Abstract Climate change poses significant threats to global health, and older adults are particularly vulnerable due to physiological, socioeconomic and environmental factors. Understanding the breadth of health impacts on this demographic is crucial for developing targeted interventions and policies. This scoping review aims to systematically map the existing literature on the health impacts of climate change on older adults, identify knowledge gaps and provide evidence-based policy recommendations. Following the PRISMA Extension for Scoping Reviews framework, we conducted a comprehensive search of peer-reviewed literature across multiple databases. Studies examining the relationship between climate change and health outcomes in older adults aged 60 years and above were included. Data were extracted and synthesised thematically across physical health, mental health and social well-being domains. The review identified multifaceted health impacts of climate change on older adults, including increased morbidity and mortality from extreme heat events, cardiovascular and respiratory complications from air pollution, mental health challenges associated with climate-related disasters and social isolation exacerbated by environmental changes. Significant gaps exist in research addressing the intersectionality of climate impacts with socioeconomic status, geographic location and pre-existing health conditions among older populations. Older adults face disproportionate health risks from climate change, necessitating age-inclusive climate adaptation strategies, enhanced healthcare infrastructure and policies that address both immediate and long-term vulnerabilities. Future research should focus on the effectiveness of interventions and on the development of resilience-building programmes tailored to ageing populations.

The escalating challenges of climate change, extreme weather events, and increasing food demand impose a significant strain on global food production. To develop and apply sustainable agriculture practices, farmers and organizations require detailed, timely information about weather, crops, and yields. While efficient agricultural monitoring relies heavily on remote sensing, the existing literature suffers from a notable lack of comprehensive, large-scale crop monitoring datasets. This paper introduces CropClimateX, a novel database built by optimizing location sampling to substantially cover cultivated areas throughout the contiguous United States. The database comprises 15,500 small 12×12 km data cubes spanning 1,527 counties. Crucially, each data cube integrates a rich array of multi-source information, including multi-sensor imagery (Sentinel-1/2, Landsat-8, MODIS), weather and extreme events (Daymet, heat/cold waves, and drought monitor maps), and environmental features (soil and terrain characteristics). This comprehensive, integrated dataset is designed to support a wide range of agricultural monitoring tasks, providing a vital resource for advancing research in sustainable farming and crop modeling.

Abstract Key Message Stands stocked with European beech ( Fagus sylvatica L.), sessile oak ( Quercus petraea (Matt.) Liebl.), and Scots pine ( Pinus sylvestris L.) show distinct deep seepage patterns. An increasing importance of extreme summer precipitation contributing to deep seepage in the northeastern German lowlands was detected. Extreme summer precipitation events contributed 71% (pine), 22% (young oak), and 15% (beech) of the annual deep seepage. Adapted forest management may promote deep seepage caused by extreme summer precipitation and by precipitation during the winter half-year. Context To date, deep seepage and groundwater recharge in temperate lowland forests occured mainly during the winter half year, the only period in which precipitation exceeds potential evapotranspiration. The increasing occurrence of extreme summer precipitation events, however, has the potential to promote deep seepage during summer. Aims This study aims to quantify the deep seepage feed by extreme summer precipitation events, utilising three large-scale lysimeters below canopies of beech ( Fagus sylvatica L.), young oak ( Quercus petraea (Matt.) Liebl.), and pine ( Pinus sylvestris L.), respectively. Methods Using a seepage hydrograph separation method, we were able to identify two major types of deep seepage: slow deep seepage due to winter precipitation and rapid deep seepage due to extreme summer precipitation events. Results Our measurements attributed substantial portions of deep seepage to extreme summer precipitation events, with distinct differences among lysimeters related to tree species and stand structure. The highest ratio of deep seepage by extreme summer precipitation to annual deep seepage occurred below pine, whereas the highest quantities of deep seepage by extreme summer precipitation were found under young oak. Conclusion Rapid deep seepage due to an increase in extreme summer precipitation events could be the most important mechanism for recharging near-surface groundwater aquifers under pine forests in the northeastern Germany lowlands. Deep seepage may be influenced by the choice of tree species and stand structure.

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 < 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.

