[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.

Under the influence of climate change, the water area of Poyang Lake exhibits seasonal fluctuations. To elucidate these changes comprehensively, we propose a water information extraction approach that integrates multi-temporal radar images and optical data. Leveraging Sentinel-1A radar images and Sentinel-2 optical images as our research datasets, we initiated a sequence of data preprocessing steps on the remote sensing image set. Employing the Sentinel-1 dual polarized water index (SDWI) and the improved normalized differential water index (MNDWI), we delineated the lake’s boundary and computed its water area using radar and optical data. The precision, timing, and change detection of our water extraction results were evaluated meticulously. Analyzing the lake area’s change trends aims to furnish scientific insights for disaster management and Poyang Lake’s conservation. Our findings revealed that: 1)The water bodies of Poyang Lake extracted from radar and optical remote sensing data were mostly congruent. Radar image extraction outperformed optical image extraction when delineating farmlands, small water bodies, and cloud-covered areas, suggesting radar images’ superiority in capturing comprehensive water information. 2)The average water area of Poyang Lake during normal, wet, and dry seasons is 3 686.49, 4 077.73, and 2 612.81 km², respectively, with the wet season’s water volume being 1.56 times that of the dry season. 3)Time-series water extraction results from radar images exhibited strong consistency with water level variation data from Xingzi Station, Duchang Station, Hukou Station, and Kangshan Station, yielding Pearson correlation coefficients of 0.89, 0.87, 0.90, and 0.81, respectively.

In the middle and lower reaches of the Yangtze River, drought has had serious impacts on economic development with increasing frequency, intensity, disaster scope and duration over the past six decades. In this study, 60-year historical precipitation dataset and 40-year remote sensing dataset were constructed to calculate the meteorological drought and agricultural drought indexes. The drought monitoring index series curves were established based on standardized precipitation index (SPI) and vegetation health index (VHI). The spatial distribution, seasonal distribution, occurrence frequency and other indicators of drought were analyzed to explore the drought’s spatial-temporal differentiation laws. The results show that drought events in the study area are notably season-dependent, with high frequency in spring and summer, and low frequency in autumn and winter. Droughts in the north and west part were more serious than those in the south and east part. Northern Hubei and southern Hunan were prone to suffer from droughts.

This paper addresses the need for emergency monitoring and intelligent simulation in response to sudden water pollution events.We established the correlations of sensor performance with time,space,and attributes and constructed a multi-sensor synergetic monitoring model to accomplish the dynamic and collaborative sensing through a network of satellite,UAV(Unmanned Aerial Vehicle),ground and water platforms.We selected Danjiangkou Reservoir as a demonstration area for dynamic network monitoring of sudden water pollution events. Based on the specific observation requirements during the occurrence,dynamic evolution,and later stages of water pollution events,we determined the corresponding platforms and sensors required. Experimental results manifest that the proposed multi-sensor network captures the water pollution monitoring information comprehensively,accurately,and rapidly. Moreover,it provides scientific support for emergency response and treatment of sudden water pollution events.

Long-term operation of pump station led to increased resistance and weakened outflow capacity, thereby impacting urban drainage efficiency. In view of this, we have developed a flow capacity check and evaluation system for pump stations utilizing the Python programming language and digital image technology. Real-time data of the axial flow pump voltmeter and ammeter is collected using cameras, while ultrasonic Doppler profilers and radar water level gauges record the outlet flow and head of the pump station. To address the issue of flow data deviation caused by complex flow patterns and ambient noise interference, we have implemented a moving median filtering method and employed Bessel interpolation for data completion. These methods significantly improve the accuracy of flow data. Our system has been successfully applied to assessing the outflow capacity of pumping stations, uncovering that the measured outflow capacity falls below the theoretical value in conditions where power and head remain constant.

By searching published research papers affiliated with the Changjiang River Scientific Research Institute (CRSRI) from 2016 to 2020 included in three major index databases, namely SCI, EI, and CPCI, we conducted a bibliometric analysis on the number, discipline, author, and author group distribution of these papers. The author group distribution includes author’s age, education level, and professional titles. By comparing the statistical data with those in the previous five years, we further examined the scientific research, discipline development, and personnel structure of CRSRI in recent years from the perspective of research papers. We observed a significant enhancement in research strength and international influence. While maintaining the research strength in traditional fields, emerging fields have shown rapid improvement in research capacity, and external collaborations have been strengthened. A higher level of research talent has emerged, with a tendency towards younger researchers. This study aims to provide valuable data for discipline construction and research performance evaluation of CRSRI.