Impact Of Hurricane Research Articles

The Impact of a Major Hurricane on Sediment Geochemistry and Organic Contaminants of a Shallow Subtropical Estuary Through Strong Resuspension

The Impact of a Major Hurricane on Sediment Geochemistry and Organic Contaminants of a Shallow Subtropical Estuary Through Strong Resuspension

A Disparate Disaster: Spatial Patterns of Building Damage Caused by Hurricane Ian and Associated Socio-Economic Factors

The literature shows that communities under different socio-economic conditions suffer different levels of damage in disasters. In addition to the physical intensity of hazards, such differences are also related to the varying abilities of communities to prepare for and respond to disasters. This study analyzes the spatial patterns of building damage in Hurricane Ian in 2022 and investigates the socio-economic disparities related to the damage. Specifically, this study employs NASA’s Damage Proxy Map (DPM2) to analyze spatial patterns of building damage caused by the hurricane. Then, it uses statistical analysis to assess the relationships between building damage and hurricane intensity, building conditions, and socio-economic variables at the building and census tract levels. Furthermore, the study applies geographically weighted regression (GWR) to examine the spatial variation of the damage factors. The results provide valuable insights into the potential factors related to building damage and the spatial variation in the factors. The results also reveal the uneven distribution of building damage among different population groups, implying socio-economic inequalities in disaster adaptation and resilience. Moreover, the study provides actionable information for policymakers, emergency responders, and community leaders in formulating strategies to mitigate the impact of future hurricanes by identifying vulnerable communities and population groups.

Current Status of Remote Sensing for Studying the Impacts of Hurricanes on Mangrove Forests in the Coastal United States

Current Status of Remote Sensing for Studying the Impacts of Hurricanes on Mangrove Forests in the Coastal United States

Abstract A010: Evaluating the aftermath of two major hurricanes and the COVID-19 pandemic on lung cancer Incidence in Puerto Rico: An interrupted time-series analysis

Abstract Background - In recent years, events like hurricanes and the COVID-19 pandemic have significantly impacted the daily lives and the healthcare system in Puerto Rico (PR). We aimed to evaluate the effect of these disruptions on lung cancer incidence. Methods - An interrupted time-series analysis (ITSA) was conducted to assess the impact of hurricanes and the COVID-19 pandemic on lung cancer incidence trends. To achieve this, the data was modeled using first-order autoregression through Prais-Winsten AR(1) regression. Monthly lung cancer cases from 2012 to 2021 were obtained from the PR Central Cancer Registry database. The analysis was divided into three periods: 1) pre-hurricanes (January 2012-August 2017), 2) post-hurricanes (September 2017-March 2020), and 3) onset of COVID-19 lockdown restrictions (April 2020-December 2021). Additionally, we measured the impact of the events by comparing the change in cases with the expected cases in the absence of the disruptions. Results - A steady trend was observed in monthly overall lung cancer cases during the pre-hurricane period. Immediately after the two major hurricanes, there was an immediate decrease of 10.2 cases, representing a 16.1% decline compared to the expected lung cancer cases. During the post-hurricanes period, lung cancer cases exhibited a significant upward trend, eventually surpassing the expected number. However, another decline in lung cancer cases was observed with the onset of the COVID-19 lockdown restrictions, representing a 22.9% decline compared to the expected cases. This decline was followed by a significant upward trend of 0.67 cases per month until the end of 2021, once again surpassing the expected number of cases. Similar patterns were observed by age group and for late-stage cases. However, early-stage lung cancer showed a higher percentage difference at the start of the COVID-19 lockdown restrictions compared to the other groups and did not surpass the expected number of cases by the end of the study. Conclusion - Our results demonstrate the impact of hurricanes and the COVID-19 lockdown on lung cancer incidence in PR. These disruptions led to limited access to healthcare, particularly diagnostic and screening services, as evidenced by the decrease in lung cancer cases. Although lung cancer cases gradually returned to the expected levels in the absence of disruptions, the long-term effects of delayed diagnoses due to these events should be closely monitored. Citation Format: Tonatiuh Suárez-Ramos, Carlos Torres-Cintrón, Yisel Pagán-Santana. Evaluating the aftermath of two major hurricanes and the COVID-19 pandemic on lung cancer Incidence in Puerto Rico: An interrupted time-series analysis [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A010.

