[Objective] This study aims to evaluate the long-term effects of the impoundment and operation of the Three Gorges Reservoir on the shoreline ecological environment of the middle and lower reaches of the Yangtze River, thereby providing scientific evidence and technical support for subsequent policy formulation and the implementation of the Yangtze River protection strategy. [Methods] The shoreline of the Zhicheng-Hukou section in the middle reaches of the Yangtze River was selected as the study area. Satellite images for four representative years (2004, 2010, 2016, and 2023) at the same water levels combined with UAV real-scene imagery from specific sampling sites during 2021-2023 and water level data from hydrological monitoring stations were utilized to systematically analyze the spatiotemporal dynamic evolution characteristics of the shoreline space after the impoundment of the Three Gorges Reservoir. Remote sensing images were used to calculate large-scale changes in shoreline water surface area for each section in the four representative years, and UAV data were employed to analyze shoreline slope and inundation at selected sampling sites. [Results] (1) After 20 years of impoundment and operation of the Three Gorges Reservoir, the water surface area and shoreline area in the Zhicheng-Hukou section of the middle reaches of the Yangtze River remained generally stable, with the standard deviation of the large-scale water surface area proportion controlled within 2.4%. Additionally, sedimentation occurred in local areas, particularly at river bends, requiring enhanced monitoring of these areas in the future. (2) Analysis of shoreline inundation and slope changes showed that from 2021 to 2023, slope variations along parts of the middle reaches of the Yangtze River shoreline were not significant. At the Zhijiang and Shishou sampling sites, the standard deviation of shoreline slope distribution remained within 2.0%, indicating overall stability. [Conclusion] The impoundment and operation of the Three Gorges Reservoir have a limited impact on the shoreline spatial patterns of the middle reaches of the Yangtze River, and overall changes in shoreline space in the middle and lower reaches of the Yangtze River remain minor. Overall water surface area and shoreline slope remain stable. However, shoreline area has changed in certain local sections, indicating that monitoring and management need to be continuously strengthened in the future. The innovations of this study lies in 1) integration of multi-source monitoring data to achieve high-precision monitoring of spatial dynamic changes in the shoreline of the middle reaches of the Yangtze River; 2) exploration of the influence mechanisms of the Three Gorges Reservoir impoundment on shoreline spatial patterns from two dimensions: large-scale water surface area changes and local shoreline morphological evolution.

[Objective] To address the problems of insufficient 3D scene accuracy, unsatisfactory dynamic water effects, and imprecise hydrodynamic process simulation in existing river-lake digital twins, this study proposes an integrated method for 3D river-lake scene construction and hydrodynamic process simulation based on Unreal Engine 5 (UE5). The proposed method aims to construct a digital twin framework that combines high-fidelity scene representation with high-accuracy hydrodynamic process simulation, and to enhance the visualization, dynamism, and interactivity of river-lake digital twins. [Methods] UE5 was used as the research platform, and a real-scene 3D hydrodynamic process simulation method for river-lake scenarios was proposed and implemented by integrating terrain construction, water body simulation, and dynamic extraction of hydrological parameters. First, high-precision 3D river-lake terrain and environmental scenes were constructed using terrain height maps and high-precision photogrammetric models. Second, the Fluid Flux water simulation plugin in UE5 was modified by incorporating bottom friction factors influenced by the Manning coefficient, as used in engineering analysis, thereby establishing a hydrodynamic process model that better conformed to engineering practice. Finally, Blueprint programs were designed to dynamically extract and compute hydrological process parameters during simulation, enabling real-time calculation and dynamic extraction of key hydrological parameters such as flow velocity, water depth, watershed area, total water volume, and river cross-sections. An interactive user interface was also developed to support parameter visualization and scene interaction. [Results] A complete river-lake digital twin framework was constructed, and its functional effectiveness was verified through multiple experiments. First, a dam-break simulation experiment in a 90° bend was constructed to simulate the diffusion process of dam-break flow. The trends of water level variations at all measurement points showed good agreement with classical experimental data, validating the reliability of the hydrodynamic model. Subsequently, inundation simulation experiments under different vegetation cover conditions were conducted. These experiments reflected the influence of vegetation density on flow resistance and inundation processes in the simulated scenarios, demonstrated the capability of surface roughness variations to affect flow simulation, and verified that the proposed method could simulate the impacts of different vegetation environments on hydrodynamic processes. Finally, a complete 3D river-lake scene integrating 3D scenarios, dynamic water simulation, real-time hydrological parameter extraction, and an interactive interface was presented. Through the interface, users could obtain hydrological parameters such as water depth, flow velocity, cross-sectional morphology, watershed area, and total water volume at any location in real time, facilitating clear data acquisition and subsequent processing. [Conclusion] This study investigates methods for 3D scene construction of rivers and lakes and for hydrodynamic process simulation within such scenes, and successfully constructs a river-lake scene framework that integrates high-precision 3D scenes with hydrodynamic process simulation using UE5. The main innovations of this study lie in clarifying the method for constructing 3D river-lake scenes in the UE5 environment, generating terrain base surfaces using the terrain system and elevation data, and introducing high-precision photogrammetric models to enrich the surface environment, thereby improving the realism of 3D river-lake scene construction. From an engineering analysis perspective, the hydrodynamic model of the Fluid Flux plugin in UE5 is improved by adding a friction term influenced by the Manning coefficient, enabling hydrodynamic process simulation in 3D scenes to more accurately reflect the influence of environmental roughness. Simulation scenarios are also designed to verify the impacts of different terrain and vegetation roughness on flow simulation. In addition, Blueprint programs are designed to dynamically extract and compute various hydrological elements during the simulation of 3D river-lake scenes, forming a complete method for hydrodynamic process simulation in 3D river-lake scenes. The proposed method provides integrated capabilities for scene construction, hydrodynamic process simulation, and hydrological parameter extraction and computation, thereby improving the efficiency of data acquisition and processing during 3D scene simulation.

