Editor’s Choice Articles

The increasing frequency of disasters caused by landslides, mainly due to climate change leading to more intense extreme events, requires reliable predictive models for risk mitigation. Italy, in particular, is a country at high risk of landslides, but the lack of an updated catalogue of landslide activation dates poses a significant challenge for defining reliable activation thresholds. This study develops a methodology for mapping landslide susceptibility based on events in a pilot area of central Italy, integrating a database of landslides with known activation dates with predisposing and triggering parameters. Two statistical techniques were compared to assess their predictive performance in discriminating landslide from non-landslide conditions during extreme precipitation events. A comparison between binary logistic regression (BLR) and decision trees (QUEST) revealed the clear superiority of the BLR model, which achieved excellent predictive accuracy (AUC = 0.913). The model identified clay-rich lithology, gentle slopes (0–16°) and maximum daily precipitation as the most significant controlling factors. This result led to the generation of three derivative products: a susceptibility map, a hazard map for an extreme precipitation scenario with a 100-year return period, and a spatially distributed map of activation thresholds. This threshold map quantifies the intensity of precipitation required to exceed a critical probability of landslide initiation (p > 0.7) at any point in the territory. The susceptibility map highlights critical areas within the study area, while the hazard map also includes the return period of the event. The threshold map is a direct and operational tool for early warning systems, transforming a statistical model into a guide for real-time risk management. The study area serves as a pilot area that could allow this methodology to be replicated. With the integration of real-time meteorological data, it could function as a real-time warning system. The proposed framework therefore provides a directly actionable tool for civil protection agencies, land-use planning authorities, and emergency managers, enabling location-specific rainfall alert thresholds to be issued rather than a single regional value, with the potential to reduce both false alarms and missed warnings. Full article

In response to land-use and land-cover (LULC) changes in South Africa, which have varied effects on biodiversity, several studies have characterized LULC changes using remote sensing data due to its cost-effectiveness, repetitiveness, spatial coverage and flexibility. However, the geotemporal and methodological characteristics of these studies remain relatively unknown. In this regard, we review remote sensing-based studies conducted in South Africa using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). From the 343 articles retrieved from Web of Science, Google Scholar, and Scopus databases, 103 studies were eligible for analysis. The analysis showed that (a) various remote sensing datasets were increasingly and effectively used to characterize LULC in South Africa over the period 2001–2024, primarily Landsat data with integration of various advanced classification algorithms; (b) most studies were conducted in the eastern seaboard, particularly in the Maputaland–Pondoland–Albany hotspot and highveld to the north, and (c) much research dealt with issues pertaining to “pristine class” conversion to urban area and other human-induced activities, mainly in biodiversity-rich landscapes. Overall, LULC studies achieved consistently reliable accuracies, largely using publicly available geospatial datasets, thereby creating an accessible foundation for all researchers. LULC research is expected to increase as conservation efforts strengthen amid ongoing developments in South Africa. Full article

Accurate river discharge estimation is fundamental for water resource management under increasingly variable hydrological conditions. While conventional in situ techniques remain hydrometric reference standards, their operational deployment is constrained by cost, accessibility, and limited spatial coverage. Advances in remote sensing and artificial intelligence (AI) have introduced non-contact discharge estimation frameworks based on image-derived observations. This systematic review, conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines, examines the evolution of river discharge measurement methods between 2004 and 2024 through a structured two-stage design. An initial search in Web of Science and Scopus identified 2809 records, of which 249 were retained for first-stage synthesis. A focused second-stage screening isolated seven studies that directly integrate image-based data with machine learning or deep learning architectures for discharge estimation. The analysis reveals a methodological transition from instrument-based hydrometry toward computationally assisted, image-driven approaches. The retained studies employ close-range and satellite imagery combined with Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and related models. Although reported validation metrics indicate strong predictive capability under specific conditions, performance remains dependent on site-specific calibration and reference discharge records. Broader operational deployment requires improved transferability, uncertainty integration, and cross-basin validation. Full article

