[Objective] The eastern region of China exhibits high drought-flood variability and faces significant disaster risks.However, the evolution of drought-flood patterns based on long-term historical data remains unclear. The aim is to reveal the long-term evolution and future trends of drought-flood patterns in eastern China. [Methods] Based on long-term meteorological data from 1470 to 2020 in China, a drought-flood level series was reconstructed. Wavelet analysis and power spectrum analysis were applied to identify the dominant cycles and stage characteristics, and a long short-term memory( LSTM) model was employed to predict the drought-flood evolution trends in eastern China from 2030 to 2100. [Results] The result showed that between 1470 and 2020, eastern China experienced two predominantly dry periods and one predominantly wet period. The drought-flood conditions exhibited variation cycles averaging 20, 50, and 180 years. The “wet north-dry south” and “dry northwet south” climate patterns alternated with an average cycle of 200 years. Spatial analysis showed that over the past 50 years,regions such as North China and the southwestern part of Northeast China have high drought-flood variability, with large interannual variance thresholds, indicating an unstable climate system. In contrast, most southern regions exhibited low variability and more stable drought-flood variations. Machine learning prediction result indicated that after the mid-21st century,the climate pattern of eastern China would shift from predominantly dry to predominantly wet. Since the late 20th century, the frequency of “wet north-dry south” events had gradually increased, reaching a phase of high occurrence around 2036, followed by a gradual decline. In contrast, “dry north-wet south” events were expected to gradually intensify afterward, reaching a phase of high occurrence in the mid-21st century before gradually decreasing. [Conclusion] The result indicate that drought-flood conditions and north-south drought-flood patterns in eastern China exhibit long-term periodicity and alternating phases. In the future, the climate system may show a transitional trend from predominantly dry to predominantly wet conditions. These findings deepen the understanding of the spatiotemporal evolution mechanisms of drought and flood in eastern China and provide scientific support for optimizing water resource allocation, assessing drought and flood disaster risks, and constructing defense systems.This is of great significance for ensuring regional water security.

[Objective] The permanently shadowed regions(PSRs) at the lunar south pole, due to their low temperatures and prolonged absence of sunlight, have become key reservoirs for lunar water ice. Clarifying the distribution, abundance, and form of occurrence of water ice in this region is a core scientific question for understanding the origin and evolution of lunar water sources. It is also a critical prerequisite for enabling in-situ resource utilization and sustainable deep space exploration on the Moon. [Methods] Existing research method include orbital remote sensing technologies and indirect analytical techniques. In terms of orbital remote sensing, neutron and gamma-ray spectroscopy, near-infrared and thermal infrared spectroscopy, and radar polarimetric imaging techniques are applied to analyze their principles and detection capabilities. For indirect analysis, methods such as impact plume analysis, thermal environment modeling, and micro-cold trap analysis are employed to evaluate the stability of water ice. [Results] In-situ drilling and thermal gas release analysis have been established as the final verification techniques for the presence of water ice. Through data analysis from missions such as Lunar Prospector, Chandrayaan-1, Lunar Reconnaissance Orbiter, and LCROSS, the accuracy, applicable scenarios, and limitations of different detection method are evaluated, revealing differences in their performance in detecting the distribution of water ice. [Conclusion] The future directions of technological development include breakthroughs in high-spatial-resolution remote sensing technologies to improve the accuracy of water ice distribution mapping; the development of lunar surface mobile robots equipped with autonomous navigation and precise sampling capabilities to support in-situ exploration in complex terrains; and the establishment of an “orbital-in-situ-simulation” multi-scale data fusion framework. This will form a comprehensive understanding of lunar water ice from macroscopic distribution to microscopic mechanisms, thereby providing technical support for lunar base construction and deep space exploration.

[Objective] In 2022, the Poyang Lake Basin experienced a rare drought event with consecutive summer and autumn aridity and extremely low water levels during the flood season, leading to sustained declines in river and reservoir water levels.Implementing coordinated water supply scheduling for the reservoir groups in the basin can alleviate the prominent imbalance between water supply and demand. [Methods] Taking the Yuanhe River Basin( a tributary of the Ganjiang River) as an example, 10 key water supply projects and 30 critical water supply targets were selected. A coordinated water supply scheduling model for reservoirs in the basin was established using the MIKE Basin model. Coordinated water supply regulation measures for reservoirs were proposed, and the water supply scheduling effects in the basin were analyzed. [Results] In typical years such as 2001, 2007, and 2022, six water consumers in the Yuanhe River Basin, including the Shankouyan irrigation area, Yuanhe Water Plant, Feijiantan irrigation area, Yuannan irrigation area, Yuanhuiqu irrigation area, and Menghe irrigation area,experienced water shortages with water shortage rates ranging from 0. 2% to 28. 9%. After implementing three regulation measures, including adjusting the limit water levels of water supply for Shankouyan, Feijiantan, and Jiangkou reservoirs,adjusting the water supply targets for Shankouyan reservoir, and adding new reservoir projects, the water shortage levels of the main water consumers significantly improved, achieving an average 7. 2% reduction in water shortage. Under three inflow frequencies of 90%, 95%, and 99%, the water shortage rates for Shankouyan irrigation area, Feijiantan irrigation area, and Yuanhuiqu irrigation area decreased by an average of 7. 41%, 3. 46%, and 2. 28%, respectively. [Conclusion] Based on the actual water shortage conditions, reasonable reservoir regulation measures are proposed, which effectively improves the water shortage levels of major water consumers, such as irrigation areas in the dry season. These measures can provide technical support for the response to extreme drought events in river basins.

