Editor’s Choice Articles

In recent decades, the suspended sediment load (SSL) of many rivers around the world has shown a significant decreasing trend, which is particularly prominent in large river basins such as the Yangtze River and the Yellow River. One of the key challenges currently faced is how to quantitatively determine the relative influence of the dominant factors on the basis of systematically assessing the changing trend of SSL. This study takes the upper reaches of the Yangtze River as the research object. Based on the observation data from representative hydrological stations during 1966–2024, it systematically analyzes the interannual variation trend of SSL in different sections of the study river reach, identifies several mutation points, and divides the SSL change process into a baseline period, change period I, and change period II. Using the SCRCQ (slope change ratio of cumulative quantity) method, the study finds that the contribution ratio of human activities to the reduction of SSL in different sections of the study river reach ranges from 87.5% to 111.9%, the contribution ratio of precipitation change ranges from −14.3% to 12.4%, and the contribution ratio of evapotranspiration change ranges from −0.1% to 0.6%. For the entire upper Yangtze River basin, the contribution ratios of human activities to the reduction of SSL during change period I and change period II are 87.5% and 95.1%, respectively, while those of climate change are 12.4% and 4.9%, respectively. Human activities play an absolutely dominant role in the reduction of SSL in the upper Yangtze River. The results of this study can provide guidance for the scientific management of river reaches with concentrated large-scale reservoirs in the upper Yangtze River and also offer references for the formulation of management measures for similar rivers worldwide. Full article

Water erosion is a major driver of soil degradation in arid and semi-arid regions, where the lack of vegetative cover and intense rainfall accelerate erosion processes. Field experiments were conducted to evaluate the effectiveness of stone walls (SW) as a soil conservation practice in reducing soil erosion using the universal soil loss equation. Furthermore, the support practice factor (P) was estimated via integrating computational measurements of changes in A horizon thickness with slope profiles. Six sites with varying slope gradients (8%, 10%, 15%, and 25%) implementing SW were compared to neighboring sites lacking this practice in the northeastern parts of Jordan. SW reduced average annual soil loss by 83%, lowering the average annual erosion rate from 58 t.ha⁻¹.yr⁻¹ (severe risk) to 10 t.ha⁻¹.yr⁻¹ (slight risk). The implementation of SW stabilized the thickness of the A horizon and organic matter contents across different slope gradients. In contrast, the absence of SW led to greater soil displacement and accumulation of organic matter at the lower slopes, indicating higher erosion risks. The average estimated P factor was 0.35. These findings underscore the effectiveness of conservation practices in controlling soil erosion, enhancing soil quality, and promoting sustainable land use in arid and semi-arid environments. Wider adoption of such measures can significantly contribute to combating soil degradation and improving agricultural productivity in similar regions worldwide. Full article

The concentration of air pollutants could be affected by climate change in industrial park zones in Hidalgo state, Mexico (IPHSs). The goals of this work were: (a) to describe the aerosols’ behavior (PM₁₀ and PM_2.5) and air pollutants (SO₂, NO₂, O₃, and CO) in the IPHSs and (b) determine the climate variable behavior regarding the presence in IPHSs. The methodology consisted of structuring the time series of climate variables and air pollutants in six analysis regions. Afterwards, an annual average calculation, a count of days exceeding the allowed limits set by the official Mexican norms, an analysis of annual behavior by season, the Sen slope calculation, and correlation among variables were performed. Results demonstrated that Zone 2 is the most polluted, exceeding the allowed limits in the annual average (PM₁₀ > 36 μg/m³, PM_2.5 > 10 μg/m³, and NO₂ > 0.021 ppm) and having more than 1000, 96, and 11 days where the daily limit was exceeded in PM₁₀, PM_2.5, and SO₂, respectively. The minimum concentrations of the pollutants were observed during the summer–autumn seasons, coinciding with the highest precipitation. Regarding the correlations, the pollutants are negatively and statistically significantly correlated with precipitation (ranging from −0.81 to −0.43); meanwhile, the maximum temperature (ranging from +0.41 to +0.51) and evaporation (ranging from +0.39 to +0.54) are positively and statistically significantly correlated. In conclusion, the results could suggest that the presence of pollutants in the atmosphere may be influenced by the behavior of nearby regional climatic conditions in the IPHSs. Full article

Land surface temperature (LST) is a key indicator reflecting the ecological environmental disturbance caused by open-pit coal mining activities and determining the ecological status in alpine permafrost regions. Thus, it is crucial to study the spatiotemporal variations and influencing mechanisms of LST throughout all stages of small-scale mining–large-scale land surface damage–ecological restoration. Landsat imagery over nine periods was extracted from the growing seasons between 1990 and 2024. This study retrieved LST while simultaneously calculating albedo, soil moisture, and normalized difference vegetation index (NDVI) for each time phase. By integrating land use/cover (LUCC) data, the spatiotemporal evolution patterns of LST in the mining area throughout all stages were revealed. Based on the Geodetector method, an identification approach for factors influencing LST spatial differentiation was established. This approach was applicable to the entire process characterized by significant land type transitions. The results indicate that the spatiotemporal variations in LST were significantly correlated with land surface damage and restoration caused by human activities in the mining area. With the implementation of ecological restoration, high and ultra-high temperatures decreased by about 25.98% compared to the period when the surface damage was the most severe. The main influencing factors of LST differentiation were identified for different land use types, i.e., natural and restored meadows (soil wetness, albedo, and NDVI), mine pits (albedo, aspect, and elevation), and mining waste dumps (aspect and albedo before restoration; aspect and NDVI after restoration). This study can provide a reference for monitoring the ecological environment changes and ecological restoration of global coalfields with the same climatic characteristics. Full article

The Mediterranean region is regarded as a hot spot on Earth because of its placement at the junction of many aerosols. Numerous studies have demonstrated that the North Atlantic Oscillation (NAO), which is closely related to the El Niño–Southern Oscillation (ENSO) phenomenon, influences the weather in the area. However, a recent study by the same author examined the ENSO effect on atmospheric processes in this area and discovered a slight but notable influence. This study builds on that earlier work, but it divides the Mediterranean region into four smaller regions during the same time span as the previous study, which is extended by two years, from 1980 to 2024. The division is based on geographical, climatological, and atmospheric process features. The findings demonstrate that volcanic eruptions significantly affect the total amount of aerosols. Additionally, the current study reveals that the Granger-causality test of the physical phenomena of solar activity, ENSO, and NAO indicates that all have a significant impact, either separately or in combination, on the atmospheric process over the four Mediterranean regions, and this effect can last up to six months. Moreover, a taxonomy of the different forms of aerosols across the four subregions is given. Full article

Understanding global precipitation trends is critical for managing water resources, anticipating extreme events, and assessing the impacts of climate change. This study analyzes spatial and temporal patterns of precipitation from 1998 to 2024 using NASA’s Global Precipitation Measurement Mission (GPM) Integrated Multi-satellite Retrievals for (IMERG) Version 7, which merges satellite observations with rain-gauge data at 0.1° resolution. A total of 324 monthly datasets were aggregated into annual and seasonal composites to evaluate annual and seasonal trends in global precipitation. The non-parametric Mann–Kendall test was applied at the pixel scale to detect statistically significant monotonic trends, and Sen’s slope estimator method was used to quantify the magnitude of change in mean annual and seasonal global precipitation. Results reveal robust and geographically consistent patterns: significant wetting trends are evident in high-latitude regions, with the Arctic and Southern Oceans showing the strongest increases across multiple seasons, including +0.04 mm/day in December–January–February for the Arctic Ocean and +0.04 mm/day in June–July–August for the Southern Ocean. Northern China also demonstrates persistent increases, aligned with recent intensification of extreme late-season precipitation. In contrast, significant drying trends are detected in the tropical East Pacific (up to −0.02 mm/day), northern South America, and some areas in central-southern Africa, highlighting regions at risk of sustained hydroclimatic stress. The North Atlantic south of Greenland emerges as a summer drying hotspot, consistent with Greenland Ice Sheet melt enhancing stratification and reducing precipitation. Collectively, the findings underscore a dual pattern of wetting at high latitudes and drying in tropical belts, emphasizing the role of polar amplification, ocean–atmosphere interactions, and climate variability in shaping Earth’s precipitation dynamics. Full article

