[Objective] The mechanism of nitrogen transport in karst water system is a critical issue in the study of karst groundwater pollution and plays a key controlling role in the migration process of contaminants within karst watershed. Due to the unique geological structure and hydrological conditions in the karst regions of Southern China, the nitrogen transport process is extremely complex, posing significant challenges in characterizing and simulating its physical processes. [Methods] Relevant domestic and international studies are systematically reviewed. It summarizes and analyzes nitrogen source identification, transformation processes, influencing factors, and simulation approaches. Key gaps and weaknesses in current research are identified. [Results] The primary limitations of current research lie in the predominant focus on the recharge and discharge zones of karst catchments, while studies on the internal processes of underground rivers remain relatively limited. Furthermore, the understanding of chemical processes such as mineralization and dissimilatory reactions and microbial mechanisms driving nitrogen transformation requires further deepening. Additionally, modeling approaches for nitrogen transport and research on emerging contaminants in karst regions are still in their nascent stages. [Conclusion] Future research should commence with enhanced monitoring of karst water system to obtain more comprehensive data for further study; delve into the relationship between microbial and nitrogen transformation to reveal the biogeochemical mechanisms of nitrogen transport in karst water system; develop nitrogen transport models tailored to karst regions to strengthen the study of solute migration mechanisms; and expand the research scope to emerging pollutants such as suspended particulate matter, investigating their impact mechanisms on nitrogen transport.

[Objective] To address the complex spatial distribution, significant heterogeneity of demand points, and insufficient efficiency of resource allocation in emergency material reserve location under flood disaster scenarios, an optimization approach integrating demand-based zoning and hierarchical facility allocation is developed. [Methods] Multi-dimensional feature vectors of demand points were constructed from three dimensions, including hazard, exposure, and accessibility. After normalization, a self-organizing map( SOM) neural network was employed to achieve topological mapping of high-dimensional features. The within-cluster sum of squared errors was calculated based on the neuron weight space, and the optimal number of demand clusters was determined using the elbow method. At the subregional level, a primary reserve location model was established, with the objective of minimizing the product of demand weight and transportation distance. At the global level, a higher-level reserve location model was constructed for optimization. [Results] The results showed that the optimal number of demand clusters was determined to be 34. Based on the classification result of the 34 subregions, primary reserves achieved effective coverage of intraregional demand. On this basis, eight higher-level reserves were identified through global optimization to enable coordinated resource allocation. Compared with conventional method, construction cost was reduced by approximately 16. 1%, and the weighted average response time was shortened by approximately 17. 1%. [Conclusion] The results indicate that the proposed method can effectively characterize the multidimensional heterogeneity of demand points under flood disaster scenarios and achieve zoning-driven two-level optimization of reserve allocation. Through the framework of “SOM-based zoning-local optimization-global coordination,” the spatial adaptability and system efficiency of emergency resource allocation can be significantly improved.

[Objective] The evolution patterns of extreme climate in cities within the north-south climate transition zone are revealed, thereby providing scientific support for disaster prevention and mitigation, water resources regulation, and climate resilience construction in Suqian City. [Methods] Daily temperature and precipitation observation data from four meteorological stations in Suqian during 1955—2024 were used. Linear trend analysis, the Mann-Kendall test, the trend-free pre-whitening MK test, and the Pettitt abrupt change test were applied to identify evolution characteristics of climatic series. A system consisting of 44 extreme climate indicators was constructed to analyze evolution patterns of extreme precipitation, extreme temperature, and compound extreme events. Partial correlation analysis, stepwise regression, piecewise multiple regression, and hierarchical variance decomposition were used to quantify the effects of three driving factors, namely, global warming, atmospheric circulation, and urbanization. [Results] Over the past 70 years, no significant trend or abrupt change was detected in annual total precipitation, while a remarkable transformation was observed in precipitation structure. The frequency of moderate rainfall events decreased, and the proportions of rainstorms, short-duration extreme precipitation, and nighttime precipitation continuously increased, leading to shorter and more evenly distributed precipitation processes. The annual average temperature increased extremely significantly at a rate of 0. 28~0. 38 ℃ per decade, and consistent abrupt changes occurred across the whole study area in 1993. Diurnal asymmetric warming characteristics were observed. The warming rate of nighttime low temperature was significantly higher than that of daytime temperature, and the diurnal temperature range was continuously reduced. The reduction in cold events was far greater than the increase in warm events. Dry-warm and dry-cold compound extreme events were dominant, while warm-wet and cold-wet events occurred at very low frequencies, indicating a shift in climate risk from cold-dry dominance to warm-dry dominance. According to variance decomposition result, an independent variance contribution of 23. 2% was obtained for global warming, and independent contributions of 3. 1% and 8. 8% were obtained for large-scale atmospheric circulation and local urbanization, respectively. A total of 13. 4% of temperature variation was explained by multi-factor coupling interaction. Piecewise regression result showed that the local response intensity of regional climate to atmospheric circulation and global warming was weakened by rapid urbanization, and no significant linear warming effect was detected for urbanization. [Conclusion] A systematic transformation of climate features is identified in Suqian during 1955—2024, with diurnal asymmetric warming, intensified extreme precipitation, and warm-dominated compound climate risks as the three core characteristics. Global warming dominates regional temperature variations. Urbanization alters climate conditions only through long-term cumulative effects, whereas its marginal warming effect is insignificant. The findings can provide a reference for climate adaptation and disaster prevention in similar cities within the north-south climate transition zone.

