Search Results (2,773)

Accurate and spatially detailed soil information is essential for supporting sustainable land use planning, particularly in data-scarce regions such as Cyprus, where soil degradation risks are intensified by land fragmentation, water scarcity, and climate change pressure. This study aimed to generate national-scale predictive maps of key soil health descriptors by integrating satellite-based indicators with a recently released geo-referenced soil dataset. A machine learning model was applied to estimate a suite of soil properties, including organic carbon, pH, texture fractions, macronutrients, and electrical conductivity. The resulting maps reflect spatial patterns consistent with previous studies focused on Cyprus and provide high resolution insights into degradation processes, such as organic carbon loss, and salinization risk. These outputs provide added value for identifying priority zones for soil conservation and evidence-based land management planning. While predictive uncertainty is greater in areas lacking ground reference data, particularly in the northeastern part of the island, the modeling framework demonstrates strong potential for a national-scale soil health assessment. The outcomes are directly relevant to ongoing soil policy developments, including the forthcoming Soil Monitoring Law, and provide spatial prediction models and indicator maps that support the assessment and mitigation of soil degradation. Full article

Groundwater level (GWL) in unconfined aquifers is highly susceptible to climate variables and human activities, exhibiting nonlinear fluctuations; these can further contribute to or exacerbate environmental hazards, such as land subsidence. Understanding the relationship between GWL changes and external conditions is essential for effective groundwater resource management and ecological protection. However, this relationship remains unclear and variable. This study systematically analyzes the correlations between climate and human factors and GWLs, using data from monitoring stations in the unconsolidated sedimentary aquifers of Beijing, China. It evaluates the importance of influencing factors on GWL simulation accuracy and tests how different inputs affect simulation performance. The results indicate that human factors are more strongly correlated with GWLs, yet climate factors hold higher importance scores. In GWL simulations, different input variables yield varying accuracy, with the inclusion of precipitation notably decreasing simulation precision because of its lagged or indirect effects on groundwater levels. The variation in accuracy across monitoring stations further suggests that the primary differences may stem from the GWL data itself. These findings underscore the need for high-resolution, localized data and tailored input selection to improve GWL projections and inform adaptive water-resource strategies under changing climatic and anthropogenic pressures. Full article

Global warming and rapid urban industrialization are profoundly transforming land-use patterns and carbon storage capacity in terrestrial ecosystems. A rigorous analysis of spatiotemporal variations in regional land-use changes and carbon storage dynamics provides critical insights for sustainable land-use planning and ecological security, particularly within the context of achieving carbon peaking and carbon neutrality targets. In this study, the PLUS-InVEST model was coupled with climate change and policy constraints to construct six future scenarios. We analyzed the characteristics of land-use evolution and the spatial and temporal changes in carbon storage in the Sanjiangyuan region from 2000 to 2020. We also predicted the potential impacts of land-use shift on carbon storage. The results show the following: (1) Land-use transitions exerted significant impacts on carbon stock. The Sanjiangyuan region experienced a net carbon stock reduction of 9.9 × 10⁶ t during 2000–2020, with the most pronounced decline (6.1 × 10⁶ t) occurring between 2000 and 2010. (2) Under the same climate scenario, the natural development (ND) scenario exhibited decreasing carbon reserves relative to 2020 baseline levels. Notably, land-use planning scenarios demonstrated spatially heterogeneous impacts, with the ecological protection (EP) scenario consistently maintaining higher carbon stocks compared to the ND scenario. (3) Multivariate driver interactions exerted stronger control over spatial carbon storage patterns than any individual factor. These findings inform targeted land-use management strategies to enhance regional carbon sequestration capacity, promote sustainable development, and support China’s carbon peaking and neutrality objectives. Full article

Vibrio species are part of the indigenous microbial flora in marine, brackish and fresh water in moderate and tropical climates that thrive and multiply in water at elevated water temperatures. The number of human non-cholera Vibrio infections due to exposure to contaminated surface water increases worldwide. To study possible climate change-related changes in Vibrio concentrations, prevalent species, and risks of illness, water samples from coastal and inland water bodies in the Netherlands were tested in 2019–2021. Data were combined with data from previous studies in 2009–2012 in order to develop a regression model to predict current and future risks of Vibrio illness. Year-to-year and site-specific variations in Vibrio concentrations and water temperature were observed, but there was no trend of increasing Vibrio concentrations or water temperature over time. In 2019–2021, Vibrio species distribution had not changed since 2009–2012; V. alginolyticus and V. parahaemolyticus were still the dominant species. Statistical analysis demonstrated a significant effect of water temperature on Vibrio concentrations. The model predicted a concentration increase of a factor of 1.5 for each degree Celsius temperature increase. Predicted risks of illness were higher at higher water temperatures, and higher for children than for adults. Based on the most recent climate change scenarios for the Netherlands, the risks of Vibrio illness will increase with factors ranging from 1.6 to 7.6 in 2050 and 2100. These outcomes warrant adequate information about Vibrio risks to water managers, public health workers and the general public. Full article

