Reclaimed water reuse is an important way to solve the water shortage， which is widely used in industrial production and urban greening. However， it contains a large number of conditionally pathogenic bacteria， which pose a potential threat to the environment and human health.. The study proposes to use Granular Activated Carbon （GAC）， Biological Activated Carbon （BAC） and Nanofiltration （NF） as a combination of filtration units for secondary effluent from wastewater plants to achieve the purpose of removing three types of conditionally pathogenic bacteria （Legionella， Pseudomonas aeruginosa， and Pseudomonas aeruginosa）， Escherichia coli and organic matter in water. The removal effect was analysed using three treatment processes， namely direct NF， GAC-NF and BAC-NF. The results showed that under the conditions of 5 cm/h filtration rate for GAC filtration and 0.4 MPa filtration pressure for NF， both direct NF and its combination process could achieve the complete removal of conditionally pathogenic bacteria from secondary effluent water； compared with direct NF and GAC-NF， BAC-NF process has the highest removal rate of DOC， UV254， microbial metabolic by-products and humic acid organic matter in water， which are 85.2%， 74.5%， 76.2% and 82.0%， respectively； there is a significant correlation between the conditionally pathogenic bacteria in membrane effluent of different combination processes with E. coli， DOC. By strengthening the removal of E. coli and organic matter in the water， we can achieve the purpose of improving the reduction of conditionally pathogenic bacteria by different combination processes； in the biofilm on the surface of BAC， Pseudomonas aeruginosa， Aeromonas aeruginosa， Nitrospiraea and Fusobacterium sp. in the bacterial under the gate level accounted for the highest proportion， and the above bacteria played an inhibitory role in the growth and reproduction of conditionally pathogenic bacteria. The highest percentage of biofilm eukaryotic microorganisms were nematodes and rotifers， which were able to achieve the reduction of conditionally pathogenic bacteria in water by trapping.

In recent years， mangroves have been widely used in coastal protection and restoration projects， and it is very important to arrange mangroves scientifically to exert their wave-dissipating effects. Factors such as the submergence degree， planting density， planting width， and arrangement of mangrove forests will have different degrees of influence on their wave dissipation effects. To explore the influence of the above parameters on mangrove wave dissipation， this paper carries out physical model experiments and analyzes the sensitivity of the impact of different parameters. Selecting the mangrove belt on the seashore of the western beach in Yueqing Bay， Zhejiang Province， as the research object， we built a 1∶5 normal water tank model， processed the Cypress tree models， and carried out mangrove wave-dissipating experiments to study the influence and sensitivity of various mangrove layout parameters on wave-dissipation. The experimental results show that the wave-dissipating effect of mangroves decreases with the increase of submersion degree； the wider the mangroves are， the better the wave-dissipating effect is， but the wave-dissipation rate per unit length of the front half of mangroves is often greater than that of the rear half； the increase of planting density can enhance the wave-dissipating effect of mangroves； under the same planting density， the wave-dissipating effect of “品”-shaped distribution is generally similar to or slightly better than that of rectangular distribution. Through sensitivity analysis， it is found that submersion degree has the most significant impact on the wave-dissipating effect of mangroves， followed by mangrove width. The research results preliminarily explore the optimal wave-dissipating effect layout parameters and their combinations of mangroves， providing references for maximizing the disaster prevention and mitigation benefits of mangrove planting projects in sea embankment safety projects.

In the realm of agricultural irrigation， the assessment of chemical oxygen demand （COD） stands as a crucial parameter， serving as a significant indicator of the level of organic matter pollution within water bodies. When the concentration of COD surpasses the threshold of 60 milligrams per liter， the adverse effects that this elevation incurs upon the quality of soil and the subsequent growth of agricultural crops manifest as a significant concern that demands attention. This phenomenon harbors the potential to exert a substantial and profound impact on both the quantity and the quality of agricultural crop yields， thereby posing a formidable and daunting challenge to the enduring sustainability and viability of crop production practices. Consequently， it is imperative to precisely anticipate the trajectory of COD concentration trends in the effluent discharged from wastewater treatment facilities， thereby facilitating its effective utilization in agricultural irrigation practices. This research combines the augmented wavelet transform technique with an improved particle swarm optimization （IPSO） algorithm as well as a sophisticated back propagation neural network to construct a combinatorial prediction model. Considering the multitude of factors influencing COD and the intricate interrelationship among these factors， principal component analysis （PCA） is employed for comprehensive feature extraction. Recognizing the inevitable noise interference during data acquisition， wavelet noise reduction techniques are implemented to preprocess the raw data. This ensures data quality and enhances model accuracy. On this basis， the prediction model of COD in effluent of sewage treatment plant is constructed based on BP neural network algorithm. To overcome the potential issue of blindness inherent in parameter selection for the BP neural network， an improved particle swarm algorithm is introduced to optimize the parameters of the model to improve the prediction accuracy. The empirical findings confirm the exemplary predictive effectiveness of the PIWT-IPSO-BP model put forth in this study. Demonstrating a mean absolute error （MAE） of 0.222， a root mean square error （RMSE） of 0.386， and a coefficient of determination of 0.984， this model showcases remarkable precision in its capacity to forecast COD levels accurately. Moreover， it adeptly navigates the challenges associated with data noise and multifactorial constraints， offering invaluable insights for the seamless integration of wastewater recycling technologies into agricultural irrigation practices.

