Hurricane Otto caused sequential changes in tropical soil microbiota over 5 years.Acidobacteria were critical early decomposers of deposited canopy debris for 3 years.Complex C degrading fungi were critical later decomposers of debris starting at 4 years.A suite of C, N and microbial indicators should prove valuable for forest managers.Hurricanes cause significant damage to tropical forests; however, little is known of their effects on decomposition and decomposer communities. This study demonstrated that canopy debris deposited during Hurricane Otto stimulated sequential changes in soil carbon (C) and nitrogen (N) components, and decomposer microbial communities over 5 years. The initial response phase occurred within 2 years post-hurricane and appeared associated with decomposition of the labile canopy debris, suggested by: increased DNA sequences (MPS) of the Acidobacterial community (as common decomposers of labile plant material), decreases in total organic C (TOC), increased biomass C, respiration, and NH4+, conversion of organic C in biomass, and decreased MPS of complex organic C decomposing (CCDec) Fungal community. After 3 years post-hurricane, the later response phase appeared associated with decomposition of the more stable components of the canopy debris, suggested by: increased MPS of the Fungal CCDec community, TOC, stabilized Respiration, decreased Biomass C, the return to pre-hurricane levels of the conversion of organic C to biomass, and decreased MPS of Acidobacterial community. These changes in the microbial community compositions resulted in progressive decomposition of the hurricane-deposited canopy material within 5 years, resulting several potential indicators of different stages of decomposition and soil recovery post-disturbance.

This study analyzes the effects of Hurricane Eta on the Chiriqui Viejo River basin, revealing the significant impact of extreme weather events on the hydrological dynamics of the region. The maximum rainfall recorded on November 4, 2020, reached 223.8 mm, while the flow in Paso Canoa reached 638.03 m3/s, demonstrating the magnitude of the event and the inability of the basin to handle such high volumes of water. Through a detailed analysis, it was observed that soil saturation resulted in direct runoff of up to 70.0 mm that same day, which shows that the infiltration capacity of the soil was quickly exceeded. Despite the damage observed, there are currently no advanced hydrological studies on extreme events in critical basins such as the Chiriqui Viejo River. This lack of research reflects a serious lack of planning and assessment of the risks associated with phenomena of this magnitude. One of the most critical problems found is the lack of specialized hydrology professionals, who are essential to carry out detailed studies and ensure sustainable management of water resources. In a context where climate change increases the frequency and intensity of extreme events, the absence of hydrologists in the region puts the resilience of the basin to future disasters at risk. The basin's hydraulic system demonstrated its inability to handle high flows, underscoring the need to improve flood control and water retention infrastructure. In addition, the lack of effective hydrological planning and coordination in the management of hydraulic infrastructures compromises both the safety of downstream communities and the sustainability of hydroelectric reservoirs, vital for the region.

