Search

Background: Climate change and anthropogenic activities are accelerating environmental changes, challenging wild animals' survival. Behavioral plasticity, such as adjusting habitat selection and foraging activity, is a key mechanism for responding to rapid environmental changes in the Anthropocene era. However, this shift may expose animals to new challenges. Moreover, not all behavioral plasticity is adaptive, as evidenced by ecological traps. This study focuses on Poyang Lake, a Ramsar wetland and a critical wintering ground for waterbirds in the East Asian–Australasian Flyway. Historically, the migratory patterns of waterbirds were synchronized with the plant life cycle. However, recent hydrological regime changes have diminished suitable habitats and food resources, thereby posing significant conservation challenges for waterbirds. Methods: Utilizing multiyear satellite tracking data, we examined the variations in wintering home range and behaviors of four herbivorous waterbird species between natural and artificial wetlands in Poyang Lake under different hydrological conditions. Results: Our results reveal significant differences in home range area and movement speed among species and across hydrological years. All species demonstrated a marked increase in their use of artificial wetlands under unfavorable conditions. Specifically, the Greater White-fronted Goose (Anser albifrons) shifted its distribution to artificial wetlands during drought years while favoring natural wetlands under normal conditions, indicating a stress-induced adaptation. In contrast, the Bean Goose (A. fabalis) and Swan Goose (A. cygnoid) displayed greater behavioral plasticity. Notably, the Siberian Crane (Leucogeranus leucogeranus) increasingly used artificial wetlands, likely due to human protection, raising concerns about potential ecological traps. Additionally, waterbirds foraging in artificial wetlands generally exhibited higher movement speeds during drought conditions. This behavior suggests maladaptation and a more dispersed distribution. Conclusions: Our study underscored the critical role of artificial wetlands in supporting migratory waterbirds during drought, though elevated movement speeds observed in these habitats suggest potential maladaptation. Species-specific responses raise concerns about ecological traps if these habitats fail to meet key ecological needs. To ensure long-term conservation, efforts should focus on preserving natural wetlands and enhancing the quality of artificial habitats. Future research should prioritize long-term monitoring to guide habitat management and address species-specific needs in the face of climate change and habitat degradation. Article highlights: Waterbirds increased utilization intensity and home range in artificial wetlands during unfavorable conditions, showcasing their behavioral plasticity in response to environmental changes. During droughts, waterbirds showed significantly increased movement speeds in artificial wetlands, indicating their maladaptation to these environments. Different species adopt various strategies to cope with the adverse effects of hydrological alternations. Wintering waterbird conservation requires species-specific management strategy. [ABSTRACT FROM AUTHOR]

This study assessed smallholder farmers’ vulnerability to climate extremes in west and southwest Showa zones, central Ethiopia. The study used 1981–2020 rainfall and temperature data obtained from the Ethiopian Meteorology Institute and employed RClimDex1 for data quality control and extreme climate indices analysis. The selected ten (10) major components of Livelihood Vulnerability Index (LV I) were used to assess farmers’ vulnerability, categorized into exposure, sensitivity, and adaptive capacity based on the livelihood vulnerability indicator intergovernmental panel for climate change (LVI-IPCC) framework. Primary data gathered through household surveys conducted across 626 selected peasant associations representing different agro ecological zones (lowland, midland, and highland). The primary data analysis performed via statistical package for social sciences (IBM SPSS) Statistics Version 26 and R software. The study finding indicated that lowland agroecology (0.47) is more vulnerable to climate extremes than highlands (0.38) and midlands (0.40) agro-ecologies In terms of smallholder farmers’ sensitivity to extreme climate events, lowland agro-ecology suggested a higher sensitivity index than the other two ecologies. The smallholder farmers’ sensitivity to extreme climate events was 0.70, 0.57, and 0.67 LVI values on lowland, midland, and highland agro-ecosystems, respectively. Midland agroecology has strong adaptive capacity (0.560) and is the least vulnerable. In lowland agroecology, smallholder farmers are more vulnerable to extreme weather because of their limited adaptive capacity and higher exposure. The study’s findings will guide policymakers in addressing the negative effects of climate extreme indices on smallholder farmers. Providing improved seeds, irrigation, credit access, and real-time weather information updates can reduce vulnerability in lowland areas. [ABSTRACT FROM AUTHOR]

