Drought caused by long-term water shortage may lead to insufficient soil moisture and a decline in groundwater, affecting plant growth and species turnover, and thereby altering the structure and function of the ecosystem. The vast majority of regions in China are facing severe ecological water shortage pressure, which poses a challenge to the health of the ecosystem and the sustainable development of the economy and society. The standardized ecological water deficit index (SEWDI) adopts the actual water consumption of vegetation to characterize the ecosystem’s water supply, and calculates the ecological water deficit (EWD) as the difference between ecological water consumption (EWC) and ecological water requirements (EWR), thereby enabling dynamic assessment of regional drought stress status. Based on the SEWDI, this study used multi-source remote sensing data to reveal the dynamic changes and driving factors of ecological drought in China from 1982 to 2024, and the following main conclusions were obtained: (1) the changing trends of ecological drought in various regions of China were different, and the drought situation has been particularly severe since 2000. Except for the Huang-Huai-Hai Plain Region (HPR) and the Middle-Lower Yangtze Plain (MYP), SEWDI showed a downward trend in the other regions, indicating that ecological drought in China generally showed an increasing trend. (2) SEWDI had two seasonal mutation points, which occurred in January 2003 (confidence interval: December 2002 to March 2003) and April 2017 (confidence interval: June 2016 to March 2018). (3) The most severe ecological drought event occurred from July 2019 to April 2020, with a duration and intensity of 10 months and 9.15, respectively. The peak of the drought occurred in February 2020 (SEWDI= –1.21). (4) From spring to winter, the mean range of the grid trend feature Zs of SEWDI was –1.12 (in winter) to 0.13 (in summer), suggesting that drought in summer showed a decreasing trend, while drought in spring, autumn and winter showed an increasing trend. (5) Under the combined influence of climate change and human activities, the three optimal variable factors driving changes in ecological drought in China were evapotranspiration, soil moisture and irrigation water. The research results aim to provide a reference for the identification of ecological drought and its driving factors, and to offer a scientific theoretical basis for China’s response to climate change and ecological environment protection.

Groundwater drought poses severe threats to water security, ecological sustainability, and agricultural production, particularly under accelerating climate change and intensified human interference. Characterizing the spatial-temporal evolution, propagation mechanisms, and dominant drivers of groundwater drought across diverse geographical regions is critical for targeted water resource management and drought risk mitigation in China. In this study, we systematically investigated the spatial-temporal dynamics of groundwater drought across China's major climate regions, quantified its long-term trend characteristics and spatiotemporal propagation patterns, and further elucidated the natural and remotely-sensed driving mechanisms behind groundwater drought variations. The results revealed significant spatially heterogeneous and temporally varying groundwater drought patterns across China. Most regions exhibited an overall intensifying trend of groundwater drought, while distinct zonal differences existed between northern arid/semi-arid regions and southern humid regions. Based on the Bayesian estimator algorithm, the mutation points of groundwater drought index (GDI) in most subzones were concentrated between 2018 and 2023, with a coverage area of 48.1% during this period. Wavelet transform analysis shows that the factor with the strongest explanatory power for GDI in the combination of three climate factors was precipitation, evapotranspiration, and soil temperature. Furthermore, vegetation exhibited obvious lag responses and cumulative effects relative to groundwater drought (3.18 months). The findings provide a solid theoretical reference and practical support for regional groundwater drought early warning, differentiated water resource regulation, and sustainable groundwater protection across China.

As the evaporation capacity of land has increased, soil drought characterized by soil moisture deficiency directly threatens national food security and ecological security, and affects the sustainable development of the social economy. This study, based on the China region, utilized soil moisture datasets based on remote sensing and in-situ site observation data to assess the temporal evolution and spatial pattern of soil drought in China and its various regions from 2000 to 2022. It identified the main segmented locations, mutation types, and spatial trend characteristics of soil drought. The direct or indirect contributions of climatic factors and circulation factors to soil drought were identified. The results showed that: (1) During the study period, the minimum value (–0.81) of the original standardized soil moisture index (SSMI) sequence occurred in August 2004, and one positive breakpoint occurred in March 2010. Soil drought in China presented a changing trend of first aggravation and then deceleration. (2) There were mainly four types of mutations in soil drought: “interrupted decrease”, “interrupted increase”, “decrease to increase”, and “increase to decrease”, and most of the mutation points occurred after 2010. (3) The most severe soil drought occurred in 2009 (SSMI= –0.55), and the drought center was mainly located in the southern Yunnan-Guizhou Plateau Region (YPR) in autumn and winter. (4) The drought in each month and quarter in the Huang-Huai-Hai Plain Region (HPR) showed an increasing trend. (5) Based on cross-wavelet transform, the three best variables for explaining soil drought changes were soil moisture, precipitation, and evapotranspiration. This study reveals the regions in China where soil drought changes are more likely to occur, which can provide a scientific reference for revealing the formation mechanism of soil drought, as well as drought adaptation and response measures.

