Search Results (2,035)

Long-term groundwater overexploitation in the North China Plain has triggered severe land subsidence. Meanwhile, the implementation of the Middle Route of the South-to-North Water Diversion (SNWD) project and extreme precipitation events driven by climate change are exerting profound impacts on the regional hydrogeological environment. However, the localized deformation response mechanisms at a sub-kilometer to kilometer scale under the combined influence of large-scale hydraulic engineering and extreme weather events remain unclear. In this study, we utilized ascending Sentinel-1A SAR data from 2017 to 2025. By employing the Small Baseline Subset InSAR (SBAS-InSAR) technique, coupled with the Generic Atmospheric Correction Online Service (GACOS) and multi-track mosaicking, we acquired high-spatiotemporal-resolution vertical land deformation fields in the piedmont of the Taihang Mountains (Northern Henan to Southern Hebei section). Furthermore, we analyzed these deformation fields by integrating deformation pattern classification with the spatial distribution of geological structures and water diversion projects. Through this approach, we explored the controlling factors and impacts of heavy rainfall events and the SNWD project on regional land deformation. Full article

With the rapid growth of tourism in Dayi County over the past decade, this study develops a meteorological disaster risk assessment framework for major tourist attractions in this region. Drawing upon daily precipitation and temperature records from 25 meteorological stations (2014–2023) alongside multi-source geospatial data, we evaluate six primary attractions: Xiling Snow Mountain, Huashuiwan, Anren Ancient Town, Xinchang Ancient Town, Tianfu Huaxigu Valley, and Shujiu Cultural Park. The evaluation model integrates four core dimensions: hazard, environmental sensitivity, asset vulnerability, and disaster mitigation capacity. Indicator weights are determined through the Analytic Hierarchy Process, and GIS-based spatial analysis is employed for risk zonation. Additionally, the 45-year ChinaMet dataset provides independent validation for the long-term stability of the hazard assessment. Results reveal a distinct west-low, east-high composite risk gradient. High-altitude mountainous regions in the west exhibit a lower overall risk. Despite frequent extreme weather events, extensive vegetation coverage and low visitor density effectively buffer the negative impacts of physical hazards. Conversely, tourist attractions on the eastern plains fall within high-risk zones. Concentrated visitor populations, dense built environments, and low-lying terrain collectively amplify exposure to severe rainstorms and extreme heatwaves. These findings demonstrate that meteorological disaster risk in tourism destinations fundamentally arises from the deep coupling of natural and human systems. Thus, this study provides a scientific basis for implementing differentiated disaster prevention, mitigation, and localized emergency management strategies. Full article

Vegetation is a key component of bioretention systems, significantly influencing their functionality and overall performance. The sustainability of these systems largely depends on the plant species’ ability to withstand the primary hydrological stresses induced by the infrastructure’s design, in combination with local climatic conditions. This study focuses on plant selection for bioretention applications in Sub-Mediterranean and Mediterranean regions of Italy, which are increasingly affected by extreme weather events, including intense rainfall, heat waves, and prolonged droughts. A review of scientific literature and stormwater management manuals developed for Mediterranean climates was conducted to evaluate both non-native species and Italian native species already used in international applications. The findings reveal a limited range of drought-tolerant non-native species in scientific literature, whereas such species are more prominently featured in California-based manuals. Moreover, among the relatively few native species identified, only a small number are truly suited to fully Mediterranean conditions, based on evaluations of Ellenberg index values. These results highlight a significant research gap, emphasizing the need for further studies, particularly focused on the target ecosystems of native flora in central and southern Italian regions. Full article

