Factors that support public health infrastructure recovery in Puerto Rico and US Virgin Islands after Hurricanes Irma and Maria.

This paper describes the factors that support recovery of public health infrastructure (PHI), including conditions that facilitated or hindered recovery in United States (US) territories impacted by hurricanes Irma and Maria. A deductive approach was used to categorize data from five organizations that received crisis hurricane recovery (CHR) funds from the Centers for Disease Control and Prevention.* Spending was grouped into five infrastructure gaps: (1) human resources, (2) informatic upgrades, (3) equipment, (4) minor repairs, and (5) preventive maintenance. Unanticipated PHI costs, facilitators, and hinderances to PHI recovery were identified. Most (72 percent) of the $53,529,823 CHR funding was used to address infrastructure gaps in (1) human resources (56 percent), (2) informatics (16 percent), (3) equipment (13 percent), (4) minor repairs (10 percent), and (5) preventive maintenance (5 percent). Most of the requests (56 percent) to redirect funds were associated with unanticipated costs in initial work plans and budgets. The use of administrative partners, planning tools, dedicated staff, streamlined procedures, eg, contracts, and cost sharing facilitated PHI recovery. The most common hindrance to PHI recovery were delays in procurement and shipping. In summary, investments in dedicated funding to upgrade, repair, or replace critical structures and systems for infectious disease surveillance, laboratory capacity, vector control, environmental health inspections, and vaccine storage and administration in Puerto Rico and the US Virgin Islands after Hurricanes Irma and Maria contributed to their recovery capacity. These findings may inform funding and resource allocation considerations for PHI recovery in the US territories.

Combining multisensor images and social network data to assess the area flooded by a hurricane event.

In this study, multisensor remote sensing datasets were used to characterize the land use and land covers (LULC) flooded by Hurricane Willa which made landfall on October 24, 2018. The landscape characterization was done using an unsupervised K-means algorithm of a cloud-free Sentinel-2 MultiSpectral Instrument (MSI) image, acquired during the dry season before Hurricane Willa. A flood map was derived using the histogram thresholding technique over a Synthetic Aperture Radar (SAR) Sentinel-1 C-band and combined with a flood map derived from a Sentinel-2 MSI image. Both, the Sentinel-1 and Sentinel-2 images were obtained after Willa landfall. While the LULC map reached an accuracy of 92%, validated using data collected during field surveys, the flood map achieved 90% overall accuracy, validated using locations extracted from social network data, that were manually georeferenced. The agriculture class was the dominant land use (about 2,624 km2), followed by deciduous forest (1,591 km2) and sub-perennial forest (1,317 km2). About 1,608 km2 represents the permanent wetlands (mangrove, salt marsh, lagoon and estuaries, and littoral classes), but only 489 km2 of this area belongs to aquatic surfaces (lagoons and estuaries). The flooded area was 1,225 km2, with the agricultural class as the most impacted (735 km2). Our analysis detected the saltmarsh class occupied 541 km2in the LULC map, and around 328 km2 were flooded during Hurricane Willa. Since the water flow receded relatively quickly, obtaining representative imagery to assess the flood event was a challenge. Still, the high overall accuracies obtained in this study allow us to assume that the outputs are reliable and can be used in the implementation of effective strategies for the protection, restoration, and management of wetlands. In addition, they will improve the capacity of local governments and residents of Marismas Nacionales to make informed decisions for the protection of vulnerable areas to the different threats derived from climate change.

Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics.

Each year, an average of 45 tropical cyclones affect coastal areas and potentially impact forests. The proportion of the most intense cyclones has increased over the past four decades and is predicted to continue to do so. Yet, it remains uncertain how topographical exposure and tree characteristics can mediate the damage caused by increasing wind speed. Here, we compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide, encompassing field data for 22,176 trees and 815 species. We reconstructed the wind structure of those tropical cyclones to estimate the maximum sustained wind speed (MSW) and wind direction at the studied plots. Then, we used a causal inference framework combined with Bayesian generalised linear mixed models to understand and quantify the causal effects of MSW, topographical exposure to wind (EXP), tree size (DBH) and species wood density (ρ) on the proportion of damaged trees at the community level, and on the probability of snapping or uprooting at the tree level. The probability of snapping or uprooting at the tree level and, hence, the proportion of damaged trees at the community level, increased with increasing MSW, and with increasing EXP accentuating the damaging effects of cyclones, in particular at higher wind speeds. Higher ρ decreased the probability of snapping and to a lesser extent of uprooting. Larger trees tended to have lower probabilities of snapping but increased probabilities of uprooting. Importantly, the effect of ρ decreasing the probabilities of snapping was more marked for smaller than larger trees and was further accentuated at higher MSW. Our work emphasises how local topography, tree size and species wood density together mediate cyclone damage to tropical forests, facilitating better predictions of the impacts of such disturbances in an increasingly windier world.