Assessing population viability and management strategies for species recovery of the critically endangered Puerto Rican parrot

AbstractRecovery of endangered species is challenging and lengthy, especially when it involves reintroduction and dynamic environmental conditions. Because managers often need to decide between many management strategies with uncertain outcomes, periodically assessing progress toward recovery using population viability analysis (PVA) can help guide decision‐making. We developed a PVA for the critically endangered Puerto Rican parrot (Amazona vittata) to evaluate current status and potential future management strategies to reach goals set in the Recovery Plan. Having grown from their nadir of 13 birds in 1976 to 686 in 2021, the recovery effort has made great progress. Using 15 years of data, we built an individual‐based PVA that included interactions between two captive and three wild populations connected via annual releases. If management continues as planned, the wild populations have no to moderate risks of extinction (0–32%) over the next 100 years. However, wild populations remain dependent on releases to sustain growth, and recovery targets for stable population growth and connectivity have not yet been reached. Our analysis suggests that hurricanes are an impediment to reaching recovery targets and impact some wild populations more than others based on geography. Projections with climate‐change‐induced higher hurricane impact resulted in wild populations being less likely to stabilize. We identified demographic rates and associated management strategies that could positively impact wild populations: increasing reproduction (e.g., via increasing artificial nests, improving nest success) and decreasing first‐year mortality (e.g., via targeted predator control). Based on our current understanding of Puerto Rican parrot demographics, species recovery will continue to be management‐dependent unless demographic rates can be altered. As more data are gathered, especially for data‐sparse populations and in the face of environmental change, future iterations of this model can re‐evaluate progress, update management strategies, and provide support for deciding if and when to delist this iconic species.

Human and Hurricane Impacts to Mangroves Modulate Overwash Deposition to a Back-Barrier Lagoon

Human and Hurricane Impacts to Mangroves Modulate Overwash Deposition to a Back-Barrier Lagoon

MODIS fPAR products do not reflect in-situ conditions in a tropical dry forest based on wavelet and cross-wavelet transforms

The fraction of Photosynthetically Active Radiation (fPAR) plays a pivotal role in determining the carbon flux in ecosystems. Although the MODIS fPAR product has demonstrated effectiveness in the Northern Hemisphere, its validity still needs to be verified in the context of Tropical Dry Forests (TDFs), which constitute 40% of all tropical forests. This study utilized a Wireless Sensor Network (WSN) to generate an in-situ Green fPAR dataset at the Santa Rosa National Park Environmental Monitoring Supersite, aiming to validate MODIS fPAR products from 2013 to 2017. This study employs a 2-flux fPAR estimation approach for the in-situ dataset, followed by Savitzky–Golay derivative-based smoothing, univariate-wavelet transforms, and cross-wavelet analysis to compare phenological variables between the in-situ and MODIS fPAR datasets. Our findings reveal a significant temporal disparity between the MODIS fPAR products and ground-based data, with MODIS consistently lagging in detecting the onset of green-up or senescence in TDFs by 18–55 days. However, the annual and inter-seasonal patterns were statistically significant (p < 0.05) and replicated in the MODIS and in-situ datasets. Notably, these patterns deviate during extreme water conditions (droughts and hurricanes), with MODIS underestimating the effects of drought and failing to represent hurricane impact. Furthermore, MODIS fPAR products do not effectively capture small-scale fPAR variations and intra-seasonal differences. Therefore, this study underscores the limited accuracy of MODIS fPAR observations in the context of TDFs. Consequently, caution is warranted when relying on MODIS fPAR products to monitor rapid phenological changes in Tropical Dry Forests.