[Objective] In response to the persistent high-temperature drought of the highest intensity since 1961 that occurred across most of the Yangtze River Basin during the summer and autumn of 2022, the Changjiang Water Resources Commission successively launched two rounds of special campaigns to combat drought, ensure water supply, and secure autumn grain harvest. A digital twin platform for drought relief scheduling in the Yangtze River Basin was developed and scenario simulations for drought defense were conducted, providing technical demonstration for the construction of a digital twin system for drought defense in the river basin. [Methods] The platform was developed in line with the operational requirements of early warning, simulation, and contingency planning. Using the platform, the severe drought in the Yangtze River Basin in 2022 were simulated. [Results] (1) Guided by the requirements of drought relief based on prediction, early warning, simulation, and contingency planning, and combining needs such as drought relief information management, emergency response, drought disaster verification and assessment, drought relief benefit evaluation, and drought relief contingency planning, a digital twin platform for drought relief and water replenishment scheduling was developed. Functions such as monitoring and alarm, prediction and early warning, scheduling simulation, contingency plan consultation, and user management were realized. (2) The severe drought in the Yangtze River Basin in 2022 was selected as a case study. Scenario simulations were conducted along the whole chain and entire process of “current drought diagnosis,future trend analysis,drought relief scheduling simulation,contingency plan consultation and decision-making”. “Current drought diagnosis” included drought monitoring data access, over-limit alarm for monitoring data, and monitoring of drought-related online public opinion, addressing the question of “where is the drought occurring?” “Future trend analysis” included drought prediction data access, prediction model calculation, and over-limit early warning for prediction data, addressing the question of “how will the drought evolve?” “Drought relief scheduling simulation” included the construction of a knowledge base of drought relief scheduling schemes and drought relief scheduling schemes based on knowledge base and scenario simulation, addressing the question of “how should the reservoirs be operated?” “Contingency plan consultation and decision-making” included drought relief emergency plan query, automatic generation of drought relief reports, and intelligent response of drought relief knowledge base, addressing the question of “what actions should be taken for drought relief?” [Conclusion] (1) A digital twin platform for drought relief and water replenishment scheduling is developed, and scenario simulation of the severe drought in the Yangtze River Basin in 2022 is conducted, forming a model case of a basin-level digital twin system for drought defense. (2) Future research should focus on water inflow and demand prediction models for the river basin, big data analysis of online public opinion, and intelligent matching and rolling optimization of drought relief scheduling scenarios, to effectively improve the intelligent level of drought relief and disaster reduction management in the Yangtze River Basin.

