Search Results (1,056)

Coastal zones are dynamic interfaces where land, ocean, and atmosphere interact, making them sensitive indicators of environmental change. However, quantifying shoreline movement across long distances and over multi-year timescales remains challenging using traditional ground-based methods alone. We conducted an analysis of environmental factors and shoreline dynamics along a 58 km stretch of the arid Cabo Pulmo shoreline in Mexico from 2020 to 2026 using the CoastSat tool. The landscape is characterized by a diverse array of geographical features, including sandy beaches, granite cliffs, estuarine systems, and various anthropogenic structures. Results indicated a sea-level rise of 2 mm/year over the last 27 years, which is consistent with the reported range for the Pacific (1.8 to 3.8 mm/year). Notably, we observed an increasing trend of Category 4 and 5 hurricanes in the Mexican Pacific, with an average of 1 additional hurricane per decade (1950–2023). A total of 457 Sentinel-2 satellite images were used for automated analysis using the CoastSat platform, all of which were acquired under tidal conditions not exceeding 1 m. Our findings indicate that the granite cliffs show no detectable horizontal changes in the satellite images; however, their minimal vertical erosion contributes sediment to adjacent beaches. The most significant shoreline erosion was observed north of a marina breakwater, measuring −19.7 m, attributed to the disruption of littoral transport toward the southeast. In contrast, sandy beaches located in front of streams and estuaries—characterized by a lack of infrastructure (houses and breakwaters) and gentle slopes of 2° to 4°—demonstrated positive accretion of up to 5.9 m. According to the autoregressive distributed lag model, wave energy and hurricane-driven wind gusts are the primary agents of shoreline retreat, displacing sediment seaward to the continental shelf. Sea level rise exacerbates this retreat, while rainfall plays a minor but contributing role by transporting sediment during hurricanes in this arid region. This study highlights the effectiveness of CoastSat as a neural network-based tool for analyzing shoreline changes; however, we faced certain limitations, such as the absence of in situ beach profiles due to restricted access. Full article

Forests worldwide are impacted by tropical cyclones which alter their structure and ecological functions. In this study, we investigated repeat track data from ICESat-2’s (Ice, Cloud and land Elevation Satellite-2’s) land and vegetation height product (ATL08) to quantify structural changes in forests, with a focus on coastal forests in Alabama and Florida affected by Hurricane Sally (2020). We evaluated pre-hurricane ATL08 along-track canopy estimates at the ATL08 100 m segment scale and 20 m sub-segment scale and quantified structural canopy changes using exact pre- and post-repeated tracks. Results demonstrated strong agreement between ATL08’s 98th percentile canopy height (RH98) and reference airborne LiDAR-derived RH98 at both spatial scales, with improved performance at the 20 m sub-segment scale (mean bias: −1.16 m; MAE: 2.28 m; RMSE: 3.44 m; r: 0.80). Samples over evergreen forests provided reduced bias (−2 m to −0.55 m), reduced RMSE (4.02 m to 2.96 m), and improved correlation (0.77 to 0.83) than woody wetlands for canopy height acquisition. Post-hurricane analyses revealed height reductions in tall canopy (20–30 m) of 1.51 m, while smaller trees (0–10 m) increased by 0.77 m, reflecting growth. Overall, findings highlight ICESat-2’s ability to monitor canopy height changes and offer prospects for integrating ICESat-2 data for damage assessments. Full article

The experiences of 48 U.S. journalists covering Donald Trump’s inaugural year as president in 2017 provide a contemporaneous account of being “enemies of the people” in a highly charged partisan environment. Using snowball sampling, participants were asked to respond via email during a period bookended by a Phoenix, Arizona, rally in which Trump berated news reporters for their coverage of demonstrations in Charlottesville, Virginia, and then, weeks later, posted disparaging “fake news” tweets. The social media barrage followed challenging news coverage of a devastating hurricane in the eastern United States. Responses came from journalists representing 41 news organizations (including broadcast, radio, cable, online, and print) in regions throughout the country. Qualitative reflexive thematic analysis surfaced deeply intertwined personal and professional concerns, suggesting a need to heighten awareness of the influence sustained trauma has on both personal and professional relationships. Their being labeled “fake” and “enemies of the people” might affect content and routine in ways that include self-censorship, sourcing, transparency, and degrees to which one would risk harm or emotional distress to cover a story. Toughing it out in the face of sustained and wounding attacks might create hidden psychological and professional time-bombs, putting journalists, journalism, and ultimately democracy, at risk. Full article

