Search Results (354)

Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource allocation, and decision making in disaster management. In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. We propose LinU-Mamba, a model with a U-Net-based vision Mamba architecture, with light spatial attention in skip connections, and an efficient linear attention mechanism in the encoder and decoder to better capture salient fire information in the dataset. The model is trained and evaluated on the two-dimensional remote sensing dataset Next Day Wildfire Spread (NDWS), which maps fire data across the United States with fire entries, topography, vegetation, weather, drought index, and population density variables. The results demonstrate that our approach achieves superior performance compared to existing deep learning methods applied to the same dataset, while showing an efficient training time. Furthermore, we highlight the impacts of pre-training and feature selection in remote sensing, as well as the impacts of linear attention use in our model. As far as we know, LinU-Mamba is the first model based on Mamba used for wildfire spread prediction, making it a strong foundation for future research. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

While water conservancy projects continuously enhance flood control and resource allocation capabilities, the adverse impacts on basin systems, particularly the structural disruption of surface water–groundwater continuity, have become increasingly pronounced. Therefore, establishing quantitative assessment of water system connectivity as a critical foundation for optimizing spatial water distribution, maintaining ecohydrological equilibrium, and enhancing flood–drought regulation efficacy is important. Focusing on the regulated reaches of the Panjiakou, Daheiting, and Taolinkou reservoirs in the Luan River Basin, this study established and integrated a three-dimensional assessment framework that synthesizes hydrological processes, hydraulic structural effects, and human activities as three fundamental drivers, and employed the Analytic Hierarchy Process (AHP) to develop a quantitative connectivity evaluation system. Results indicate that water conservancy projects significantly altered basin connectivity: surface water connectivity decreased by 0.40, while groundwater connectivity experienced a minor reduction (0.25) primarily through reservoir seepage. Consequently, the integrated surface–groundwater system declined by 0.39. Critically, project scale governs surface connectivity attenuation intensity, which substantially exceeds impacts on groundwater systems. The comprehensive assessment system developed in this study provides theoretical and methodological support for diagnosing river connectivity, formulating ecological restoration strategies, and protecting basin ecosystems. Full article

The Inner Mongolia Plateau (IMP), situated in the arid and semi-arid ecological transition zone of northern China, is particularly vulnerable to both climate change and human activities. Understanding the spatiotemporal vegetation dynamics and their driving forces is essential for regional ecological management. This study employs Sen’s slope estimation, BFAST analysis, residual trend method and Geodetector to analyze the spatial patterns of Normalized Difference Vegetation Index (NDVI) variability and distinguish between climatic and anthropogenic influences. Key findings include the following: (1) From 1982 to 2022, vegetation cover across the IMP exhibited a significant greening trend. Zonal analysis showed that this spatial heterogeneity was strongly regulated by regional hydrothermal conditions, with varied responses across land cover types and pronounced recovery observed in high-altitude areas. (2) In the western arid regions, vegetation trends were unstable, often marked by interruptions and reversals, contrasting with the sustained greening observed in the eastern zones. (3) Vegetation growth was primarily temperature-driven in the eastern forested areas, precipitation-driven in the central grasslands, and severely limited in the western deserts due to warming-induced drought. (4) Human activities exerted dual effects: significant positive residual trends were observed in the Hetao Plain and southern Horqin Sandy Land, while widespread negative residuals emerged across the southern deserts and central grasslands. (5) Vegetation change was driven by climate and human factors, with recovery mainly due to climate improvement and degradation linked to their combined impact. These findings highlight the interactive mechanisms of climate change and human disturbance in regulating terrestrial vegetation dynamics, offering insights for sustainable development and ecosystem education in climate-sensitive systems. Full article

