Search Results (169)

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. 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

Climate change is impacting atmospheric patterns and therefore wave conditions, with ports being among the most affected infrastructures, making it crucial to ensure their operability under changing climatic conditions. Most scientific studies on climate change focus on coastal erosion and flooding, whereas research on its impact on port operability remains relatively scarce. This challenge could be tackled with the emergence of Artificial Intelligence (AI), where alternative modeling approaches can be developed. Thus, a novel AI-based model specifically designed for studying port agitation is introduced herein. By integrating a hybrid deep learning approach, combining Feedforward Neural Networks (FFNNs) to model wave climate and Convolutional Neural Networks (CNNs) for port image analysis, port agitation has been successfully predicted compared to linear wave propagation models. This marks the first instance of utilizing image processing tools to analyze port agitation, resulting in a model with a remarkably low error rate, while offering a significant reduction in computational time compared to traditional wave propagation models, reducing computational time by a factor of four to ten. The accuracy of the proposed model has been investigated and validated for the Port of Valencia, located in the Spanish section of the Mediterranean Sea. Full article

Tidal prediction is essential for navigation safety, coastal risk management, and climate adaptation. This study develops and validates a hybrid harmonic analysis–artificial intelligence (HA–AI) framework to improve decadal tidal forecasting at five tide gauge stations along the Korean coast. Using ten years of hourly sea-level observations (2015–2025), harmonic decomposition captures deterministic astronomical components, while station-specific long short-term memory (LSTM) models learn residual nonlinear dynamics. Validation against the independent 2025 dataset demonstrates substantial accuracy gains compared with harmonic analysis alone. Across all stations, the hybrid approach reduced root mean square error (RMSE) by 16–40% (average 32.3%), with RMSE values of 8.1–10.8 cm, mean absolute errors (MAEs) of 6.3–8.9 cm, and correlation coefficients (R) ranging from 0.76 to 0.96. At Busan, RMSE was reduced from 15.1 cm (HA) to 9.9 cm (hybrid), while at Sokcho, improvement reached 40.1%. Uncertainty analysis further confirmed reliability, with 46.2% of residuals contained within ±2σ bounds. These results highlight the hybrid framework’s ability to integrate physical interpretability with adaptive skill, ensuring robust and transferable forecasts across heterogeneous coastal settings. The findings provide practical value for navigation, flood preparedness, and climate-resilient coastal planning, and demonstrate the potential of hybrid models as an operational forecasting tool. Full article

Coastal regions are among the areas most affected by climate change, facing rising sea levels, frequent flooding, and accelerated erosion that place renewable energy infrastructures under serious threat. Solar farms, which are often built along shorelines to maximize sunlight, are particularly vulnerable to salt-induced corrosion, storm surges, and wind damage. These challenges call for monitoring solutions that are not only accurate but also scalable and privacy-preserving. To address this need, Q-MobiGraphNet, a quantum-inspired multimodal classification framework, is proposed for federated coastal vulnerability analysis and solar infrastructure assessment. The framework integrates IoT sensor telemetry, UAV imagery, and geospatial metadata through a Multimodal Feature Harmonization Suite (MFHS), which reduces heterogeneity and ensures consistency across diverse data sources. A quantum sinusoidal encoding layer enriches feature representations, while lightweight MobileNet-based convolution and graph convolutional reasoning capture both local patterns and structural dependencies. For interpretability, the Q-SHAPE module extends Shapley value analysis with quantum-weighted sampling, and a Hybrid Jellyfish–Sailfish Optimization (HJFSO) strategy enables efficient hyperparameter tuning in federated environments. Extensive experiments on datasets from Norwegian coastal solar farms show that Q-MobiGraphNet achieves 98.6% accuracy, and 97.2% F1-score, and 90.8% Prediction Agreement Consistency (PAC), outperforming state-of-the-art multimodal fusion models. With only 16.2 M parameters and an inference time of 46 ms, the framework is lightweight enough for real-time deployment. By combining accuracy, interpretability, and fairness across distributed clients, Q-MobiGraphNet offers actionable insights to enhance the resilience of coastal renewable energy systems. Full article

