To investigate the non-stationary characteristics of potential evapotranspiration (PET) and the driving mechanisms of the East Asian monsoon on basin-scale, this study takes the Huaihe River Basin as a representative study area. Based on daily meteorological data from 29 stations spanning 1960–2020, PET was calculated using the FAO56 Penman–Monteith method. An improved pre-whitening Mann–Kendall test, moving-window periodic analysis, cross-wavelet transform, and sensitivity analysis were jointly applied to reveal the non-stationarity of PET and its monsoon-driven mechanisms. The results show that: (1) Temporally, the East Asian monsoon exhibits a three-stage interdecadal transition pattern of “strong–weak–strong” at the interannual scale, while PET shows a trend of strengthening followed by weakening; the interdecadal turning points of them are highly synchronized in time. (2) Spatially, basin-wide PET displays an east–high to west–low distribution pattern, and spring PET exhibits a north–south gradient characterized by decreases in the north and increases in the south. In summer, PET at most stations shows a significant decreasing trend, with the rate of decrease increasing from east to west. In autumn and winter, PET changes are relatively gentle, and decreasing trends dominate across the basin except in the southeastern area and a few western stations. (3) The East Asian monsoon index and PET exhibit significant resonant periodicities at the interannual time scale, indicating a pronounced influence of the monsoon on PET. It demonstrates that the East Asian monsoon affects seasonal wind direction, thereby modulating basin-scale meteorological factors and ultimately driving the non-stationary behavior of PET. This forms a distinctive “East Asian monsoon–meteorological factors–PET” driving framework, which not only provides key support for understanding the response of the hydrological cycle to climate change in the East Asian monsoon region, but also offers insights for related studies in other monsoon regions worldwide.

Hydropower scheduling is highly sensitive to intra-annual inflow variability, yet the influence of distinct inflow patterns beyond simple metrics of unevenness remains underexplored. This study investigates how intra-annual inflow patterns affect the annual scheduling of a large hydropower plant, using Longyangxia in the Upper Yellow River as a case study. Sixty seven years of monthly inflow data are analyzed using k-means clustering to identify nine distinct intra-annual inflow patterns, each characterized by unique morphological features including peak timing and shape. Despite stationarity in annual inflow volumes, the frequency of specific patterns shifts significantly over time, with uniform patterns becoming more prevalent during prolonged dry periods. An annual optimization scheduling model solved with a parallel cuckoo search algorithm is applied across 96 scenarios combining different inflow levels, patterns, and initial water levels. Results show that under identical annual inflow volume and initial conditions, pattern selection can alter annual energy output by up to 6.6 percent with an average of 3.6 percent. Uneven patterns consistently yield higher energy output by exploiting the nonlinear relationship between water head and power generation through strategic storage and release. Patterns with moderate unevenness achieve the most stable intra-annual generation. Water abandonment occurs only under the highest inflow levels and is most severe for patterns with late concentrated peaks. The study reveals that the impact of inflow patterns is fundamentally mediated by reservoir regulation capacity, providing a unifying framework for understanding pattern effects across different hydropower systems. These findings demonstrate that incorporating pattern-based inflow information is essential for robust hydropower scheduling, and that morphological characteristics of inflow distributions provide predictive power beyond that of conventional unevenness indicators alone.