Abstract Sediment yield prediction is vital for sustainable watershed management, particularly in data-scarce regions. This study, conducted in the Göksun Çayı Karaahmet sub-basin, Türkiye, evaluated whether sediment connectivity indices can reproduce outputs from the Revised Universal Soil Loss Equation (RUSLE) and Modified Universal Soil Loss Equation (MUSLE). Sediment yield was modeled for 196 sub-catchments and 69 rainfall events over 10 years using GIS-based factors: rainfall erosivity, soil erodibility, slope length-steepness, land cover, and hydrological parameters. Despite different assumptions (rainfall erosivity versus runoff and peak discharge), RUSLE and MUSLE showed strong agreement (R² = 0.87 at the event scale; R² = 0.93 at the sub-catchment scale). Predicted sediment yields ranged from 0.02 to 16.46 t ha -1 (MUSLE) and 0.04–10.63 t ha -1 (RUSLE/SDR), with mean values of 0.89 and 0.96 t ha -1 , respectively. Sediment connectivity indices-including the Index of Connectivity (IC), Sediment Delivery Ratio (SDR), and Topographic Wetness Index (TWI), were applied as inputs to five machine learning (ML) models (XGBoost, Random Forest, k-NN, SVR, and ANN). XGBoost and Random Forest achieved the best performance (R² = 0.912–0.942, RMSE = 0.065–0.089, MAE = 0.047–0.055), reproducing empirical outputs. IC, SDR, and TWI were dominant predictors. These results demonstrate that connectivity metrics integrated with ML can emulate empirical erosion models, offering a scalable, data-efficient alternative for ungauged basins. However, because the models were trained on RUSLE/MUSLE outputs from 69 events under static land use and climate, they may underpredict extreme sediment events and require field validation before operational use.

Complex network uncovers key propagation patterns of extreme freezing rain events in China

Flood disasters are increasing worldwide due to climate change, posing growing risks to infrastructure and human life. Korea, where nearly 70% of annual rainfall occurs during the summer monsoon, is particularly vulnerable to extreme precipitation events intensified by El Niño and La Niña. This study investigates how terrain resolution influences flood simulation accuracy by comparing a 1 m LiDAR digital elevation model (DEM) with a DEM generated from a 1:5000 topographic map. Flood depth and velocity fields produced by the two DEMs show notable quantitative differences: for final flood depth, the 1:5000 DEM yields a mean absolute error of approximately 56.9 cm and an RMSE of 76.4 cm relative to LiDAR results, with substantial local over- and underestimations. Flow velocity and maximum velocity also show significant deviations, with RMSE values of 58.0 cm/s and 68.4 cm/s, respectively. Although the 1:5000 DEM captures the general inundation pattern, these discrepancies—particularly in narrow channels and urbanized floodplains—demonstrate that coarse-resolution terrain data cannot reliably reproduce hydrodynamic behavior. We conclude that while 1:5000 DEMs may be acceptable for reconnaissance-level hazard screening, high-resolution LiDAR DEMs are essential for accurate flood depth and velocity simulation, supporting their integration into engineering design, urban flood risk assessment, and disaster management frameworks.

Abstract Small Island Developing States (SIDS) are among the most vulnerable regions to sea-level rise (SLR) and coastal climate hazards, which pose increasing threats to populations, economies, and critical infrastructure. Seychelles face mounting risks from coastal flooding and erosion, yet detailed assessments of future climate impacts to inform national policy remain limited. This study evaluates future impacts of SLR-induced coastal flooding in Seychelles using high-resolution hydrodynamic modelling combined with socio-economic projections based on the Shared Socioeconomic Pathways (SSP). We simulate extreme water level events under three SLR scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) from 2030 to 2150 and assess exposure of population, buildings, and road networks on the islands of Mahé, Praslin, and La Digue. Our results indicate a sharp rise in land, population, and infrastructure exposure to an extreme water level event across all scenarios: Mahé is projected to face population exposure of up to 16,200 people by 2100 under SSP3-7.0, and over 150 km of roads will be at risk of flooding from by 2150, while Praslin and La Digue show delayed but accelerating exposure later in the century. Crucially, scenarios with high population growth show intensification of potential risks, sometimes surpassing the higher sea-level scenarios with lower demographic pressure. These findings can inform climate resilient planning and coastal adaptation policies in Seychelles and underscore an urgent need for integrated adaptation strategies. Without proactive implementation of measures, SLR could have large impacts on Seychelles’ economy. Our subnational projections demonstrate the type of evidence that is required for supporting adaptive governance in order to safeguard SIDS against escalating climate risks.