Assessing the contribution of tropical cyclones to regional precipitation variability is essential for understanding the associated hydrometeorological benefits and risks. This study quantifies the contribution of tropical cyclones to annual precipitation in the northernmost part of South America from 2016 to 2025, utilizing data from surface rain gauges. Simulations using the Weather Research and Forecasting (WRF) model, configured with 2 km grid spacing and 38 vertical levels, estimate the influence of relative humidity at 850 hPa and ambient temperature at 500 hPa on precipitation over the continental region when each convective system is nearest to the coastline. During Hurricanes Matthew (2016) and Melissa (2025), contributions to the annual average precipitation reached 51% and 47%, respectively, with the highest values observed near the northern South American coastline. The contributions of Harvey (2017), Iota (2020), Julia (2022), and Beryl (2024) to annual precipitation were 0–26%, 0–18%, 0–12%, and 0–19%, respectively. Precipitation distribution was heterogeneous during the passage of tropical storms. The extent of accumulated precipitation was influenced by the cyclone’s trajectory and proximity to mountainous regions. Patterns of relative humidity at 850 hPa did not correspond to a uniform precipitation distribution. Between 6% and 30% of rain gauges did not record precipitation during the analyzed tropical cyclone events. These findings highlight that tropical cyclone-induced precipitation is strongly influenced by complex interactions between atmospheric dynamics and topography. Future research should assess the contributions of these systems to groundwater and surface reservoirs that support indigenous communities in rural areas. Full article

We developed a class of multivariate integer-valued time series models using copula theory. Each count time series is modeled as a Markov chain, with serial dependence characterized through copula-based transition probabilities for Poisson and negative binomial marginals. Cross-sectional dependence is modeled via a trivariate Gaussian or a “t-copula”, allowing for both positive and negative correlations and providing a flexible dependence structure. Model parameters are estimated using likelihood-based inference, where the trivariate Gaussian or t-copula integrals are evaluated through standard randomized Monte Carlo methods. Simulation results, along with an analysis of annual counts of major hurricanes (Category 3+) across the North Atlantic, Eastern North Pacific, and Western North Pacific basins, demonstrate the effectiveness of the proposed model. Full article

This study investigates the mechanical and thermodynamic effects of evaporating ocean spray on the structure and dynamics of a hurricane marine atmospheric boundary layer using Eulerian multifluid and mixture model approaches coupled with the $E-ϵ$ turbulence closure. The multifluid framework treats air and spray as interpenetrating phases, enabling a physically consistent representation of air–droplet interactions governing momentum transfer, enthalpy exchange, and turbulence modulation. The mixture approach is based on a simplified description that captures only part of the underlying physics yet offers an advantage in its ability to yield analytical insight. Mechanically, spray produces competing effects: on one hand, droplet inertia causes wind deceleration, and on the other, spray-induced turbulence attenuation, primarily resulting from the air–droplet friction, leads to strengthening the wind. Analytical and numerical results show that the latter effect prevails for typical spray droplet sizes leading to wind acceleration and drag reduction at hurricane wind speeds. Thermodynamically, evaporating droplets redistribute total heat flux in favor of its latent component, with effects strongly dependent on the droplet size. Small droplets suppress turbulence and reduce the total enthalpy flux, whereas large ones enhance it. Furthermore, spray significantly increases the total enthalpy-to-drag coefficient ratio with wind speed, which agrees with field observations. Full article