Climate change brings new challenges to the sustainable development of agriculture in the new era. Accurately grasping the patterns of climate change impacts on agricultural systems is crucial for ensuring agricultural sustainability and food security. Taking the Loess Plateau (LP), China as an example, this study used a coupling coordination degree model and spatial autocorrelation analysis to portray the spatial and temporal features of crop-cropland coupling relationship from 2000 to 2020 and explored the impact law of climate change through geographically and temporally weighted regression (GTWR). The results were as follows: 1) the crop-cropland coupling coordination degree of the LP showed a gradual upward trend from 2000 to 2020, forming a spatial pattern with lower values in the central region and higher values in the surrounding areas. 2) There was a positive correlation in the spatial distribution of crop-cropland coupling coordination degree in the LP from 2000 to 2020, and the high value-low value (H-L) and low value-low value (L-L) agglomerations continued to expand eastward, while the spatial and temporal evolution of the high value-high value (H-H) and low value-high value (L-H) agglomerations was not obvious. 3) The impacts of climatic elements on crop-cropland coupling coordination degree in the LP showed strong heterogeneity in time scales. The inhibitory impacts of summer days (SU) and frost days (FD) accounted for a higher proportion, while the annual average temperature (TEM) had both promoting and inhibiting impacts. The impacts proportion and intensity of extreme heavy precipitation day (R25), continuous drought days (CDD), and annual precipitation (PRE) all experienced significant changes. 4) In space, the impacts of SU and FD on the crop-cropland coupling coordination degree varied with latitude and altitude. The adaptability of the LP to R25 gradually strengthened, and the extensions of CDD and increase of PRE led to the increasing inhibition beyond the eastern region of LP, and TEM showed a promoting impact in the Fenwei Plain. As an important grain-producing area in China, the LP should actively deal with the impacts of climate change on the crop-cropland coupling relationship, vigorously safeguard food security, and promote sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Prisons are frequently excluded from hazard risk management, disaster mitigation planning particularly in the aftermath of climate disasters. People deprived of their liberty are wholly reliant on the State, and unable to protect themselves from climate related threats. This is especially the case in Africa where over one million people are incarcerated. We report from Malawi, a least developed country in sub-Saharan Africa, where the system is operating over 234 per cent capacity. With no climate change responsive prison policy, nor research on this topic despite evidence for disrupted agriculture and seasonal cyclone activity; we conducted a rapid situation assessment to evaluate the impact of climate change on the Malawi prison system operations. Interviews and small focus groups were conducted with professional stakeholders (n = 12), prison officers (n = 15), juvenile prisoners (n = 10), and former prisoners (n = 50). Reflexive thematic analysis revealed six themes: climate impacts and infrastructural challenges; extreme life-threatening events and (in)ability to evacuate; fluctuating temperatures, prison system congestion and ventilation; water crisis and environmental health; navigating crop failures, interrupted grain supply and food (in)security; and circulation of disease and amplified health vulnerability. Findings illustrate how climate change poses substantial challenges to congested prison operations in Malawi; infrastructure protection, sanitation and maintenance; (in)ability to safely evacuate; power, water and food shortages; and circulation of disease. Vulnerable prisoners include women, children and people with disabilities. The assessment evidence will inform the development of a climate responsive prison action plan for the Malawi Prison system and represent an exemplar for the Southern African region. [ABSTRACT FROM AUTHOR]

Ethiopia's vulnerability to climate change is exacerbated by high poverty rates, rapid population growth, increasing prevalence of vector-borne diseases, and heavy reliance on rain-fed agriculture. This narrative review aims to compile existing data on the impacts of climate extremes on the physical environment, public health, and livelihoods in Ethiopia, thereby highlighting the significance of this region for such a study. Data were sourced from peer-reviewed journal articles from databases like PubMed, Scopus, and Web of Science, as well as reports and other unpublished documents. Results show that Ethiopia is facing increasing frequency, severity, duration, and timing of climate-related extreme events. Key challenges include environmental degradation, reduced crop yields, recurring floods, droughts, famines, increased heat waves, and spread of infectious diseases. Average daily rainfall is projected to decrease from 2.04 mm (1961–1990) to 1.97 mm (2070–2099), indicating a worsening climate trend. Moreover, the average annual temperature has risen by 1.3°C since 1960, at a rate of 0.28°C per decade. Flood records indicate a sharp rise, with 274 flood incidents recorded in 2020, causing extensive damage, including an annual soil loss of 1 billion tons in the Ethiopian highlands, reducing land productivity by 2.2% annually. Droughts from 1964 to 2023 affected 96.5 million people, reduced GDP by 4%, decreased agricultural output by 12%, and increased inflation rates by 15%. The regions of Afar, Somali, Gambella, and Benshangul Gumuz exhibit extreme vulnerability to health impacts due to rising temperatures. Addressing climate extremes is critical to mitigate their adverse effects on Ethiopia's environment, public health, and livelihoods. [ABSTRACT FROM AUTHOR]