Multimodal Quanvolutional and Convolutional Neural Networks for Multi-Class Image Classification

By utilizing hybrid quantum–classical neural networks (HNNs), this research aims to enhance the efficiency of image classification tasks. HNNs allow us to utilize quantum computing to solve machine learning problems, which can be highly power-efficient and provide significant computation speedup compared to classical operations. This is particularly relevant in sustainable applications where reducing computational resources and energy consumption is crucial. This study explores the feasibility of a novel architecture by leveraging quantum devices as the first layer of the neural network, which proved to be useful for scaling HNNs’ training process. Understanding the role of quanvolutional operations and how they interact with classical neural networks can lead to optimized model architectures that are more efficient and effective for image classification tasks. This research investigates the performance of HNNs across different datasets, including CIFAR100 and Satellite Images of Hurricane Damage by evaluating the performance of HNNs on these datasets in comparison with the performance of reference classical models. By evaluating the scalability of HNNs on diverse datasets, the study provides insights into their applicability across various real-world scenarios, which is essential for building sustainable machine learning solutions that can adapt to different environments. Leveraging transfer learning techniques with pre-trained models such as ResNet, EfficientNet, and VGG16 demonstrates the potential for HNNs to benefit from existing knowledge in classical neural networks. This approach can significantly reduce the computational cost of training HNNs from scratch while still achieving competitive performance. The feasibility study conducted in this research assesses the practicality and viability of deploying HNNs for real-world image classification tasks. By comparing the performance of HNNs with classical reference models like ResNet, EfficientNet, and VGG-16, this study provides evidence of the potential advantages of HNNs in certain scenarios. Overall, the findings of this research contribute to advancing sustainable applications of machine learning by proposing novel techniques, optimizing model architectures, and demonstrating the feasibility of adopting HNNs for real-world image classification problems. These insights can inform the development of more efficient and environmentally friendly machine learning solutions.

Spatially and Temporally Variable Impacts of Hurricanes on Shallow Sediment Structure

AbstractSediment dynamics are fundamental to understanding coastal resiliency to climate change in the coming decades. Tropical cyclones can radically alter shallow sediment properties; however, the uncertain and destructive nature of tropical cyclones make understanding and predicting their impacts on sediments challenging. Here, grain size sampling in conjunction with continuous hydrodynamic data provided an unprecedented perspective of the impacts of two tropical cyclones, including Hurricane Sally (2020), in which the inner core of the storm passed directly over the field sites, on shallow coastal sediments in Alabama (USA). Sampling directly before and after Sally as well as out to ∼7 months after the second storm event, Hurricane Zeta, showed that the changes in sediments following storm events exhibited notable site‐to‐site variability. This variability during the first storm event was consistent with low sand supply and flow interactions driven by local bathymetry that led to sand transport and deposition at some previously‐muddy sites, near‐surface mud loss at some sandy sites, or little change at others. Post‐Sally impacts to grain size were well preserved 8 months after the storm, despite passage of Zeta as well as seasonal winds and riverine inputs during winter and spring. Overall, high temporal‐resolution sampling over a relatively large area (&lt;500 km2) revealed relatively small‐scale spatial variability (on the order of 5–10 km) of hurricane impacts to sediment structure. These observations demonstrate a critical limitation for accurately predicting changes to coastal sediment dynamics in the face of a changing climate and its impact on tropical cyclones.

Estimating pre-impact and post-impact evacuation behaviors – An empirical study of hurricane Ida in coastal Louisiana and Mississippi

Evacuation after hurricane impacts appear (post-impact evacuation) has been underemphasized in empirical evacuation studies. This study uses well-examined factors for pre-impact evacuation and novel factors for post-impact evacuation in a sequential logit model for pre- and post-impact evacuation choices. Results show that the evacuation warning is the only factor that affected both pre-impact and post-impact evacuations. Demographics and housing characteristics are significant factors for pre-impact evacuation but not for post-impact evacuation, while residential damages and durations of utility outages are significant situational factors for post-impact evacuation. The durations of water and power outages had additive effects on the probability of evacuating after hurricane impact. Based on the results, we argue that the conventional assumption that sheltered-in-place residents will remain in the affected area, and the restoration planning and assistance generated with that premise will not be aligned with the demand of residents facing inhabitable living situations with damaged residences and prolonged utility outages. Agencies should consider extending the evacuation planning time horizon for storm events likely to induce severe damages and outages and prepare for evacuation during disrupted conditions.