The Pinglu Canal in Guangxi is a 100-million-ton water transportation artery connecting the Beibu Gulf Port in the western Zhuhai Basin. Scientific and reasonable assessment of the ecological health of the inland section of the Pinglu Canal can provide a basis for the planning and construction of water conservancy and ecological environment. As an important means to monitor the health of water bodies and guide the restoration and protection of damaged water ecology， river health assessment has always been a research focus， but there is a lack of research on quantitative comprehensive indicators. In this study， we took five sample points as an example， which roughly covered the two major basins of the Qinjiang River and the Shaping River， so as to construct a quantitative evaluation system of habitat health in the inner section of the Pinglu Canal. Based on the river health of the Pinglu Canal， this paper constructed a river health evaluation index system in the inner section of the Pinglu Canal from four criterion layers： aquatic biological index， hydrological index， water quality index and river morphological index， and determined 13 evaluation indicators. Since the CRITIC weight method can comprehensively measure the objective weight of the indicators based on the contrast intensity of the evaluation indicators and the conflict between the indicators， and clearly characterize the habitat health status in the inland section of the Pinglu Canal， this method is adopted. The results show that there are 2 sites in the healthy state and 3 sites in the sub-health state in the sample points of the inner section of the Pinglu Canal， and there are no general， poor or extremely poor points. On the whole， the habitat health status of the inland reach of the Pinglu Canal canal is in a healthy state. Finally， the changes and problems that need to be solved urgently in the four criterion layers of aquatic biological index， hydrological index， water quality index and river morphology after the completion of the Pinglu Canal are discussed. In this paper， the countermeasures and suggestions for habitat restoration in the inland section of the Pinglu Canal are put forward.

Under natural conditions， the karst water in the Jinan Spring Area exhibits excellent water quality. However， with rapid socio-economic development， the environmental quality of karst water has changed significantly. To investigate the evolution pattern of the chemical characteristics of groundwater in the spring area， comprehensive methods including Piper trilinear diagrams， Gibbs models， ion ratios， and factor analysis were employed based on the collection of multi-year data on the major ion chemistry of karst water in the spring area. The results indicated that： ① The dominant anions and cations in the karst water in the spring area were HCO₃ ^- and Ca²⁺， originating from the weathering of carbonate rocks. The overall chemical types of karst water in the spring area gradually changed from HCO₃·SO^- ₄Ca in the indirect recharge zone to HCO₃-Ca in the discharge zone. ② The chemical evolution of karst water in the spring area was mainly influenced by rock weathering， cation exchange， and human activities. Na⁺ and Cl^- showed a multi-source nature， mainly derived from the dissolution of rock salt and human activities.③ The intensity of the influencing factors on the chemical characteristics of karst water varied at different time stages. The impact of human activities on the spring area increased year by year， and the combined effects reflected the influence of human activities on spring water. The interaction of multiple factors made the chemical characteristics and evolution of water in the spring area more complex. Investigating the evolution of karst water chemistry in the spring area has guiding significance for the protection of karst water environments.

Understanding river network connectivity is an important prerequisite for the efficient use of water resources， which is particularly important for the healthy functioning of river basin ecosystems and water resources management. In order to evaluate the impact of river network connectivity on water resources management， this study proposes a judgement matrix-based river network connectivity evaluation model according to analytic hierarchy process， and the model is verified by simulation experiments. The experimental results show that the river network connectivity evaluation model based on the judgement matrix can effectively evaluate the flow of material and energy between different waters， and better reflect the water volume， flow and energy distribution capacity of the river.The connectivity of a point is strongly correlated with water depth， moderately correlated with flow rate and energy， and weakly correlated with flow velocity. The model of this study has good application value and provides a reference for the management and planning of rivers and water bodies

In order to solve the problem of insufficient solution to the ecological flow of multiple species in simulating the habitat of spawning grounds for drift spawning fish， a solution method for ecological flow and threshold calculation is proposed by simulating the habitat of fish spawning grounds and coupling the ecological needs of multiple drift spawning fish. The method includes： ① Using fuzzy logic to simulate the habitat of spawning grounds； ② Improving the dominance model and using the entropy weight method to determine the ecological flow weight of fish species， enhancing the applicability of ecological flow results in fish spawning grounds； ③ Coupling the spawning needs of multiple fish species to solve the ecological flow， and based on the optimal ecological flow of single and multiple fish species， solving the comprehensive ecological flow threshold. This method takes into account the ecological flow demand of various fish species during spawning， increasing the reliability of fish species in indicating river ecological demand. The research results can provide a theoretical basis for reservoir ecological operation and necessary reference for river water resource management.