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

The Nile, Indus, and Yellow River deltas are historically significant and have experienced extensive shoreline changes over the past 50 years, yet the roles of human interventions and natural events remain unclear. In this study, the Net Shoreline Movement and End Point Rate (EPR) were calculated to quantify the erosion and accretion of the shoreline, respectively. Subsequently, linear trend analysis was employed to identify potential directional shifts in shoreline behavior. These measures are combined with segment-scale cumulative area and the EPR trend to reveal where erosion or accretion intensifies, weakens, or reverses through time. Results show distinct, system-specific trajectories, the Nile lost ~27 km² from 1972 to1997 as a result of the dam construction and sediment reduction, and lost only ~3 km² more from 1997 to 2022, with local stabilization. The Indus switched from intermittent gains before 1990s to sustained loss after that, totaling ~300 km² of cumulative land loss mainly due to upstream dam constructions and storm events. The Yellow River gained ~500 km² from 1973 to 1996 then lost ~200 km² after main-channel relocation and reduced sediment supply despite active-mouth management. These outcomes indicate that deltas are very vulnerable to system wide human activities and natural events. Combined, satellite-derived metrics can help prioritize locations, guide feasible interventions, establish annual monitoring and trigger action. A major caveat of this study is that yearly shoreline rates and 5–10-yearaverages can mask short-lived or very local shifts. Targeted field surveys and finer-scale modeling (hydrodynamics, subsidence monitoring, bathymetry) are therefore needed to refine the design and inform better policy choices. Full article

Rapid urbanization and climate change demand innovative green solutions in city planning. Tiny forests—small artificial wooded areas in urban or peri-urban settings—are gaining attention. This paper explores the use of the Miyawaki method to establish such forests in Italy, highlighting their environmental and educational benefits. The study defines micro-forests (100–200 m²) and mini-forests (200–2000 m²) per legislative standards and describes the qualitative features needed for self-sustaining ecosystems. Mimicking natural succession, these forests support biodiversity, reduce urban heat, improve air quality, and act as carbon sinks. Beyond ecological functions, they offer strong pedagogical value, fostering naturalistic intelligence and reconnecting people with natural rhythms and ecosystems. Case studies from Vigevano and Rome show practical applications, demonstrating how tiny forests can enhance sustainability, community well-being, and environmental awareness in cities. Full article

In Mexico, 95% of the population has access to drinking water sources, but only about 65% of domestic waste water is treated to safe levels. This study analyzes forty years of Mexican scientific production on water and waste water treatment through a bibliometric and conceptual approach, evaluating its contribution Sustainable Development Goal (SDG) 6. The analysis identified three major research clusters: (1) biological processes for water treatment, (2) development and optimization of physical–chemical processes, and (3) water quality and management. These themes reflect the evolution of biological approaches for identifying and removing organic contaminants, the application of advanced techniques for improving water quality, and the promotion of sustainable water use. The study also highlights the growing attention to emerging contaminants, nanotechnology, integrated water resource management, and persistent challenges in sanitation. With respect to SDG 6, Mexican research has mainly focused on targets 6.1 (universal and equitable access to drinking water), 6.3 (water quality), and 6.5 (water resources management), while targets 6.2 (sanitation), 6.a (international cooperation), and 6.b (community participation) remain underrepresented compared with the international benchmarks, where the research trend is on water management, resources, and the water–food–energy nexus. Finally, the findings also show synergies with SDGs 11 (sustainable cities and communities), 9 (industry, innovation, and infrastructure), and 3 (good health and well-being), although gaps persist in addressing equitable access to water and society participation. Full article

Understanding the dispersion of volcanic sulphur dioxide (SO₂) plumes is crucial for assessing their environmental and climatic impacts. This study integrates satellite-based and reanalysis datasets to simulate as well as visualise the dispersion patterns of volcanic SO₂ under diverse atmospheric conditions. By incorporating data from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2), CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), and OMI (Ozone Monitoring Instrument) datasets, we are able to provide comprehensive insights into the vertical and horizontal trajectories of SO₂ plumes. The methodology involves modelling SO₂ dispersion across various atmospheric pressure surfaces, incorporating wind directions, wind speeds, and vertical column mass densities. This approach allows us to trace the evolution of SO₂ plumes from their source through varying meteorological conditions, capturing detailed vertical distributions and plume paths. Combining these datasets allows for a comprehensive analysis of both natural and human-induced factors affecting SO₂ dispersion. Visual and statistical interpretations in the paper reveal overall SO₂ concentrations, first injection dates, and dissipation patterns detected across altitudes of up to ±20 km in the stratosphere. This work highlights the significance of combining satellite-based and global atmospheric reanalysis datasets to validate and enhance the accuracy of plume dispersion models while having a general agreement that OMI daily data and MERRA-2 reanalysis hourly data are capable of accurately accounting for SO₂ plume dispersion patterns under varying meteorological conditions. Full article

Frequent grassland fires have severely affected regional ecosystems as well as the production and living conditions of local residents. Grassland fire prevention capabilities constitute an integral part of the disaster prevention and mitigation system and play an important role in improving grassroots governance. To gain a deeper understanding of the practical foundation and influencing mechanisms of grassland fire prevention capabilities, establish an evaluation index system for prevention capabilities covering the four dimensions of disaster prevention, disaster resistance, disaster relief, and recovery. Combining micro-level survey data, a quantile regression model is used to analyze the influencing factors. The research findings indicate that (1) disaster resistance (0.49) plays a prominent role in grassland fire prevention capabilities, with economic foundations and individual disaster relief capabilities being particularly critical for overall improvement. Although residents have strong fire prevention awareness, their organizational collaboration capabilities are relatively weak, and there are significant differences in prevention capabilities across regions, necessitating tailored, precise enhancements. (2) There are significant differences in prevention capabilities among residents of different agricultural and pastoral production types, with semi-agricultural and semi-pastoral areas having the strongest comprehensive capabilities and pastoral areas relatively weaker. (3) A significant analysis of factors influencing grassland fire prevention capabilities: effective and diverse risk communication is a prerequisite for enhancing residents’ prevention capabilities; the level of panic regarding grassland fires and road infrastructure are important influencing factors, but residents’ understanding of climate change and grassroots organizations’ capacity for mechanism construction have insignificant impacts. Therefore, in future grassland fire disaster prevention and mitigation efforts, it is essential to strengthen risk communication, improve infrastructure, monitor environmental changes and the spatiotemporal patterns of grassland fires, enhance residents’ understanding of climate change, reinforce the emergency response capabilities of grassroots organizations, and stimulate public participation awareness to collectively build a multi-tiered grassland fire prevention system. Full article