As the frequency and severity of extreme weather events may increase due to climate change, understanding their impacts on water systems, resources, and infrastructure becomes very important. This study contributes to the growing body of knowledge on how extreme storms and floods disrupt interrelated elements comprising water systems by examining the case of Storm Daniel, which struck the Thessaly region of Greece in September 2023. Using a multi-source approach, including field data, institutional reports, scientific assessments, and publications, the study systematically identifies and categorizes the impacts of the storm and the ensuing flood across surface waters, drinking water supply, and wastewater infrastructure and other water-related systems through various mechanisms. The findings provide an overview of how such extreme storms may affect such systems and reveal widespread, interconnected disruptions that highlight systemic vulnerabilities in both natural and engineered systems, synthesizing these impact pathways. The study presents evidence of poor resilience against extreme events and climate change hazards in water-related infrastructure.

This paper proposes a Federated Multi-Agent Deep Reinforcement Learning (FMADRL) framework to enhance the resilience of highway service area microgrids against extreme weather events. The method integrates Generative Adversarial Networks with Monte Carlo simulations to generate high-fidelity weather scenarios, enabling privacy-preserving collaborative optimization across distributed microgrids. A multi-objective approach using the Ripple-Spreading Algorithm yields balanced solutions for economic efficiency, reliability, and response speed. Large-scale simulations demonstrate significant improvements: the proposed method achieves an 88.3 score on the comprehensive system resilience metric, reduces the average fault recovery time from 46.6 min to 8.4 min, lowers annual operating costs by 69.3%, equivalent to 536,945.1 USD, and achieves annual carbon emissions reductions of 285 Mg. This approach provides an innovative solution for enhancing the resilience of distributed microgrids during extreme weather events.

Rainstorm floods constitute one of the major natural hazards threatening the safe and stable operation of power grid facilities. Constructing a rapid and accurate prediction model is of great significance in order to enhance the disaster prevention capacity of the power grid. This study proposes a rapid prediction model for urban rainstorm flood targeting power grid facilities based on deep learning. The model utilizes computational results of high-precision mechanism models as data-driven input and adopts a dual-branch prediction architecture of space and time: the spatial prediction module employs a multi-layer perceptron (MLP), and the temporal prediction module integrates convolutional neural network (CNN), long short-term memory network (LSTM), and attention mechanism (ATT). The constructed water dynamics model of the right bank of Liangshui River in Fengtai District of Beijing has been verified to be reliable in the simulation of the July 2023 (“23·7”) extreme rainstorm event in Beijing (the July 2023 event), which provides high-quality training and validation data for the deep learning-based surrogate model (SM model). Compared with traditional high-precision mechanism models, the SM model shows distinctive advantages: the R2 value of the overall inundation water depth prediction of the spatial prediction module reaches 0.9939, and the average absolute error of water depth is 0.013 m; the R2 values of temporal water depth processes prediction at all substations made by the temporal prediction module are all higher than 0.92. Only by inputting rainfall data can the water depth at power grid facilities be output within seconds, providing an effective tool for rapid assessment of flood risks to power grid facilities. In a word, the main contribution of this study lies in the proposal of the SM model driven by the high-precision mechanism model. This model, through a dual-branch module in both space and time, has achieved second-level high-precision prediction from rainfall input to water depth output in scenarios where the power grid is at risk of flooding for the first time, providing an expandable method for real-time simulation of complex physical processes.

As extreme temperature events increase globally, the influences of non-optimal temperature on health outcomes are of increasing concern. However, whether there is a correlation between non-optimal apparent temperature (AT) and acne is unknown. To illustrate the association between non-optimal AT and acne, data of daily acne outpatient visits, meteorological factors, and air pollutants from 2015 to 2019 in Chongqing, China were obtained. A multi-center study using distributed lag non-linear model (DLNM) was employed to explore the possible association between short-term non-optimal AT and outpatient visits for acne. Stratified analyses by age and gender were carried out to verify vulnerable populations. Results showed that both cold and hot AT were significantly associated with acne. The significant single-lag effects of cold AT lasted from lag0 to lag3, and cumulative-lag effects lasted from lag00 to lag07, with the highest relative risk of 1.09 (95%CI: 1.03-1.17) on lag0, and 1.34 (95%CI: 1.17-1.53) on lag07, respectively. In term of hot AT, the significant single-lag effects were observed from lag0 to lag3 with cumulative-lag effects from lag00 to lag07, with peak relative risk of 1.08 (95%CI: 1.02-1.14) on lag0 and 1.17 (95%CI: 1.08-1.27) on lag03, respectively. Stratified analyses showed that young patients (< 25 years old) and males were more susceptible to non-optimal AT. We provide the first evidence that non-optimal AT can increase the risk of acne, particularly for young people and males. Our findings add new sights regarding the potential adverse effects of non-optimal temperature on skin diseases especially acne.