[Objective] Global climate change and human activities have profoundly altered drought dynamics, particularly in ecologically sensitive regions. This study analyzes the propagation characteristics and mechanisms of meteorological drought(MD) and hydrological drought(HD) on both sides of the southern Gaoligong Mountains within the Longitudinal Range-Gorge Region( LRGR), aiming to provide scientific foundation for effective water resource management and disaster prevention. [Methods] Based on the significant hydrometeorological differentiation between both sides of the southern Gaoligong Mountains,and using monthly precipitation and runoff data between 1981 and 2020 from representative river basins, the intensity and frequency of MD and HD were evaluated using the standardized precipitation index(SPI) and standardized runoff index(SRI).Variation trends of drought indices were analyzed through Mann-Kendall trend tests. Run theory was employed to evaluate the propagation time lag from MD to HD, followed by a Bayesian ordinal probit regression model to quantify the relationship between cumulative precipitation deficits(SPIm) and HD severity. [Results] The result showed that over the past four decades, MD intensity in the LRGR had increased significantly. The frequency of MD events( approximately 2. 2 to 2. 5 events/year) was notably higher than that of HD events(approximately 1. 1 to 1. 5 events/year). HD events demonstrated longer duration(2. 18 to 3. 04 months) and greater severity(1. 18 to 1. 76). The propagation from MD to HD was relatively rapid, with an average time lag of 0. 5 to 0. 7 months, while the recovery process showed longer lags(1. 2 to 2. 2 months). The Bayesian analysis revealed a negative correlation between SPIm and HD severity, with increased uncertainty in this relationship under extreme MD conditions. [Conclusion] High-intensity HD may constrain agricultural and socio-economic activities in the region. The rapid response to MD and the prolonged HD recovery highlight emerging challenges for sustainable water resource utilization in this area. These findings enhance the understanding of drought propagation processes in mountainous ecosystems and provide scientific support for adapting water resource management strategies to climate change.

[Objective] The Shanmei Reservoir Basin, located in a subtropical monsoon climate zone, is frequently affected by tropical cyclones, subtropical high-pressure systems, and complex topography, leading to frequent drought-flood abrupt alternation(DFAA) events that severely threaten regional water security. Accurately identifying and predicting the spatiotemporal evolution of DFAA under global change is crucial for regional disaster prevention and mitigation. [Methods] Shanmei Reservoir Basin was taken as study area. Using the Standardized Precipitation Index with a 12-day antecedent precipitation period(SPI-12d), the spatiotemporal characteristics of DFAA from 1980 to 2018 were analyzed. Additionally, CMIP6 multi-model ensemble projections under SSP2-4. 5 and SSP5-8. 5 scenarios were employed to predict the spatiotemporal evolution of DFAA in the 21st century. [Results] Key findings include:(1) The frequency of drought-to-flood(DTF) and flood-to-drought(FTD) events initially increased significantly and then decreased gradually. DTF intensity rose significantly, while FTD intensity declined. DTF events were concentrated in January, July, and September-December, whereas FTD events mainly occurred from August to November.(2) High-frequency and high-intensity DTF areas were located in the southern watershed, while high-frequency FTD areas were in the northeast and high-intensity FTD areas in the southeast.( 3) Future projections indicate increasing DTF frequency and intensity, with greater increases under SSP5-8. 5. FTD frequency showed insignificant declines under SSP2-4. 5and slight rises under SSP5-8. 5, while FTD intensity decreased. DTF increases were most pronounced in February and December, whereas FTD increases were notable in January, August, and October.(4) The central and southern watershed will face higher risks, with both DTF and FTD frequency and intensity projected to increase. [Conclusion] DFAA impacts in the Shanmei Reservoir Basin are intensifying overall, with the central-southern region as a high-risk zone. DTF poses a greater threat than FTD. These findings provide a scientific basis for DFAA monitoring and response.