Hurricanes cause severe impacts on lives, livelihoods, and essential systems. Hurricane Melissa impacted Jamaica as a Category 5 cyclone, resulting in estimated losses of approximately 41% of national GDP (US$8.8 billion) and eliciting widespread damage to housing, healthcare, agriculture, and urban infrastructure. Agriculture sustained heavy losses, with 41,000 hectares of damaged farmland and the loss of more than 1 million livestock animals. These impacts resulted in exposed animal closures with biological hazards. Using systems thinking, the PESTHEEL framework, and a One Health lens, we argue for viewing Hurricane Melissa as series of cascading inter-related One Health threats of waterborne and vector-borne diseases, zoonoses, antimicrobial resistance, degraded indoor and outdoor air quality, chemical pollution, and shifting migration and border dynamics. These each unfold at different timings. A structured synthesis for Jamaica and other Caribbean Small Island Developing States is provided by integrating systems thinking, One Health, and the PESTHEEL framework. Immediate and lagged risk pathways are identified, and practical risk reduction actions are proposed to support anticipatory, multisectoral recovery: enhanced syndromic, laboratory, wastewater, vector, and rodent surveillance; resilient WASH and shelter systems; non-insecticidal and integrated vector management; biosecure aid and border protocols; environmental toxicology monitoring; and climate–health intelligence. Full article

The development of offshore wind energy in tropical cyclone-prone regions requires analytical frameworks that capture non-stationary climate dynamics. This study presents a multi-scale spectral approach to characterize Atlantic tropical cyclone variability and assess implications for offshore wind resilience in the Caribbean Basin. The methodology integrates Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) to resolve temporal variability in sea surface temperature, cyclone frequency, and intensity, complemented by two-dimensional kernel density estimation (KDE) and non-stationarity analysis. Using NOAA and National Hurricane Center datasets, results identify dominant periodicities at annual and ENSO (2–7 year) scales, a post-1995 spectral energy shift associated with the positive AMO phase, and a thermodynamically consistent energy corridor along 12–16° N. A statistically significant change point in 1987 (Pettitt test, p < 0.05) is detected, although spatial displacement is not significant. An integrated Wind Risk Index highlights the central-western Caribbean as a high-exposure zone overlapping offshore wind development areas. Exceedance analysis shows that 39.8% of observations surpass 25 m/s, 6.0% exceed 50 m/s, and 1.3% approach 70 m/s, indicating relevant design considerations. These findings support the need for non-stationary, multi-scale approaches in offshore wind risk assessment under tropical cyclone influence. Full article

Intravenous fluids are integral to pediatric perioperative care, yet optimal fluid volumes during adenotonsillectomy remain debated. In 2024, Hurricane Helene disrupted IV fluid supply chains, necessitating an involuntary shift to restrictive intraoperative fluid administration. This event created an opportunity to evaluate whether reduced intraoperative fluids affected postoperative pain or complication rates in pediatric adenotonsillectomies. We conducted a retrospective cohort study of children under 12 years who underwent adenotonsillectomy between 1 October 2024, and 31 January 2025. Patients were stratified into a restrictive fluid group (<10 mL/kg) and a non-restrictive group (≥10 mL/kg). Collected data included demographics, intraoperative fluid volumes, postoperative FLACC pain scores, and documented complications. Pain scores were compared using Mann–Whitney U tests, due to non-normal data distribution with descriptive analysis. A total of 133 patients were included (63 restrictive, 70 non-restrictive). Mean postoperative FLACC scores were similar between groups (4.53 ± 2.62 vs. 4.57 ± 3.44; p = 0.50), with comparable operative times. Complications occurred in both groups without a consistent association with fluid strategy. These findings suggest that intraoperative fluid restriction below 10 mL/kg does not significantly affect postoperative pain or overall complication rates in pediatric adenotonsillectomy. Short-term fluid restriction may be safe in resource-limited settings, though prospective studies are warranted. Full article

Southern New Jersey faces increasing flood risk due to several factors including rapid development, climate change, and aging infrastructure. This study evaluated the flood vulnerability of two municipal solid waste landfills located in Gloucester and Cumberland Counties. These sites are located near rural communities that rely on shallow groundwater for drinking water, which may be contaminated by floods. To assess these challenges, this research applies a hydrologic–hydraulic model to evaluate future flood vulnerability at the Cumberland County Improvement Authority (CCIA) landfill and the Gloucester County Solid Waste Complex (GCSWC) landfill. The method uses HEC-HMS and HEC-RAS 2D model simulations with climate-adjusted precipitation data derived from global climate models. Model performance was evaluated using Hurricane Ida (31 August–2 September 2021) by comparing HEC-RAS-simulated inundation extents with independently derived Sentinel-1 SAR flood maps generated in Google Earth Engine. Climate forcing was developed by deriving climate-adjusted 24 h precipitation–frequency (PF) design depths for 50-year and 100-year design storms under the Shared Socioeconomic Pathway (SSP) emissions pathways SSP2-4.5 (moderate) and SSP5-8.5 (high) for mid-century (2025–2050) and late-century (2070–2100) periods. These PF storm totals were converted to rainfall hyetographs using a fixed alternating variability method (AVM) temporal pattern within the coupled HEC-HMS/HEC-RAS modeling chain. Hazard amplification was primarily expressed through lateral inundation expansion and longer persistence of shallow flooding in low-relief operational zones, rather than uniform increases in peak depth across landfill interiors. Across both facilities, the landfill toe and adjacent access corridors were consistently identified as the most sensitive operational areas. Full article