[Objective] To systematically review the development trends in terrestrial carbon sink research, this paper employs bibliometric methods to analyze Chinese and English literature from 1994 to 2024, aiming to reveal its evolution, research hotspot shifts, distribution of core research entities, and future directions, thereby providing a reference for grasping field frontiers and supporting relevant decision-making. [Methods] Data were sourced from the Web of Science Core Collection (SCI-E) and the China National Knowledge Infrastructure (CNKI) database, retrieving 8 431 relevant publications on terrestrial carbon sinks from 1994 to 2024 (4 727 in English, 3 704 in Chinese). Using CiteSpace software, knowledge graphs including literature co-citation, author collaboration, and keyword timeline maps were constructed. Combined with the burst detection algorithm, the analysis covered the temporal, disciplinary, journal, and country distributions of publications, identified high-impact institutions, prolific authors, high-centrality and highly-cited literature, and investigated the evolutionary stages and frontier hotspots of keywords. [Results] (1) Publication trends: The number of publications showed significant growth over the past three decades, with an average annual increase of approximately 12% after 2008 and 15% after 2019. The development process could be divided into three stages: slow inception (1994-2008), steady development (2008-2019), and rapid advancement (post-2019). (2) Distribution by country, journal, and discipline: China published the most papers (2 149 in WOS, 3 704 in CNKI), followed by the United States and Germany. Key journals included Global Change Biology, Science of the Total Environment, Agricultural and Forest Meteorology, Acta Ecologica Sinica, and Ecological Economy. Environmental science and technology and resources science and technology were core disciplines. Chinese literature emphasized forestry and agricultural economics, while English literature focused on ecology and geosciences. (3) Core research entities: The Chinese Academy of Sciences was the most prolific institution (832 papers), followed by the University of Chinese Academy of Sciences and the French National Centre for Scientific Research (CNRS). High-centrality and highly-cited literature concentrated on three areas: the dynamics of terrestrial carbon sinks, carbon flux monitoring models, and the coupling between climate change and carbon sinks. (4) Evolution of research hotspots: Keyword burst detection revealed three distinct stages. Stage 1 (1994-2008): focused on fundamental carbon cycle theory, with keywords such as “carbon cycle”,“carbon balance”, and “eddy covariance”. Stage 2 (2008-2019): research expanded to socio-economic dimensions, featuring keywords like “net primary production”,“low-carbon economy”, and “ecological compensation”. Stage 3 (2019-2024): closely aligned with global carbon reduction goals and ecosystem value realization, highlighted by keywords including “carbon neutrality”, “carbon peak”,“carbon emissions”, “temperature sensitivity”, “ecological product accounting”, and “carbon trading”. [Conclusion] This bibliometric analysis indicates that terrestrial carbon sink research is developing rapidly, with China being a major contributor. Research hotspots have evolved from fundamental mechanisms to socio-economic integration, and further toward a trajectory driven by carbon neutrality goals and market mechanisms. Future research directions mainly include: enhancing carbon sink monitoring, accounting, and assessment accuracy; studying carbon process mechanisms in coupled multiple ecosystems; evaluating regional emission reduction and sink enhancement potential along with technological applications; developing economic valuation and market trading mechanisms for carbon sinks; and strengthening international cooperation and data sharing. The findings can provide a basis for understanding the field’s development trajectory, predicting future trends, and supporting policies related to China’s “Dual Carbon” goals.

[Objective] To address the challenges of incomplete remote sensing imagery and insufficient measured terrain data in large-scale river numerical simulations, this study proposes a novel method for river channel terrain reconstruction that integrates Copernicus DEM 30 data with limited measured data, aiming to solve the problem of constructing large-scale river channel DEMs in data-scarce areas and to verify its applicability in MIKE 21 hydrodynamic-sediment numerical simulations. [Methods] This study took the Alar-Xinquman river section of the Tarim River as the study area and utilized relevant hydrological data and measured terrain elevation data from 2011 as the basic dataset. The specific reconstruction process was as follows: (1) Using ArcGIS software, combined with Google Earth historical imagery and Copernicus DEM 30, the inner and outer bank lines of the river channel were delineated by scaling overlapping imagery proportionally to determine the main channel boundary.(2) The river DEM was corrected stepwise. First, the quadratic interpolation method was applied to densify the elevation data of the main channel cross-sections. Second, the mean difference method was used to calculate the deviations in elevation between measured points and the Copernicus DEM at corresponding locations, enabling an overall vertical correction of the Copernicus DEM data. Next, a locally weighted regression (LOESS) algorithm was introduced to correct the longitudinal cross-section elevations of the main channel, smoothing the riverbed terrain and generating the main channel DEM. Finally, the main channel and floodplain DEM data were merged to construct the complete river channel DEM.(3) The reconstructed DEM was imported into the MIKE 21 hydrodynamic-sediment module. Measured data such as water level, flow, and sediment concentration were selected as boundary conditions for numerical simulation. The accuracy and reliability of the reconstructed DEM were evaluated by comparing the errors between the simulated and measured values. [Results] Comparison between numerical simulation results and measured data revealed that the results for flow velocity, flow-stage relationships, and river channel erosion-deposition tests all met the allowable deviation requirements specified in relevant technical standards. This indicated that the river channel DEM constructed in this study was reasonably suitable for two-dimensional hydrodynamic-sediment numerical simulations. [Conclusion] (1) The river channel terrain reconstruction method proposed in this study, integrating Copernicus DEM 30, quadratic interpolation, mean difference method, and locally weighted regression (LOESS) algorithm, can effectively address the lack of measured underwater terrain data for large-scale rivers. It demonstrates superior continuity and accuracy compared to traditional single-interpolation methods.(2) The hydrodynamic model established based on the reconstructed DEM achieves simulation accuracy within the allowable deviation limits specified by relevant technical standards. This method is characterized by low cost, high efficiency, and strong applicability, providing a practical new approach and technical support for the numerical simulation of large-scale rivers with scarce data, such as the Tarim River.

