Cracking in concrete structures has always been a concern for researchers， designers and constructors. In hydraulic and hydropower engineering， structures such as concrete dams and the linings of water conveyance tunnels often operate under water pressure， and cracking is to some extent inevitable. However， most cracked concrete structures are still able to function safely. This raises questions about whether cracks can be permitted in concrete structures， what size cracks will not cause failure， and what reasonable criteria should be used to describe and evaluate the extent of concrete cracking. In this paper a method is proposed for establishing the cracking criteria of hydraulic concrete based on the anti-seepage function， which can be used for the cracking analysis of hydraulic concrete structures with water-retaining faces. First， the anti-seepage requirements of hydraulic concrete are determined in accordance with relevant codes and actual engineering practices. Then， experimental studies are conducted to establish the relationship between the uniaxial tensile strain and both the permeability coefficient and crack width of concrete. Subsequently， numerical simulations are employed to derive the correlation between the uniaxial tensile strain and the equivalent plastic strain， which is further validated for its applicability under complex stress states in hydraulic structures. Accordingly， thresholds for equivalent plastic strain and crack width， based on the anti-seepage function， are then proposed as the cracking criteria for hydraulic concrete structures. The research of this paper provides new criteria for the cracking analysis of hydraulic concrete which promise good practical application value.

Reservoir scheduling rules are pivotal for guiding reservoir operations. To mitigate water abandonment and power shortage risks in integrated hydro-wind-solar systems， this study proposes a method for deriving main reservoir scheduling rules， accounting for joint operation risks. Multi-timescale inflow assessment criterion for water abandonment and power shortage risks are constructed， with Monte Carlo simulation and K-means clustering generating extreme condition scenario combinations to characterize risks and serve as model inputs. Considering hydraulic， electrical， and renewable energy consumption constraints， optimized scheduling strategies for cascade hydropower stations are developed， and a main reservoir scheduling model is formulated to ensure no water abandonment or power shortages within fixed future periods. This model is transformed into a mixed-integer linear programming （MILP） model， solved hourly within each scheduling cycle， to determine the upper bounds for water abandonment risk and the lower bounds for power supply assurance. Applied to a hydro-wind-solar base in southwest China， the effectiveness of the method is validated through comparative rules and multi-scenario tests， achieving no water abandonment in the flood season and no power shortages in the dry season compared to baseline rules. The approach consistently derives robust scheduling rules across scenarios， providing theoretical support for stable hydro-wind-solar operations under rapid renewable energy growth.

Flash flood disasters are the main cause of fatalities among flood-related hazards in China. Risk identification and early warning represent crucial technologies for the proactive defense against flash floods. This paper systematically reviews conventional techniques for flash flood risk identification and early warning both within China and abroad， providing a detailed analysis of the advantages， limitations and bottlenecks of various methods. By tracing the development of artificial intelligence （AI） and its role in transforming research paradigms in hydrological science， the study highlights the significant potential of AI in flash flood disaster prevention. It identifies six major challenges confronting large AI models in the context of flash flood risk identification and early warning： data acquisition and quality， generalization and interpretability， balancing complexity with emergent capabilities， computational efficiency and parallel acceleration， and intelligent decision-making. Targeted solutions and future trends are discussed in response to these challenges. The paper proposes that future research should focus on big data governance， and the integration technology of AI and physical models. Additionally， efforts should focus on continuously improving heterogeneous hybrid parallel computing framework and training strategies， advancing automated optimization of large model parameters and intelligent prediction， and enhancing learning capacity， generalization ability and risk prediction ability. The aim of these efforts is to provide both theoretical insights and practical guidance for the application of large AI models in flash flood risk identification and early warning.