[Objective] To solve the problem of inaccurate prediction and difficulty in real application of traditional waterlogging models due to data loss and insufficient accuracy, a method of coupling data model with hydraulic simulation model is studied. [Methods] Taking the Lianghe area in the central urban area of Jiujiang as the research area, data model for data cleaning,reconstruction and correction of boundary conditions is used to compensate for the shortcomings of measured data and provide data support for the operation of hydraulic simulation model. At the same time, hydraulic simulation models are used to calculate various possible working conditions and supplement training basic data for data mining models of waterlogging pridiction, in order to improve and verify their accuracy. [Results] The results show that after optimizing the data model, the average NSE of the hydraulic simulation result is improved, and the highest increase is about 20%. The SVM machine learning model can reflect the inundation range of the designed rainfall conditions, and the predicted maximum water depth is close to that simulated by the hydraulic simulation model. The RMSE is close to 0. 007. [Conclusion] Coupling data model with hydraulic simulation model to construct a new urban waterlogging model, data model can improve the boundary conditions for the operation of the mechanism model, and hydraulic simulation model can enhance the applicability of data model. The mutual coupling of the two models can achieve good application effects.

[Objective] In the context of climate change, extreme rainstorms occur with increasing frequency, posing growing challenges of flood disasters in urban old communities. Due to deteriorating infrastructure, low disaster resistance of buildings,and unreasonable planning and design, urban old communities generally exhibit weak disaster resilience. Therefore, enhancing the flood disaster resilience of urban old communities has become a top priority in China' s disaster prevention and mitigation efforts. [Methods] First, the understanding of urban old communities and flood disaster resilience was elaborated, and the characteristics of such communities were summarized from institutional, technical, spatial, organizational, and social dimensions. Then, the basic approach to flood disaster resilience in urban old communities was explained from five aspects:resilience governance elements, collaborative governance elements, spatial governance elements, interactive mechanisms among governance elements, and theoretical linkages between governance elements and resilience. Subsequently, the resilience dimensions of urban old communities were defined: institutional resilience, organizational resilience, social resilience,technological resilience, and infrastructural resilience. Finally, an analytical framework for flood disaster resilience in urban old communities was constructed, and its connections with each resilience dimension were established. [Results] The analytical framework for flood disaster resilience in urban old communities consists of three core components: governance philosophy,governance system, and governance capacity. The governance philosophy included resilience governance, collaborative governance, and spatial governance. The governance system comprised leadership structure, operational mechanisms, and governance institutions. The governance capacity included modern technological support capabilities, risk management abilities of leading officials, and mobilization capacity of social organizations. [Conclusion] This analytical framework provides systematic theoretical support and practical pathways for flood disaster governance in urban old communities. It helps enrich and improve the governance theoretical system, broadens research perspectives on flood disaster resilience, and enhances the disaster prevention capacity of urban old communities. In addition, the framework serves as a reference for other urban old communities to transform their flood disaster governance concepts and method and to enhance their flood disaster resilience.

[Objective] Under the influence of global climate change, extreme weather events in China have shown increasing frequency, intensity, and recurrence, posing serious threats and losses to social development. Research has shown that there are still problems in China's emergency management of extreme weather events, including “inadequate emergency management systems and mechanisms, shortcomings in meteorological forecasting and early warning technologies, and the urgent need to enhance social disaster prevention and mitigation capabilities”. Research on the collaborative governance of extreme weather emergency management can help improve the level of meteorological disaster emergency management in China. [Theory] In terms of theoretical construction, a “tripartite collaboration model” framework composed of “meteorological forecasting-government decision-making-social response” was proposed, providing new insights and solutions for the collaborative governance of extreme weather events. [Methods] A “four-dimensional collaborative governance framework” was proposed to achieve the shift from a single-subject perspective to a multi-subject collaborative perspective. The framework examined four dimensions: crosssystem collaborative governance mechanisms, digital governance paradigm transformation, open collaborative innovation networks, and proactive defense system provision, providing a new approach and model for the collaborative governance of extreme weather events. [Conclusion] The implementation pathways for collaborative governance of extreme weather emergency management are proposed as follows: advancing disaster prevention through “conceptual pre-positioning, early warning prepositioning, and scenario pre-positioning”; achieving high-quality emergency response through “internal government coordination, inter-departmental and inter-regional coordination, and government-society coordination”; and maximizing the effectiveness of disaster prevention and mitigation through “interactive sensing, emergency coordination, and effective response”.

[Objective] To effectively address the issues of damaged river habitat structure and functional degradation. [Methods] The Lingshou section of the Hutuo River Basin was selected as the study area. Based on field surveys, ecological monitoring, literature research, and numerical simulations, the concept of hydraulic units was introduced. Target fish species were scientifically selected, and their hydrological and geomorphic habitat requirements were systematically summarized. The spatial distribution of habitats for different fish species during key stages under hydrological variations was quantitatively analyzed. [Results] The result revealed significant differences in the spatial distribution of hydraulic units at different flow rates. The hydraulic unit diversity index reached its maximum value at a flow rate of 200 m³/s. When the flow rate was 120 m³/s, the area of deep pools reached a maximum of 796 956 m². At a flow rate of 240 m³/s, the area of rapids reached a maximum of 1 005 619 m². Based on the habitat requirements of four target fish species, the ecological flow process in the study area was determined. During the concentrated spawning period from May to June, the ecological flow ranged from 200 to 240 m³/s. During the overwintering period, it ranged from 80 to 120 m³/s, and during the foraging period, it ranged from 120to 200 m³/s. [Conclusion] Through quantitative analysis of the spatiotemporal distribution of hydraulic units and comprehensive consideration of the needs of key species at different stages of life cycle, the ecological flow process of the damaged river was dynamically determined in stages. The findings provide theoretical support for the ecological restoration of regional rivers and lakes.