Land cover change due to rapid urbanization and industrialization has been known to increase the land surface temperature around the world. For this reason, examining the variation of land surface temperatures and mitigating the related impacts remain a challenge. The changing land cover pattern in urban areas also affects the quality of life in urban areas by altering its environment, deteriorating air quality and increasing the frequency of extreme climatic events like high-intensity rainfall, drought, and development of urban heat islands conditions. Düzce, located in the Western Black Sea Region, where urbanization and industrialization have been expanding rapidly in Türkiye since 2000s, was selected as the study area. In the study area, natural disasters with hydrometeorological character have occurred frequently in recent years. Düzce is climate change sensitive and ecologically vulnerable areas. For this purpose, temporal and spatial changes in the land surface temperature were determined by Land Surface Temperature and Normalized Difference Vegetation Index Analysis with Landsat 7 ETM + in 2000 and Landsat 9 OLI in 2022. Landsat 7 ETM + in 2000 and Sentinel-2 in 2022 were analysed for land cover with object-based classification. Daily temperature and precipitation data were used for climatological analysis. Statistical data were used for population growth and hydrometeorological disaster. According to the results, settlement areas expanded, and an average temperature increase was observed in 2022 compared with 2000. The areas where land surface temperature values increase are determined as settlement areas, industrial and agricultural areas. [ABSTRACT FROM AUTHOR]

This study examines the relationship between extreme weather conditions and the S&P500 return index, representing the U.S. stock market. The literature review and analysis show extreme weather events can impact the S&P500 return index. This effect is observed in two ways. First, extreme weather events create a market anomaly in the U.S. stock market, indicating that prices move in a way that cannot be explained by a rational model. Second, extreme weather events create financial uncertainty and have a negative impact on firms' future cash flows. These findings suggest that investors and financial markets should be more cautious about extreme weather events. In addition, the impact of extreme weather on the U.S. stock market is weak. This can be explained in two ways. First, extreme weather events are predictable and seasonally recurring. Second, the American stock market is close to the efficient market hypothesis. [ABSTRACT FROM AUTHOR]

Climate change is increasing the occurrence of extreme weather events, such as intense windstorms, with a trend expected to worsen due to global warming. The growing intensity and frequency of these events are causing a significant number of failures in power distribution grids. However, understanding the nature of extreme wind events and predicting their impact on distribution grids can help and prevent these issues, potentially mitigating their adverse effects. This study analyzes a structured method to predict distribution grid disruptions caused by extreme wind events. The method utilizes Machine Learning (ML) models, including K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Decision Trees (DTs), Gradient Boosting Machine (GBM), Gaussian Process (GP), Deep Neural Network (DNN), and Ensemble Learning which combines RF, SVM and GP to analyze synthetic failure data and predict power grid outages. The study utilized meteorological information, physical fragility curves, and scenario generation for distribution systems. The approach is validated by using five-fold cross-validation on the dataset, demonstrating its effectiveness in enhancing predictive capabilities against extreme wind events. Experimental results showed that the Ensemble Learning, GP, and SVM models outperformed other predictive models in the binary classification task of identifying failures or non-failures, achieving the highest performance metrics. [ABSTRACT FROM AUTHOR]

The aim of this study is to assess future hazards due to extreme meteorological events in the Wielkopolska region, Poland, based on five climate model projections and three scenarios: SSP126, 370, and 585. The paper analyzes the changes of mean and extreme precipitation, mean and extreme temperatures, and humidity index, as well as changes in difference between maximum temperatures observed from day to day and changes in difference between mean atmospheric pressure at the sea level observed from day to day. Additionally, we look at possible future occurrence of wildfires due to changes in fire weather conditions. Based on climate model projections, future hazard due to extreme meteorological events in Wielkopolska region is to be more serious and will be most noticeable in the end of twenty-first century and for two higher scenarios: SSP370 and SSP585. For near future, 2021–2050, projected conditions of meteorological extremes for analyzed scenarios are quite consistent. Therefore, there is a strong need for implementing adaptation actions. Nevertheless, such activities are so far lacking, and several adaptation options are not present in local and national legislation, even though they are recognized as effective. [ABSTRACT FROM AUTHOR]

Climate change has intensified extreme weather events, posing major challenges to agriculture-dependent regions like Northern Maharashtra. This study analyzed temperature and precipitation extremes across five districts--Nashik, Dhule, Nandurbar, Jalgaon and Ahmednagar using data from 1982 to 2022 with the help of RClimDex model. Key temperature indices, including tropical nights (TR25), warm days (TX90p), and frost days (FD13) showed an increase in warm events and a decline in cool nights and frost days. Reduced diurnal temperature range (DTR) indicated less nighttime cooling, consistent with global warming. For precipitation, extreme rainfall events are rising as indicated by the maximum 1-day precipitation (Rx1day), while consecutive dry days (CDD) are shortening. These shifts heighten risks such as crop heat stress, altered growing seasons, soil erosion, and water management challenges. The study underscores the urgent need for adaptive agricultural strategies, improved irrigation, and early warning systems to mitigate the impacts of climate change and enhance resilience in Northern Maharashtra. [ABSTRACT FROM AUTHOR]