Changes in fronts regulate nitrate cycling in Zhanjiang Bay: A comparative study during the normal wet season, rainstorm, and typhoon periods.

Typhoons and rainstorms (>250 mm/day) are extreme weather events changing hydrological characteristics and thus nitrogen (N) cycle in coastal waters. However, responses of N cycle to rainstorms and typhoons and their underlying mechanisms need to be elucidated. In this study, we conducted an analysis of a comparative dataset encompassing concentrations of nitrate (NO3-), ammonium (NH4+), dissolved oxygen (DO), chlorophyll a (Chl a), hydrological parameters, dual isotopic composition of NO3- (δ15N-NO3- and δ18O-NO3-) in Zhanjiang Bay during three distinct periods: the normal wet season, rainstorm, and typhoon periods. After the rainstorm, the salinity front in Zhanjiang Bay was more weakened and steadier than that during the normal wet season, mainly because onshore wind and a large amount of freshwater was inputted into the ocean surface. This weakened and steady salinity front strengthened water stratification and provided a favorable condition for phytoplankton blooms. Correspondingly, evident NO3- deficits coincided with elevated δ15N-NO3- and δ18O-NO3- values indicated that sufficient NO3- sustained phytoplankton blooms, leading to NO3- assimilation during the rainstorm period. By contrast, due to the onshore wind induced by the typhoon, the salinity front in Zhanjiang Bay was more intensified and unsteady after the typhoon than the normal wet season. The salinity front after the typhoon was unsteady enough to enhance vertical mixing in the water column. Relatively high DO concentrations suggested that enhanced vertical mixing after the typhoon support freshly organic matter decomposition and nitrification via oxygen injection from the air into the water column. In addition, NO3- deficits coincided with elevated δ15N-NO3- values and δ18O-NO3- values demonstrated the coexistence of NO3- assimilation during the typhoon period. This study suggests that the changing processes involved in NO3- cycling after typhoons and rainstorms are associated with the stability and intensity of the salinity front altered by these weather events.

Impact of climate-induced floods and typhoons on geriatric disabling health among older Chinese and Filipinos: a cross-country systematic review.

BACKGROUND: Apart from both China and the Philippines continuing to be exposed to and affected by different climate-induced hazards, in particular floods and typhoons, they are also reported to be witnessing rapid ageing populations of 60 years and older. As such, this systematic review synthesized the existing evidence about the impacts aggravated by floods and typhoons on the geriatric disabling health of older Chinese and Filipinos, respectively. METHODS: Four (4) electronic databases were systematically searched to identify eligible studies published between 2000 and early 2023. This process had to confirm the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA), as well as the standard protocol registered with PROSPERO (CRD42023420549). RESULTS: Out of 317 and 216 initial records retrieved for China and the Philippines, respectively, 27 (China) and 25 (Philippines) studies were eligible for final review. The disabling conditions they reported to affect the health of older adults were grouped into 4 categories: cognitive and intellectual, physical, chronic and terminal illnesses, and mental and psychological, with the latter identified as the most prevalent condition to affect older Chinese and Filipinos. On a sub-category level, posttraumatic stress disorder (PTSD) was the most common condition reported in 27 flood-related studies in China, while injuries and wounds prevailed in the Philippines, according to 25 typhoon-related studies. CONCLUSION: The increasing occurrence of extreme climate hazards, especially floods and typhoons in China and the Philippines, respectively, impacted the health of their older adults with various disabling effects or conditions. Therefore, this calls for appropriate geriatric-informed interventions in the context of climate change and rapidly ageing settings beyond China and the Philippines to others that are also prone to floods and typhoons.

Typhoon disaster emergency forecasting method based on big data.