The Northern Rocky Mountains, USA contain a vast forested landscape, managed primarily by the federal government. This region contains some of the highest elevations forests and most iconic endangered and threatened species in the contiguous United States. The influence of human impacts and climate change are evident on the landscape today, with larger and more frequent fires impacting vegetation composition and recovery. This project uses paleoecological data from six lake sediment cores to investigate what drives fire across this region over the Holocene. Count regression was used to predict charcoal influx as a function of Pinus pollen accumulation rates (PAR) and percent. The results show that fire activity increases significantly with Pinus pollen, and that baseline fire activity varies significantly across sites, largely following an elevation gradient. The results of this analysis illustrate a novel way to use paleoecological data to provide valuable information to federal agencies as they prepare for future management of these ecologically valuable areas. Full article

This paper explores how uncertainty in climate change projections both shapes and is shaped by key epistemic, methodological, ethical, and political concerns. Drawing on a review of key philosophical sources, it examines the relationship between scientific objectivity and the influence of social, ethical, and political values in contexts of deep uncertainty. Authors such as Wendy Parker and Heather Douglas debate the role that social values play in the estimation and communication of scientific uncertainty, particularly when decisions carry significant ethical and political consequences. At the same time, several studies emphasize that, beyond the influence of values, there are structural limitations inherent to complex climate models that prevent uncertainty from being fully reduced. Taken together, these perspectives suggest that both evaluative judgments and technical constraints must be considered when interpreting and managing uncertainty in climate science, especially insofar as it informs collective decision-making processes. The article also examines how certain institutional practices tend to downplay uncertainty, generating biases that affect both scientific communication and public decision-making. It, therefore, explores potential solutions through more integrative approaches, such as robust modeling, risk assessments focused on low-probability but high-impact events (HILL), and collective ethical deliberation. The paper further discusses the concept of normative uncertainty, illustrated through the case of the Tempisque River water conflict, which highlights the difficulty of reconciling competing values. It concludes that, far from being eliminated, uncertainty must be managed through tools that integrate technical rationality, ethical sensitivity, and adaptive governance. Full article

Since 2005, landscape ecology has emerged as a structured scientific field in the Democratic Republic of Congo, notably shaped by the contributions of Professor Jan Bogaert. The evolution of research in this field can be divided into three main phases. The first phase (2005–2012) focused on the quantitative analysis of forest fragmentation using Geographic Information Systems and landscape metrics. From 2013 to 2019, research approaches broadened to include the social sciences, marking a shift toward a socio-ecological perspective on landscapes. Since 2020, the field has increasingly adopted holistic frameworks that integrate climatic factors and forward-looking modeling. Key research themes now include ecological flows across landscape mosaics, land-use dynamics, and the anthropogenic transformation of ecosystems. However, several challenges persist, including the lack of long-term temporal datasets, uneven geographic coverage, and limited integration of local knowledge systems. Notable advances have been made through high-resolution remote sensing and participatory methods, although their application is still limited by technical and financial constraints. This manuscript advocates for stronger interdisciplinary collaboration, improved field methodologies, and the development of context-appropriate tools to support sustainable and locally grounded landscape management in the Congolese context. Full article

The Cau River Basin in northeastern Vietnam is an ecologically and economically important watershed, yet it has lacked comprehensive hydrological modelling to date. Characterised by highly complex topography, diverse land use/land cover, and limited hydrometeorological data, the basin presents challenges for water resource assessment and management. This study applies the SWAT hydrological model to simulate streamflow dynamics in the Cau River Basin over a 31-year period (1990–2020) using multiple-source geospatial data, including a 30 m digital elevation model, official soil and land use maps, and daily climate records from six meteorological stations. Model calibration (1997–2008) and validation (2009–2020) were conducted using the SWAT-CUP tool, achieving strong performance with a Nash–Sutcliffe Efficiency (NSE) of 0.95 and 0.90, and R² of 0.95 and 0.91, respectively. Sensitivity analysis identified four key parameters most influential on streamflow (curve number, saturated hydraulic conductivity, soil evaporation compensation factor, and available water capacity), supporting a more focused and effective calibration process. Model results revealed substantial spatio-temporal variability in runoff, with annual surface runoff ranging from 19.8 mm (2011) to 56.4 mm (2013), generally lower in upstream sub-watersheds (<30 mm) and higher in downstream areas (>60 mm). The simulations also showed a clear seasonal contrast between the wet and dry periods. These findings support evidence-based strategies for flood and drought mitigation, inform agricultural and land use planning, and offer a transferable modelling framework for similarly complex watersheds. Full article