This study aims to assess the applicability of various observation systems for evaluating the quality of concrete cut-off walls using the elastic wave CT method， focusing on their accuracy and reliability in detecting internal defects within the concrete. Five distinct observation system layouts were employed to capture the propagation signals of the simulating elastic wave source within the concrete cut-off wall. The internal structure of the concrete cut-off wall was reconstructed utilizing the shortest-path ray algorithm and the Gauss-Newton inversion method. The performance of each system layout in signal acquisition， data processing， and image reconstruction was compared and analyzed through experiments. The results show that the fixed observation system layout demonstrates superior performance when the depth of the cut-off wall is shallow and the equipment can provide full coverage. The positioning error can be controlled within 0.5 m， with a signal-to-noise ratio higher than 25 dB， ensuring high detection precision. For deep walls exceeding the coverage range of the equipment， mobile observation system layouts show significant advantages. Among them， the balanced and optimized mobile observation system layout is more suitable for rapid， large-scale screening. A practical engineering case confirmed that the balanced and optimized observation system can successfully identify the local defects in a 61 m deep cut-off wall. Through comparative analysis， this study eatablishes a quantitative selection criteria for observation systems based on indicators such as positioning error， relative wave velocity error， and signal-to-noise ratio.

With global climate change and accelerating urbanization， urban waterlogging disasters have become increasingly frequent and severe， making rapid waterlogging forecasting a key research focus. Compared with traditional numerical simulation methods， deep-learning-based artificial intelligence （AI） models can significantly improve computational efficiency. However， they often encounter training bottlenecks due to limited GPU memory. To address this， this study proposes an efficient AI urban waterlogging forecasting model named as UNet-KAN-SR. This model first employs the UNet-KAN module to efficiently simulate the spatio-temporal evolution of waterlogging over low-resolution grids， and then leverages the SR （super-resolution） module， along with high-resolution surface information， to progressively map the low-resolution waterlogging distribution to high-resolution distribution. This spatiotemporal decoupling strategy can ensure simulation accuracy while substantially reducing the computational resources required for training AI models. Experimental results demonstrate that the UNet-KAN-SR model can simulate a 3-hour waterlogging distribution within 3 minutes， achieving a root mean square error （RMSE） of 9 cm and a probability of detection （POD） of 0.84， demonstrating high accuracy and computational efficiency. Further analysis reveals that the integration of the KAN module can significantly enhance the model’s capability to capture nonlinear flood dynamics when compared with common CNN modules， reducing RMSE by 10%. Furthermore， this study finds that incorporating high-resolution features， such as surface topography， building coverage ratio， and land use， can significantly improve the simulation performance but performance improvement is similar under different feature combinations. This indicates that by optimizing the combination of input features during AI model construction， training speed can be enhanced， modeling costs controlled， and efficient intelligent forecasting achieved.

Exposed rocks on the lift joint surface of Rock-filled Concrete （RFC） dams can significantly enhance the shear capacity of the lift joints. While relevant codes and standards stipulate specific requirements regarding exposure of rocks at lift joints， there is currently a lack of rapid and effective detection and evaluation methods. This paper proposes a rapid segmentation and recognition algorithm for exposed rocks at lift joints， based on YOLOv11-Seg and incorporating the Slim-Neck attention mechanism and the FASFFHead detection head. Furthermore， by integrating actual engineering data and the latest code specifications， an evaluation method for exposure of rocks on lift joint surface is established based on the aforementioned segmentation algorithm. The research results indicate that the improved YOLOv11-Seg model achieves detection precision and recall rates of 91.4% and 89.33%， respectively， representing increases of 8.6% and 7.8% compared to the initial model， while maintaining high training and recognition efficiency. The measured exposure ratio of rocks at lift joints in actual RFC dam placements exhibits significant variation， and the detection results for individual lift joints conform to a Weibull distribution. In conjunction with the newly revised standard NB/T 10077-2024， Code for Design of Rock-Filled Concrete Dams， the proposed method can effectively evaluate the condition of surface-exposed rock on-site. The horizontal projected area of the exposed rocks shows a hyperbolic relationship with the outropping height of rocks and a linear correlation with the vertical projected area； this relationship can be utilized to further assess the enhancement of the interfacial shear capacity contributed by the exposed rocks. This research provides technical means and a foundation for monitoring and evaluating the quality of lift joints in Rock-filled Concrete dams.

