Search Results (7,115)

This study reconstructs and projects relative sea-level change (RSLC) along Chesapeake Bay, a global hotspot for sea-level rise, from 1900 to 2100 by statistically extrapolating observed tide gauge trends, rather than employing climate model-based scenarios. The approach integrates global mean sea-level rise (GMSLR), regional sea-level rise (RSLR), and local land subsidence (LS) to evaluate both past and future behavior. Tide gauge data reveal that Chesapeake Bay’s sea level has accelerated at 0.099 ± 0.013 mm/year² since 1992, with a linear rate of 1.26 mm/year since 1900, slightly outpacing global averages. LS, primarily driven by glacial isostatic adjustment (GIA) and sediment compaction, has been the dominant contributor to RSLC since the early 20th century, accounting for up to 71% of the RSLC prior to 1992 across 15 tide gauge stations. However, with GMSLR accelerating at 0.120 ± 0.025 mm/year², the relative contribution of LS to RSLC is projected to decline to 31–43% by 2100. The reconstructed RSLC for the 20th century ranges between 32 and 44 cm, while extrapolated projections for the 21st century indicate a further increase of 53–99 cm. By 2100, GMSLR is expected to contribute to 60–70% of total RSLC. Spatial variability in RSLC across 15 tide gauge stations reflects differing geological conditions and anthropogenic influences such as groundwater withdrawal and construction-induced subsidence. These findings highlight the critical need for adaptive strategies to mitigate the impact of rising sea levels on coastal communities and infrastructure in the Chesapeake Bay region. Continued monitoring, improved modeling, and targeted resilience planning are essential to address the accelerating threats posed by sea-level rise and to ensure the sustainability of vulnerable coastal areas. Full article

Blue carbon ecosystems, particularly mangroves, seagrasses, and salt marshes, play a crucial role in climate regulation by capturing and storing huge stocks of carbon. Together with supporting fisheries production, protecting shorelines from erosion, and supplying timber and non-timber products to communities, blue carbon ecosystems offer investment opportunities through carbon markets, thus supporting climate change mitigation and sustainable livelihoods. The current study assessed above- and below-ground biomass, sediment carbon, and the capacity of the blue carbon ecosystems in Kwale and Lamu Counties, Kenya, to capture and store carbon. This was followed by mapping of hotspot areas of degradation and the identification of investment opportunities in blue carbon credits. Carbon densities in mangroves were estimated at 560.23 Mg C ha⁻¹ in Lamu and 526.34 Mg C ha⁻¹ in Kwale, with sediments accounting for more than 70% of the stored carbon. In seagrass ecosystems, carbon densities measured 171.65 Mg C ha⁻¹ in Lamu and 220.29 Mg C ha⁻¹ in Kwale, values that surpass the national average but are consistent with global figures. Mangrove cover is declining at 0.49% yr⁻¹ in Kwale and 0.16% yr⁻¹ in Lamu, while seagrass losses in Lamu are 0.67% yr⁻¹, with a 0.34% yr⁻¹ increase in Kwale. Under a business-as-usual scenario, mangrove loss over 30 years will result in emissions of 4.43 million tCO₂e in Kwale and 18.96 million tCO₂e in Lamu. Effective interventions could enhance carbon sequestration from 0.12 to 3.86 million tCO₂e in Kwale and 0.62 to 19.52 million tCO₂e in Lamu. At the same period, seagrass losses in Lamu would emit 5.21 million tCO₂e. With a conservative carbon price of 20 USD per tCO₂e, projected annual revenues from mangrove carbon credits amount to USD 3.59 million in both Lamu and Kwale, and USD 216,040 for seagrass carbon credits in Lamu. These findings highlight the substantial climate and financial benefits of investing in the restoration and protection of the two ecosystems. Full article