As the coupling and interaction among subsystems in integrated energy systems (IESs) increase, these systems are becoming increasingly vulnerable to failures under extreme weather events and natural disasters, which threatens overall operational security and stability. This paper reviews recent studies on the resilience-oriented management of IESs, with a focus on the characterization and assessment of energy system resilience covering various types of resilience-challenging extreme events, the related quantification metrics, methods, and resilience enhancement applications. Based on the reviewed studies, this paper attempts to figure out how internal and external adversities impact the systems, and identifies effective methods to detect, assess, and quantify system vulnerabilities and weak links under extreme events scenarios. Rather than confining the analysis to single-carrier systems, this study bridges cross-disciplinary perspectives to construct a resilience-oriented conceptual framework specifically designed for IESs, centering on the core logical, analytical, and technical strategies for both characterizing and advancing IESs’ resilience. Also, this review tries to reveal research opportunities to address significant gaps in the existing literature. Findings from the review can inform future research and help to develop scalable and effective ways to enhance IESs’ resilience. Full article

This Perspective is dedicated to the memory of Professor Joseph Mark (Joe) Gani (1924–2016) and Professor Christopher Charles (Chris) Heyde (1939–2008), two scholars whose intellectual leadership profoundly shaped applied probability, mathematical statistics, and their interface with finance, insurance, and risk management. Their contributions extend beyond specific technical results to the development of research cultures grounded in probabilistic rigor, empirical relevance, and methodological transparency. We emphasize three enduring themes central to modern quantitative risk analysis. First, the systematic incorporation of heavy-tailed and non-Gaussian features in stochastic modeling, reflecting persistent empirical deviations from classical Gaussian assumptions in financial data. Second, the development of stochastic and time-series methodologies capable of handling dependence structures, including conditional heteroskedasticity and long-range dependence. Third, the principled integration of probabilistic modeling with data-driven and machine learning approaches, ensuring predictive performance is accompanied by interpretability and robustness. We situate these contributions within contemporary challenges in financial risk management, including systemic risk, environmental, social and governance (ESG) considerations, and climate finance. In particular, climate-related financial risks arise from both physical impacts (such as extreme weather events and long-term environmental change) and transition dynamics associated with the shift toward a low-carbon economy (including policy, technological, and market adjustments). These sources of risk introduce additional forms of dependence, nonlinearity, and model uncertainty, particularly in high-dimensional, data-rich settings. This Perspective highlights a forward-looking research agenda that preserves the foundational principles of applied probability while adapting them to modern financial systems characterized by real-time information flows and evolving risk structures. This legacy continues to shape how financial risk is modeled, measured, and understood in increasingly complex and interconnected environments. Full article

Elderly individuals in rural China are highly vulnerable to extreme weather events and temperature fluctuations due to inadequate infrastructure in the built environment and constrained economic conditions, thereby increasing their health risks. Outdoor spaces represent one of the primary daily activity settings for rural older adults. However, existing research rarely links spatiotemporal patterns of outdoor activities to evidence-based thermal environment optimization, leaving a critical knowledge gap for age-friendly and sustainable rural design. This study focuses on the spatiotemporal differentiation patterns of daily outdoor activities among elderly people aged 60 years and above in rural Xi’an, as well as the optimization of spatial variations in thermal environments. Using on-site interviews, thermal environment measurements, thermal comfort questionnaires, continuous thermal environment monitoring, and machine learning based on random forest, this study drew the following conclusions: (1) outdoor activities in winter were concentrated between 9:00–11:00 and 13:00–17:00, while in summer, they shifted to the morning and evening periods, namely 6:00–9:00 and 17:00–21:00. (2) Models for outdoor clothing adjustment, thermal sensation, and thermal acceptability among elderly residents were established. The calculated neutral temperature was 10.19 °C, with a 90% outdoor thermal acceptability range of 9.6–27.2 °C and an 80% outdoor thermal acceptability range of 6.2–30.6 °C. These findings differ from those documented in regions with distinct climate zones and geographical settings. This discrepancy stems from regional climatic features, lifestyle variations between urban and rural older adults, and differences in the thermal environment quality of elderly-oriented outdoor activity spaces. (3) In winter, the acceptable period of the Universal Thermal Climate Index (UTCI) at south-facing entrances (10:30–16:30) was significantly longer than that in the courtyard (13:30–14:00). In summer, the comfortable period in the courtyard (before 10:00 and after 20:00) was longer than that at north-facing entrances (before 09:00). A random forest model for thermal sensation was established, and the relative importance of each parameter influencing thermal sensation was analyzed. On this basis, priority improvement pathways and strategies for the thermal environment, as well as suggestions for the subjective adaptive behaviors of elderly residents, were proposed. The research results of this study can provide technical solutions for age-friendly thermal environment design in rural areas, thereby safeguarding the comfort, health, and social well-being of the elderly population in rural areas. Full article