Due to the uncertainty of wind power output， curtailment or deviation penalties occur when executing medium and long-term contracts. To compensate for the lack of flexibility in medium and long-term contract transactions and enhance the absorption ratio of wind power， this paper proposes a clearing model for wind-hydro-thermal medium and long-term transaction considering contract transfer. Considering the uncertainty of wind power output， the probability distribution of wind power output is used to divide the quality range of wind power output into a certain part and an uncertain part. The certain part of wind power participates in the medium and long-term market transaction of the upper layer， with the goal of minimizing the overall purchase cost of the power system， and an bidding model is established for wind power， hydroelectric power， and thermal power， which obtains the contract volume and price of the medium and long-term transaction. The clearing results are passed down to the lower layer. The remaining uncertain part of wind power participates in the lower-layer contract transfer transaction. The Monte Carlo method is used to generate a typical scenario set for the uncertain part of wind power， and with the goal of maximizing the benefits of wind power and the interests of hydropower and with thermal power as constraints， a transaction model is for autonomous negotiation among wind power， hydroelectric power， and thermal power is established. This model fully exploits the flexible regulation capacity of hydroelectric power and thermal power， transferring the electricity volume originally undertaken by hydroelectric power and thermal power to wind power， and feeding back the transaction results to the upper layer to further optimize the upper-layer clearing results. The analysis of the improved IEEE 30-node system case study indicates that the proposed model not only enhances the consumption level of wind power in the medium and long-term market， but also reduces the emission of carbon dioxide and other pollutants， and effectively safeguards the interests of all parties involved in the contract transfer transaction， thereby enhancing the willingness and initiative of market participants to participate in contract transfer transactions.

The sediment concentration in inflowing water directly affects the safe operation of hydropower stations. Accurate prediction of sediment concentration supports decision-making for peak shaving during shutdowns of hydroelectric plants and is crucial for reducing sediment abrasion on turbine units， thereby extending their operational lifespan. To develop a more effective and precise short-term sediment concentration forecast， this study employs Long Short-Term Memory （LSTM）， Support Vector Regression （SVR）， and Random Forest （RF） models to construct a forecast model based on hydro-meteorological monitoring data， including temperature， water level， and flow rate. The applicability and reliability of the models are validated using the reservoir area of the Taledesayi （TLDSY） power station in the Kashi River （KSH） basin as the research subject. The results indicate that the SVR model can effectively predict the fluctuation trend of sediment concentration in inflowing water. However， the model exhibits some errors in quantitatively predicting sediment concentration， with generally overestimated results. The RF model can predict the sediment concentration in inflowing water for the next 1-3 hours fairly accurately based on hydro-meteorological information from the past 10 hours. However， as the forecast period increases， the stability of the RF model decreases significantly， leading to potentially large local errors. The LSTM model can also accurately predict the sediment concentration in inflowing water for the next 1~3 hours， and it demonstrates more stable predictions for a forecast period of 4~5 hours， with NSE value consistently above 0.6， MAE value below 0.15 kg/m3， and the peak sediment prediction error within 15%. In conclusion， the short-term sediment concentration forecast model constructed based on the LSTM algorithm performs best. It can achieve a more accurate short-term sediment concentration forecast for inflowing water based on historical hydro-meteorological monitoring information， providing quantitative data support for the safe and efficient operation of hydroelectric plants.

Hebei is a major agricultural province in China， and has always been an agricultural drought prone area， which is caused by the serious shortage of regional water resources on the one hand， and the waste of water resources caused by extensive irrigation on the other hand. The key to solve the above problems is to improve the efficiency of irrigation water. To do this work well， it is very important to fully grasp the actual planting structure and irrigation information of crops. In this paper， Qiu County of Handan City was selected as the study area. Based on Landsat 8， GF-1 image data and UAV data， combined with the phenological characteristics of winter wheat， the winter wheat growing season time series data set was constructed， and winter wheat information was extracted by Support Vector Machine（SVM） classification method. On this basis， the land surface temperature （LST）， vegetation water supply index （VSWI） and temperature vegetation drought index （TVDI） were retrieved according to the changes of remote sensing index before and after irrigation， and the regional crop irrigation information extraction model experiment was carried out. The results show that： ① The SVM classification method of remote sensing image time series data has high information extraction accuracy， and the Kappa coefficient is 0.92； ② The comparison of the results of LST， VSWI and TVDI irrigated area extraction in Landsat 8 images shows that the three indexes accounted for more than 60% of the winter wheat area， which has a good consistency. The investigation confirms that the inversion of irrigated area based on VSWI is the best. ③ The superposition analysis of the extraction results of the three indexes obtains an area of 128.357 km²， in which the overlap rate between VSWI index and this area reaches 88.48%. The above research methods can accurately identify the planting area and irrigation information of winter wheat， which can be used as theoretical support and decision-making reference for regional water resource scheduling， planting structure adjustment， and drought prevention and control.

Exploring the optimal scale of rice cultivation in northern coastal regions holds significance for water-saving and salt-mitigating. This study takes Tangshan City as an example and analyzes the spatiotemporal distribution of rice planting scale based on groundwater salinity monitoring and Landsat remote-sensing images. Combining the salinity threshold of rice growth， we propose appropriate planting scale and adjustment measures through overlay analysis. The findings reveal that due to the shortage of water resources， rice cultivation area in the coastal area of Tangshan has been decreasing annually since 1991， with the current area stabilizing at approximately 62 800 hectares. Areas with groundwater mineralization levels above 3 g/L， between 1 and 3 g/L， and below 1 g/L are designated as rice preservation， reduction， and prohibition zones， covering 48 000， 16 900 and 10 500 hectares， respectively. Considering national food security requirements， the suitable rice cultivation area is estimated between 50 000 to 53 300 hectares. The research results offer new insights into the strategies for water-saving and yield-increasing in grain production in northern coastal areas.