Arundo donax L. has been introduced in markets worldwide due to its economic value. However, it is listed in the world’s 100 worst alien invasive species because it easily escapes from cultivation, and forms dense monospecific stands in riparian areas, agricultural areas, and grassland areas along roadsides, including in protected areas. This species grows rapidly and produces large amounts of biomass due to its high photosynthetic ability. It spreads asexually through ramets, in addition to stem and rhizome fragments. Wildfires, flooding, and human activity promote its distribution and domination. It can adapt to various habitats and tolerate various adverse environmental conditions, such as cold temperatures, drought, flooding, and high salinity. A. donax exhibits defense mechanisms against biotic stressors, including herbivores and pathogens. It produces indole alkaloids, such as bufotenidine and gramine, as well as other alkaloids that are toxic to herbivorous mammals, insects, parasitic nematodes, and pathogenic fungi and oomycetes. A. donax accumulates high concentrations of phytoliths, which also protect against pathogen infection and herbivory. Only a few herbivores and pathogens have been reported to significantly damage A. donax growth and populations. Additionally, A. donax exhibits allelopathic activity against competing plant species, though the allelochemicals involved have yet to be identified. These characteristics may contribute to its infestation, survival, and population expansion in new habitats as an invasive plant species. Dense monospecific stands of A. donax alter ecosystem structures and functions. These stands impact abiotic processes in ecosystems by reducing water availability, and increasing the risk of erosion, flooding, and intense fires. The stands also negatively affect biotic processes by reducing plant diversity and richness, as well as the fitness of habitats for invertebrates and vertebrates. Eradicating A. donax from a habitat requires an ongoing, long-term integrated management approach based on an understanding of its invasive mechanisms. Human activity has also contributed to the spread of A. donax populations. There is an urgent need to address its invasive traits. This is the first review focusing on the invasive mechanisms of this plant in terms of adaptation to abiotic and biotic stressors, particularly physiological adaptation. Full article

Ponderosa pine (Pinus ponderosa Lawson & C. Lawson) forests in the western United States have experienced reduced fire frequency since Euro-American settlement, usually because of successful fire suppression policies and even without such human impacts at remote sites in the Great Basin and Mojave Deserts. In an effort to improve our understanding of long-term environmental dynamics in sky-island ecosystems, we developed tree-ring chronologies from ponderosa pines located in the Sheep Mountain Range of southern Nevada, inside the Desert National Wildlife Refuge (DNWR). After comparing those dendrochronological records with other ones available for the south-central Great Basin, we analyzed their climatic response using station-recorded monthly precipitation and air temperature data from 1950 to 2024. The main climatic signal was December through May total precipitation, which was then reconstructed at annual resolution over the past five centuries, from 1490 to 2011 CE. The mean episode duration was 2.6 years, and the maximum drought duration was 11 years (1924–1934; the “Dust Bowl” period), while the longest episode, 19 years (1905–1923), is known throughout North America as the “early 1900s pluvial”. By quantifying multi-annual dry and wet episodes, the period since DNWR establishment was placed in a long-term dendroclimatic framework, allowing us to estimate the potential drought resilience of its unique, tree-dominated environments. Full article

In recent years, under the influence of climate change and human activities, droughts and floods have occurred frequently in the Yangtze River Basin (YRB), seriously threatening socioeconomic development and ecological security. The topography and climate of the YRB are complex, so it is crucial to develop appropriate drought and flood policies based on the drought and flood characteristics of different sub-basins. This study calculated the water storage deficit index (WSDI) based on the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GFO) mascon model, extended WSDI to the bidirectional monitoring of droughts and floods in the YRB, and verified the reliability of WSDI in monitoring hydrological events through historical documented events. Combined with the wavelet method, it revealed the heterogeneity of climate responses in the three sub-basins of the upper, middle, and lower reaches. The results showed the following. (1) Compared and verified with the Standardized Precipitation Evapotranspiration Index (SPEI), self-calibrating Palmer Drought Severity Index (scPDSI), and documented events, WSDI overcame the limitations of traditional indices and had higher reliability. A total of 21 drought events and 18 flood events were identified in the three sub-basins, with the lowest frequency of drought and flood events in the upper reaches. (2) Most areas of the YRB showed different degrees of wetting on the monthly and seasonal scales, and the slowest trend of wetting was in the lower reaches of the YRB. (3) The degree of influence of teleconnection factors in the upper, middle, and lower reaches of the YRB had gradually increased over time, and, in particular, El Niño Southern Oscillation (ENSO) had a significant impact on the droughts and floods. This study provided a new basis for the early warning of droughts and floods in different sub-basins of the YRB. Full article