Typhoons are natural disasters characterized by their high frequency of occurrence and significant impact, often leading to secondary disasters. In this study, we propose a prediction model for the trend of typhoon disasters. Utilizing neural networks, we calculate the forgetting gate, update gate, and output gate to forecast typhoon intensity, position, and disaster trends. By employing the concept of big data, we collected typhoon data using Python technology and verified the model's performance. Overall, the model exhibited a good fit, particularly for strong tropical storms. However, improvements are needed to enhance the forecasting accuracy for tropical depressions, typhoons, and strong typhoons. The model demonstrated a small average error in predicting the latitude and longitude of the typhoon's center position, and the predicted path closely aligned with the actual trajectory.

Long-term Impact of Tropical Cyclones on Disease Exacerbation Among Children with Asthma in the Eastern United States, 2000-2018.

BACKGROUND: Tropical cyclones are associated with acute increases in mortality and morbidity, but few studies have examined their longer-term health consequences. We assessed whether tropical cyclones are associated with a higher frequency of symptom exacerbation among children with asthma in the following 12 months in eastern United States counties, 2000-2018. METHODS: We defined exposure to tropical cyclones as a maximum sustained windspeed >21 meters/second at the county center and used coarsened exact matching to match each exposed county to one or more unexposed counties. We used longitudinal, de-identified administrative claims data to estimate the county-level, monthly risk of experiencing at least one asthma exacerbation requiring medical attention among commercially insured children aged 5-17 with prior diagnosis of asthma. We used a difference-in-differences approach implemented via a Poisson fixed effects model to compare the risk of asthma exacerbation in the 12 months before versus after each storm in exposed versus unexposed counties. RESULTS: Across 43 tropical cyclones impacting the eastern United States, we did not observe evidence of an increase in the risk of symptom exacerbation in the 12 months following the storm (random-effects meta-analytic summary estimate: risk ratio = 1.03 [95% confidence interval = 0.96, 1.10], I2 = 17%). However, certain storms, such as Hurricane Sandy, were associated with a higher risk of symptom exacerbation. CONCLUSIONS: These findings are consistent with the hypothesis that some tropical cyclones are detrimental to children's respiratory health. However, tropical cyclones were not associated in aggregate with long-term exacerbation of clinically apparent asthma symptoms among a population of children with commercial health insurance.

Ocean state rising: Storm simulation and vulnerability mapping to predict hurricane impacts for Rhode Island's critical infrastructure.

Predicting the consequences of a major coastal storm is increasingly difficult as the result of global climate change and growing societal dependence on critical infrastructure (CI). Past storms are no longer a reliable predictor of future weather events, and the traditional approach to vulnerability assessment presents accumulated loss in largely quantitative terms that lack the specificity local emergency managers need to develop effective plans and mitigation strategies. The Rhode Island Coastal Hazards Modeling and Prediction (RI-CHAMP) system is a geographic information system (GIS)-based modeling tool that combines high-resolution storm simulations with geolocated vulnerability data to predict specific consequences based on local concerns about impacts to CI. This case study discusses implementing RI-CHAMP for the State of Rhode Island to predict impacts of wind and inundation on its CI during a hurricane, tropical storm, or nor'easter. This paper addresses the collection and field verification of vulnerability data, along with RI-CHAMP's process for integrating those data with storm models. The project deeply engaged end-users (emergency managers, facility managers, and other stakeholders) in developing RI-CHAMP's ArcGIS Online dashboard to ensure it provides specific, actionable data. The results of real and synthetic storm models are presented along with discussion of how the data in these simulations are being used by state and local emergency managers, facility owners, and others.

Microorganisms uniquely capture and predict stony coral tissue loss disease and hurricane disturbance impacts on US Virgin Island reefs.

Coral reef ecosystems are now commonly affected by major climate and disease disturbances. Disturbance impacts are typically recorded using reef benthic cover, but this may be less reflective of other ecosystem processes. To explore the potential for reef water-based disturbance indicators, we conducted a 7-year time series on US Virgin Island reefs where we examined benthic cover and reef water nutrients and microorganisms from 2016 to 2022, which included two major disturbances: hurricanes Irma and Maria in 2017 and the stony coral tissue loss disease outbreak starting in 2020. The disease outbreak coincided with the largest changes in the benthic habitat, with increases in the percent cover of turf algae and Ramicrusta, an invasive alga. While sampling timepoint contributed most to changes in reef water nutrient composition and microbial community beta diversity, both disturbances led to increases in ammonium concentration, a mechanism likely contributing to observed microbial community shifts. We identified 10 microbial taxa that were sensitive and predictive of increasing ammonium concentration. This included the decline of the oligotrophic and photoautotrophic Prochlorococcus and the enrichment of heterotrophic taxa. As disturbances impact reefs, the changing nutrient and microbial regimes may foster a type of microbialization, a process that hastens reef degradation.

