Search Results (1,297)

Rural and coastal communities in areas of socio-economic deprivation face increasing exposure to compound climate-related hazards, including flooding, erosion and extreme heat. Effective adaptation planning in these contexts requires approaches that integrate physical hazard modelling with measures of social vulnerability in a transparent and reproducible way. This study develops and applies the Adaptive and Resilient Rural-Coastal Communities in Lincolnshire (ARRCC-L) framework, a sequential process combining data collation, two-dimensional hydraulic simulation using LISFLOOD-FP, and composite vulnerability mapping. The framework is versioned and protocolised to support replication, and is applied to Lincolnshire, UK, integrating UKCP18 climate projections, high-resolution flood models, infrastructure accessibility data and deprivation indices to generate multi-scenario flood exposure assessments for 2020–2100. The findings demonstrate how open, reproducible modelling can underpin inclusive stakeholder engagement and inform equitable adaptation strategies. By situating hazard analysis within a socio-economic context, the ARRCC-L framework offers a transferable decision support tool for embedding resilience considerations into regional planning, supporting both local adaptation measures and national risk governance. Full article

The Yellow River Delta region is not only a concentrated area of human activities in coastal zones, but also a zone strongly influenced by regional environmental changes, where land cover changes are significantly affected by natural factors. Current historical LUCC datasets overlook the importance of partitioning to obtain accurate information on the potential maximum distribution range, which may lead to uncertainties in climate and environmental predictions. This study aims to reconstruct historical cropland changes in the Yellow River Delta via a region-adapted allocation model, supporting improved LUCC data accuracy and related research. Based on historical river course, settlement, and cropland survey data, this study identifies natural factors using historical settlement density through correlation analysis. Subsequently, a reclamation suitability model conforming to regional characteristics was constructed, and it obtains the cropland changes in the Yellow River Delta Basin at a spatial resolution of 0.5′ × 0.5′ over five time periods since 1855. The research indicates the following: (1) Through the method of analyzing the correlation between historical settlement density and natural factors, it is found that elevation (−), soil pH (+), soil organic carbon density (−), and NDVI (+) are the primary natural factors influencing the distribution of farmland in the Yellow River Delta. (2) The amount of farmland in the Yellow River Delta increased initially and then decreased after 1885; the average reclamation rate increased from 5.65%, peaked at 23.46% in the early 20th century, and then fell back to 7.68%. Spatially, the reclamation area expanded from scattered local areas along the Yellow River towards the sea, with a distinct coastal distribution. (3) Evaluation through absolute difference analysis shows that, compared with the HYDE 3.2 data, our reconstruction reflects the impacts of coastal changes, river distribution, and regional policy history on the allocation results. Based on the findings of this study, relevant issues can be improved from two aspects: first, by correlating settlement density with natural factors to identify key regional natural factors, which can then be applied to the update of LUCC data in small spatial units and similar regions to enhance data accuracy; second, by referring to the historical laws of cropland reclamation and suitability conditions, to optimize the current land planning of the Yellow River Delta and balance cropland utilization with ecological protection. Full article

Land-use change and its interaction with petrochemical accident risk are critical for sustainable coastal development. This study established a multi-source data-integrated risk assessment framework, employing fuzzy C-means clustering to stratify petrochemical accident risk into six distinct levels. The analysis revealed the relationship between these risk levels and land-use type changes. Furthermore, the Takagi–Sugeno fuzzy dynamic model was applied to evaluate potential risks at representative coastal petrochemical enterprises. The findings were as follows: (1) Risk concentrates in small-to-medium private, newly established firms, primarily as explosion accidents. (2) The highest risk occurs in Bohai Bay, followed by Jiangsu, Zhejiang, and Guangdong; national policies have reduced affected zones from 352.61 km² (2019) to 43.67 km² (2022). (3) The total potential risk zone spans 2986.21 km², with high-risk cores in Hebei, Zhejiang, and Fujian (36.52%) and medium-risk in Shandong Peninsula (32.01%). (4) Risk primarily affects farmland and construction land; urban expansion has increased affected built-up areas from 16.36% (2012) to 47.02% (2022), shifting effects from ecological to combined socio-ecological consequences. These findings provide critical theoretical support and actionable management recommendations for integrating coastal land-use planning, urban expansion control, and coordinated petrochemical risk governance. Full article