Hurricanes are one of the most destructive natural disasters that can cause catastrophic losses to both communities and infrastructure. Assessment of hurricane risk furnishes a spatial depiction of the interplay among hazard, vulnerability, exposure, and mitigation capacity, crucial for understanding and managing the risks hurricanes pose to communities. These assessments aid in gauging the efficacy of existing hurricane mitigation strategies and gauging their resilience across diverse climate change scenarios. A systematic review was conducted, encompassing 94 articles, to scrutinize the structure, data inputs, assumptions, methodologies, perils modelled, and key predictors of hurricane risk. This review identified key research gaps essential for enhancing future risk assessments. The complex interaction between hurricane perils may be disastrous and underestimated in the majority of risk assessments which focus on a single peril, commonly storm surge and flood, resulting in inadequacies in disaster resilience planning. Most risk assessments were based on hurricane frequency rather than hurricane damage, which is more insightful for policymakers. Furthermore, considering secondary indirect impacts stemming from hurricanes, including real estate market and business interruption, could enrich economic impact assessments. Hurricane mitigation measures were the most under-utilised category of predictors leveraged in only 5% of studies. The top six predictive factors for hurricane risk were land use, slope, precipitation, elevation, population density, and soil texture/drainage. Another notable research gap identified was the potential of machine learning techniques in risk assessments, offering advantages over traditional MCDM and numerical models due to their ability to capture complex nonlinear relationships and adaptability to different study regions. Existing machine learning based risk assessments leverage random forest models (42% of studies) followed by neural network models (19% of studies), with further research required to investigate diverse machine learning algorithms such as ensemble models. A further research gap is model validation, in particular assessing transferability to a new study region. Additionally, harnessing simulated data and refining projections related to demographic and built environment dynamics can bolster the sophistication of climate change scenario assessments. By addressing these research gaps, hurricane risk assessments can furnish invaluable insights for national policymakers, facilitating the development of robust hurricane mitigation strategies and the construction of hurricaneresilient communities. To the authors' knowledge, this represents the first literature review specifically dedicated to quantitative hurricane risk assessments, encompassing a comparison of Multi-criteria Decision Making (MCDM), numerical models, and machine learning models. Ultimately, advancements in hurricane risk assessments and modelling stand poised to mitigate potential losses to communities and infrastructure both in the immediate and long-term future. (c) 2025 China University of Geosciences (Beijing) and Peking University. Published by Elsevier B.V. on behalf of China University of Geosciences (Beijing). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Hurricane Helene triggered 1792 landslides across western North Carolina and has caused damage to 79 bridges to date. Helene hit western North Carolina days after a low-pressure system dropped up to 254 mm of rain in some locations of western North Carolina (e.g., Asheville Regional Airport). The already waterlogged region experienced devastation as significant additional rainfall occurred during Helene, where some areas, like Asheville, North Carolina received an additional 356 mm of rain (National Weather Service, 2024). In this study, machine learning (ML)-generated multi-hazard landslide susceptibility maps are compared to the documented landslides from Helene. The landslide models use the North Carolina landslide database, soil survey, rainfall, USGS digital elevation model (DEM), and distance to rivers to create the landslide variables. From the DEM, aspect factors and slope are computed. Because recent research in western North Carolina suggests fault movement is destabilizing slopes, distance to fault was also incorporated as a predictor variable. Finally, soil types were used as a wildfire predictor variable. In total, 4794 landslides were used for model training. Random Forest and logistic regression machine learning algorithms were used to develop the landslide susceptibility map. Furthermore, landslide susceptibility was also examined with and without consideration of wildfires. Ultimately, this study indicates heavy rainfall and debris-laden floodwaters were critical in triggering both landslides and scour, posing a dual threat to bridge stability. Field investigations from Hurricane Helene revealed that bridge damage was concentrated at bridge abutments, with scour and sediment deposition exacerbating structural vulnerability. We evaluated the assumed flooding potential (AFP) of damaged bridges in the study area, finding that bridges with lower AFP values were particularly vulnerable to scour and submersion during flood events. Differentiating between landslide-induced and scour-induced damage is essential for accurately assessing risks to infrastructure. The findings emphasize the importance of comprehensive hazard mapping to guide infrastructure resilience planning in mountainous regions.

Florida's unique climatic and geographical features have profoundly influenced its hurricane history. This study quantitatively examines the effects of Hurricane Ian on urban vegetation in Fort Myers, Florida, using remote sensing data. We analyzed pre- and post-hurricane vegetation indices, including NDVI (Normalized Difference Vegetation Index), ARVI (Atmospherically Resistant Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index). Our findings reveal varied spatial impacts, with NDVI changes ranging from -0.03 to 0.333, ARVI changes from -0.016 to 0.25, and SAVI changes from -0.04 to 0.5. Negative values indicate vegetation damage, while positive values suggest resilience or recovery. The study area experienced a 63.75% reduction in vegetation cover, from 67.10 km(2) before Hurricane Ian to 24.325 km(2) after. Pre-hurricane NDVI ranged from -0.2298 to 0.5663, while post-hurricane values ranged from -0.189 to 0.521, indicating overall vegetation stress. ARVI maxima decreased from 0.379 to 0.352, and SAVI maxima from 0.849 to 0.782, further confirming vegetation damage. Support Vector Machine classification achieved 89% accuracy (Kappa = 0.85) for prehurricane and 87% (Kappa = 0.83) for post-hurricane vegetation mapping. These findings enhance our understanding of hurricane impacts on urban green infrastructure, with significant implications for urban planning and disaster preparedness in coastal cities prone to extreme weather events. The outcomes enhance damage assessment methodologies and provide valuable insights into the ecological consequences of hurricanes on urban ecosystems.