Accurate wind power forecasting is essential for ensuring power grid stability and facilitating the large-scale integration of renewable energy, yet it faces significant challenges due to the randomness, variability, and intermittency of wind resources and the increasing frequency of extreme weather events. Existing data-driven approaches often struggle to balance temporal continuity with meteorological sensitivity, leading to lag effects during rapid fluctuations, and frequently generate predictions that violate physical domain knowledge. To address these limitations, this paper proposes a dual-stream architecture to decouple temporal dependencies and spatial–meteorological mappings, utilizing a Physics-Informed GRU (PI-GRU) and an Enhanced Random Forest (ERF). Both streams are strictly bounded by physical constraints. Furthermore, a scenario-aware adaptive fusion mechanism is introduced to dynamically adjust the model’s reliance on each stream based on real-time wind speed gradients and volatility indices. Extensive experiments were conducted using a comprehensive dataset from three coastal wind farms over 8 months, encompassing stable regimes and extreme weather events. Evaluating across both 1-day and 4-day forecast horizons, the results demonstrate that our method significantly outperforms state-of-the-art baselines, proving its robustness and practical value for grid security and dispatch optimization. Full article

The Niger Delta region of Nigeria experiences multiple environmental stresses due to intensive oil exploration and pervasive gas flaring, both of which contribute to local and regional climate changes, extreme weather events, and excessive and erratic rainfall. Consequently, flooding remains a recurrent natural disaster, disproportionately impacting the low-lying states of Delta, Bayelsa, and Rivers. This study employs remotely sensed geospatial data and a GIS-based weighted overlay analysis to delineate flood-prone areas on a regional scale in the central Niger Delta states. Flood susceptibility was determined through a weighted overlay of digital elevation model (DEM), slope, proximity to streams, rainfall, and LULC data, among others. Weights of criteria were derived through an analytical hierarchy process (AHP) with a very good consistency ratio of 2.5%. Land use and land cover (LULC) and rainfall data were further analyzed to detect trends of changes between 2012 and 2022. The results show that relatively 77% of the study region is prone to flooding. Areas prone to very high flooding are about 16%, high is 29%, moderate is 32%, while low and very low flood-prone areas cover 18% and 5% of the study region, respectively. There is also a notable increase in average annual rainfall and land cover changes. Average rainfall increased by 58.1% between 2012 and 2017, and by 11.5% between 2017 and 2022. Land cover change analysis further indicates that approximately 1.3% of the study area was converted predominantly to flooded zones and water bodies from 2017 to 2022. The results of this study could be useful for urban regional planning, flood mitigation, and resettlement policies aimed at reducing flood vulnerability and enhancing resilience in the central Niger Delta, as well as other places where similar challenges exist. Full article

Observed increases in wildfire activity across the contiguous United States (U.S.), together with continued warming and expanding development in fire-prone landscapes, highlight the need to anticipate near-term changes in fire regimes. We apply a Bayesian statistical model that integrates projected population density (SSP2) and downscaled climate simulations under a moderate emissions scenario (RCP 4.5) to estimate future wildfire occurrence, maximum fire size (using the 90th percentile of fire size distribution), and total area burned for large fires (>1000 acres) across all EPA Level III ecoregions for 2020–2060. Relative to 1984–2019, we project nationwide increases of 56% in fire occurrence and 59% in area burned, with larger increases in maximum fire size (63%) in 2020–2060. Spatial patterns vary substantially: fire occurrence increases most strongly in the eastern U.S., including regions where large fires have historically been rare, while western ecoregions experience the largest absolute increases in burned area and extreme fire size. The disproportionate growth in maximum fire size suggests that changes in fire weather will amplify extreme events beyond increases in ignition frequency alone. These projections indicate expanding wildfire risk across diverse U.S. landscapes and underscore the need for regionally tailored fire management and preparedness strategies. Full article