[Objective] In recent years, climate warming and other factors have led to a rise in extreme weather events globally. Simultaneously, under the "carbon peak - carbon neutral" goal, the integration of fluctuating renewable energy including wind and solar energy, extensive use of power electronics and long-distance ultra-high-voltage transmission have reshaped traditional power grids. These changes have increased the exposure of primary equipment to harsh environments, intensifying the vulnerability of power systems to extreme weather impacts. [Method] This paper reviewed the impact of recent extreme weather events, such as typhoons, heavy rainfall and ice storms, on power system equipment and operations. It explored systemic risk performance in depth in new power systems through case studies and proposed strategies for refined modeling, systemic risk assessment and cross-disciplinary collaboration across disaster scenarios. [Result] The research shows that extreme weather significantly increases the risks of equipment failures and system instability, with high renewable energy penetration amplifying the impact of failure propagation. [Conclusion] The safe operation of power systems under the dual pressure of in-depth construction of new power systems and intensified impact of extreme weather relies on improvement of equipment risk modeling, systemic risk control and cross- disciplinary collaboration mechanisms. Therefore, future research should focus on building multi-dimensional joint risk assessment of power systems. In addition, the paper points out the significance of strengthening in-depth integration of power systems and meteorological systems for enhancing risk prediction and dynamic response capabilities of the system, thus providing scientific basis and technical support for improving the system resilience.

Accurate short-term load forecasting (LF) under extreme weather is vital for the sustainable development of energy systems. This paper proposes a basic framework for future load forecasting researches of sustainable energy systems under extreme weather events and provides new direction for membrane computing model in terms of load forecasting. Inspired by nonlinear spiking mechanisms in nonlinear spiking neural P systems, the gated spiking neural P (GSNP) model is a new recurrent-like network. In this study, we develop an innovative membrane computing model, termed frequency attention temporal convolutional network-load forecasting-frequency attention gated spiking neural P (FATCN-LF-FAGSNP) model. Frequency enhanced channel attention mechanism (FECAM) is utilized to enhance the features extraction ability of temporal convolutional network (TCN) and improve prediction ability of GSNP systems. FATCN fully extracts the temporal relationship of features, the features of each channel interact with each frequency component to learn more temporal information effectively and comprehensively in frequency domain. Moreover, adding FECAM to extract features from the data fully reveals the relationship between influencing factors and the load series, which improves the quality of data features and the forecasting accuracy of the FAGSNP model. Then inspired by the interaction mechanism of impulses between biological neuronal cells, FAGSNP is able to consider the load variability and effectively predict load trends. In addition, to address load prediction challenges posed by extreme weather and promote the sustainable development of power systems, the proposed model integrates many models to solve this problem. First, optimized variational mode decomposition (VMD) is used to decompose the load series and the sub-sequences are combined with relevant features, to form the different input sequences of the prediction model. Then, FATCN-LF-FAGSNP model is developed to accurately forecast each high frequency component. Subsequently inverted Transformer model and Informer model are utilized to predict low frequency components and residual component, respectively. Finally all predicted components are reconstructed to get the final predicted results. We conducted extensive comparative experiments with ten baseline models on three real-world datasets, compared with GSNP model and TCN-GSNP model, the coefficient of determination (R2) of the FATCN-LF-FAGSNP model increases and mean absolute percentage error (MAPE), mean absolute error (MAE) and relative absolute error (RAE) reduce, the LF accuracy (measured by R2) of the proposed hybrid model gets 99.7% in seasonal LF task. In addition, the proposed hybrid model gets the best in MAPE, MAE, R2 and RAE metrics in all cases, which demonstrates the effectiveness of the proposed model in LF tasks under both extreme weather scenarios and seasonal prediction scenarios.

The main objective of this study was to explore the causes and consequences of flood-induced temporary displacement in southwest coastal Bangladesh. To investigate this, we purposively select one village; Hazrakhali, under the Satkhira district as our study area. This project has been carried out by following a qualitative research approach. Data for this study is collected through in-depth interviews conducted with 20 household heads who were selected using snowball sampling. The findings show that the dominant causes of temporary displacement in that area include economic challenges, the fear of losing their social connections, and a deep attachment to their cultural heritage. This temporary displacement had long-term negative consequences for their livelihoods in terms of poverty, rehabilitation problems, food insecurity, income diversification, hygiene and sanitation troubles, scarcity of edible water, etc. These findings will advocate for policymakers and disaster management bodies to address and mitigate the impacts of flood-affected miseries induced by displacement in the region.

This study examines the relationship between extreme weather conditions and the S&P500 return index, representing the U.S. stock market. The literature review and analysis show extreme weather events can impact the S&P500 return index. This effect is observed in two ways. First, extreme weather events create a market anomaly in the U.S. stock market, indicating that prices move in a way that cannot be explained by a rational model. Second, extreme weather events create financial uncertainty and have a negative impact on firms’ future cash flows. These findings suggest that investors and financial markets should be more cautious about extreme weather events. In addition, the impact of extreme weather on the U.S. stock market is weak. This can be explained in two ways. First, extreme weather events are predictable and seasonally recurring. Second, the American stock market is close to the efficient market hypothesis.