Existing drainage systems have limited capacity to mitigate future climate change-induced flooding problems effectively. However, some studies have evaluated the effectiveness of integrating Water-Sensitive Urban Design (WSUD) with existing drainage systems in mitigating flooding in tropical regions. This study examined the performance of drainage systems and integrated WSUD options under current and future climate scenarios in a sub-catchment of Saltwater Creek, a tropical catchment located in Cairns, Australia. A combination of one-dimensional (1D) and two-dimensional (1D2D) runoff generation and routing models (RORB, storm injector, and MIKE+) is used for simulating runoff and inundation. Several types of WSUDs are tested alongside different climate change scenarios to assess the impact of WSUD in flood mitigation. The results indicate that the existing grey infrastructure is insufficient to address the anticipated increase in precipitation intensity and the resulting flooding caused by climate change in the Engineers Park sub-catchment. Under future climate change scenarios, moderate rainfall events contribute to a 25% increase in peak flow (95% confidence interval = [1.5%, 0.8%]) and total runoff volume (95% confidence interval = [1.05%, 6.5%]), as per the Representative Concentration Pathway 8.5 in the 2090 scenario. Integrating WSUD with existing grey infrastructure positively contributed to reducing the flooded area by 18–54% under RCP 8.5 in 2090. However, the efficiency of these combined systems is governed by several factors such as rainfall characteristics, the climate change scenario, rain barrel and porous pavement systems, and the size and physical characteristics of the study area. In the tropics, the flooding problem is estimated to increase under future climatic conditions, and the integration of WSUD with grey infrastructure can play a positive role in reducing floods and their impacts. However, careful interpretation of results is required with an additional assessment clarifying how these systems perform in large catchments and their economic viability for extensive applications. Full article

Quantifying land–atmosphere transport remains crucial for advancing climate prediction and weather forecasting efforts. To improve turbulent flux estimation, the anisotropy of turbulence is taken into consideration. The parameters $x_B$ and $y_B$, which quantify anisotropy degrees across motion scales, form trajectories in the barycentric map. Using the Hilbert–Huang transform, the scale-dependent properties of anisotropy in observational data from multiple sites are investigated. Analysis reveals consistent patterns in the average $y_B-x_B$ trajectories across stratification conditions: as scale increases, $x_B$ increases from 0.4 to 0.9, while $y_B$ initially climbs from 0.5 to 0.7 before declining to 0. Meanwhile, individual case trajectories sometimes deviate from this pattern, indicating contamination by non-turbulent motions that typically cause turbulent flux overestimation. Crucially, identifying the scale at which deviations occur allows effective separation of atmospheric turbulence from non-turbulent motions, which enables the reconstruction of turbulence data. Results demonstrate that corrected fluxes reduce overestimation inherent in traditional eddy covariance systems by approximately 30%, with enhancements for CO₂ and air pollutants reaching 45–83%. Furthermore, the correlation between anisotropy and stratification suggests potential for refining similarity theories into a broader scope, such as carbon cycle assessment and pollution control. Therefore, anisotropy shows promise in quantifying the land–atmosphere transport. Full article

The Tibetan Plateau (TP) is experiencing higher warming rates than elsewhere, which may affect regional vegetation growth. Particularly on the Eastern Tibetan Plateau (ETP), where the topography is diverse and rich in biodiversity, it is necessary to clarify the drivers of climate and topography on vegetation cover. In this research, we selected the Shaluli Mountains (SLLM) in the ETP as the study area, monitored the spatial and temporal dynamics of the regional vegetation cover using remote sensing methods, and quantified the drivers of vegetation change using Geodetector (GD). The results showed a decreasing trend in annual precipitation (PRE) (−2.4054 mm/year) and the Palmer Drought Severity Index (PDSI) (−0.1813/year) in the SLLM. Annual maximum temperature (TMX) on the spatial and temporal scales showed an overall increasing trend, and the regional climate tended to become warmer and drier. Since 2000, fractional vegetation cover (FVC) has shown a fluctuating upward trend, with an average value of 0.6710, and FVC has spatially shown a pattern of “low in the middle and high in the surroundings”. The areas with non-significant increases (p > 0.05) and significant increases (p < 0.05) in FVC accounted for 46.03% and 5.76% of the SLLM. Altitude (q = 0.3517) and TMX (q = 0.3158) were the main drivers of FVC changes. As altitude and TMX increased, FVC showed a trend of increasing and then decreasing. The results of this study help us to clarify the influence of climate and topography on the vegetation ecosystem of the ETP and provide a scientific basis for regional biodiversity conservation and sustainable development. Full article

This study presents a hybrid approach to hydrological forecasting by integrating the physically based Soil and Water Assessment Tool (SWAT) model with Prophet time-series modeling and machine learning–based multi-output regression. Applied to the Cahaba watershed, the objective is to predict key environmental variables (precipitation, evapotranspiration (ET), potential evapotranspiration (PET), and snowmelt) and their influence on hydrological responses (surface runoff, groundwater flow, soil water, sediment yield, and water yield) under present (2010–2022) and future (2030–2042) climate scenarios. Using SWAT outputs for calibration, the integrated SWAT-Prophet-ML model predicted ET and PET with RMSE values between 10 and 20 mm. Performance was lower for high-variability events such as precipitation (RMSE = 30–50 mm). Under current climate conditions, R² values of 0.75 (water yield) and 0.70 (surface runoff) were achieved. Groundwater and sediment yields were underpredicted, particularly during peak years. The model’s limitations relate to its dependence on historical trends and its limited representation of physical processes, which constrain its performance under future climate scenarios. Suggested improvements include scenario-based training and integration of physical constraints. The approach offers a scalable, data-driven method for enhancing monthly water balance prediction and supports applications in watershed planning. Full article

In early May 2025, extreme wildfires swept across Manitoba, Canada, fueled by unseasonably warm temperatures, prolonged drought, and stressed vegetation. We explore how multi-source satellite indicators—such as anomalies in snow cover, precipitation, temperature, vegetation indices, and soil moisture in April–May—jointly signal landscape preconditioning for fire, highlighting the potential of these compound anomalies to inform fire risk awareness in boreal regions. Results indicate that rainfall deficits and diminished snowpack significantly reduced soil moisture, which subsequently decreased vegetative greenness and created a flammable environment prior to ignition. This concept captures how multiple moderate anomalies, when occurring simultaneously, can converge to create high-impact fire conditions that would not be flagged by individual thresholds alone. These findings underscore the importance of integrating climate and biosphere anomalies into wildfire risk monitoring to enhance preparedness in boreal regions under accelerating climate change. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Climate change has accelerated the retreat of Andean glaciers, with significant recent losses in the tropical Andes. This study evaluates the extinction of the Carihuairazo volcano glacier (Ecuador), quantifying its area from 1312.5 m² in September 2023 to 101.2 m² in January 2024, its thickness (from 2.5 m to 0.71 m), and its volume (from 2638.85 m³ to 457.18 m³), before its complete deglaciation in February 2024; this rapid melting and its small size classify it as a glacierette. Multivariate analyses (PCA and biclustering) were performed to correlate climatic variables (temperature, solar radiation, precipitation, relative humidity, vapor pressure, and wind) with glacier surface and thickness. The PCA explained 70.26% of the total variance, with Axis 1 (28.01%) associated with extreme thermal conditions (temperatures up to 8.18 °C and radiation up to 16.14 kJ m⁻² day⁻¹), which probably drove its disappearance. Likewise, Axis 2 (21.56%) was related to favorable hydric conditions (precipitation between 39 and 94 mm) during the initial phase of glacier monitoring. Biclustering identified three groups of variables: Group 1 (temperature, solar radiation, and vapor pressure) contributed most to deglaciation; Group 2 (precipitation, humidity) apparently benefited initial stability; and Group 3 (wind) played a secondary role. These results, validated through in situ measurements, provide scientific evidence of the disappearance of the Carihuairazo volcano glacier by February 2024. They also corroborate earlier projections that anticipated its extinction by the middle of this decade. The early disappearance of this glacier highlights the vulnerability of small tropical Andean glaciers and underscores the urgent need for water security strategies focused on management, adaptation, and resilience. Full article

