Search Results (38,902)

The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, is promising in this context. The coastline of Northern Tunisia is currently showing geomorphic process, such as increasing erosion associated with lateral sedimentation. This study aims to investigate the potential of time-series optical data, namely Landsat (from 1985–2019) and Google Earth^® satellite imagery (from 2007 to 2023), to analyze shoreline changes and morphosedimentary and geomorphological processes between Cape Serrat and Kef Abbed, Northern Tunisia. The Digital Shoreline Analysis System (DSAS) was used to quantify the multitemporal rates of shoreline using two metrics: the net shoreline movement (NSM) and the end-point rate (EPR). Erosion was observed around the tombolo and near river mouths, exacerbated by the presence of surrounding dams, where the NSM is up to −8.31 m/year. Despite a total NSM of −15 m, seasonal dynamics revealed a maximum erosion in winter (71% negative NSM) and accretion in spring (57% positive NSM). The effects of currents, winds, and dams on dune dynamics were studied using historical images of Google Earth^®. In the period from 1994 to 2023, the area is marked by dune face retreat and removal in more than 40% of the site, showing the increasing erosion. At finer spatial resolution and according to the synergy of field observations and photointerpretation, four key geomorphic processes shaping the coastline were identified: wave/tide action, wind transport, pedogenesis, and deposition. Given the frequent changes in coastal areas, this method facilitates the maintenance and updating of coastline databases, which are essential for analyzing the impacts of the sea level rise in the southern Mediterranean region. Furthermore, the developed approach could be implemented with a range of forecast scenarios to simulate the impacts of a higher future sea-level enhanced climate change. Full article

Cold ecosystems are experiencing a warming rate that is twice as fast as the global average and are particularly vulnerable to the consequences of climate change. In mountain ecosystems, it is particularly important to monitor vegetation to understand ecosystem dynamics, biodiversity conservation, and the resilience of these fragile ecosystems to global change. Hence, we used satellite data acquired by Sentinel-2 to perform a comparative assessment of the Normalized Difference Vegetation Index (NDVI) and the Plant Phenology Index (PPI) in mountainous regions (canton of Valais-Switzerland in the European Alps) for monitoring vegetation dynamics of four types: deciduous trees, coniferous trees, grasslands, and shrublands. Results indicate that the NDVI is particularly noisy in the seasonal cycle at the beginning/end of the snow season and for coniferous trees, which is consistent with its known snow sensitivity issue and difficulties in retrieving signal variation in dense and evergreen vegetation. The PPI seems to deal with these problems but tends to overestimate peak values, which could be attributed to its logarithmic formula and derived high sensitivity to variations in near-infrared (NIR) and red reflectance during the peak growing season. Concerning seasonal parameters retrieval, we find close concordance in the results for the start of season (SOS) and end of season (EOS) between indices, except for coniferous trees. Peak of season (POS) results exhibit important differences between the indices. Our findings suggest that PPI is a robust remote sensed index for vegetation monitoring in seasonal snow-covered and complex mountain environments. Full article

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma,ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts. Full article

This study aims to assess the adaptability of coastal areas in the Republic of Korea to future climate change-induced flooding. Coastal areas can be susceptible to complex external factors, including rainfall, tide levels, storm surge wave overtopping, etc. The study employs an integrated approach to address this, connecting hydrological and marine engineering technologies. The models utilized in this study encompass XP-SWMM, ADCIRC, SWAN, and FLOW-3D. This study analyzed floods in 2050 and 2100, considering expected rainfall patterns, sea level rising, and an increase in typhoon intensity based on climate change scenarios for six coastal areas in the Republic of Korea. We reviewed the adaptability of flooding to climate change in each region. Full article

Rainfall irregularity in Mediterranean regions is a characterizing feature of their climate. The aim of this manuscript is to analyze, in a climate change context, the evolution of this irregularity in Barcelona. A very long monthly database (1786–2023) enables detailed analysis of rainfall evolution, with its irregularity quantified using the concept of disparity, the trends of which are assessed using moving windows and a modified Mann–Kendall test. The relationship between disparity and the Western Mediterranean Oscillation index (WeMOi) is also explored. Additionally, the study compares rainfall amounts to the 1961–1990 reference period and evaluates autumn’s contribution to annual totals. A significant and increasing disparity trend over the years is detected for the autumn months. While correlations between disparity and WeMOi are limited, the WeMOi and monthly precipitation are significantly correlated for two autumn months, October and November, and for December, aligning with previous studies. This suggests the potential influence of the WeMOi fluctuations on future rainfall during these three months. Recent evidence of the increasing autumn irregularity is seen in the consecutive low-rainfall years of 2021, 2022 and 2023, which stand out as the driest since 1835, with the last two autumns ranking among the 5% driest. Full article