Extreme climate events—such as heatwaves, floods, and droughts—are increasingly affecting ecosystems, with the global average temperature projected to rise by up to 3 °C (IPCC, 2023) due to anthropogenic greenhouse gas emissions. These changes pose critical challenges to food security, as evidenced by 733 million people facing hunger in 2024. In response, crop modeling considering different climate change scenarios has become a valuable tool to guide the development of climate-resilient agricultural strategies. Despite its nutritional importance and capacity to thrive across diverse environments, Ipomoea batatas (sweet potato) remains understudied in terms of potential spatial distribution forecasting, particularly in regions of high agrobiodiversity such as northwestern South America. Therefore, in this study we modeled the projected distribution of wild and landrace sweet potato genepools in the northern Andes under four future timeframes using seven machine learning algorithms. Our results predicted a 50% reduction in the climatically suitable range for the wild genepool by 2081, coupled with an average altitudinal shift from 1537 to 2216 m above sea level (a.s.l.). For landraces, a 36% reduction was projected by 2080, with a shift from 62 to 1995 m a.s.l. By the end of the century, suitable zones for both wild and cultivated genepools are expected to converge in high-altitude regions such as the Colombian Massif, with additional remnants of wild populations near the mountain range of Farallones de Cali. This modeling approach provides essential insights into the spatial dynamics of I. batatas under climate change, highlighting the need for ex situ conservation planning in vulnerable regions as well as assisted migration to more suitable areas. Future research should integrate edaphic and biotic interaction data to better approach the realized niche of the species and understand potential responses under a niche conservatism assumption, as well as genomic data to account for the species’ intrinsic adaptative potential, overall informing conservation, germplasm mobilization, and pre-breeding strategies that may ultimately secure the role of sweet potato in resilient food systems. Full article

Understanding the spatiotemporal dynamics of coastal intertidal meta-ecosystems in response to sea-level rise (SLR) is essential for understanding the interactions between terrestrial and aquatic meta-ecosystems. However, given that annual SLR changes are typically measured in millimeters, ecosystems may take decades to exhibit noticeable shifts. As a result, the extent of lateral responses at a single point is constrained by the fragmented temporal and spatial scales. We integrated the tidal inundation gradient of a coastal meta-ecosystem—comprising a high-elevation flat (H), low-elevation flat (L), and mudflat—to quantify the potential application of inferring the spatiotemporal impact of environmental features, using China’s Yangtze Estuary, which is one of the largest and most dynamic estuaries in the world. We employed both flood ratio data and tidal elevation modeling, underscoring the utility of spatial modeling of the role of SLR. Our results show that along the tidal inundation gradient, SLR alters hydrological dynamics, leading to environmental changes such as reduced aboveground biomass, increased plant diversity, decreased total soil, carbon, and nitrogen, and a lower leaf area index (LAI). Furthermore, composite indices combining the enhanced vegetation index (EVI) and the land surface water index (LSWI) were used to characterize the rapid responses of vegetation and soil between sites to predict future ecosystem shifts in environmental properties over time due to SLR. To effectively capture both vegetation characteristics and the soil surface water content, we propose the use of the ratio and difference between the EVI and LSWI as a composite indicator (ELR), which effectively reflects vegetation responses to SLR, with high-elevation sites driven by tides and high ELRs. The EVI-LSWI difference (ELD) was also found to be effective for detecting flood dynamics and vegetation along the tidal inundation gradient. Our findings offer a heuristic scenario of the response of coastal intertidal meta-ecosystems in the Yangtze Estuary to SLR and provide valuable insights for conservation strategies in the context of climate change. Full article

Storm Daniel is arguably one of the most severe Mediterranean tropical-like cyclones (medicanes) ever recorded. Greece was one of the most affected areas, especially the central part of the country. The extreme precipitation that was observed along with the subsequent extensive flooding was considered a critical challenge to validate the regional version of the ICON (Icosahedral Non-Hydrostatic) numerical weather prediction (NWP) model. From a methodological standpoint, the short-range nature of the model was realized with 48 h runs over a sequence of cases that covered the storm period. The development of the medicane was highlighted via the tracking of the minimum mean sea level pressure (MSLP) in reference to the corresponding analysis of the European Center for Medium-Range Weather Forecasts (ECMWF). In a similar fashion, snapshots regarding the 500 hPa geopotential associated with the 850 hPa temperature were addressed at the 24th forecast hour of the model runs. Although the model’s performance over the four most affected synoptic stations of the Hellenic National Meteorological Service (HNMS) was mixed, the overall accumulated forecasted precipitation was in very good agreement with the corresponding total value of the observations over all the available synoptic stations. Full article