ABSTRACT Background Against the backdrop of accelerating climate change and more frequent extreme weather events, typhoon disasters have become a major challenge to mental health. Based on the Social Determinants of Health theory and integrating the Cumulative Disadvantage Model with Structural Causal Influence analysis, this study evaluates how typhoon exposure affects the burden of mental health disorders and how these effects vary with social structural differences. Objective To investigate the mechanisms linking typhoon exposure to the burden of mental health disorders, and to quantify the moderating roles of macro-level social structural variables. Methods By constructing both main effect and year-on-year difference models, combined with structural equation modelling and multinational panel data, this research quantifies the moderating roles of macro-level social variables, including gross national income, Human Development Index, Gini coefficient, government health expenditure, out-of-pocket health spending, educational attainment, and life expectancy. Results Typhoons were found to increase prevalence, incidence, and disability-adjusted life years (DALYs) related to mental disorders, with the strongest impact in the 25–34 age group. High income, education, HDI, and public health investment were linked to greater resilience, while low income, high OOP, and high inequality indicated vulnerability. Secondary disaster frequency and the number of people affected acted as mediators, forming a pathway from ‘typhoon’ to ‘social stress’ to ‘mental disorders.’ Conclusions Typhoon impacts on mental health are shaped by both direct exposure and structural inequalities. Improving socioeconomic conditions, lowering OOP costs, reducing inequality, and increasing public health investment can strengthen psychological resilience and disaster response capacity.

Introduction: Global climate change has caused an increase in the frequency and intensity of extreme rain events, including in Jambi Province which is vulnerable due to geographical conditions, land use intensity and the dominance of the plantation sector. Extreme rain events have the potential to cause flooding, damage infrastructure and disrupt food security, resulting in future climate projections to support mitigation and adaptation efforts. . Objective: The aim of the research is to project changes in extreme climate indices and analyze their spatial distribution patterns and impacts. Methods: The data used includes observed rainfall from 41 BMKG rain posts, CHIRPS reanalysis data, and CMIP6 model data for historical and projection periods. Results and Discussion: The research results show that most extreme rainfall indices have increased until the end of the 21st century. Intensity indices such as PRCPTot, RX1day, RX5day, R95p, and R99p show a significant upward trend, indicating an increase in very heavy rain events. Conclusion: Spatially, the central region is the area most vulnerable to increased rainfall extremes.

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

The advance and delay of the rainy season is among the most frequently cited effects of climate change in the central Ecuadorian Andes. However, its assessment is not feasible using the indicators recommended by the standardized indices of the Expert Team on Climate Change Detection and Indices (ETCCDI), designed to detect changes in intensity, frequency, or duration of intense events. This study aims to analyze such advances and delays through harmonic analysis in Tungurahua, a predominantly agricultural province in the Tropical Central Andes, where in situ data are scarce. Daily in situ data from five meteorological stations were used, including precipitation, maximum, and minimum temperature records spanning 39 to 68 years. The study involved an analysis of the region’s climatology, climate change indices, and harmonic analysis using Cross-Wavelet Transform (XWT) and Wavelet Coherence Transform (WCT) to identify seasonal patterns and their variability (advance or delay) by comparing historical and recent time series, and Krigging for regionalization. The year 2000 was used as a study point for comparing past and present trends. Results show a generalized increase in both minimum and maximum temperatures. In the case of extreme rainfall events, no significant changes were detected. Harmonic analysis was found to be fruitful despite of the missing data. Furthermore, the observed advances and delays in seasonality were not statistically significant and appeared to be more closely related to the geographic location of the stations than to temporal shifts.

The rapid expansion of urban areas has intensified the Urban Heat Island (UHI) effect, particularly in high-density cities characterized by extensive impervious surfaces and limited green spaces. Rising surface temperatures increase energy consumption, reduce outdoor thermal comfort, and pose serious public health risks during extreme heat events. Urban greening is widely recognized as an effective heat mitigation strategy; however, in densely built environments, indiscriminate or uniform distribution of green spaces often fails to achieve optimal cooling benefits. This study proposes an artificial intelligence–based framework for strategically optimizing urban green space placement to maximize heat reduction while accounting for land-use constraints. The proposed approach integrates multisource remote sensing data, vegetation indices, land surface temperature measurements, and urban morphological indicators with machine learning–based thermal modeling. A Random Forest Regression model is employed to capture the nonlinear relationships between vegetation cover, built-up density, and surface temperature, followed by a spatial optimization process to identify priority locations for greening interventions. Experimental results demonstrate a strong negative relationship between vegetation density and land surface temperature, with optimized greening scenarios achieving temperature reductions of up to 2.6°C, significantly outperforming uniform greening strategies with equivalent green area allocation. The findings highlight that the spatial configuration and targeted placement of green spaces are more influential than total green cover alone. By incorporating explainable AI techniques, the framework also provides interpretable insights into the dominant drivers of urban heat, enhancing transparency for planning applications. Overall, this study offers a data-driven and decision-oriented methodology that can support urban planners and policymakers in designing effective, climate-resilient strategies for mitigating heat stress in high-density urban environments.

