence of climate change and intensive human activities. [Methods] We collected meteorological and hydrological data from 1959 to 2023 from three typical watersheds (the Shushui, Wujiang, and Tongjiang Rivers) in the Jitai Basin, and adopted the Pettitt test to divide the study period into a baseline period, a transition period, and a change period. By using the improved two-parameter monthly water balance model, we analyzed the drought characteristics and the quantitative effects of driving factors in each stage, and clarified the dominant role of different driving factors as well as their nonlinear regulation mechanisms. [Results] 1) The runoff generation mechanisms of the three typical watersheds shifted around 1980 and 2008. Specifically, in baseline period (1959-1980), the underlying surface conditions of the watersheds were relatively stable, hydrological processes were dominated by natural climate drivers, and runoff variations were directly controlled by the precipitation-evaporation balance, with no obvious disturbance from human activities. In transition period (1981-2008 ), the intensifying regional human activities began to alter the original runoff generation mechanisms of the watersheds. In the change period (2009-2023), underlying surface modification and water conservancy project regulation became dominant factors. The runoff coefficient α increased significantly in transition period and then declined in the change period, which also indicated that the regulatory intensity of human activities on runoff exceeded natural fluctuations. In terms of drought characteristic variations, drought severity and duration decreased notably in the transition period compared with the baseline period, but rebounded in the change period; the average drought severity of the three typical watersheds increased by 46.2%, 26.9% and 25.9% respectively relative to the transition period. 2) By introducing a regulating coefficient of parameter C during wet and dry periods, the improved two-parameter monthly water balance model effectively improved the overall simulation accuracy of runoff series, especially for low-flow and drought months. The Nash-Sutcliffe efficiency coefficient (NSE) was higher than 0.7 and the correlation coefficient R exceeded 0.85 in both the baseline and transition periods, with the water balance error controlled within ±1%. In the change period, the measured runoff series was heavily disturbed by human activities, which increased the difficulty of simulating monthly-scale runoff series. Parameter calibration results showed that the value of C for each typical watershed in transition period was lower than that in baseline period, but rebounded in the change period, reflecting regular variations in the precipitation-evaporation relationship of the watersheds across different stages. High temperature and low rainfall in summer and autumn were critical driving factors of hydrological drought in the Jitai Basin, with strong sensitivity to drought severity. A 10% reduction in precipitation during this period led to an increase of 0.14-0.23 in drought severity, accompanied by a marginally diminishing effect. Nevertheless, compared with the baseline period, human activities played a dominant role in the change period, causing greater variations in runoff depth and drought severity than climate change factors. [Conclusion] The multi-stage quantitative method for driving factors constructed in this study reveals the nonlinear regulatory effects of climate change and human activities on hydrological drought severity in the Jitai Basin at different stages, clarifies the influence intensity, sensitivity and stage characteristics of each driving factor, and identifies the nonlinear regulation of these factors on drought severity.

[Objective] The operation of cascade reservoirs in the lower reaches of Jinsha River has significantly altered the inflow flood characteristics of the Three Gorges Reservoir (TGR), posing new requirements for the adaptability of existing flood control scheduling. This study aims to reveal changes in inflow flood processes and flood propagation characteristics within the TGR after the impoundment of cascade reservoirs in the lower reaches of Jinsha River, providing a theoretical basis and data support for scientific flood control scheduling of the reservoir. [Methods] Based on the measured hydrological and topographic data of the TGR from 2003 to 2020, mathematical statistical methods were employed to analyze changes in inflow flood characteristics before and after the impoundment of cascade reservoirs. A one-dimensional hydrodynamic model was adopted. After calibration and validation, multiple comparative scenarios were designed to conduct simulation and analysis of flood peak propagation processes in the TGR area. [Results] Statistical analysis of the measured data showed that around 2013, the average inflow flood volume of the TGR decreased by approximately 9.9%, while the rising and falling durations decreased by 4.9% and 9.9%, respectively, and the average rising and falling rates increased by 10.3% and 9.8%, respectively. These results indicated that the flood recession became faster, the flood peaks occurred earlier, the average peak discharge decreased, and the risk of extreme floods increased. The mathematical model results showed that, in terms of discharge characteristics, the flood peak propagation time was positively correlated with the flood peak discharge, while the influence of changes in baseflow was not significant. In terms of hydrograph shape, the flood peak propagation time was negatively correlated with the rising duration and positively correlated with the falling duration. In terms of boundary conditions, the flood peak propagation time was negatively correlated with the water level upstream of the dam. [Conclusion] The operation of cascade reservoirs significantly reduces inflow flood peak, and the attenuation effect on the flood hydrograph also leads to a significant increase in flood duration. The flood peak propagation is faster under conditions of lower peak discharge, longer rising duration, shorter falling duration, and higher water level upstream of the dam.

