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/).

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.

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.