[Objective] With the development of deep learning and computer vision technologies, the application of machine vision in flood monitoring has gradually become a research hotspot. This study aims to overcome the limitations of traditional manual and satellite remote sensing methods in flood monitoring and early warning, including insufficient accuracy and high costs, and to explore the advantages and limitations of the Segformer model in extracting flood boundaries, while proposing future research directions and improvement strategies. [Methods] Using deep learning and machine vision techniques, a specialized “RiverDataset” for flood monitoring was constructed, and the performance of the Segformer model in extracting flood inundation range was evaluated based on this dataset. Additionally, the Segformer model was compared with the U-Net models based on ResNet50 and VGG16 to assess their performance in water body segmentation tasks. Taking Shashi District, Jingzhou City, Hubei Province as an example, UAV remote sensing imagery was used to accurately extract the flood contours in the region. [Results] The U-Net (VGG16) model demonstrated excellent performance on the training set but was slightly inferior to the Segformer model on the validation and test sets. The Segformer model achieved superior performance across most indicators, particularly outperforming the U-Net (ResNet50) model in complex scenarios. Although the U-Net (ResNet50) model achieved a slightly higher IoU, its higher loss value and lower mIoU and mAP indicated that its overall performance was inferior to that of the Segformer model and the U-Net model (VGG16). Consequently, the U-Net model based on VGG16 performed well across all evaluation indicators, demonstrating strong fitting capability during training. However, when processing complex water bodies, this model struggled to capture contextual information in narrow regions due to its limited receptive field, leading to frequent occurrences of information voids. Furthermore, the U-Net model failed to effectively eliminate gridding effects, compromising the local consistency of feature information. In contrast, the Segformer model did not exhibit this issue. This difference was mainly due to the relatively small receptive field of the convolutional kernels used in the U-Net model, which restricted its ability to interpret contextual information within narrow regions and hindered the establishment of long-range information continuity. The Segformer model, not constrained by the limited receptive field of conventional convolutional kernels, could better capture broader contextual information in the image. Additionally, the U-Net model failed to effectively eliminate the impact of the grid effect on feature information, resulting in extracted features that disrupted the local consistency of information. The absence of structures for perceiving local regional information made the model incapable of effectively eliminating the influence of interfering objects in complex aquatic environments, particularly in scenarios requiring precise boundary extraction. [Conclusions] In complex aquatic environments, the Segformer model demonstrates superior segmentation performance and robustness. This study validates the efficiency of the Segformer model in extracting flood inundation range, highlighting its potential for practical water body monitoring applications. Future research should further optimize the model, expand the dataset, and explore its potential for real-time application to enhance the efficiency and accuracy of flood early warning systems.

[Objective] Curtain grouting is a commonly used method for anti-seepage reinforcement in pumped storage projects, and injection rate is an important monitoring parameter that directly affects grouting quality. The fluctuation section of injection rate is a critical stage in the grouting process. At present, research on the analysis of injection rate monitoring data is scarce, scientifically sound criteria for reasonable injection rate are lacking, and it is difficult to guide the adjustment of grouting pressure. This paper proposes a method for analysing and calculating the injection-rate interval of the fluctuation section, providing scientifically sound intervals for different grouting scenarios. [Methods] By analysing the temporal evolution of injection rate during curtain grouting, the rate-time curve was divided into three stages—fluctuation, sharp-decline, and termination—and its patterns classified into four types: normal, sharp-decline, low-level injection, and non-convergent. Normal-pattern sections were selected as standard grouting segments, and two key parameters of the fluctuation section—unit-average injection rate and average slurry density—were calculated to indirectly represent the average geological conditions of the treated strata. Owing to the large scale of the grouting area and inherent geological variability of the treated strata, the unit-average injection rate exhibited high dispersion. Therefore, based on the intrinsic correlation between resource allocation decisions and geological information, the concept of “grouting similarity” and a dynamic geological-zoning approach were proposed. Correlation analysis between grouting parameters and unit-average injection rate selected GIN value, average slurry density, and hole sequence as energy, fracture, and sequence indices, respectively. Clustering was applied to standard grouting segments based on these indices to reduce dispersion. After de-noising the target parameters within each category using DBSCAN, based on the 3σ control principle in risk management, if the data followed a normal distribution, points falling outside the ±3σ range could, under the principle of controlling type Ⅰ and type Ⅱ errors, be identified as extreme outliers. The Shapiro-Wilk method was used to test the normality of each cluster, and for those that passed, the ±3σ interval was calculated as the injection rate interval for the fluctuation section. Based on the degree to which each segment's parameters deviated from the interval center, the specific interval of the fluctuation-section unit-average injection rate of completed segments was determined, and a preliminary post-grouting geological assessment was provided, thereby achieving data-driven quality management of curtain grouting. [Results] This method was applied to the curtain-grouting project of Wuyue Pumped-Storage Power Station for clustering analysis of completed grouting sections. Results showed that parameter distributions within most clusters satisfy the normality assumption. The calculated fluctuation-section injection rate intervals for each grouting-similarity pattern conformed to the similarity hypothesis and provided an effective control tool for injection rate process control and management. Based on these intervals, preliminary relative geological assessments precisely identified outlier sections with significant deviations from a large number of grouting segments and the proportion of outliers met quality-risk-management standards. [Conclusions] In summary, the proposed method for determining the injection rate interval of the fluctuation section is logically rigorous and reliable, significantly improving the scientific management of curtain-grouting construction in large-scale pumped-storage projects.