The water supply services in the large and medium-sized irrigation areas should take into account the mobility， cyclicity， systematic and other natural attributes of water resources， and should comprehensively consider the allocation efficiency of the entire process of water flow in the irrigation area， including the upstream and downstream， the left and right bank of irrigation areas， and the surface and underground from the perspective of system governance. In this paper， for the common problems of quantity and measurement of water facilities in the construction layout in current large and medium-sized irrigation areas， the overall planning points of the irrigation areas quantity measurement and control facilities have been proposed. The overall layout of the measurement and control facilities in the irrigation area has been studied， and the on-site layout model of the channel measurement and control facilities has been refined. The purpose is to realize the efficient allocation and economical and intensive utilization of irrigation water in the irrigation area. It can be used as a reference for the overall planning and overall layout for the construction of the measurement and control facilities in the large and medium-sized irrigation areas.

In order to investigate the influence of environmental changes on waterlogging disasters in farmland， a multi-layer joint tank model was constructed to investigate the influence of rainfall， the percentage of pond and weir area and the initial water layer of rice fields on the flooding peak in the irrigation area. The results show that： the multi-layer joint tank model can accurately simulate the runoff change process in the irrigation area， and the simulation accuracy of the model rate periodical and the validation period reaches the B class； the peak value of flooded water in the irrigation area increases with the increase of rainfall， and the peak value of waterlogging produced by the rainfall with a return period of 100 years is 18.91 m， which increases by 0.35 m and 0.87 m compared to the rainfall with a return period of 50 years and 20 years respectively； the engineering scheduling of the pumping station can effectively control the change of the peak value of waterlogging brought about by the change of the area of the pond and weir； rice fields have an obvious regulatory effect on rainfall drainage， and the regional flood disaster can be effectively prevented by pre-decreasing the initial water layer of rice fields.

Conducting research on the impact of climate change on the water requirements of major food crops is of significant importance for enhancing the resilience of regional agriculture to meteorological disasters， developing rational agricultural water resource management strategies， promoting sustainable development in regional agriculture， and ensuring food security. This study focuses on rice， a major food crop， and establishes a field water balance model based on the single crop coefficient method recommended by the Food and Agriculture Organization of the United Nations. By integrating mathematical and statistical methods such as the Pettitt test for detecting abrupt changes and the Mann-Kendall trend analysis， a comprehensive assessment framework is developed to evaluate the impact of climate change on the irrigation water requirements of rice during its early， middle， and late growth stages. Taking Nanchang， a city located in China's Yangtze River Basin， as a case study， this research quantitatively analyzes the evolution of major meteorological elements from 1956 to 2021 in Nanchang， compares the characteristics and trends of irrigation water requirements for early-， middle-， and late-season rice in the study area， and identifies the main drivers of inter-annual variations in irrigation water requirements for rice at different growth stages. The results show that over the past sixty years， there has been a significant increase in temperature during the rice growing season in Nanchang City， with no significant increase in precipitation， and a significant decrease in sunshine duration and wind speed. Overall， the irrigation water requirements for rice have shown a downward trend， with a significant decrease for middle-season rice and a nonsignificant decrease for early and late rice. The decrease in rice irrigation water requirements is mainly attributed to changes in rainfall， wind speed， and sunshine duration.

In order to control the negative effects of blasting construction of water tunnel， based on the blasting construction of Shiziling tunnel in Quanmutang Reservoir project as the background， based on the on-site multi-scheme blasting vibration monitoring and ANSYS/LS-DYNA numerical model， the surface propagation characteristics of blasting vibration were studied， and the correlation between the maximum principal stress of adjacent buildings and the peak resultant vibration velocity was discussed. The results show that there are differences in the distribution of vibration velocity in different directions， and the attenuation of vibration velocity in the direction of vertical tunnel axis is slower. In the three-direction vibration velocity， the horizontal vibration velocity has a higher energy distribution in the low frequency band. Under the action of blasting load， stress concentration will occur at the junction of adjacent building structural members， and the correlation between the maximum principal stress of the structure and the peak combined vibration velocity is good. Based on the numerical simulation results， the measured main frequency of vibration and the allowable value of the code， the safety threshold of site blasting vibration velocity is determined to be 2.0 cm/s. The research results can provide reference for the safety protection of similar adjacent existing buildings.

In order to improve the bad flow patterns such as deviation， backflow and vortex in forebay and the inlet pool of the side inlet pump station of short approach channel with large deflection angle， the paper takes a large-scale side inlet pump station project of short approach channel with large deflection angle as the research object. The flow patterns in the forebay and inlet pool under the preliminary design scheme were analyzed by means of physical model test and numerical simulation， and the velocity deviation coefficient λ was proposed as the evaluation index. The project was optimized， and the effect of structural transformation of the forebay on water flow rectification was discussed. It was concluded that the combination scheme of “curved block + change of bottom slope + opening of vortex elimination hole” was the recommended rectification scheme， which effectively solved the bad flow pattern existing in the original design of the forebay and inlet pool. The research results show that the rectification scheme reduces the deviation coefficients of each unit's inlet pool by 37.03%， 12.99%， 22.29% and 22.04%， respectively， and significantly improves the flow pattern of the front pool and inlet pool. The research results can provide a reference for the optimization of the flow field of the forebay and the inlet pool of the similar large deflection angle short approach channel lateral intake pump station， as well as for the design of the similar short approach channel pumping station and intake buildings.

