Journal of Hydroelectric Engineering

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水力发电学报

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2026 Vol. 45, No. 8 Published: 2026-08-25

	1	Spatial source analysis of flood events in middle and lower Yangtze River after Three Gorges dam construction ^Hot!
		Zhang Hongyang, Hu Chunhong, Gao Yu, Wang Dayu, Ren Shi, Li Linqi, Guan Jianzhao
		DOI: 10.11660/slfdxb.20260801
		Scientific identification of the spatial sources of mainstream flood events in the middle and lower Yangtze River basin is of great significance to the design of a regional flood control strategy and its engineering application. This study uses an improved method of principal component analysis (PCA) to screen significant flood fluctuation samples, based on the 2003-2022 daily average discharge series from this basin’s mainstream and major tributaries. Major flood events are determined using warning water levels, through combining a three-zone Flood Combination Index (FCI), high-FCI years, and primary discharge combination patterns for different zones. The results indicate that the improved PCA extracts discharge fluctuating characteristics effectively, and significant samples with the first principal component (PC1) score above the threshold 1.36 are in good agreement with the real cases in the high range of flow discharge. Inter-annual variations occur in the distribution of consecutive significant-sample periods. In 2021, significant flood periods occurred quite late under the influence of autumn floods from the Han River, while in 2022, early-season inflow impacts were evident due to a "dry flood season". The year of 2020 was marked by typical basin-wide flood events, and regional floods dominated 2010, 2012, 2016, and 2017, consistent with historical flood records.
		2026 Vol. 45 (8): 1-12 [Abstract] ( 77 ) PDF (3899 KB) ( 82 )

	13	Study on accurate identification of irrigated farmland by integrating mechanism analysis and remote sensing inversion
		Fan Fuquan, Weng Baisha, Gao Haibo, Sun Yingwei
		DOI: 10.11660/slfdxb.20260802
		Accurate identification of irrigated farmland is crucial for optimizing water resource allocation, improving irrigation efficiency, monitoring agricultural drought, guiding hydraulic engineering operations, and promoting food security and sustainable water use. This study focuses on the entire region of Uxin Banner, Inner Mongolia, an arid–semiarid agro-pastoral ecotone. Differences between irrigated and non-irrigated farmland are compared and examined from four dimensions－moisture dynamics, energy dynamics, vegetation physiology, and vegetation phenology－leading to 10 key factors selected across four categories. We retrieve these factors from multi-source remote sensing data, and construct a comprehensive feature index library comprising 43 indicators. Then, we select the optimal feature subset through feature-importance evaluation, and use it to train a Random Forest (RF) model for irrigated farmland identification. Application to the 2019-2024 Uxin Banner data for analysis of the spatiotemporal variations shows that the model achieves an overall accuracy of 95.38% and a Kappa coefficient of 0.9128, demonstrating its strong capability of irrigated farmland extraction. In Uxin Banner, the irrigated area was 812.2733 km2 in 2024, mainly distributed in its southern and southwestern regions. Over the five years of 2019-2024, the irrigated area increased first and then decline with a turning point occurring in 2022. The new framework in this study—mechanism analysis, factor identification, remote sensing inversion, and model identification—would help identify irrigated farmland and promote interpretable machine learning applications.
		2026 Vol. 45 (8): 13-28 [Abstract] ( 56 ) PDF (8537 KB) ( 37 )

