Tropical Storm Research Articles

Waterborne Infectious Diseases Associated with Exposure to Tropical Cyclonic Storms, United States, 1996-2018.

In the United States, tropical cyclones cause destructive flooding that can lead to adverse health outcomes. Storm-driven flooding contaminates environmental, recreational, and drinking water sources, but few studies have examined effects on specific infections over time. We used 23 years of exposure and case data to assess the effects of tropical cyclones on 6 waterborne diseases in a conditional quasi-Poisson model. We separately defined storm exposure for windspeed, rainfall, and proximity to the storm track. Exposure to storm-related rainfall was associated with a 48% (95% CI 27%-69%) increase in Shiga toxin-producing Escherichia coli infections 1 week after storms and a 42% (95% CI 22%-62%) in increase Legionnaires' disease 2 weeks after storms. Cryptosporidiosis cases increased 52% (95% CI 42%-62%) during storm weeks but declined over ensuing weeks. Cyclones are a risk to public health that will likely become more serious with climate change and aging water infrastructure systems.

Modeling tropical storm Elsa: Flood map simulation using multisensory precipitation in Connecticut

A flood map simulation in the Fenton River watershed, Connecticut, was conducted for Tropical Storm Elsa occurred in early July 2021, using Multi Radar Multi Sensor-Quantitative Precipitation Estimation (MRMS-QPE) as input to force the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) to simulate discharges in the mainstream of the watershed. The simulated discharges were calibrated using observed discharges at the Old Turnpike Bridge USGS station, and they were used to force a Hydrologic Engineering Center-River Analysis System (HEC-RAS) 2D model of the Fenton River watershed. The simulated stages were calibrated using observed stages at Old Turnpike Bridge USGS station to simulate flood maps in the mainstream of the watershed. The resulting use of HEC-HMS and HEC-RAS 2D models coupled with MRMS-QPE precipitation shows that these models set up is user-friendly. The model shows stability and the capacity to simulate flood maps along the whole mainstream of the Fenton River with good accuracy.

Risk perception—A lens for understanding adaptive behaviour in the age of climate change? Narratives from the Global South

Risk perception, a judgment of how risk is perceived individually and communicated scientifically, helps clarify local knowledge, know-how, and experience. However, community disaster preparedness and DRR planning have often overlooked community risk perception. This study investigates how risk perception at the community level influences and facilitates local DRR initiatives. It explores southwest Bangladesh as a case study, an area highly vulnerable to climatic impacts such as tropical storms, floods, and rising sea-level. This study analyses media and local narratives, in-depth interviews, and field observations. We find that community risk perception (a) tends to be ignored in local adaptation planning and (b) is shaped by four influential factors relating to local residents: their varied interests; socio-economic opportunities; attachment to place and community; and risk-tolerance capacity. The study advances theory in revealing a dynamic relationship among perception of risk, adaptive behaviour, and tolerance of risk. Practically, it helps local authorities in southwest Bangladesh and similar locales enhance DRR practices by considering community experiences and perceptions of risk. Though oft challenged by the contemporary socio-political dynamics of the Global South, transferring knowledge of risk perception to inform decision-making is critical to strategic planning in the face of disasters.

Learnings from rapid response efforts to remotely detect landslides triggered by the August 2021 Nippes earthquake and Tropical Storm Grace in Haiti

AbstractOn August 14, 2021, a Mw 7.2 earthquake struck the Tiburon Peninsula of western Haiti triggering thousands of landslides. Three days after the earthquake on August 17, 2021, Tropical Storm Grace crossed shallow waters offshore of southern Haiti triggering more landslides worsening the situation. In the aftermath of these events, several organizations with disaster response capabilities or programs activated to provide information on the location of landslides to first responders on the ground. Utilizing remote sensing to support rapid response, one organization manually mapped initiation point of landslides and three automatically detected landslides. The 2021 Haiti event also provided a unique opportunity to test different automated landslide detection methods that utilized both SAR and optical data in a rapid response scenario where rapid situational awareness was critical. As the methods used are highly replicable, the main goal of this study is to summarize the landslide rapid response products released by the organizations, detection methods, quantify accuracy and provide guidelines on how some of the shortcomings encountered in this effort might be addressed in the future. To support this validation, a manually mapped polygon-based landslide inventory covering the entire affected area was created and is also released through this effort.