The large-scale integration of wind power into coastal power systems introduces significant challenges to reserve planning, especially under the threat of typhoons, which can cause extensive generation loss and threaten system security. Conventional reserve planning methods often fail to account for such extreme typhoon events. To fill the gap, this paper proposes a novel two-stage reserve planning framework that integrates economic optimization with operational security verification. In the first stage, a diverse set of high-impact typhoon scenarios are generated using a multivariate Markov chain Monte Carlo (MMCMC)–based path reconstruction method, which captures the dynamic evolution of key typhoon characteristics. In the second stage, the economically optimal reserve capacity is identified through cost-benefit analysis and then validated against the typhoon scenarios via N − 1 security verification. A case study on the modified IEEE RTS79 test system indicates that economically optimal reserve may be inadequate for ensuring security under severe typhoon conditions. However, a small increase in reserve capacity can effectively enhance system resilience with minimal additional cost. These results highlight the importance of incorporating typhoon scenario-based security verification into reserve planning especially for high-penetration wind power systems in coastal regions. Full article

The accurate monitoring of short-term bathymetric changes in shallow waters is essential for effective coastal management and planning. Machine Learning (ML) applied to Unmanned Aerial Vehicle (UAV)-based multispectral imagery offers a rapid and cost-effective solution for bathymetric surveys. However, models based solely on multispectral imagery are inherently limited by confounding factors such as shadow effects, poor water quality, and complex seafloor textures, which obscure the spectral–depth relationship, particularly in heterogeneous coastal environments. To address these issues, we developed a hybrid bathymetric inversion model that integrates digital surface model (DSM) data—providing high-resolution topographic information—with ML applied to UAV-based multispectral imagery. The model training was supported by multibeam sonar measurements collected from an Unmanned Surface Vehicle (USV), ensuring high accuracy and adaptability to diverse underwater terrains. The study area, located around Lazarus Island, Singapore, encompasses a sandy beach slope transitioning into seagrass meadows, coral reef communities, and a fine-sediment seabed. Incorporating DSM-derived topographic information substantially improved prediction accuracy and correlation, particularly in complex environments. Compared with linear and bio-optical models, the proposed approach achieved accuracy improvements exceeding 20% in shallow-water regions, with performance reaching an R² > 0.93. The results highlighted the effectiveness of DSM integration in disentangling spectral ambiguities caused by environmental variability and improving bathymetric prediction accuracy. By combining UAV-based remote sensing with the ML model, this study presents a scalable and high-precision approach for bathymetric mapping in complex shallow-water environments, thereby enhancing the reliability of UAV-based surveys and supporting the broader application of ML in coastal monitoring and management. Full article

To explore the spatiotemporal evolution of extreme temperature events in Guangxi (1940–2023), reveal regional response mechanisms, and assess future trends of persistence under climate warming, a multi-scale analysis was conducted using ERA5 reanalysis data. Methodologies included RH tests for homogeneity correction, collaborative kriging for data optimisation, Mann–Kendall tests for trend and abrupt change detection, Morlet wavelet analysis for cyclic pattern identification, Exploratory Spatio-Temporal Data Analysis (ESTDA) for spatial heterogeneity quantification, and Rescaled Range (R/S) analysis to calculate Hurst indices for future persistence assessment. Results showed the following: (1) The ERA5 dataset exhibited high applicability in Guangxi (R = 0.9989, RMSE = 1.9492 °C), supporting robust evidence of continuous warming—warm indices (e.g., SU25, TX90p) increased significantly (SU25 at 0.2044 d/10a), while cold indices (e.g., TN10p, FD0) declined (TN10p at −0.0519 d/10a); abrupt changes of cold indices were concentrated in 1942–1950, with warm indices accelerating post-2000 and TXx exhibited the highest warming rate (0.23 °C/decade). (2) Extreme temperature indices displayed a primary 19–21-year oscillation cycle (dominant in warm indices) and a secondary 13-year cycle (prominent in cold indices). (3) Spatial heterogeneity featured northwest–southeast cold–heat inversion, coastal–inland intensity gradients, and latitudinal zonation of extreme indices; ESTDA revealed intensified polarisation, with warm indices clustering in low-latitude regions (e.g., Baise) and cold indices declining homogeneously in mountainous areas (e.g., Guilin), indicating an irreversible transition to a warming steady state. (4) R/S analysis indicated all indices had Hurst indices of 0.65–0.92, reflecting persistent future trends consistent with historical evolution, with warm indices (e.g., TNn, SU25) showing stronger persistence (H > 0.85). This work clarifies the spatial polarisation mechanism and future persistence of extreme temperature dynamics in Guangxi, providing a multi-scale scientific basis for disaster early warning and adaptation planning in climate-sensitive karst-monsoon regions. Full article