Study region: Yellow River Basin (YRB) Study focus: This study aims to systematically investigate the spatiotemporal evolution patterns and driving mechanisms of ecological drought in the Yellow River Basin (YRB) from 1982 to 2022. A novel Standardized Ecological Water Deficit Index (SEWDI) integrating vegetation dynamics and hydrological processes is developed. Using multi-source data and an integrated methodology including improved run theory, Copula joint probability models, Modified MannKendall method (MMK) trend analysis, and XGBoost-SHAP machine learning framework, the research quantitatively assesses drought characteristics across multiple time scales and identifies key driving factors. New hydrological insights for the region: A significant basin-wide drying trend (-0.017/10a) with strongest intensification in western upstream regions (-0.043/10a). Distinct westward migration of drought centers, with over 91% of western areas affected in the 2010s. The July 2019-April 2020 event was the most severe on record, peaking in February 2020 with 98.08% of the basin affected, a severity of 9.14, and a 10-month duration. Drought intensification is particularly pronounced in December (Zs = -1.33) and winter (Zs = -1.46), with 97.79% and 94.25% of the basin showing aggravating trends, respectively. Evapotranspiration (ET) emerges as dominant climatic driver, while Atlantic Multidecadal Oscillation (AMO) is primary circulation factor exacerbating drought. These findings provide crucial insights for ecological drought early warning and adaptive water resource management in the YRB.

With the escalation of global warming, the potential for land evaporation in the Yellow River Basin (YRB) has increased remarkably, and the problem of soil drought has become increasingly prominent. This not only restricts the development of the agricultural economy in the basin, but also intensifies the disparity between water availability and demand, and exerts a profound influence on the ecological security of the basin. In light of this, this study focuses on soil moisture in the YRB from 2000 to 2022, and conducts an in-depth analysis of the temporal evolution, spatial patterns, abrupt change characteristics as well as the driving forces of soil drought by calculating the Standardised Soil Moisture Index (SSMI). The results show that: (1) Soil drought in the YRB from 2000 to 2022 exhibited a decreasing trend. A negative abrupt change point occurred in February 2004, indicating a sudden intensification of arid conditions. The soil drought in the Above Longyangxia (AL) region exhibited the most significant decreasing trend in September. (2) The most severe soil drought occurred in 2006, and the drought was most intense in November of that year, when the drought-affected area reaching 86.96%. The severely drought-stricken areas were predominantly concentrated in the central and northern regions of the basin. (3) The SSMI in the second-level sub-regions of the YRB exhibits three types of abrupt changes: increase to decrease, intermittent increase, and decrease to increase, and shows a clear trend from upstream to downstream. (4) The results of cross-wavelet analysis indicate that soil moisture-absolute humidity-soil temperature is the best explanatory combination for soil drought. (5) The slopes of the small- and medium-scale fittings in the AL region are 0.5021 and 0.2584, respectively, and the corresponding mean values of meteorological drought severity are 0.87 and 2.57, respectively. Drought events in the YRB are mainly of medium/short duration and low severity, and soil drought severity tends to increase with the intensification of meteorological drought. This study reveals the spatiotemporal variation patterns of soil drought in the YRB, as well as the responses of climate to soil drought. These findings provide a scientific basis for drought resistance and ecological protection within the basin.