Thorough perception of hydraulic structures is essential for building their digital twins and promoting high-quality water resources development. With the advancement of dam safety monitoring and the establishment of integrated “sky-space-ground-water-structure based” sensing networks， effectively assimilating multi-source data to achieve full-domain perception has become a key research focus. This study proposes a Multiphysics-Informed Neural Network （MPINN） data assimilation framework， which embeds stress-seepage coupling theory and random field parameters into neural networks to integrate physical consistency with data-driven flexibility. Taking the Lianghekou core wall rockfill dam as an example， the MPINN was used to assimilate monitoring data of pore water pressure， earth pressure in the gravelly soil core wall， as well as detection data of permeability coefficient and compression modulus to reconstruct the full-field pore water pressure and earth pressure distributions within the core wall from sparse data. Comparative experiments showed that the MPINN outperforms other methods in both prediction accuracy and robustness， verifying the effectiveness of the data assimilation. This provides a new pathway for achieving thorough perception and offers technical support for the development of digital twin projects and intelligent dams.

Shield tunnel excavation beneath levees induces settlement deformation that poses a direct threat to levee integrity. Focusing on the planar oblique crossing between a shield tunnel and an embankment， this study investigates the effect of the horizontal oblique angle on the parameters of the classical Peck formula within an oblique coordinate framework. A modified approach for determining Peck formula parameters， incorporating a correction for the horizontal oblique angle， is proposed. Based on the Huanggang Road Tunnel project crossing beneath the Yellow River in Jinan， a field monitoring system was established to measure post-construction embankment settlement. The ground loss ratio and settlement trough width parameter， considering the influence of the horizontal oblique angle， were back-analyzed using the least squares regression method. The results show that the average settlement trough width parameter for the embankment strata in the Jinan section of the Yellow River is approximately 0.43， and the average ground loss ratio is about 0.31%. Neglecting the horizontal oblique angle leads to an overestimation of these parameters by approximately 12%， which may result in improper judgments regarding code compliance. Validation using measured data from the Jiluo Road Tunnel crossing beneath the Yellow River demonstrates that the proposed parameters offer satisfactory predictive capability and practical engineering applicability.

In response to climate change， the glacier retreat， snow cover reduction， and permafrost degradation in the Yarlung Tsangpo （YTR） basin have altered hydrological processes， sediment sources， and river sediment flux. This paper systematically reviews sediment data collected since the 1980s， combining data on meteorology， cryosphere （glaciers， snow， and permafrost）， and vegetation， to comprehensively assess the sediment source and transport mechanisms， spatiotemporal variations， and engineering and ecological impacts in the basin. Sediment in the YTR basin mainly originates from rainfall erosion， glacier erosion， snowmelt erosion， freeze-thaw erosion， and geological disasters. In the upper reaches （above Lazi）， sediment flux is relatively low due to low temperatures and limited precipitation； in the middle reach （Lazi to Nuxia）， sediment flux increases initially due to the tributary inputs but then decreases due to channel widening and long-term sediment accumulation； in the lower reach （Nuxia to Baxika）， glacier erosion and frequent geological disasters produce high sediment supplies， dominating the sediment dynamics of the entire basin. Climate warming has accelerated glacier melt and permafrost degradation， enhancing sediment availability， connectivity， and transport capacity. The observed sediment flux at Nuxia generally showed an increase trend from 1981 to 2009. Increasing river sediment flux can lead to issues such as reservoir sedimentation and turbine abrasion， while suspended sediments alter water turbidity and carbon flux， affecting water quality and the carbon cycle. It is necessary to strengthen an integrated “space-air-ground” based monitoring framework， cryosphere-oriented sediment-water models， and muti-spere interaction research to support sustainable river management under climate change.