For a long time, drought disasters have brought about a wide range of negative impacts on human socio-economics. Especially in large basins with many tributaries, once hydrological drought occurs synchronously in several tributaries, the hydrological drought condition in the mainstream will be aggravated, which will lead to more serious losses. However, there is still a lack of research on the probabilistic risk of simultaneous hydrologic droughts in various areas of large watersheds. In this study, the Standardized Runoff Index was used to characterize hydrological drought, and the Standardized Runoff Index (SRI) sequence characteristics of each region were analyzed. Subsequently, a multiregional hazard encounter probability distribution model with an R-vine structure was constructed with the help of the vine copula function to study the risk pattern of simultaneous hydrological drought in multiple tributaries under environmental changes. The model results showed that the probability of the four basins gradually decreased from 7.5% to 0.16% when the SRI changed from ≤−0.5 to ≤−2.0, indicating that the likelihood of the joint distribution of the compound disaster decreases with increase in the drought extremes. Meanwhile, the probability of hydrological drought in the three major basins showed significant spatial differences, and the risk ranking was Dongting Lake Basin > Poyang Lake Basin > Han River Basin. The model constructed in this study reveals the disaster risk law, provides theoretical support for the measurement of hydrological drought risk in multiple regions at the same time, and is of great significance for the prediction of compound drought disaster risk. Full article

Climate change is a pressing global issue with severe consequences for the planet and human health. The Earth’s temperature has risen by 2 °C from 1901 to 2023, and this warming trend is expected to continue, causing potentially dangerous shifts in climate. Climate change impacts are already visible, with more frequent and severe heat waves, droughts, intense rain, and floods becoming increasingly common. Therefore, hydropower can contribute to addressing the global climate change issue and help to achieve global energy transition and stabilize global energy security. A Long Short-Term Memory (LSTM)-based model implemented in Python for global and regional hydropower forecasting was developed for a study period of 2023 to 2060 by taking the input data from 1980 to 2022. The results revealed that Asian countries have greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh in 2030, 2772.06 TWh in 2040, 2811.41 TWh in 2050, and 3195.79 TWh in 2060, as compared with the other regions of the world like the Middle East, Africa, Asia, Common Wealth of Independent State (CIS), Europe, North America, and South and Central America. The global hydropower potential is also expected to increase from 4350.12 TWh in 2023 to 4806.26 TWh in 2030, 5393.80 TWh in 2040, 6003.53 TWh in 2050, and 6644.06 TWh in 2060, which is sufficient for achieving energy transition and energy security goals. Furthermore, the performance and accuracy of the LSTM-based model were found to be 98%. This study will help in the efficient scheduling and management of hydropower resources, reducing uncertainties caused by environmental variability such as precipitation and runoff. The proposed model contributes to the energy transition and security that is needed to meet the global climate targets. Full article

With climate warming, the global precipitation patterns have undergone significant changes, which will profoundly impact flood–drought disaster regimes and socioeconomic development in key regions of human activity worldwide. The convergence zone of the Indian Ocean monsoon and Pacific monsoon in China covers most of the middle and lower reaches of the Yangtze River (MLRYR), which is located in the transitional area of the second and third steps of China’s terrain. Changes in precipitation patterns in this region will significantly impact flood and drought control in the MLRYR, as well as the socioeconomic development of the MLRYR Economic Belt. In this study, Huaihua area in China was selected as the study area to study the characteristics of regional precipitation change, and to analyze the evolution in the trends in annual precipitation, extreme precipitation events, and their spatiotemporal distribution, so as to provide a reference for the study of precipitation change patterns in the intersection zone. This study utilizes precipitation data from meteorological stations and the China Meteorological Forcing Dataset (CMFD) reanalysis data for the period 1979–2023 in Huaihua region. The spatiotemporal variation in precipitation in the study area was analyzed by using linear regression, the Mann–Kendall trend test, the moving average method, the Mann–Kendall–Sneyers test, wavelet analysis, and R/S analysis. The results demonstrate the following: (1) The annual precipitation in the study area is on the rise as a whole, the climate tendency rate is 9 mm/10 a, and the precipitation fluctuates greatly, showing an alternating change of “dry–wet–dry–wet”. (2) Wavelet analysis reveals that there are 28-year, 9-year, and 4-year main cycles in annual precipitation, and the precipitation patterns at different timescales are different. (3) The results of R/S analysis show that the future precipitation trend will continue to increase, with a strong long-term memory. (4) Extreme precipitation events generally show an upward trend, indicating that their intensity and frequency have increased. (5) Spatial distribution analysis shows that the precipitation in the study area is mainly concentrated in the northeast and south of Jingzhou and Tongdao, and the precipitation level in the west is lower. The comprehensive analysis shows that the annual precipitation in the study area is on the rise and has a certain periodic precipitation law. The spatial distribution is greatly affected by other factors and the distribution is uneven. Extreme precipitation events show an increasing trend, which may lead to increased flood risk in the region and downstream areas. In the future, it is necessary to strengthen countermeasures to reduce the impact of changes in precipitation patterns on local and downstream economic and social activities. Full article