With the development of Pelton turbine towards large-capacity， the water-sand-air multiphase flow characteristics as well as sediment erosion in the distributor have received extensive attention. A six-nozzles crescent-rib spherical bifurcation distributor is presented in this article. The hydraulic characteristics of the distributor are simulated using the RANS-ELES mixed turbulence numerical model， the water-air interface is tracked using the Volume of Fluid （VOF） model， and the effects of different rib-width ratios on the flow field and sediment erosion are analyzed. The results indicate that a smaller rib-width ratio can improve the quality of water-jet and is beneficial for the anti-sediment erosion of the bifurcation part. The findings can provide reference for the design and operation of similar Pelton turbines.

In order to solve the problems of vibration of the pump unit and low pump system efficiency caused by the poor inflow pattern of the intake tank for the Xuliu Electric Pumping Station in Huai′an City， based on the design parameters of the pumping station， the method of three-dimensional turbulence numerical simulation was used to study on the hydraulic performance of the intake tank and the reconstructed elbow inlet conduit. The energy performance of the pump device was predicted and calculated for the two types of inlet conduits； the flow pattern and hydraulic performance of the conduits and pump devices before and after the reconstruction were compared and analyzed. The research results show that the intake tank of the station is suitable to be reconstructed into elbow inlet conduit； the hydraulic loss of the original scheme of intake tank is large， and the flow pattern in the intake tank is disordered， while the hydraulic loss of the elbow inlet conduit is significantly reduced after reconstruction， the flow field is smooth without vortex， and the hydraulic performance is greatly improved； the hydraulic performance of the reconstructed pump device is significantly improved compared with that before the reconstruction， and the efficiency of the pump device is increased by more than 5% under the design flow rate. After the reconstruction， the on-site pump unit runs stably， showing that it is successful to reconstruct the intake tank into elbow inlet conduit， which can provide reference for the construction and reconstruction of small scale pumping stations of the same type.

The drag-type turbine is extensively utilized for small-scale hydropower generation in low-flow scenarios and can be installed in pipelines to enable self-powering for wireless monitoring devices. To enhance the efficiency of the drag-type pipeline turbine， CFD software is employed to analyze the hydrodynamic performance of four different blade cross-sectional shapes of the turbine， and investigate the impact of these different blade cross-sectional shapes on the performance. Meanwhile， a bending optimization scheme is proposed to bend the blade with the best hydrodynamic performance， and the impact of different blade bending angles on the hydrodynamic performance of turbine is investigated. The research results show that the crescent-shaped JJ type blade outperforms the other three blade cross-sectional shaped analyzed. Moreover， the turbine’s efficiency is further enhanced by incorporating a bending angle to the JJ type blade， and the optimal range of the blade bending angle is 10°~20°. These findings offer valuable insights for optimizing the drag-type blades in pipeline turbines.

As one of the key storage lakes in The Yangtze-to-Huai Water Diversion Project， the change of water level in Caizi Lake is affected by both natural precipitation and engineering storage. In order to accurately simulate and forecast the water level of Caizi Lake， this paper constructs a model of Xin?an River， the four water sources in Caizi Lake basin， and a neural network （LSTM） model of Caizi Lake water level forecasting. On this basis， an external coupling method is used to construct the four water sources Xin′an River-LSTM coupling model， and the inlet flow simulated by the physical mechanism model is further used as a complementary factor to drive the neural network （LSTM） model to simulate the level of Caizi Lake， so as to realize the coupling application of the two different models in the lake water level forecasting. The results show that the flood error of the directly simulated water level is less than 0.1 m， and the flood error of the coupled simulated water level is less than 0.02 m， and the accuracy of the water level error of the latter is improved by 0.08 m compared with that of the former. The flood error of the directly simulated water level during the validation period is within 0.02 m， and the Nash coefficients， R ²， are 0.89， 0.75， and 0.88， respectively， and the root-mean-square errors， RMSE， are 0.034， 0.027， and 0.015， respectively； the flood error of the coupled simulated water level validation period is within 0.015 m， the Nash coefficient R ² is 0.91， 0.82 and 0.88， and the root mean square error RMSE is 0.019， 0.021 and 0.008， respectively. The results of the study show that compared with the results derived from a single driving factor， the results derived from the dual driving factor are more effective in improving the water level simulation accuracy. Meanwhile， in considering the flooding process corresponding to rainfall， the combination of data-driven and physical mechanisms effectively improves the accuracy of the field flood water level prediction and obtains more accurate simulation results compared with the direct prediction of water level error. It provides an important reference basis for the water transfer of The Yangtze-to-Huai Water Diversion Project， and also provides some references for the flood level prediction of similar water transfer projects.

The ‘7.20’ rainstorm caused a rare extreme heavy rainfall process in Henan Province. The rainfall lasted for a long time， covered a large area， had a high rainfall intensity and a large amount of precipitation， and the daily rainfall exceeded the historical extreme value. The rainstorm caused serious waterlogging in Zhengzhou and other cities， and many reservoirs and embankment projects were in serious danger， which aroused great concern of the society. The study of rainstorm transplantation helps to improve the city's ability to cope with super-standard floods. The flood discharge capacity of Kaifeng City is limited. In order to explore the risk control ability and countermeasures of Kaifeng City under extreme flood， and make preparations for flood prevention， the Zhengzhou ‘7.20’ rainstorm center is transplanted to the center of Kaifeng City in this study. The rainfall level and time history distribution remain unchanged. The ‘7.20’ rainstorm rainfall period is taken from 8∶00 on July 17 to 8∶00 on July 21， a total of 4 days. In this paper， the Chinese flood forecasting system is used to simulate the flood process， flow and peak arrival time of each control section by using the three water source Xin'anjiang model and Muskingum method， and determine the submerged range. The calculation results show that after the rainstorm transplantation in Kaifeng City， the coverage area of cumulative rainfall greater than 800 mm is 115 km²， the coverage area of cumulative rainfall greater than 600 mm is 1 154 km²， the coverage area of cumulative rainfall greater than 400 mm is 2 793 km²， and the coverage area of cumulative rainfall less than 400 mm is 3 651 km². The average submerged depth is 0.61 m， the maximum submerged depth is 2.78 m， and the total water volume is 92 million m³. Among them， 22.9 million m³ was discharged by Huiji River， and the remaining 69.1 million m³ of water needed at least 3.6 days to complete drainage. The research results have great warning and reference significance for Kaifeng City to prevent urban waterlogging， super standard flood， super standard flood monitoring and early warning， flood control planning， emergency plan preparation， and response to the “four pre” requirements of the Ministry of Water Resources.

