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Special issue on Chinese practice of dam construction, operation, and management
Issue 08,2026

Attenuation mechanisms and attribution analysis of estuarine geomorphic response to floods under human modification

WANG Xiaoguang;HE Houjun;AN Dong;ZHANG Wei;

Extreme floods are critical natural drivers of estuarine geomorphic evolution. However, how longterm human modifications quantitatively regulate this process remains poorly understood. Although the 2020 flood in the Yangtze Estuary exhibited peak discharge and water levels comparable to the 1998 event, the resulting geomorphic adjustment was significantly reduced. High-resolution bathymetric surveys indicate a 39.5% reduction in deep-channel scouring and a 37% decline in shoal deposition during the 2020 flood. Attribution analysis, integrating hydrological data, long-term tidal records, and numerical modeling, reveals that this dampened response stems not only from shortened flood duration, reduced cumulative flood volume, and weakened tidal dynamics(associated with the 18.6-year nodal cycle), but more critically from reduced sediment supply driven by upstream reservoir operations and over two decades of estuarine engineering. While reduced sediment supply has shifted the estuary's baseline state from net accretion to erosion, estuarine engineering has stabilized the river regime by confining flows to the main channel and inhibiting overbank flow. This promotes a “shoal accretion and channel scouring” pattern, thus optimizing the river regime, stabilizing the shoal-channel configuration, and significantly reducing geomorphic changes during extreme floods. This study clarified how human activities altered the morphodynamic mechanisms of the Yangtze Estuary, significantly weakening the bed adjustment induced by extreme floods. These findings provide a general framework for attributing flood impacts in estuaries worldwide and offer a scientific basis for engineering strategies to enhance resilience to hydrological extremes.

Issue 08 ,2026 No.1034 ;
[Downloads: 13 ] [Citations: 0 ] [Reads: 146 ] PDF Cite this article

Ecological water demand pattern and regulation strategy under national water network in Yellow River's “Ji-shaped” bend region

LI Zhanbin;DENG Mingjiang;LI Peng;ZHAO Yong;JIA Lu;YU Kunxia;

The Yellow River's “Ji-shaped” bend region, rich in energy resources and home to diverse ethnic groups, faces ecological vulnerability and frequent natural disasters. As a central area in the Yellow River basin, its ecological and socio-economic development is severely constrained by water scarcity. This study collected the historical meteorological data, leaf area index, soil composition, and soil moisture data from 1982 to 2019, as well as future climate projections under different carbon emission scenarios, using a comprehensive calculation method that considers potential evapotranspiration, vegetation coefficient, and soil moisture coefficient to assess the spatiotemporal variation of ecological water demand in the region. The results show that ecological water demand exhibits an increasing trend over 97.16% of the area in the Yellow River's “Jishaped” bend region. Under medium and high carbon emission scenarios, future ecological water demand will increase further. Vegetation type significantly influences the spatial distribution of ecological water demand, with areas such as southern forests exhibiting higher leaf area indices and greater water requirements. In the context of constructing the national water network, graded planning of water supply and demand should be implemented to improve the operational efficiency of the water network. Coordinated regulation of ecological protection and water resources should be carried out to enhance the resilience and recovery capacity of ecosystems. Additionally, the promotion of water-saving technologies and green development will improve water use efficiency. It is also necessary to combine large-scale terraced fields, Yudiba dams, cellars, and water resources management in the local area to construct a “water, energy, food, and ecosystem” integrated development model and improve the efficiency of water and soil resource utilization, while strengthening drought tolerance in vegetation and ecological restoration to reduce ecological water consumption.

Issue 08 ,2026 No.1034 ;
[Downloads: 44 ] [Citations: 0 ] [Reads: 137 ] PDF Cite this article

Flood retrospective simulation and risk analysis of “7·20” extreme rainstorm flood in Zhengzhou City

LI Kuang;DOU Haiying;LIU Huanyu;GENG Chuanyu;DONG Yongli;HAN Bin;China Institute of Water Resources and Hydropower Research;