Dominant species are crucial in regulating the structure and productivity of plant communities. Adaptation strategies to climate change vary among the dominant species of different life types. However, the responses of the ecological niches of dominant species to warming and precipitation in semi-arid grasslands and their impacts on community structure and function are unknown. This study involved conducting a long-term experimental simulation of warming and increased precipitation on grasslands in Inner Mongolia and studying population dynamics, ecological niches, and their responses to the structure and function of the community species of two dominant plants, L. chinensis (perennial rhizome grass) and S. krylovii (perennial clumped grass). The results show that the niche width of L. chinensis increased and S. krylovii decreased under warming and increased precipitation conditions. The overlap of L. chinensis and S. krylovii decreased under the same conditions. The niche widths of L. chinensis and S. krylovii were 1.22 for the control (C), 1.19 and 1.04 under warming (W) conditions, 1.27 and 0.97 under warming plus precipitation (WP) conditions, and 1.27 and 1.24 under the conditions of precipitation addition (P). The niche overlap of L. chinensis and S. krylovii were 0.72 in C, 0.69 in W, 0.68 in WP, and 0.82 in P. The biomass share and importance value of L. chinensis increased, and those of S. krylovii decreased in response to warming and precipitation. The effects of warming on species diversity and community stability are primarily influenced by the effects on the niche breadth of S. krylovii. Combined with our previous study, L. chinensis will offer more resources in communities in warmer and wetter steppe climates in the future. However, this is not conducive to community diversity. Full article

Coastal forests in the eastern United States are increasingly threatened by hurricanes; however, monitoring their initial impacts and subsequent recovery is challenging across scales. Understanding disturbance impacts and responses is essential for sustainable forest management, biodiversity conservation, and climate change adaptation. Using Sentinel-2 imagery, we calculated the annual Normalized Difference Vegetation Index change (∆NDVI) of forests before and after Hurricane Michael (HM) in Florida to determine how different forest use types were impacted, including the initial wind damage in 2018 and subsequent recovery or reactive management for two focal areas located near and far from the coast. We used detailed parcel data to define forest use types and characterized multi-year impacts using sampling and k-means clustering. We analyzed five years of timberland logging activity up to the fall of 2023 to identify changes in logging rates that may be attributable to post-hurricane salvage efforts. We found uniform impacts across forest use types near the coast, where winds were the most intense but differences inland. Forest use types showed a wide range of multi-year responses. Urban forests had the fastest 3-year recovery, and the timberland response was delayed, apparently due to salvage logging that increased post-hurricane, peaked in 2021–2022, and returned to the pre-hurricane rate by 2023. The initial and secondary consequences of HM on forests were complex, as they varied across local and landscape gradients. These insights reveal the importance of considering forest use types to understand the resilience of coastal forests in the face of potentially increasing hurricane activity. Full article

This work presents a novel approach to rainfall–runoff modeling. We incorporate GAN-based data compaction into a spatial-attention-enhanced transductive long short-term memory (TLSTM) network. The GAN component reduces data dimensions while retaining essential features. This compaction enables the TLSTM to capture complex temporal dependencies in rainfall–runoff patterns more effectively. When tested on the CAMELS dataset, the model significantly outperforms benchmark LSTM-based models. For 8-day runoff forecasts, our model achieves an NSE of 0.536, compared to 0.326 from the closest competitor. The integration of GAN-based feature extraction with spatial attention mechanisms improves predictive accuracy, particularly for peak-flow events. This method offers a powerful solution for addressing current challenges in water resource management and disaster planning under extreme climate conditions. Full article

