Search Results (1,041)

Tropical forests are major contributors to the global carbon budget but are affected by disturbances such as hurricanes, which cause extensive yet spatially variable tree damage and mortality. High-resolution maps of forest aboveground biomass (AGB) and its temporal change aid in quantifying disturbance impacts, assessing resilience, and supporting forest management. This study presents wall-to-wall, high-resolution mapping of pre- and post-hurricane AGB and AGB change across Puerto Rico. The maps represent forest AGB measured 0–2 years before and after two major hurricanes (Irma and Maria), as well as longer-term conditions up to four years post-disturbance. AGB was modeled using Random Forest (RF) algorithms that integrated Forest Inventory and Analysis (FIA) plot data with canopy height and cover derived from discrete-return LiDAR, multi-temporal satellite imagery, and additional geospatial predictors. Model performance was evaluated using a 10% holdout dataset. Predicted versus observed regressions yielded, at 10 m and 90 m spatial resolutions, respectively, r = 0.75 and 0.79 with model residual mean standard deviation (RMSD) = 87.7 and 39.2 Mg ha⁻¹ for pre-hurricane AGB, and r = 0.77 and 0.74 with RMSD = 69.7 and 58.1 Mg ha⁻¹ for post-hurricane AGB. AGB change models at 10 m and 90 m resolutions yielded r = 0.58 and 0.73 with RMSD = 17.0 and 18.7 Mg ha⁻¹, respectively. Ten-fold cross-validation produced stronger correlations and reduced RMSD values. Frequency distributions of mapped pixels of forest AGB and AGB change, in comparison with previously published maps and island-wide field-based estimates, indicate that, although hurricane-driven biomass reductions of up to 20% were recorded in field data, patterns consistent with longer-term recovery from historical deforestation are evident within four years after the hurricanes. The 10 m maps capture fine-scale heterogeneity in canopy damage and regrowth, whereas the 90 m maps emphasize broader regional patterns. This integrated framework provides a transferable approach for monitoring forest structure and biomass dynamics in disturbance-prone tropical ecosystems. Full article

Extreme sea levels along the Mexican coasts pose an increasing risk to coastal infrastructure and communities, particularly under the combined influence of tropical cyclones and ongoing sea-level rise. This study analyzes tide-gauge records from the Mexican Pacific and Gulf of Mexico–Caribbean coasts to characterize the statistical behavior and seasonal modulation of extreme sea-level residuals. Astronomical tides were removed through harmonic analysis to isolate the meteorological residual associated with storm-driven processes. Extreme events were evaluated using complementary extreme-value frameworks, including Generalized Extreme Value (GEV) distributions applied to monthly maxima and a Peaks-Over-Threshold (POT) approach applied to the continuous residual series with temporal declustering and Generalized Pareto Distribution (GPD) fitting. While both approaches consistently capture regional patterns, the POT–GPD framework is adopted as the primary basis for return-level estimation due to its explicit representation of event-scale extremes. The results reveal marked regional variability. Pacific stations exhibit bounded or near-Gumbel behavior (ξ ≈ −0.30 to −0.02) and a strong seasonal concentration of extremes during the tropical cyclone season. In contrast, Gulf of Mexico–Caribbean stations display higher absolute extremes and a broader seasonal footprint, with Veracruz showing a tendency toward heavier-tailed behavior (ξ ≈ 0.13). Return levels for a 25-year return period range from approximately 0.85–0.95 m in the Pacific to about 1.7 m in Veracruz. Longer return periods (e.g., 100 years) exceed 2.2 m in Veracruz but are associated with substantial uncertainty due to record-length limitations. The analysis of ENSO variability indicates that ENSO acts primarily as a secondary modulator of background sea-level variability rather than a deterministic driver of extreme events, with the largest anomalies typically associated with tropical cyclone activity. Overall, the results demonstrate that extreme sea levels along the Mexican coasts are governed by region-specific forcing and tail behavior requiring localized extreme-value modeling strategies. The proposed framework provides a robust and reproducible baseline for coastal hazard assessment and supports the integration of sea-level rise into future risk and design analyses. Full article

In March 2021, a trial operation set of ocean radar was first introduced in Hong Kong, and then in early 2022 became stably paired up with one operated by the South China Sea Bureau of the Ministry of Natural Resources of China for filling up the meteorological data void over the eastern part of the South China coastal waters. The ocean radar has undergone various enhancements of hardware and software over the years and has reached a stage of providing useful wind observations for weather monitoring purposes in the majority of cases. This paper documents the novel features of the hardware and software of the ocean radar. The performance of the derived wind and other data from the ocean radar is studied by comparing with two sets of weather buoy observations over an extended period of time (one year from June 2024 to July 2025). The quality of the wind data is considered to be reasonable as compared with the international standards of wind measurement errors. The application of the ocean radar wind observations in monitoring different weather systems is also described, including monsoon surges, surface troughs of low pressure, rainstorms and tropical cyclones. The radar is still found to have difficulties in retrieving the winds of high strength (hurricane force winds) and the circulating flow at the same time. Further research work with the ocean radar is also discussed. Full article