[Objective] Phosphorus retention has a significant impact on the environmental security of river and reservoir systems,especially in phosphorus-rich reservoir watersheds. [Methods] The Soil and Water Assessment Tool( SWAT) model was employed to analyze the spatiotemporal distribution patterns of river total phosphorus(TP) retention volumes and retention rates in the Xuanmiaoguan reservoir watershed, located in the upper reaches of the Huangbai River, from 2014 to 2018. Redundancy analysis(RDA) was conducted to perform qualitative analysis of the influencing factors. [Results] The result showed that:(1)the SWAT model effectively simulated TP loads in the phosphate mining area. It achieved Nash-Sutcliffe efficiency(NSE) values of 0. 86 and 0. 82, Percent Bias(PBIAS) of 12. 54% and 13. 42%, Coefficient of Determination(R²) values of 0. 88 and 0. 82,and Ratio of Root Mean Square Error to Standard Deviation(RSR) values of 0. 37 and 0. 42 during the calibration and validation periods, respectively.(2) On multi-year scales, the retention rate of the reservoir(53. 72%) was higher than that of the river(maximum 38. 11%), while the retention rate of tributaries(18. 88%) exceeded that of the main stream(8. 46%). However,at smaller time scales( annual or monthly), the retention process exhibited considerable variability and may even exhibit a “source” effect under certain conditions.(3) Both river retention rates and volumes showed significant positive correlations with slope length, while the monthly reservoir retention rates were positively correlated with inflow volumes and inflow-to-outflow ratios. [Conclusion] The result indicate that a large amount of phosphorus loss from phosphate mining watersheds tends to be retained within river and reservoir systems, which may serve as a new phosphorus source under specific conditions. Measures should be implemented to mitigate internal phosphorus pollution. These research findings provide valuable references for phosphorus pollution control in phosphorus-rich watersheds.

[Objective] The utilization of stormwater resources plays a critical role in enhancing integrated small watershed management and alleviating water shortage in semi-arid regions. The small watershed ecosystems are highly sensitive to groundwater level fluctuations, making the evaluation of stormwater resource utilization for groundwater recharge essential. This also constitutes a key aspect of feasibility studies for underground reservoir construction in semi-arid regions. [Methods] An underground reservoir project in a small watershed within northern semi-arid regions was selected as the study background.Surface runoff generated by rainfall from 1981 to 2023 was analyzed using a modified SCS-CN(curve number) model. The Pearson Type Ⅲ(P-Ⅲ) curve fitting method was employed to determine representative runoff values at 25%, 50%, and 75% rainfall frequencies under different conditions. A coupled surface water-groundwater numerical model was established using the MODFLOW boundary condition method to simulate groundwater flow field variations under different conditions. Furthermore, the potential of groundwater reservoirs to regulate stormwater during flood seasons and balance seasonal water availability was quantified. [Results] The result showed that:(1) utilizing regional analytical rainfall data instead of rain gauge observations effectively addressed issues of data anomalies and high dispersion, with average monthly and seasonal rainfall fitting coefficients of 0. 73.(2) The initial abstraction ratio in the modified SCS-CN model was determined to be 0. 02, and the predicted runoff result closely matched design runoff values derived from the hydrological analogy method.(3) Groundwater recharge through stormwater conservation significantly increased groundwater levels, with a stable rise of 1. 66 m under different rainfall frequency conditions.Additionally, runoff retention in flood detention areas showed exponential growth, accounting for 41. 9%, 73. 2%, and 109. 15%of total runoff, respectively. [Conclusion] The result demonstrate that the modified SCS-CN model accurately predicts rainfallinduced surface runoff in arid and semi-arid regions, especially for small agricultural watersheds where direct measurements are challenging and soil information is limited. The coupled surface water-groundwater model established using the switching boundary condition method effectively captures variations in upstream groundwater flow fields before and after the implementation of stormwater conservation project. Furthermore, stormwater collected in detention areas can be converted into a water supply for the non-flood season, ensuring the rational utilization of stormwater resources. Therefore, the collection potential of surface runoff is crucial for the construction of underground reservoir and largely determines project feasibility. The findings provide theoretical support and technical references for integrated watershed management in northern semi-arid regions and similar mountainous small watersheds.