Cyclonic storms (i.e., hurricanes) are powerful disturbance events that often cause widespread forest damage. Storm-related canopy damage reduces rainfall interception and evapotranspiration, but impacts on streamflow regimes are poorly understood. We quantify streamflow changes in Puerto Rico following Hurricane Maria in September 2017, and evaluate whether forest cover and storm-related canopy damage account for the differences. Streams are particularly vulnerable to flooding in early post-disturbance stages during hurricane season, so we focus on 3 months (Oct-Dec) following the hurricane. To discern changes in rainfall responses, we partitioned streamflow into baseflow and quickflow using a digital filter. We collected 2010-2017 streamflow and rainfall data from 18 watersheds and compared the relative magnitude of post- to pre-hurricane double mass curve slopes of baseflow and quickflow volumes against rainfall. Several watersheds displayed higher post-hurricane quickflow and baseflow, however, the response was variable. The magnitude of quickflow increase was greater in watersheds with high forest damage. Under the same level of relative damage, watersheds with low initial forest cover had greater quickflow increases than highly forested ones. Conversely, baseflow generally increased, but increases were greater in highly forested watersheds and smaller in highly damaged watersheds. These results suggest that post-storm baseflow increases were due to recharge of hurricane-related rainfall, as well as forest transpiration interruption and soil disturbance enhancing recharge of post-hurricane rainfall, while increases to quickflow are related to loss of canopy rainfall interception and higher soil saturation decreasing infiltration. Our research demonstrates that forest damage from disturbance lowers quickflow and elevates baseflow in highly forested watersheds, and elevates quickflow and lowers baseflow in less-forested watersheds. Less-forested watersheds may be closer to the forest cover loss threshold needed to elicit a streamflow response following disturbance, suggesting higher flooding potential downstream, and a lower storm-related forest disturbance threshold than in heavily forested watersheds. We quantify streamflow component changes following a severe hurricane and relate these changes to watershed forest cover and canopy damage. Several watersheds displayed higher post-hurricane quickflow and baseflow, however, the response was variable. Quickflow increases were greater in watersheds with high forest damage. Under the same level of relative damage, watersheds with low forest cover had greater quickflow increases than highly forested ones. Conversely, baseflow increases were greater in highly forested watersheds and smaller in highly damaged watersheds. image

Approximately 11% of the world's population lives within 10 km of an ocean coastline, a percentage that is likely to increase during the remainder of the 21st century due to urbanization and economic development. In the presence of climate change, coastal communities will be threatened by increasing damages due to sea-level rise (SLR), accompanied by hurricanes, storm surges and coastal inundation, shoreline erosion, and seawater intrusion into the soil. While the past decade has seen numerous proposals for coastal protection using adaptation methods to deal with the deep uncertainties associated with a changing climate, our review of the potential impact of SLR on the resilience of coastal communities reveals that these adaptation methods have not been informed by community resilience or recovery goals. Moreover, since SLR is likely to continue over the next century, periodic changes to these community goals may be necessary for public planning and risk mitigation. Finally, community policy development must be based on a quantitative risk-informed life-cycle basis to develop public support for the substantial public investments required. We propose potential research directions to identify effective adaptation methods based on the gaps identified in our review, culminating in a decision framework that is informed by community resilience goals and metrics and risk analysis over community infrastructure life cycles.