Assessing hurricane impact on vegetation and endangered deer habitat using airborne lidar and multispectral images

In the lower Florida Keys, the endangered Florida Key deer and numerous other wildlife species inhabit a vulnerable island environment susceptible to storm surges and rising seawater due to low elevation and flat terrain. Timely and reliable assessment of vegetation damage from natural disasters, such as Hurricane Irma, is crucial for effective habitat management. The study’s overall objective is to examine Hurricane Irma’s impact on vegetation on No Name Key, Florida, using remote sensing. The study relates the area change in vegetation obtained from remote sensing analysis to Florida Key deer population changes following the storm. The methodology involved performing a thematic change detection analysis using the following data sources: (1) aerial multispectral images (for pre- and post-Hurricane), (2) airborne lidar data (for pre- and post-Hurricane), (3) an existing vegetation map, and (4) soil data. A Support Vector Machine (SVM) image classification algorithm was applied to pre- and post-storm input image stacks to create pre- and post-Hurricane Irma vegetation maps. We were then able to obtain the area change information (for various vegetation categories) by performing the change detection analysis of the 2 SVM-classified images. The differences in areas following the storm were calculated for 7 affected vegetation types. Using the area change information following Hurricane Irma, we estimated the number of deer supported by the storm-affected vegetation. These estimated deer numbers, based on the area differences in post-Hurricane Irma vegetation types, were compared to observed deer numbers collected during the post-Hurricane Irma Texas A&M Natural Resources Institute (NRI) deer field survey. The results showed the following: mangroves had the largest negative area changes (area loss), followed by pinelands, hardwoods/hammocks, developed areas, and buttonwoods. Freshwater marshes had the largest positive area changes (area gain). The deer's preferred vegetation areas had decreased post-Hurricane Irma, resulting in a reduced deer population compared to pre-storm numbers. The predicted number of the Key deer post-Hurricane Irma fell within a 95% confidence interval of the observed deer population from the post-storm field survey. The study findings and techniques could be applied to study climate change impact, especially sea level rise. This methodology can be valuable in assessing the impact of storms on other wildlife species in similar environments. The applications and methodology are especially relevant considering the increasing frequency and intensity of storm surges and the accelerating rate of sea level rise.

The creative–destructive force of hurricanes: evidence from technological adoption in colonial Jamaican sugar estates

AbstractWhile in the early part of the nineteenth-century Jamaica was one of the world’s leading sugar producers, the abolition of slavery, the flooding of sugar markets with cheap European beet sugar, and the equalization and finally elimination of sugar import duties across the British empire led to a need for more efficient ways to produce sugar. However, it has been widely noted that Jamaica sugar estates were late in adopting more efficient production techniques, arguably due to inadequate financing. This paper investigates what role the destructive forces of hurricanes may have played in inducing Jamaica to finally modernize its sugar production. To this end, we combine a geo-referenced exhaustive data set of Jamaican sugar estates with a measure of localized hurricane damage constructed from historical hurricane tracks over the period 1882 to 1930. Our econometric analysis shows that hurricane strikes increased the probability that a surviving estate upgraded its sugar processing technology, particularly when the price of sugar was high and the price of the other main exporting crop (bananas) was low. Additionally, while a government hurricane loan programme working through local loan banks did help plantations to adopt new machinery, this depended on the damage not being too large.

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. These quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

The first neurosurgical service in Texas: neurosurgery at the University of Texas Medical Branch (1937-2023).

The authors present a historical analysis of the first neurosurgical service in Texas. Initially established as a subdivision within the Department of Surgery in the early 1900s, this service eventually evolved into the Department of Neurosurgery at the University of Texas Medical Branch (UTMB). The pivotal contributions of individual chiefs of neurosurgery throughout the years are highlighted, emphasizing their roles in shaping the growth of the neurosurgery division. The challenges faced by the neurosurgical division are documented, with particular attention given to the impact of hurricanes on Galveston Island, Texas, which significantly disrupted hospital operations. Additionally, a detailed account of recent clinical and research expansions is presented, along with the future directions envisioned for the Department of Neurosurgery. This work offers a comprehensive historical narrative of the neurosurgical service at UTMB, chronicling its journey of growth and innovation, and underscoring its profound contributions to Galveston's healthcare services, extending its impact beyond the local community.