This study investigates the drivers of individual and joint adoption of sustainable land use (SLU) practices—specifically crop choice and soil and water conservation—and their impact on farm performance (crop revenue) and production risk (crop yield skewness). Using a farm-level dataset of 504 households across three agro-ecological zones in Punjab, Pakistan, we address selectivity bias through the newly developed multinomial endogenous switching regression (MESR) model. Additionally, we assess land use sustainability across ecological, social, and economic dimensions using a comprehensive non-parametric approach. Our findings identify key determinants of SLU adoption, including farmer education, access to advisory services, FBO membership, hired labor, climate information, farm size, and perceptions of drought and heatwaves. We demonstrate that joint adoption of SLU practices maximizes crop revenue and reduces production risk, lowering the likelihood of crop failure. The study further suggests complementarity between these SLU practices in enhancing crop revenue. Moreover, joint adopters of SLU practices significantly outperform non-adopters in ecological, social, and economic sustainability dimensions. We recommend improving access to public sector farm advisory services and climate information to enable farmers to make well-informed decisions based on reliable data. Implementing these measures can support the transition toward sustainable land management, helping to mitigate risks like crop failure and declining revenues, which threaten farm income. Full article

Critical infrastructure, such as transport networks, energy facilities, and urban installations, is increasingly vulnerable to natural hazards and climate change. Remote sensing technologies, namely satellite imagery, offer solutions for monitoring, evaluating, and enhancing the resilience of these vital assets. This paper explores how applications based on synthetic aperture radar (SAR) and optical satellite imagery contribute to the protection of critical infrastructure by enabling near real-time monitoring and early detection of natural hazards for actionable insights across various European critical infrastructure sectors. Case studies demonstrate the integration of remote sensing data into geographic information systems (GISs) for promoting situational awareness, risk assessment, and predictive modeling of natural disasters. These include floods, landslides, wildfires, and earthquakes. Accordingly, this study underlines the role of remote sensing in supporting long-term infrastructure planning and climate adaptation strategies. The presented work supports the goals of the European Union (EU-HORIZON)-sponsored ATLANTIS project, which focuses on strengthening the resilience of critical EU infrastructures by providing authorities and civil protection services with effective tools for managing natural hazards. Full article

The Ecological Green Heart Area of the Chang–Zhu–Tan Urban Agglomeration in Central China faces increasing forest health threats due to rapid urbanization and land use change. This study assessed the spatiotemporal dynamics and drivers of forest health from 2005 to 2023 using a multi-dimensional framework based on vitality, organizational structure, and anti-interference capacity. A forest health index (FHI) was constructed using multi-source data, and the optimal parameter geographic detector (OPGD) model was applied to identify dominant and interacting factors. The results show the following: (1) FHI declined from 0.62 (2005) to 0.55 (2015) and rebounded to 0.60 (2023). (2) Healthier forests were concentrated in the east and center, with degradation in the west and south; (3) Topography was the leading driver (q = 0.17), followed by climate, while socioeconomic factors gained influence over time. (4) Interactions among factors showed strong nonlinear enhancement. This research demonstrates the effectiveness of the OPGD model in capturing spatial heterogeneity and interaction effects, underscoring the need for differentiated, spatially informed conservation and land management strategies. This research provides scientific support for integrating ecological protection with urban planning, contributing to the broader goals of ecosystem resilience, sustainable land use, and regional sustainability. Full article

Sustainability has an ever-increasing importance in our lives, mainly due to climate changes, finite resources, and a growing population, where each of us is called to make a change. Although climate change is a global phenomenon, our individual choices can make the difference. The transportation sector is one of the largest contributors to global carbon emissions, making the transition toward sustainable mobility a critical priority. The adoption of electric vehicles is widely recognized as a key solution to reduce the environmental impact of transportation. However, their widespread acceptance depends on various technological, behavioral, and economical factors. Within this research we use as an artifact the CO₂ Emission Management Gauge (CEMG) devices to better understand how the manufacturers, with integrated features on vehicles, could significantly enhance sales and drive the movement towards electric vehicle adoption. This study proposes an innovative new theoretical model based on Task-Technology Fit, Technology Acceptance, and the Theory of Planned Behavior to understand the main drivers that may foster electric vehicle adoption, tested in a quantitative study with structural equation modelling (SEM), and conducted in a South European country. Our findings, not without some limitations, reveal that while technological innovations like CEMG provide consumers with valuable transparency regarding emissions, its influence on the intention of adoption is dependent on the attitude towards electric vehicles and subjective norm. Our results also support the influence of task-technology fit on perceived usefulness and perceived ease-of-use, the influence of perceived usefulness on consumer attitude towards electric vehicles, and the influence of perceived ease-of-use on perceived usefulness. A challenge is also presented within our work to expand CEMG usage in the future to more intrinsic urban contexts, combined with smart city algorithms, collecting and proving CO₂ emission information to citizens in locations such as traffic lights, illumination posts, streets, and public areas, allowing the needed information to better manage the city’s quality of air and traffic. Full article