[Objective] Lake salinization has become increasingly severe due to global climate change and human activities,posing significant threats to ecosystems and water resources. The aims are to evaluate the current status of model research on lake salinization and summarize research directions. [Methods] Bibliometric tool VOSviewer was employed, with Web of Science Core Collection database serving as the data source, to conduct a quantitative analysis of publications in the field of lake salinization and model research spanning from 1900 to present. [Results] The result showed that the bibliometric visualization exhibited distinct phased changes. The research domain showed interdisciplinary characteristics across multiple directions. Key research hotspots were systematically summarized, and future trends were projected based on keyword burst detection analysis. The results demonstrated that the field went through three developmental phases: a slow exploration phase, a stable development phase, and a rapid growth phase. Since 2009, influenced by heightened interest in climate change research, the volume of publications surged with sustained increases projected for the future. Research hotspots included ecosystem dynamics, environmental variation, and hydrological modeling. Keyword burst analysis highlighted a transition toward integrated research, with interdisciplinary knowledge synthesis and methodological integration becoming increasingly crucial. Future research is anticipated to focus on salinization in high-altitude and arid regions(e. g., Qinghai-Xizang Plateau and Xinjiang). Cutting-edge technologies such as remote sensing and machine learning are expected to be widely applied for salinity dynamics analysis and water resource management, thereby promoting advancements in this field. [Conclusion] Current research status of this field across hydrological, geological, and ecological disciplines is comprehensively reviewed, with research hotspots and emerging trends identified, providing a scientific foundation for ecological conservation and sustainable water resource utilization in lakes.

[Objective] By comparing the inversion result of chlorophyll-a from two models in Poyang Lake, the model with higher inversion accuracy is selected, enabling more accurate and efficient application in water quality monitoring and management of shallow lakes. [Methods] Chlorophyll-a is a key indicator for water quality monitoring and a critical parameter for eutrophication assessment in aquatic environments. Poyang Lake was selected as a representative study area. Chlorophyll-a concentration in Poyang Lake was inverted based on measured chlorophyll-a concentration and Landsat-8 OLI satellite remote sensing data. After preprocessing the remote sensing images, correlation analysis was conducted between single band and band combination data and chlorophyll-a concentration data. A chlorophyll-a band ratio model was developed, and relevant band combinations were selected to further establish a back propagation( BP) neural network model. The correlation between the measured chlorophyll-a concentrations and the inversion result from the BP neural network model was compared. [Results] The result showed that the developed BP neural network model led to an improvement in the coefficient of determination(R2) between the predicted and measured values, from 0. 624~0. 855 to 0. 745~0. 921, compared to the band ratio model. The mean absolute percentage error(MAPE) and root mean square error(RMSE) were reduced by more than 46% compared to the band ratio model. [Conclusion] The BP neural network model outperforms the band ratio model in inversion accuracy. Temporally, chlorophyll-a concentrations inverted by the BP neural network model are higher during wet seasons and lower during dry seasons, with chlorophyll-a concentrations increasing in summer and decreasing in winter. Spatially, chlorophyll-a concentration is lower in the central lake and areas with high water flow, and higher along the shoreline and in regions with intense human activities, with the southern lake area showing higher concentrations than the northern area. The established BP neural network model demonstrates excellent performance in chlorophyll-a inversion in shallow lakes, providing important support for the conservation of ecological environment in lakes.

[Objective] Due to the influence of global climate change and human activities, extreme meteorological and hydrological events in the Zishui Basin are increasing day by day, resulting in the destruction of the consistency of hydrological processes, which makes the calculation result obtained by traditional hydrological frequency calculation method uncertain. [Methods] Therefore, based on hydrological data and meteorological data of Taojiang Station in the Zishui Basin from 1963 to 2022, a non-consistent model with different distribution of multiple covariates was constructed using the GAMLSS model. The good degree of fit of each model was comprehensively compared, and the design value of annual maximum daily runoff under different design frequencies was calculated using the optimal model. [Results] It is shown by the result that the contribution rate of precipitation to runoff change is 66. 52%, which is identified as the main reason for the increase of annual maximum daily runoff series in the Zishui basin. In addition, the best fitting effect is shown by the GA distribution model with precipitation,temperature, and reservoir index as covariables, which can be used to effectively describe the dynamic change characteristics of the annual maximum daily runoff in the Zishui basin under the influence of changes in precipitation and human activities. [Conclusion] As the annual maximum daily runoff of the urban development watershed is significantly affected by precipitation,the flood threat is being increased, and some reference for water resources management and water security maintenance in the watershed under the changing environment can be provided by the research result.