Flash floods and debris flows pose significant threats to human safety and socioeconomic development. It is necessary to analyze the evolutionary process of flash floods and debris flows. Numerical modeling is a commonly used tool to study the evolution of disaster processes. However, existing hydrological or hydrodynamic numerical models for flash floods and debris flows often fail to effectively capture the rapid changes and complex fluid dynamics within fluvial systems, which results in low simulation accuracy. This paper innovatively constructs the HiPIMS-FLO-2D hybrid model for highly accurate simulating the compound disasters of flash floods and debris flows, and conducts a hazard assessment by integrating debris flow occurrence frequencies with multi-scenario simulations. Results show that: 1) The mean fit statistic F for flash-flood simulation was 0.8 (F ranges from 0 to 1, with larger values indicating better agreement), and the mean overall accuracy index Ω for debris-flow simulation was 1.673 (Ω ranges from -2 to 2, with values closer to 2 indicating higher accuracy). 2) High hazard areas for debris flows are primarily located in the middle of gullies and at the middle fronts of accumulation fans. Medium hazard areas are concentrated in the upper gullies and at the edges of the fans. Low hazard areas are distributed along the sides of the gullies and the wings of the fans. 3) Hipims-Flow-2D simulation accuracy is highly correlated with the DEM, and local DEM errors can affect the overall simulation accuracy. Corrections to the DEM result in a 19.05

The Yangtze River Basin (YRB), serving as a crucial ecological shield and economic lifeline in China, has witnessed a gradual increase in the frequency of drought disasters, posing a significant ecological security challenge to the region. Addressing the typical characteristics of “high temperature-high evapotranspiration” droughts in the YRB, this study employs the Standardized Vegetation Water-deficit Index (SVWI) (1982–2022) in the basin, which overcomes the limitations of traditional greenness indices in distinguishing between water stress and abiotic stress. The aim is to precisely identify typical drought events, capture trend mutations, and quantify the statistical associations and relative importance of meteorological and teleconnection factors with vegetation drought. The results show that: (1) from 1982 to 2022, drought with the highest intensity (6.73) in the YRB occurred from August 2019 to June 2020; (2) the grid-based trend characteristic value Zs was –0.06 in summer and –0.86 in winter; (3) the most severe vegetation drought occurred in January 2014 (SVWI=–1.33). The mutation point of SVWI in the YRB appeared in December 2002, showing a monotonically decreasing trend; (4) there are 4 main mutation types of vegetation drought in the YRB, including interrupted decline, increase to decrease, decrease to increase, and monotonic decrease, with most mutation points occurring around 2002; (5) among circulation factors, the Atlantic Multidecadal Oscillation (AMO), Sunspot Index (SSI), and El Niño-Southern Oscillation (ENSO) have the most significant teleconnection driving effects on vegetation drought; and (6) the trivariate combination of evapotranspiration-soil moisture-precipitation is the optimal multi-factor combination for explaining the dynamic changes of vegetation drought. The results help to clarify the evolutionary characteristics of vegetation drought and dominant driving factors, and provide a scientific basis for the adaptive management of ecosystems and the prevention of drought risks.

Soil drought impact on irrigation areas is not merely a single reduction in crop yields, but rather a chain reaction that occurs from multiple dimensions including crop growth, water resource allocation, soil environment, operation of irrigation area projects, agricultural economy and ecosystems. The changing trend and mutation characteristics of soil drought are unclear in the People's Victory Canal Irrigation District (PVCID). The Standardized Soil Moisture Index (SSMI) and the breaks for additive seasons and trend (BFAST) decomposition algorithm were adopted, combined with the eXtreme Gradient Boosting (XGBoost) model, to explore spatio-temporal evolution characteristics, driving factors and response to meteorological drought of soil drought. During the research period, the area percentage of SSMI showing a downward trend was 97.30%. The most severe soil drought occurred in 2019. In addition, the optimal trivariate combination is precipitation, evapotranspiration, and air temperature. This study has clarified the spatio-temporal evolution laws and driving mechanisms of soil drought in the PVCID, providing an important theoretical basis for the early warning, prevention and control of soil drought and the adaptive management of the ecosystem.