Amid global environmental changes, urbanisation erodes nature connectedness, an important driver of pro-environmental behaviours and human well-being, exacerbating human-made risks like biodiversity loss and climate change. This study introduces a novel hybrid agent-based model (ABM), calibrated with historical urbanisation data, to explore how urbanisation, opportunity and orientation to engage with nature, and intergenerational transmission have shaped nature connectedness over time. The model simulates historical trends (1800–2020) against target data, with projections extending to 2125. The ABM revealed a significant nature connectedness decline with excellent fit to the target data, derived from nature word use in cultural products. Although a lifetime ‘extinction of experience’ mechanism refined the fit, intergenerational transmission emerged as the dominant driver—supporting a socio-ecological tipping point in human–nature disconnection. Even with transformative interventions like dramatic urban greening and enhanced nature engagement, projections suggest a persistent disconnection from nature through to 2050, highlighting locked-in risks to environmental stewardship. After 2050, the most transformative interventions trigger a self-sustaining recovery, highlighting the need for sustained, systemic policies that embed nature connectedness into urban planning and education. Full article

This study explores the use of ensemble machine learning models to develop wildfire susceptibility maps (WFSMs) in Greece, focusing on their application as regressors. We provide a continuous assessment of wildfire risk, enhancing the interpretability and accuracy of predictions. Two key metrics were developed: Ensemble Mean and Ensemble Max. This dual-metric approach improves predictive robustness and provides critical insights for wildfire management strategies. The ensemble mode effectively handles complex, high-dimensional data, addressing challenges such as over fitting and data heterogeneity. Utilizing advanced techniques like XGBoost, GBM, LightGBM, and CatBoost regressors, our research demonstrates the potential of these methods to enhance wildfire risk estimation. The Ensemble Mean model classified 50% of the land as low risk and 21% as high risk, while the Ensemble Max model identified 38% as low risk and 33% as high risk. Notably, 83% of wildfires between 2000 and 2024 occurred in areas marked as high-risk by both models. The findings reveal that a significant proportion of wildfires occurred in areas identified as high risk by both ensemble models, underscoring their effectiveness. This approach offers significant potential to mitigate wildfires’ environmental, economic, and social impacts, enhance climate resilience, and strengthen preparedness for future wildfire events. Full article

This study analyzes the evolution of hydrometeorological risks and severe weather events in Catalonia through an extensive review of 21,312 news reports aired by Televisió de Catalunya (TVC) between 1984 and 2019, 10,686 (50.1%) of which focused on events within Catalonia. The reports are categorized by the type of phenomenon, geographic location, and reported impact, enabling the identification of temporal trends. The results indicate a general increase in the frequency of news coverage of hydrometeorological and severe weather events—particularly floods and heavy rainfall—both in Catalonia and the broader Mediterranean region. This rise is attributed not only to a potential increase in such events, but also to the expansion and evolution of media coverage over time. In the Catalan context, the most frequently reported hazards are snowfalls and cold waves (3203 reports), followed by rainfall and flooding (3065), agrometeorological risks (2589), and wind or sea storms (1456). The study highlights that rainfall and flooding pose the most significant risks in Catalonia, as they account for the majority of the reports involving serious impacts—1273 cases of material damage and 150 involving fatalities. The normalized data reveal a growing proportion of reports on violent weather and floods, and a relative decline in snow-related events. Full article

This study presents the first comprehensive nationwide assessment of mangrove ecosystems in the Maldives. Surveys were conducted across 162 islands in 20 administrative atolls, integrating field data, the literature, and secondary sources to map mangrove distribution, confirm species presence, and classify habitat types. Twelve true mangrove species were identified, with Bruguiera cylindrica, Rhizophora mucronata, and Lumnitzera racemosa emerging as dominant. Species diversity was evaluated using Shannon (H′), Margalef (d′), Pielou’s evenness (J′), and Simpson’s dominance (λ′) indices. Atolls within the northern and southern regions, particularly Laamu, Noonu, and Shaviyani, exhibited the highest diversity and evenness, while central atolls such as Ari and Faafu supported mono-specific or degraded stands. Mangrove habitats were classified into four geomorphological types: marsh based, pond based, embayment, and fringing systems. Field sampling was conducted using standardized belt transects and quadrats, with species verified using photographic documentation and expert validation. Species distributions showed strong habitat associations, with B. cylindrica dominant in marshes, R. mucronata and B. gymnorrhiza in ponds, and Ceriops tagal and L. racemosa in embayments. Rare species like Bruguiera hainesii and Heritiera littoralis were confined to stable hydrological niches. This study establishes a critical, island-level baseline for mangrove conservation and ecosystem-based planning in the Maldives, providing a reference point for tracking future responses to climate change, sea-level rise, and hydrological disturbances, emphasizing the need for habitat-specific strategies to protect biodiversity. Full article

A vital step for any hydrochemical assessment is properly carrying out quality assurance and quality control (QA/QC) techniques to evaluate data confidence before performing the assessment. Understanding the processes governing groundwater evolution in coastal aquifers is critical for managing freshwater resources under increasing anthropogenic and climatic pressures. This study reassesses the hydrochemical and isotopic data from the Deep Confined Aquifer System (DCAS) in the Jiangsu Coastal Plain, China, by firstly applying QA/QC protocols. Anomalously high Fe and Mn concentrations in several samples were identified and excluded, yielding a refined dataset that enabled a more accurate interpretation of hydrogeochemical processes. Using hierarchical cluster analysis (HCA), principal component analysis (PCA), and stable and radioactive isotope data (δ²H, δ¹⁸O, ³H, and ¹⁴C), we identify three dominant drivers of groundwater evolution: water–rock interaction, evaporation, and seawater intrusion. In contrast to earlier interpretations, we present clear evidence of active seawater intrusion into the DCAS, supported by salinity patterns, isotopic signatures, and local hydrodynamics. Furthermore, inconsistencies between tritium- and radiocarbon-derived residence times—modern recharge indicated by ³H versus Pleistocene ages from ¹⁴C—highlight the unreliability of previous paleoclimatic reconstructions based on unvalidated datasets. These findings underscore the crucial role of robust QA/QC and integrated tracer analysis in groundwater studies. Full article

The voluntary basis on which recycling and energy saving are performed at households brings forward the need to better understand the profile of recyclers and non-recyclers and to make meaningful comparisons between them. Hence, the aim of this study is to compare recyclers’ and non-recyclers’ profiles and practices in order to detect areas that require policy and educational interventions. To achieve this aim, this study collected a representative sample of 384 citizens in a fast-growing urban center and compared recyclers and non-recyclers in terms of their environmental practices. The results showed that both groups identified environmental protection as their leading motive to recycle, while plastic and paper were the most recycled materials. An interesting difference between the two groups was that recyclers were more engaged in energy-saving, suggesting that recycling engagement may be associated with the adoption of energy-saving practices. The Internet was the leading information source across both groups, emphasizing the role it can play in spreading accurate and motivating messages about recycling and energy-saving. This study provides a useful and nuanced picture of recyclers’ and non-recyclers’ profiles and their differences, and as such, it can introduce new angles for the design of strategies for encouraging pro-environmental behavior. 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