Mountain ecosystems, strongly affected by climate-related variability and human impact, are degrading faster than other terrestrial ecosystems. Currently, the United Nations (UN) utilizes Sustainable Development Goal (SDG) 15: Life on Land (Target 15.4 and Sub-indicators 15.4.2a and 15.4.2b), along with the System for Earth Observation Data Access, Processing and Analysis for Land Monitoring, commonly referred to as SEPAL, to track mountain degradation. This SEPAL analysis does not include soil data, which is critical to understanding mountain degradation. The present research focuses on improving the tracking and evaluation of mountain land degradation (LD) utilizing soil data in the state of Colorado (CO) in the United States of America (USA) as an example. Total anthropogenic LD affects an estimated 19% of Colorado’s territory as of 2024, driven mainly by agricultural activities (80%). Between 2001 and 2024, overall LD in CO decreased (−0.4%), but LD from development increased by 23.3%. For mountain areas in CO, the mountain green cover index (MGCI) was 96% for 2024, and it decreased (−0.4%) between 2001 and 2024. The mountain LD proportion was 2.5% as determined by the SEPAL method compared to 4.4% by LULC analysis. Incorporation of soil data into LULC analysis found that between 2001 and 2024 LD increased to 6.6%. All soil types in the mountains exhibited anthropogenic LD due to development with a total developed area of 1385.1 km². Current total mountain LD (inherent + anthropogenic) in CO may be as high as 38.9%. Future estimates of total mountain LD should include both inherent and anthropogenic LD. Full article

Air pollution remains a major public health and environmental challenge in China, driven by complex non-linear interactions among anthropogenic activities, meteorological conditions, and topographic features that go beyond simple linear relationships. This study presents a comprehensive spatio-temporal assessment of key air pollutants (CO, NO₂, SO₂, and PM_2.5) and their relationships with Total Column Ozone (TCO) across China’s provinces from 2019 to 2023. Multi-source high-resolution satellite data from Sentinel-5P/TROPOMI, the China High PM_2.5 dataset, MODIS, and ERA5-Land reanalysis were integrated. A tiered analytical framework was applied, combining linear Pearson correlations, non-linear Spearman rank correlations, and interpretable XGBoost machine learning with SHAP values. Results reveal a distinct seasonal “seesaw” pattern, with primary pollutants peaking during winter stagnation and TCO reaching maximum levels in late winter and spring. Non-linear analyses uncover critical threshold effects, including exponential increases in PM_2.5 and SO₂ when surface temperatures drop below 0 °C, very strong SO₂-TCO coupling (ρ = 0.93), and significant pollutant trapping in low-elevation regions (CO-elevation ρ = −0.82). These findings support the development of precision environmental policies with dynamic, temperature-threshold-based emission controls and topography-specific strategies to effectively mitigate air pollution in China. Full article

The Seulawah Agam volcano, located in Aceh, hosts one of Indonesia’s largest unexploited geothermal resources that is included in the Indonesian Green Energy Program. Previous studies of the Seulawah geothermal system (SGS) have used partial data and methods without developing a comprehensive conceptual model of the reservoir and its fluid flow and heat transfer patterns. This study aims to characterize the groundwater flow and heat transfer patterns of the SGS through numerical modeling based on integrated geological, geophysical, and geochemical data. Numerical modeling was conducted along two representative transects: Ie Seum, Ie Jue, and Kawah van Heutsz manifestations. MODFLOW 6 was used to model groundwater flow and heat transfer using a new conceptual model derived from magnetotelluric data, chemical composition and physical properties of the fluid, isotopic data, and mineragraphic data. The low resistivity anomalies are closely related to fluid discharges beneath the Ie Seum and Ie Jue areas. The depth of the Ie Seum reservoir is around 1.0–2.5 km, with estimated temperatures of 120–242 °C, while the depth of the Ie Jue and Kawah van Heutsz reservoirs is between 0.8 and 2.5 km, with estimated temperatures of 150–316 °C. The modeling suggests that the Ie Seum and the Ie Jue–Kawah van Heutsz systems represent regional groundwater and intermediate-local flow regimes, respectively. It is suggested that drilling be conducted around the local Ie Jue hydrothermal system, which is more economical given the shallower reservoir and higher temperature. Full article