The North China Plain (NCP) is China’s vital grain-producing core and a typical water-scarce region threatened by frequent soil drought under global climate warming and intensive human disturbances. In this study, the 0.1° monthly FLDAS datasets spanning 1982–2024 were adopted to construct the Standardized Soil Moisture Index (SSMI). We systematically analyzed the spatiotemporal variations, seasonal differentiation, extreme drought dynamics and long-term trend persistence of soil drought across the whole NCP and its five sub-regions. The results revealed that: (1) The most serious drought event occurred from July 2002 to November 2002, with September being the driest month and an average SSMI value of –1.33 in the NCP. (2) Anti-persistence dominated the drought situation in most areas, implying that the current trend is likely to reverse in the future. (3) Wavelet coherence analysis indicated strong coupling interactions among soil moisture, soil temperature and air temperature, which dominate drought evolution, while precipitation and evapotranspiration exert stable regulatory effects across monthly, interannual and interdecadal scales. (4) Soil drought severity rises synchronously with the intensification of meteorological drought, with distinct spatial heterogeneity in drought response among sub-regions. The findings provide a theoretical support for agricultural drought early warning, differentiated water resource allocation and regional drought disaster prevention and mitigation.

Rapid urbanization in Guangdong Province has severely impacted river systems due to limited understanding of the sustainability of river connectivity and functions. This study applies the Minimum Cumulative Resistance Model (MCRM) to quantify expansion costs of natural and social processes, with their difference defined as the River Ecological Sensitivity (RES) index. Higher RES values indicate that surrounding land is more prone to conversion into built-up areas, whereas lower values favor river ecological land. RES values were assigned to river segments using a multi-ring buffer approach and integrated into a complex network framework to simulate potential evolution of river network connectivity and functions under coupled natural–social drivers. Conservation scenarios were constructed by sequentially resetting segments with RES > 0 to zero in descending order of sensitivity, representing proactive protection of highly sensitive segments. Results show: (1) River segments with RES > 0 account for 63.4 63.4

Rapid urbanization has greatly altered urban ecological spaces and habitat quality functions, threatening regional biodiversity and the sustainability of landscapes. Therefore, constructing a comprehensive ecological network and ecological safety patterns is crucial for ecosystem management and regional development. However, simple quantification of ecological networks fails to meet the construction needs of ecological safety patterns, and most studies focus solely on network quantification analysis, thus overlooking the importance of spatial analysis. This study proposes a method of ecological network quantification assessment combined with hotspot analysis and coupled with standard deviational ellipse spatial analysis, which not only satisfies quantitative analysis but also adds spatial analysis methods, facilitating a more comprehensive construction of safety patterns. Firstly, through Morphological Spatial Pattern Analysis (MSPA) and landscape connectivity indices, ecological source areas in the main urban area of Kunming were identified, integrating various resistance factors and corrective factors to construct an ecological resistance surface. The Minimum Cumulative Resistance (MCR) model was used to identify potential ecological corridors, and their importance was evaluated using the gravity model, thus establishing an ecological network. Secondly, based on network structure indices, the ecological network was assessed and optimized. On this basis, combined with hotspot analysis coupled with standard deviational ellipse spatial analysis, an ecological safety pattern was constructed. The results show: the core area of the study region is 2402.28 km², accounting for 52.07% of the total area; there are 13 ecological source areas totaling 2102.89 km², accounting for 45.58% of the total area; there are 178 potential ecological corridors, including 15 level one ecological corridors and 19 level two ecological corridors; 103 ecological nodes, 70 'stepping stones', and 48 ecological breakpoints were identified. In terms of ecological network optimization, 6 new ecological source areas were added, covering an area of 16.22 km², and the potential ecological corridors increased to 324, including 11 new level two ecological corridors, 51 new ecological nodes, 15 'stepping stones', and 24 major ecological breakpoints. After optimization, the network closure index (α), network connectivity index (β), and network connectivity rate index (γ) improved by 15.16%, 24.56%, and 17.79%, respectively. Based on the network structure quantitative analysis and hotspot analysis coupled with the standard deviational ellipse's spatial analysis, an 'one axis, two belts, five zones' ecological safety pattern was constructed.