In this work, we report the realization of curved distributed Bragg reflectors (DBRs) without the need for lithography to achieve strong lateral confinement in a GaN-based vertical cavity. By embedding SiO₂ nanospheres during deposition, curved DBRs with a funnel-shaped cross-section were fabricated. Based on the formed curved DBRs, a vertical cavity with a quality factor exceeding 2800 and a mode volume below 0.14 μm³ was successfully fabricated. The optical pumping threshold power of a vertical cavity surface-emitting laser (VCSEL) with a curved DBR was reduced to 76 nW, which is one order of magnitude lower than that of the same VCSEL with double-planar DBRs. Near-field patterns revealed that the curved-DBR VCSEL emits a circularly symmetric TEM₀₀ mode with a full width at half maximum (FWHM) of only 1.8 μm. We believe this is an effective technique for fabricating low-threshold or small-aperture VCSELs. Full article

Hurricane Florence (2018) proved to be a damaging tropical cyclone that formed off the coast of the Cabo Verde Islands. On 12 UTC 14 September 2018, Florence made landfall as a weakened category 1 Hurricane in Wrightsville Beach, NC. In the midst of landfall, Florence’s ground speed stalled considerably to near zero. Because of this stall, Florence continued to accumulate feet of rain along the coastline, and the inundation of seawater became extreme. Due to the impacts of Florence, the Weather Research and Forecasting Model (WRF-ARW) was used to simulate the tropical cyclone and provide insight into the thermodynamics and dynamics that played a significant role at the time of landfall. After the control case, several sensitivity experiments were conducted. The historical sensitivity experiments utilize the thermodynamic and moisture fields of ERA5 reanalysis data from 1968 and 1998, respectively, to modify the thermodynamic and moisture fields in the initial conditions of the WRF–ARW control case. In addition, to study the potential future climate impacts of Florence, the NCAR CESM Global Bias-Corrected CMIP5 Output to Support WRF/MPAS Research dataset was utilized. The same approach was taken as the historical versions of Florence for sensitivity experiments for future climate, i.e., thermodynamic and moisture fields for both 2038 and 2068 under the RCP6.0 and RCP8.5 climate scenarios, respectively. Results suggest a corresponding intensity shift with minor track deflections. Based on these modifications, synoptic and mesoscale dynamics will be studied to provide insight into how Florence-like hurricanes may change based on certain climate scenarios. Full article

Availability of phosphorus (P) is thought to limit bole growth in wet tropical forests, raising concern that removing P through repeated logging in P-limited stands may be unsustainable. Motivated by a study in Indonesia, we analyzed Olsen extractable and total soil P in the upper 10 cm in paired samples we collected under vs. near decaying boles of two contrasting species in a wet tropical forest in Puerto Rico. Guarea guidonia had higher wood and leaf P concentrations than Dacryodes excelsa. G. guidonia colonized valleys with higher soil P concentrations than ridge sites dominated by D. excelsa. We used two age cohorts of trees > 30 cm diameter, felled by hurricanes Hugo in 1989 (11 years old) and Georges in 1998 (1.5 years old), but soil P did not differ with age. Soil Olsen P concentrations were significantly higher under versus away from boles of both species. Paradoxically, augmentation of soil P was greater under boles of D. excelsa than G. guidonia despite having lower wood P. Soil % C and Olsen P were strongly positively correlated in D. excelsa but not in G. guidonia, suggesting that regulation of soil P-availability differs between ridges and valleys. Both soil C and P may be critical for maintaining soil fertility on ridges in a wet tropical forest. Our results are discussed in the context of prior experiments at our site, including two where bole growth increased with wood addition and/or decreased after removal of woody debris. These studies in Puerto Rico, together with others elsewhere, suggest that reduced forest productivity could potentially result from repeated logging of forest stands on ridges with low P-availability in humid tropical areas since decaying wood could directly and indirectly maintain P-availability in sites with low soil P-availability. We suggest several hypotheses on P-cycling in montane humid tropical forests that need further research to elucidate mechanisms controlling soil P-availability and identify sites where repeated logging is likely to be unsustainable. Full article