Understanding coastal hydraulic processes is essential for sustainable coastal planning and management, especially in semi-arid regions where data scarcity represents a significant challenge. This study sought to apply the Brazilian Coastal Modelling System (SMC-Brasil) to analyse the coastal hydraulic processes present on the Brazilian semi-arid coast in Rio Grande do Norte, seeking to understand its boundary conditions given the scarcity of data and limited monitoring network. The methodological procedures followed five main stages: data collection and processing, running the models, statistical analysis, and interpretation of the results. The simulations identified wave propagation and dissipation patterns influenced by local bathymetric features such as sandy banks and submarine canyons. The modelling indicated waves with an average Hs₅₀% of 1.14 m, with dominant directions from ENE to ESE. Longitudinal flows ranged from 1 to 8 m³/h, with a predominance of east to west at medium and high tides. The modelling indicated spatial gradients of energy and sediment transport compatible with historical records and field observations. The results show that submerged relief irregularities play a central role in regional coastal dynamics, conditioning current flows and deposition. The application of SMC-Brasil has shown potential to fill monitoring gaps in regions with low infrastructure, offering affordable and effective technical support for adaptive coastal planning in the face of climate change impacts. Full article

Understanding the spatio-temporal evolution of land consolidation is essential for optimizing regional land resource allocation and mitigating human–land conflicts during socio-economic development. This study introduced a novel framework for analyzing such patterns in China. Utilizing a two-decade (2002–2022) prefecture-level city dataset of land consolidation projects in Lianyungang, Jiangsu Province, we developed a “land consolidation intensity” metric and applied quantitative techniques—including land use transfer matrices, landscape pattern indices, Sankey diagrams, and standard deviation ellipses—to assess spatio-temporal dynamics and centroid shifts. Key findings included: (1) Land consolidation intensity exhibited distinct stages, evolving from initial development to rapid growth and eventual stabilization, closely aligning with national policy shifts. (2) The primary sources for supplemented cultivated land were ponds, rivers, and tidal flats, followed by grassland, construction land, and forest land, with cultivated land consistently dominating the consolidated landscape. (3) Land consolidation projects distribution concentrated in economic and political centers, with a spatial shift from inland western region towards the eastern coastal region. (4) Gray relational analysis identified economic development as the predominant driver, with policy and social factors providing secondary guidance. This research elucidates the spatio-temporal evolution characteristics of land consolidation at the prefecture-level city and demonstrates the utility of the proposed framework for similar analyses, offering insights relevant to national land use planning and policy formulation. Full article

Social facilities play a crucial role in urban development. However, there are currently few studies on the rationality of the spatial layout of social facilities in inland coastal cross-river cities. In view of this, we choose Nanjing City, China as an example, based on the point of interest (POI) data of social facility, and use the techniques including kernel density analysis, standard error ellipses, and spatial correlation analysis to systematically investigate the spatial distribution characteristics and patterns of social facilities in Nanjing. The research results show that there are significant differences in the spatial distribution of different types of social facilities in Nanjing, and the overall layout presents a pattern of denser distribution in the central urban area and more dispersed distribution in the peripheral areas. Shopping and transportation facilities are mostly concentrated in the core area of the main urban district, medical facilities are relatively concentrated, and cultural and educational facilities are located in all regions. The expert weighting analysis based on the Delphi method indicates that the influence weights of shopping consumption and transportation facilities on urban facilities are relatively greater than those of other factors. Overall, the social service facilities in the central urban area of Nanjing are well developed and well arranged, whereas the construction of facilities in several new districts and suburbs still needs to be further strengthened. The findings offer a scientific foundation for improving the layout of social facilities and urban planning in Nanjing, while also serving as a valuable reference for the development of other inland coastal cities spanning rivers. Full article