With climate change and accelerated urbanisation, the urban flood risk has increased significantly. Previous studies predominantly used administrative districts and regular grids as the vulnerability calculation units, and mostly relied on static indicators that fail to adequately reflect the dynamic heterogeneity of the vulnerability within intra-city functions. This paper established a novel framework that utilises urban functional zones (UFZs) as the computational unit to capture differences between urban functions. This framework employs dynamic population data at various times to reflect dynamic changes in the vulnerability. The indicators are selected to represent exposure, sensitivity, and adaptability. Particle swarm optimisation is employed to integrate subjective and objective assignments, and the high-performance integrated hydrodynamic modelling system is used to simulate flood inundation under different return periods for joint vulnerability analysis. The results demonstrate that using UFZs as computational units reveals significant vulnerability differences across different functional types, times, and spatial contexts. From T=0 to T=18, the vulnerability of the business and park zones continuously increased, university zone continuously decreased, and the company, primary and secondary school, and hospital zones increased and then decreased, while the residence zone decreased and then increased. The vulnerability hotspot areas shifted between northwest and southwest over time. The high vulnerability-high inundation depth UFZs were identified as priorities for flood management, and their number and functional types varied over time and across different return periods. This paper provides new support for assessing urban flood vulnerability, which is crucial for developing sustainable urban planning and flood risk management strategies.

Necroptosis, a type of programmed cell death, has been increasingly linked to cardiovascular disease development, yet its role in dilated cardiomyopathy (DCM) remains unclear. In this study, we analyzed the GSE5406 dataset from the GEO database to explore necroptosis-related prognostic signatures in DCM using LASSO regression. We identified five necroptosis-related genes (BID, CAMK2B, GLUL, HSP90AB1, CHMP5) that define a necroptosis-related signature with strong predictive value, evidenced by ROC curve areas of 0.852 and 0.957 in training and test sets, respectively. Our analyses, including GO and GSEA enrichment, focused on pathways associated with high necroptosis-related scores (NRS) and revealed significant immune cell infiltration. Notably, nTreg and iTreg cells were enriched in the high NRS group, while CD8 naive T cells and CD8 T cells positively correlated with NRS. Small molecule drugs fenofibrate, procyclidine, and tienilic acid emerged as potential therapeutic agents for high-risk patients, with fenofibrate showing efficacy in inhibiting DCM progression in an inflammatory animal model. These findings underscore the clinical relevance of necroptosis-related genes in assessing DCM progression and prognosis and highlight their potential for targeted therapeutic development.

Multiple sensors are strategically deployed within concrete dams to monitor structural behavior under intricate environmental conditions. The diverse monitoring parameters, spatial configurations, and temporal variations across these sensors often engender performance conflicts. It is different to obtain the comprehensive dam safety status under the intricate relations and behavior conflicts. To solve the problem, the proposed model integrates anomaly detection, trust propagation, consensus measurement, and information fusion for dam safety assessment. The multi-expert variational autoencoder facilitates anomaly score computations. The social trust network delineates spatiotemporal relationships among sensors. The consensus measurement mitigates information conflicts for data integration using the interval-valued fusion strategy. Empirical validation through a case study involving an arch dam underscores the model's efficacy in identifying anomalies. Through the results analysis, the spatial relationships exhibit divergent attributes in response to changes in water levels. It indicates that the spatial relations are necessary factors in the dam safety assessment.

Ecological drought in terrestrial systems is a vegetation-functional degradation phenomenon triggered by the long-term imbalance between ecosystem water supply and demand. This process involves nonlinear coupling of multiple climatic factors, ultimately forming a compound ecological stress mechanism characterized by spatiotemporal heterogeneity. Based on meteorological and remote sensing datasets from 1982 to 2022, this study identified the spatial distribution and temporal variability of ecological drought in China, elucidated the dynamic evolution and return periods of typical drought events, unveiled the scale-dependent effects of climatic factors under both univariate dominance and multivariate coupling, as well as deciphered the response mechanisms of ecological drought to meteorological drought. The results demonstrated that (1) terrestrial ecological drought in China exhibited a pronounced intensification trend during the study period, with the standardized ecological water deficit index (SEWDI) reaching its minimum value of −1.21 in February 2020. Notably, the Alpine Vegetation Region (AVR) displayed the most significant deterioration in ecological drought severity (−0.032/10a). (2) A seasonal abrupt change in SEWDI was detected in January 2003 (probability: 99.42%), while the trend component revealed two mutation points in January 2003 (probability: 96.35%) and November 2017 (probability: 43.67%). (3) The drought event with the maximum severity (6.28) occurred from September 2019 to April 2020, exhibiting a return period exceeding the 10-year return level. (4) The mean values of gridded trend eigenvalues ranged from −1.06 in winter to 0.19 in summer; 87.01% of the area exhibited aggravated ecological drought in winter, with the peak period (88.51%) occurring in January. (5) Evapotranspiration (ET) was identified as the dominant univariate driver, contributing a percentage of significant power (POSP) of 18.75%. Under multivariate driving factors, the synergistic effects of ET, soil moisture (SM), and air humidity (AH) exhibited the strongest explanatory power (POSP = 19.21%). (6) The response of ecological drought to meteorological drought exhibited regional asynchrony, with the maximum correlation coefficient averaging 0.48 and lag times spanning 1–6 months. Through systematic analysis of ecological drought dynamics and driving mechanisms, a dynamic assessment framework was constructed. These outcomes strengthen the scientific basis for regional drought risk early-warning systems and spatially tailored adaptive management strategies.