Hurricanes are extreme climatic events frequently affecting tropical regions such as the tropical dry forests (TDFs) in Mexico, where its frequency/intensity is expected to increase toward the year 2100. To answer how resistant is a Mexican tropical dry forest to a high-intensity hurricane, and if its degree of resistance was mediated by its conservation degree, we evaluated the effect of a category 4 hurricane over the tree community, soil nutrients, and soil enzymatic activity in two contrasting TDF ecosystems: Old-Growth Forest (OGF) and Secondary Forest (SF). In general, vegetation richness and diversity showed very high resistance one year after the hurricane, but several structural attributes did not, especially in the OGF where the tree mortality related to vegetation structure and spatial distribution of individuals was higher. Then, in the short term, SF vegetation appeared to be more resistant, whereas the OGF, with more biomass to lose, appeared to be more vulnerable. Conversely, most soil attributes showed low resistance in both stages, but especially in SF which could face more severe nutrient limitations. The response of TDF to high-intensity hurricanes, in terms of above- and belowground processes, was in part dependent on its disturbance level. Moreover, an increase in the intensity/frequency of hurricanes could lead this TDF toward a high nutrient limitation (especially by phosphorus) for the plants and consequently toward a loss of soil functioning, especially in the SF. This eventually could produce a severe degradation in fundamental attributes and functions of the ecosystem.

Tropical Cyclones (TCs) inflict substantial coastal damages, making it pertinent to understand changing storm characteristics in the important nearshore region. Past work examined several aspects of TCs relevant for impacts in coastal regions. However, few studies explored nearshore storm intensification and its response to climate change at the global scale. Here, we address this using a suite of observations and numerical model simulations. Over the historical period 1979-2020, observations reveal a global mean TC intensification rate increase of about 3 kt per 24-hr in regions close to the coast. Analysis of the observed large-scale environment shows that stronger decreases in vertical wind shear and larger increases in relative humidity relative to the open oceans are responsible. Further, high-resolution climate model simulations suggest that nearshore TC intensification will continue to rise under global warming. Idealized numerical experiments with an intermediate complexity model reveal that decreasing shear near coastlines, driven by amplified warming in the upper troposphere and changes in heating patterns, is the major pathway for these projected increases in nearshore TC intensification. Tropical cyclones (TCs) that intensify close to the coast pose a major socio-economic threat and are a substantial challenge from an operational standpoint. Therefore understanding historical trends in nearshore storm intensification and how they may change in future is of considerable significance. Despite this, few studies examined this key aspect of TCs at the global scale. Here we show, using an analysis of observations and atmospheric reanalyses, that the mean TC intensification rate has increased significantly over the period 1979-2020 primarily aided by increases in relative humidity and decreases in vertical wind shear. Further, high-resolution climate models, which explicitly resolve TCs, suggest that nearshore TC intensification will continue to increase in future. These increases in coastal TC intensification rates can mainly be attributed to stronger projected decreases in vertical wind shear. To better understand wind shear projections, a suite of idealized numerical experiments with an intermediate complexity model were conducted. The experiments indicate that enhanced warming in the upper-troposphere and changing heating patterns are likely responsible. Tropical cyclone (TC) intensification rates have increased in near coastal regions over the 42-year period 1979-2020 Increases in relative humidity along with decreases in vertical wind shear are responsible Climate models project a continued increase in nearshore TC intensification rates with decreasing wind shear playing a crucial role

Modern forestry research emphasizes infusing management practices with an understanding of natural disturbance regimes-often called ecological forestry. Forestry practices that emulate aspects of natural disturbance regimes are considered an effective approach to balance silvicultural and ecological objectives. Silvicultural research is often available to guide successful regeneration in many forest types, but little information is available about gap patterns from common disturbances in the eastern U.S. like hurricanes. We examined the size, shape, and spatial distribution of canopy gaps formed in a longleaf pine woodland by Hurricane Michael across multiple landscape factors including stand size, composition, and soil types. We found high variation in many gap characteristics but no significant differences in gap size or shape among landscape factors. However, spatial distribution of gaps differed among landscape types in nuanced ways. We also found that stand size complexity may prevent the formation of very large gaps that can disrupt fire continuity in systems managed with frequent fire. The results highlight the ecological importance of hurricane events and provide insight into hurricane gap formation at the landscape scale. The implementation of silviculture practices that emulate a large, rapid, single disturbance event may be more practically applied than management based on disturbances such as lightning or insects which occur over longer timeframes.