This paper examines the concept of human vulnerability within the context of the current environmental crisis, with particular attention to older adults as one of the population groups most exposed to the differentiated effects of climate change and extreme events. The study adopts a multidimensional understanding of vulnerability, in which natural and social phenomena are closely interconnected, so that environmental risks cannot be separated from socioeconomic, political, cultural, and institutional factors that shape the unequal distribution of impacts across society. From a qualitative and documentary perspective, the analysis is based on the systematization and interpretation of normative frameworks applicable to the human rights of older persons, incorporating dogmatic and systemic-structural methods of legal analysis. In this context, vulnerability is understood as being configured through dimensions such as exposure, sensitivity, and adaptive capacity, which acquire particular relevance in aging processes marked by inequality, poverty, exclusion, and limitations in support networks. Furthermore, the environmental crisis is highlighted as a central challenge to the effective guarantee of fundamental rights, including the right to a healthy environment, health, adequate housing, accessibility, and non-discrimination. Consequently, the study emphasizes the need to strengthen comprehensive public policies, differentiated civil protection protocols, and accessible mechanisms for rights enforcement, guided by principles of dignity, social justice, and inclusion. The paper concludes that reducing the vulnerability of older adults in the face of environmental crisis requires structural transformations that articulate sustainability, human rights, and intersectional approaches to protection.

Climate change is increasingly linked to a surge of extreme weather events, raising the risk of disease outbreaks and food insecurity. Meanwhile, an increase in emerging and re-emerging infectious diseases, many of which do not respond to available antibiotics owing to antimicrobial resistance (AMR), poses another great challenge to public health. Although some studies have shown that climate change and extreme weather events are associated with higher levels of AMR, much work remains to determine whether these are causal linkages or merely parallel reflections of an anthropogenic change. In this Review, we explore evidence on the relationship between climate and AMR, highlighting pathways through which rising temperatures and extreme weather events might intensify this pressing issue. Beyond existing ecological evidence demonstrating correlations between temperature and AMR prevalence in clinically important pathogens, a growing body of work suggests that the predominant impact of climate change on AMR manifests through an increase in infectious disease prevalence and a demand for antimicrobial use. Current evidence on the relationship between climate and AMR is insufficient in addressing issues related to temporality and causality, and underscores the need for further research to understand the nature of these complex relationships.

Climate change and pesticide resistance: A synthesis on emerging mechanisms and evolutionary dynamics.

Marine heatwaves (MHWs), discrete and prolonged periods of anomalously high ocean temperatures, have been occurring with increasing frequency and intensity in recent years, threatening global fisheries. This study employs nighttime imageries and random forest models to analyze the impacts of MHWs on Japanese flying squid (Todarodes pacificus) nighttime fisheries in the Sea of Japan (East Sea) (2014–2023). For the first time, critical MHWs thresholds were identified: monthly duration exceeding 16 days and cumulative intensity surpassing 50 °C·days trigger significant contraction of core fishing grounds, particularly in summer/autumn, exhibiting strong negative correlations to catch and vessel numbers (|r| > 0.7, p < 0.05). Sea surface temperature (SST) is the primary driver of spatiotemporal patterns of squid fishing grounds (45% model contribution), with optimal fishing conditions at 17.08 °C. Regional MHWs sensitivity varies across exclusive economic zones (EEZs), with the South Korean EEZ responding strongest (R² = 0.77) to MHWs characteristics. This research establishes novel MHWs impact thresholds for migratory squid fisheries and provides a data-driven framework for adaptive management in semi-enclosed seas, highlighting the urgency of addressing extreme climate events in fisheries governance.

Canine leptospirosis and environmental risk factors in coastal New South Wales, Australia (2021 - 2023).

Spatiotemporal variation of marine microbes in the Taiwan strait ecosystem.

Climate change is altering the frequency and intensity of extreme climatic events such as tropical cyclones, raising concerns about ecological stability under these disturbances. This study examined how functional richness and functional redundancy influenced the stability of two key ecological functions-insect predation and seed dispersal-in forest bird communities with contrasting histories of cyclone exposure. Using total biomass of insectivores and frugivores as functional indicators, we found that the stability of insect predation increased with redundancy in function-delivering traits under cyclone disturbances, whereas the stability of seed dispersal was enhanced by redundancy in both function-delivering and response-related traits. In forests frequently exposed to cyclones, both functional groups occupied a smaller overall trait space, while frugivores showed greater trait similarity among individuals. These results suggest that the stability in insect predation and seed dispersal largely stems from the abundance of species performing similar functional roles. High cyclone frequency appears to exclude species with extreme traits and concentrate functional space around a few dominant species with convergent traits. Because such convergence buffers cyclone impacts, the loss of even a few redundant species could increase functional uniqueness and, consequently, reduce the functional stability of forest bird communities.