Soil erosion poses a significant global environmental challenge, causing land degradation, deforestation, river siltation, and reduced agricultural productivity. Although the Revised Universal Soil Loss Equation (RUSLE) has been widely applied in Brazil, its use in the tropical river basins of the Amazon remains limited. This study aimed to apply a GIS-integrated RUSLE model and compare its soil loss estimates with multiple linear regression (MLR) models based on terrain attributes, aiming to identify priority areas and key geomorphometric drivers of soil erosion in a tropical Amazonian river basin. A digital elevation model based on Shuttle Radar Topography Mission (SRTM) data, land use and land cover (LULC) maps, and rainfall and soil data were applied to the GIS-integrated RUSLE model; we then defined six risk classes—slight (0–2.5 t ha⁻¹ yr⁻¹), slight–moderate (2.5–5), moderate (5–10), moderate–high (10–15), high (15–25), and very high (>25)—and identified priority zones as those in the top two risk classes. The Caeté River Basin (CRB) was classified into six erosion risk categories: low (81.14%), low to moderate (2.97%), moderate (11.88%), moderate to high (0.93%), high (0.03%), and very high (3.05%). The CRB predominantly exhibited a low erosion risk, with higher erosion rates linked to intense rainfall, gentle slopes covered by Arenosols, and human activities. The average annual soil loss was estimated at 2.0 t ha⁻¹ yr⁻¹, with a total loss of 1005.44 t ha⁻¹ yr⁻¹. Additionally, geomorphological and multiple linear regression (MLR) analyses identified seven key variables influencing soil erosion: the convergence index, closed depressions, the topographic wetness index, the channel network distance, and the local curvature, upslope curvature, and local downslope curvature. These variables collectively explained 26% of the variability in soil loss (R² = 0.26), highlighting the significant role of terrain characteristics in erosion processes. These findings indicate that soil erosion control efforts should focus primarily on areas with Arenosols and regions experiencing increased anthropogenic activity, where the erosion risks are higher. The identification of priority erosion areas enables the development of targeted conservation strategies, particularly for Arenosols and regions under anthropogenic pressure, where the soil losses exceed the tolerance threshold of 10.48 t ha⁻¹ yr⁻¹. These findings directly support the formulation of local environmental policies aimed at mitigating soil degradation by stabilizing vulnerable soils, regulating high-impact land uses, and promoting sustainable practices in critical zones. The GIS-RUSLE framework is supported by consistent rainfall data, as verified by a double mass curve analysis (R² ranging from 0.64 to 0.77), and offers a replicable methodology for soil conservation planning in tropical basins with similar erosion drivers. This approach offers a science-based foundation to guide soil conservation planning in tropical basins. While effective in identifying erosion-prone areas, it should be complemented in future studies by dynamic models and temporal analyses to better capture the complex erosion processes and land use change impacts in the Amazon. Full article

At the global level, numerous reservoirs exhibit a pronounced water degradation. Inadequate land use and climate change effects contribute to freshwater degradation and disrupt the ecosystem balances. This study aimed to evaluate the temporal and spatial effects of the surrounding area on two Portuguese reservoirs: Rabagão and Aguieira. For each reservoir sub-watershed scale, the evolution of land use and soil occupation and the pressures reported over the past decade were analyzed. Additionally, official records of water quality parameters were collected, and water quality was assessed according to the Water Framework Directive (WFD). Both reservoirs show anthropogenic pressure, reflected in the water quality. Rabagão has good water quality, associated with undeveloped lands (47%), agriculture (26%), and one pressure on the aquaculture sector. Aguieira is characterized by high nutrient concentrations, low transparency, and phytoplankton. This is linked to various land uses, including forestry (75%), and agriculture (19%), as well as multiple environmental pressures. Key contributors include urban discharge (27 sites) and water catchments allocated for agricultural purposes (89 sites) and others. The long-term data showed an increase in chlorophyll a concentration, water temperature, and pH values, and a decrease in the concentration of total phosphorus, but higher than the reference value. Additionally, the usage of the surrounding area of the hydrographic basin shows that it is extremely important for water quality and should be included in the WFD. Addressing the problems in the surrounding areas reservoirs is essential to adopting measures that improve water quality, therefore guaranteeing the health of the environment as expected under the One Health concept. Full article

The Cheremskyi Nature Reserve, situated in the Volyn region of Ukraine, constitutes a pivotal element of the European ecological network, distinguished by its distinctive mosaic of peatlands, bogs, and floodplain forests. This study utilizes Sentinel-2 satellite imagery and the Google Earth Engine (GEE) to assess the spatiotemporal patterns of various vegetation indices (NDVI, EVI, SAVI, MSAVI, GNDVI, NDRE, NDWI) from 2017 to 2024. The study aims to select the most suitable combination of vegetation spectral indices for future research. The analysis reveals significant negative trends in NDVI, SAVI, MSAVI, GNDVI, and NDRE, indicating a decline in vegetation health, while NDWI shows a positive trend, suggesting an increased vegetation water content. Correlation analysis underscores robust interrelationships among the indices, with NDVI and SAVI identified as the most significant through random forest feature importance analysis. Principal component analysis (PCA) further elucidates the primary axes of variability, emphasizing the complex interplay between vegetation greenness and moisture content. The findings underscore the utility of multi-index analyses in enhancing predictive capabilities for ecosystem monitoring and support targeted conservation strategies for the sustainable management of the Cheremskyi Nature Reserve. Full article

This study investigates the estimation of the boundary layer height (PBLH) in Huelva, Spain, in November 2023, using different methods: Richardson number, humidity gradient and refractivity gradient. From the virtual potential profiles of temperature and specific humidity, in the case of daytime PBLH, which method works best in some situations when there are discrepancies between results is discussed. The results are then compared with the PBLH values obtained from the ERA-5 reanalysis. The synoptic analysis shows that the decrease in PBLH in the central weeks of the month is compatible with a thermal inversion by subsidence due to a persistent anticyclonic situation. Regarding air quality, the NO₂ concentrations in the air quality station of Matalascañas, which is a background station, show negative correlations with the PBLH. Full article

The Anthropocene is a concept that highlights the profound changes humans have made to nearly every aspect of the Earth. It serves as a compelling narrative that challenges us to examine public perceptions and interests regarding human–nature interactions in an integrated way. These interactions are widespread but can vary significantly over time, across cultures and under different economic conditions, making them difficult to monitor effectively on a large scale. Recent advancements in digital technology, such as the ability to track online searches through tools, like Google Trends-Glimpse, and the near real-time monitoring of news broadcasts via the GDELT Project, present new opportunities. These tools can analyze data in multiple languages around the world, encouraging innovative approaches to integrate the diverse and complex information generated within this multi-language, multi-concept, and varied time-scale environment of human activity and beliefs. We propose a transformed version of Markowitz’s multi-asset optimization theory that encompasses over 5.5 billion people, several languages, and concepts since 2004. This approach is a functional ensemble where ecology and economics intersect, at least mechanistically. Our findings indicate that while there is a general increase in people’s interest in Anthropocene-related issues, significant differences exist across cultures. We also identify several sources of data noise and evidence that interfere with the overall methodology. Addressing these issues in future research will help to extend the validity of our approach, especially if it increases interest in conservation culturomics. Full article

The concept of nature-based solutions (NBS) is widely promoted as an approach to effectively counteract climate change and land degradation (LD) as well as simultaneously add environmental and socio-economic benefits. To have a worldwide impact from NBS, it is important to consider potential land and soil resources at various scales, including administrative units (e.g., country, state, county, etc.). Nature-based solutions are considered by many United Nations (UN) initiatives, including the Paris Agreement and the UN Convention to Combat Desertification (UNCCD). Currently, there is no consensus on how to define NBS and their indicators. The innovation of this study is that it defines and evaluates soil- and land-based NBS potential while suggesting indicators for land- and soil-based NBS using the contiguous United States of America (USA) as an example. This study defines potential land for NBS as the sum of the individual satellite-identified areas of barren, shrub/scrub, and herbaceous land covers, which are linked to climate and inherent soil quality (SQ), so that NBS could be implemented without changing other land uses. The potential soil for NBS, based on SQ, is a subset of land available for potential NBS. As of 2021, anthropogenic LD affected 2,092,539.0 km² in the contiguous USA, with 928,618.0 km² (15.1% of the contiguous US area) of actual potential land for NBS. The contiguous USA had a negative balance between anthropogenic LD and actual potential land for NBS to compensate for anthropogenic LD of −1,163,921.0 km². Thirty-seven states also exhibited a negative balance for LD compensation with Iowa having the worst balance of −124,497.0 km². Many states with positive anthropogenic LD and NBS balances showed that most of the potential NBS land was of low SQ and, therefore, may not be suitable for NBS. Planning for NBS should involve a feasibility analysis of “nationally determined NBS” (NDNBS) through site and context-specific NBS. Full article