[Objective] Complex causal coupling effects and cascading propagation exist among operational risks in hydropower stations, which accelerate the dynamic evolution of risk events and increase the unpredictability and uncontrollability of their consequences. Scientific identification of risk events and their evolution characteristics is essential for ensuring the safe operation of hydropower stations. [Methods] Through text data mining of historical risk event logs from hydropower stations, triggering events, process events, consequence events, and their developmental relationships were extracted. A complex network model of risk events was constructed, and the network topology and evolutionary characteristics of the risk event development process were quantitatively analyzed. [Results] The constructed risk propagation network for hydropower stations consisted of 102 nodes and 125 directed edges. The “unit cut-off” node had an out-degree of 29, the “circuit breaker trip” node had an in-degree of 5, and the “abnormal downstream water level” node had both closeness centrality and eigenvector centrality of 1. The edge “unit cut-off → external transmission channel interruption” had the highest edge betweenness. The chain “isolating switch equipment failure → three-phase current imbalance → external transmission channel interruption → all circuit breakers disconnected → total station blackout” had the highest transmission probability. [Conclusion] Significant functional differences are observed among nodes and chains in risk propagation within the hydropower station risk network. Nodes with high out-degree and high closeness centrality are potential risk sources. Nodes with high eigenvector centrality occupy core positions in the network structure, and nodes with high in-degree tend to become risk aggregation points. Chains with high edge betweenness are critical transmission channels with significant bridging effects. High-probability risk evolution paths reveal strong causal coupling relationships among risk events. Key chains such as “unit cut-off → external transmission channel interruption → all circuit breakers disconnected” exhibit strong propagation capability within the network, requiring prioritized deployment of monitoring and coordinated prevention and control mechanisms.

[Objective] Improving the rainwater retention capacity(RRC) of green roofs has been recognized as an important part of urban stormwater management. As the substrate layer is a key structural element of green roofs, investigating the influence mechanism of layered soil substrates on the RRC of green roofs has a positive impact on green roof design. [Methods] Green roof models were established using layered soil and two single-soil configurations with different material mix ratios. Hydraulic parameters of the layered soil were obtained using the inverse solution module of the HYDRUS-1D software. Through four months of dry-wet cycles, the RRC of the three models was studied. Simulations were performed to examine the effects of initial water content, saturated water content, and other factors on the RRC of layered-soil green roofs, followed by the sensitivity analysis of these parameters. Additionally, simulations were conducted to examine the RRC of layered-soil green roofs under different rainstorm return periods. [Results] The result showed that the RRC of layered soil was 5% and 1% higher, respectively, than that of the single-soil configurations. The layered soil showed greater reduction in peak drainage and longer peak drainage delay. Initial water content, saturated water content, permeability, and depth ratio of upper to lower layers all have certain effects on the RRC of layered-soil green roofs, with saturated water content having the strongest effect. When the maximum surface water storage depth was 10 mm, peak surface runoff of the green roofs was reduced by 22%, 15%, 14%, and 12% under different rainstorm return periods, respectively. [Conclusion] The findings show that layered soil exhibits higher RRC than single-soil configurations, demonstrating better hydrological performance. To achieve better hydrological performance of layered-soil green roofs, the substrate layer should comprise a high-permeability upper soil layer and a low-permeability lower soil layer, with the depth ratio of the two layers maintained within a reasonable range. Allowing surface ponding infiltration during rainstorms can effectively reduce peak runoff and delay the onset of surface runoff.

[Objective] To optimize the drainage and flood control system in urban self-draining areas and enhance their capacity to cope with extreme rainfall. [Methods] Taking the Central Business District of Qingpu New City in Shanghai as a case study, a 1D~2D coupled drainage pipe network model was constructed based on Infoworks ICM. The effects of multiple factors, such as river water level and layout proportion, on the waterlogging reduction benefits of green facilities were analyzed. Three typical types of green facilities were selected: sunken greenbelt, rain garden, and permeable pavement. The total waterlogging volume and waterlogging area under short-duration( 120 min) and long-duration( 24 h) rainfall conditions were simulated to quantitatively evaluate the benefits of these facilities under different layout proportions and water level boundary conditions. [Results] The result showed that under both short-and long-duration rainfall conditions, the reduction effectiveness of green facilities increased with increasing layout proportion. However, the waterlogging reduction benefit of the sunken greenbelt was significantly higher than that of rain gardens and permeable pavements. The three types of green facilities exhibited superior performance in reduction benefits under short-duration rainfall conditions compared to long-duration conditions. Variations in river water level had a minor influence on the waterlogging reduction benefits of green facilities. [Conclusion] The optimization framework for green facility layout proportion under multi-factor coupling proposed in this research provides an important reference for the optimized design of urban drainage systems and the construction of sponge cities.