Mining rents, climate extremes, and water stress in China: An econometric assessment of emissions and water-related climate vulnerability.

Abstract Extreme climate events are increasingly impacting human health and vulnerability, particularly in urban areas. This study examines whether perceptions of climate change in Columbus, Ohio, align with observed environmental factors and social determinants of health. Using Natural Language Processing (NLP) on open-ended survey responses from 200 participants, we identified five dominant concern themes: flooding and water health, urban heat and air quality, heatwaves and environmental vulnerability, seasonal shifts and ecosystem loss, and climate impacts on lake health. The analysis of climate records (1985–2023) revealed an increase in the frequency of heatwaves, extreme precipitation, and dry days, coupled with a decline in cold events. Land cover analysis over the same period revealed significant expansions in developed (+30.7%) and barren land (+68.1%), alongside reductions in forest (−14.0%) and shrubland cover (−82.8%). Social vulnerability metrics (2009–2020) indicated growing proportions of elderly residents, immigrants, non-English speakers, households in multi-unit housing or with limited resources, and Hispanic populations. Cross-domain synthesis found perceptions generally aligned with observed trends, particularly for heat, flooding, and environmental vulnerability. Seasonal shifts were partially supported by more heatwaves, fewer cold events, and vegetation decline, although additional indicators such as frost dates would strengthen the evidence. Similarly, concerns over lake health require further ecological data for validation, and air quality would need support from particulate matter concentrations. These findings highlight the value of integrating perceptions with objective climate, land cover, and vulnerability data. Comparing perceptions with measurable trends clarifies which concerns reflect observed change and which may stem from information gaps, supporting targeted adaptation and communication strategies.

Droughts are recognized as one of the most devastating extreme climate events, leading to severe socioeconomic losses and ecological degradation globally under climate change. With global warming, the frequency and intensity of extreme droughts are increasing, posing critical challenges to water resource management. The Standardized Precipitation Conversion Index (SPCI) has demonstrated potential in drought monitoring; however, its applicability across diverse climatic zones and multiple temporal scales remains inadequately validated. This study addresses this gap by establishing a novel multi-scale inversion analysis using ERA5-based precipitable water vapor (PWV) and precipitation data. SPCI is selected for its advantage in eliminating climatic background biases through probability normalization, overcoming limitations of traditional indices such as the Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI). We systematically evaluated the spatiotemporal evolution of Precipitation Efficiency (PE) and SPCI across four climatic zones in China. Results show that the first two principal components explain over 85% of the spatiotemporal variability of PE, with PC1 independently contributing from 82.05% to 83.80%. This high variance contribution underscores that the spatiotemporal patterns of PE are dominated by a few key climatic drivers, validating the robustness of the principal component analysis. SPCI exhibits strong correlation with SPI, exceeding 0.95 in the Tropical Monsoon Zone (TMZ) at scales of 1–6 months, indicating its utility for short-to-medium-term drought monitoring. Distinct zonal differentiation in PE patterns is revealed, such as the bimodal annual cycle in the Tropical-Subtropical Monsoon Composite Zone (TSMCZ). This study evaluates the performance of the SPCI against the widely used SPI and SPEI across four major climatic zones in China. It validates the SPCI’s applicability across China’s complex climates, providing a scientific basis for region-specific drought early warning and water resource optimization.

Global supply chains face unprecedented disruptions, from pandemics and geopolitical shocks to extreme climate events. These shocks expose limitations of traditional, reactive risk management approaches such as buffer inventory and backup suppliers. This study adopts a conceptual review and framework-based research design to develop an AI-driven risk mitigation cycle across four phases: risk identification, risk assessment, risk response, and continuous learning. Drawing on illustrative industry cases (Amazon, UPS, and Walmart), the paper explains how predictive analytics, machine learning, and sensor-enabled monitoring can improve early warning, real-time decision support, and adaptive resilience. The study’s contribution is a structured framework that links AI-enabled risk mitigation to both firm-level resilience and broader U.S. competitiveness. Practical implications are provided for managers (governance, capability building, and implementation priorities) and for policy stakeholders concerned with supply chain security and performance in volatile global markets.