Climate communication scientists search for effective message strategies to engage the ambivalent public in support of climate advocacy. The personal experience of wildfire is expected to render climate change impacts more concretely, pointing to a potential message strategy to engage the public. This study examined Twitter discourse related to climate change during the onset of 20 wildfires in California between the years 2017 and 2021. In this mixed method study, we analyzed tweets geographically and temporally proximal to the occurrence of wildfires to discover framings and examined how frequencies in climate framings changed before and after fires. Results identified three predominant climate framings: linking wildfire to climate change, suggesting climate actions, and attributing climate change to adversities besides wildfires. Mean tweet frequencies linking wildfire to climate change and attributing adversities increased significantly after the onset of fire. While suggesting climate action tweets also increased, the increase was not statistically significant. Temporal analysis of tweet frequencies for the three themes of tweets showed that discussion increased after the onset of a fire but persisted typically no more than 2 weeks. For fires that burned for longer periods of more than a month, external events triggered climate discussions. Our findings contribute to identifying how the personal experience of wildfire shapes Twitter discussion related to climate change, and how these framings change over time during wildfire events, leading to insights into critical time points after wildfire for implementing message strategies to increase public engagement on climate change impacts and policy.

Climate change is increasing the occurrence of extreme weather events, such as intense windstorms, with a trend expected to worsen due to global warming. The growing intensity and frequency of these events are causing a significant number of failures in power distribution grids. However, understanding the nature of extreme wind events and predicting their impact on distribution grids can help and prevent these issues, potentially mitigating their adverse effects. This study analyzes a structured method to predict distribution grid disruptions caused by extreme wind events. The method utilizes Machine Learning (ML) models, including K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Decision Trees (DTs), Gradient Boosting Machine (GBM), Gaussian Process (GP), Deep Neural Network (DNN), and Ensemble Learning which combines RF, SVM and GP to analyze synthetic failure data and predict power grid outages. The study utilized meteorological information, physical fragility curves, and scenario generation for distribution systems. The approach is validated by using five-fold cross-validation on the dataset, demonstrating its effectiveness in enhancing predictive capabilities against extreme wind events. Experimental results showed that the Ensemble Learning, GP, and SVM models outperformed other predictive models in the binary classification task of identifying failures or non-failures, achieving the highest performance metrics.

Abstract Background Extreme weather events affect health by directly and indirectly increasing illness burdens and changing healthcare usage patterns. These effects can be especially severe in rural and remote areas, exacerbating existing health disparities, and necessitating urgent mitigation or adaptation strategies. Despite increased research on health and climate change, studies focusing on rural and remote populations remain limited. This study aimed to review the relationships among extreme weather events, healthcare utilization, and health outcomes in rural and remote populations, identify research gaps, and inform policy development for adaptation and disaster management in these settings. Methods A systematic scoping review was registered and conducted following the PRISMA-ScR guidelines. The search databases included PubMed, Web of Science, Scopus, the Cochrane Library, ProQuest, and the WHO IRIS. The included studies were primary research, focused on rural or remote areas, and investigated the effects of extreme weather events on either health outcomes or healthcare utilization. There were no methodological, date or language restrictions. We excluded protocols, reviews, letters, editorials, and commentaries. Two reviewers screened and extracted all data, other reviewers were invited to resolve conflicts. Findings are presented numerically or narratively as appropriate. Results The review included 135 studies from 31 countries, with most from high-income countries. Extreme weather events exacerbate communicable and noncommunicable diseases, including cardiorespiratory, mental health, and malnutrition, and lead to secondary impacts such as mass migration and increased poverty. Healthcare utilization patterns changed during these events, with increased demand for emergency services but reduced access to routine care due to disrupted services and financial constraints. Conclusions The results highlighted the essential role of community and social support in rural and remote areas during extreme weather events and the importance of primary healthcare services in disaster management. Future research should focus on developing and implementing effective mitigation and adaptation programs tailored to the unique challenges faced by these populations.