The climate of the south of Brazil is characterized by northern winds in a hegemonic way for the transfer of moisture. Thus, the goal here is to verify the impact of the meridional water vapor transport on the rainfall of the Mirim–São Gonçalo Watershed (MSGW), located in the extreme south of Brazil and essential for regional development. The study is based on the precipitation data from MSGW weather stations and ERA5 reanalysis data for the period 1981–2020, which allowed the analysis of the interactions between different climatological variables. The water vapor transport was analyzed using the vertically integrated water vapor flux (VIVF). Coefficients were obtained according to the VIVF values in two locations placed between the Amazon basin and southern Brazil, namely in Bolivia and Paraguay. The results show that the MSGW is directly impacted by moisture transport from the north in all seasons, and this transport is most significant at the 850 hPa level. In addition, the moisture and rainfall in the MSGW are also influenced by changes in the magnitude and direction of this flow, with an increase in transport in periods of El Niño, especially during spring. Therefore, the study brings insights into how changes in tropical South American climate, through a cascading effect, may affect the Mirim–São Gonçalo Watershed development in the middle latitudes from changes in the meridional water vapor transport, highlighting the importance of studying the tropical and extratropical interactions in South America for the MSGW management and sustainable development. Full article

This study seeks to examine the development of scientific literature concerning the Danube Delta, an exceptional ecosystem characterized by its rich biodiversity, which is facing challenges from both climate change and human activities. It aims to identify significant trends in research publications from 1862 to 2023. The methodology employed involves a thorough bibliometric examination of articles catalogued in the Scopus database, utilizing specific criteria to ensure the direct applicability of the research to the Danube Delta. The analysis centers on factors such as publication frequency, citation rates, as well as collaborations among institutions and across international borders, thus shedding light on the scientific contributions and their practical implications in protecting the region’s unique ecosystem. The research findings indicate a notable surge in scholarly interest in the Danube Delta, particularly amidst growing global concerns regarding climate change. Furthermore, it is observed that highly cited studies often address issues related to habitat preservation, human impacts, and strategies for adapting to changing environmental conditions. The significance of international collaboration emerges as a crucial aspect in enhancing the caliber and relevance of research, underscoring the necessity for a coordinated global endeavor to study and safeguard this vital ecosystem. The research emphasizes the necessity of adopting a comprehensive and interdisciplinary methodology in studying the Danube Delta, offering insights for crafting conservation policies that address both local and global environmental concerns. Its findings offer a robust framework for steering future research endeavors and conservation initiatives, underscoring the crucial significance of international scientific cooperation in sustainably managing biodiversity amidst climate change challenges. While the study offers valuable insights, it is essential to acknowledge certain limitations, like underrepresentation of non-English language studies and methodological or modeling limitations. By acknowledging these limitations and exploring the suggested research avenues, future studies can further enhance our comprehension and management of the Danube Delta within the context of prevailing and forthcoming global challenges. Full article

Resolving the status of soil carbon with land cover is critical for addressing the impacts of climate change arising from land cover conversion in boreal regions. However, many conventional inferential approaches inadequately gauge statistical significance for this issue, due to limited sample sizes or skewness of soil properties. This study aimed to address this drawback by adopting inferential approaches suitable for smaller samples sizes, where normal distributions of soil properties were not assumed. A two-step inference process was proposed. The Kruskal–Wallis (KW) test was first employed to evaluate disparities amongst soil properties. Generalized estimating equations (GEEs) were then wielded for a more thorough analysis. The proposed method was applied to soil samples (n = 431) extracted within the southern transition zone of the boreal forest (49°–50° N, 80°40′–84° W) in northern Ontario, Canada. Sites representative of eight land cover types and seven dominant tree species were sampled, investigating the total carbon (C), carbon-to-nitrogen ratio (C:N), clay percentage, and bulk density (BD). The KW test analysis corroborated significance (p-values < 0.05) for median differences between soil properties across the cover types. GEEs supported refined robust statistical evidence of mean differences in soil C between specific tree species groupings and land covers, particularly for black spruce (Picea mariana) and wetlands. In addition to the proposed method, the results of this study provided application for the selection of appropriate predictors for C with digital soil mapping. Full article

Species distribution modeling (SDM) is a vital tool for ecological and biogeographical research, allowing precise predictions of species distributions based on environmental variables. This study reviews the evolution of SDM techniques from 1985 to 2023, focusing on model development and applications in conservation, climate change adaptation, and invasive species management. We employed a mixed review with bibliometric and systematic element approaches using the Scopus database, analyzing 982 documents from 275 sources. The MaxEnt model emerged as the most frequently used technique, applied in 85% of the studies due to its adaptability and accuracy. Our findings highlight the increasing trend in international collaboration, particularly between China, the United Kingdom, and Brazil. The study reveals a significant annual growth rate of 11.99%, driven by technological advancements and the urgency to address biodiversity loss. Our analysis also shows that while MaxEnt remains dominant, deep learning and other advanced computational techniques are gaining traction, reflecting a shift toward integrating AI in ecological modeling. The results emphasize the importance of global cooperation and the continued evolution of SDM methodologies, projecting further integration of real-time data sources like UAVs and satellite imagery to enhance model precision and applicability in future conservation efforts. Full article

South America has experienced significant changes in climate patterns over recent decades, particularly in terms of precipitation and temperature extremes. This study analyzes trends in climate extremes from 1979 to 2020 across South America, focusing on their relationships with sea surface temperature (SST) anomalies in the Pacific and Atlantic Oceans. The analysis uses precipitation and temperature indices, such as the number of heavy rainfall days (R10mm, R20mm, R30mm), total annual precipitation (PRCPTOT), hottest day (TXx), and heatwave duration (WSDI), to assess changes over time. The results show a widespread decline in total annual precipitation across the continent, although some regions, particularly in the northeast and southeast, experienced an increase in the intensity and frequency of extreme precipitation events. Extreme temperatures have also risen consistently across South America, with an increase in both the frequency and duration of heat extremes, indicating an ongoing warming trend. The study also highlights the significant role of SST anomalies in both the Pacific and Atlantic Oceans in driving these climate extremes. Strong correlations were found between Pacific SST anomalies (Niño 3.4 region) and extreme precipitation events in the northern and southern regions of South America. Similarly, Atlantic SST anomalies, especially in the Northern Atlantic (TNA), exhibited notable impacts on temperature extremes, particularly heatwaves. These findings underscore the complex interactions between SST anomalies and climate variability in South America, providing crucial insights into the dynamics of climate extremes in the region. Understanding these relationships is essential for developing effective adaptation and mitigation strategies in response to the increasing frequency and intensity of climate extremes. Full article