With the vigorous promotion in China of efforts to reduce pollution and carbon emissions, examining their synergies becomes increasingly crucial. This study used the multi-regional input–output (MRIO) table to build the consumption-based industrial emissions inventories of CO₂ and three major air pollutants (PM_2.5, NO_x, and SO₂) and constructed synergistic emission indices of the intensity and magnitude to identify and evaluate the synergy between carbon emissions and air pollutants in inter-industrial trade among 30 provinces in mainland China. The results show that more than 85% and 40% of inter-provincial and inter-industrial trades have synergistic emissions between CO₂ and air pollutants, respectively. We identified 77 inter-provincial trades and 84 inter-industrial trades among provinces with strong synergistic emissions. They are mainly reflected in the demand of the construction industry in Zhejiang and Guangdong for the nonmetal mineral products manufacturing industry in Henan, and the metal smelting and processing industry in Hebei, along with the demand of the service industry in Beijing for the electric power, steam, and hot water production and supply industry in Inner Mongolia. Our study provides new insights into the synergistic reduction of CO₂ and air pollutants within the supply chain, thereby enriching the discourse on regional and industrial synergies in achieving sustainable development goals. Full article

Urban energy systems planning presents significant challenges, requiring the integration of multiple objectives such as economic feasibility, technical reliability, and environmental sustainability. Although previous studies have focused on optimizing renewable energy systems, many lack comprehensive decision frameworks that address the complex trade-offs between these objectives in urban settings. Addressing these challenges, this study introduces a novel Multi-Criteria Decision Analysis (MCDA) framework tailored for the evaluation and prioritization of energy scenarios in urban contexts, with a specific application to the city of Bozen-Bolzano. The proposed framework integrates various performance indicators to provide a comprehensive assessment tool, enabling urban planners to make informed decisions that balance different strategic priorities. At the core of this framework is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is employed to systematically rank energy scenarios based on their proximity to an ideal solution. This method allows for a clear, quantifiable comparison of diverse energy strategies, facilitating the identification of scenarios that best align with the city’s overall objectives. The flexibility of the MCDA framework, particularly through the adjustable criteria weights in TOPSIS, allows it to accommodate the shifting priorities of urban planners, whether they emphasize economic, environmental, or technical outcomes. The study’s findings underscore the importance of a holistic approach to energy planning, where trade-offs are inevitable but can be managed effectively through a structured decision-making process. Finally, the study addresses key gaps in the literature by providing a flexible and adaptable tool that can be replicated in different urban contexts to support the transition toward 100% renewable energy systems. Full article

The Gate rudder system (GRS) was recently introduced as an innovative energy-saving device (ESD) for ships, and it is the most attractive ESD currently used in the market, with double figures of fuel savings in full-scale (>10–35%) compared with a ship with a conventional rudder system (CRS). Although there are few new ship applications of GRS, the recently completed EC-H2020 GATERS project successfully demonstrated its unique energy-saving and manoeuvrability benefits as a “retrofit” solution for an existing general cargo vessel for the first time. The project results suggested that the GRS holds significant potential for retrofitting existing ships to enhance fuel efficiency (~35%) and improve manoeuvrability. Nevertheless, the application was a comprehensive undertaking requiring various work tasks such as component manufacturing, removing existing systems, and modification and upgrading works, with substantial energy consumption and environmental impacts. Therefore, it was insightful to study energy use and environmental impacts in a GRS retrofit process. This study developed and implemented a comprehensive energy consumption and carbon footprint assessment framework for the GRS retrofit in the GATERS project. A detailed assessment of energy consumption and related carbon emissions was performed during the major stages of manufacturing, system removals, and modifications and assembly in the GRS retrofit. Also, the potential savings in energy use and emissions were addressed. The results demonstrated that the manufacturing stage was the most energy-intensive phase, being responsible for 91.4% of total electricity and 46.7% of fuel-based thermal energy use. The system removals accounted for 53.3% of the fuel-based thermal energy, whereas the modification and assembly work accounted for about 7.7% of the total electricity use. Additionally, various measures such as clean electrification, energy efficiency, mould/tool reuse, and component reuse to reduce the energy consumption and related carbon emissions in future GRS retrofit applications were addressed and discussed together with their reduction potentials. Full article

Giant water bugs (Hemiptera: Belostomatidae) are top predators in wetland ecosystems, serving as biological indicators of the health of lentic ecosystems and as effective biological control agents for freshwater snails and mosquitoes. This study aimed to predict the current and future distribution of two Korean giant water bugs, Appasus japonicus and Diplonychus esakii, under three climate change scenarios, contributing to the sustainable management of wetland ecosystems in South Korea. Using MaxEnt models, we employed seven climatic and three non-climatic variables to investigate the habitat preferences and distribution patterns of the species. The results revealed that A. japonicus is likely to experience a northward range contraction due to climate change, while D. esakii is predicted to expand its distribution northward without losing its current range. These responses may lead to occupancy turnover between the two species, potentially driving reassembly in aquatic organism community. Elevation was the primary factor influencing the distribution of A. japonicus, whereas annual mean temperature was the most informative variable for D. esakii, both factors derived under the current climate conditions. These findings suggest that both species are highly sensitive to climate change, with potential range shifts toward higher latitudes and elevations. This study provides insights into how climate change could impact two giant water bugs, thereby supporting future efforts to manage and conserve wetland ecosystems in this country. Full article