[Objective] Emergency response to breaches has high requirements for timeliness, information accuracy, overall situational awareness, and coordinated observation. The aim of this study is to meet the urgent practical need for dynamic real-time monitoring of breaches, providing technical support for rapid post-disaster assessment and decision-making for flood control emergency rescue. [Methods] Considering the constraints of time, space, frequency, and spectrum, we developed a multi-source remote sensing collaborative monitoring method by using the fuzzy multi-attribute decision-making method to evaluate the dynamic monitoring capabilities of different collaborative schemes in response to breaches and their secondary flood disasters. The breach progression can be traced by inverting indicators such as breach width and inundated area within the embankment area. We further selected the “breach in the Tuanzhou Embankment of the Dongting Lake on July 5” as a case study for experimental analysis and validation. [Results] (1) under the breach monitoring scenario, the derived collaborative monitoring scheme achieved a comprehensive fitness value of 0.654. The breach width variation curve exhibited a general consistency with the trend of in-situ hydrological monitoring data. The relative error between the breach width derived from collaborative inversion and hydrological measurement was 0.98%, demonstrating high accuracy suitable for emergency monitoring. (2) The coordination monitoring scheme under the flood monitoring scenario achieved a comprehensive fitness value of 0.591. The inundation area variation curve within the embankment area was overall consistent with the trend of values calculated by the flood re-simulation model, with a relative error of -2.39%. This error primarily stemmed from the fitting accuracy of discrete values derived from multi-source remote sensing inversion and systematic errors in the re-simulation model calculations. [Conclusions] The developed multi-source coordination combination method integrates both the coordination monitoring process and the coordination data inversion, covering the entire monitoring period before, during, after the breach. It accurately reflects the piping and seepage processes and their propagation trends before the occurrence of breaches, thereby addressing the practical needs for timeliness, comprehensiveness, and coordination in responding to breaches and secondary flood disasters. In addition, the effectiveness of the experiments and applications in this study depends on the types and quantities of satellite resources that can be scheduled under emergency monitoring modes, as well as their alignment with monitoring task requirements. The more satellite resources involved in coordination and evaluation, the higher the monitoring frequency and coverage cycle can be increased, potentially improving the comprehensive fitness value.

[Objective] In water conservancy and hydropower engineering, grouting is a key construction process to ensure the stability and leakage safety of hydraulic retaining structures. However, traditional grouting monitoring equipment exhibits deficiencies in accuracy, level of intelligence, and construction efficiency. This study aims to achieve fully automated operation and early warning feedback control of the grouting process by innovatively designing a new generation of intelligent sensing and efficient control equipment for grouting parameters, thereby improving engineering management efficiency, construction quality, and safety. [Methods] This study designed an intelligent centralized slurry station, an intelligent pressure regulation and flushing device, an intelligent grouting control coordination center, and a digital grouting recording unit, and integrated them into a new generation of intelligent sensing and efficient control equipment for grouting parameters. Additionally, a multi-channel, user-friendly human-computer interaction interface based on multi-touch control was developed to realize the fully automated operation and unified coordination management of the grouting process, including slurry preparation, slurry delivery, pressure regulation, and grouting. [Results] Through experiments and practical engineering applications, this equipment was verified to achieve fully automated operation of the grouting process, significantly improving the accuracy and intelligence of grouting construction. By implementing early warning feedback control and unified coordination management functions during the grouting process, construction safety was effectively ensured, and project management efficiency was enhanced. Compared to traditional grouting monitoring equipment, construction efficiency was increased by more than 30%, and construction quality was significantly improved. [Conclusions] The new generation of intelligent sensing and efficient control equipment for grouting parameters shows remarkable effectiveness in the grouting construction of water conservancy and hydropower projects, achieving automated, intelligent, and efficient grouting process, providing strong support for high-quality project construction and showing broad application prospects.