Clarifying the new characteristics of Dongting Lake’s backwater effect on the lower Jingjiang River after the impoundment of the Three Gorges Reservoir is a critical issue at present. Focusing on the river section from Jianli to Chenglingji upstream of the confluence between the river and the lake， Copula functions were employed to investigate the characteristics of flow coincidences between the river and the lake in different periods. Based on the flow dynamics of alluvial rivers， a balanced-state flow pairing relationship was proposed， and an index for assessing the backwater intensity of the mainstream was developed. On this basis， the occurrence probability and timing of backwater events as well as the variation of backwater intensity during different periods within a year for the lower Jingjiang River before and after the impoundment of the Three Gorges Reservoir were quantitatively analyzed. The results indicate that， under the balanced state， the flow pairing between the river and the lake follows a nonlinear functional relationship. This relationship is close to the long-term mean fitted line of river-lake discharges and can be used to determine whether the mainstream is in a backwater or drawdown state. After the impoundment of the Three Gorges Reservoir， the duration of Jianli discharges at the medium-low flow level increased， resulting in a 44.6% rise in the coincidence probability of both the mainstream and lake inflows being at medium-low flow. The coincidence probability of the mainstream at medium flow and lake inflows at flood level （exceeding 30，000 m³/s） increased by 5.4%. As a result， the erosional base level at the river-lake confluence decreased， and on an annual scale， the mean backwater intensity of the lake on the mainstream weakened. However， the timing of the mainstream being in a backwater state shifted from above-bankfull flow levels to medium flow levels. The occurrence of extreme backwater intensity far exceeding the historical values during April-July has increased， warranting long-term attention.

Non-stationary hydrological frequency analysis is a critical scientific research issue for engineering hydrologic design under changing environments. Existing methods exhibit high uncertainty and often fail to account for the spatial dependences among data series of different stations. This paper proposes a regional non-stationary hydrological frequency analysis methodology based on hierarchical Bayesian framework and extreme hydrological regionalization. Firstly， the partitioning around medoids （PAM） clustering algorithm based on the F-madogram variogram is utilized to partition the basin into hydrologically homogeneous regions. And then regional non-stationary hydrological frequency analysis models based on hierarchical Bayesian are constructed. They can be classified into no pooling， partial pooling and full pooling models. Finally， a case study is conducted using the annual maximum 24 hour extreme rainfall data from the Xiangjiang River Basin. Results indicate that based on the spatial clustering algorithm adapting with extreme value theory， the Xiangjiang River Basin with 36 rainfall gauges is divided into three hydrological regions. For Region I， FMA Nino12 is identified as a climatic driver significantly positively correlated with most stations. Compared with the at-site non-stationary model， the partial pooling model exhibits superior performance， effectively capturing the regionally homogeneous response to climatic drivers while preserving individual site characteristics. Simultaneously， the regional models exhibit a great benefit with the uncertainty for regional parameters at each station reduced by about 10% to 35%. Moreover， as the return period increases， the non-stationary return periods consistently decrease， compared with the stationary conditions. For instance， at the 50-year return period， the reduction rate approaches 60% for some stations， indicating an increased probability of extreme rainfall events within the region driven by climatic factors. This methodology enriches the methodological framework of non-stationary hydrological frequency analysis and can provide a scientific basis for determining the design rainstorms in basins and formulating disaster prevention strategies.

To overcome the limitations of existing research in effectively addressing the high-dimensional and nonlinear hydraulic processes in open-channel sections of water transfer projects under water diversion disturbances during the icing period， and the high reliance on manual experience in actual operations， this study takes the Wangnou-ruwugou section of the Jiaodong Water Transfer Project as a case study to investigate real-time intelligent hydraulic regulation of gate-pump groups in open channels. Through one-dimensional hydrodynamic simulations of gate-pump groups， step disturbances were applied to the diversion flows of canal pools to reveal the coupling mechanism between hydraulic processes in open channels and the operational responses of gate-pump groups under various diversion disturbances， and thus determine safety thresholds for water diversion disturbances during the icing period. On this basis， a real-time intelligent regulation model for gate-pump groups in open channels was developed by coupling a hydraulic model with a deep reinforcement learning algorithm， which excels at handling nonlinear， high-dimensional problems and requires minimal modeling data. The robustness of the proposed model was validated under various operational conditions. Application results demonstrate that the derived scheduling strategy effectively raises and stabilizes the water level at control sections to the target ice-period regulation level while ensuring operational safety and reducing the gate adjustment frequency.