After the “7·20” extreme rainstorm flood in Zhengzhou City in 2021, a series of engineering and nonengineering measures were implemented in Zhengzhou City, and the flood control capacity was systematically improved. In order to test the actual effect of the current working conditions to deal with extreme rainstorm floods, this study was based on the hourly rainfall, topography, and current working conditions data of rainstorm, relying on the “five pre”(forecasting, pre-assessment, early-warning, rehearsal and contingency planning) system of flood control in Zhengzhou City, using the coupling technology of three-source Xin'anjiang model, water engineering dispatching model, and one-dimensional river hydrodynamic model to carry out flood retrospective simulation and risk analysis. The whole process was integrated into the pre-judgment function to realize the advanced research and judgment of flood risk and the optimization of engineering joint dispatching. The applicability of the model method and the reliability of the simulation results were verified by comparing with the actual situation of the “7·20” disaster. The results show that the operation of 15 reservoirs under the current operating conditions can give full play to the role of flood storage and regulation. The highest water level adjusted according to the “7·20” actual water level does not exceed the design water level, and the highest water level adjusted according to the current flood limit water level does not exceed the check water level. The river channel in the main urban area has been significantly improved through comprehensive management and drainage capacity. The Jialu River(from Science Avenue to Beijing-Hong Kong-Macao Expressway section) and the whole section of the Jinshui River have reached the flood control standard of 100-year return period, and only the local sections of the Chaohe River and Qilihe River have overflow danger. The risk of flood overflowing into the channel of the two cross-buildings of the middle line of the South-to-North Water Diversion Project in Jiayu River and Jialu River is greatly reduced, which can effectively guarantee the water conveyance safety of the middle line of the South-to-North Water Diversion Project. The research results can provide technical support for extreme precipitation defense, unconventional rainstorm flood forecasting and early warning, and flood control system optimization and improvement in Zhengzhou City.

Issue 08 ,2026 No.1034 ;
[Downloads: 64 ] [Citations: 0 ] [Reads: 148 ] PDF Cite this article

Research and application on a flood control scheduling model integrating physical constraints with deep learning

ZHAO Zhao;CHENG Yinyi;YANG Xiaodong;

In response to the highly nonlinear and sudden characteristics of flood processes of the section from the Xiaolangdi Reservoir to the Huayuankou Hydrological Stationin the middle reaches of the Yellow River, as well as the computational complexity and lack of timeliness associated with traditional optimization scheduling models, this paper proposed a flood control scheduling model(CNN-BiLSTM-SA) that integrated physical constraints with deep learning. The model employed a one-dimensional convolutional neural network(1D-CNN) to extract local correlation features from hydrological time series, utilized a bidirectional long shortterm memory(BiLSTM) network to model global temporal dependencies, and incorporated a self-attention mechanism(SA) to dynamically capture flood lag characteristics. To enhance the model's physical consistency, the principle of water balance was formulated as a physical constraint and incorporated into the loss function. Application results indicate that the model's simulation accuracy on the test set outperforms the baseline model: the Nash-Sutcliffe Efficiency(NSE) for outflow from Xiaolangdi Reservoir and flow at Huayuankou Station reaches 0.9171 and 0.9691, respectively, while the root mean square error(RMSE), mean absolute error(MAE), and mean absolute percentage error(MAPE) are all significantly reduced. It demonstrates the superiority of the hybrid network architecture in handling complex scheduling logic. This study leverages the bidirectional information propagation mechanism of BiLSTM and the dynamic weighting modeling of the self-attention mechanism to more accurately characterize the nonlinearity, long-term delay, and hysteresis of flood wave propagation on long-distance river channels. The proposed approach provides a modeling method for flood control scheduling simulation in basins that balances computational efficiency with physical consistency.

Issue 08 ,2026 No.1034 ;
[Downloads: 27 ] [Citations: 0 ] [Reads: 155 ] PDF Cite this article

A deep learning flood forecasting model based on dual-source water balance constraints

LI Danning;CHAI Hua;WANG Xinyi;WANG Dian;HOU Aizhong;FU Xudong;Tsinghua University;

Global climate change, coupled with anthropogenic impacts, has led to frequent flood disasters, making high-accuracy and long-lead-time hydrological forecasting a critical support for enhancing disaster prevention and mitigation capabilities and ensuring water resource security. Existing artificial intelligencebased flood forecasting methods predominantly rely on extrapolation from single historical discharge series, failing to adequately consider the dynamic interaction mechanism of dual water sources from local precipitation and upstream inflow, as well as the water balance constraint. Consequently, these models exhibit weak representation of the physical mechanisms governing runoff generation and confluence, resulting in limited prediction accuracy during flash flood events. This paper proposed HydroFormer-TS, a deep learning flood forecasting model based on dual-source water balance constraints. By designing a network architecture that integrates dual-source information and embeds physics-guided features, the model was forced to satisfy the water balance principle, thereby enhancing the physical plausibility and interpretability of the simulation process. This approach advanced the modeling paradigm from purely “data-driven” to “data-physics fusiondriven”. By taking the Beijiang River basin, a typical humid watershed in southern China, as the study area, multiple typical flood events and sudden extreme floods were selected for validation. The model demonstrates outstanding performance across all lead times, achieving a maximum Nash Sutcliffe efficiency coefficient of 0.94. It shows an improvement of 85.4% over the LSTM model and 38.2% over the iTransformer at the 72-hour lead time, while reducing the mean absolute percentage error to 23.05% for sudden flood events. This work provides a novel modeling perspective and technical pathway for high-precision hydrological forecasting.

Issue 08 ,2026 No.1034 ;
[Downloads: 38 ] [Citations: 0 ] [Reads: 144 ] PDF Cite this article
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