Search Results (137)

Extreme weather events, such as marine heatwaves (MHWs), pose serious threats to the aquaculture sector, facilitating the occurrence of disease outbreaks and compromising farmed animals’ welfare and survival. Hence, finding eco-innovative strategies to improve animal immunocompetence is essential to assure aquaculture’s sustainability and resilience in a rapidly changing ocean. This study evaluated the immunostimulatory potential of Laminaria digitata powder (0.3% and 1.5%) and extract (0.3%) in juvenile gilthead seabream (Sparus aurata) exposed to a Vibrio harveyi outbreak during a Category III MHW event (T = 25.7 °C). Overall, L. digitata supplementation did not significantly affect fish immunocompetence under optimal rearing conditions (T = 21.4 °C; no infection), nor did it induce any adverse effects. However, both the powder (1.5%) and extract (0.3%) forms of L. digitata supplementation effectively mitigated the negative impacts prompted by the MHW and Vibrio harveyi infection—evidenced by improvements in fish health indicators, hematological parameters, leukocyte viability, granulocyte proportions, and reductions in peroxidase activity and immunoglobulin M levels. From an economic standpoint, supplementation with 1.5% L. digitata powder emerged as the most promising strategy, offering a practical balance between effectiveness and affordability for large-scale applications. These findings highlight the potential of L. digitata as an immunostimulatory aquafeed supplement, with promising benefits for fish health and resilience under adverse rearing conditions. Full article

The urban heat island (UHI) effect and its associated extreme weather events have adverse impacts on human environment-coupled systems. However, the spatiotemporal variations in the UHI effect, as well as potential influencing factors, across climate zones remain poorly understood. This study explored how climate zones influenced the spatiotemporal variation in, trends in, and drivers of summer daytime surface UHI intensity (SUHII) in 220 Chinese cities located in five climate zones from 2000 to 2020. SUHII was quantified using MODIS land surface temperature (LST) data and remote sensing-derived urban built-up area masks were used to quantify SUHII. The Mann–Kendall test was applied to detect long-term SUHII trends, while Pearson correlation and stepwise multiple regression analyses were performed to identify key climatic and geographic drivers across different climate zones. The results indicated summer daytime SUHII values of 1.75 °C ± 1.19 °C, 1.74 °C ± 0.81 °C, 2.37 °C ± 0.75 °C, 2.14 °C ± 1.00 °C, and 2.36 °C ± 0.91 °C for the middle temperate zone (MTZ), south temperate zone (STZ), north subtropical zone (NSZ), middle subtropical zone (MSZ), and south subtropical zone (SSZ), respectively. In most cities, the SUHII increased significantly over time (p < 0.05). Pearson’s correlation analysis indicated that the enhanced vegetation index (EVI) and net radiation (NR) were moderately correlated with the SUHII in the MTZ, with correlation coefficients (r) of 0.465 and 0.42 (p < 0.05). Using a multivariate stepwise regression model, the relative contributions of various influencing factors to the UHI effect were quantified, explaining 27.1% to 57.2% of the variation across different climate zones. In particular, the economic vulnerability index and population density were the main factors affecting the SUHII in the MTZ and SSZ. Our findings support the development of policies aimed at mitigating the UHI effect by addressing the specific requirements of different climate zones to reduce. Full article