With the aging of China’s expressway network, the number of maintenance projects continues to increase, and issues such as construction safety, driving risk, and traffic efficiency have become increasingly prominent. This paper systematically reviews relevant research progress from four aspects: safety characteristics, traffic capacity, work-zone layout, and speed limit management. The review indicates that Western scholars have made extensive use of rich data resources—such as traffic parameters and accident records from expressway maintenance road sections—and have developed relatively systematic and well-established research frameworks in theoretical analysis, practical application, and evaluation methods. In contrast, Chinese studies have mainly relied on specific maintenance projects, commonly employing on-site investigations and traffic simulations to address particular problems, with limited systematization and generalization. Looking forward, it is essential to further strengthen the standardized collection and statistical analysis of traffic data (including accident data) for expressway maintenance road sections. Meanwhile, for complex scenarios such as multi-lane segments, special road sections, reconstruction and expansion sections, as well as extreme climatic conditions and nighttime operations, comprehensive research should be conducted by leveraging new-generation driving simulation, big data analytics, and artificial intelligence technologies, thereby providing scientific support and methodological foundations for building a systematic theoretical framework for traffic safety in expressway maintenance road sections. Full article

The 2011 Great Flood in Thailand exposed critical deficiencies in water management across the Chao Phraya River Basin, particularly in controlling inflows and discharges from major reservoirs such as Sirikit and Bhumibol. Inadequate rainfall monitoring at the Nakhon Sawan station further intensified the disaster’s impact. As climate change continues to amplify extreme weather events, this study aims to improve flood forecasting accuracy and promote sustainable water resource management aligned with the Sustainable Development Goals (SDGs 6, 11, and 13). Advanced climate data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were spatially refined and integrated with hydrological models to enhance regional accuracy. The Discrete Wavelet Transform (DWT) was applied for feature extraction to capture hydrological variability, while the Nonlinear Autoregressive Model with Exogenous Factors (NARX) was employed to model complex temporal relationships. A multi-model ensemble framework was developed to merge climate forecasts with real-time hydrological data. Results demonstrate significant model performance improvements, with DWT-NARX achieving 55–98% lower prediction errors (RMSE) compared to baseline methods and correlation coefficients exceeding 0.91 across all forecasting scenarios. Marked seasonal variations emerge, with higher inflows during wet periods and reduced inflows during dry seasons. Under RCP8.5 climate scenarios, wet-season inflows are projected to increase by 15.8–17.4% by 2099, while dry-season flows may decline by up to 33.5%, potentially challenging future water availability and flood control operations. These findings highlight the need for adaptive and sustainable water management strategies to enhance climate resilience and advance SDG targets on water security, disaster risk reduction, and climate adaptation. Full article

Flooding poses a major hazard to rapidly urbanising cities in Southeast Asia, and risks are projected to intensify under climate change. Accurate risk assessment, however, is hindered by scarcity of hydrological and topographic data. Focusing on the Lower Prek Thnot River Basin, a peri-urban catchment of Phnom Penh, Cambodia, the study applied the Rainfall–Runoff–Inundation model and systematically augmented inputs: hourly satellite rainfall data, field-surveyed river cross-sections and representation of hydraulic infrastructure such as weirs and pumping. Validation used Sentinel-1 SAR-derived flood-extent maps for the October 2020 event. Scenario comparison shows that rainfall input and channel geometry act synergistically: omitting either degrades performance and spatial realism. The best configuration (Sim. 5) Accuracy = 0.891, Hit Ratio = 0.546 and True Ratio = 0.701 against Sentinel-1, and reproduced inundation upstream of weirs while reducing overestimation in urban districts through pumping emulation. At the study’s 500 m grid, updating land use from 2002 to 2020 had only a minor effect relative to rainfall, geometry and infrastructure. The results demonstrate that targeted data augmentation—combining satellite products, field surveys and operational infrastructure—can deliver robust inundation maps under data scarcity, supporting hazard mapping and resilience-oriented flood management in rapidly urbanising basins. Full article

Early prediction of common bean (Phaseolus vulgaris L.) yield is essential for improving productivity in tropical agricultural systems. In this study, we integrated canopy structural metrics obtained with the Tracing Radiation and Architecture of Canopies (TRAC) system, unmanned aerial vehicle (UAV)-based multispectral measurements (normalized difference vegetation index—NDVI, projected canopy area), and phenological variables collected from stages R6 to R8 under non-limiting nitrogen conditions. Exploratory analyses (correlation, variance inflation factors—VIF), dimensionality reduction (principal component analysis—PCA), and regularized regression (Elastic Net/LASSO), combined with bootstrap stability selection, were applied to identify a parsimonious subset of robust predictors. The final model, composed of six variables, explained approximately 72% of the variability in plant-level grain yield, with acceptable errors (RMSE ≈ 10.67 g; MAE ≈ 7.91 g). The results demonstrate that combining early vigor, radiation interception, and canopy architecture provides complementary information beyond simple spectral indices. This non-destructive framework delivers an efficient model for early yield estimation and supports site-specific management decisions in common bean with high spatial resolution. By enhancing input-use efficiency and reducing waste, this approach contributes to sustainable development and aligns with the global Sustainable Development Goals (SDGs) for climate-resilient agriculture. Full article