Maize is one of the top five field crops worldwide and is indispensable as animal feed, serves as a raw material in many industries, and is a staple for human food. However, its production is under increasing pressure mainly due to abiotic stress. Drought and excessive precipitation, air temperature fluctuations, and reduced soil fertility due to inadequate soil pH reactions are among the biggest challenges that must be overcome. Therefore, the goal of this study was to determine the effects of these combined stressful abiotic conditions on maize grain yield and quality and to determine the genetic-specific response of maize genotypes in such conditions. The experiment was set up in eastern Croatia according to the randomized complete block design in four replications. A total of 10 maize hybrids of different FAO maturity groups were evaluated across four diverse environments, each subjected to one or two abiotic stresses (extreme precipitation, drought, high air temperatures, and acidic soil). Analysis of variance revealed that all treatment effects were statistically significant, except for the effect of hybrids on grain yield. Depending on the effect of abiotic stress, the variations among environments were up to 51.4% for yield and up to 12.1%, 18.9%, and 0.81% for protein, oil, and starch content, respectively. Differences among hybrids were less pronounced for yield (7.9%), while for protein (13.5%), oil (17.3%), and starch content (1.5%) were similar. However, the largest variation was found for the interaction effect. In the conducted research, ENV2 recorded the highest grain yield, along with the highest oil and starch content, as well as the second-highest protein content, while the hybrid effect remained unclear. Generally, ENV4 had the greatest negative impact due to the combined effects of extreme abiotic stresses, including soil acidity, drought, and high air temperatures. Full article

It is essential to emphasize the significant impacts of climate change, which are evident in the form of severe and prolonged droughts, hurricanes, snowstorms, and other climatic disturbances. These challenges are particularly pronounced in urban environments and among human populations. The situation is further aggravated by the increasing utilization of available open spaces for residential and industrial development, leading to heightened energy consumption, elevated pollution levels, and increased carbon emissions, all of which negatively affect public health. The primary objective of this review article is to provide a comprehensive evaluation of current research, with a particular focus on the innovative use of residual biomass from urban vegetation for biochar production in the United States. This research entails an exhaustive review of existing literature to assess the implementation of a carbon-negative wood biomass biochar system as a strategic approach to sustainable urban management. By transforming urban wood waste—including tree trimmings, construction debris, and storm-damaged timber—into biochar through pyrolysis, a thermochemical process that sequesters carbon while generating renewable energy, we can leverage this valuable resource. The resulting biochar offers a range of co-benefits: it enhances soil health, improves water retention, reduces stormwater runoff, and lowers greenhouse gas emissions when applied in urban green spaces, agriculture, and land restoration projects. This review highlights the advantages and potential of converting urban wood waste into biochar while exploring how municipalities can strengthen their green ecosystems. Furthermore, it aims to provide a thorough understanding of how the utilization of woody biomass biochar can contribute to mitigating urban carbon emissions across the United States. Full article

The increase in chronic diseases and climate change in recent decades has been driven by food systems that affect both human health and the environment. This study investigated the interrelation between food consumption, obesity, undernutrition, and climate change, aiming to understand how these factors connect within the global syndemic. The methodology used was a scoping review, in which 12 articles were analyzed after an initial search that resulted in 11,208 references. The references were screened using the Rayyan software (Rayyan Systems Inc. (Doha, Qatar), version 1.6.1 and web-based version), removing duplicates and assessing the studies based on eligibility criteria. The articles addressed different aspects, such as the relationship between food consumption, obesity, undernutrition, and climate change, providing data on how food insecurity and socioeconomic conditions influence these conditions. In sequence, we developed a conceptual model to offer a detailed view of the factors affecting the global syndemic, considering the availability of food, its accessibility, stability in supply, and its use in the diet. The model recognizes that climate change affects food consumption both directly and indirectly. Direct effects include the impact of extreme weather events—such as floods and droughts—on the availability, access, quantity, and quality of food. Indirectly, climate change exacerbates socioeconomic vulnerabilities and disrupts food systems in more structural ways, contributing to increased food insecurity. The findings revealed that food insecurity, in turn, can lead to both obesity and undernutrition, particularly among vulnerable populations. There was a scarcity of studies that integrated the relationship between undernutrition, climate change, and food consumption, especially in certain regional contexts such as Latin America. The evidence gathered in the literature and the conceptual model provide a foundation for future research and the development of more effective public policies that integrate food issues, public health, and climate change in a more holistic and interconnected approach. Full article