Tree-ring width records provide crucial insights into historical vegetation dynamics and climate change. This study integrates tree-ring width index (RWI), MODIS NDVI remote sensing data, and 11 monthly extreme climate indices from Larix sibirica Ledeb. chronologies in the Burqin and Two-River Source regions of the Altai Mountains, Xinjiang, to investigate tree-ring-NDVI relationships and reconstruct vegetation coverage since the 19th century. Using LASSO regression to identify dominant extreme climate drivers and CMIP6 future climate scenarios, we projected radial growth trends and potential tree decline risks. Results demonstrate significant positive correlations between RWI and growing-season NDVI ( p &amp;lt; 0.05), reflecting tree-ring sensitivity to vegetation productivity changes. Pettitt tests revealed significant pre-mutation declining trends in historical vegetation coverage at both sites. Pearson correlation analysis revealed distinct response patterns of tree-ring width to extreme climate events between the two sampling sites. At the Burqin site, extreme precipitation during the previous autumn (October RX1day) significantly constrained radial growth. Conversely, elevated daytime temperatures (TX90p), greater diurnal temperature ranges (DTR) prior to the growing season, and short-duration heavy precipitation events (RX5day) during transitional periods and critical growth months exerted positive effects on tree growth. The Two-River Source site exhibited contrasting responses: anomalously warm autumn conditions (TN90p) following the previous growing season led to subsequent growth suppression. Extreme temperature events during the current year demonstrated dual effects - while temperature extremes (TXx) and warm events (TX90p/TN90p) inhibited radial growth, cold extremes (TX10p/TN10p) and increased diurnal temperature ranges (DTR) exhibited moderate growth-enhancing effects. CMIP6-based projections indicate significant future growth declines across the Altai Mountains. This study advances understanding of extreme climate impacts on forest ecosystems through a novel multi-proxy approach combining dendrochronology and remote sensing. Our findings provide scientific foundations for conservation, restoration, and adaptive management of forest ecosystems under climate change.

Reconstruction of temperature, precipitation, and identification of extreme climate events in high mountain Asia over 500 years using multi-method EnKF.

Elevation shapes thermal breadth and climate sensitivity in Moltrecht's treefrog tadpoles.

In developing countries, climate variability poses significant challenges to urban water resource management and faecal sludge management, particularly in flood- and drought-prone areas. Inadequate consideration of climate variability in faecal sludge management planning has led to service disruptions in flood- and drought-prone urban areas. This study integrates multi-timescale climate trend analysis with faecal sludge management challenges, providing novel insights into climate-resilient sanitation strategies. The extreme climate events and their implications on faecal sludge management were assessed by applying the Standardized Precipitation Index (SPI) at multiple timescales (SPI-12, SPI-6, and SPI-3). The Mann–Kendall test and Sen’s slope estimator were applied to analyse long-term trends and the magnitude of change at annual, seasonal, and monthly levels. Climate Hazards Group InfraRed Precipitation with Station rainfall data (1981–2021) from four stations were used, with validation conducted through Pearson correlation against observed data from Dar es Salaam International Airport. Results indicate increasing precipitation variability, with statistically significant positive trends in some seasons, particularly during the long rains. Extreme wet periods (2019–2020, SPI: 2.48–2.92) have increased flood risks, while severe droughts (2002–2003, SPI: -1.57 to -2.08) have exacerbated water scarcity, thereby impacting the reliability of faecal sludge management services. The Sinza River Catchment, a lowland urban area where over 99% of residents rely on onsite sanitation, remains highly vulnerable to climate extremes, causing pit latrine failures due to flooding and limited desludging options during droughts. Addressing these issues requires climate-resilient faecal sludge management strategies, including flood-resistant latrines, water-efficient sanitation technologies, and the integration of policy into urban resilience planning.