This contribution examines the luxury real estate sector in the Caribbean Island of Sint Maarten. Drawing upon an analysis of ethnographic observations, interviews, property market data and marketing materials, we pose two core questions to the data: (1) How are fragments of the Dutch-Caribbean past deployed in luxury real estate marketing? (2) How does cyclical hurricane damage influence the luxury real estate market and heritage preservation? Proportionally very few of the luxury real estate listings directly reference cultural history. Yet when “Dutch-style and “plantation-era” esthetics are referenced, they appear to add value to the properties while enhancing a sense of exclusivity but erase the history of colonial violence. In conjunction with these discursive effects are the material realities of the cyclical destruction of property by hurricanes through which distressed properties are sold at a discount to be redeveloped for luxury builds aimed largely at foreign purchasers. This disaster development model systematically destroys artifacts of tangible heritage while displacing residents from communal spaces. As climate change intensifies, we raise questions about the sustainability of this model on the island going forward. Full article

Long-term monitoring is essential for understanding how recurring disturbances, such as hurricanes and coral bleaching, affect reef fish communities and ecosystem processes. This study evaluates temporal trends (2013–2025) in fish assemblage composition, functional diversity, and nutrient excretion proxies (C, N, and P) across three reef sites on San Andrés Island in the Colombian Caribbean. Our results reveal significant shifts in community structure following major disturbances in 2020 (Hurricanes Eta, Iota) and 2023 (mass bleaching event). Taxonomic and functional richness (TRich, FRich) fluctuated throughout the study period, whereas functional divergence (FDiv) declined earlier (2016), highlighting site-specific differences. A trait-based nutrient-excretion proxy (NPC composite score) identified key species that maintain nutrient cycling. Despite recent coral bleaching, certain sites exhibited functional resilience, sustained by the persistence of high-performing nutrient providing species. However, the overall disconnect between taxonomic recovery and functional stability suggests that ecosystem-level processes remain vulnerable, even when species richness appears to recover. This highlights the importance of integrating functional traits and nutrient recycling proxies into monitoring programs to better predict long-term variability in San Andrés Island reefs under a changing climate. Our findings provide a framework for prioritizing management efforts in the Seaflower Biosphere Reserve with emphasis on maintaining ecosystem services. Full article

Hurricanes routinely damage forests across the North Atlantic Basin, yet efforts to characterize their impacts have had mixed subregional success. To elucidate these challenges, this study analyzed pre- and post-hurricane land surface phenology (LSP) for 44 moderate and strong hurricanes over 22 years using the Enhanced Vegetation Index (EVI). We statistically grouped storms based on their long-term climate attributes, then compared subregional impacts with wind speed and land cover. After accounting for wind speed, responses differed among the six subregions. The Southeast U.S. showed declines in EVI for the first winter and first year post storm, but this response was weak or absent elsewhere. The Central America region declined in the first winter but not in the subsequent growing season, while four other regions showed no increased impact with wind speed in either season. We then examined six category 4 hurricanes using a forest mask. In dry areas, drought-sensitive vegetation explained weak responses, whereas in the humid tropics, rapid refoliation or sprouting was common. These factors complicate optical remote sensing assessments. Rapid evaluations can mistake defoliation for more substantial damage, and delayed assessments can confuse EVI recovery with structural recovery. Results underscore the need for ecologically tailored monitoring approaches. Full article

Hurricanes create compound hazards such as storm surge, flooding, and wind-driven debris that can degrade roadway capacity, fragment network connectivity, and hinder evacuation and shelter operations. From a sustainability perspective, improving evacuation planning is essential for reducing disaster-related losses, protecting vulnerable populations, and strengthening the resilience of coastal communities facing intensifying climate-driven hazards. This paper develops a micro-scale, agent-based evacuation modeling framework to assess evacuation performance under baseline and compound-hazard conditions, with emphasis on municipal decision support. The framework is demonstrated for Westbrook, Connecticut, at the census block-group scale in AnyLogic by integrating household locations, vehicle availability, road-network connectivity, and shelter capacities from publicly available datasets. Evacuation propensity and destination choice are parameterized using survey data, enabling empirically grounded decisions for in-town versus out-of-town evacuation among household-vehicle agents. Compound disruptions are represented through flood-related road closures derived from SLOSH storm-surge outputs and stochastic wind-related disruptions that dynamically constrain accessibility during the simulation. Scenarios are evaluated for Saffir–Simpson Category 1–2 and Category 3–4 hurricanes under baseline and compound conditions. Model outputs quantify normalized evacuation time, congestion and critical intersections, shelter demand and unmet capacity, evacuation failure, and spatial heterogeneity across block groups. Results indicate that compound flooding substantially increases evacuation times and failure rates, with the largest performance degradation concentrated in higher-vulnerability areas. Optimization experiments further compare the effectiveness of behavioral shifts, shelter-capacity expansion, and earlier departure timing in reducing delays and unmet shelter demand. Overall, the proposed framework provides transparent, reproducible, and scalable analytics that town engineers and emergency planners can use to evaluate evacuation readiness under compound hurricane impacts. Full article