Objective: Over the last five decades, extreme weather events (EWEs) have made a substantial contribution to the overall climate change impacts in India. Intergovernmental Panel on Climate Change (IPCC) has predicted that India will experience an increase in extreme weather events in the future. The primary aim of the study was to gain insights into India's overall occurrence of EWEs between 1970 and 2019 across different states. Methods: In the present study, data from the Indian Meteorological Department (IMD) spanning the last five decades (1970–2019) was examined to understand how specific EWEs have changed in India over varying regions and time spans. The analysis involved descriptive statistics using heatmaps, and trend analysis using the Mann- Kendall test. Findings: In the past 50 years (1970–2019), around 11,158 EWEs occurred in India. EM-DAT data shows a rise from 4 + events in the 1950s to 20 + in 2018, while IMD data indicates an increase from 50 + events in the 1970s to 400 + in 2019. The event-wise trend analysis of 50 years of data on EWEs in India revealed a consistent increase in the incidence of each EWE. The Mann–Kendall test, conducted to detect trends in EWEs over 50 years revealed a significant upward trend at 1% level for total EWEs, including heat waves, floods, heavy rains, and thunderstorms. Heat map and spatial analysis revealed that significant regions for heat waves include Maharashtra, Odisha, West Bengal, Uttar Pradesh, Rajasthan, and Uttarakhand. Cold waves have increased in Uttar Pradesh, Uttarakhand, Rajasthan, Bihar, and Jharkhand. Floods, heavy rains, thunderstorms, and lightning are common in Maharashtra, West Bengal, Kerala, Assam, and Karnataka. Conclusion: The findings of the study have critical implications for climate resilience strategies and disaster management in India. The increasing frequency and geographical concentration of EWEs call for region-specific preparedness and mitigation plans. By knowing the trends of EWEs and hotspot states through retrospective data records, the Government can proactively plan for future adverse events to prioritize high-risk areas, implementing targeted preparedness and mitigation strategies to prevent significant mortality and morbidity. [ABSTRACT FROM AUTHOR]

Across the globe, mobile species are key components of ecosystems. Migratory birds and nomadic antelope can have considerable conservation, economic or societal value, while irruptive insects can be major pests and threaten food security. Extreme weather events, which are increasing in frequency and intensity under ongoing climate change, are driving rapid and unforeseen shifts in mobile species distributions. This challenges their management, potentially leading to population declines, or exacerbating the adverse impacts of pests. Near‐term, within‐year forecasting may have the potential to anticipate mobile species distribution changes during extreme weather events, thus informing adaptive management strategies. Here, for the first time, we assess the robustness of near‐term forecasting of the distribution of a terrestrial species under extreme weather. For this, we generated near‐term (2 weeks to 7 months ahead) distribution forecasts for a crop pest that is a threat to food security in southern Africa, the red‐billed quelea Quelea quelea. To assess performance, we generated hindcasts of the species distribution across 13 years (2004–2016) that encompassed two major droughts. We show that, using dynamic species distribution models (D‐SDMs), environmental suitability for quelea can be accurately forecast with seasonal lead times (up to 7 months ahead), at high resolution, and across a large spatial scale, including in extreme drought conditions. D‐SDM predictive accuracy and near‐term hindcast reliability were primarily driven by the availability of training data rather than overarching weather conditions. We discuss how a forecasting system could be used to inform adaptive management of mobile species and mitigate impacts of extreme weather, including by anticipating sites and times for transient management and proactively mobilising resources for prepared responses. Our results suggest that such techniques could be widely applied to inform more resilient, adaptive management of mobile species worldwide. [ABSTRACT FROM AUTHOR]

Aim: As global climate changes, there is a clear mismatch between the temporal and spatial characteristics of body temperature and environmental temperature, confounding the assessment of thermal stress for organisms in many ecological studies. Here, we hindcast the hourly body temperatures of intertidal molluscs to explore the differences between them and environmental temperatures (air and water temperatures) in multiple metrics of thermal stress. Location: Intertidal shores in East Asia (0°–45°N, 100°E–140°E). Time Period: 40 years, 1980 to 2019. Major Taxa Studied: Mollusca. Methods: We collected habitat zonation data and measured the morphological characteristics of 25 intertidal molluscs living in East Asia. For three different types of intertidal molluscs (i.e., bivalves, limpets and snails), we built corresponding heat budget models (HBMs) to hindcast the hourly body temperatures from 1980 to 2019. We analysed the thermal stress of intertidal species faced in three metrics, annual extreme high temperatures (T99), seasonal daily maximum temperatures (DMT) and heatwaves, and compared them with environmental temperatures. Results: We found that T99 of body temperatures and their interannual warming rates are significantly higher than those of environmental temperatures. Moreover, there were non‐negligible mismatches between the seasonal thermal pattern and heatwaves of body temperatures and environmental temperatures, suggesting that the deleterious impacts of global warming on intertidal species are underestimated and cannot be directly revealed by environmental temperatures. Main Conclusions: Thermal stress patterns of body temperature were significantly different from those of environmental temperature, and the thermal stress faced by intertidal species had been persistently underestimated. These results emphasise that body temperature should be used as the appropriate metric for evaluating and predicting the impacts of global warming and weather extremes in the intertidal biological system. [ABSTRACT FROM AUTHOR]

