Search Results (121)

Climate change is increasing the frequency and intensity of Marine Heatwave (MHW) events, threatening Western Tropical Pacific Small Island Developing States (SIDSs). MHWs critically threaten the fisheries sector which vitally supports food and nutrition security in local communities and local livelihoods. Currently, MHW risk to fisheries in Western Tropical Pacific SIDSs remains underexplored. Vanuatu is a Western Tropical Pacific SIDS which requires expanded MHW risk knowledge to improve the adaptive capacity of fisheries. A fundamental method for expanding MHW risk knowledge is tailored risk assessment. This study conducts the initial steps in a tailored MHW risk assessment methodology, displaying how a tailored indicator selection and weighting process can inform effective MHW risk assessment for fisheries in Western Tropical Pacific SIDSs. Hazard, vulnerability, and exposure indicators were selected through a combined process utilising a literature review and participatory research survey. Survey results were also used to develop a user-informed indicator weighting scheme. Selected indicators included sea surface temperature (SST), coral bleaching/mortality, and chlorophyll-a concentration (hazard); terrestrial-based food and income generation, fishing skills and technology, fishery fish diversity/fishery flexibility, and primary production of commercial fisheries (vulnerability); seagrass population/C content, coral habitat health/crown-of-thorns prevalence, crab stock health, and fish mortality/fish stock health (exposure). These indicators and their assigned weights are recommended for use in a future MHW risk assessment for Vanuatu fisheries. A tailored, fisheries-specific MHW risk assessment could advise local decision-makers on where/when MHW risk is high and aid the implementation of more effective fisheries risk management. Full article

Bangladesh is among the world’s most climate-vulnerable countries, facing recurrent hazards that disrupt lives and livelihoods. Among these, heatwaves and cold snaps strongly affect electricity consumption, representing a key socio-economic impact of climate extremes. In this study, we used meteorological and electricity data from six sub-regions of Bangladesh to examine long-term changes in extreme temperature days and their effects on electricity usage. Results showed that western inland stations (Chuadanga, Jashore) experienced hotter summers and colder winters, whereas coastal sites (Barishal, Patuakhali) were moderated by maritime influences. Trend analysis revealed significant increases in hot-day frequency since 1961 (up to 1.8 days yr⁻¹ at coastal areas, while cold-day frequencies generally declined but with regional variability. Electricity demand followed a clear pattern, being highest on hot days, lowest on cold days, and intermediate on normal days. Among the regions, Khulna consistently recorded the greatest demand (up to 161 MWh), while Patuakhali remained the lowest (~19–32 MWh). Regression analysis further showed that demand rises with maximum temperature, with slopes up to 5.7 MWh °C⁻¹ and moderate correlations (r = 0.27–0.47). Importantly, the temperature–demand relationship has strengthened in recent years, as similar climatic conditions now correspond to higher electricity use, reflecting both climatic pressures and socio-economic growth. These findings highlight the challenge of temperature extremes for electricity demand and the need to integrate climate–energy linkages into adaptation planning. Full article

As climate variability intensifies, its impacts are increasingly visible through disrupted agricultural systems and rising food insecurity, especially in climate-sensitive regions. This study explores the complex relationships between environmental stressors, such as rising temperatures, erratic rainfall, and soil degradation, with food insecurity outcomes in selected districts of Uttarakhand, India. Using the Fuzzy DEMATEL method, this study analyzes 19 stressors affecting the food supply chain and identifies the nine most influential factors. An Environmental Stressor Index (ESI) is constructed, integrating climatic, hydrological, and land-use dimensions. The ESI is applied to three districts—Rudraprayag, Udham Singh Nagar, and Almora—to assess their vulnerability. The results suggest that Rudraprayag faces high exposure to climate extremes (heatwaves, floods, and droughts) but benefits from a relatively stronger infrastructure. Udham Singh Nagar exhibits the highest overall vulnerability, driven by water stress, air pollution, and salinity, whereas Almora remains relatively less exposed, apart from moderate drought and connectivity stress. Simulations based on RCP 4.5 and RCP 8.5 scenarios indicate increasing stress across all regions, with Udham Singh Nagar consistently identified as the most vulnerable. Rudraprayag experiences increased stress under the RCP 8.5 scenario, while Almora is the least vulnerable, though still at risk from drought and pest outbreaks. By incorporating crop yield models into the ESI framework, this study advances a systems-level tool for assessing agricultural vulnerability to climate change. This research holds global relevance, as food supply chains in climate-sensitive regions such as Africa, Southeast Asia, and Latin America face similar compound stressors. Its novelty lies in integrating a Fuzzy DEMATEL-based Environmental Stressor Index with crop yield modeling. The findings highlight the urgent need for climate-informed food system planning and policies that integrate environmental and social vulnerabilities. Full article