Immediate Injury Deaths Related to the Remnants From Hurricane Ida in New York City, September 1-2, 2021.

The remnants from Hurricane Ida in September 2021 caused unprecedented rainfall and inland flooding in New York City (NYC) and resulted in many immediate deaths. We reviewed death records (electronic death certificates and medical examiner reports) to systematically document the circumstances of death and demographics of decedents to inform injury prevention and climate adaptation actions for future extreme precipitation events. There were 14 Ida-related injury deaths in NYC, of which 13 (93%) were directly caused by Ida, and 1 (7%) was indirectly related. Most decedents were Asian (71%) and foreign-born (71%). The most common circumstance of death was drowning in unregulated basement apartments (71%). Themes that emerged from the death records review included the suddenness of flooding, inadequate exits, nighttime risks, and multiple household members were sometimes affected. These deaths reflect interacting housing and climate crises, and their disproportionate impact on disadvantaged populations needing safe and affordable housing. Climate adaptation actions, such as improving stormwater management infrastructure, informing residents about flood risk, implementing Federal Emergency Management Agency recommendations to make basements safer, and expanding emergency notification measures can mitigate risk. As climate change increases extreme precipitation events, multi-layered efforts are needed to keep residents safe.

Evacuation and Health Care Outcomes Among Assisted Living Residents After Hurricane Irma.

Importance: Evacuation has been found to be associated with adverse outcomes among nursing home residents during hurricanes, but the outcomes for assisted living (AL) residents remain unknown. Objective: To examine the association between evacuation and health care outcomes (ie, emergency department visits, hospitalizations, mortality, and nursing home visits) among Florida AL residents exposed to Hurricane Irma. Design, Setting, and Participants: Retrospective cohort study using 2017 Medicare claims data. Participants were a cohort of Florida AL residents who were aged 65 years or older, enrolled in Medicare fee-for-service, and resided in 9-digit zip codes corresponding to US assisted living communities with 25 or more beds on September 10, 2017, the day of Hurricane Irma's landfall. Propensity score matching was used to match evacuated residents to those that sheltered-in-place based on resident and AL characteristics. Data were analyzed from September 2022 to February 2024. Exposure: Whether the AL community evacuated or sheltered-in-place before Hurricane Irma made landfall. Main Outcomes and Measures: Thirty- and 90-day emergency department visits, hospitalizations, mortality, and nursing home admissions. Results: The study cohort included 25 130 Florida AL residents (mean [SD] age 81 [9] years); 3402 (13.5%) evacuated and 21 728 (86.5%) did not evacuate. The evacuated group had 2223 women (65.3%), and the group that sheltered-in-place had 14 556 women (67.0%). In the evacuated group, 42 residents (1.2%) were Black, 93 (2.7%) were Hispanic, and 3225 (94.8%) were White. In the group that sheltered in place, 490 residents (2.3%) were Black, 707 (3.3%) were Hispanic, and 20 212 (93.0%) were White. After 1:4 propensity score matching, when compared with sheltering-in-place, evacuation was associated with a 16% greater odds of emergency department visits (adjusted odds ratio [AOR], 1.16; 95% CI, 1.01-1.33; P = .04) and 51% greater odds of nursing home visits (AOR, 1.51; 95% CI, 1.14-2.00; P = .01) within 30 days of Hurricane Irma's landfall. Hospitalization and mortality did not vary significantly by evacuation status within 30 or 90 days after the landfall date. Conclusions and Relevance: In this cohort study of Florida AL residents, there was an increased risk of nursing home and emergency department visits within 30 days of Hurricane Irma's landfall among residents from communities that evacuated before the storm when compared with residents from communities that sheltered-in-place. The stress and disruption caused by evacuation may yield poorer immediate health outcomes after a major storm for AL residents. Therefore, the potential benefits and harms of evacuating vs sheltering-in-place must be carefully considered when developing emergency planning and response.

Hurricanes pose a substantial risk to New England forest carbon stocks.