[Objective] To address the issues of insufficient real-time sensing capability and low level of intelligent decision-making in water resource scheduling and management in irrigation areas, this study focuses on the design and implementation of a digital twin water resource scheduling and management platform for irrigation areas. [Methods] By integrating the Internet of Things, digital twins, hydraulic models, and irrigation area water resource scheduling and management systems, a water resource sensing and control system for irrigation areas was established. This system gathered massive sensing data and combined it with digital twin visualization technologies such as full-factor digital representation, multi-source heterogeneous data fusion, and high-fidelity 3D visualization. It enabled synchronous simulation and diagnostic analysis of the operational status of various components of the irrigation area (canal headworks, canal sections, regulating gates, distribution gates, and irrigation area) and multiple factors (water level, flow, water quality, engineering safety, gate opening, etc.). Based on the canal system hydrodynamic model and water resource scheduling evaluation model, a water resource scheduling decision-making system of “forward simulation of scheduling impacts and backward deduction of scheduling plans” was constructed. The dynamic simulation and analysis of water allocation plans generated by the integrated model of “inflow prediction-water demand prediction-dynamic canal system water distribution” were performed, continuously iterating and optimizing scheduling plans to assist in the scientific development of joint water resource scheduling plans for the canal gate groups in the irrigation area. [Results and Conclusions] The practical application of the digital twin Dujiangyan Irrigation Project (canal head hub) demonstrates that through digital mapping and intelligent simulation of all elements of the physical watershed and water resources management activities in the irrigation area, the digital twin irrigation area water resource scheduling and management platform can timely perceive the supply and demand status of water resources, as well as the operational conditions of irrigation water conservancy projects and sensing devices. This enables intelligent and refined allocation of water resources, comprehensively enhancing the modernization level of water resource management in irrigation areas.

[Objectives] This study aims to explore the feasibility of employing DeepSeek, a large language model, to promote intelligent hydrological analysis through its natural language interaction and code generation functions. This research innovatively applies DeepSeek to engineering hydrological analysis, promoting intelligent development in the field of engineering hydrology. [Methods] First, based on the core concepts and characteristics of engineering hydrology discipline, it was concluded that DeepSeek’s application scenarios such as code generation, code rewriting, and code explanation were highly suitable for engineering hydrology, a field heavily dependent on data. Focusing on the typical task of frequency analysis of hydrological data, this study used a case-driven method and designed a two-stage experiment. During the data cleaning phase, daily water level data incorporating compound water level recording methods were fed into the system, and MATLAB cleaning code was iteratively generated using structured prompts. In the data analysis phase, the annual maximum water levels, 3-day and 5-day moving average maximum sequences during the flood season were generated, and the Pearson Type III (P-III) distribution was used to calculate key frequency design values such as 1% and 5%. Finally, a quantitative comparison was conducted between DeepSeek’s calculated results and conventional eye-fitting curve outcomes to evaluate the accuracy of the results. [Results] In terms of efficiency, the processing time for multiple prompts ranged from 33 to 109 seconds. Standardized tasks (such as moving average calculations) achieved “prompt as code”, substantially reducing programming time and significantly enhancing workflow efficiency. Additionally, the automated optimization of existing inefficient code notably improved efficiency. Regarding accuracy, DeepSeek could accurately identify user requirements and precisely interpret professional concepts. It achieved a 100% accuracy rate in the first attempt when interpreting key concepts such as the P-III distribution and flood season averages. However, for low-frequency terms (e.g., compound recording method), 2-3 rounds of prompt iteration were required. Additionally, DeepSeek’s calculated average and C_v parameters were consistent with those obtained using conventional methods, further demonstrating its high precision. [Conclusions] DeepSeek significantly lowers the technical barriers to engineering hydrological analysis. Its natural language interaction capability serves as an “intelligent bridge” between professional requirements and code implementation, while its automated data processing and model calculation alleviate practitioners’ workload, promoting the integration of AI technology from academic research into engineering practice. In the future, with in-depth research and expanded applications, DeepSeek is expected to evolve from an auxiliary tool into a core engine driving the transformation of engineering hydrology from “experience-based decision-making” to “knowledge-data collaborative decision-making,” thereby providing foundational support for intelligent water conservancy.