Method for Spatiotemporal Solar Power Profile Estimation for a Proposed U.S.–Caribbean–South America Super Grid under Hurricanes

Solar photovoltaic (PV) generation technology stands out as a scalable and cost-effective solution to enable the transition toward decarbonization. However, PV solar output, beyond the daily solar irradiance variability and unavailability during nights, is very sensitive to weather events like hurricanes. Hurricanes nucleate massive amounts of clouds around their centers, shading hundreds of kilometers in their path, reducing PV power output. This research proposes a spatiotemporal method, implemented in MATLAB R2023b coding, to estimate the shading effect of hurricanes over a wide distribution of PV solar plants connected to a high-voltage power infrastructure called the U.S.–Caribbean–South America super grid. The complete interconnection of the U.S., the Caribbean, and South America results in the lowest power valley levels, i.e., an overall percentual reduction in PV power output caused by hurricane shading. The simulations assess the impact of hurricanes in 10 synthetic trajectories spanning from Texas to Florida. The Caribbean would also experience lower power valleys with expanded interconnectivity schemes. The U.S.–Caribbean–South America super grid reduces Caribbean variability from 37.8% to 8.9% in the case study. The proposed spatiotemporal method for PV power profile estimation is a valuable tool for future solar power generation expansion, transmission planning, and system design considering the impact of hurricanes.

Impact of Hurricanes, Tropical Storms, and Coastal Extratropical Storms on Indoor Air VOC Concentrations

AbstractUnderstanding vapor intrusion (VI) temporal variability is key for the design of sampling strategies intended to assess reasonable maximum exposure of indoor air concentrations of volatile organic compounds (VOCs) as well as risk evaluation and mitigation planning. VI temporal variability has previously been shown to be dependent on the complex interactions of multiple independent variables—meteorological, hydrogeological, and human behavioral. Several meteorological variables, including barometric pressure, wind speed, and rainfall, are linked during tropical and extratropical storm events. High‐frequency meteorological and indoor VOC data from a series of seven tropical storms and four extratropical storms were collected at a single industrial building with multiple heating, ventilation, and air conditioning (HVAC) zones. The storms and sampling zones showed a variety of effects on trichloroethylene (TCE) concentrations in indoor air. In one zone (supply room), increases in TCE concentrations often, but not always, closely coincided with decreasing barometric pressure, sustained wind speeds over 32 km/h (20 mph), and differential pressures indicating subslab to indoor flow. A second zone, in a restroom, did not show a consistent pattern of temporal correlation between meteorological factors and indoor air concentrations. While peak indoor air concentrations may be associated with the passage of cyclonic storms at some sampling locations, this does not appear to be generalizable to all sampling locations. The observed increase in indoor air concentration potentially attributable to these storms is typically less than an order of magnitude and the duration ranges from a day to a week.

Catastrophic storms, forest disturbance, and the natural history of Swainson's warbler.

The core breeding range of Swainson's warbler (Limnothlypis swainsonii) overlaps a zone of exceptionally high tornado frequency in southeastern North America. The importance of tornadoes in creating breeding habitat for this globally rare warbler and other disturbance-dependent species has been largely overlooked. This paper estimates tornado frequency (1950-2021) and forest disturbance in the 240 counties and parishes in which breeding was documented from 1988 to 2014. The frequency of destructive tornadoes (EF1-EF5) varied 6-fold across the breeding range with a peak in the Gulf Coast states. Counties from east Texas to Alabama experienced the lowest median return interval of 5.4 years per 1000 km2, resulting in approximately 2477 ha of forest damage per 1000 km2 per century, based on current forestland cover. Tornadoes were significantly less frequent north and east of the core breeding range, with return intervals increasing to 9.1 years per 1000 km2 for breeding counties on the Atlantic coastal plain, 10.2 years per 1000 km2 in the Ozark Mountains, and 32.3 years per 1000 km2 in the Appalachian Mountains. Breeding counties within 150 km of the coastline from east Texas to North Carolina are also subjected to the highest frequency of hurricanes in the Western Hemisphere. Hurricanes often cause massive forest damage but archived meteorological and forestry data are insufficient to estimate the aggregate extent of forest disturbance in breeding counties. Nevertheless, the combined impact of tornadoes and hurricanes in the pre-Anthropogenic era was likely sufficient to produce a dynamic mosaic of early-successional forest crucial for the breeding ecology of Swainson's warbler. To ensure the long-term survival of this rare warbler, it is advisable to develop habitat management plans that incorporate remote sensing data on early-successional forest generated by catastrophic storms as well as anthropogenic activities.