Understanding how climate change will reshape drought dynamics is essential for planning sustainable water and agricultural systems in tropical regions. However, large uncertainties in existing projections limit effective adaptation. To address this, we applied machine learning-enhanced climate projections and satellite-based drought indices to assess drought dynamics in Ethiopia’s Ganale Dawa Basin as a case study. Agricultural and hydrological droughts were analyzed for a historical baseline (1982–2014) and three future periods (2015–2040, 2041–2070, 2071–2100) under SSP2-4.5 (a moderate-emission pathway) and SSP5-8.5 (a high-emission pathway) scenarios. Results show that agricultural droughts occurred 34 times during the historical baseline. Under SSP2-4.5, their frequency declined to 10 in the mid-future, before rising to 16 events in the far future. In contrast, SSP5-8.5 projected increased variability with 33 events in the near future, dropping to 2 in the mid-future, and increasing again to 19 in the far future. Hydrological droughts were more persistent, with a baseline frequency of 31 events, and 26–36 events over future periods under both scenarios. These findings reveal increasing variability in agricultural drought and continued recurrence of hydrological drought. The findings emphasize a dual adaptation approach combining immediate agricultural responses with sustained water management and climate mitigation. Full article

As global urbanization accelerates, cities increasingly shape economic growth and environmental outcomes, making sustainable urban and transport planning critical. Sustainable city branding (SCB) is emerging as a strategic tool that not only enhances a city’s global competitiveness but actively drives urban sustainability by integrating environmental, social, and economic dimensions aligned with the UN Sustainable Development Goals (SDGs). However, the direct link between SCB and transport planning remains largely unexplored, limiting actionable policy. This study introduces a novel conceptual framework connecting SCB with transport planning, positioning public transportation as a key lever for sustainable urban development. It identifies core interactions between city branding and sustainable mobility, proposes methodologies to evaluate SCB effectiveness, and addresses potential risks, challenges, and research gaps. A policy roadmap for decision-makers based on the framework is outlined. This roadmap is structured into three phases spanning a five-year program. In Phase 1, cities should lay the foundation by integrating SCB into municipal transport and sustainability plans and establishing measurable indicators aligned with the SDGs. Phase 2 focuses on engagement and experimentation, encouraging the creation of participatory branding platforms and the implementation of pilot projects, such as green mobility corridors or climate-resilient transit hubs. Finally, Phase 3 emphasizes monitoring and scaling, utilizing digital technologies for real-time tracking, evaluating pilot outcomes, and expanding successful initiatives based on key performance indicators, including ridership growth, carbon reduction, and citizen engagement. By linking SCB explicitly to transport planning and providing a concrete roadmap, this study offers a unique contribution to both urban sustainability research and practical policy-making, enabling cities to simultaneously strengthen their brand, enhance mobility, and achieve measurable sustainability outcomes. Full article

Climate change is increasing the drought frequency and severity, so projecting spatiotemporal drought evolution across climate zones is critical for drought mitigation. Model biases, the choice of drought index, and neglecting CO₂ effects on potential evapotranspiration (PET) add large uncertainties to future drought projections. We selected 10 global climate models (GCMs) that participated in the Coupled Model Intercomparison Project Phase 6 and downscaled model outputs using the bias correction and spatial downscaling (BCSD) method. We then developed a CO₂-aware standardized moisture anomaly index (SZI[CO₂]) and used a three-dimensional drought identification method to extract the duration, area, and severity; we then analyzed their spatiotemporal dynamics. To account for nonstationarity, Copula-based approaches were used to estimate joint drought probabilities with time-varying parameters. Projections indicate wetting in Southern Northwest China, Inner Mongolia, and the Western Tibetan Plateau (reduced drought frequency, duration, intensity), while Central and Southern China show a drying trend in the 21st century. Three-dimensional drought metrics exhibit strong nonstationarity; nonstationary log-normal and generalized extreme value distributions fit most regions best. Under equal drought characteristic values, co-occurrence probabilities are higher under SSP5-8.5 scenarios than SSP2-4.5 scenarios, with the largest scenario differences over the Tibetan Plateau and Central and Southern China. Full article