[Objective] In the context of big data from mobile internet, social media data with tags such as posting time and location has received widespread attention for its critical role in natural disaster response. In China, research on social attention and online public opinion regarding drought events remains limited, especially for the analysis of spatiotemporal and thematic characteristics of extreme drought events at the river basin scale, with no relevant reports yet. [Methods] This study used the 2022 extreme drought in China’s Yangtze River Basin as a representative case. Utilizing texts from Weibo, a mainstream social media platform in China, as data sources, this study used machine learning and artificial intelligence algorithms to collect Weibo text data throughout the drought progression process. The Latent Dirichlet Allocation (LDA) topic model was employed to perform term clustering and thematic characterization. Through this methodology, an in-depth mining of spatiotemporal and thematic characteristics of drought-related public opinion was conducted, along with sentiment analysis. [Results] (1) The temporal evolution of attention levels on social media was relatively synchronized with the progression of the drought event, with peak drought stage particularly prone to attracting heightened public attention. Across the entire Yangtze River Basin, drought-related discussions on social media remained relatively low in July 2022, rose dramatically in early August, peaked in mid-to-late August, gradually declined in mid-September, and returned to zero in early December. In terms of drought progression, an inverse correlation between the temporal variation characteristics of Weibo discussion level in severely affected provinces and municipalities including Sichuan, Chongqing, and Jiangxi and local hydrological flow data was observed. (2) The spatial characteristics of attention levels on social media basically matched the distribution of drought severity. The proportion of Weibo discussions in high-attention provinces and municipalities (e.g., Sichuan, Chongqing, and Jiangxi) exceeded 50%, reflecting widespread public concern about the drought and indirectly indicating severe socioeconomic impacts caused by the drought in these regions. In contrast, provinces and municipalities such as Yunnan, Tibet, Shanghai, and Qinghai showed relatively low levels of Weibo discussions. (3) The thematic characteristics of drought-related content on social media showed significant regional differences, with public attention levels being closely related to the severity of drought impacts. In Jiangxi and Hunan, key terms related to the drought were “shrinking of Poyang Lake” and “declining water levels” In Sichuan and Chongqing, key terms were secondary disasters such as “wildfires”, “earthquakes”, as well as drought-induced issues such as “reduced crop production by farmers” and “electricity supply shortages”. Other provinces primarily focused on “continuous high-temperature weather” and “meteorological drought”. As the drought progressed, the sentiment of public opinion on drought gradually transitioned from negative to positive. [Conclusion] Weibo texts serve as an effective data source for online public opinion analysis of sudden-onset disasters. The research findings can provide technical support for drought tracking analysis and mobilization efforts of the public for drought relief in river basins.

Irrigated area is the basic data required for effective agricultural water conservation, yet traditional survey and statistical methods no longer meet current monitoring needs. In this research, GF-1 and Sentinel-2 satellite images were fused to construct the sample spectrum of crop growth period. Based on the pixel-scale spectral matching method, the crop planting structure and actual irrigated area of Zaohe irrigation district in Suqian City, Jiangsu Province from 2017 to 2022 were synergistically extracted. Results show that the main planting pattern in Zaohe irrigation district is rice-wheat rotation. From 2017 to 2022, the actual irrigated area was 85.11 km², 91.91 km², 103.65 km², 95.85 km², 97.72 km² and 88.24 km². respectively. Validation using sample points and a confusion matrix yielded an overall accuracy of 89.71% and a Kappa coefficient of 0.80, indicating higher accuracy and better extraction effects compared to existing products like IrriMap_Syn and IWMI products. This method is suitable for extracting the irrigated area in south China, and can provide technical and data support for the daily supervision of management departments and the optimization of water resource allocation.

The Three Gorges Reservoir (TGR) area features a large number of tributaries, but the lack of basic hydrological station monitoring leads to the scarcity of hydrological and water-resources information for these tributaries, affecting regional water resource management and flood control safety. To address this issue, we selected 20 typical tributaries in the TGR area, among which 18 are ungauged, to build 3D digital river models for these tributaries by self-developed remotely-sensed hydrological station technology along with satellite and unmanned aerial vehicle (UAV) remote-sensing data. Based on this model, we calculated the river discharges, relative water levels, water-surface widths, and other information from January 2016 to July 2023 for the monitoring sections. Results revealed that: 1) The cross-sections of 20 typical tributaries in the TGR area display U-shape, demonstrating mountainous characteristics. The remotely-sensed hydrological station technology demonstrates high accuracy in calculating ungauged tributary discharges in the TGR area, with the average Nash-Sutcliffe efficiency coefficient (NSE) and coefficient of determination (R²) reaching 0.74 and 0.76, respectively. 2) During the study period, the relative water levels of the tributaries changed minimally. The percentages of months with monthly relative water-level fluctuations below 1.2 m in the upper, middle, and lower tributaries of the reservoir were 93.1%, 86.8%, and 87.4% respectively. 3) The average discharges of typical ungauged tributaries were generally stable. However, trend analysis indicated that the discharges of 13 ungauged tributaries have being decreasing, suggesting an overall downward trend in tributary discharges in the reservoir area. 4) The 20 tributaries have abundant annual average and total discharges, with the annual average inflow reaching 16.475 billion m³, accounting for approximately 4.8% of the total annual inflow of the reservoir area, offering substantial water resource support for regional economic development.