The global carbon cycle has become increasingly unbalanced over the past century as anthropogenic fluxes into the atmosphere far exceed the sequestration capacity of land and ocean systems. Data from 2025 show estimated annual anthropogenic emissions of ≈11.2 gigatonnes of carbon (GtC), while only ≈5.6 GtC are sequestered by land and ocean sinks mainly provided by photosynthetic CO₂ fixation. The resulting surplus of carbon emissions has led to a doubling of atmospheric CO₂ concentrations above pre-industrial values to ≈430 ppm, which is a major driver of increasingly erratic climatic phenomena. Recent data indicate that fossil fuel use will continue rising up to and beyond 2050, largely negating the drive to cut CO₂ emissions as recommended by the IPCC and other reputable transnational bodies. Hence, there is an urgent need to reduce atmospheric CO₂ levels via carbon sequestration. This review focuses on the proven capacity of biological mechanisms to sequester CO₂ at a global scale with an annual capacity in the range of gigatonnes of carbon. New measures such as re- and a-forestation, plus improved and more sustainable management of tropical tree crops, can further increase the carbon sequestration potential of these plants. By implementing these and other nature-based solutions, the highly productive tropical vegetation belt could contribute an additional 1–2 Gt of carbon sequestration via natural forests and perennial tree crops. In order to expedite this process, we examine the use of new modalities of transparent carbon trading systems that include selected tropical crops. As highlighted at COP30 in Brazil and elsewhere, this would enable tropical countries to derive benefit for costs incurred in land management changes such as reforestation, regenerative farming, and intercropping to benefit smallholders and other rural communities. In particular, carbon finance is emerging as a critical driver, with appropriately regulated and transparent carbon credit schemes offering fungible monetary compensation for climate-positive land management. Full article

Aridity represents a fundamental climatic constraint governing water resources, ecosystem functioning, and agricultural systems in transitional climate zones. This study examines the spatial organization and temporal variability of aridity and thermal continentality in North Macedonia using observational records from 13 meteorological stations distributed across contrasting altitudinal and physiographic settings. The analysis is based on homogenized monthly and annual air temperature and precipitation series covering the period 1991–2020. Aridity and continentality were quantified using the Johansson Continentality Index (JCI), the De Martonne Aridity Index (I_DM), and the Pinna Combinative Index (I_P). Temporal consistency and trend behavior were evaluated using Pettitt’s nonparametric change-point test, linear regression, the Mann–Kendall test, and Sen’s slope estimator. Links between aridity variability and large-scale atmospheric circulation were examined using correlations with the North Atlantic Oscillation (NAO) and the Southern Oscillation Index (SOI). The results show a spatially consistent and statistically significant increase in mean annual air temperature, with a common change point around 2006, while precipitation displays strong spatial variability and limited temporal coherence. Aridity patterns display a strong altitudinal control, with extremely humid to very humid conditions prevailing in mountainous western regions and semi-humid to semi-dry conditions dominating lowland and southeastern areas, particularly during summer. Trend analyses do not reveal statistically significant long-term changes in aridity or continentality over the study period, although low-elevation stations exhibit weak drying tendencies. A moderate positive association between I_DM and I_P (r = 0.66) confirms internal consistency among aridity indices, while summer aridity shows a statistically significant relationship with the NAO. These results provide a robust climatic reference for North Macedonia, establishing a first climatological baseline of aridity conditions based on multiple indices applied to homogenized observations, and contributing to regional assessments of hydroclimatic variability relevant to climate adaptation planning. Full article