	29	Flow velocity attenuation and sediment suspension suppression by submerged flexible vegetation
		Jia Hao, Tang Caihong, Zhang Shanghong, Yan Jiachen, Peng Yang
		DOI: 10.11660/slfdxb.20260803
		Aquatic vegetation, as a key part of open-channel and lake ecosystems, plays critical ecological roles. It impacts flow turbulence and sediment suspension in a water body, thereby altering its turbidity—a basic indicator of aquatic ecosystem health. However, the mechanism of turbidity altered by natural aquatic vegetation remains complicated. This study conducts a flume experiment using Vallisneria natans arranged in a rectangular pattern, to observe vertical variations in the flow velocity and sediment concentration of water flows under different flowrates and densities of natural flexible submerged vegetation. Analysis on the measured vertical profiles shows the morphology of flexible vegetation markedly alters the flow velocity pattern and turbulence structure. Under different vegetation densities, the time-averaged velocity, lateral and vertical Reynolds stresses, and turbulent kinetic energy all vary significantly, particularly in the near-bed and canopy layers. A turning point of the mean velocity profile occurs at the canopy top; similar vertical patterns are observed in turbulent kinetic energy and Reynolds stresses, with the maximum values located near the top of the vegetation canopy. Compared with the flow on a bare bed, suspended sediment concentration is reduced significantly in the vegetation condition, and decreases further with the vegetation density increasing. Flow rate and density conditions impact the canopy layer remarkably. Thus, we demonstrate that the primary reason for the reduction of suspended sediment in the flow on a submerged vegetation riverbed is that vegetation lowers the time-averaged flow velocity within the canopy and thereby suppresses sediment suspension.
		2026 Vol. 45 (8): 29-38 [Abstract] ( 57 ) PDF (2193 KB) ( 27 )

	39	Analysis of flow velocity differences at vertical pipe inlet-outlet orifices measured by different velocimeters
		Yuan Ye, Gao Xueping, Zhu Hongtao, Liu Yinzhu
		DOI: 10.11660/slfdxb.20260804
		The reversible inlet-outlet device of a vertical pipe is a major type of the bidirectional intake-outlet structures adopted by pumped storage power stations. Backflows usually occur at the bottom of its orifice in the outflow mode due to the structural characteristics, but the backflow velocity and its spatial extent are relatively small, requiring a very sensitive velocimeter and a careful arrangement of the gauge positions. Such backflows could not be detected in the model tests if the measurements were inappropriate. This paper compares the differences in the measurements of velocity distributions at the orifice section, produced by adopting different velocimeters or different gauge arrangements. The results show that the backflow occurs in our test cases, and they are not detectable unless the velocimeter produces interference low enough and keeps effective when the gauge is very close to the orifice bottom. The backflow was missed in the previous studies that adopted high-interference velocimeters, failed to measure the positions close enough to the bottom, or arranged only a small number of gauge points over the cross-section. We find that significant differences in the measured velocity at the same position by adopting different types of velocimeters that are different in the structures and measuring principles, especially in the low flow velocity cases, reaching the range of 75.0% - 150%. For a cross section with uneven velocity distribution, multiple gauge lines should be arranged; each line should have enough points, such as more than 7 - 9. The velocity pattern and hydraulic parameters cannot be obtained accurately unless the complete vertical profiles of flow velocity are observed. This study would be useful for further experimental study of the flow patterns in the reversible inlet-outlet device.
		2026 Vol. 45 (8): 39-49 [Abstract] ( 57 ) PDF (4541 KB) ( 23 )

	50	An adjoint method for multi-parameter inversion in shallow lake water quality modeling
		Chen Yan, Liu Zhaowei
		DOI: 10.11660/slfdxb.20260805
		In large shallow lakes, where hydrodynamic conditions are relatively weak, biochemical processes play a dominant role in water quality evolution. This makes key parameters－such as the degradation coefficient and internal source strength－significantly impact water quality simulation. However, these parameters are often difficult to estimate directly due to the high complexity of biochemical processes in a natural water body, thereby posing a key challenge to the improvement of simulation accuracy. This paper formulates a gradient analytical expression of objective function with respect to parameters, focusing on degradation coefficient and internal source strength, through deriving an adjoint system of the depth-averaged convection-diffusion equation based on the adjoint equation method. Then, by applying the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm, we construct a collaborative inversion framework for water quality parameters, and implement it on the OpenFOAM numerical platform. This joint inversion method is applied in a case study of CODMn analysis for the Naisi Lake of the East Route of the South-to-North Water Diversion Project. It shows conceptually reasonable ranges of the optimized degradation coefficient k = 0.0185 d-1 and internal source strength S = 0.179 g·m-2·d-1, and achieves the model simulations with relative RMSE values less than 16.6%, a significant reduction compared to the initial parameter scheme. The application also demonstrates its stable high-efficiency convergence in optimization calculations. Thus, this method can effectively improve the identification accuracy of parameters in the shallow lake water quality models, a useful approach to parameter inversion under complicated water environmental conditions.
		2026 Vol. 45 (8): 50-59 [Abstract] ( 55 ) PDF (2107 KB) ( 25 )