[Objective] Rainfall, as an important input variable in hydrological models, significantly affects the prediction capabilities of these models. Studying the temporal and spatial variation characteristics of rainfall at different scales is crucial for enhancing the stability and prediction accuracy of hydrological models. [Methods] The Xiagushan watershed in Henan Province was selected as the study area. Historical flood events were simulated using the SCS-CN and Xin'anjiang models. The temporal and spatial characteristics of rainfall and the simulation accuracy of the models were statistically analyzed to explore the impact of rainfall variability on the modeling capabilities of these hydrological models and hydrological response. [Results] The average coefficient of determination for the Xin'anjiang model and the SCS-CN model were 0. 83 and 0. 85, respectively, with the SCSCN model slightly outperforming the Xin'anjiang model. Both models showed the best performance in simulating floods with short durations and concentrated rainfall centers, whereas the simulation accuracy was lower when the rainfall center was closer to the watershed outlet. [Conclusion] Under low flood conditions, the accuracy of both hydrological models is significantly influenced by the average rainfall intensity. Under moderate flood conditions, the Xin'anjiang model is most affected by the average rainfall intensity and the rainfall spatial variability coefficient, while the SCS-CN model is mainly influenced by the rainfall location index. Under high flood conditions, the rainfall location index becomes the main factor affecting the simulation performance of both models, with contribution rates of 30. 23% and 33. 97%, respectively. Therefore, model parameters can be optimized based on the temporal and spatial characteristics of rainfall to improve the simulation accuracy and reliability of the models.

[Objective] The process of converting precipitation into runoff in arid regions is intricate, and accurately characterizing and simulating flood process triggered by rainfall presents a significant challenge for regional hydrological research. [Methods] A distributed rainfall-runoff model, designated as the GA-XAJ-CW model (hereafter referred to as the GX-CW model), was developed based on the adaptive transformation of infiltration-excess and saturation-excess runoff. This model takes soil field capacity and infiltration capacity as discriminant thresholds, and integrates the Green-Ampt model(GA), the Xin'anjiang model(XAJ), and a confluence method based on grid water drops(CW). For model calibration and validation, ten precipitation events that occurred between 2013 and 2019 in the Suyukougou watershed, situated at the eastern foothills of the Helan Mountain, were selected. The distributed Green-Ampt(Grid-GA) model served as a comparative benchmark. [Results] The result indicate that the GX-CW model, which is based on the adaptive transformation of infiltration-excess and saturation-excess runoff, has a better simulation effect. During the model calibration process, 80% of the fields exhibited relative flood errors within 20% and peak timing errors within 1 hour, with all achieving a Nash-Sutcliffe Efficiency(NSE) exceeding 0. 7. In the model validation phase, the performance was somewhat diminished compared to the calibration phase; however, 60% of the fields still maintained relative flood errors within 20% and peak timing errors within 1 hour, with 60% achieving a NSE above 0. 7. [Conclusion] Overall, the GX-CW model demonstrated significantly superior performance compared to the Grid-GA model,providing enhanced insights into rainfall-runoff processes. This model exhibits promising potential for simulating flash flood events in small catchments in arid regions.

[Objective] Runoff in glacial basins on the cold and arid northern slope of the Tianshan Mountains is jointly influenced by vertical zonality and the nonlinearity of glacier ablation. However, the significant vertical gradient and high glacier runoff contribution rate lead to prominent challenges in this region, namely poor snowmelt runoff simulation and insufficient characterization of the physical mechanism of baseflow. Taking the Manas River Basin as the study area, the performance adaptation patterns of physically-based and data-driven models under vertical differentiation are investigated. [Methods] Integrating the CMADS dataset, a parallel runoff simulation framework combining SWAT, LSTM, and CNN-LSTM was established. The Nash-Sutcliffe Efficiency(NSE), Root Mean Square Error(RMSE), and Percent Bias(PBIAS) were used to quantitatively evaluate the performance of monthly runoff simulation from 1979 to 2018, and the causes of model errors during the snowmelt period(April-July) and low-flow period(November-March) were analyzed. [Results] The result showed that: CNNLSTM exhibited superior overall simulation performance, with an overall NSE of 0. 829—representing a 3. 6% and 2. 2%improvement over LSTM(0. 800) and SWAT(0. 811), respectively. During the snowmelt period(April—July), CNN-LSTM still performed best(NSE= 0. 787), which was 6. 8% and 3. 0% higher than that of SWAT(0. 737) and LSTM(0. 764),respectively. In the low-flow period( November-March), CNN-LSTM and LSTM showed similar performance( both NSE=0. 859). Given the small runoff base in the low-flow period, although SWAT had a lower NSE(0. 782) than the two models, its explicit physical parameterization of groundwater processes still endowed the result with higher process robustness and interpretability, thus possessing unique analytical value. [Conclusion] Under the vertical zonality gradient, differences in model performance are jointly driven by hydrological process mechanisms and model structural advantages. CNN-LSTM learns the spatiotemporal patterns of meteorological factors via a data-driven approach and exhibits excellent adaptability to nonlinear processes. SWAT, with explicit parameterization based on physical mechanisms, exhibits unique process consistency and interpretability. The advantage boundaries and applicable scenarios of different models in runoff simulation in cold and arid regions are identified, providing a key scientific basis for the development of integrated physically-based and data-driven runoff simulation models.