Florida (FL)’s agriculture sector is undergoing rapid transformation due to biological shocks, environmental stressors, import competition, and accelerating urbanization. Citrus greening, laurel wilt, and hurricane-related damage have sharply reduced yields and acreage, while rising imports from Mexico and Brazil erode market share and depress prices. Urban development and recreational land-use expansion are accelerating land-value increases, which in turn drives farmland loss and abandonment. This policy-oriented review synthesizes these pressures and evaluates state policy responses. Our findings highlight the need for integrated strategies that improve resilience, strengthen land conservation, and enhance the long-term competitiveness of FL’s agricultural sector. Full article

Tropical cyclones kill thousands and inflict vast destruction annually. While ocean temperatures and atmospheric conditions dominate their formation and behaviour, forests’ potential influence has received little systematic attention. This review examines whether and how forests may affect tropical cyclone frequency, intensity, and behaviour. Support varies by mechanism and stage. Post-landfall effects have the strongest support: forests slow storms, moderate wind speeds and curb flooding through enhanced soil infiltration. Forests also influence storm tracks, though magnitudes are uncertain. Pre-landfall effects are less certain. These include processes that modify offshore humidity, temperature, and aerosols. The Biotic Pump theory proposes that forest cover creates pressure gradients drawing moisture inland, reducing its availability for ocean storms. Forest influences are likely to be most evident near thresholds for storm formation or intensification, where small perturbations in conditions can alter outcomes. This context-dependency reconciles divergent findings and aids the integration of forests into climate risk assessments. Forest conservation provides clear post-landfall protection; pre-landfall effects, while uncertain, further strengthen the case for protection and highlight research priorities. Full article

Communities susceptible to disasters frequently endure severe socio-economic and psychological repercussions. Therefore, it is essential to thoroughly understand the various vulnerabilities encountered by different groups. Residents of New Orleans, Louisiana, faced significant hardships after Hurricane Katrina hit on 29 August 2005. A multitude of individuals lost their residences, while others, regrettably, lost family members. The Displaced New Orleans Residents Survey (DNORS) offered significant insights into individuals and households living in New Orleans immediately prior to Hurricane Katrina’s impact in August 2005. The survey interview was conducted from mid-2009 until mid-2010. This study utilizes DNORS public data files to evaluate socio-demographic characteristics pertinent to the analysis, including age, gender, race/ethnicity, marital status, household income, education level, employment status in 2005, and insurance coverage, alongside psychological measures such as mental health symptoms, posttraumatic stress, depression, and perceived stress. The research employs various regression techniques to identify the at-risk categories affected psychologically and physically by the hurricane. These findings may aid policymakers in developing targeted post-disaster recovery strategies, thereby promoting more resilient and sustainable communities. Full article

Hurricanes have intensified and become more persistent under a changing climate, increasing the risk of infrastructure damage and property loss in coastal regions, threatening their sustainability. This study examines how hurricane intensity and persistence influence infrastructure loss, contributing to a more comprehensive understanding of climate-related risks. Using data from the National Oceanic and Atmospheric Administration (NOAA) Storm Events Database from 1996 to 2024, we develop a series of machine learning models to predict property losses based on storm characteristics and contextual vulnerability factors. Narrative-based text analysis and time-series feature engineering were applied to extract meteorological and temporal attributes, while regression and ensemble models were used for predictive evaluation. Results show that storm intensity alone explains only a small portion of loss variance, with persistence influencing damage primarily through rainfall and hydrological effects. The findings highlight that vulnerability, exposure, and cumulative risk dynamics are essential for accurate long-term prediction and for assessing infrastructure sustainability. Overall, the study demonstrates that combining machine learning techniques with climate and vulnerability data can inform future research on infrastructure sustainability. The quantified vulnerability-versus-intensity breakdown presented here can support post-disaster resource allocation, insurance risk modeling, and the prioritization of infrastructure maintenance in hurricane-prone regions. Full article