The conversion of wetlands into croplands often leads to significant losses of peat soil salinity and soil organic matter (SOM), though quantifying these changes is challenging due to limited historical data. In this study, we compared current soil physicochemical properties with rare historical data from the Mezzano Lowland (ML) in Northeastern Italy, a former wetland drained over 60 years ago. The transformation, which affected approximately 18,100 hectares, was achieved through the construction of a network of drainage canals and pumping stations capable of removing large volumes of water, enabling intensive agricultural use. Results showed a marked decrease in electrical conductivity (EC) and sulphate concentration, indicating extensive salt leaching from the upper peat soil layers. EC dropped from historical values up to 196 $\mathrm{m}$$\mathrm{S}$/cm (1967–1968) to a current maximum of 4.93 $\mathrm{m}$$\mathrm{S}$/cm, while sulphate levels declined by over 90%. SOM also showed significant depletion, especially in deeper layers (50–100 cm), with losses ranging from 50 to 60 $\mathrm{wt}\%$, due to increased aeration and microbial activity post-drainage. These climatic and environmental changes, including a marked reduction in soil salinity and sulphate concentrations due to prolonged leaching, have likely shifted the Mezzano Lowland from a carbon sink to a net source of CO₂ and CH₄ by promoting microbial processes that enhance methane production under anaerobic conditions. To detect residual peat layers, we used Ground-Penetrating Radar (GPR), which, combined with soil sampling, proved effective for tracking long-term peat soil changes. This approach can inform sustainable land management strategies to prevent further carbon loss and maintain peat soil stability. Full article

Levee failures caused by prolonged flooding and elevated upstream water levels pose a significant risk to floodplain communities, especially as the number of extreme hydrological events increases under climate change. Understanding seepage-induced weakening and failure mechanisms is essential for improving levee design and resilience. This study develops a numerical framework that integrates unsaturated and saturated seepage analysis with slope stability evaluation to simulate seepage front progression and predict failure initiation. The model employs van Genuchten-based soil water retention properties and experimentally derived hydraulic conductivities, with results validated against five experimental cases with varying hydraulic conductivity contrasts between the levee body and foundation soils. The simulations reproduced seepage front evolution and slope deformation patterns with good agreement with experimental observations. In cases with high permeability contrasts, the model captured foundation-dominant seepage behavior, while moderate- and low-contrast scenarios showed close alignment with observed phreatic line development. Slight deviations were noted in failure timing, but the framework demonstrated potential for reproducing seepage-induced instability in levees. The findings contribute to understanding how the internal soil composition governs levee performance under flooding and provide a basis for developing seepage countermeasures and early warning tools. This approach offers practical value for risk-informed levee design and flood management. Full article

Cultural customs often condition solid waste management, especially in developing countries. The decomposition of solid waste depends on climatic conditions and is related to geomorphology and anthropogenic practices. Leachate generated in landfills can migrate superficially and underground, contaminating soils and aquifers. Knowing the level of contaminant load in leachate is important for proper solid waste management. However, in the Andean regions of Peru, there is scarce data on the polluting potential of leachates. This research aimed to determine the Leachate Pollution Index (LPI) according to the sub-indexes of organic, inorganic, and heavy metals from landfills in the high Andean regions of Peru. Physical, chemical, and microbiological parameters were evaluated in fresh and retained leachate samples, in both dry and rainy seasons, from two landfills located at around 3000 m of altitude. The results showed high contamination levels, particularly in BOD₅, COD, NH₃-N, and total coliforms, with high organic and inorganic sub-indexes that affect the LPI, indicating high levels of contamination and posing a potential risk to surrounding ecosystems. It was also found that the high Andean landfills studied have a good-to-high biodegradability. This research contributes essential baseline information for environmental monitoring and supports the need for improved leachate management in high-altitude landfills in Peru and similar Andean contexts. Full article