Wetlands are an important part of coastal ecosystems but are under increasing pressure from climate change-induced sea-level rise and flooding, in addition to development pressures associated with increasing human populations. The change in tidal events and their intensity due to sea-level rise is also reshaping and challenging the vitality of existing wetland systems, requiring more intensive localized studies to identify future-focused restoration and conservation strategies. To support this endeavor, this study utilizes tide gauge datasets from the Australian Bureau of Meteorology (BOM) for maximum sea-level (Hmax) prediction and Landsat Collection surface reflectance datasets obtained from the United States Geological Survey (USGS) database to detect and project patterns of change in the Maroochy River floodplain of Queensland, Australia. This study developed an efficient hybrid deep learning model combining a Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNNBiLSTM) architecture for the prediction of maximum sea-level and tidal events. The proposed model significantly outperformed three benchmark models (Multiple Linear Regression (MLR), Support Vector Regression (SVR), and CatBoost) in achieving a high correlation coefficient (r = 0.9748) for maximum sea-level prediction. To further address the increasing frequency and intensity of tidal events linked to sea-level rise, a CNNBiLSTM classification model was also developed, achieving 96.72% accuracy in predicting extreme tidal occurrences. This study identified a significant positive linear increase in sea-level rise of 0.016 m/year between 2014 and 2024. Wetland change detection using Landsat imagery along the Maroochy River floodplain also identified a substantial vegetation loss of 395.64 hectares from 2009 to 2023. These findings highlight the strong potential of integrating deep learning and remote sensing for improved prediction and assessment of sea-level extremes and coastal ecosystem changes. The study outcomes provide valuable insights for informing not only conservation and restoration activities but also for providing localized projections of future change necessary for the progression of effective climate adaptation and mitigation strategies. Full article

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration. Full article

Rising sea levels and increasing storm wave heights are two clear indicators of climate change affecting coastal environments worldwide. Coastal cities and infrastructure are particularly vulnerable to these hazards, highlighting the need for accurate predictions and effective adaptation and resilience strategies to protect human lives and economic activities. This study focuses on the Andalusia coast of southern Spain, from Cádiz to Almería, analyzing twelve years of sea level and wave height records using an Extreme Value Analysis. A key challenge lies in selecting the most suitable statistical distribution for long-term predictions. To address this, we propose a modified application of the Cramér–Rao Lower Bound and compare it with the Akaike Information Criteria and the Bayesian Information Criteria. Our results indicate that sea level extremes generally follow a Gumbel distribution, while wave height extremes align more closely with the Fisher–Tippett I distribution. Additionally, a high-resolution digital elevation model of the Navantia Puerto Real shipyard, generated with LiDAR scanning, was used to identify flood-prone areas and assess potential operational impacts. This approach allows for the development of practical recommendations for enhancing infrastructure resilience. The main contribution of this work includes the estimation of extreme regimes for sea level and wave stations, a novel and more efficient application of the Cramér–Rao Lower Bound, a comparative analysis with Bayesian criteria, and providing recommendations to improve the resilience of shipyard operations. Full article

As sea levels rise and extreme weather events become more frequent due to climate change, coastal urban areas are increasingly vulnerable to wave overtopping and flooding. Retrofitting existing vertical seawalls with retreated storm walls represents a key adaptive strategy, especially in the Mediterranean, where steep foreshores and limited public space constrain conventional coastal defenses. This study investigates the effectiveness of storm walls in reducing wave overtopping on vertical walls with steep foreshores (1:7 to 1:10) through high-fidelity numerical simulations using the SWASH model. A comprehensive parametric study, involving 450 test cases, was conducted using Latin Hypercube Sampling to explore the influence of geometric and hydrodynamic variables on overtopping rate. Model validation against Eurotop/CLASH physical data demonstrated strong agreement (r = 0.96), confirming the reliability of SWASH for such applications. Key findings indicate that longer promenades (Gc) and reduced impulsiveness of the wave conditions reduce overtopping. A new empirical reduction factor, calibrated for integration into the Eurotop overtopping equation for plain vertical walls, is proposed based on dimensionless promenade width and water depth. The modified empirical model shows strong predictive performance (r = 0.94) against SWASH-calculated overtopping rates. This work highlights the practical value of integrating storm walls into urban seawall design and offers engineers a validated tool for enhancing coastal resilience. Future research should extend the framework to other superstructure adaptations, such as parapets or stilling basins, to further improve flood protection in the face of climate change. Full article