Between 14 and 16 June 2023, an extratropical cyclone affected the south-southeastern coast of Brazil, causing significant damage and loss of life. In the state of Rio Grande do Sul, Civil Defense authorities reported at least 16 fatalities. Although numerical models can simulate the general characteristics of extratropical cyclones, they often struggle to accurately represent the intensity and timing of strong winds and heavy precipitation. One approach to improving such simulations is the use of convective-permitting models (CPMs), in which convection is explicitly resolved. In this context, the main objective of this study is to assess the performance of the Weather Research and Forecasting (WRF) model in CP mode, nested in the ERA5 reanalysis, in representing both the synoptic and mesoscale structures of the cyclone, as well as its associated strong winds and precipitation. The WRF-CP successfully simulated the cyclone’s track, though with some discrepancies in the cyclone location during the first 12 h. Comparisons with radar-based precipitation estimates indicated that the WRF-CP captured the location of the observed precipitation bands. During the cyclone’s occlusion phase—when precipitation was particularly intense—hourly simulated precipitation and 10 m wind (speed, zonal, and meridional components) were evaluated against observations from meteorological stations. WRF-CP demonstrated strong skill in simulating both the timing and intensity of precipitation, with correlation coefficients exceeding 0.4 and biases below 0.5 mm h⁻¹. Some limitations were observed in the simulation of 10 m wind speed, which tended to be overestimated. However, the model performed well in simulating the wind components, particularly the zonal component, as indicated by predominantly high correlation values (most above 0.4), suggesting a good representation of wind direction, which is a function of the zonal and meridional components. Overall, the simulation highlights the potential of WRF-CP for studying extreme weather events, including the small-scale structures embedded within synoptic-scale cyclones responsible for producing adverse weather. Full article

Climate change and variability are characterized by unpredictable and extreme weather events. They adversely impact the highly susceptible smallholder farmers in sub-Saharan Africa, who heavily rely on rain-fed agriculture. Climate smart agriculture (CSA) practices have been extensively promoted as offering long-term solutions to changing climate conditions, while enhancing the productivity and sustainability of African agricultural systems. Despite this, the adoption rate remains low among smallholder farmers. Understanding the factors that influence adoption of these practices among this key farming community is therefore necessary to increase their adoption. In this paper, we review and summarize findings from existing studies on the factors that influence the adoption of CSA practices by smallholder farmers in sub-Saharan Africa. Our review reveals that land tenure security, access to information and extension services, and affiliation to group membership positively influence adoption. On the other hand, gender, risk perception, and off-farm income had conflicting effects by reporting both positive and negative influences on CSA adoption. We conclude that CSA adoption options are local-specific, and their development and implementation should emphasize locally tailored knowledge, skills, and resources. Full article

This study integrated the strengths of ensemble learning and soft computing to develop a future regional rainfall model for evaluating the complex characteristics of island precipitation. Soft computing uses the well-developed adaptive neuro-fuzzy inference system, which has been successfully applied in atmospheric hydrology and combines the features of neural networks and fuzzy logic. This combination enables artificial intelligence (AI) to effectively represent reasoning derived from complex data and expert experience. Due to the multiple atmospheric and hydrological factors that influence rainfall, the nonlinear interrelations among them are highly intricate. Nonlinear principal component analysis can extract nonlinear features from the data, reduce dimensionality, and minimize the adverse effects of data noise and excessive input factors on soft computing, which may otherwise result in poor model performance. Ultimately, ensemble learning enhances prediction accuracy and reduces uncertainty. This study used Tamsui and Kaohsiung in Taiwan as case study locations. Historical monthly rainfall data (January 1950 to December 2005) from Tamsui Station and Kaohsiung Station of the Central Weather Administration, along with historical and varied emission scenario data (RCP 4.5 and RCP 8.5) from three AR5 GCM models (ACCESS 1.0, CSIRO-MK3.6.0, MRI-CGCM3), were used to evaluate future regional rainfall trends and uncertainties through the method proposed in this study. The research findings indicate the following: (1) Ensemble learning results demonstrate that all examined general circulation models effectively simulate historical rainfall trends. (2) The average rainfall trends under the RCP 4.5 emission scenario are generally consistent with historical rainfall trends. (3) The exceedance probabilities of future rainfall during the mid-term (2061–2080) and long-term (2081–2100) suggest that Kaohsiung may experience precipitation events with higher rainfall than historical data during dry seasons (October to April of next year), while Tamsui Station may exhibit greater variability in terms of exceedance probabilities. (4) Under both the RCP 4.5 and RCP 8.5 emission scenarios, the percentage changes in future rainfall variability at Kaohsiung Station during dry seasons are higher than those during wet seasons (May to September), indicating an increased risk of extreme precipitation events during dry seasons. Full article

Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R² = 0.87, p < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions. Full article

Climate change negatively affects agricultural productivity by altering weather patterns, increasing the frequency of extreme events, and disrupting growing seasons. These changes lead to reduced crop yields, soil degradation, and challenges to food security. This research investigates how climate change affects plant cultivation and agricultural farming operations in Khemisset Province of Morocco. A research study based on surveys of 120 farmers combines quantitative and qualitative methods to determine their views about climate change and their adaptive measures. The researchers select their farmers according to land conditions, plant life, and livestock management patterns. The obtained data demonstrate climate parameter deterioration throughout the period extending from 1985 to 2015, which corresponds with meteorological measurements. Climate variability produces adverse environmental effects which negatively affect agricultural output. The Zemmour tribe members and other farmers use different agricultural adaptation strategies, including fertilizer application, rotational cropping, and planting maturation-premature seeds. The research findings highlight the necessity of developing specific adaptation methods that defend agricultural sectors against climate change risks and secure food supplies. This investigation adds to climate resilience knowledge by delivering important findings that guide agricultural sustainability policy development and implementation. Full article

Bamboo forest ecosystems are an important component of the Earth’s terrestrial ecosystems and play an important role in addressing the global timber crisis as well as climate change. Bamboo is a typical shallow-rooted, fast-growing clonal plant species whose developed rhizome system and high canopy closure play an important role in soil and water conservation. The function of soil and water conservation services of bamboo forests can intuitively reflect the regional regulation of precipitation, the redistribution function of precipitation, and the function of soil fixation, which is one of the crucial ecological service functions in regional ecosystems. Bamboo forests are divided into monopodial bamboo forests, sympodial bamboo forests, and mixed bamboo forests, which are mainly distributed in tropical and subtropical mountainous areas. The region’s variable climate, abundant precipitation, and high potential risk of soil erosion, in conjunction with the frequent operation of bamboo forests and frequent occurrence of extreme weather events, have the potential to adversely affect the ecosystem function of bamboo forests. Presently, bamboo forests are primarily managed through the cultivation of bamboo, with the objective of enhancing productivity. Extensive research has been conducted on the long-term maintenance of bamboo forest productivity. However, there is a paucity of research on the mechanisms of management measures for ecosystem stability and the development of adaptive management technology systems suitable for soil and water conservation, carbon sequestration and sink enhancement, and biodiversity conservation. This paper is predicated on the biological characteristics of bamboo and, thus, aims to compile the extant research progress on the following subjects: the role of rainfall redistribution in bamboo forest canopies, the role of deadfall interception, and the mechanism of soil fixation mechanics of the root system. It also synthesizes the current status of research on the impact of traditional management measures on the soil and water conservation function of bamboo forests. Finally, it discusses the problems of current research and the direction of future development. Full article

The Atlantic puffin (Fratercula arctica) is a seabird species characterized by great diving capabilities and transoceanic migratory behavior. These movements contribute to the dispersion of the species during migration, and episodes of mortality associated with migration may be a normal event in the dynamic of the Atlantic puffin populations. This study aimed to describe the anatomopathological findings of an unusual mortality event of Atlantic puffins observed during the non-breeding period along the coast of the Canary Islands. The most consistent gross finding during necropsy was generalized muscle atrophy and fat depletion. The main histological findings were centered in the urinary tract, with dilation and inflammation of the primary ureter branch and medullary cones, and intraluminal trematodes identified as Renicola sloanei based on morphology and molecular analysis. Influenza virus infection was ruled out. The postmortem investigations performed in this mortality event of Atlantic puffins indicate that the animals were severely emaciated and suffered from nephropathy. The etiopathological investigation performed in relation to this mortality event of Atlantic puffins indicates starvation associated with bad weather conditions during migratory movement as the most likely cause of the unusual mortality event. Full article