	60	Study on fatigue optimization and fixed-pitch emergency strategy of Kaplan runners under AGC mode
		Zhang Bingxue, Lai Zhenming, Wang Yuliang, Yang Bohan, Wang Xianzheng, Yin Yanhui, Yan Tianliu, Liu Shan, Shi Guangtai
		DOI: 10.11660/slfdxb.20260806
		The new-type power system is subjected to a sharp increase in its unit regulation frequency under the Automatic Generation Control (AGC) mode, and long-term high-frequency alternating stress in a large-scale axial-flow Kaplan turbine runner causes fatigue damage to its key transmission components such as the operating frame and rocker arm. To meet the system’s operation demand and prevent such damage, this paper presents a systematic study on the fatigue prevention and control method and how to handle the key runner component emergency. We develop a three-dimensional simulation model of the operating frame and the rocker arm in the framework of the ANSYS Workbench finite element code, based on the real crack fault cases of key runner components at the Shawan and Angu hydropower stations on the Dadu River. The fatigue characteristics of core components under the AGC conditions are examined in detail, structural weak areas identified, and targeted optimization schemes suggested. We work out an emergency strategy for fixed-pitch operation of the Kaplan runners under the extreme working conditions that usually cause a failure of the runner transmission mechanism. Specific points for implementing engineering measures are clarified from the aspects of feasibility demonstration, blade angle selection, regulation mode adjustment, blade positioning technology, and operation & maintenance control. We have achieved a full-process technical system of "fatigue analysis–structural optimization–emergency disposal". Our results help the safe and stable operation of axial-flow Kaplan runners under the AGC mode, and would guide designing engineering measures for fatigue damage prevention and emergency handling of similar generating units.
		2026 Vol. 45 (8): 60-70 [Abstract] ( 60 ) PDF (1978 KB) ( 22 )

	71	State-dependent final rheological model for rockfill materials and its engineering application
		Wang Liujiang, Wang Qi, Han Huaqiang, Liu Sihong, Shen Chaomin, Mao Hangyu
		DOI: 10.11660/slfdxb.20260807
		The rheological properties of rockfill materials are a primary factor contributing to the post-construction settlement of a rockfill dam, and improving compaction quality can reduce such settlement effectively. In this study, large-scale triaxial creep tests are conducted on rockfill specimens with different initial void ratios. We develop a compaction state-dependent final rheological model considering the void ratio effect, and examine the influence of compaction quality on the long-term deformation of a rockfill dam. The results indicate the rheological behavior of rockfill materials depends significantly on the compaction state－the smaller the initial void ratio, the bigger the attenuation rate of creep velocity and the smaller the final creep deformation. The dilatancy equation of the modified Cam-clay model well describes the rheological dilatancy relationship of rockfill materials, and the rheological critical dilatancy stress ratio decreases exponentially with the void ratio. Our final rheological model, equipped with 5 parameters, can uniformly characterize the final rheological behavior of rockfill materials under different compaction states; It is applicable to refined simulations of the effect of spatial variability in compaction quality on the long-term deformation of a rockfill dam. Compaction quality imposes a remarkable influence on the post-construction settlement of rockfill dams－the larger the initial void ratio, the more significant the increase in settlement, while the growth of downstream displacement is relatively smaller.
		2026 Vol. 45 (8): 71-83 [Abstract] ( 69 ) PDF (4245 KB) ( 109 )