[Objective] Long-distance tunnel engineering plays a critical role in modern infrastructure construction, where tunnel monitoring is essential to ensure safety and reliability. Traditional electrical measurement method face challenges in long-distance tunnel monitoring due to power supply constraints, data acquisition limitations, and signal attenuation over extended transmission distances. [Methods] To address these challenges, a fiber Bragg grating(FBG)-based automated multi-source monitoring and transmission scheme was proposed. An integrated monitoring system architecture was constructed, comprising perception,transmission, and application layers. Automated transmission, storage, statistical analysis, and visualization of deformation,seepage pressure, and bolt stress data were achieved through 5 G network technology and cloud platforms, enabling efficient data processing and real-time monitoring. The proposed scheme was applied to a tunnel project in Hunan, China, achieving continuous monitoring over 20 km, which validated the stability and feasibility of FBG technology in long-distance data transmission. [Results] Monitoring result indicated that fiber-optic and electrical measurements exhibited closely aligned mean values across monitoring phases, with minimum relative errors of 0. 46%, 3. 23%, and 2. 37% in displacement, seepage pressure, and bolt stress monitoring, respectively. [Conclusion] Over four months of monitoring demonstrated that fiber-optic sensing technology stably acquired and transmitted large volumes of data meeting accuracy requirements. Additionally, the proximity in data magnitudes and consistency in variation patterns between the two method confirmed the reliability of fiber-optic monitoring. The proposed FBG-based sensing scheme offers a viable new approach for safety monitoring in long-distance tunnel engineering.

[Objective] Accurate calculation of flow resistance in alluvial rivers is of great significance for river regulation and flood control engineering. Conventional resistance formulas and existing machine learning method still have multiple limitations.To improve the performance and generalization ability of resistance models, a flow resistance estimation method based on deep learning is proposed. [Methods] Hydrological features, including Froude number, volumetric sediment concentration, width-todepth ratio, diameter-to-depth ratio, annual runoff, and annual sediment load, were selected as model inputs, and a flow resistance calculation model based on deep forest was established. The model was trained and tested using measured data from hydrological stations in the lower Yellow River, and comprehensively evaluated in terms of spatiotemporal generalization ability and transfer learning performance. [Results] The model achieved a Nash-Sutcliffe efficiency(NSE) of 0. 785, a mean absolute error(MAE) of 0. 002, a root mean square error(RMSE) of 0. 003, and a mean absolute percentage error(MAPE) of 14. 618% on the test dataset. After the incorporation of spatiotemporal average features, the NSE of the model increased from 0. 681 4 to 0. 742 7, the MAE decreased from 0. 002 3 to 0. 002 1, the RMSE dropped from 0. 003 2 to 0. 002 8, and the MAPE reduced from 14. 978% to 13. 689%. When the model was transferred to completely new water-sediment conditions, the maximum decline in NSE reached 65. 35%, and the maximum increases in MAE, RMSE, and MAPE were 100%, 150%, and 123. 98%, respectively. [Conclusion] Compared with traditional resistance formulas and machine learning method, the deep forest model demonstrates superior accuracy in predicting flow resistance under general conditions in alluvial rivers. By introducing large-scale spatiotemporal average features, the model's calculation accuracy across different hydrological stations and hydrological periods is effectively improved, and its generalization ability is significantly enhanced. However, under special water-sediment conditions, the model still shows performance fluctuations. In certain cases, its calculation accuracy is even lower than that of physically based roughness formulas. Therefore, it is urgent to incorporate physical mechanisms to enhance its transfer learning capability. When addressing the calculation of movable bed resistance in the Yellow River under complex environments, emphasis should be placed on mutual verification with reliable traditional formulas.

[Objective] Water temperature is a key factor affecting the evolution of river ecosystems, playing an important role in the conversion of biogenic elements, biological habitat, and other physical, chemical, and biological processes. Hydropower development has obstructed the continuity of rivers and altered the transmission of river water temperature along the river,especially under the cascade hydropower development, which has seriously affected the rhythm of river water temperature. It is necessary to reveal the impact patterns of cascade dam construction on water temperature in the Yangtze River mainstream. [Methods] Based on the Google Earth Engine(GEE) platform, Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 TIRS remote sensing datasets were utilized to retrieve surface water temperature of the Yangtze River from 2000 to 2020 through atmospheric radiative transfer models. The retrieved result were validated by comparison with measured data from Zhutuo hydrological station,and the spatiotemporal characteristics of surface water temperature before dam construction( 2000—2005) and after dam construction(2015—2020) were analyzed in the Liyuan-Guanyin section, Wuduode-Xiangjiaba section, and Three Gorges Reservoir area. [Results] The result show that the average annual surface water temperatures before the construction of dams in the Liyuan-Guanyin section, Wuduode-Xiangjiaba section, and Three Gorges Reservoir area were 21. 58 ℃, 21. 31 ℃, and 17. 79 ℃, respectively. After the construction of dams, the water temperature in the middle reaches of the Jinsha River reservoir area decreased significantly to 15. 47 ℃, with an average decrease of 5. 43 ℃ in the dry season and an average decrease of 2. 46 ℃ in the rainy season. The average annual water temperature downstream of the dams was close to the natural river water temperature, and the correlation with the surrounding measured air temperature was significant. Water temperature within the reservoir areas exhibited fragmented distribution, with maximum temperature differences observed at the heads of cascade dams.The Three Gorges Reservoir area exhibited phenomena of “cold retention” and “temperature retention”. The Nash efficiency coefficient between remote sensing inversion and measured data reached 0. 907, with a root mean square error of 1. 24 ℃,indicating high reliability of the result. [Conclusion] The continuity of river water temperature has been disrupted by cascade dam construction, significantly weakening the temperature transmission effect. The water temperature in the head of each reservoir area and the backwater area showed a significant correlation, but the correlation between the water temperature in the reservoir area and the water temperature discharged from dam was weak. Remote sensing method can effectively retrieve river surface water temperature, providing method ological support and theoretical references for river water temperature inversion and watershed water temperature management.