The intensified concerns related to agrochemicals’ ecological and health risks have encouraged the exploration of microbial agents as eco-friendly alternatives. Some members of Bacillus spp. are potential plant-growth-promoting agents and benefit numerous crop plants globally. This study aimed to explore the beneficial effects of two Bacillus strains (B. subtilis strain IS1 and B. amyloliquificiens strain IS6) capable of alleviating the growth of tomato plants against salinity stress and Fusarium wilt disease. These strains were able to significantly promote the growth of tomato plants and biomass accumulation in pot trials in the absence of any stress. Under salinity stress conditions (150 mM NaCl), B. subtilis strain IS1 demonstrated superior performance and significantly increased shoot length (45.74%), root length (101.39%), fresh biomass (62.17%), and dry biomass (49.69%) contents compared to control plants. Similarly, B. subtilis strain IS1 (63.7%) and B. amyloliquificiens strain IS6 (32.1%) effectively suppressed Fusarium wilt disease and significantly increased plant growth indices compared to the pathogen control. Furthermore, these strains increased the production of chlorophyll, carotenoid, and total phenolic contents. They significantly affected the activities of enzymes involved in antioxidant machinery and the phenylpropanoid pathway. Hence, this study effectively demonstrates that these Bacillus strains can effectively alleviate the growth of tomato plants under multiple stress conditions and can be used to develop bio-based formulations for use in the fields. Full article

The Hudson Bay seasonal sea ice record has been well known since the advent of satellite reconnaissance, with a continuous record since 1971. To extend the record to earlier decades, a thermal variability framework is used with the surface temperature climatological records from four climate stations along the Hudson Bay shoreline: Churchill, Manitoba; Kuujjurapik, Quebec; Inukjuak, Quebec; and Coral Harbour, Nunavut. The day-to-day surface temperature variation for the minimum temperature of the day was found to be well correlated to the known seasonal sea ice distribution in the Bay. The sea ice/thermal variability relationship was able to reproduce the existing sea ice record (the average breakup and freeze-up dates for the Bay) largely within the error limits of the sea ice data (1 week), as well as filling in some gaps in the existing sea ice record. The breakup dates, freeze-up dates, and ice-free season lengths were generated for the period of 1922 to 1970, with varying degrees of confidence, adding close to 50 years to the sea ice record. Key periods in the spring and fall were found to be critical, signaling the time when the changes in the sea conditions are first notable in the temperature variability record, often well in advance of the 5/10th ice coverage used for the sea ice record derived from ice charts. These key periods in advance of the breakup and freeze-up could be potentially used, in season, as a predictor for navigation. The results are suggestive of a fundamental change in the nature of the breakup (faster) and freeze-up (longer) in recent years. Full article

The installation of offshore wind farms is rising, driven by the goal of changing the global energy matrix. However, many of their possible impacts are still unknown. Increased noise levels, disruptions to food chains, pollution due to traffic, and impacts on fishing communities and tourism are all potential effects to consider. Marine habitats are essential carbon dioxide sinks. Therefore, losing marine biodiversity due to offshore wind farms can be counterproductive in mitigating climate change. Balancing biodiversity conservation, wind potential, and political interests is challenging. Today, Uruguay has significantly decreased the fossil share in its electricity generation, incorporating electricity generation from wind, solar, and biomass energy alongside hydroelectricity. In line with this, the country’s Hydrogen Roadmap highlights green hydrogen as relevant, potentially serving as a fuel for both domestic and export transportation. Combining the country’s strong base of wind energy production experience with its sustainable policy, it plans to implement offshore wind farms to produce green hydrogen, making studies of its impacts crucial. This paper reviews the current social and environmental information on the Uruguayan coastal habitat, analyzes onshore wind farms’ ecological studies, and examines offshore wind farms’ global environmental and social impacts. Finally, it proposes studies for environmental approval of offshore wind farms. Full article