Under the influence of urbanization and climate change， urban water problems such as urban flooding and water pollution have become more and more serious. Sponge city， as a new urban stormwater management concept， contributes to runoff control through the methods of “source reduction， process control and end treatment” to improve urban environmental adaptability. Most of the existing studies focus on the runoff control effect of a single facility， rather than the parameter combination analysis of multiple facilities. In order to explore the influence mechanism of the structural parameters of sponge facilities on the effect of runoff control， this study selected a typical residential area in Chengdu as the example to construct a SWMM model of urban stormwater management， and carried out the sensitivity analysis and optimization of the structural parameters of four types of sponge facilities， including the bioretention cell， the green roof， the permeable pavement and the grass swales， and used the optimization results to simulate the combined facilities under different rainfall scenarios. The results showed that the thickness of the planting layer and soil porosity of the bioretention cell and the green roof， the thickness and porosity of the aquifer of permeable pavement， the Manning coefficient of the grass swale and the depth of the aquifer were the factors with the highest sensitivity to runoff. The greater the rainfall return period， the more significant the influence of structural parameters， moreover， the change of planting soil thickness and porosity of green roof has the most significant influence on runoff coefficient. After optimizing the structural parameters， the sponge facility can achieve a runoff peak reduction rate of 40.49%~65.67% and a runoff peak delay time of 10~20 minutes. The research results provide a theoretical basis and practical cases for the optimization of the structure and layout of sponge city facilities， which is helpful to achieve the optimal runoff control effect of sponge city construction.

The Xijiang River is the mainstem of the largest water system in South China， the Pearl River Basin， and its runoff variability is critical to the water supply in South China. In this paper， based on the day-by-day meteorological observation data， five climate models containing seven scenarios （SSP1-1.9， SSP1-2.6， SSP2-4.5， SSP3-7.0， SSP4-3.4， SSP4-6.0， SSP5-8.5） in the sixth phase of the Coupled Models International Intercomparison Program （CMIP6） are revised in terms of downscaling and bias， and the SWAT hydrological model is predicted and analyzed in combination with the hydrological observation data. The SWAT hydrological model was rate-determined and validated with hydrological observation data， and the runoff change characteristics of the Xijiang River Basin under the global temperature rise levels of 1.5， 2.0， 3.0 and 4.0 ℃ were predicted and analyzed. The results show that： ① From 1961 to 2020， the annual mean temperature in the Xijiang River basin shows a significant upward trend at the rate of 0.15 °C/10 a； the annual precipitation shows a weak downward trend at the rate of -0.9 mm/10 a. At the global warming of 1.5~4.0 °C， the annual mean temperature in the Xijiang River basin will increase by 1.7 ℃ （model range： 1.2~2.2 ℃）~4.0 ℃ （3.7~4.3 ℃） compared with the pre-industrial revolution； the annual precipitation in the basin increases compared with the base period， and the increase is more obvious at the global warming of 3.0 ℃ and 4.0 ℃. ② From 1961 to 2020， the annual runoff in the Xijiang River Basin is 6 923.5 m3/s， with a decreasing trend at a rate of -19.0 （m3·s）/10 a. Compared with the base period （1995-2014）， the annual runoff in the Xijiang River Basin will increase by 3.5%（-20.4%~28.4%）~10.5%（-18.7%~43.2%）under the global warming of 1.5~4.0 ℃； the runoff from July to November shows an increasing trend compared with that of the base period， and the runoff from April to June runoff showed a decreasing trend， and runoff from December to March of the following year all showed a decrease from the base period at a temperature rise of 3.0 ℃， and a slight increase at other global warming levels. ③ Compared with the base period， the risk of flooding and dry water in the basin under the four temperature rise levels shows an increasing trend， and the higher the temperature rise level， the higher the risk of flooding and dry water in the basin， and the flood with one in a hundred years in the historical period will become one in less than 45~50 years， one in 15~28 years， one in 10~18 years， and one in 5~8 years under the level of global temperature rise level of 1.5~4.0 ℃； the length of one-in-a-hundred-year dry water events will become one in less than 40~46 years， one in 22~25 years， one in 12~13 years， and one in 8~10 years under the different levels of global temperature rise. With the increase of global temperatures， the risk of abundant and depleted water events in the basin will increase， which may pose a threat to water resources management and flood and drought control projects in the Xijiang River Basin.