[Objective] Monitoring and managing effluent quality indicators of wastewater treatment plants is critical for environmental protection. However, wastewater treatment involves highly variable and unpredictable complex biochemical processes. [Methods] A prediction model for effluent COD and total nitrogen in wastewater treatment plants was established based on a Long Short-Term Memory(LSTM)-Informer. The LSTM module was first used to extract temporal dependencies from the influent quality indicators. Subsequently, the extracted temporal features were integrated into the Informer module to address the issue of long-term dependency. Additionally, through the analysis of performance evaluation indicators of several typical models(Convolutional Neural Network, CNN, LSTM, CNN-LSTM, and Informer), the fitting performance and prediction performance of each model were compared. [Results] The experimental result showed that the LSTM-Informer model could accurately predict the effluent COD and TN concentrations. On the experimental dataset, the mean squared errors between the predicted and actual values were 0. 630 mg · L^-1 and 0. 105 mg · L^-1, respectively. The root mean square errors were 0. 79 mg·L^-1 and 0. 32 mg · L^-1, the mean absolute errors were 0. 60 mg · L^-1 and 0. 24 mg · L^-1, the mean absolute percentage errors were 2. 16% and 2. 26%, and the coefficients of determination(R²) were 0. 97 and 0. 90, respectively. All these indicators outperformed those of several typical neural network models. In the statistical significance test, it was verified that the p-values of the LSTM-Informer model were all greater than those of the other comparative models at a significance level of 0. 05. [Conclusion] The result demonstrate that the LSTM-Informer model, which captures short-term temporal dependencies via the LSTM network and extracts long-term temporal features through the multi-head sparse self-attention mechanism of the Informer, provides a reliable basis for the long-term monitoring and prediction of wastewater effluent quality. Its accuracy demonstrates advantages in both evaluation indicators and statistical tests. Overall, this model can provide early warning of water quality changes for wastewater treatment plants, which is crucial for achieving intelligent control and optimizing treatment processes.

[Objective] Against the background of China's comprehensive victory in poverty alleviation and the overall improvement of drinking water safety, the economic sustainability of safe drinking water supply in some water-scarce and environmentally fragile areas, especially brackish-water areas, remains challenging. Existing studies using brackish water as a drinking water source report large differences in water production costs and high uncertainty, mainly because the relationship among raw-water quality, water treatment processes, and drinking water standards has not been studied jointly, and the reported result reflect only one part of the whole process. [Methods] Starting from the complete chain of drinking water production, the whole-process cascaded combination relationship of " raw water quality-water treatment process-drinking water standard" and its economic evaluation model were constructed. Using raw-water total dissolved solids( TDS) as the key indicator, the model systematically analyzed the water production costs of raw water with different TDS concentrations under different treatment processes for meeting different drinking water standards, and then compared them with the current first-tier water prices in major provincial capitals and municipality with independent planning status in China to evaluate the economic sustainability of different cascaded combinations. [Results] Under China's current drinking water standards, when raw-water TDS was ≤1 000 mg/L, conventional treatment or ultrafiltration could stably meet the standard, with low water production cost and strong economic sustainability. When TDS was 1 000 ~ 1 500 mg/L, nanofiltration was required, and the cost increased but remained within a sustainable range. When TDS further rose to 1 500~2 000 mg/L, reverse osmosis was required to meet the standard, at which point the water production cost increased significantly and economic sustainability approached a critical threshold. When TDS exceeded 2 210 mg/L, the water production cost exceeded the current highest water price of 4. 16 CNY/m³, and economic sustainability became a concern. Application of the method to a prefecture-level city in northwestern China showed that, under existing source-water conditions, conventional processes alone could provide compliant water to only 21. 46% of the population. After introducing nanofiltration, this proportion increased to 58. 92%, while the remaining 41. 08% must rely on the higher-cost reverse osmosis process to ensure drinking water safety. [Conclusion] The complete cascade from raw water to finished water is an inseparable chain in drinking water production. The proposed economic evaluation method for cascaded combinations can provide a scientific method for configuring different treatment processes and selecting water supply sources for drinking water based on raw-water quality, and can serve as a reference for guiding urban and rural water supply planning and ensuring supply safety against the background of continuously rising drinking water standards.