Reliable performance of the electricity distribution networks is critical to avoid outages and disruptions which may lead to substantial economic losses. Some of the important components of the electricity distribution networks include overhead cables, utility poles, cross-arms, substations, and transformers. These components are subjected to deterioration with aging and pose risks of failure creating safety concerns, specially under extreme weather conditions. Efficient maintenance and the use of accurate condition assessment techniques to monitor the integrity of the aforementioned components are key factors to be considered in managing the electricity distribution assets. This article presents a review of different condition assessment techniques employed for monitoring the electricity distribution infrastructure. The conventional inspection techniques and advancements in non-destructive testing methods are discussed outlining the relative advantages and disadvantages. Other than the normal failures of distribution infrastructure due to component ageing, unexpected and less frequent extreme weather events can have significant impacts on the resilience of the electrical power infrastructure. Thus, the effects of extreme weather conditions on the reliability of the electricity distribution networks and the failure rates of assets are reviewed in this study. Further, the maintenance and decision-making approaches implemented in the power distribution networks are also discussed. [ABSTRACT FROM AUTHOR]

Considering the projected impacts of climate change in upcoming decades, innovative educational approaches should encourage inventive problem-solving techniques and societal change, fostering transformative climate adaptation. The value of drama in climate adaptation education remains a novel area in the environmental education research literature and requires further exploration of its potential benefits to Climate Change Education (CCE). This article presents a proposal for CCE to include various elements in a drama workshop by evaluating a methodological framework. Participants in the workshop studied the vulnerabilities that arose from flooding and droughts while dramatizing different social conflicts to develop building adaptation scenarios. Through the exploration of problems via playful activities, participants collaboratively construct narratives and texts rich with meaning, based on a critical and creative perception of themes, needs, desires, and overlapping ideologies. This short-term experience manifests efficacy in elucidating the underpinnings of social systems structures, human values, and motivations. This article analyzes workshop results, providing a pedagogical structure and theoretical foundation, contributing to a better comprehension of drama in education and the creation of capacities towards CCE. [ABSTRACT FROM AUTHOR]

Grain filling is a critical process for improving crop production under adverse conditions caused by climate change. Here, using a quantitative method, we quantified post-anthesis source–sink relationships of a large dataset to assess the contribution of remobilized pre-anthesis assimilates to grain growth for both biomass and nitrogen. The dataset came from 13 years of semi-controlled field experimentation, in which six bread wheat genotypes were grown at plot scale under contrasting temperature, water, and nitrogen regimes. On average, grain biomass was ~10% higher than post-anthesis above-ground biomass accumulation across regimes and genotypes. Overall, the estimated relative contribution (%) of remobilized assimilates to grain biomass became increasingly significant with increasing stress intensity, ranging from virtually nil to 100%. This percentage was altered more by water and nitrogen regimes than by temperature, indicating the greater impact of water or nitrogen regimes relative to high temperatures under our experimental conditions. Relationships between grain nitrogen demand and post-anthesis nitrogen uptake were generally insensitive to environmental conditions, as there was always significant remobilization of nitrogen from vegetative organs, which helped to stabilize the amount of grain nitrogen. Moreover, variations in the relative contribution of remobilized assimilates with environmental variables were genotype dependent. Our analysis provides an overall picture of post-anthesis source–sink relationships and pre-anthesis assimilate contributions to grain filling across (non-)environmental factors, and highlights that designing wheat adaptation to climate change should account for complex multifactor interactions. [ABSTRACT FROM AUTHOR]

This study advances the precision and efficiency of precipitation nowcasting, particularly under extreme weather conditions. Traditional forecasting methods struggle with precision, spatial feature generalization, and recognizing long-range spatial correlations, challenges that intensify during extreme weather events. The Enhanced Temporal Correlation Jump Prediction Network (ETCJ-PredNet) introduces a novel attention mechanism that optimally leverages spatiotemporal data correlations. This model scrutinizes and encodes information from previous frames, enhancing predictions of high-intensity radar echoes. Additionally, ETCJ-PredNet addresses the issue of gradient vanishing through an innovative jump connection strategy. Comparative experiments on the Moving Modified National Institute of Standards and Technology (Moving-MNIST) and Hong Kong Observatory Dataset Number 7 (HKO-7) validate that ETCJ-PredNet outperforms existing models, particularly under extreme precipitation conditions. Detailed evaluations using Critical Success Index (CSI), Heidke Skill Score (HSS), Probability of Detection (POD), and False Alarm Ratio (FAR) across various rainfall intensities further underscore its superior predictive capabilities, especially as rainfall intensity exceeds 30 dbz,40 dbz, and 50 dbz. These results confirm ETCJ-PredNet's robustness and utility in real-time extreme weather forecasting. [ABSTRACT FROM AUTHOR]