On the morning of 7 March 2025, the Argentine district of Bahía Blanca experienced a severe flooding that led to at least 15 fatalities. This study presents the main aspects of the event based on different data sources that helped to explain the exceptional precipitation of about 300 mm and rapid flooding. The results indicated that Bahía Blanca district presented flooded areas of approximately 33 km² (1.4% of the total area) on 10 March, most of them concentrated in the non-urbanized zones. However, a total of 18 km² (0.8% of the total area) was still identified on 11 March, with a greater impact on the low-lying urban areas of the Bahía Blanca, General Daniel Cerri, and Ingeniero White towns. The likelihood of severe weather development was confirmed from instability indices. The very high moisture content along a low-level convergence line, jointly with upper-level divergence, contributed to deep convective cloud development that affected Bahía Blanca for at least 6 h. Increasing knowledge of urban floods from different data sources can support weather forecasts to provide timely warnings, essential to mitigate the adverse impacts of these extreme weather events on low-lying urban areas. Full article

Global environmental and climatic changes have become an ever-increasing trepidation worldwide due to the unprecedented changes in temperatures, precipitation, weather, and ecosystems at the international, regional, and local levels. This study attempts to find out the impacts and vulnerabilities of climate-induced hazards and environmental degradation on the lives of Indigenous people in the Chittagong Hill Tracts (CHT), along with finding out the factors of environmental degradation in Indigenous communities. This study also explores the adaptation strategies of Indigenous people in adverse situations in the Chittagong Hill Tracts. For conducting this study, a critical ethnographic approach is adopted, together with participant observation, in-depth interviews (N = 55), and focus group discussions (FGDs) (N = 5, 48 participants), following purposive sampling over one year (October 2019 to October 2020). The findings of this study indicate that climate-induced hazards and environmental crisis brings unprecedented suffering to the lives and livelihoods of Indigenous people. Climate change and variability together with human causes contribute to creating an environmental crisis that threatens food security, creates water scarcity, and disrupts agricultural activities and household properties in Indigenous communities. The findings also indicate that Indigenous people are bound to change their traditional jhum cultivation and adjust their traditional housing structure apart from searching for alternative livelihoods due to climatic events and environmental crises. However, Indigenous people try to adapt to this adverse situation following their life-long experiences and local knowledge. This study helps policymakers as well as governments to understand the major drivers of environmental crisis and climatic events in Indigenous communities, together with finding out the solutions and recuperating the living conditions of the marginalized groups in Bangladesh and beyond. Full article

Thunderstorms are weather phenomena that comprise thunder and lightning. They typically result in heavy precipitation, including rain, snow, and hail. Thunderstorms have adverse effects on flight at both the ground and the upper levels of the troposphere. The characteristics of the thunderstorm of Istanbul International Airport (International Civil Aviation Organization (ICAO) code: LTFM) have been investigated because it is currently one of the busiest airports in Europe and the seventh-busiest airport in the world. Geopotential height (m), temperature (°C), dewpoint temperature (°C), relative humidity (%), mixing ratio (g kg⁻¹), wind direction (°), and wind speed (knots) data for the ground level and upper levels of the İstanbul radiosonde station were obtained from the Turkish State Meteorological Service (TSMS) for 29 October 2018 and 1 January 2023. Surface data were regularly collected by the automatic weather stations near the runway and the upper-level data were collected by the radiosonde system located in the Kartal district of İstanbul. Thunderstorm statistics, stability indices, and meteorological variables at the upper levels were evaluated for this period. Thunderstorms were observed to be more frequent during the summer, with a total of 51 events. June had the highest number of thunderstorm events with a total of 32. This averages eight events per year. A total of 72.22% occurred during trough and cold front transitions. The K index and total totals index represented the thunderstorm events better than other stability indices. In total, 75% of the thunderstorm days were represented by these two stability indices. The results are similar to the covering of this area: the convective available potential energy (CAPE) values which are commonly used for atmospheric instability are low during thunderstorm events, and the K and total totals indices are better represented for thunderstorm events. This study investigates thunderstorm events at the LTFM, providing critical insights into aviation safety and operational efficiency. The research aims to improve flight planning, reduce weather-related disruptions, and increase safety and also serves as a reference for airports with similar climatic conditions. Full article