This study pioneers a fully remote sensing-based framework for mapping heatwave susceptibility, integrating the TabTransformer deep learning model with Particle Swarm Optimization (PSO) for robust hyperparameter tuning. The central question addressed is whether a fully remote sensing-driven, PSO-optimized TabTransformer can achieve accurate, scalable, and spatially detailed heatwave susceptibility mapping in data-scarce regions such as Central Asia. Utilizing ERA5-derived heatwave evidence and thirteen environmental and socio-economic predictors, the workflow produces high-resolution susceptibility maps spanning five Central Asian countries. Comparative analysis evidences that the PSO-optimized TabTransformer model outperforms the baseline across multiple metrics. On the test set, the optimized model achieved an RMSE of 0.123, MAE of 0.034, and R² of 0.938, outperforming the standalone TabTransformer (RMSE = 0.132, MAE = 0.038, R² = 0.93). Discriminative capacity also improved, with AUROC increasing from 0.933 to 0.940. The PSO-tuned model delivered faster convergence, lower final loss, and more stable accuracy during training and validation. Spatial outputs reveal heightened susceptibility in southern and southwestern sectors—Turkmenistan, Uzbekistan, southern Kazakhstan, and adjacent lowlands—with statistically significant improvements in spatial precision and class delineation confirmed by Chi-squared, Friedman, and Wilcoxon tests, all with congruent p-values of <0.0001. Feature importance analysis consistently identifies maximum temperature, frequency of hot days, and rainfall as dominant predictors. These advancements validate the potential of data-driven, deep learning approaches for reliable, scalable environmental hazard assessment, crucial for climate adaptation planning in vulnerable regions. Full article

Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to study population exposure to heat stress. This research focuses on Naples, Italy’s most densely populated city, where intense human activity and unique geomorphological conditions influence local temperatures. The presence of a Surface Urban Heat Island (SUHI) is assessed by deriving high-resolution Land Surface Temperature (LST) in a time series ranging from 2013 to 2023, processed with the Statistical Mono Window (SMW) algorithm in the Google Earth Engine (GEE) environment. SMW needs brightness temperature (Tb) extracted from a Landsat 8 (L8) Thermal InfraRed Sensor (TIRS), emissivity from Advanced Spaceborne and Thermal Emission Radiometer Global Emissivity Database (ASTERGED), and atmospheric correction coefficients from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR). A total of 64 nighttime images were processed and analyzed to assess long-term trends and identify the main heat islands in Naples. The hottest image was compared with population data, including demographic categories such as children, elderly people, and pregnant women. A risk index was calculated by combining temperature values, exposure levels, and the vulnerability of each group. Results identified three major heat islands, showing that risk is strongly linked to both population density and heat island distribution. Incorporating Local Climate Zone (LCZ) classification further highlighted the urban areas most prone to extreme heat based on morphology. Full article