In-depth analysis of the evolution of ecosystem services (ESs) in the basin at different spatial scales, scientific identification of ecosystem service clusters, and revelation of their spatial and temporal characteristics as well as coupling mechanisms of interactions are the key prerequisites for effective implementation of ES management. This paper assessed the spatial and temporal changes of six key ESs covering food provisioning (FP), water yield (WY), soil retention (SR), water conservation (WC), habitat quality (HQ), and carbon sequestration (CS) in the Xijiang River Basin (XRB), China, between 2000 and 2020. Given that the scale effects of ESs and their spatial heterogeneity in the XRB are still subject to large uncertainties, a combination of Spearman correlation analysis and geographically weighted regression (GWR) modelling systematically revealed the trade-offs and synergistic relationships between ESs and the scale effects from a grid, watershed, and county perspective. Additionally, we applied the self-organizing mapping (SOM) method to identify multiple ecosystem service bundles (ESBs) and propose corresponding sustainable spatial planning and management strategies for each cluster. The results reveal the following key findings: (1) Spatial distribution and heterogeneity: The six ESs demonstrated pronounced spatial variability across the study area during the two-decade period from 2000 to 2020. The downstream areas had higher levels of ESs, while the upstream regions showed comparatively lower levels. This trend was particularly evident in areas with extensive arable land, higher population density, and more developed economic activity, where ESs levels were lower. (2) Trade-offs/synergies: The analysis highlighted the prevalence of synergistic effects among ESs, with food provisioning-related services exhibiting notable trade-offs. Trade-off/Synergistic effects were weaker at the grid scale but more pronounced at the sub-basin and county scales, with significant spatial heterogeneity. (3) Identification of ESBs: We identified five distinct ESBs: the HQ-CS synergy bundle (HCSB), the integrated ecological bundle (IEB), the agricultural bundle (AB), the key synergetic bundle lacking HQ (KSB), and the supply service bundle (SSB). These clusters suggest that the overall ecological environment of the study area has significantly improved, the supply functions have strengthened, and ecosystem vulnerability has been effectively mitigated. Building upon the identified multi-scale spatiotemporal heterogeneity patterns of ESBs in the XRB, this study proposes an integrated framework for territorial spatial planning and adaptive land management, aiming to optimize regional ecosystem service provisioning and enhance socio-ecological sustainability.

Urbanization has greatly accelerated the degradation of river systems, a trend likely to intensify in the future. While extensive research has examined the historical impacts of urbanization on river morphology, less attention has been paid to the present-day sustainability of river network structural connectivity and functions. To fill this gap, this study applies the Minimum Cumulative Resistance (MCR) model in Guangdong Province to characterize spatial competition between ecological conservation and urban expansion. A River Ecological Sensitivity Index is derived from river buffer zones and incorporated into a complex network framework to adjust edge weights, allowing simulations of potential structural and functional changes in the current river network under a coordinated land-use expansion scenario. In addition, a conservation-priority scenario is simulated to evaluate its capacity to sustain and optimize network functionality. The results show that: (1) highly sensitive segments are concentrated in the Pearl River Delta but remain scattered in other regions; (2) high-sensitivity river segments are concentrated in urban agglomerations, while low-sensitivity segments are mainly distributed in suburban areas; (3) the current river network is dominated by nodes of degree 1 and 3, while coordinated expansion reduces level-4 closeness centrality nodes by 9.74% and increases level-1 connectivity nodes by 12.73%; (4) without proactive conservation, Guangdong’s network functionality will decline—protecting 24% of highly sensitive segments is sufficient to maintain ecological and water-supply functions, while 32% is required to secure flood control capacity. These findings provide practical insights for guiding urban river governance and advancing sustainable development. Graphic abstract