Urban flooding poses an escalating threat to riverine cities in Southern Asia’s tropical regions, primarily driven by rapid urban expansion. This study develops future projections of Intensity–Duration–Frequency (IDF) curves for major urban centers in Punjab, Pakistan, utilizing downscaled satellite-derived precipitation data. Baseline IDF curves were established using historical rainfall records from multiple meteorological stations. Among eight General Circulation Models (GCMs) assessed, EC-Earth3-Veg-LR demonstrated the highest skill in capturing extreme rainfall patterns relevant to the region. Future precipitation projections from this model were downscaled using the Equidistant Quantile Matching (EQM) technique and applied to generate IDF curves under two CMIP6 scenarios: SSP2-4.5 and SSP5-8.5. The results reveal a substantial increase in extreme rainfall intensities, particularly under the SSP5-8.5 scenario, with projected 100-year return period rainfall intensities rising by approximately 30–55% across key cities. The downscaled projections reveal more pronounced variations than the raw GCM outputs, emphasizing the importance of high-resolution climate data for accurate regional hydrological risk evaluation and effective urban flood resilience planning. Full article

This study analyses the dynamics of snow cover in the Neelum Watershed of Pakistan and the expected changes in temperature and precipitation. Google Earth Engine was used to analyze the variability of winter snow cover with the help of MODIS 8-day data from 2000 to 2020. Two model combinations totaling five CMIP6 General Circulation Models were used to interpret future climate projections based on three Shared Socioeconomic Pathways (SSP2-4.5, SSP3-7.0, and SSP5-8.5) for 2021–2050. The modified Mann–Kendall test was used to identify trends, and the Theil–Sen estimator was used to analyze the impact. The results demonstrate that the extent of snow-covered area increased significantly between 2000 and 2020, and approximately 6448.83 km² (approximately 87% of the watershed) was covered by snow in winter. All SSP scenarios indicated positive trends in winter precipitation with average rates of 1.87, 0.44, and 0.80 mm/yr under SSP2-4.5, SSP3-7.0, and SSP5-8.5. In all the scenarios, the minimum temperature (0.0405 °C yr⁻¹) and maximum temperature (0.0305 °C yr⁻¹) are consistently growing, as per temperature predictions. These projected changes indicate the danger of more frequent extreme weather events that will put a strain on the region’s ecosystems, agriculture, and hydropower operations. The findings offer the necessary information to inform strategies regarding climate adaptation and mitigation in the Neelum River basin. Full article

This study examines the contribution of ERASMUS+ Key Action 2 (KA2) projects, funded between 2014 and 2020, to the dissemination and promotion of the United Nations Sustainable Development Goals (SDGs). A predominantly quantitative content analysis was conducted based on metadata extracted from the official European Commission database, focusing on the presence of SDG-related keywords within the titles, topics, and abstracts of the projects. In total, 24,838 KA2 projects were examined. The findings reveal a growing alignment between funded projects and certain SDGs, particularly SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), SDG 13 (Climate Action), SDG 7 (Affordable and Clean Energy), and SDG 9 (Industry, Innovation, and Infrastructure). In contrast, goals such as SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), and SDG 14 (Life Below Water) are scarcely represented. Overall, the results demonstrate an increasing commitment to sustainability themes over time and also highlight notable gaps in the promotion of several SDGs. This analysis offers valuable insights into the strategic alignment of ERASMUS+ funding with the 2030 Agenda and identifies opportunities for strengthening its future contributions to sustainable development. Full article