The rapid urbanization in developing countries has resulted in altered land-use patterns, surface energy imbalances, and heightened urban heat stress, exacerbating the urban heat island effect and vulnerability to heatwaves. The abandonment of agricultural lands, while a global challenge, presents cities with a unique opportunity to meet tree cover targets and improve resilience to these climatic challenges. Building on prior studies, this research employs the combined use of ENVI-met 4.4.6 and Ray-Man 3.1 simulation models to assess the efficacy of nature-based solutions in revegetating abandoned urban agricultural lands with the aim of enhancing outdoor thermal comfort. As a vital component of urban ecosystem services, thermal comfort, particularly through microclimate cooling, is essential for improving public health and livability in cities. This investigation focuses on the integration of broadleaf, evergreen, and edible woody species as bioclimatic interventions to mitigate urban heat stress. Simulation results showed that species such as Quercus spp. (broadleaf) and Cupressus arizonica (evergreen) substantially reduced the Mean Radiant Temperature (Tmrt) index by up to 26.76 °C, primarily due to their shading effects and large canopies. Combining these vegetation types with crops emerged as the most effective strategy to mitigate heat stress and optimize land-use. This study demonstrates how cities can incorporate nature-based solutions to adapt and mitigate the health risks posed by climate change while fostering resilience. These findings offer valuable knowledge for other developing countries facing similar challenges, highlighting the importance of revegetating abandoned urban agricultural lands for thermal comfort and ecosystem service provision, with the advantages of reducing mortality and morbidity during heatwaves. Consequently, these results should inform urban climate policies aimed at promoting resilience, public health, and ecological sustainability in a changing climate. Full article

Heatwaves (HWs) are becoming more frequent and severe, posing a significant threat to human health. Studies have shown that extreme heat, whether as incremental temperature increases or prolonged HWs, is associated with an increased risk of heat-related illnesses. However, there is still limited understanding of how these impacts unfold in Brazil, given its unique social, environmental, and health-system contexts. I this perspective article, we explore the effects of HWs on human physiology, examine the social and biological factors that contribute to heat stress, and identify vulnerable populations at disproportionate risk. We also discuss the potential consequences of extreme heat in other aspects of society, such as agriculture and energy, and assess the challenges of strengthening resilience in Brazil’s health sector. Our key contribution are to make visible the hidden burden of heat-related mortality, to examine how fragmented governance constrains the adaptive capacity of Brazil’s health sector, and to reflect on pathways to strengthen resilience to heatwaves. Full article

Climate change has intensified the frequency and severity of heatwaves globally, posing significant public health risks, particularly in Mediterranean countries such as Greece, where rising temperatures coincide with vulnerable aging populations. This study develops a machine learning framework employing XGBoost models to predict monthly heatwave-attributable mortality from cardiovascular and respiratory diseases across Greek regions, stratified by age groups. Using high-resolution climate projections under RCP4.5 and RCP8.5 scenarios, the models integrate meteorological and demographic data to capture complex nonlinear relationships and regional heterogeneity. Model performance was rigorously validated with a temporally held-out dataset, demonstrating high predictive accuracy (R² > 0.96). Projections indicate a sharp increase in elderly mortality due to heat exposure by mid-century, with marked geographic disparities emphasizing urban centers like Attica. This work advances prior studies by incorporating detailed spatial and demographic stratification and applying robust machine learning techniques beyond traditional statistical approaches. The model offers a valuable tool for public health planning and climate adaptation in Greece and similar Mediterranean contexts. Our findings highlight the urgent need for targeted mitigation strategies to address the growing burden of heatwave-related mortality under changing climate conditions. Full article

This study evaluates the passive thermal resilience of two full-scale residential buildings during natural summer heatwaves and blackout-like conditions in a temperate European climate. The buildings share identical geometry and ventilation but differ in envelope mass and ground coupling. Building B1 is a masonry structure with a slab-on-ground floor, while B2 is a lightweight timber-frame house. In 2019, B1 underwent a retrofit in which floor insulation was removed to enable direct subsoil heat exchange. Three complementary frameworks were applied: model IOD, AWD, OEF, the indicators AF and αIOD, and the health-based scenario rating HE, HIHH, and WBGT. Across all metrics, B1 demonstrated superior resilience, with overheating fully eliminated after ground coupling was introduced. B2, in contrast, remained vulnerable under both moderate and extreme events. The findings highlight the critical role of thermal mass and soil buffering in maintaining safe indoor conditions without active systems. Under certain circumstances, omitting under-slab insulation can improve summer resilience without significantly compromising winter performance. A companion life-cycle analysis confirms lower cumulative carbon emissions for B1 under all SSP scenarios to 2100. Passive ground coupling thus emerges as a low-cost, maintenance-free adaptation strategy with co-benefits for mitigation and occupant safety. Full article