Abstract Recent studies highlight a clear warming trend in Amazon Basin with projections indicating an increase of climatic extreme events like droughts and heatwaves (HWs). The repeated occurrence of these extreme events threatens to a transition from its established energy-limited hydrological regime to one increasingly constrained by water availability. Co-occurring drought and HWs conditions are associated with high evaporation rates and rapid soil moisture depletion. These drying trends can reach a point where the land cannot satisfy atmospheric water demand, triggering a feedback loop where soil desiccation amplifies surface temperatures through sharp decline of the evaporative fraction. The unprecedented 2023-2024 drought provides a striking case study of this relation between extreme dryness and heat over the region. This study aims to investigate the interaction between soil moisture and land surface temperature (LST) during the 2023–2024 drought–heatwave events over the Amazon Basin, assessing the role of soil moisture deficits in amplifying heat extremes. Specifically, we aim to analyze patterns of soil moisture and LST and compare the responses across four Amazon subareas (NE, NW, SE, SW). Our analysis combines an all-sky LST product and soil moisture in the root zone provided by the Satellite Applications Facility on Support to Operational Hydrology and Water Management (H-SAF). Findings suggest that prolonged droughts, combined with persistent HWs, may accelerate soil desiccation and disrupt evapotranspiration processes. Analyses for specific Amazon areas reveal distinct patterns. NE, SE and SW Amazon regions showed soil moisture deficits and/or high LST values during the September-November periods of 2023 and 2024, highlighting their susceptibility to extreme climatic events. In contrast, the NW region exhibited relative resilience. Our findings highlight the vulnerability of the Amazon Basin and the crucial role of regional resilience in mitigating the escalating impacts of extreme events.

With the increasing frequency of extreme climatic events in cities, the heavy rainfall-geological hazard-flood urban hazard chain has become more prominent, while traditional single-hazard assessments fail to reveal its systemic transmission mechanisms. This study constructs a hazard chain risk assessment framework based on directed weighted networks to support precise urban disaster identification and control. Using Event Tree Analysis (ETA), 23 key hazard nodes were identified to build a causal loop network, and a Bayesian network was developed to quantify node dependencies. The disaster-bearing body was divided into actual and functional subsystems, and its exposure-vulnerability and disaster resistance capacity were evaluated using an improved Analytic Hierarchy Process and the brittle entropy method. A multi-influence matrix integrating node degree, shortest path, and global influence was designed to calculate the risk weights of 38 nodes and establish the directed weighted network. Applied to Mentougou District, Beijing, the comprehensive hazard chain risk value was calculated as 34.23, and key high-risk nodes were identified. The results show that this model surpasses traditional unweighted or single-hazard methods by enabling dynamic and quantitative evaluation of complex urban hazard chains, offering new insights for enhancing urban resilience and disaster prevention.

Climate change and anthropogenic activities have significantly altered the carbon (C) balance of rivers. However, the current understanding of how riverine C biogeochemistry responds to global climate change remains limited. Here we evaluate the spatiotemporal dynamics of carbon dioxide (CO2) flux and phytoplankton primary productivity (PP) in the main stem and tributaries of the Three Gorges Reservoir Region between 2010 and 2023. We found that the response of the CO2 flux and PP was evident in the extremely rainy months when, on average, the CO2 flux increased by 42.5% and PP decreased by 10.7%. Spatially, we also observed significant CO2 flux and PP differences between the backwater and upstream areas of the tributaries. CO2 flux at the upstream sites was 60-266% higher than at the backwater sites, while PP at the backwater sites was 4-13 times the amount of PP at the upstream sites. Differences in flow velocity and chlorophyll concentrations between the backwater and upstream sites, which primarily originate from the water level regulation operations of the dam, are believed to dominate spatial CO2 flux and PP gradients, respectively. This study reveals the response patterns and feedback mechanisms between riverine C biogeochemistry and global climate change factors in a large dammed river and associated tributaries.

Informing atmospheric pollution hotspots and exposure risks under climate change using machine learning: Evidence from 2843 Chinese regions.

Abstract Extreme climate events are increasingly frequent in Inner Mongolia (IM), threatening vegetation productivity. The impacts of upcoming extreme climate stressors on future vegetation dynamics are not well understood, even though earlier research has looked at how vegetation responded to a small number of historical extreme climate events. Here, we simulated historical Net Primary Productivity (NPP) (1982-2020) using the Carnegie-Ames-Stanford model, identified key extreme climate factors through geographical detector method (GDM), and projected future NPP variations (2023-2100) under different climate scenarios using a partial least squares regression model. Result showed that 72.3% of IM experienced increasing NPP during 1982-2020, with 28.2% showing significant increase, mainly in northeastern and eastern regions. Consecutive wet days, heavy precipitation days, cold nights, and maximum temperature were identified as key contributors to NPP changes using GDM, with explanatory powers of 0.903, 0.781, 0.704 and 0.63, respectively. In southern IM, there were considerable increases in NPP as predicted by the SSP2-4.5 and SSP5-8.5 scenarios, while eastern and western regions showed significant decreases exposure to combined extreme climate events. Our study provides the comprehensive assessment of future NPP reactions to combined extreme climate events in IM. The findings highlight vulnerable regions requiring targeted vegetation management strategies and demonstrate the importance of considering multiple extreme climate factors in vegetation prediction models.