[Objective] The continuous rise in the water level of Qinghai Lake poses a threat to surrounding ecological security and infrastructure.Clarifying the dynamic changes in its water balance and the core driving factors is crucial for ecological protection and risk prevention and control. [Methods] The distributed hydrological model SWAT,isotopic tracers,and structural equation modeling(SEM) were used to investigate the medium-to long-term hydrological evolution and driving mechanisms of Qinghai Lake under climate change. [Results] The result indicated that the water level changes of Qinghai Lake exhibited distinct phased characteristics.Before 2004,it declined at an average rate of 0.078 m per year,and then recovered at a rate of 0.187 m per year,with the recovery rate being 2.4 times that of the previous decline rate.Water balance exhibited two typical states.The positive balance state was primarily characterized by runoff dominance(37.8%) synergized with precipitation(31.6%),while the negative balance state was characterized by evaporation dominance(41.5%) and suppressed runoff(25.9%).A critical threshold for system imbalance was identified as evaporation exceeding 40×10⁸ m³/a and runoff below 14×10⁸ m³/a.Groundwater was a key regulatory component,with an average annual recharge of 8.11×10⁸ m³.Notably,the recharge in 2024 reached(19.01±3.52)×10⁸ m³,with a peak summer recharge rate of(6.99±1.24)×10⁶ m³/d.Furthermore,the recharge in the northern region was significantly higher than in other regions. [Conclusion] The spatiotemporal coupled driving mechanisms of precipitation-runoff-groundwater recharge are revealed,providing a scientific basis for the water resource management of Qinghai Lake.

[Objective] The Yellow River Basin serves as a critical ecological barrier and major agricultural production area in China, characterized by a fragile ecological environment and high sensitivity to climate change. Investigating the response characteristics of vegetation to drought events in this region, quantifying the probability of vegetation loss under different drought scenarios, and identifying the drought thresholds that trigger vegetation loss are of great significance for regional ecological security and sustainable development. [Methods] Based on monthly precipitation and leaf area index(LAI) data from 1982 to 2020, the standardized precipitation index(SPI) was used to identify drought events, and a bivariate Copula-based conditional probability model was constructed to assess vegetation loss probability and inversely identify disaster-triggering thresholds. [Results] 1) LAI was significantly positively correlated with SPI across 94. 3% of the grid cells in the Yellow River Basin, and the optimal cumulative time scale of vegetation response to drought exhibited a pronounced north-south divergence, ranging from 4—9 months in the southern region to over 17 months in the northeastern region. The basin-wide average probability of vegetation loss increased from 52. 0% under mild drought to 67. 7% under extreme drought(LAI ≤ 40 th percentile), with values in the grassland-dominated central and northern areas substantially exceeding those in the forest-and cropland-dominated southeastern areas(by 12% ~18%).(2) Drought thresholds differed markedly among the four vegetation types. The mean SPI required to trigger LAI ≤ 40 th percentile loss was only-0. 70 for grasslands, whereas deciduous broadleaf forests required an SPI as low as-1. 12, indicating a 1. 6-fold difference in drought sensitivity between the two.(3) As the loss level decreased from the 40 th to the 10 th percentile, the proportion of areas with identifiable thresholds declined sharply, suggesting that extreme vegetation loss was predominantly governed by localized drought events in grassland regions. [Conclusion] Vegetation drought vulnerability in the Yellow River Basin is jointly governed by vegetation type and regional moisture availability. Grasslands represent the most drought-prone ecosystem type, and the grassland-dominated central and northern areas constitute priority regions for drought disaster prevention and control. These findings provide a scientific basis for drought risk zoning and management, ecological conservation prioritization, and optimized water resource allocation across the river basin.

[Objective] Under the background of global warming, drought frequency has shown an increasing trend in southern China. However, research on drought in the humid river basins of the southeastern coastal area remains insufficient. Investigating the spatiotemporal evolution characteristics and driving factors of drought in the Minjiang River Basin is of great significance for drought risk assessment and the formulation of scientific water resource management strategies. [Methods] Based on daily precipitation and temperature data from 26 meteorological stations in the Minjiang River Basin, the standardized precipitation evapotranspiration index(SPEI) was calculated as the drought indicator. [Methods] including the Mann-Kendall trend test, cross wavelet analysis, and wavelet coherence analysis were used to analyze the spatiotemporal variation characteristics of drought across different time scales in the river basin. Additionally, the effects of climate conditions and large-scale circulation factors on drought characteristics were explored. [Results] The result showed that from 1962 to 2021, the annual-scale SPEI in the river basin increased at a rate of 0. 030/10 a. The change rates of SPEI in spring, summer, autumn, and winter were-0. 013/10 a, 0. 051/10 a,-0. 016/10 a, and-0. 003/10 a, respectively. Spatially, 19 stations(73. 08% of the total) exhibited a drying trend at the annual scale. The proportions of stations showing drying trends in spring, summer, autumn, and winter were 65. 38%, 26. 92%, 76. 92%, and 69. 23%, respectively. [Conclusion] Over the past 60 years, the Minjiang River Basin has exhibited an insignificant wetting trend, but with seasonal differences. Spring, autumn, and winter exhibit a wetting trend, while a wetting trend is observed in summer. Significant wetting trends are observed in the northern and southeastern parts of the river basin, while drying tendencies are observed in the central and southern regions. The drought frequency is higher in the northeastern and southwestern parts of the river basin, with the eastern region prone to drought in spring and autumn, and the southern region prone to drought in winter. Precipitation is a key factor influencing drought at different time scales in the Minjiang River Basin, and temperature exhibits significantly negative correlations with drought in spring, autumn, and winter. Drought in the Minjiang River Basin is closely related to the El Niño-Southern Oscillation(ENSO) and the Arctic Oscillation(AO). The SPEI shows a resonance period of approximately 0~5 months with ENSO and 3~6 months with AO.