Based on ERA5 reanalysis data, best-track data of tropical cyclones, and satellite nighttime light data from 1995 to 2024, this study employs a statistical composite method to analyse spatiotemporal evolution characteristics and impact mechanisms of gale events in the South China Sea. The results indicate: ① The gale days exhibit a pattern of ‘high in the northeast and southwest, low in the middle’ with three high-value regions located in the Taiwan Strait, the Bashi Strait, and the offshore region southeast of Vietnam, where the average wind speed at the centres reaches 8 m/s. Maximum wind speeds show a ‘high in the north, low in the south’ pattern, with the dividing line near 10° N. The number of gale days peaks in winter, while maximum wind speeds are higher in summer and autumn than in winter and spring. ② The spatial distribution of gales is primarily influenced by the combined effects of land–sea topography and weather systems. Cold air masses in winter and spring are the dominant cause of gales in the South China Sea. Although typhoons in summer and autumn occur less frequently, they are more likely to trigger extreme gales. ③ Most regions of the South China Sea show an increasing trend in the gale days, while a few areas in the south and near Guangdong exhibit a decrease. The overall increase is primarily attributed to the intensification of the subtropical high, whereas the reduction near Guangdong is mainly due to increased surface roughness caused by urbanisation, which enhances friction and suppresses wind speeds. Full article

In recent years, the frequency, damage and impact scope of extreme weather events have increased and expanded significantly. Based on the official secondary panel data of 26 provinces in China from 2006 to 2022, this paper explores the impact of extreme weather on the urban–rural income gap. Employing benchmark regression, mediating effect and moderating effect models, this study empirically analyzed the transmission mechanism by which extreme weather affects the urban–rural income gap through crop damage caused by disasters and the mitigating role of agricultural insurance. The key findings reveal that extreme weather significantly widens the urban–rural income gap, with the severity of disaster losses serving as the primary transmission path. Furthermore, agricultural insurance effectively mitigates this shock by hedging against the loss of rural residents’ disposable income. Heterogeneity analysis shows that extreme precipitation and droughts exert the most pronounced effects, and the widening of the income gap is particularly significant in the western region of China. Consequently, it is imperative to promote the integration of meteorological services and agricultural insurance risk reduction services, improve the core infrastructure of rural disaster resistance, and build a differentiated agricultural insurance policy system for risk zones to narrow the income gap between urban and rural areas. Full article

Climate change and intensifying extreme weather events challenge plant adaptability, making the evaluation of adaptive potential imperative. This study aims to identify climatically stable habitats for Rhododendron dauricum, a nationally protected (Class II) shrub species in China. Species occurrence records were integrated with multiple environmental datasets, and habitat suitability was inferred using a maximum entropy model under current and future climate scenarios. The model outputs indicate that habitat suitability is primarily driven by temperature and moisture, vegetation plays a secondary role, and topographic and soil factors are less influential. Projections show a consistent contraction of suitable habitats, particularly in highly suitable areas, with stronger declines under higher emission scenarios and longer time horizons. Spatial patterns shift from continuous to fragmented distributions, with suitable habitats increasingly concentrated in the northeastern regions and northern mountain ranges. Core areas that remain suitable across scenarios are identified through multi-scenario consistency analysis, representing climatically stable regions. These areas should be prioritized for in situ conservation, while populations maintaining high suitability across scenarios may serve as candidate provenances for ex situ conservation and future landscape deployment. This study elucidates the adaptive potential of R. dauricum under future climate scenarios and identifies key environmental drivers, informing conservation, breeding, and climate-adaptive management. Full article