Natural threats are becoming increasingly frequent and difficult to anticipate, urging public authorities and stakeholders to adopt sustainable methodologies and tools capable of continuously supplying historical and real-time data on hazards and their impacts. Such tools enable the prompt activation of recovery actions, enhance the resilience of citizens and the built environment, and contribute to the achievement of the Sustainable Development Goals (SDGs). This paper presents an interoperable and multipurpose framework developed within the MULTICLIMACT project (GA n. 101123538), designed to enhance urban smartness and sustainability, and to support and improve resilience in municipal decision-making. The framework integrates heterogeneous data sources into a unified environment, covering infrastructures, buildings, and social systems. It also includes physiological monitoring, which collects physiological parameters from wearable sensors in a privacy-preserving way, and microclimate monitoring, which records indoor air quality in inhabited environments. Simulation-based analyses are applied to capture cascading effects of disruptions, while multidimensional indicators (societal, economic, operational, and health-related) are used to quantify resilience. The approach was implemented in the Italian municipality of Camerino, where hazard monitoring systems, impact assessment tools, and indoor comfort data were integrated and validated in the SCP-MULTICLIMACT platform. The proposed approach offers a replicable model for integrating environmental and health data in support of climate resilience and sustainable urban development. Full article

Achieving a decarbonized built environment in Canada requires proven, resilient, and scalable building envelope assemblies. In 2022, building operations accounted for 18% of Canada’s greenhouse gas (GHG) emissions, with space heating responsible for nearly two-thirds of this total. Alongside operational carbon reductions, embodied carbon emissions—stemming from the production and transport of building materials—must be prioritized during the design phase. Without intervention, construction materials could consume up to half of the remaining global 1.5 °C carbon budget by 2050. This paper highlights NRCan’s prototype, low-carbon, prefabricated panels filled with chopped straw and cellulose insulation under the Prefabricated Exterior Energy Retrofit (PEER) research project. The research advances confidence in performance and durability of biogenic materials by conducting controlled experiments, guarded hot box testing, and hygrothermal modelling. These panels present a promising pathway to drastically lower embodied carbon in the built environment. The validated hygrothermal model, accurate to between 3% and 7, enables assessment of hygrothermal performance across Canadian climates, retrofit scenarios and future climate conditions. This work supports the evidence for low-carbon or bio-based materials as a solution for Canada’s built environment. Full article

The latest climate change predictions indicate that the sea level will accelerate in the coming decades as a direct consequence of global warming. This is expected to seriously threaten low-lying coastal areas worldwide, resulting in severe coastal flooding with significant socio-economic impacts, leading to the loss of coastal settlements, exploitable land, and natural ecosystems. The main objective of this study is to provide a first-order preliminary estimation of potential inundation extents along the northern coastline of the Amvrakikos Gulf, a deltaic complex formed by the Arachthos, Louros, and Vouvos rivers in Western Greece, resulting from long-term sea-level rise induced by climate change, using the integrated Bathtub and Hydraulic Connectivity (HC) inundation method. A 2 m resolution Digital Elevation Model (DEM) was used, along with local long-term sea-level projections, for the years 2050 and 2100. Additionally, subsidence rates due to the compaction of deltaic sediments were taken into account. To assess the area’s proneness to inundation caused or enhanced by sea-level rise, the extent of each land cover type, the Natura 2000 Network protected area, the settlements, the total length of the road network, and the cultural assets located within the inundation zones under each climate change scenario were considered. The analysis revealed that under the optimistic SSP1-1.9 scenario of the Intergovernmental Panel on Climate Change (IPCC), areas of 40.81 km² (min 20.34 km², max 63.55 km²) and 69.10 km² (min 41.75 km², max 88.02 km²) could potentially be inundated by 2050 and 2100, respectively. Under the pessimistic SSP5-8.5 scenario, the inundation zone expands to 42.56 km² (min 37.05 km², max 66.31 km²) by 2050 and 84.55 km² (min 67.54 km², max 116.86 km²) by 2100, affecting a significant portion of ecologically valuable wetlands and water bodies within the Natura 2000 protected area. Specifically, the inundated Natura 2000 area is projected to range from 37.77 km² (min 20.30 km², max 46.82 km²) by 2050 to 50.74 km² (min 38.71 km², max 62.84 km²) by 2100 under the SSP1-1.9 scenario, and from 39.34 km² (min 34.53 km², max 49.09 km²) by 2050 to 60.48 km² (min 49.73 km², max 82.5 km²) by 2100 under the SSP5-8.5 scenario. Four settlements with a total population of approximately 800 people, as well as 32 economic facilities most of which operate in the secondary and tertiary sectors and are small to medium-sized economic units, such as olive mills, farms, gas stations, spare parts stores, construction companies, and food service establishments, are expected to experience significant exposure to coastal flooding and operational disruptions in the near future due to sea-level rise. Full article