[Objective] In the context of climate change, compound dry-hot events(CDHEs) have shown an increasing trend in both frequency and intensity, posing severe threats to social and ecological systems. To enhance the early warning and prevention capability for CDHEs, it is imperative to establish a high-precision forecasting model. [Methods] Preceding sea surface temperature fluctuation patterns(PSFP) were constructed as predictors based on SST variations during different periods. The data were processed using the Loess seasonal-trend decomposition method. Three machine learning models—support vector machine(SVM), random forest( RF), and extreme learning machine( ELM)—were employed to develop deterministic, ensemble, and probabilistic forecasting models(PSFP-SVM, PSFP-RF, and PSFP-ELM) for CDHEs. Taking the Pearl River Basin as the study area, four representative summer CDHEs in 1989, 1991, 1999, and 2011 were selected for model validation. [Results] The result showed that the models could effectively predict the spatiotemporal distribution characteristics of summer CDHEs in the Pearl River Basin with 1-and 3-month lead time. Among them, the PSFP-ELM model performed best, achieving an R² higher than 0. 5 and an accuracy exceeding 0. 8. Its forecast result showed strong agreement with observations and superior performance at the 3-month lead time. The forecasting accuracy of the PSFP-based models was significantly higher than that of traditional circulation factors-based models, with performance improvements exceeding tenfold. [Conclusion] The findings provide a scientific basis for risk early warning and decision-making of disaster prevention and mitigation regarding CDHEs.

[Objective] Influenced by the monsoon climate, the spatiotemporal distribution of precipitation within a year in China is extremely uneven. The annual distribution of runoff is mainly concentrated during the flood season and often exists in the form of floods. To ensure flood control safety, traditional flood control strategies mainly rely on rapid flood discharge. However, excessive reliance on a single flood discharge mode has led to a large amount of rain and floodwater resources failing to be effectively stored and utilized, exacerbating the shortage of water resources in the region during the non-flood season. Moreover, flood disasters often occur simultaneously with drought events in other regions. Floodwater resource utilization can not only aid in peak flow reduction and flood retention, but also alleviate regional water shortages caused by the uneven spatiotemporal distribution of precipitation. [Methods] Domestic and international research progress on the fundamental concepts and methodologies of floodwater resource utilization is reviewed, and prospects for inter-basin floodwater resource utilization are explored. [Results] The specific content includes:(1) outlining the concept of floodwater resource utilization potential and its evaluation method;(2) summarizing the regulation strategies for the utilization of floodwater resources in reservoirs and flood detention areas;(3) proposing regional and inter-basin floodwater resource utilization pathways and research directions. [Conclusion] Through a systematic analysis of the frontier advancements and challenges in floodwater resource utilization, the analysis can provide critical theoretical and methodological support for flood disaster analysis and scientific management. It offers new perspectives for alleviating the supply-demand contradiction of water resources across regions and basins, thereby supporting coordinated regional economic development.

[Objective] To investigate the effects of cascade hydropower development in the lower Jinsha River on the distribution of spawning grounds and early-life resources of Coreius guichenoti. [Methods] Based on literature review and field survey data recorded from 1981 to 2023, combined with the progression of hydropower project construction, the spatiotemporal variation characteristics of the spawning scale, early-life resources, and spawning grounds of C.guichenoti and their driving factors were analyzed. [Results] The result showed that current spawning activities were primarily distributed in river reaches including Pingdi Town, Yuanmou County, and Jiaopingdu, with only sporadic spawning remaining in areas such as Qiaojia, Huidong, and Wuding, indicating a continuous reduction in overall scale. Following the operation of the second-phase projects, spawning grounds in Qiaojia, Jiaopingdu, and Yuanmou County may further disappear, and the major spawning grounds were projected to shift towards the tributaries and mainstem upstream of the Wudongde Dam. Under the combined influence of environmental factors such as water temperature, discharge, flow velocity, and physical barriers, the age at first sexual maturity of C.guichenoti was delayed, the reproductive period was shortened, the spawning frequency decreased, and both spawning quantity and hatching rate declined, leading to a year-by-year decreasing trend in early-life resource abundance. [Conclusion] It is recommended to adopt ecological regulation and habitat substitution technologies to improve spawning habitat conditions. Furthermore, artificial proliferation and release should be enhanced, and fish passage facilities should be constructed to enhance the reproductive population size and genetic diversity, thereby mitigating the negative impact of cascade hydropower development on C.guichenoti population resources.