Soils as the important components of the global carbon cycle play a critical role in food security as well as in supporting adaptation to climate change. The current review presents recent research on interactions between soil systems and climate dynamics. Climate change and poor land-use practices pose significant threats to soil health. In this context, the application of Sustainable Soil Management (SSM) strategies provides important benefits. These practices contribute to climate change mitigation by increasing carbon storage in soils and improving soil resilience to extreme climate conditions. Regenerative agriculture practices, including Conservation Agriculture (CA), cover crops, organic materials, and diversified cropping systems can store carbon at rates of about 0.1 to 1.2 t C ha⁻¹ yr⁻¹. Moreover, these practices improve biodiversity and enhance soil properties, with yield responses varying depending on environmental and management conditions. Climate change accelerates soil degradation by raising temperatures, altering rainfall patterns, and increasing the frequency of extreme weather events. Consequently, these factors lead to marked reductions in Soil Organic Carbon (SOC) stocks and degrade essential soil properties. This review places SSM within an extensive sustainability framework that is closely linked to the United Nations Sustainable Development Goals (SDGs). Key goals addressed include SDG 2 (Zero Hunger), SDG 13 (Climate Action), and SDG 15 (Life on Land). It also examines related policies, presents case studies from different agroecological regions, and discusses future research directions. Wider adoption of SSM requires strong economic incentives and inclusive governance. These measures can support climate-resilient agriculture and net-zero emission goals. Full article

Climate change is intensifying the frequency and severity of extreme weather events, posing significant challenges to crop productivity and agroclimatic management in subtropical regions. However, quantitative insights into how different cropping systems respond to climate extremes remain limited. In this study, crop net primary productivity (CNPP) of two representative cropping systems, early–late rice (ER–LR) and dry rapeseed–sweet potato (DR–SP), was analyzed in Pingxiang, a typical subtropical agricultural region of China. Nineteen extreme temperature and precipitation indices were evaluated using an integrated Trend–Prediction–Sensitivity–Threshold (TPST) framework combining statistical and machine learning approaches. CNPP exhibited an upward trend (slope = 4.29 g C m⁻² yr⁻¹) from 2000 to 2023, with ER–LR showing faster growth (slope = 4.54 g C m⁻² yr⁻¹) and higher stability (high-volatility area: 1.25%) than DR–SP (slope = 4.11 g C m⁻² yr⁻¹; 4.94%). Temperature extremes were the dominant drivers, exhibiting nonlinear responses with threshold effects. DR–SP was more climate-sensitive, while ER–LR showed greater tolerance, highlighting the role of cropping systems in enhancing resilience. The TPST framework provides a transferable approach for assessing agroecosystem productivity responses to climate extremes and supports climate-resilient cropland management in subtropical regions. Full article

With the increasing frequency of extreme weather and rapid growth of civil aviation, severe convective weather (thunderstorms, short-term heavy precipitation, and strong winds) poses growing threats to flight safety. This study proposes a multi-label CNN-ConvLSTM framework that fuses airport Doppler radar echoes, Himawari-8 satellite imagery, surface observations, and radar optical flow features to nowcast multiple severe convective events within the next 30 min. The model uses 2D-CNN for spatial extraction, ConvLSTM for temporal dynamics, and a weighted joint loss (Focal Loss and Dice Loss) to address class imbalance. Trained on 396 samples (positive-to-negative ratio 1:2.5) from 83 events at Guanghan Airport (2021–2024), incorporating optical flow features significantly boosted performance: macro-F1 increased from 0.719 to 0.792, and Threat Score (TS) from 0.567 to 0.705. Notably, false negatives for minority classes dropped sharply, with strong winds F1-score rising from 0.15 to 1.00. Ablation analysis showed optical flow as the top contributor (Mean Decrease in TS ≈ 0.5). Through multi-modal fusion and motion enhancement, this interpretable model provides high-precision nowcasting for airport severe convective weather, offering substantial value for aviation safety. Full article