The limitation and low accuracy of hydrological data seriously affects the accuracy of flood forecasting. To address the issue, this study proposes a novel flood forecasting method that combines the CE-QUAL-W2 model with the Physical Information Neural Network (PINN) model. Real time observation data correct using the CE-QUAL-W2 model, the corrected data were input into the Xin-An-Jiang (XAJ) model, the Long Short-Term Memory Neural Network (LSTM) model, and the PINN model, respectively. The predictive performance of the CE-QUAL-W2&PINN coupled model was comprehensively evaluated by six evaluation metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Nash Sutcliffe Efficiency Coefficient (NSE), Percentage Deviation (PBIAS), Kline Gupta Efficiency Coefficient (KGE), and Wilmot Consistency Index (WI). This study selects Luoma Lake as the study area, selecting 35 representative floods that occurred between 1960 and 2022. The results show that: (1) The CE-QUAL-W2 model simulated the water level and flow of 35 floods, and the R-2 between the simulation results and the observed values was greater than 0.71. (2) Four out of 35 floods were randomly selected for the analysis, and the results showed that the 3-hour predictive lead-time (R-2 > 0.90) provided better forecasting compared to the 6-hour predictive lead-time (R-2 < 0.87). The CE-QUAL-W2&PINN coupled model maintained its RE within 18 % for all 35 floods predicted within the 3-hour predictive lead-time, and the R-2 values were all greater than 0.8. (3) Based on a 3-hour predictive lead-time, the optimal values of the CE-QUAL-W2&PINN coupled model in forecasting water levels for six metrics are MAE = 3.894, RMSE = 5.721, NSE = 0.968, PBIAS = - 0.982, KGE = 0.963, and WI = 0.971, respectively. The optimal values for forecasting flow are MAE = 5.943, RMSE = 14.755, NSE = 0.954, PBIAS = - 0.723, KGE = 0.951, and WI = 0.978, respectively, which have higher forecasting accuracy compared to XAJ and LSTM models.

The issue of reservoir flood control optimization (RFCO) is inherently intricate as it involves a large number of variables and multiple objectives. In the realm of RFCO, numerous multi-objective algorithms are prone to the dimensional disaster, often becoming ensnared in local optima and failing to provide decision-makers with diverse solutions. This paper introduces a modified multi-objective grey wolf optimizer (MMOGWO) that integrates multiple search strategy, enhancing the autonomous exploration capabilities of individual grey wolves. It addresses the weaknesses of original multi-objective grey wolf optimizer (MOGWO) in exploration and its propensity to converge to local optima. MMOGWO was evaluated on well-known multi-objective optimization benchmark functions UF8-UF10, DTLZ2 and DTLZ7. Experimental results from Wilcoxon signed-rank tests and Friedman tests demonstrate the algorithm's strong competitiveness, as it holds an advantage in comparisons with MOGWO, non-dominated Sorting Dung Beetle Optimizer (NSDBO), multi-objective golden eagle optimizer (MOGEO), and multi-objective Manta ray foraging optimizer (MOMRFO). Subsequently, MMOGWO was applied to a flood control operation model that considers the safety of cascade reservoirs, the safety of downstream protected objects, and the ability of cascade reservoirs to manage consecutive floods. The results show that MMOGWO significantly outperforms widely-used algorithms such as NSGA-III and MOEA/D in high-dimensional RFCO problems. This can be attributed to MMOGWO's broader solution coverage, whereas the solutions of NSGA-III and MOEA/D are mostly concentrated in a narrow range, indicating their entrapment in local optima while MMOGWO achieves global optimization and provides a more diverse set of feasible solutions. The MMOGWO algorithm presented in this paper emerges as a reliable optimizer for flood control operation of cascade reservoirs and can be regarded as a competitive multi-objective optimization algorithm.

Potential evapotranspiration (ETp) is an important component of the water and energy cycle. This study investigated the changing patterns of both summer ETp and its drivers in the Huai River Basin for the first time using the newly proposed anomaly contribution analysis method, as summer is usually the peak period of ETp but little has been done to study it specifically. The anomaly contribution analysis method is able to calculate the contribution rates of climate factors to summer ETp for every year, which helps to reveal the dynamic changes in the contribution of climate factors to summer ETp. The results show that the evaporation paradox is not accurate for the basin since summer ETp declines significantly while the trend of summer Tm is insignificant. Influenced by the abrupt changes in summer Sh and Ws, summer ETp underwent a mutation around the 1970s and 1980s. Sensitivity analysis and contribution analysis show that the most sensitive meteorological factors may not contribute the most to summer ETp. Contribution analysis at a multi–year scale and the results of the anomaly contribution analysis method demonstrate that dominant factors of ETp may be different at multi–year and seasonal scales in the same region. Moreover, the dominant meteorological factors of summer ETp are also different at station and basin scales due to scale effects. Further, dynamic changes in contribution rates show that contributions of summer climate factors have clear positive–negative alterations. Additionally, there are also differences in the spatial distribution of contribution rates between the north–south and east–west directions. These findings will not only provide valuable information for regional water resources management but also provide new insights into the evolution of ETp under climate change.