[Objective] To address the issue of ecological water shortage in inland river basins within the arid regions of northwestern China, the Manas River Basin is selected as the study area to assess the spatiotemporal dynamic changes of vegetation ecological water demand and to reveal the interaction mechanism of natural and human-driven factors, thereby providing a scientific basis for the optimal allocation of regional water resources. [Methods] Based on the land use data of the Manas River Basin, the vegetation area was extracted. An improved Penman-Monteith model, coupled with vegetation coefficients and soil moisture limitation coefficients, was employed to construct a calculation framework for vegetation ecological water demand. Combined with the geodetector, the influence of climatic and human factors on vegetation ecological water demand was analyzed. [Results] From 2000 to 2020, the vegetation coverage area decreased at a rate of 1 047. 9 km² per decade, and the vegetation ecological water demand declined at a rate of 8. 88×10⁸ m³ per decade. Land use was identified as the core driving factor of the changes in the ecological water demand of vegetation in the study area. The q value remained consistently stable within a high level ranging from 0. 93 to 0. 96. A nonlinear enhancement effect was observed between land use and climatic factors. [Conclusion] The study shows that the changes in vegetation ecological water demand in the Manas River Basin are mainly driven by the coupling of land use and climatic factors, and the changes in grassland area dominate the trend of total water demand. The conclusion can provide a reference for the management of vegetation ecological water demand in similar river basins in arid regions.

[Objective] Multi-source data synergistic remote sensing is a crucial pathway for enhancing the monitoring frequency of river water bodies. However, the accuracy of river water body monitoring varies among satellites with different spatial resolutions,leading to uncertainties in the time-series analysis of river water bodies derived from multi-source satellite synergy. A quantitative evaluation of the accuracy differences in water surface area monitoring for river sections by satellites with different spatial resolutions, and the development of a correction model for multi-source satellite-based water surface area monitoring, hold significant importance for achieving high-precision, long-term, and high-frequency satellite synergistic monitoring of river water bodies. [Methods] Three river sections of the upper reaches of the Jinsha River with different channel patterns—Benzilan(straight, channel count index = 1), Shigu(anabranched, channel count index = 1~2), and Zhengkexiang(braided, channel count index = 2 ~ 6)—were selected as the study areas. Data acquired from the MS600 unmanned aerial vehicle( UAV),Landsat series satellites, Sentinel-1/2 satellites, GF-1/2/6 satellites, and HJ-2A/2B satellites were utilized as the main data sources. Spatiotemporally matched sample pairs of river water surface area were established by pairing high-spatial-resolution data with medium-to-high-spatial-resolution satellite data for the three river sections. The accuracy of water surface area monitoring using multi-source medium-to-high-resolution satellites for different river sections was validated, and correction models were constructed. Additionally, the monitoring accuracy of different satellites along with the model' s correction effectiveness were quantitatively evaluated. The factors influencing the accuracy of multi-source satellite monitoring for river sections, the applicability of correction models, and the advantages of multi-source collaborative monitoring were explored. [Results] The result showed that the RMSE of multi-source satellite-monitored water surface area for the Zhengkexiang, Shigu, and Benzilan river sections ranged from 0. 180 9 km² to 0. 706 8 km², 0. 040 8 km² to 0. 096 3 km², and 0. 015 8 km² to 0. 041 4 km²,respectively. After model correction, the average MAPE of Landsat-8/9 OLI, Sentinel-2 MSI, Sentinel-1 SAR, and the 16-meter domestic satellites across the three river sections reduced from 14. 16%, 7. 26%, 20. 06%, and 9. 37% to 10. 30%, 5. 33%,7. 59%, and 7. 32%, respectively. The standard deviation of the differences between the water surface area monitored by multisource satellite synergy and the reference values decreased from 0. 221 8 km², 0. 037 4 km², and 0. 019 1 km² to 0. 159 1 km²,0. 023 4 km², and 0. 012 8 km². The effective monitoring frequency increased from an average of 12 times per year to 260 times per year. Furthermore, the relationships between river water body monitoring accuracy and factors such as satellite spatial resolution, satellite sensor type, and channel morphology were analyzed. [Conclusion] The result demonstrate significant differences in the accuracy of water body monitoring for different morphologies of river sections using satellites of different spatial resolutions. Satellite spatial resolution, satellite sensor type, and the complexity of river section morphology are identified as important factors influencing the accuracy of river water surface area monitoring. The correction model for multi-source satellite remote sensing of water surface area can improve both the accuracy and consistency of water surface area monitoring for river sections from satellites with different spatial resolutions. Multi-source satellite synergistic monitoring can significantly enhance the monitoring frequency of river section water bodies. The research findings have potential for broader application in aspects such as the long-term reconstruction of river water surface area, water level and discharge monitoring, and hydrological model calibration.