Against the backdrop of global warming， the frequency， intensity and range of compound dry-hot events， that is drought and extreme high temperature event occurring simultaneously are increasing and causing serious natural disasters and socio-economic losses. However， in a region with complex climate and terrain conditions like China， the evolutionary characteristics of compound dry-hot events are still unclear， and the potential driving mechanisms require further study. In order to understand and deal with these events， the spatiotemporal variation of summer compound dry-hot events in China from 1961 to 2015 are studied based on the Standardized Dry and Hot Index （SDHI）. The frequency and intensity changes of compound dry-hot events in different regions are compared and analyzed. Additionally， the relationship between these events and large-scale climate modes are explored by wavelet coherence， multiple linear regression and other correlation analysis method. The results show that： The frequency and intensity of the summer compound dry-hot events in China have increased， and the increase of temperature leads to the increase of the severity of compound dry-hot events； The El Ni?o-Southern Oscillation （ENSO） and Atlantic Multidecadal Oscillation （AMO） are negatively correlated with SDHI， while the Pacific Decadal Oscillation （PDO） and North Atlantic Oscillation （NAO） are positively correlated with SDHI； The AMO has a significant influence on compound dry-hot events in the northeastern， southern， and southwestern regions， whereas the NAO primarily impacts compound dry-hot events in the northwestern and northern regions； Except for East and Central China， the synergistic contribution of the four climate modes to the compound dry-hot events was more than 15%. The findings of this study offer valuable insights and scientific basis for addressing climate change， enhancing disaster resilience， and mitigating risk losses.

The rapid expansion of urban impervious surfaces has greatly altered the natural hydrological cycle process and is one of the main causes of urban water syndrome such as urban flooding and degradation of rivers and lakes. Sponge city construction and Low Impact Development are important measures to alleviate the urban syndrome. They use sponge facilities or urban green areas to disconnect the impervious surface to reduce its impact on hydrology. It is of great significance to explore the hydrological effect of imperviousness disconnection for sponge city construction in China. Based on the fully distributed physical hydrological model ParFlow.CLM， we explored the hydrological impacts of building roof disconnection in a representative residential compound in Xining City during continuous rainfall and evaporation under different soil texture scenarios. The urban underground structures were also fully considered in the simulation. The results show that roof disconnection using the natural green space can have a good runoff control performance： when the saturated hydraulic conductivity （K_s ） of the soil is greater than 0.01 m/h， the annual reduction rate of roof runoff can reach more than 72.9%. The roof disconnection generated concentrated infiltration near the roof downspout， which increased the soil moisture in the root zone， and this effect is more pronounced when the soil permeability is higher. The increase in soil moisture in root zone further promoted evapotranspiration from the green space： the annual total evapotranspiration of the residential compound increased by 6.2% to 7.8% in different soil texture scenarios compared to the scenarios with no roof disconnection. On the other hand， the concentrated infiltration creates local deeper wetting front， which allows more infiltrated water to escape from the evapotranspiration of the root zone and promotes deep drainage. The simulation results show that although the underground parking lot in the residential compound limits deep drainage over most of the area， the annual total deep drainage under imperviousness disconnection can still exceed pre-urbanization levels when soil K_s is greater than 0.01 m/h.

In order to improve the efficiency of agricultural water resource utilization in the northern agricultural-pastoral intertwined zone in China， this study aims to propose a framework for evaluating and analyzing the efficiency of agricultural water resource utilization based on the eXtreme Gradient Boosting （XGBoost） algorithm of machine learning. Firstly， the entropy-weighted TOPSIS（Technique for Order Preference by Similarity to Ideal Solution） model was used to measure the agricultural water use efficiency of the seven provinces and regions in the northern northern agricultural-pastoral intertwined belt from 2008 to 2021； Secondly， the efficiency values were used as the prior samples for the XGBoost regression prediction algorithm to be trained and tested， and the hyper-parameters of the XGBoost regression prediction model were optimized using the Bayesian Optimization（BO） algorithm. In addition， five-fold cross-validation was applied to test the robustness of the TOPSIS-XGBoost regression model results； Finally， the SHAP（SHapley Additive exPlanations） model was used to systematically analyze the key drivers affecting agricultural water use efficiency in the seven provinces and districts of the northern agricultural-pastoral intertwined zone.The results of the study show that the overall agricultural water use efficiency of the seven provinces and regions in the northern agricultural-pastoral intertwined zone from 2008 to 2021 has improved， with the average efficiency value increasing from 0.328 in 2008 to 0.437 in 2021， but the overall average value of efficiency is relatively low；In 2021， the agricultural water resources utilization efficiency in Hebei Province， Ningxia Hui Autonomous Region， Liaoning Province， Shaanxi Province and Inner Mongolia Autonomous Region was relatively high， with efficiency values ranging from 0.40 to 0.59； The comprehensive utilization efficiency of agricultural water resources in Gansu and Shanxi Provinces was low， with efficiency values of 0.33 and 0.31， respectively；The R ² of the test set of the BO-XGBoost regression prediction model improved by 2.63% compared with the benchmark XGBoost model， and the mean value of the R² of the five-fold cross-validation was 0.96， which indicated that the model had a small error and good prediction performance and robustness； The modulus of water supply， the effective irrigation rate， and the degree of agricultural scaling were the key drivers affecting the efficiency of agricultural water resources use in the seven provinces and regions. The TOPSIS-BO-XGBoost-SHAP model can provide scientific reference and technical support for the sustainable development of agriculture in China.