Artisanal and small-scale gold mining (ASCGM) provides a livelihood for many communities worldwide, but it has profound environmental impacts, especially on the quality of nearby water resources. This study assessed the impacts of ASCGM on the physicochemical quality of water and sediments from Kitengure stream, Buhweju Plateau, Western Uganda. Surface water (n = 94) and superficial sediments (n = 36) were sampled between October 2021 and January 2022 from three different sections of Kitengure stream (upstream, midstream around the ASCGM area, and downstream). The samples were analyzed for various physicochemical parameters and selected potentially toxic elements (PTXEs), namely: zinc (Zn), cadmium (Cd), lead (Pb), copper (Cu), and arsenic (As). A health risk assessment was performed using the hazard index and incremental life cancer risk methods. Pearson’s bivariate correlation, geoaccumulation, and pollution indices were used to establish the sources and potential risks that PTXEs in sediments could pose to aquatic organisms. The results indicated that water in Kitengure stream draining the ASCGM site was highly colored (1230.00 ± 134.09 Pt-co units; range = 924.00–1576.00 Pt-co units) and turbid (194.75 ± 23.51 NTU; range = 148–257 NTU). Among the five analyzed PTXEs, only Cd (0.082 ± 0.200–0.092 ± 0.001 mg/L) and Cu (0.022 ± 0.004–0.058 ± 0.005 mg/L) were detected in water, and Cd was above the permissible limit of 0.003 mg/L for potable water. Upon calculating the water quality index (WQI), the water samples were categorized as very poor for upstream samples (WQI = 227) and unfit for use (WQI = 965 and 432) for midstream and downstream samples, respectively. In sediments, the mean concentration ranges of Zn, Cd, Pb, Cu, and As were 0.991 ± 0.038–1.161 ± 0.051, 0.121 ± 0.014–0.145 ± 0.025, 0.260 ± 0.027–0.770 ± 0.037, 0.107 ± 0.017–0.422 ± 0.056, and 0.022 ± 0.002–0.073 ± 0.003 mg/kg, respectively, and they were all below their average shale, toxicity reference, and consensus-based sediment quality guidelines. Geoaccumulation indices suggested that there was no enrichment of the elements in the sedimentary phase and the associated ecological risks were low. However, there were potential non-carcinogenic health risks that maybe experienced by children who drink water from Kitengure stream. No discernable health risks were likely due to dermal contact with water and sediments during dredging or panning activities. It is recommended that further studies should determine the total mercury content of water, sediments, and crops grown along the stream as well as the associated ecological and human health risks. Full article

This systematic review evaluates the health impacts of climate-induced extreme weather events and the effectiveness of various adaptation strategies. Seventeen studies were analyzed, focusing on adaptation measures such as agricultural adjustments, renewable energy, ecosystem restoration, infrastructure redesign, and public health interventions. Significant health impacts were identified, including increased morbidity and mortality due to heatwaves, floods, and vector-borne diseases. The success of adaptation strategies was found to be highly dependent on local context, implementation capacity, and sustainability. This review underscores gaps in data quality, the generalizability of findings, and the integration of adaptation measures into public health policies. An urgent need exists for interdisciplinary approaches and community engagement to ensure sustainable, equitable health outcomes in the face of climate change. Future research should focus on these areas to strengthen public health resilience. Full article

Conflicts between wildlife and humans are a major ecological issue. During migration, wildlife, especially wildebeest, often encounter significant environmental pressures from human activities. However, relatively few studies have been conducted to provide a concise, quantitative description of wildebeest migration in the Maasai Mara National Reserve (MMNR). In this study, we identified changes in the location of the wildebeest population over time in the Maasai Mara National Reserve. We then used a K-means algorithm (R² = 0.926) to fit coordinates representing the changes in the location of the wildebeests to enable a quantitative representation of their migration routes. Subsequently, we developed an environmental stress model to assess the changes in environmental stresses faced by wildebeests along their migration routes. We proposed a model of “migratory ecological corridors and customized buffer zones” and determined the response coefficient T_res. We used the response coefficients T_res = 0.06, 0.09, and 0.12 as the critical values to categorize the areas along the routes into weak, medium, and strong response regions. Then, we set the width of the buffer zones on both sides of the routes as 5 km, 7 km, and 9 km, respectively, and evaluated the buffer effect. This type of model achieved a good effect of reducing the environmental pressure by 54.06%. The “Migratory Ecological Corridor and Customized Buffer Zone” model demonstrated a high degree of economic feasibility while showing good practicality in mitigating the environmental conflicts between humans and migratory wildlife. The variability in the environmental pressures across the region indicates that the Nairobi and Nakuru districts may be undergoing a particular stage of urbanization that unleashes potential threats to the migration of wildebeests. Further research is essential to assess the feasibility of larger buffer zones. Full article

Being located in the middle of Southern Europe, and thus likely representing a particularly dynamic member of Mediterranean Europe, Italy has experienced a sudden increase in early desertification risk because of multiple factors of change. Long-term research initiatives have provided relatively well-known examples of the continuous assessment of the desertification risk carried out via multiple exercises from different academic and practitioner stakeholders, frequently using the Environmentally Sensitive Area Index (ESAI). This composite index based on a large number of elementary variables and individual indicators—spanning from the climate to soil quality and from vegetation cover to land-use intensity—facilitated the comprehensive, long-term monitoring of the early desertification risk at disaggregated spatial scales, being of some relevance for policy implementation. The present study summarizes the main evidence of environmental monitoring in Italy by analyzing a relatively long time series of ESAI scores using administrative boundaries for a better representation of the biophysical and socioeconomic trends of interest for early desertification monitoring. The descriptive analysis of the ESAI scores offers a refined representation of economic spaces in the country during past (1960–2010 on a decadal basis), present (2020), and future (2030, exploring four different scenarios, S1–S4) times. Taken as a proxy of the early desertification risk in advanced economies, the ESAI scores increased over time as a result of worse climate regimes (namely, drier and warmer conditions), landscape change, and rising human pressure that exacerbated related processes, such as soil erosion, salinization, compaction, sealing, water scarcity, wildfires, and overgrazing. Full article

Agroforestry systems are recognized as sustainable land use practices that foster environmental health and promote adaptive responses to global change. By harnessing the synergies between trees and agricultural activities, agroforestry systems provide multiple benefits, including soil conservation, biodiversity enhancement, and carbon sequestration. Windbreaks form integral elements of Hungarian agricultural landscapes, and the enhanced agroforestry subsidy framework might have a favorable impact on their expansion, underscoring the importance of evaluating their potential for carbon sequestration. In the present study, we assess the implications of doubling the extent of windbreak plantations in Hungary by planting an additional 14,256 hectares of windbreaks. We evaluate the total carbon sequestration and the annual climate change mitigation potential of the new plantations up to 2050. For the modeling, we use the recently developed Windbreak module of the Forest Industry Carbon Model, which is a yield table-based model specific to Hungary and allows for the estimation of living biomass, dead organic matter, and soil carbon balance. We project that new windbreak plantations will sequester 913 kt C by 2050, representing an average annual climate change mitigation potential of 144 kt CO₂ eq. Our findings reveal that doubling the extent of windbreak plantations could achieve an extra 5% carbon sequestration in forested areas as compared to business-as-usual (BAU) conditions. We conclude that new windbreak plantations on agricultural field boundaries have substantial climate change mitigation potential, underscoring agroforestry’s contribution to agricultural resilience and achieving Hungary’s climate goals set for the land-use (LULUCF) sector. Full article

Arctic coastlines are the most vulnerable regions of the Earth, and local communities in those areas are being affected by rising sea levels and temperature. Therefore, Earth Observation combined with up-to-date geoinformation tools offers a dependable, cost-effective, and time-efficient approach to understanding the socioeconomic impact of climate changes in Arctic coastal areas. A promising approach is the Coastal Vulnerability Index (CVI), which takes into account different factors such as geomorphology, sea factors, and shoreline retreat or advance, to estimate the grade of vulnerability of a coastal area. Notwithstanding its potential, its application in the Arctic is still challenging. This study targets to estimate CVI to value the vulnerability of the coastal areas of Norway located in the Arctic. For the application of CVI and specifically for geomorphological and sea factors, data were acquired from international and national institutes. After the collection of all the necessary parameters for CVI was completed, all datasets were imported into a GIS software program (ArcGIS Pro) where the vulnerability classes of CVI were estimated. The results show that most of the coast of Northern Norway is characterized by a low to high degree of vulnerability, while in the island of Tromsø the vulnerability is mainly high and very high. Full article