Wetland ecosystems are critical for biodiversity conservation and carbon sequestration, underpinning climate regulation and sustainable development. Accurate prediction of wetland evolution is therefore essential for informed regional planning, particularly in International Wetland Cities. As one of the first designated International Wetland Cities, Haikou exemplifies the intensifying pressures faced by coastal wetlands in rapidly urbanizing regions, balancing economic development imperatives with ecological conservation. This study addresses this challenge by employing the PLUS model to simulate the spatiotemporal dynamics of wetland evolution in Haikou from 2010 to 2030 under four distinct scenarios: Business-as-Usual (BAU), Ecological Conservation (EC), Economic Development (ED), and Multi-Objective Development (MOD). The integrated approach combines landscape pattern dynamics analysis, land-use transition matrices, and quantitative assessment of driving factor contributions. Key findings reveal significant historical wetland loss between 2010 and 2020 (21.01 km²), characterized by substantial declines in artificial wetlands (paddy fields: −14.43 km²; agricultural ponds: −8.99 km²) alongside resilient growth in natural wetlands (rivers: +2.70 km²; mangroves: +1.25 km²), highlighting fundamental trade-offs between economic and ecological priorities. Scenario projections indicate that unregulated development (ED) would exacerbate wetland loss (−26.33 km²; dynamic change rate: −0.61%), including unprecedented river fragmentation (−16.0%). Conversely, strict conservation (EC) achieves near net-zero wetland loss (−0.05%) but constrains economic development capacity by 24%. Critically, the MOD scenario demonstrates an effective balance, maintaining 86% of EC’s wetland preservation efficacy while satisfying 73% of ED’s development demand. This is achieved through strategic interventions including establishing wetland protection constraints and optimizing bidirectional land conversion rules, yielding synergistic benefits. Spatial analysis identifies key conflict hotspots such as Nandu River shoreline, Dongzhai Port mangroves, necessitating targeted management strategies aligned with the heterogeneity of driving factors. This study advances the framework for sustainable wetland governance by demonstrating how multi-objective spatial planning can transform ecological-economic trade-offs into synergistic co-benefits. It provides a transferable methodological approach for coastal cities in the Global South and other International Wetland City. Full article

Visibility of the sea plays a significant role in shaping spatial perception, property value, and planning decisions in coastal areas. While traditional GIS-based viewshed analysis provides useful tools for modeling visibility, it remains limited by its 2.5D nature and simplified representations of terrain and vegetation. This study presents a 3D ray-tracing-based method for analyzing water surface visibility using high-resolution LIDAR data and physically based rendering techniques within a fully 3D environment. The methodology allows for realistic modeling of visibility from a human perspective, accounting for complex occlusions caused by buildings, terrain, and vegetation. Unlike conventional GIS tools, the proposed approach identifies visible areas beneath tree canopies and enables vertical exploration of visibility from different elevations and building floors. The method was applied in a case study of the coastal city of Świnoujście, Poland. The resulting viewshed was validated through photographic field verification from observer height (1.7 m), confirming the accuracy of visibility predictions. This research demonstrates the potential of ray-tracing methods in landscape and urban visibility analysis, offering a flexible and perceptually accurate alternative to traditional GIS-based approaches. Future work will focus on quantifying the visible extent of the water surface to support more detailed assessments of visual exposure in planning and conservation context. Full article

Climate change significantly affects hydrological processes in forest ecosystems, particularly in sensitive coastal areas such as the Croatan National Forest (CNF) in North Carolina. Accurate projections of future water yield are essential for managing agriculture, forestry, and natural ecosystems. This study investigates the potential impacts of climate change on water yield using a combination of statistical downscaling and machine learning. Two downscaling methods, a Statistical DownScaling Model (SDSM) and Multivariate Adaptive Constructed Analogs (MACA), were evaluated, with the SDSM providing superior performance for local climate conditions. To improve precipitation input accuracy, twenty ensemble scenarios were generated using the SDSM, and various machine learning algorithms were applied to identify the optimal ensemble. Among these, the Extreme Gradient Boosting (XGBoost) algorithm exhibited the lowest error and was selected for producing high-quality precipitation time series. This methodology is integrated into the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach, which leverages machine learning to enhance climate input precision and reduce uncertainty in hydrological modeling. Water yield was simulated over the period 1961–2060, combining observed and projected climate data to capture both historical trends and future changes. The results show that combining statistical downscaling with machine learning algorithms can help improve the accuracy of water yield projections under climate change and be useful for water resource planning, forest management, and climate adaptation. Full article