In the remote sensing monitoring for cyanobacteria blooms, the bloom area is a critical indicator for assessing the severity of the bloom and is crucial for relevant authorities in selecting preventive measures and determining emergency response levels. Traditional methods using medium-to-low-resolution imagery has limited precision in estimating bloom area. To address this issue, the cyanobacteria bloom areas of Taihu Lake as the study area extracted from Sentinel-2 and Sentinel-3 data were compared. Furthermore, the relationship between the NDVI from Sentinel-3 imagery and the cyanobacteria bloom area proportion within mixed pixels were analyzed. Based on these analyses, a corrected model for estimating bloom area was established using the NDVI density segmentation method to refine the bloom area extracted from Sentinel-3 images. The statistical results of bloom areas derived from Sentinel-3 with correction, Sentinel-3 without correction, and Sentinel-2 were compared and analyzed. The findings demonstrate that the corrected model significantly improves the accuracy and reliability of bloom area estimation using Sentinel-3 imagery compared to traditional methods, thereby enhancing its practical application value in cyanobacteria bloom monitoring.

Forecasting and early warning of water quality are essential for advancing the modernization of water pollution prevention. In this study, we developed a technical framework for a phosphorus risk assessment platform and implemented a water environment platform for phosphorus risk forecasting and early warning. The platform automatically collects and integrates multi-source heterogeneous foundational data, including meteorological, hydrological, water quality monitoring, and pollution source discharge data, and generates boundary and process conditions for an air-land-water coupled model. The platform forecasts rainfall, hydrology, and total phosphorus concentrations, assesses phosphorus-related risks, and issues early warnings. The platform was applied to the Xiangxi River basin in 2016 and 2020, and has provided technical support for enhancing intelligent basin management and mitigating phosphorus risks in the Xiangxi River basin.

In addressing the requirements of water resources monitoring tasks, we propose an adaptive multi-sensor performance measurement scheme by integrating time-space coverage, sensor parameters, imaging quality, and target importance based on the prototype of the monitoring task requirements. To achieve optimal benefit for water resources monitoring tasks, we constructed a combined optimization method using fuzzy greedy search decision algorithm and validated the adaptability of the proposed method by applying it to the Danjiangkou Reservoir area. Results demonstrate that the combined observation method effectively meet the requirements of reservoir water quality monitoring, reservoir bank environmental monitoring, as well as routine drought monitoring tasks within specific space-time constraints, thereby providing a sound decision-making basis for the daily monitoring of water resources.

To investigate the ecological status of Shanghai’s offshore waters, we applied the water color remote sensing technology to estimate chlorophyll-a concentration. Considering the impact of BRDF (Bidirectional Reflectance Distribution Function) on inversion accuracy, we utilized LandSat-8 remote sensing data and measured water quality data in association with Lee’s model and QAA (Quasi-analytical algorithm) for BRDF correction. Results indicate that suspended sediment concentration is a major factor affecting BRDF. The angle of sunlight has minimal impact on BRDF when the sun is not vertically incident. After BRDF correction, the mean value of R² increased by 22.2% to 0.9, whereas mean RMSE decreased to 0.74. Before the correction, chlorophyll-a concentrations in Shanghai’s offshore waters were overestimated by an average of 2 mg/m³. BRDF correction notably enhances chlorophyll-a inversion accuracy, presenting innovative insights for offshore water color inversion.

Accurate monitoring of leakage losses in water supply pipeline network is crucial for preventing water resource waste. This study proposes a multi-criteria decision analysis method to optimize sensor arrangement for monitoring the pipeline leakage loss. To address uncertainties in weights, difference thresholds, and preference thresholds in sensor arrangement, we used information entropy to screen initial sensor positions within the decision space. Based on monitoring objectives, we defined criteria for sensor arrangement optimization and applied the multi-criteria decision analysis method to rank the initial schemes. Taking into account multiple sets of parameters and different preference scenarios, we obtained the probabilistic ranking of the schemes. To validate the proposed method, we conducted simulation experiments using the k1 model of the baseline test pipe network and compared several preference scenarios. The results demonstrate that the proposed method effectively handles sequential priorities, rankings, and pairwise comparisons, avoiding the use of “black box” in selecting sensor placement scenarios.

River simulation technology plays a crucial role in watershed digital twin. However, current precise modelling of physical rivers to virtual watershed faces several deficiencies. To address such deficiencies, a 3D hydrodynamic model was developed in this study based on terrain measurements, remote sensing images,hydrological data, and water surface line calculated by hydrological model. The UE5 engine and Fluid Flux plug-in rendering was employed in the model. Blueprints were reconstructed and compiled, data assets were trained and coordinate system disparities were reconciled to finally achieve high fidelity simulation of the water flow of Hudu River. Calibration results indicate that the present model accurately reflects real-world conditions with small errors. The simulation model depicts the water flow dynamics during different periods, the relative height difference between water level and embankment, the artificial flood diversion blasting, and underwater scenarios. Parameter adjustments enable comparative analysis of different scenarios, offering visualization support for flood control decision-making.