The present study aims to assess the tectonic activity in the South Setifian allochthonous complex, providing insights into the evolution of the landscape. A morphometric analysis of Jebel Youcef Mountain (JYM) in Eastern Algeria was conducted to assess neotectonic activity. Six quantitative parameters were analyzed: stream length-gradient index, asymmetric factor, hypsometric integral, valley floor width-to-valley height ratio, index of drainage basin shape, and index of mountain front sinuosity across the 16 river basins in the region. The geomorphic indices are combined into a single index of relative tectonic activity (IRTA), categorized into four classes: very high, high, moderate, and low. The results identified two major lineament sets. The NE-SW lineament set is the dominant structural feature, playing a key role in driving recent geological processes and deformation in the study area. In contrast, the E-W and NW-SE lineament sets exert a more localized influence, primarily affecting the Jurassic formations at Kef El Ahmar’s central peak in Jebel Youcef, though they exhibit relatively lower tectonic activity compared to the NE-SW lineament set. Based on the relative active tectonic classes, significant neotectonic activity is evident in the study area, as shown by distinctive basement fracturing. The findings contribute to understanding the structural processes in the study area. Furthermore, the study establishes a systematic framework for analyzing tectonic activity and landscape morphology evolution, enhancing our perception of the convergence between the North African Alpine zones and the Atlas range. Full article

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century, has been considerably affecting short-term weather conditions, such as increasing weather variability and the incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, the spatial error model (SEM) and the spatial Durbin model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches, and then over that threshold, milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance. Full article

The growing population and expansion of rural activities, along with changing climatic patterns and the need for water during drought periods, have led to a rise in the water demand worldwide. As a result, the construction of water storage structures such as dams has increased in recent years to meet the water needs. However, dam construction can bring significant alterations to the natural flow regime of rivers, and it is therefore essential to understand the potential effects of human structures on the hydrological regime of rivers to reduce their destructive impacts. This study analyzes the hydrological changes in the Shahrchai River in response to the Shahrchai Dam construction in Urmia, Iran. The study period was from 1950 to 2017 at the Urmia Band station. The Indicators of Hydrological Alteration (IHA) were used to analyze the hydrological changes before and after regulating, accounting for land use changes and climatic factors. The results revealed the adverse effects of the Shahrchai Dam on the hydrological indices. The analysis showed an increase in the average flow rate during the summer season and a decrease in other seasons. However, the combined effects of water transferring for drinking purposes, a decrease in permanent snow cover upstream of the dam, and an increase in water use for irrigation and agricultural purposes resulted in a decrease in the released river flow. Furthermore, the minimum and maximum daily flow rates decreased by approximately 85% and 65%, respectively, after the construction of the Shahrchai Dam. Additionally, the number of days with maximum flow rates increased from 117 days in the pre-dam period to 181 days in the post-dam period. As a concluding remark, the construction of the Shahrchai Dam, land use/cover changes, and a decrease in permanent snow cover had unfavorable effects on the hydrological regime of the river. Therefore, the hydrological indicators should be adjusted to an acceptable level compared to the natural state to preserve the river ecosystem. The findings of this study are expected to guide water resource managers in regulating the sustainable flow regime of permanent rivers. Full article

The North Aegean Sea region in Greece is located at the convergence of the Eurasian, African, and Anatolian tectonic plates. The region experiences frequent seismicity ranging from moderate to large-magnitude earthquakes. Tectonic interactions and seismic events in this area have far-reaching implications for understanding the broader geological processes in the eastern Mediterranean region. This study aims to conduct a comprehensive investigation of the seismic activity of the North Aegean Sea region by employing advanced seismological techniques and data analyses. Data from onshore seismological networks were collected and analyzed to assess the characteristics of the earthquakes in the region. Seismicity patterns, focal mechanisms, and seismic moment calculations were performed to assess current seismic activity. The present study combined spatiotemporal analysis with the analysis of genesis mechanisms, and this resulted in more results than those of previous studies. Detailed analysis of the seismic data showed patterns in the occurrence of earthquakes over time, with periodic episodes of increased seismic activity compared to activities followed by quieter periods. Finally, this study proves that recent earthquakes in the study area (2017, 2020) highlight the complexity of seismicity as well as the consequences of strong earthquakes on people and buildings. Overall, these findings suggest that the North Aegean Sea is becoming increasingly seismically active and is a potential risk zone for adjacent regions. Full article

In the global carbon cycle, atmospheric carbon emissions, both ‘natural’ and anthropogenic, are balanced by carbon uptake (i.e., sequestration) that mostly occurs via photosynthesis, plus a much smaller proportion via geological processes. Since the formation of the Earth about 4.54 billion years ago, the ratio between emitted and sequestered carbon has varied considerably, with atmospheric CO₂ levels ranging from 100,000 ppm to a mere 100 ppm. Over this time, a huge amount of carbon has been sequestered due to photosynthesis and essentially removed from the cycle, being buried as fossil deposits of coal, oil, and gas. Relatively low atmospheric CO₂ levels were the norm for the past 10 million years, and during the past million years, they averaged about 220 ppm. More recently, the Holocene epoch, starting ~11,700 years ago, has been a period of unusual climatic stability with relatively warm, moist conditions and low atmospheric CO₂ levels of between 260 and 280 ppm. During the Holocene, stable conditions facilitated a social revolution with the domestication of crops and livestock, leading to urbanisation and the development of complex technologies. As part of the latter process, immense quantities of sequestered fossil carbon have recently been used as energy sources, resulting in a particularly rapid increase in CO₂ emissions after 1950 CE to the current value of 424 ppm, with further rises to >800 ppm predicted by 2100. This is already perturbing the previously stable Holocene climate and threatening future food production and social stability. Today, the global carbon cycle has been shifted such that carbon sequestration is no longer keeping up with recent anthropogenic emissions. In order to address this imbalance, it is important to understand the roles of potential biological carbon sequestration systems and to devise strategies to facilitate net CO₂ uptake; for example, via changes in the patterns of land use, such as afforestation, preventing deforestation, and facilitating agriculture–agroforestry transitions. Full article

This study examines heat wave projections across Ecuador’s Coastal, Highlands, and Amazon regions for 1975–2004 and 2070–2099 under Representative Concentration Pathways (RCP) scenarios 2.6, 4.5, and 8.5. Employing dynamic downscaling, we identify significant increases in heatwave intensity and maximum air temperatures ($T_{max}$), particularly under RCP 8.5, with the Coastal region facing the most severe impacts. A moderate positive correlation between Tmax and climate indices such as the Pacific Decadal Oscillation (PDO) and the Oceanic Niño Index (ONI) suggests regional climatic influences on heatwave trends. These findings highlight the critical need for integrated climate adaptation strategies in Ecuador, focusing on mitigating risks to health, agriculture, and ecosystems. Proposed measures include urban forestry initiatives and the promotion of cool surfaces, alongside enhancing public awareness and access to cooling resources. This research contributes to the understanding of climate change impacts in Latin America, underscoring the urgency of adopting targeted adaptation and resilience strategies against urban heat island effects in Ecuador’s urban centers. Full article

This study employs the Soil and Water Assessment Tool (SWAT) model to evaluate soil loss within the Shilabati and Dwarkeswar River Basin of West Bengal, serving as a pilot investigation into soil erosion levels at ungauged stations during the post-monsoon season. Detailed data for temperature, precipitation, wind speed, solar radiation, and relative humidity for 2000–2022 were collected. A land use map, soil map, and slope map were prepared to execute the model. The model categorizes the watershed region into 19 sub-basins and 227 Hydrological Response Units (HRUs). A detailed study with regard to soil loss was carried out. A detailed examination of soil erosion patterns over four distinct time periods (2003–2007, 2007–2012, 2013–2017, and 2018–2022) indicated variability in soil loss severity across sub-basins. The years 2008–2012, characterized by lower precipitation, witnessed reduced soil erosion. Sub-basins 6, 16, 17, and 19 consistently faced substantial soil loss, while minimal erosion was observed in sub-basins 14 and 18. The absence of a definitive soil loss pattern highlights the region’s susceptibility to climatic variables. Reduced soil erosion from 2018 to 2022 is attributed to diminished precipitation and subsequent lower discharge levels. The study emphasizes the intricate relationship between climatic factors and soil erosion dynamics. Full article