This study advances coastal flood risk assessment by incorporating El Niño–Southern Oscillation (ENSO) phase information into the Joint Probability of Maxima Method (ENSO-JPMM) for extreme water level prediction in Annapolis, MD. Using data from GLOSS/Extended Sea 135 Level Analysis Version 3 (GESLA-3) dataset and water level records from 1950–2021, we demonstrate that ENSO phases significantly affects flood risk probabilities through their influence on mean sea level, astronomical tides, and skew surge components. We introduce an enhanced JPMM framework that employs phase-specific scaling factors and vertical offsets derived from historical observations, with El Niño conditions associated with higher mean water levels (0.433 m) compared to La Niña (0.403 m) and Neutral phases (0.409 m). The ENSO-JPMM demonstrates improved predictive accuracy across all phases, with root mean square error reductions of up to 5.96% during Neutral conditions and 3.56% during El Niño phases. By implementing a detailed methodology for mean sea level estimation and skew surge analysis, our approach provides a more detailed framework for separating tidal and non-tidal components while accounting for climate variability. The results indicate that traditional extreme value analyses may underestimate flood risks by failing to account for ENSO-driven variability, which can modulate mean water levels by up to 3.0 cm in Annapolis. This research provides insight for coastal infrastructure planning and flood risk management, particularly as climate change potentially alters ENSO characteristics and their influence on extreme water levels. The methodology presented here, while specific to Annapolis MD, can be adapted for other coastal regions to improve flood risk assessments and enhance community resilience planning. Full article

The modelling and forecasting of the impact of extreme rainfall events in urban environments is becoming increasingly challenging as historical tools have been found to need refinement to acquire improved flood risk predictions for river and coastal basins. This article discusses some of the key challenges faced by flood modellers addressing the growing effects of climate change, with the key findings reported in this article being that (i) improved flood models are needed for accurately predicting extreme flood elevations and inundation extents through the inclusion of shock-capturing algorithms; (ii) improved flood hazard risk formulae are need to predict the stability and vulnerability of vehicles and people in extreme flood events; and (iii) assessing the impact of floods on water quality in river and coastal basins can only be delivered accurately when storm events are modelled holistically from the source to sea (S2S), with a systems-based approach to dynamically integrate surface and sub-surface flows etc. Full article

Climate change has continued to cause severe coastal flooding, erosion, and storm surge in the northeastern U.S. region. Compounding the coastal storm challenge, this region also experienced multiple 1-in-100-, 1-in-200-, and 1-in-500-year rainfall events in 2024. In recent years, community-based flood risk management has become an important component for generating locally viable mitigation strategies to build environmental sustainability. At the heart of this community engagement paradigm is flood risk communication, which aims to bring together community stakeholders to strengthen their social resilience to collaborate in generating flood risk management solutions. Extant research has rarely examined the direct connection between theory-driven risk communication factors and community-based flood risk management. To better understand the role of risk communication in facilitating participatory flood risk management planning, this study integrated risk communication constructs with the relevant Health Belief Model components to propose and test a conceptual framework. Specifically, this study conducted a survey with 302 residents of a coastal community highly vulnerable to sea level rise, storm surge, and year-round flooding in the coastal region of northeastern U.S. Study results suggested that flood information exposure could drive greater perceived flood risk severity and mitigation barriers, in addition to furthering flood risk information-seeking behavior and affiliated community-engaged flood risk communication. Community-engaged communication was positively linked to perceived social efficacy beliefs in tackling flood risk management, aside from being linked to perceived flood risk mitigation response efficacy. Both perceived social efficacy and response efficacy in flood risk management positively predicted interest in participatory flood risk management planning. Full article