The multi-reservoir flood control operation (MRFCO) problem is characterized by high dimensions and multiple constraints. These features pose significant challenges to algorithms aiming to solve the MRFCO problem, requiring them not only to handle high-dimensional variables effectively but also to manage constraints efficiently. The Horned Lizard Optimization Algorithm (HLOA) performs excellently in handling high-dimensional problems and effectively integrates with penalty functions to manage constraints. However, it still exhibits poor convergence when dealing with certain benchmark functions. Therefore, this paper proposes the Enhanced Horned Lizard Optimization Algorithm (EHLOA), which incorporates Circle initialization and two strategies for avoiding local optima, thereby enhancing HLOA’s convergence performance. Firstly, EHLOA was tested on benchmark functions, where it demonstrated strong robustness and scalability. Then, EHLOA was applied to the MRFCO problem at the upper section of Lanzhou of the Yellow River in China, showing excellent convergence capabilities and the ability to escape local optima. The reduction rates of flood peaks achieved by EHLOA for the two millennial floods and two decamillennial floods were 55.6%, 52.8%, 58.1%, and 56.4%, respectively. Additionally, the generated operation schemes showed that the reservoir volumes changes were reasonable, and the discharge processes were stable under EHLOA’s operation. Overall, EHLOA can be considered a reliable algorithm for addressing the MRFCO problem.

【Objective】 Study the characteristics of land use change in Huaihe River basin and predict the land use change in Huaihe River basin in 2030, in order to achieve reasonable development and utilization of land resources in the basin. 【Method】 The land use transfer matrix and land use dynamic were used to analyze the land use change characteristics based on the land use data of Huaihe River basin from 1990 to 2020. The land use pattern of Huaihe River basin in 2010 and 2015 were simulated based on the cellular automata-Markov (CA-Markov) model, and the land use change trend of the basin in 2030 is predicted under the condition of meeting a certain degree of accuracy. 【Result】 ①Cultivated land and construction land area account for more than 80% of the land use area, which were the two most dominant land use types in Huaihe River basin; ②The decrease of cultivated land and the continuous expansion of construction land were the most obvious change characteristics of land use in Huaihe River basin in the past 30 years; ③The land use in 2010 and 2015 simulated by the CA-Markov model based on the Kappa coefficients of 2010 and 2015 simulated based on CA-Markov model are 0.937 and 0.944, respectively, with high simulation accuracy; ④The predicted land use changes in Huaihe River basin in 2030 showed that the reduction of arable land and the expansion of land for construction are still the main trends, but the magnitude of the changes in both of them is slowing down, and the changes in forest land and grassland are not significant, but the area of watershed continues to increase. 【Conclusion】 The substantial expansion of construction land and the continuous decrease of arable land in Huaihe River basin should be emphasized, and the land use change in 2030 simulated based on CA-Markov model can provide a reference for the future land use development of the basin.

Study region: The Huai River Basin, China. Study focus: This study chooses the daily flow data of Huai River and its tributaries, Shi River and Hong River during 1959–2016. The co-occurrence of floods in different tributaries of the catchment is assessed by analyzing the flood peak and flood timing at three different gauging stations located in the river network. What’s more, the parameters(estimated from the 30-year scale time windows) of the marginal and joint distributions are assumed stationary and nonstationary respectively to explore how the trend of coincidence probability(CP) is effected by them. New hydrological insights for the region: The results show that, over the period analyzed, the most probable time of flood co-occurrence over the gauge stations in the study area tends to move backwards from 10 July to about 20 July. The probability of flood co-occurrence at the Bantai and Jiangjiaji gauging stations, which singularly experience an increase in flood peak, increases from 9.25×10−9(the 1st window) to 1.17×10−5(the 25th window) when the return period of flood is 20 year, while in the same condition, the CP of Bantai and Wangjiaba decreased from 6.16×10−6 to 2.48×10−7.