Nature-based climate solutions (NCS) are championed as a primary tool to mitigate climate change, especially in forested regions capable of storing and sequestering vast amounts of carbon. New England is one of the most heavily forested regions in the United States (>75% forested by land area), and forest carbon is a significant component of climate mitigation policies. Large infrequent disturbances, such as hurricanes, are a major source of uncertainty and risk for policies relying on forest carbon for climate mitigation, especially as climate change is projected to alter the intensity and extent of hurricanes. To date, most research into disturbance impacts on forest carbon stocks has focused on fire. Here, we show that a single hurricane in the region can down between 121 and 250 MMTCO2e or 4.6%-9.4% of the total aboveground forest carbon, much greater than the carbon sequestered annually by New England's forests (16 MMTCO2e year-1). However, emissions from hurricanes are not instantaneous; it takes approximately 19 years for downed carbon to become a net emission and 100 years for 90% of the downed carbon to be emitted. Reconstructing hurricanes with the HURRECON and EXPOS models across a range of historical and projected wind speeds, we find that an 8% and 16% increase in hurricane wind speeds leads to a 10.7- and 24.8-fold increase in the extent of high-severity damaged areas (widespread tree mortality). Increased wind speed also leads to unprecedented geographical shifts in damage, both inland and northward, into heavily forested regions traditionally less affected by hurricanes. Given that a single hurricane can emit the equivalent of 10+ years of carbon sequestered by forests in New England, the status of these forests as a durable carbon sink is uncertain. Understanding the risks to forest carbon stocks from disturbances is necessary for decision-makers relying on forests as a NCS.

Characteristics of mesoscale eddies in the Mozambique Channel.

The mesoscale eddy characteristics of the Mozambique Warm Current were investigated by detecting and tracking satellite altimetry data from 2010 to 2019. A total of 1,086 eddies were identified in the Mozambique Channel, comprising 509 cyclonic eddies and 577 anticyclonic eddies. The results revealed that the bay area on the northwest coast of Madagascar was the main hotspot of eddy generation, and the mean amplitude and radius of the anticyclonic eddies in the Mozambique Channel were 24.23 cm and 82.7 km, respectively, which are larger than those of the cyclonic eddies. Local wind forcing had a significant impact on the formation of mesoscale eddies in the Mozambique Channel. In winter, the wind stress in the northern and southern areas of the Mozambique Channel exhibited a strong correlation with the distribution of eddy kinetic energy (EKE), where both monsoonal winds in the north and trade winds in the south could facilitate mesoscale anticyclonic eddy formation. In addition, the variability in the number of anticyclonic and cyclonic eddies in the Mozambique Channel may have exerted a significant influence on the seasonal anomalous fluctuations in local sea surface temperatures (SSTs). This study presented a novel analysis of the mesoscale eddy characteristics in the Mozambique Channel.

Impact of typhoons on anthropogenic nitrogen sources in Lake Sihwa, South Korea.

This study investigated the nitrate dual isotopic compositions (δ15NNO3 and δ18ONO3) of water samples to trace nitrate sources in Lake Sihwa, which encompasses various land-use types (e.g., urban, industry, wetland, and agriculture). The biogeochemical interactions of anthropogenic nitrogen sources (e.g., soil, road dust, and septic water) were also evaluated through multiple pathways from terrestrial boundaries to the water column. Based on increased concentrations of dissolved total nitrogen (DTN; 3.1 ± 1.6 mg/L) after typhoon, the variation of element stoichiometry (N:P:Si) in this system shifted to the relatively N-rich conditions (DIN/DIP; 14.1 ± 8.1, DIN/DSi; 1.4 ± 1.8), potentially triggering the occurrence of harmful algal blooms. Furthermore, discriminative isotopic compositions (δ15NNO3; 4.0 ± 2.1 ‰, δ18ONO3; 6.1 ± 4.3 ‰) after the typhoon suggested the increased DTN input of anthropogenic origins within Lake Sihwa would be mainly transported from urban sources (76 ± 9 %). Consequently, the isotopic-based approach may be useful for effective water quality management under increased anthropogenic activities near aquatic systems.

Hurricanes are limiting the mangrove canopy heights in the Gulf of Mexico.