[Objective] The Dadu River Basin, a transition between the Qinghai-Xizang Plateau and Sichuan Basin, features towering mountains, deep valleys, and dramatic relief. Complex geology renders it highly prone to geological hazards. [Methods] Via multi-source data fusion, this work integrates geological surveys, remote sensing, spatiotemporal statistics, and case inversion to establish a framework. It explores landslide spatial-temporal distribution and synergies with topography,geology, rock-soil, slope structure, hydrology, and human activities, clarifying evolution patterns and multi-scale coupled disaster mechanisms in the main basin. A multi-factor landslide model is built, and geological-human activity synergies identified. [Results] Key findings:(1) 97% of landslides occur within 5 km of faults, concentrated in strong unloading zones(500~ 3 500 m elevation, 10° ~ 50° slope);(2) 94. 18% of landslides happen in May-September rainy season, correlating significantly with rainfall;(3) Landslide density within 5 km of hydropower hubs hits 0. 113/km², triple that of other areas. [Conclusion] Result show landslide spatial distribution is strongly structure-controlled; rainfall is the main trigger, with prominent engineering disturbance effects.

[Objective] Reservoir bank collapse is a typical engineering geological hazard in reservoir areas, directly threatening the lives and property of residents and the safety of infrastructure along the banks. Its prevention and risk management represent a major challenge in the field of hydraulic engineering. [Methods] The current state of research was systematically reviewed on reservoir bank collapse domestically and internationally. It begins by elucidating its causative mechanisms and typical failure modes. It then summarizes the technical method ology system, from investigation and identification to prediction and evaluation,analyzing the applicability of various method, including graphical, mathematical analysis, and numerical simulation approaches.The paper discusses integrated prevention and mitigation measures that combine engineering governance with ecological restoration, as well as the technical system encompassing professional “ sky-air-ground-water ” integrated monitoring and community-based monitoring, forecasting, and prevention. Building on this foundation and from a management perspective, this paper constructs a systematic risk management framework for reservoir bank collapse. This framework uses risk assessment as its scientific basis and the “Four Pre” s(Forecasting, Early Warning, Simulation, and Preparedness) as its core process, detailing its closed-loop management process and implementation strategies. [Results] The result indicate that the causes of reservoir bank collapse are complex. Reservoir water level fluctuation is the dominant external triggering factor, while the geological structure of the bank slope and the properties of the rock and soil mass form the internal basis controlling its stability. Identification and prediction technologies are showing a trend of integration between empirical and mechanistic models. Mechanistic and data-driven method such as numerical simulation and machine learning represent the development direction of high-precision and intelligent prediction. Prevention and mitigation measures are shifting from single engineering solutions to “ engineering-ecological ”collaborative governance, with measures like ecological slope protection receiving increasing attention. The monitoring and early warning system has evolved into a mature paradigm characterized by the “ integration of professional and community-based monitoring” and “stereoscopic collaboration”. Risk management has preliminarily formed a systematic management framework using the “Four Pre” s as the main line and risk assessment as the scientific basis, achieving a strategic shift from passive response to active prevention and control, significantly enhancing the comprehensive risk management capability of reservoir areas. [Conclusion] Current understanding of the dynamic coupling mechanisms involving multiple factors such as water level,rainfall, seepage, and geological structure remains insufficient. There is a need to deepen research on multi-field coupling mechanisms, develop “ grey-box” models that integrate physical mechanisms and data-driven approaches, and promote the intellectualization and standardization of prediction technologies to overcome the challenges of predicting bank collapse under complex conditions. Research on quantitative models of vegetation root soil reinforcement mechanisms, the durability of ecological slope protection materials, and their synergistic mechanisms with engineering structures should be conducted to develop ecofriendly and resilient prevention and mitigation technologies. It is essential to build an intelligent management “brain” to achieve closed-loop management of the “Four Pre” s for risk.