Oceanfront relief varies along coastlines and serves as the first barrier to wave and surge damage. However, forecasted increases in storm frequency and sea levels are anticipated to enhance coastal erosion, potentially weakening this protection. The land–sea transition is variable along the New England coast, USA, and this variability has produced a range of coastal morphologies that can vary over short distances. It is important to track the beach transition zone to better understand transformations of the system and related hazard risks. A combination of field and computer-based methods was used to evaluate the beach transition zone of southwestern Rhode Island to determine alongshore variability and dynamics. More specifically, a decadal-scale study was conducted to examine changes in morphology from 2011 to 2022, and a short-term study at South Kingstown Town Beach examined changes from November 2023 to January 2024 using time-series drone-derived elevations. Classification of over 500 cross-shore transects illustrated the dominance of sedimentary shorelines, with smaller areas of rocky outcrops and hardening. Analysis of four different years (2011, 2014, 2018, and 2022) determined that beaches with dune morphology were the most common type of transition zone (41–47% of the transects) and transects with a high bank upland were the next most frequent class (34–41%). Following Hurricane Sandy in 2012, a 6% decrease in the number of dune-classified transects was measured; however, one-third of those recovered dune morphology by 2022. The greatest beach transformations over the short-term study occurred in response to strong storms in the 2023–2024 winter season, during which lateral beach movement (erosion) exceeded 15 m in portions of South Kingstown Town Beach. Dune erosion was accompanied by overwash flooding and deposition, and the area remained low-lying and thus vulnerable to future impacts. The beach transition zone classification and insights from this research will be informative for future planning by coastal communities by determining at-risk shorelines based on underlying geology and the stability of morphological features. Full article

Accurate forecasts of tropical cyclone (TC) track and intensity with a sufficient lead time are critical for disaster preparedness and risk mitigation. Traditional numerical weather prediction models, while fundamental to operational forecasting, often exhibit systematic errors due to limitations in observations, physical parameterizations, and model resolution. In recent years, machine learning (ML) and deep learning (DL) approaches have emerged as promising data-driven alternatives for improving TC forecasts. This study presents a comparative evaluation of six ML and DL models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN)—for forecasting TC track and intensity in the North Atlantic basin. The models are trained using the National Hurricane Center’s (NHC) HURDAT2 best-track dataset for storms from 1990 to 2019 and evaluated on an independent test set from the 2020 season. Model performance is compared across all models and benchmarked against the 2020 mean Decay-SHIFOR5 intensity error, CLIPER5 track errors, and the NHC official forecast (OFCL) errors. Forecast skill is assessed using mean absolute error (MAE) with 95% bootstrap confidence intervals and the coefficient of determination ($R^2$) across lead times of 6, 12, 18, 24, 48, and 72 h. The results show that: (1) several ML and DL models achieve intensity forecast performance that is broadly comparable in magnitude to the 2020 mean OFCL benchmarks, with an average error reduction of 5–11% at the 24 h lead time; (2) among the ML models, XGBoost and CatBoost slightly outperform LightGBM and RF in accuracy, while LightGBM demonstrates the highest computational efficiency; and (3) among the DL models, CNNs outperform ANNs in predictive accuracy and intensity forecasting efficiency, while ANNs exhibit lower computational cost for track forecast. Bootstrap confidence intervals indicate relatively low variability in model errors, supporting the statistical stability of the results within the 2020 season. However, these results reflect within-season variability and do not necessarily generalize across different years or climatological conditions. Overall, the findings demonstrate the potential of ML/DL-based approaches to complement existing operational forecast systems and enhance TC track and intensity forecasting in the North Atlantic basin. Full article