Smart cities require effective disaster management (like flooding, solar storms, sandstorms, or hurricanes), as it directly impacts people’s lives. The key challenges of disaster management are timely detection and effective notification during the crisis. This research presents a smart multi-layer framework for notification classification and management before and during flooding disasters. The framework includes an early detection module as the main phase in the alerting process. This step depends on an Ensemble Learning (EL) model based on a triad of the three best selected models (Deep Learning (DL), Random Forest (RF), and K-nearest Neighbor (KNN)) to analyze data collected continuously from the Internet of Things (IoT) layer. In the boosting phase, the framework utilizes Large Language Models (LLMs) with DL to analyze social textual crowdsourcing data. The results will enable the framework to identify the most affected areas during a flood. The framework adds a fog computing layer alongside a cloud layer to enable instantaneous processing of user responses and generate specialized alerts based on contextual factors such as location, time, risk level, alert type, and user characteristics. Through testing and implementation, the proposed algorithms demonstrated an accuracy rate of over 98% in detecting threats using a dataset of real, collected weather and flooding data. Additionally, the framework proposes a centralized control panel and a design of a smartphone application that offers essential services and facilitates communication among managed civil defense teams, citizens, and volunteers. Full article

The catastrophic impact of Hurricane Helene proved that standard FEMA flood maps are often inadequate for assessing risk in complex mountainous terrain. Using Buncombe County, North Carolina, as a case study, this research introduces a replicable framework for siting emergency shelters based on a multi-dimensional Flood Risk Index. By synthesizing HAND-derived inundation data, land-use intensity, and a machine learning-based Socio-Economic Vulnerability Index (SEVI), we mapped the intersection of hazard and vulnerability. Our analysis reveals a significant misalignment—a large portion of the current shelter network sits in high-risk zones, while safer upland corridors in the north and west remain underutilized. This study delivers a data-driven roadmap for disaster preparedness, ensuring that future shelter placement is not only safe from terrain-driven floods but also strategically and equitably located. Full article

This multi-university, multi-disaster study examined associations among prior trauma exposure, disaster exposure, and post-disaster life stressors with mental health outcomes, as well as the potential protective roles of a perceived altruistic community, post-disaster social support exchanges, and changes in world beliefs. University students in disaster-affected areas of the mainland United States and Puerto Rico (N = 666; 77.5% female; M age = 21.26) completed an online survey assessing disaster exposure, post-disaster life stressors, perceptions of community unity, social support exchanges, post-disaster changes in world beliefs, and symptoms of posttraumatic stress (PTSS), depression, and anxiety. Younger age emerged as a risk factor for depression and anxiety, and Black participants reported higher PTSS than White participants. Greater lifetime trauma exposure, experiencing the hurricanes in Puerto Rico or the California wildfires (compared to mainland hurricanes), and reporting more post-disaster life stressors were each associated with elevated PTSS, depression, and anxiety symptoms. In contrast, a stronger sense of an altruistic community was associated with lower levels of these symptoms. More positive post-disaster changes in beliefs about the world were related to lower PTSS and depression, whereas greater involvement in social support exchanges was associated with higher PTSS. Findings underscore the importance of identifying both risk and protective factors that shape young adults’ post-disaster adjustment. Full article

This study examines how research in the United States has addressed infrastructure resilience across different disaster contexts, situating the topic within broader discussions on climate-related risks and adaptation. Infrastructure resilience has gained increasing importance as communities face more frequent and severe natural hazards and as infrastructure systems become more complex and interconnected. A database of more than 7000 studies published over the past century by universities, research centers, and government agencies was compiled and organized, including supplemental works from regions such as Europe, Australia, Japan, Africa, and South America. The dataset provides a long-term perspective on the evolution of resilience-related research and reflects the scope of accessible literature indexed in major research repositories. Using systematic classification, each study was categorized by disaster type (i.e., floods, hurricanes, wildfires, heatwaves, and snowstorms) and by infrastructure system (i.e., transportation, water, energy, telecommunications, and buildings). A keyword-based relevance scoring method was applied to distinguish studies in which resilience is a central analytical focus from those in which it appears as a secondary or contextual concept. The results are presented through an interactive web-based platform that enables users to explore resilience research by state, year, disaster type, infrastructure category, and level of relevance. The analysis reveals a substantial increase in resilience-related publications in recent decades, with notable geographic and thematic concentrations. Transportation and water infrastructure dominates the literature, while energy systems, telecommunications, and digital infrastructure remain underrepresented. These findings highlight both progress and persistent gaps in infrastructure resilience research and support more integrated, system-oriented, and future-focused resilience planning. Full article