Recent studies have revealed a rise in extreme heat events worldwide, while extreme cold has reduced. It is highly likely that human-induced climate forcing will double the risk of exceptionally severe heat waves by the end of the century. Although extreme heat is expected to have more significant socioeconomic impacts than cold extremes, the latter contributes to a wide range of adverse effects on the environment, various economic sectors and human health. The present research aims to evaluate the contemporary spatio-temporal variations of extreme cold events in Southeastern Europe through the intensity–duration cold spell model developed for quantitative assessment of cold weather in Bulgaria. We defined and analyzed the suitability of three indicators, based on minimum temperature thresholds, for evaluating the severity of extreme cold in the period 1961–2020 across the Köppen–Geiger climate zones, using daily temperature data from 70 selected meteorological stations. All indicators show a statistically significant decreasing trend for the Cfb and Dfb climate zones. The proposed intensity–duration model demonstrated good spatio-temporal conformity with the Excess Cold Factor (ECF) severity index in classifying and estimating the severity of extreme cold events on a yearly basis. Full article

Climate change and global warming have been shown to increase the frequency and intensity of extreme weather events. Concurrently, substantial efforts are being directed toward fostering the transition to a low-carbon economy. These concurrent trends result in the emergence of both physical and transition climate risks. This study investigates the impacts of climate risks, both physical and transition, on the return of major industrial metals (aluminum, copper, iron, lead, tin, nickel, and zinc) between January 2005 and December 2023. Employing the novel multivariate quantile-on-quantile regression (m-QQR) approach, this study examines how climate risks affect metal markets under different market conditions and risk levels. The results reveal that transition risks exert a more significant adverse impact on metal returns during bearish markets conditions, particularly for metals linked to high-emission industries, while physical risks affect metal returns across a wider range of quantiles, often increasing volatility during extreme market conditions. Furthermore, copper and nickel, both of which are crucial for renewable energy development, demonstrate resilience at higher quantiles, highlighting their role in the transition to a low-carbon economy. Finally, these two metals may serve as effective hedges against losses in other metals that are more vulnerable to transition risks, like aluminum and lead. Full article

With the increasing frequency of floods in recent decades, particularly in West Africa, many regions have faced unusual and recurrent flooding events. Communities in flood-prone areas experience heightened insecurity, loss of property, and, in some cases, serious injuries or fatalities. Consequently, flood risk assessment and mitigation have become essential. This comparative study between Niamey and Lokoja employs Geographic Information Systems (GIS) and the Analytical Hierarchy Process (AHP) to delineate flood susceptibility, vulnerability, and risk zones. The study utilized a comprehensive range of thematic layers, with weight percentages assigned to each parameter as follows: 29% for elevation, 24% for slope, 15% for the Topographic Wetness Index (TWI), 9% for drainage density, 9% for distance from rivers, 4% for both precipitation and the Normalized Difference Water Index (NDWI), and 2% each for the Normalized Difference Vegetation Index (NDVI) and soil type. To validate these weightings, a consistency ratio was calculated, ensuring it remained below 10%. The findings reveal that 32% of the Niamey study area is at risk of flooding, compared to approximately 15% in Lokoja. The results highlight a very high flood potential, particularly in areas near the Niger River, with this potential decreasing as elevation increases. Given the current prevalence of extreme weather events in West Africa, it is crucial to employ effective tools to mitigate their adverse impacts. This research will assist decision-makers in quantifying the spatial vulnerability of flood-prone areas and developing effective flood risk assessment and mitigation strategies in the region. Full article