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The combined effect of global warming and urbanization have an impact on the occurrences of floods in the Yangtze River Basin (YRB) and Huaihe River Basin (HRB), causing potential risks to the safe operation of watersheds. Therefore, this study proposed a dynamic von Mises-based (DvM) framework for detecting the impact of urbanization and climate change on flood timing (FT) across HRB and YRB from the nonstationary frequency included: (1) visualization of empirical probability of FT series to ascertain the number of mixed von Mises distributions; (2) selection of the multi-covariate-based model in consideration of the combined effect of multiple physical covariates, following the phase-wise strategy which involves the likelihood ratio test, Kolmogorov-Smirnov goodness-of-fit test, and the Akaike information criterion; (3) development of the linear regression-based comparative approach to isolate and distinguish the delaying and advancing effects of various factors (mainly climate change and urbanization-induced impact) on FT. In terms of the urbanization-induced impact on peak flow timing, HRB suffered an advanced effect by 4% and YRB suffered an advanced effect by 2%, which can be attributed to the divergence of the urbanization development level quantified by the impervious surfaces of sub-basins controlled by the stations in these two basins. In contrast to the modest advancement caused by urbanization (4% for HRB and 2% for YRB) in influencing the timing of flood peaks in the two basins, climate change factors, specifically the timing of soil moisture (TSM) and maximum rainfall (TPRE), play a more significant role in contributing to a delayed trend observed at a total of seven stations in the Huaihe River Basin and Yangtze River Basin. The main driver behind the trend towards delayed flood timing in both the Huaihe River Basin (HRB) and the Yangtze River Basin (YRB) is predominantly influenced by the timing of soil moisture. According to TSM, this factor contributes 16% in HRB and 10% in YRB. In contrast, the timing of rainfall has a lesser impact, accounting for 13% in HRB and 8% in YRB according to TPRE.

【Objective】Various methods have been proposed to classify changes in runoff in catchments， but how to assess their reliability remains a challenge. In this paper， we present a method to assess the reliability of the annual runoff classification for wet and dry years calculated by different methods. Its effectiveness was tested against data measured from a watershed.【Method】The reliability of the methods for classifying annual runoff for wet and dry years is analyzed based on their stability and predictability. The assessment is based on the cross-validation method and Markov chain method. We evaluate the stability and predictability of the classified results obtained by the mean-standard deviation method （MSD）， gray relational analysis （GRA）， and set-pair analysis （SPA）. The difference in the classification and the transfer probability of the indices is established to evaluate the stability and predictability of the classified results. The proposed model is tested against annual runoff measured from 1956 - 2021 at the Tangnaihai Hydrological Station in the upper reaches of the Yellow River basin.【Result】（1） Analysis using the cross-validation method and Markov chain showed that the results calculated by different classification methods vary， indicating that the stability and predictability of different methods are different. （2） The classification difference index indicates that the GRA method is most stable and the MSD method is least stable. The transfer probability differences indicates that the GRA method has the best predictability and the MSD has the worst. （3） Considering stability and predictability， the GRA method is most reliable for classifying annual runoff abundance and depletion， and the MSD method is the least.【Conclusion】The reliability of different methods for classifying annual runoff for wet and dry years varies for the same watershed. The method we developed from the cross-validation method and Markov chain can effectively assess the reliability of the results calculated by different classification methods.