Inequities in the Impacts of Hurricanes and Other Extreme Weather Events for Cancer Survivors.

In this minireview, we examine the impacts of hurricanes and other extreme weather events on cancer survivors, focusing on structural and social determinants of health. We briefly explore influences on biological, psychosocial, and behavioral outcomes and discuss risk and resilience factors in cancer survivorship during and after hurricanes. Our goal is to inform future directions for research that can identify areas in which we can most efficiently improve cancer outcomes and inform changes in health systems, clinical practice, and public health policies. This timely minireview provides researchers and clinicians with an overview of challenges and opportunities for improving disaster preparedness and response for cancer survivors.

Impacts of hurricanes and disease on Diadema antillarum in shallow water reef and mangrove locations in St John, USVI.

The 1983-1984 mortality event of the long-spined sea urchin Diadema antillarum reduced their population by up to 99% and was accompanied by a phase shift from coral dominated to algal dominated reefs in the Caribbean. Modest rebounds of D. antillarum populations in the Caribbean have been noted, and here we document the impacts of two major hurricanes (2017, Irma and Maria) and the 2022 disease outbreak on populations of D. antillarum found by targeted surveys in the urchin zone at nine fringing reef and three mangrove sites on St. John, USVI. D. antillarum populations at the reef sites had declined by 66% five months after the hurricanes but showed significant recovery just one year later. The impact of recent disease on these populations was much more profound, with all reef populations exhibiting a significant decline (96.4% overall). Fifteen months after the disease was first noted, D. antillarum at reef sites exhibited a modest yet significant recovery (15% pre-disease density). D. antillarum populations in mangrove sites were impacted by the hurricanes but exhibited much higher density than reef sites after the disease outbreak, suggesting that at D. antillarum in some locations may be less vulnerable to disease.

Wavelet Analysis and Forecasting using Open-Access Lumber Market Indices for Assessing the Impact of Hurricanes on Southern US Stumpage Prices

Abstract In the aftermath of events such as hurricanes, the economic impact of downed timber can reach billions of dollars. Accurate forecasting of stumpage prices after such events is crucial for maximizing recovery value while minimizing salvage costs. However, this poses challenges because of the inherent nature of the data. This study addresses these challenges by exploring the application of wavelet analysis, a novel approach in the field of forestry economic analysis. Wavelet analysis offers a unique framework for studying periodic phenomena in time series data, particularly when frequency changes over time are present. By leveraging wavelet analysis, this study uncovers relationships between timber market indices and hurricane seasons. The combination of traditional correlation analysis and wavelet coherence analysis enhances the statistical analysis in this study, offering a comprehensive examination of the relationship between the Timber Market Survey data and market indices. This innovative analytical approach enables a deeper understanding of the dynamics of the timber market and the potential effects of hurricanes on timber prices. Furthermore, this research highlights the recent advancements in wavelet methodology and the availability of open-source packages in the programming language R, such as WaveletComp and WaveletArima, that facilitate wavelet analysis and time series forecasting. The Wavelet-ARIMA model used in this study demonstrates its effectiveness in reducing noise and improving prediction accuracy. The study incorporates an extensive data set consisting of 10 Consumer Price Indices, 7 Producer Price Indices, 30 state-wide Timber Market Survey indices, 54 TMS subregions, and 6 open market series.