Uncertain parameters exert a significant influence on the outcomes of water resource allocation. However， there remains a dearth of quantitative assessment regarding the variations in system responses stemming from these uncertain parameter inputs. This study employed interval numbers to represent uncertainties within water resource allocation， constructing an inexact two-stage stochastic programming model with multidimensional uncertain parameters， employing a layer-by-layer experimental design approach， which systematically quantifies the main effects， interaction effects， and contribution rates of uncertain parameters on critical boundary features of allocation outcomes， including upper bounds， median values， and lower bounds of target values and allocation decisions. The practical application of this methodology was demonstrated through a case study of the Nandu River Basin. Results revealed that by the year 2030， managers in the Nandu River Basin could supply water up to a predetermined upper limit， resulting in an anticipated total water supply revenue ranging from 21.311 billion to 30.589 billion yuan. The layer-by-layer experimental design facilitated a quantitative analysis of the influence of multidimensional uncertain parameters on allocation outcomes. Notably， the benefit coefficients of agricultural and municipal water supply in the middle and lower reaches contributed more than 60% to the lower bound， median， and upper bound of total water supply benefits. Variations in the benifical coefficient of agricultural water supply in the middle reaches had the most significant impact on total water supply revenue， with main effects on the lower bound， median， and upper bound of total water supply benefits being 44.22， 48.28， and 43.01， respectively. Additionally， the contribution rates of water inflow at a 95% probability level during particularly dry years to the lower bound， median， and upper bound of total water shortage exceed 50%， with main effects of -11.04， -22.30， and -24.12， respectively， resulting in the most substantial changes in total water shortage. The primary influencing parameter types for total water supply benefits or total water shortage across the lower bound， median， and upper bound remained consistent， although the contribution rates varied across different boundaries， with a low impact of interaction effects. These research findings have significant implications for informing the formulation of water resource allocation schemes and analyzing water security assurance in the Nandu River Basin.

To address the issue of multicollinearity among features and low prediction accuracy in meteorological data-driven models for streamflow forecasting， a combination prediction model VIF-GBRT-MC is proposed by integrating Variance Inflation Factor （VIF）， Gradient Boosting Regression Trees （GBRT）， and Markov Chain （MC） error correction model. Daily streamflow data from Yangxian hydrological station in the Han River basin are selected for case study analysis. The predictive performance of the VIF-GBRT-MC model is compared with that of single models： GBRT， Long Short-Term Memory Neural Network （LSTM）， Support Vector Machine （SVM）， and corresponding combination models： VIF-GBRT， VIF-LSTM， VIF-SVM， VIF-LSTM-MC， and VIF-SVM-MC. Evaluation of the models' predictions is conducted using Nash-Sutcliffe Efficiency （NSE）， Normalized Root Mean Square Error （NRMSE）， Mean Absolute Percentage Error ［MAPE（%）］， Peak Performance at Threshold Statistics ［PPTS（5）］， and Qualification Rate［QR（%）］. Results indicate： ① VIF effectively selects features favorable for prediction， mitigates multicollinearity issues， and reduces the risk of model overfitting， thereby enhancing prediction accuracy. ② The MC error correction model accurately identifies potential error states in future streamflow predictions and adjusts accordingly， further improving prediction accuracy. ③ Compared to LSTM and SVM models， GBRT exhibits superior capability in accommodating the nonlinear characteristics of streamflow and meteorological factors， resulting in stronger predictive power. The combination of GBRT with VIF and MC models in the VIF-GBRT-MC model effectively mitigates inconsistencies in streamflow， significantly improving prediction accuracy. This study provides effective forecasting methods for practical streamflow prediction tasks and offers a feasible solution to address the challenges posed by climate change and human activities on streamflow prediction.

In order to investigate the effects of well covers on node overflow and two-dimensional water accumulation characteristics during the coupling process of 1D-2D dimensional model in urban waterlogging， this paper takes a drainage area of Yuelai Convention and Exhibition City of Chongqing as the research area. The study employs the Storm Water Management Model （SWMM） for stormwater runoff management and couples it with the Finite-Volume Coastal Ocean Model （FVCOM）. By analyzing the role of inspection well covers in the head of water during the overflow process， corrective adjustments are made to the head calculations for overflow nodes in the coupled model. The model is validated based on historical rainfall， and the coupled model is applied to simulate and analyze the differences in the number of overflow nodes， their spatial distribution， inundation depth， and inundation area under different well cover generalization scenarios. The results indicate that during short return periods of heavy rainfall （such as less than once every 50 years）， the degree of network overflow is relatively light， with no significant difference in node overflow between the two generalized manhole cover scenarios. Compared to not considering the impact of manhole covers， the number of overflowing inspection wells in the model considering the impact of manhole covers decreased from 533 and 597 to 189 and 431 for return periods of 50 years and 100 years respectively. The maximum waterlogging depth decreased from 0.46 m and 0.53 m to 0.38 m and 0.48 m， and the maximum waterlogging area decreased from 3.50 km2 and 3.85 km2 to 2.10 km2 and 3.05 km2. Three main waterlogging-prone points were identified， which were located at the intersection of Binjiang Road and Tongmao Avenue， the north section of Yuecheng Road and the south section of Cuigang Road. Therefore， when conducting extreme urban inundation risk simulation and analysis using 1D-2D coupled hydrodynamic model in densely networked urban areas， it is crucial to adequately consider the rational generalization of rainwater manhole covers in the model to enhance the accuracy of simulation results.