[Objective] The Hanjiang River Basin, as the core water source area of the Middle Route of the South-to-North Water Diversion Project, has runoff variations that are of great significance to water supply security and regional sustainable development. This study aims to identify the main driving factors of runoff evolution in the upper, middle, and lower reaches of the Hanjiang River Basin. By following the framework of “pattern identification-hydrological modeling-attribution analysis of runoff changes,” this study quantitatively and qualitatively assesses the impacts of climate change and human activities on runoff variations, thereby providing a scientific basis for rational water resources utilization and management decisions in the river basin. [Methods] Statistical methods, including linear regression, moving average, rescaled range (R/S) analysis, cumulative anomaly, Mann-Kendall trend test, and sliding t-test, were employed to identify the evolution patterns of hydrological elements. A distributed Xin’anjiang model was constructed to simulate and reconstruct natural runoff, and the contribution rates of climate change and human activities to runoff changes were quantified. [Results] The results showed that from 1959 to 2019, precipitation in the upper reaches exhibited a decreasing trend at a rate of -0.59 mm/a, whereas precipitation in the middle and lower reaches showed increasing trends at rates of 0.24 mm/a and 0.05 mm/a, respectively. Temperature differences among the three reaches were minimal. Runoff exhibited significant interannual variability, with an abrupt change occurring in 1990. The decline in runoff at the Xiantao station in the lower reaches was significantly larger than that at the Huangjiagang station in the middle reaches and the Shiquan station in the upper reaches. Climate change contributed to a reduction in runoff at Shiquan station by 75.97 mm, accounting for 59.98% of the total change. Human activities led to runoff reductions of 83.32 mm at Huangjiagang station and 78.45 mm at Xiantao station, with contribution rates of 54.89% and 77.20%, respectively. [Conclusion] The impact of human activities on runoff evolution is gradually intensifying and becomes more pronounced in the downstream areas. The upper reaches, characterized by higher elevation and dominated by forest and grassland with relatively limited human activities, experience a smaller degree of anthropogenic influence. In the middle reaches, higher population density and economic development drive greater water demand, while regulated water transfer from hydraulic engineering has led to an overall decline in runoff. In the lower reaches, intensive human modifications to the underlying surface, frequent human activities, high water demand, and large-scale regulated water transfers collectively result in a significant reduction in runoff. The findings of this study provide valuable insights for water resources development, utilization, and watershed planning in the Hanjiang River Basin.

[Objective] Although artificial intelligence-based water level forecasting methods have achieved promising results in predicting upstream water levels at various power stations, including the Three Gorges Reservoir, there remains room for improvement. To obtain more accurate water level predictions for the Three Gorges Reservoir, this study develops an ultra-short-term forecasting model with a 15-minute time scale based on deep learning techniques, providing enhanced technical support for real-time reservoir operation. [Methods] The dataset comprises four categories: (1) water level data from the Three Gorges Reservoir and downstream areas; (2) inflow and spillage flow rates of the Three Gorges; (3) total power output of the Three Gorges power plant; and (4) precipitation between Cuntan and the Three Gorges area. Four water level forecasting models, including a baseline model and three comparative models, were developed to predict water level changes over the next 24 hours. Each model was constructed using both LSTM and RNN neural networks. The primary distinctions among these models lie in the processing of input and output data as well as the temporal scales of the data. By comparing the performance of different models under varying conditions, we analyze how model configurations impact prediction accuracy. [Results and Conclusion] (1) Regardless of input conditions, water level forecasting models built with LSTM outperform those using RNN, achieving Mean Absolute Errors (MAE) of 3.58 to 4.40 cm and maximum absolute errors of 56.99 to 110.03 cm. (2) All four water level forecasting models constructed using LSTM exhibit good performance, with the best-performing model incorporating dynamic reservoir capacity and interval rainfall impacts, achieving an MAE of 3.58 cm and a maximum absolute error of 56.99 cm. (3) Differences in model input variables are the dominant factor affecting forecast accuracy across various conditions. Incorporating reservoir water level information allows the model to better account for dynamic reservoir capacity effects, while adding interval rainfall data provides more precise inflow estimates, significantly enhancing prediction accuracy. This approach reduces the MAE by 18.64% compared to the baseline model. This study demonstrates that integrating relevant hydrological and meteorological factors into LSTM-based models can substantially improve the precision of short-term water level forecasts, thereby supporting effective reservoir management.