[Objective] The undulating terrain and uneven spatiotemporal distribution of rainfall in hilly regions result in rapid runoff generation and concentration, making it difficult to effectively collect water resources. This leads to issues such as frequent flooding in lowlands and droughts in uplands. Therefore, a coordinated water allocation and supply system for hilly regions is required to alleviate the conflict between water supply and demand. [Methods] Based on the integrated water supply approach in hilly regions, a multi-objective optimal allocation model for water resources in hilly regions was established. The Non-dominated Sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ) was improved in three aspects: crossover, mutation, and sorting processes within the same non-dominated layer, for solving the model. A comprehensive evaluation indicator system for water resources was developed. The cloud model was improved using the Analytic Hierarchy Process(AHP) for evaluating the set of schemes and selecting the overall best multi-objective optimal allocation scheme for water resources. Finally, the theoretical method was applied to the Liuhe District, Nanjing City, to conduct a case study. [Results] (1) The improved NSGA-Ⅱ algorithm was employed to obtain the Pareto solution sets for Liuhe District in 2025 and 2035. These sets were screened based on economic,social, ecological benefits, along with optimal coordination, resultsing in seven allocation scheme sets for each year.(2) After the seven scheme sets for 2025 and 2035 were evaluated using the cloud model rating, the optimal scheme set for both 2025 and 2035 was scheme 4, representing the optimal coordination between economic and social benefits. The scores were 89. 87 and 90. 95, respectively. [Conclusion] A multi-objective optimal allocation model for water resources in hilly regions is established.By improving the traditional NSGA-Ⅱ algorithm in three aspects, the quality and rationality of the solution sets are significantly enhanced. A multi-level evaluation indicator system is established. An improved cloud model is innovatively applied to quantitatively evaluate the schemes, and the optimal scheme is selected through comparison. This approach provides a complete technical pathway of “model construction-algorithm optimization-scheme comparison and selection” for efficient water resource allocation in hilly regions. It can provide a reference for decision-makers, alleviate water supply-demand conflict, and promote comprehensive and coordinated development in hilly regions.

[Objective] With the frequent occurrence of extreme drought weather, urban water shortage has become a common challenge during rapid urbanization. To alleviate urban water shortage under tight supply-demand balance conditions, a technical framework for the construction of an optimized joint water supply scheduling model for multi-source urban water supply and the extraction of joint scheduling rules based on linear programming is established. [Methods] Taking the main urban area of Kunming as an example, a joint optimized scheduling model for water supply of reservoir groups was developed using CPLEX linear programming and the lexicographic method. The variable constraints of water supply capacity of water source projects under different storage conditions were considered. Using the long-term inflow series from 1956 to 2021, optimized scheduling studies were conducted for water demand in 2021 and 2025. Scheduling rules were summarized based on the optimized scheduling process, and a joint scheduling method for the reservoir groups to ensure water supply for the urban area of Kunming was proposed. [Results] The result indicate that the average annual runoff of the conventional water-source reservoir group in the main urban area of Kunming from 1956 to 2021 is 0. 621 billion m³. The actual water demand in 2021 is approximately 0. 480 billion m³, and the forecasted water demand in 2025 is about 0. 588 billion m³. Under the water-demand conditions of 2021 and 2025, the water-supply guarantee rates of joint dispatching of conventional water sources are 86% and 57%, respectively. When the Dez e Reservoir is included in the joint operation, the water-supply guarantee rates increase to 92% and 74%, respectively.Regarding the causes of water shortage in each supply zone, in the main urban area, 67. 9% of water shortages are attributed to the supply capacity limitations of Yunlong Reservoir, while 32. 1% are caused by insufficient water resources. In contrast, water shortages in the Qingshuihai area are entirely(100%) due to insufficient water resources. [Conclusion] The result indicate that the water supply in the main urban area of Kunming is under tight supply-demand balance conditions. When encountering dry or extremely dry years, the water supply will face great challenges. Resource-based water shortage and limitations in engineering water transmission capacity are the main causes of water shortage. The joint scheduling model of reservoir groups constructed using CPLEX can effectively coordinate the synergistic water supply process of multiple water sources. Through optimized scheduling, the water shortage problem under the normal year conditions in 2021 can be basically solved, and the water supply pressures under the conditions in 2025 can be significantly alleviated. Based on the optimized scheduling process, scheduling rules of “stable supply from primary water sources, regulation by auxiliary water sources, and supplementation by scattered small water sources” which adapt to the supply-demand characteristics and engineering system are proposed. These rules can provide a reference for the joint water supply scheduling of the water source reservoir groups in Kunming. Through the water supply scheduling simulation of the main urban area of Kunming, the adaptability of using the CPLEX linear programming solver for the joint optimal scheduling of reservoir groups is demonstrated, and a method for extracting joint scheduling rules for reservoir groups based on the optimized scheduling process is further explored. This can provide a theoretical basis and a practical framework for the optimized scheduling of multi-source water supply in similar water-scarce areas.