For inter-basin water transfer (IBWT) projects, the conflict between social, economic, and ecological objectives makes water allocation processes more complex. Specific to the problem of water resource conflict in IBWT projects, we established an optimal allocation model of generalized (conventional) water resources (G (C) model) to demonstrate the advantages of the G model. The improved multi-objective cuckoo optimization algorithm (IMOCS) was applied to search the Pareto frontiers of the two models under normal, dry, and extremely dry conditions. The optimal allocation scheme set of generalized (conventional) water resources (G (C) scheme set) consists of ten Pareto optimal solutions with the minimum water shortage selected from the Pareto optimal solutions of the G (C) model. The analytic hierarchy process (AHP) combined with criteria importance using the inter-criteria correlation (CRITIC) method was used to assign weights of evaluation indexes in the evaluation index system. The non-negative matrix method was employed to evaluate the G (C) scheme set to determine the best G (C) scheme for the Jiangsu section of the South-to-North Water Transfer (J-SNWT) Project. The results show that (1) the Pareto frontier of the G model is better than that of the C model, and (2) the best G scheme shows better index values compared to the best C scheme. The total water shortages are reduced by 254.2 million m3 and 827.9 million m3 under the dry condition, respectively, and the water losses are reduced by 145.1 million m3 and 141.1 million m3 under the extremely dry condition, respectively. These findings could not only provide J-SNWT Project managers with guidelines for water allocation under normal, dry, and extremely dry conditions but also demonstrate that the G model could achieve better water-allocation benefits than the C model for inter-basin water transfer projects.

Vegetation is one of the main participants in the global carbon cycle and plays an important role in land surface water transport and energy transmission.Based on the normalized difference vegetation index (NDVI) of the Huaihe River Basin (HRB) from 1999 to 2018,the spatial-temporal evolution law of NDVI was studied on three time scales (month,quarter,and year) through the Mann-Kendall trend test and empirical orthogonal function (EOF) decomposition.The following findings are obtained.① Affected by crop maturity and different landforms,the multi-year average values of monthly NDVI in the HRB present an M-shaped distribution as a whole,and there are some differences in the mean value and variation range of NDVI in different sub-basins.② In the downstream area with more farmlands,the NDVI shows an insignificant downward trend in summer and winter,while in other three sub-basins of the river,the NDVI follows an upward trend in all seasons,especially in spring and autumn.③ The annual NDVI of the basin exhibits a significant upward trend,which is the same as the global greening trend.This also indicates that returning cropland back to forests and other soil and water conservation measures have achieved remarkable results in vegetation restoration and ecological protection in the basin for the past 20 years.④ The spatial distribution of NDVI modes in the HRB shows simultaneous increases or decreases overall,with a great difference existing between the western and eastern areas.Vegetation coverage gradually decreases from inland to coastal.These research results can provide a reference basis for understanding the vegetation restoration characteristics and protecting the ecological environment in the HRB.

Ecological flows in rivers are critical to the health and stability of river ecosystems, especially for inland drylands where ecological conditions are rapidly deteriorating. Climate change and human activities lead to hydrological variation, which in turn alters the hydrological and ecological balance of local ecosystems. Therefore, it is important to study the ecological flow under hydrological variation. In this study, the second-largest inland river basin in China, the Hei River Basin, was selected as the case study. The heuristic segmentation method, monthly minimum average flow method, the Lyon method, the average flow in the driest month method, and the monthly frequency method were employed to calculate the minimum and suitable ecological flow considering hydrological variation. Then, the results of the minimum and suitable ecological flow were evaluated and compared by the Tennant method. Finally, the ecological flows were recommended for the Hei River Basin after comparison and evaluation. Results show that: (1) It is necessary and feasible to calculate ecological flow demand considering hydrological variation in the Hei River Basin. (2) The evaluation results of the minimum ecological flow are mostly at a good level or above, and those of the suitable ecological flows are mostly at the optimum range. (3) Three scenarios with different periods and frequencies were set up to obtain suitable ecological flow; and it shows that the suitable ecological flow of scenario 3 (50% frequency in all months) has the best ecological benefits, and scenario 2 (frequency is taken as 75% in spring and autumn, 50% in summer, and 80% in winter) has the best comprehensive benefits. This study can provide important reference for water resources development and utilization and ecological protection in the Hei River Basin.