Climate change poses a significant threat to Ethiopian agriculture, impacting both cereal and livestock production through rising temperatures, erratic rainfall, prolonged droughts, and increased pest and disease outbreaks. These challenges intensify food insecurity, particularly for smallholder farmers and pastoralists who rely on climate-sensitive agricultural systems. This systematic review aims to synthesize the impacts of climate change on Ethiopian agriculture, with a specific focus on cereal production and livestock feed quality, while exploring effective adaptation strategies that can support resilience in the sector. The review synthesizes 50 peer-reviewed publications (2020–2024) from the Climate Change Effects on Food Security project, which supports young African academics and Higher Education Institutions (HEIs) in addressing Sustainable Development Goals (SDGs). Using PRISMA guidelines, the review assesses climate change impacts on major cereal crops and livestock feed in Ethiopia and explores adaptation strategies. Over the past 30 years, Ethiopia has experienced rising temperatures (0.3–0.66 °C), with future projections indicating increases of 0.6–0.8 °C per decade resulting in more frequent and severe droughts, floods, and landslides. These shifts have led to declining yields of wheat, maize, and barley, shrinking arable land, and deteriorating feed quality and water availability, severely affecting livestock health and productivity. The study identifies key on-the-ground adaptation strategies, including adjusted planting dates, crop diversification, drought-tolerant varieties, soil and water conservation, agroforestry, supplemental irrigation, and integrated fertilizer use. Livestock adaptations include improved breeding practices, fodder enhancement using legumes and local browse species, and seasonal climate forecasting. These results have significant practical implications: they offer a robust evidence base for policymakers, extension agents, and development practitioners to design and implement targeted, context-specific adaptation strategies. Moreover, the findings support the integration of climate resilience into national agricultural policies and food security planning. The Climate Change Effects on Food Security project’s role in generating scientific knowledge and fostering interdisciplinary collaboration is vital for building institutional and human capacity to confront climate challenges. Ultimately, this review contributes actionable insights for promoting sustainable, climate-resilient agriculture across Ethiopia. Full article

Middle East (ME) countries have arid and semi-arid climates with low annual precipitation and considerable geographical and temporal variability, which contribute to their extremely erratic rainfall. The generation of timely and accurate climatic information for the ME is anticipated to be aided by global reanalysis products and satellite-based precipitation estimations. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Hazards Group Infra-Red Precipitation (CHIRPS) on Google Earth Engine (GEE) were used to study rainfall in eleven chosen ME counties from 2000 to 2023. This study shows that Saudi Arabia (509.64 mm/December–January–February; DJF), Iraq (211.50 mm/September–October–November; SON), Iran (306.35 mm/SON), Jordan (161.28 mm/DJF), Kuwait (44.66 mm), Syria (246.51 mm/DJF), UAE–Qatar–Bahrain (28.62 mm/SON), Oman (64.90 mm/June–July–August; JJA), and Yemen (240.27 mm/SON) were the countries with the highest rainfall. Due to improved ground station integration, CHIRPS also reports larger rainfall anomalies, with a peak of 59.15 mm in DJF, mainly in northern Iran, Iraq, and Syria. PERSIANN understates heavy rainfall, probably because it relies on infrared satellite data, with a maximum anomaly of 4.15 mm. Saudi Arabia saw heavy rain during the JJA months, while others received less. More accurate rainfall forecasts in the ME can lessen the effects of floods and droughts, promoting environmental resilience and regional economic stability. Therefore, a more comprehensive understanding of all the relevant components is necessary to address these difficulties. Both environmental and human impacts must be taken into account for sustainable solutions. Full article