Mangrove canopy height (MCH) has been described as a leading characteristic of mangrove forests, protecting coastal economic interests from hurricanes. Meanwhile, winter temperature has been considered the main factor controlling the MCH along subtropical coastlines. However, the MCH in Cedar Key, Florida (∼12 m), is significantly higher than in Port Fourchon, Louisiana (∼2.5 m), even though these two subtropical locations have similar winter temperatures. Port Fourchon has been more frequently impacted by hurricanes than Cedar Key, suggesting that hurricanes may have limited the MCH in Port Fourchon rather than simply winter temperatures. This hypothesis was evaluated using novel high-resolution remote sensing techniques that tracked the MCH changes between 2002 and 2023. Results indicate that hurricanes were the limiting factor keeping the mean MCH at Port Fourchon to <1 m (2002-2013), as the absence of hurricane impacts between 2013 and 2018 allowed the mean MCH to increase by 60 cm despite the winter freezes in Jan/2014 and Jan/2018. Hurricanes Zeta (2020) and Ida (2021) caused a decrease in the mean MCH by 20 cm, breaking branches, defoliating the canopy, and toppling trees. The mean MCH (∼1.6 m) attained before Zeta and Ida has not yet been recovered as of August 2023 (∼1.4 m), suggesting a longer-lasting impact (>4 years) of hurricanes on mangroves than winter freezes (<1 year). The high frequency of hurricanes affecting mangroves at Port Fourchon has acted as a periodic "pruning," particularly of the tallest Avicennia trees, inhibiting their natural growth rates even during quiet periods following hurricane events (e.g., 12 cm/yr, 2013-2018). By contrast, the absence of hurricanes in Cedar Key (2000-2020) has allowed the MCH to reach 12 m (44-50 cm/yr), implying that, besides the winter temperature, the frequency and intensity of hurricanes are important factors limiting the MCH on their latitudinal range limits in the Gulf of Mexico.

Can cyclone exposure explain behavioural and demographic variation among lemur species?

Madagascar has a harsh and stochastic climate because of regular natural disturbances. This history of regular cyclones has been hypothesised to have directed evolutionary changes to lemur behaviour and morphology that make them more resilient to sudden environmental change. These adaptations may include: small group sizes, high degrees of energy-conserving behaviours, generalist habitat use, small home ranges, small body size, and a limited number of frugivorous species. To date, however, no one has tested how variation in cyclone exposure across Madagascar is associated with variation in these resilience traits. In this study, we created a detailed cyclone impact map for Madagascar using Koppen-Geiger climate class, historical cyclone tracks, the Saffir Class of cyclone and hurricane intensity, and precipitation data. We also used existing literature to calculate a resilience score for 26 lemur species for which data existed on resilience traits. Our cyclone impact map was then overlaid on known geographic ranges of these species and compared to resilience score while controlling for phylogenetic non-independence and spatial autocorrelation. We found no association between cyclone impact in a lemur range and their resilience score. When assessing traits individually, however, we found that cyclone impact was positively associated with body size, suggesting that the more impacted a species is by cyclones the smaller they are. We also found cyclone impact to be negatively associated with frugivory, with species in higher impact zones eating more fruit. While unexpected, this could reflect an increased production in fruit in tree fall gaps following cyclones. While we did not find a pattern between cyclone impact and behavioural resilience in lemurs, we suggest a similar study at a global scale across all primates would allow for more taxonomic variation and reveal larger patterns key to understanding past and future vulnerability to natural disturbances in primates.

Memristor-based circuit design of episodic memory neural network and its application in hurricane category prediction.

Episodic memory, as a type of long-term memory (LTM), is used to learn and store the unique personal experience. Based on the episodic memory biological mechanism, this paper proposes a bionic episodic memory memristive neural network circuit. The proposed memristive neural network circuit includes a neocortical module, a parahippocampal module and a hippocampus module. The neocortical module with the two paths structure is used to receive the sensory signal, and is also used to separate and transmit the spatial information and the non-spatial information involved in the sensory signal. The parahippocampal module is composed of the parahippocampal cortex-MEA and the perirhinal cortex-LEA, which receives the two types of information from the neocortical module respectively. As the last module, the hippocampus module receives and integrates the output information of the parahippocampal module as well as generates the corresponding episodic memory. Meanwhile, the specific scenario information with the certain temporal signal from the generated episodic memory is also extracted by the hippocampus module. The simulation results in PSPICE show that the proposed memristive neural network circuit can generate the various episodic memories and extract the specific scenario information successfully. By configuring the memristor parameters, the proposed bionic episodic memory memristive neural network circuit can be applied to the hurricane category prediction, which verifies the feasibility of this work.