The accuracy of ERA5 reanalysis datasets and their applicability in the coastal area of Bohai Bay are crucial for weather forecasting and environmental protection research. However, synthesis evaluation of ERA5 in this region remains lacking. In this study, using a tropospheric wind profile radar (CFL-06L) placed in coastal Huanghua city, the deviations of ERA5 reanalysis data are assessed from the ground to an altitude of 5 km. The results indicate that the wind speed of ERA5 reanalysis data exhibits good consistency from the surface to the tropospheric level of about 5 km, with R2 values ranging from 0.5 to 0.85. The lowest mean wind speed error, less than 3 m/s, occurs in the middle layer, while larger errors are observed at the surface and upper layers. Specifically, at 150 m, the R2 is as low as 0.5, with numerous outliers around 5000 m. Seasonal analysis shows that the ERA5 wind field performs best in summer and worst in autumn and winter, especially at lower levels affected by circulation systems, high stratus clouds, and aerosols, with errors reaching up to 10 m/s. Further analysis of extreme weather events, such as heavy rain; hot, dry winds; and snowstorms, reveals that the effects of sea-land winds and strong convective systems significantly impact the observation of wind profiles and the assimilation of reanalysis data, particularly under the constrain of boundary layer height. Additionally, we also find that the transition of sea-land breeze is capable of triggering the nighttime low-level jet, thereby downward transporting the aloft ozone to the ground and resulting in an abnormal increase in the surface ozone concentration. The study provides a scientific basis for improving meteorological forecasting, optimizing wind energy resource utilization, and formulating environmental protection policies, highlighting its significant scientific and practical application value. [ABSTRACT FROM AUTHOR]

The Sundarbans is the world's largest contiguous mangrove forest with an area of about 10,000 square kilometers and shared between Bangladesh and India. This world-renowned mangrove forest, located on the lower Ganges floodplain and facing the Bay of Bengal, has long served as a crucial barrier, shielding southern coastal Bangladesh from cyclone hazards. However, the Sundarbans mangrove ecosystem is now increasingly threatened by climate-induced hazards, particularly tropical cyclones originating from the Indian Ocean. To assess the cyclone vulnerability of this unique ecosystem, using geospatial techniques, we analyzed the damage caused by past cyclones and the subsequent recovery across three salinity zones, i.e., Oligohaline, Mesohaline, and Polyhaline. Our study also examined the relationship between cyclone intensity with the extent of damage and forest recovery. The findings of our study indicate that the Polyhaline zone, the largest in terms of area and with the lowest elevation, suffered the most significant damage from cyclones in the Sundarbans region, likely due to its proximity to the most cyclone paths. A correlation analysis revealed that cyclone damage positively correlated with wind speed and negatively correlated with the distance of landfall from the center of the Sundarbans. With the expectation of more extreme weather events in the near future, the Sundarbans mangrove forest faces a potentially devastating outlook unless both natural protection processes and human interventions are undertaken to safeguard this critical ecosystem. [ABSTRACT FROM AUTHOR]

This paper analyzes the theoretical foundation and practical implications of recognizing the right to a stable climate as a fundamental human right. Further, it examines the intersection of human rights, right to climate, and the intensification of extreme climate events. Through a bibliometric analysis, the study highlights the increase in scholarly attention paid to this nexus. The intensification of extreme climate events, such as the Cumbre Vieja volcano in Spain, is also analyzed as a catalyst for recognizing the right to climate as a human right, as a fundamental requirement for its enactment. Indeed, it is argued that this recognition is necessary to achieve climate justice. These thoughts about the necessity of recognizing the right to climate as a human right are also based on a similar case, the enactment of the rights to water and sanitation, which is presented as a case study, demonstrating how specific environmental rights can be integrated into human rights discourse. The results and discussion section synthesizes these findings, highlighting the imperative of recognizing climate rights to ensure justice and sustainability amidst escalating climate challenges. [ABSTRACT FROM AUTHOR]

There is an ongoing debate on the relationship between accelerated warming in the Arctic and extreme weather patterns in mid-latitudes. As extreme weather events have dramatic socioeconomic costs, it is important to investigate the possibility of the increased risk of such events in mid-latitudes. We investigated changes in the frequency of extreme weather events in Eastern Europe and Western to Central Asia (30–60 ∘ N, 20–75 ∘ E) in the post-Arctic amplification (2002–2022) compared to the pre-Arctic amplification (1979–1999) period. We analyzed the daily detrended near-surface temperature and precipitation of the ERA5 data. There is no robust evidence for the contribution of Arctic amplification to changes in extreme precipitation in Eastern Europe and Western to Central Asia. Most regions of the study area have experienced a decrease in annual precipitation in the post-Arctic amplification period. We also identified an increase in consecutive dry days in most parts of Central Asia by approximately 16 days per year, which could be attributed to a warmer climate because dry areas generally become drier in a warmer climate. Greater warming of the Earth in the more recent period has been associated with a significant increase in both warm days and nights and a significant decrease in cold days and nights over Eastern Europe and Western to Central Asia. Depending on the season, we identified both intensification and weakening of the upper tropospheric jet stream in the post-Arctic amplification period. The jet stream is intensified from the eastern Black Sea toward northern Kazakhstan and southeastern Russia in spring. In contrast, it has significantly weakened in the northern Mediterranean Sea and western Kazakhstan in summer and the Caspian Sea and Caucasus in autumn. Our results have important implications for a better understanding of the potential impact of rapid Arctic warming on extreme weather events in mid-latitudes. [ABSTRACT FROM AUTHOR]