[Objective] Accurate prediction of Tunnel Boring Machine(TBM) cutterhead thrust is crucial for ensuring construction safety and improving excavation efficiency in long-distance water tunnel projects. However, traditional deep learning models such as LSTM, GRU, and Transformer, while capable of achieving high prediction accuracy, generally suffer from large parameter sizes, long training times, and poor adaptability to on-site data, making them unsuitable for real-time updates in practical engineering scenarios. [Methods] A rapid prediction and excavation safety early warning framework for TBM cutterhead thrust in long-distance water conveyance tunnels is proposed, based on the coupling of clustering, transfer learning, and ensemble learning. First, clustering algorithms are applied to classify the ascending phase data of cutterhead thrust into different variation forms, constructing sub-databases. Then, transfer learning is used to train variation form classification models, enhancing the adaptability to new data. Finally, an ensemble learning method is employed to achieve high-precision regression prediction of cutterhead thrust within each variation form sub-database, incorporating the Leading Averaged Force-Penetration Index(LAFPI) as a sensitive feature for identifying surrounding rock transitions, thus providing effective early warning. [Results] The result show that the cutterhead thrust prediction framework, with only 0. 015 billion parameters and just two minutes of training time, can achieve high-precision prediction(R² > 0. 85) for cutterhead thrust data containing over 1, 000 time points. [Conclusion] Compared with traditional large neural network models, the training time is reduced by 86. 9%, and the model size is reduced by 97. 4%. The proposed framework, by integrating clustering analysis, transfer learning, and ensemble learning techniques, significantly improves real-time training and application efficiency. It possesses strong real-time prediction capabilities, effectively addressing the complex geological conditions in long-distance tunnel construction, and holds significant engineering application prospects, particularly in enhancing construction safety and excavation efficiency.

[Objective] To address the problem of insufficient model accuracy caused by data uncertainty in landslide susceptibility assessment, a modeling approach is proposed that integrates confidence-weighted samples with coefficient of variation(COV) of factors. [Methods] Taking Pingjiang County, Hunan Province as the study area, a three-level sample confidence evaluation system(1. 0, 0. 8, 0. 5) was established to weight landslide samples, and COV was used to quantify the uncertainties of key factors such as slope(COV= 0. 15) and rainfall intensity(COV= 0. 12). On this basis, Bayesian neural network(BNN) and XGBoost models were employed to develop a dual-dimensional uncertainty integration model. [Results] The results showed that the proposed dual-dimensional integration method significantly improved model performance, with the accuracy of the XGBoost model increasing from 0. 721 to 0. 867, and its AUC value rising from 0. 745 to 0. 892. The BNN model achieved an accuracy improvement from 0. 715 to 0. 861, with its AUC value increasing from 0. 738 to 0. 885. BNN visually demonstrated the uncertainty of landslide probability distribution through probability density function, while XGBoost reflected prediction deviations via confidence intervals. [Conclusion] The findings provide a new paradigm for landslide susceptibility assessment in data-scarce and geologically complex regions. In the future, model robustness can be further improved through multi-source data fusion and physical model coupling.

[Objective] The accurate accounting of irrigated area is of great significance for guiding agricultural irrigation and water consumption allocation. However, due to limited availability of optical remote sensing imagery, insufficient ground observation data in irrigated area, and low update frequency of public data, there are still many challenges in achieving rapid identification of irrigated area at the regional scale. The aim of the study is to support the rapid monitoring of irrigated area using multi-source satellite data. [Methods] Based on optical and radar long time-series remote sensing images, a method for acquiring monthly historical samples from published datasets was proposed on the Google Earth Engine cloud computing platform. By integrating multi-source features, including radar, spectral, hydrothermal, environmental, and temporal data, rapid identification of monthly synthesized irrigated area at the regional scale was achieved for Washington State, USA, from 2016 to 2017. [Results] The result showed that 10% ~17% of the land in the core area of the study area had obvious irrigation activities during the growing season from April to October, with the irrigated area at the initial stage reaching 800 km² to 1 100 km², the irrigated area during peak growing season exceeding 1 600 km², and the total annual irrigated area reaching 2 200 km². The overall accuracy of monthly irrigated area identification in the study area was 84. 76%(Kappa coefficient = 0. 694 4). [Conclusion] Compared with the publicly available remote sensing irrigation datasets and regional annual agricultural census data, the proposed multi-source image cloud computing method enables more effective and accurate identification of monthly irrigated area distribution. Meanwhile, it can improve the frequency of irrigation information updates and reduce the risk of underestimating irrigated area caused by agricultural census, thereby providing a crucial basis for research on crop water demand and irrigation water consumption assessment.