Extreme climate events—such as heatwaves, floods, and droughts—are increasingly affecting ecosystems, with the global average temperature projected to rise by up to 3 °C (IPCC, 2023) due to anthropogenic greenhouse gas emissions. These changes pose critical challenges to food security, as evidenced by 733 million people facing hunger in 2024. In response, crop modeling considering different climate change scenarios has become a valuable tool to guide the development of climate-resilient agricultural strategies. Despite its nutritional importance and capacity to thrive across diverse environments, Ipomoea batatas (sweet potato) remains understudied in terms of potential spatial distribution forecasting, particularly in regions of high agrobiodiversity such as northwestern South America. Therefore, in this study we modeled the projected distribution of wild and landrace sweet potato genepools in the northern Andes under four future timeframes using seven machine learning algorithms. Our results predicted a 50% reduction in the climatically suitable range for the wild genepool by 2081, coupled with an average altitudinal shift from 1537 to 2216 m above sea level (a.s.l.). For landraces, a 36% reduction was projected by 2080, with a shift from 62 to 1995 m a.s.l. By the end of the century, suitable zones for both wild and cultivated genepools are expected to converge in high-altitude regions such as the Colombian Massif, with additional remnants of wild populations near the mountain range of Farallones de Cali. This modeling approach provides essential insights into the spatial dynamics of I. batatas under climate change, highlighting the need for ex situ conservation planning in vulnerable regions as well as assisted migration to more suitable areas. Future research should integrate edaphic and biotic interaction data to better approach the realized niche of the species and understand potential responses under a niche conservatism assumption, as well as genomic data to account for the species’ intrinsic adaptative potential, overall informing conservation, germplasm mobilization, and pre-breeding strategies that may ultimately secure the role of sweet potato in resilient food systems. Full article

Heatwaves are increasing in frequency and intensity owing to climate change, posing severe health risks to urban populations, particularly vulnerable groups. This study investigates public perceptions, adaptive behavior, and policy awareness regarding extreme heat in Gwangju Metropolitan City, South Korea, a heat-prone urban area. Using a mixed-methods approach, we analyzed primary survey data from 814 residents and secondary data from the 2020 Gwangju Citizen Heatwave Awareness Survey. Statistical analyses, including chi-squared and t-tests, examined differences across socioeconomic age groups. Results indicate that while general awareness of heatwave risks is high, low-income residents exhibit lower perceived severity, limited access to mechanical cooling, and greater reliance on passive avoidance behaviors. Awareness and use of municipal cooling shelters were low, with satisfaction hindered by concerns over accessibility, cleanliness, and operational hours. Television and emergency text alerts were the main information channels; however, trust and perceived usefulness were limited. Policy recommendations include spatially targeted shelter placement informed by vulnerability mapping, improved operational standards, diversified risk communication, and enhanced community engagement. This study underscores the importance of equity-driven adaptation strategies and provides practical insights for global municipalities facing similar climate-related heat risks. Full article

This study assesses the impact of heatwaves on hospital admissions in the Matam region of Senegal by combining climatic indices with machine learning methods. Using daily maximum temperature (TMAX) and heat index (HI), heatwave events were identified from 2017 to 2022. Hospital data from Ourossogui Regional Hospital were analyzed, and three predictive models, Random Forest (RF), Extreme Gradient Boosting (XGB), and Generalized Additive Models (GAMs), were compared. A bootstrapping approach with 1000 iterations was used to evaluate model robustness. The findings reveal a significant delayed effect of heatwaves, with increased hospitalizations occurring three to five days after the event. RF outperformed the other models with R² values ranging from 0.51 to 0.72. These findings highlight the need to enhance heatwave monitoring and promote the integration of impact-based climate forecasting into health early warning systems, particularly to protect vulnerable groups such as the elderly, children, and outdoor workers. Full article