Eutrophication remains a persistent challenge in the Romanian Black Sea coastal zone, driven by excess nutrient inputs from riverine and coastal sources and further intensified by climate change. This study assesses eutrophication dynamics and explores mitigation options using an integrated framework that combines in situ observations, satellite-derived chlorophyll a data, machine learning, and system dynamics modelling. Water samples collected during two field campaigns (2023–2024) were analyzed for nutrient concentrations and linked with chlorophyll a products from the Copernicus Marine Service. Random Forest analysis identified dissolved inorganic nitrogen, phosphate, salinity, and temperature as the most influential predictors of chlorophyll a distribution. A system dynamics model was subsequently used to explore relative ecosystem responses under multiple management scenarios, including nutrient reduction, enhanced zooplankton grazing, and combined interventions. Scenario-based simulations indicate that nutrient reduction alone produces a moderate decrease in chlorophyll a (45% relative to baseline conditions), while restoration of grazing pressure yields a comparable response. The strongest reduction is achieved under the combined scenario, which integrates nutrient reduction with biological control and lowers normalized chlorophyll a levels by approximately two thirds (71%) relative to baseline. In contrast, a bloom-favourable scenario results in a several-fold increase in chlorophyll a of 160%. Spatial analysis highlights persistent eutrophication hotspots near the Danube mouths and urban discharge areas. These results demonstrate that integrated strategies combining nutrient source control with ecological restoration are substantially more effective than single-measure interventions. The proposed framework provides a scenario-based decision-support tool for ecosystem-based management and supports progress toward achieving Good Environmental Status under the Marine Strategy Framework Directive. Full article

Forests play a vital role in the global carbon cycle but face growing anthropogenic pressures, with climate change and forest fragmentation among the most critical. In West Africa, particularly in Ghana, the interaction between increasing aridity and forest fragmentation remains underexplored, despite its significance for forest biomass dynamics and carbon storage processes. This study examined how spatial variation in climatic aridity (Aridity Index, AI) affects above-ground biomass (AGB) in Ghana’s ecological zones, both directly and indirectly through forest fragmentation and biodiversity, using structural equation modeling (SEM) and generalized additive models (GAMs). Results from this study show that AGB declines along the aridity gradient, with humid zones supporting the highest biomass and semi-arid zones the lowest. The SEM analysis revealed that areas with a lower aridity index (drier conditions) had significantly lower AGB, indicating that arid conditions are associated with lower forest biomass. Fragmentation patterns align with this relationship, while biodiversity (as measured by species richness) showed weak associations, likely reflecting both ecological and data limitations. GAMs highlighted nonlinear fragmentation effects: mean patch area (AREA_MN) was the strongest predictor, showing a unimodal relationship with biomass, whereas number of patches (NP), edge density (ED), and landscape shape index (LSI) reduced AGB. Overall, these findings demonstrate that aridity and spatial configuration jointly control biomass, with fragmentation acting as a key mediator of this relationship. Dry and transitional forests emerge as particularly vulnerable, emphasizing the need for management strategies that maintain large, connected forest patches and integrate restoration into climate adaptation policies. Full article

Lake Batur, located within a volcanic caldera in Bali, Indonesia, is subjected to anthropogenic pressures related to agriculture, aquaculture, tourism, and religious activities, which may affect its water quality and ecology condition. This study investigates the physicochemical properties of lake water and diatom assemblages preserved in lake sediments to provide insight into environmental conditions in this volcanic alkaline ecosystem. Water quality parameters, including pH, temperature, electrical conductivity (EC), and total dissolved solids (TDS), were measured. Vertical profiles of temperature and conductivity revealed stable stratification, with minimal variation below 20 m water depth. Elevated nitrogen concentrations, including nitrate (NO₃⁻), nitrite (NO₂⁻), and ammonium (NH₄⁺), were observed, particularly in the southern basin, suggesting localized nutrient enrichment. Scanning electron microscopy (SEM) analysis of lake sediment samples identified ten diatom genera, including Ulnaria, Denticula, and Discostella, which are commonly associated with nutrient-enriched freshwater environments. Overall, the results indicate that Lake Batur exhibits conditions consistent with early-stage eutrophication in localized areas, highlighting the importance of continuous monitoring and targeted management strategies to protect the ecological integrity of this volcanic lake system. Full article

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

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

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

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