Abstract The Mediterranean Sea is a weak sink for the atmospheric CO 2 with the October‐March extended winter season characterized by the occurrence of high CO 2 sink events. Here, we analyzed state‐of‐the‐art ocean and atmospheric reanalyses and observational data sets to investigate the variability of the winter sink and its relation with synoptic atmospheric features crossing the region in the period 1999–2020. High CO 2 sink events are identified using classical extreme event approach with fixed threshold (95p) based on the CO 2 daily flux distribution. First, we showed that these events are driven by large‐scale atmospheric configurations that produce stronger‐than‐average wind speed and colder‐than‐average 2 m and sea surface temperature patterns in the region. Second, a co‐location analysis was applied to assess the probability to detect an extra‐tropical cyclone at a fixed distance from the location of the events showing that the larger the event's magnitude, the higher the probability. In most of the cases, these cyclones originate within the Mediterranean region and are usually deeper, bigger in terms of size and characterized by a stronger circulation with respect to the systems that usually cross the region. By establishing a statistical relationship between high CO 2 sink events and synoptic atmospheric activity, we emphasize the potential influence of the cyclone activity on the carbon budget of the Mediterranean Sea.

Climate change and the resulting projected rise in sea level put densely populated urban communities at risk of river flooding, storm surges, and subsurface flooding. Miami finds itself in an increasingly vulnerable position, as compound inundation seems to be a constant and unavoidable occurrence due to its low elevation and limestone geomorphology. Several recent studies on compound overflows have been conducted in Miami-Dade County. However, in-depth research has yet to be conducted on its economic epicenter. Owing to the lack of resilience to tidal surges and extreme precipitation events, Miami’s infrastructure and the well-being of its population may be at risk of flooding. This study applied HEC-RAS 2D to develop one- and two-dimensional water flow models to understand and estimate Miami’s vulnerability to extreme flood events, such as 50- and 100-year return storms. It used Hurricane Irma as a validation and calibration event for extreme event reproduction. The study also explores novel machine learning metamodels to produce a robust sensitivity analysis for the hydrologic model. This research is expected to provide insights into vulnerability thresholds and inform flood mitigation strategies, particularly in today’s unprecedented and intensified weather events. The study revealed that Miami’s inner bay coastline, particularly the downtown coastline, is severely impacted by extreme hydrometeorological events. Under extreme event circumstances, the 35.4 km2 area of Miami is at risk of flooding, with 38% of the areas classified as having medium to extreme risk by FEMA, indicating severe infrastructural and community vulnerability.

Krill is a central organism in the food web of many marine ecosystems and eastern boundary current upwelling regions specifically. Here, a superensemble of climate and ecological models is used to determine drivers of future change, variability, and uncertainty in krill abundance for the California Current. While krill is projected to slowly decrease throughout the 21st century, the long-term trend consistently exceeds natural variability only under extreme warming. Similarly, unprecedented low krill years are expected to progressively increase, but their frequency of occurrence will depend on background abundances tied to low-frequency climate variability. The relative contributions of warming rate and ecological model formulation to projected uncertainty are comparable and reflect latitudinal changes in the magnitude of climate forcing and availability of empirical data to parameterize krill models. This finding highlights the fact that uncertainty in climate change impacts on coastal upwelling ecosystems may depend as strongly on model formulation as they do on anthropogenic forcing. Furthermore, the increasingly divergent krill model responses outside of the core domain for which they were originally implemented advocate for regionally tailored projections and models to reduce overall uncertainty. By identifying and quantifying uncertainty sources in future krill abundance across relevant time scales, the present study lays the foundation for understanding how the superposition of long-term trends, low-frequency variability, and extreme events may lead to unprecedented ecosystem states, and for assessing their broader impacts on altered presence, distribution, and recovery of species that directly or indirectly depend on krill.