【Objective】 Statistical characteristics and frequency of runoff is crucial for water resources planning and water resource project construction, especially when runoff in watersheds shows significant variation due to environmental changes. Taking upper reach of the Heihe River basin as an example, this paper examines the impact of runoff variation on statistical characteristics and frequency of the runoff analysis. 【Method】 Annual runoff and annual maximum flooding flow measured from the reach were used in our study. Two types of analysis windows, the fixed-point variable-width window (Type A window) and the variable-point fixed-width window (Type B window), were used to extract the runoff sequences (window sequences) for analysis. The P-Ш type curves were utilized to analyze the statistical characteristics of the window series, from which we calculated the runoff design values. The trends and discontinuity in the window series were identified using the linear trend analysis, MMK trend test, and heuristic segmentation method. 【Result】 ① The estimated annual runoff parameters (Ex) showed a significant increasing trend and abrupt changes in both Type A and Type B window analyses. The coefficient of variation (Cv) showed a significant increasing trend in Type A window analysis and abrupt changes in Type B window analysis. The coefficient of skewness (Cs) showed abrupt changes in Type B window analysis. Design values for different frequencies of the annual runoff showed a significant increasing trend and abrupt changes in both types of windows. ② The estimated parameters in the annual maximum showed abrupt changes in Type A window analysis, while the Ex showed abrupt changes in Type B window analysis. Cs showed a significant increasing trend and abrupt changes in Type B window analysis. Design values for different frequencies of the annual maximum flooding did not identify any significant increasing trend in Type A window analysis but showed sudden changes. In Type B window analysis, there was a significant increasing trend and abrupt changes. 【Conclusion】 Analyzing runoff processes should use long sequences to capture the whole spectrum of the runoff pattern. Using long runoff series can mitigate the influence of extreme runoffs on statistical characteristics and frequency of runoff analysis.

[目的]揭示流域潜在蒸散发(ET0)时空演变规律,阐明淮河流域ET0变化的主要原因,为淮河流域水资源管理、干旱评估及农业用水评估和规划等提供参考研究.[方法]基于淮河流域内及其周边29个气象站点1960-2020年逐日实测气象数据,采用Penman-Monteith公式、Mann-Kendall法研究了 ET0的基本变化趋势,并运用经验正交函数分解法(EOF)分解了流域各站点组成的ET0矩阵,得到流域年和四季ET.气象场的时空分布结构,通过分析典型分布模态对应的时间系数揭示了流域ET0气象场的变化规律.[结果]流域年ET0 多年均值为858.4 mm,除春季外,其他三季和年ET0均呈下降趋势.流域年ET0整体以同增同减、流域西北和东南区域反相变化两种空间分布模态为主,其中,模态一以1980年为界,有从"全流域高ET0"的空间分布形式转变为"全流域低ET0"的显著变化趋势;模态二也有从"西北ET0偏高东南ET0偏低"向"西北ET0偏低东南ET0偏高"的显著变化趋势.流域四季ET0 整体以同增同减的空间分布为主,流域年ET0 空间分布模态的转变主要是由夏季ET.的空间模态转变造成的.[结论]淮河流域ET0时空变化存在明显的季节和区域性差异,夏季ET0对年ET0的变化贡献率最大,显著减少的日照时数和风速是导致流域存在"蒸发悖论"的主要原因,未来可针对性地加强夏季蒸散发的实际观测和研究.

推进中国特色水利现代化伟大事业不断发展,需要加强水利类专业课程思政建设,贯彻落实立德树人的根本任务,不断提升水利人才培养质量.《水文水利计算》是水文与水资源工程及相关专业学科的基础专业核心课程,课程贴近工程实践,侧重实际应用.

"地理信息系统原理"是水文与水资源工程专业的学科基础课,针对课程性质和特点,本文以扬州大学水文与水资源工程专业为例,深入分析了当前课程线上线下教学现状.面对课程教学存在的问题,本文从教学内容、教学手段和师资队伍建设等方面提出了相应的改革措施和建议,以期为严峻复杂形势下的课程教学改革创新提供一些参考和借鉴.