Dam safety assessment systems play a pivotal role in evaluating the structural integrity of critical hydraulic infrastructures. Current implementations frequently exhibit limitations in critical functionalities including multi-source data integration and automated anomaly detection. This study proposes an ontology-enhanced intelligent assessment system featuring three technical innovations. A multi-level semantic representation framework is proposed to formally model structural components, sensor networks, and their spatiotemporal relationships through domain-specific ontology engineering. A hybrid anomaly detection architecture employs spatiotemporal variational autoencoder to enable unsupervised identification of abnormal signals. A knowledge-informed reasoning framework integrates empirical safety rules and detection results through Semantic Web Rule Language and SPARQL Protocol and Resource Description Framework Query Language query. Experimental validation on a double-curvature arch dam demonstrated superior performance. The proposed system achieves 89.1% anomaly detection accuracy, simplifies the semantic query through ontology-driven knowledge indexing, and enables automated diagnostic reasoning that identifies the causal relationships between abnormal signals and environmental triggers.

In recent decades, China's multiple cities have expanded rapidly, intensifying urban heat island (UHI) effects. The spatiotemporal patterns of UHI intensity and their driving factors have become a research focus. Some studies focus on machine learning or statistical methods regarding the spatiotemporal patterns of the UHI intensity in multiple cities and their driving factors, with few exploring UHI intensity variations using interpretable neural networks. We calculated the UHI of 31 provincial capital cities across seven physical geographic regions over two decades, analyzed cluster characteristics with Fourier fitting and K-means algorithms, and disclosed their driving factor using the explainable deep learning model (TabNET). Results show that (1) Between 2000 and 2020, the spatial expansion rate of first-tier cities was 176.92 %, while that of second-tier cities reached 197.12 %. (2) 94.62 % of the cities experienced increases in UHI during the summer months, with Urumqi showing the smallest change in UHI across the seasons and Shenyang showing the greatest change. (3) The four distinct UHI patterns observed across 31 Chinese cities can be categorized into six clusters that closely correspond to China's six natural geographical regions, indicating its dominant influence on UHI spatial variation. (4) The geographical location, population, GDP, elevation, month, and vegetation cover are significant drivers of UHI, with weights of 20.3 %, 14 %, 13.3 %, 13.2 %, 12.2 % and 10 %, respectively. Our results visually summarize the regional UHI patterns and disclose driving factors, providing intuitive guidance for mitigating UHI and urban thermal environment optimization.

As monitoring technologies and data collection methodologies advance, landslide disaster data reflects attributes such as diverse sources, heterogeneity, substantial volumes, and stringent real-time requirements. To bolster the data support capabilities for the monitoring, prevention, and management of landslide disasters, the efficient integration of multi-source heterogeneous data is of paramount importance. The present study proposes an innovative approach to integrate multi-source landslide disaster data by combining the Flink-oriented framework with load balancing task scheduling based on an improved particle swarm optimization (APSO) algorithm. It utilizes Flink’s streaming processing capabilities to efficiently process and store multi-source landslide data. To tackle the issue of uneven cluster load distribution during the integration process, the APSO algorithm is proposed to facilitate cluster load balancing. The findings indicate the following: (1) The multi-source data integration method for landslide disaster based on Flink and APSO proposed in this article, combined with the structural characteristics of landslide disaster data, adopts different integration methods for data in different formats, which can effectively achieve the integration of multi-source landslide data. (2) A multi-source landslide data integration framework based on Flink has been established. Utilizing Kafka as a message queue, a real-time data pipeline was constructed, with Flink facilitating data processing and read/write operations for the database. This implementation achieves efficient integration of multi-source landslide data. (3) Compared to Flink’s default task scheduling strategy, the cluster load balancing strategy based on APSO demonstrated a reduction of approximately 4.7% in average task execution time and an improvement of approximately 5.4% in average system throughput during actual tests using landslide data sets. The research findings illustrate a significant improvement in the efficiency of data integration processing and system performance.