In the coastal megacities of the Global South, where urbanization is steeply accelerating, it is a complex undertaking to navigate and govern for ecological sustainability while working to address mounting pressures to develop the physical and natural environment. In this study, we closely analyze the legality of coastal mangroves in Greater Mumbai through the lens of Blue-Green Infrastructure (BGI), sustainable governance, and environmental policy processes. While there is the constitutional and legislative protection of mangroves, they continue to disappear from the Greater Mumbai landscape, raising legitimate concerns about governance failures writ large. Using a mixed-method approach, we employ geospatial analysis of mangrove cover change from 1994 through to 2024, along with a thematic review of policy and institutional perspectives. The geospatial analysis indicated a −3.91% reduction in mangrove cover because of land-use developments, infrastructure encroachments, and the weak enforcement of existing regulations. The policy review identified limited regulatory coherence, institutional fragmentation, and low levels of community engagement. We advocate for the conservation of mangrove ecosystems in Mumbai, not just as an environmental resource, but also as vital urban infrastructure. We argue for the need to identify opportunities for reform, such as enhanced community contribution and participation, policy harmonization, and uniform incorporation of BGI principles into spatial planning and climate adaptation planning in Greater Mumbai. Full article

Urban flooding, intensified by climate change, poses significant threats to sustainable development, necessitating accurate precipitation projections for effective risk management. This study utilized Bayesian Model Averaging (BMA) to optimize CMIP6 multi-model ensemble precipitation projections for Shanghai, integrating Delta statistical downscaling with observational data to enhance spatial accuracy and reduce uncertainty. After downscaling, RMSE values of daily precipitation for individual models range from 10.158 to 12.512, with correlation coefficients between −0.009 and 0.0047. The BMA exhibits an RMSE of 8.105 and a correlation coefficient of 0.056, demonstrating better accuracy compared to individual models. The BMA-weighted projections, coupled with Soil Conservation Service Curve Number (SCS-CN) hydrological model and drainage capacity constraints, reveal spatiotemporal flood risk patterns under Shared Socioeconomic Pathway (SSP) 245 and SSP585 scenarios. Key findings indicate that while SSP245 shows stable extreme precipitation intensity, SSP585 drives substantial increases—particularly for 50-year and 100-year return periods, with late 21st century maximums rising by 24.9% and 32.6%, respectively, compared to mid-century. Spatially, flood risk concentrates in peripheral districts due to higher precipitation exposure and average drainage capacity, contrasting with the lower-risk central urban core. This study establishes a watershed-based risk assessment framework linking climate projections directly to urban drainage planning, proposing differentiated strategies: green infrastructure for runoff reduction in high-risk areas, drainage system integration for vulnerable suburbs, and ecological restoration for coastal zones. This integrated methodology provides a replicable approach for climate-resilient urban flood management, demonstrating that effective adaptation requires scenario-specific spatial targeting. Full article

China has entered an era of population decline, yet urbanization continues as rural-to-urban migration persists. This demographic transition has prompted a strategic shift in urban development from extensive spatial expansion toward quality-oriented, intensive growth models. However, evolving human–land supply–demand dynamics in cities historically characterized by population inflows remain insufficiently understood. This study focuses on Xiamen, a prototypical coastal migrant-receiving city, to investigate land use simulation under demographic transition. By integrating the cohort-component method with the Patch-generating Land Use Simulation (PLUS) model, we project Xiamen’s population under three scenarios by 2030: Stable Continuation (SCS), Natural Development (NDS), and National 2030 Population Planning (NPP), with projected increases of 5.56%, 6.76%, and 24.69%, respectively. Results show continued but decelerating population growth, with adequate labor supply and persistent demographic dividend. Notably, the NPP scenario reveals a negative correlation between population growth and construction land expansion. In NPP-High, prioritizing compact development and ecological conservation, population grows by 1.27 million while construction land decreases by 2.85% and forest land increases by 4.09%. This framework provides empirical evidence for compact urban development under the dual constraints of land-use efficiency and ecological protection. Full article