	84	Development of intelligent safety hazards recognition system and its application in underground engineering construction
		Liu Kuigang
		DOI: 10.11660/slfdxb.20260808
		This paper presents an intelligent hidden hazard recognition model based on the Swin Transformer architecture, and an analysis of its test applications to underground engineering construction, aimed at the industry pain points at construction sites, such as manual hazard inspections, low efficiency, high rates of missed detections, and limited hazard coverage. We construct a two-stage recognition framework comprising self-supervised pre-training - supervised fine-tuning, and adopt a video frame extraction technology based on the cross-cosine similarity threshold control method to build a comprehensive hazard dataset. This model completes pre-training through semantic-constrained random mask reconstruction tasks, and uses a transfer learning strategy in its accurate recognition of 55 types of construction hazards. The test set results show that it achieves an accuracy of 89.8%, precision of 90.5%, and an F1 of 89.4%, and maintains recognition accuracies of 83.6% and 85.4% under the simulated conditions of low light and dust interference respectively. The system based on the model can be seamlessly integrated with the existing safety management platforms, forming a closed-loop governance process of AI recognition - construction rectification - online review by supervisors - random inspection by construction units. Through the test operation of 14 lines and 65 sections of the Beijing's rail transit system, we have applied the system to nearly 29,000 hazard identification cases in total, and achieved stable control effect on key risks such as high-altitude falls and object strikes. This study demonstrates key construction technologies and application paths and their importance for intelligent supervision, helping refined safety governance in underground engineering construction.
		2026 Vol. 45 (8): 84-97 [Abstract] ( 46 ) PDF (6116 KB) ( 29 )

	98	Interpretable intelligent prediction model for concrete dam deformation and its engineering application
		Li Yongdeng, Zhang Meng, Wang Jian, Lu Yang
		DOI: 10.11660/slfdxb.20260809
		Accurate forecasting of the evolution of deformation patterns of a concrete dam is critical to ensuring its safe operation and facilitating long-term maintenance. However, conventional prediction models often suffer from insufficient accuracy and poor interpretability in dealing with nonlinear, multi-dimensional, and long-term deformation time series data. In this work, the Convolutional Neural Network (CNN) is adopted to extract local spatial features from deformation data; the Inverted Transformer (iTransformer) with its multi-head attention mechanism are used to capture long-time series dependencies. And, we use Long Short-Term Memory (LSTM) to enhance the memory of time series data, and optimize globally the key model hyperparameters using the Particle Swarm Optimization algorithm (PSO). Then, we develop an interpretable hybrid model integrating PSO, CNN, iTransformer, and LSTM, and conduct global sensitivity analysis to explain the model’s results. We compare and verify multiple gauge points and multiple models in a case study involving 11 years of displacement measurements on the Xiluodu large concrete arch dam, with model performance assessed via the root mean square error (RMSE) metric. The results show our new model achieves more accurate predictions of the dam deformation trend, featured with higher accuracy, stronger generalization ability, and better stability, and good interpretability.
		2026 Vol. 45 (8): 98-107 [Abstract] ( 75 ) PDF (2823 KB) ( 17 )

	108	Study on deep semantic understanding and structured modeling path of dam safety knowledge graphs
		Gong Linling, Chen Bo, Yan Kewu, Lü Guoxu
		DOI: 10.11660/slfdxb.20260810
		Aimed at the issue of knowledge islands between water conservancy project data texts, this paper presents a dam safety knowledge graph construction method based on the UIE framework and the SE-RE-Joint model. We use a seven-step method to construct a multi-layer domain ontology library of main structure, affiliated buildings, and comprehensive evaluation. After data preprocessing, fine-tuning of small samples is used to realize preliminary entity recognition. We complete entity recognition and relationship extraction through semantic enhanced coding, relying on the bidirectional GRU to capture sequence dependence and CRF to optimize label sequence consistency. And, a Neo4j graph database is used to store knowledge. Test results show that the UIE+SE-RE-Joint model extracts 25,000 entities and 31,000 relational pairs, and achieves a prediction accuracy higher than 84%. The four types of errors－entity conflicts, boundary ambiguities, type confusions, and relationship misjudgments－all are lowered significantly relative to the comparison model. The map supports knowledge visualization and data retrieval; Through Python-based implementation, it completes knowledge dynamic updating, knowledge reasoning, and risk positioning, assisting the intelligent diagnosis in dam safety management.
		2026 Vol. 45 (8): 108-124 [Abstract] ( 63 ) PDF (7848 KB) ( 26 )