Shallow warm-water coral reefs are among the most biodiverse and valuable ecosystems on Earth, supporting a quarter of all marine life and delivering critical ecosystem services such as coastal protection, food security, and economic benefits through tourism and fisheries. However, these ecosystems are under escalating threat from anthropogenic climate change, with tropical cyclones representing their most significant high-energy storm disturbances. Approximately 70% of the world’s coral reefs lie within the tropical cyclone belt, where the frequency, intensity, and rainfall associated with tropical cyclones are changing due to global warming. Coral reefs already compromised by climate-induced stressors—such as marine heatwaves, ocean acidification, and sea-level rise—are increasingly vulnerable to the compounding impacts of more intense and slower-moving cyclones. Projected changes in cyclone behaviour, including regional variations in storm intensity and rainfall, may further undermine coral reef resilience, pushing many reef systems toward irreversible degradation. Future impacts will be regionally variable but increasingly severe without immediate climate mitigation. Building reef resilience will require a combination of rapid global carbon emission reductions and ambitious adaptation strategies, including enhanced reef management and restoration and conservation efforts. The long-term survival of coral reefs now hinges on coordinated global action and support for reef-dependent communities. Full article

Compound dry and hot events or extremes (CDHEs) have emerged as major climatic threats to agricultural production and food security in the middle reaches of the Yangtze River Basin (MRYRB), a critical grain-producing region in China. However, agricultural risks associated with CDHEs, incorporating both natural and socio-economic factors, remain poorly understood in this area. Using a Hazard-Exposure-Vulnerability (HEV) framework integrated with a weighting quantification method and supported by remote sensing technology and integrated geographic data, we systematically assessed the spatiotemporal dynamics of agricultural CDHE risks and corresponding crop responses in the MRYRB from 2000 to 2019. Results indicated an increasing trend in agricultural risks across the region, particularly in the Poyang Lake Plain (by 21.9%) and Jianghan Plain (by 9.9%), whereas a decreasing trend was observed in the Dongting Lake Plain (by 15.2%). Spatial autocorrelation analysis further demonstrated a significant negative relationship between gross primary production (GPP) and high agricultural risks of CDHEs, with a spatial concordance rate of 52.6%. These findings underscore the importance of incorporating CDHE risk assessments into agricultural management. To mitigate future risks, we suggest targeted adaptation strategies, including strengthening water resource management and developing multi-source irrigation systems in the Poyang Lake Plain, Dongting Lake, and the Jianghan Plain, improving hydraulic infrastructure and water source conservation capacity in northern and southwestern Hunan Province, and prioritizing regional risk-based adaptive planning to reduce agricultural losses. Our findings rectify the longstanding assumption that hydrological abundance inherently confers robust resistance to compound drought and heatwave stresses in lacustrine plains. Full article

This study investigates urban heat vulnerabilities in Seville, Spain, using a multidimensional framework that integrates remote sensing, Space Syntax, and social vulnerability metrics. This research identifies Heat Boundaries (HBs), which are critical urban entities with elevated Land Surface Temperatures (LSTs) that act as barriers to adjacent vulnerable neighbourhoods, disrupting both physical and social continuity and environmental equity, and examines their relationship with the urban syntax and social vulnerability. The analysis spans two temporal scenarios: a Category 3 heatwave on 26 June 2023 and a normal summer day on 14 July 2024, incorporating both daytime and nighttime satellite-derived LST data (Landsat 9 and ECOSTRESS). The results reveal pronounced spatial disparities in thermal exposure. During the heatwave, peripheral zones recorded extreme LSTs exceeding 53 °C, while river-adjacent neighbourhoods recorded up to 7.28 °C less LST averages. In the non-heatwave scenario, LSTs for advantaged neighbourhoods close to the Guadalquivir River were 2.55 °C lower than vulnerable high-density zones and 3.77 °C lower than the peripheries. Nocturnal patterns showed a reversal, with central high-density districts retaining more heat than the peripheries. Correlation analyses indicate strong associations between LST and built-up intensity (NDBI) and a significant inverse correlation with vegetation cover (NDVI). Syntactic indicators revealed that higher Mean Depth values—indicative of spatial segregation—correspond with elevated thermal stress, particularly during nighttime and heatwave scenarios. HBs occupy 17% of the city, predominantly composed of barren land (42%), industrial zones (30%), and transportation infrastructure (28%), and often border areas with high social vulnerability. This study underscores the critical role of spatial configuration in shaping heat exposure and advocates for targeted climate adaptation measures, such as HB rehabilitation, greening interventions, and Connectivity-based design. It also presents preliminary insights for future deep learning applications to automate HB detection and support predictive urban heat resilience planning. Full article