Characterization of the 2012 and 2013 Metro Manila “Enhanced Habagat” Heavy Rainfall Events

The strong southwest monsoon episodes in August 2012 and 2013, locally referred to as the “Enhanced Habagat ” 2012 and 2013, respectively, resulted in rainfall that exceeded the monthly mean rainfall of the affected regions along western Luzon, including Metro Manila. The prolonged heavy rainfall events were the result of tropical cyclone enhancement of the monsoon winds. The synoptic environment in the two events was characterized by the deepening of the Asian monsoon trough depicted by the zonally-oriented and eastward-extended 1000 hPa isobar. The deep troughs were caused each year by a combination of tropical cyclones located to the northeast of the Philippines – Typhoon Haikui in 2012 and Severe Tropical Storm Trami in 2013, and remnant tropical cyclones in the northern South China Sea region that formed several days prior to the enhanced Habagat episodes. The monsoon trough induced a low-level westerly jet in the South China Sea toward the western Luzon region that transported a narrow stream of moisture-laden air mass to Metro Manila and surrounding areas. Consequently, heavy precipitation ensued. Analysis showed the remnant lows are as important as the enhancing tropical cyclones Haikui and Trami in inducing the westerly jets. Removal of the enhancing tropical cyclones in numerical model simulations still showed a relatively deep monsoon trough that led to heavy rainfall along western Luzon. In addition, the intrusion of low potential vorticity area to the eastern and northern flanks of the tropical cyclones facilitated the strengthening of the steering ridge that resulted in their westward and slow translational motion, making the events last for several days and exacerbating the impacts. Furthermore, the Madden-Julian Oscillation possibly serves as a precursor to heavy rainfall events. Compounded with a populous megacity, understanding the mechanisms that lead to these extreme hazards is vital to future forecasting and disaster risk management of similar events.

A test of adaptive strategy theory using fifteen years of change in coral abundance

Universal Adaptive Strategy Theory aims to predict how taxa and assemblages respond to disturbances on the basis of adaptive strategy group (ASG) membership. Here, we test such predictions using the adaptive strategy scheme for reef-building corals developed by Darling et al. (Ecol Lett 15:1378–1386, 2012) and a long-term dataset of coral assemblage structure from inshore reefs on the central Great Barrier Reef. Several disturbances including mass bleaching and tropical storms were recorded in this 15-year interval from 1998 to 2013. ASG membership did not predict how a given taxon responded to disturbance. In fact, all ASGs were on average equally affected by bleaching and a period of multiple disturbances. Furthermore, there were no consistent winners at these sites in response to the 1998 bleaching in contrast to previous work suggesting clear hierarchies in susceptibility to bleaching. In conclusion, while further efforts to re-evaluate the utility of ASGs for reef corals should be encouraged our results and a re-examination of the literature suggests that direct trait-based approaches might prove more useful when exploring how corals respond to disturbance.

Examining data imbalance in crowdsourced reports for improving flash flood situational awareness

The use of crowdsourced data has been finding practical use for enhancing situational awareness during disasters. While recent studies have shown promising results regarding the potential of crowdsourced data (such as user-generated flood reports) for flash flood mapping and situational awareness, little attention has been paid to data imbalance issues that could introduce biases in data and assessment. To address this gap, in this study, we examine biases present in crowdsourced reports to identify data imbalance with a goal of improving disaster situational awareness. Three biases are examined: sample bias, spatial bias, and demographic bias. To examine these biases, we analyzed reported flooding from 3-1-1 reports (which is a citizen hotline allowing the community to report problems such as flooding) and Waze reports (which is a GPS navigation app that allows drivers to report flooded roads) with respect to FEMA damage data collected in the aftermaths of Tropical Storm Imelda in Harris County, Texas, in 2019 and Hurricane Ida in New York City in 2021. First, sample bias is assessed by expanding the flood-related categories in 3-1-1 reports. Integrating other flooding related topics into the Global Moran's I and Local Indicator of Spatial Association (LISA) revealed more communities that were impacted by floods. To examine spatial bias, we perform the LISA and BI-LISA tests on the data sets—FEMA damage, 3-1-1 reports, and Waze reports—at the census tract level and census block group level. By looking at two geographical aggregations, we found that the larger spatial aggregations, census tracts, show less data imbalance in the results. Through a regression analysis, we found that 3-1-1 reports and Waze reports have data imbalance limitations in areas where minority populations and single parent households reside. The findings of this study advance understanding of data imbalance and biases in crowdsourced datasets that are growingly used for disaster situational awareness. Through addressing data imbalance issues, researchers and practitioners can proactively mitigate biases in crowdsourced data and prevent biased and inequitable decisions and actions.

Changes in the spatial distribution of total phosphorus in sediment and water column of a shallow subtropical lake

Julian PJ II, Schafer T, Cohen MJ, Jones P, Osborne TZ. 2023. Changes in the spatial distribution of total phosphorus in sediment and water column of a shallow subtropical lake. Lake Reserv Manage. 39:213–230. In shallow lakes, interactions between bed sediment and the water column strongly influence the availability and transport of nutrients. Lake Okeechobee, in South Florida, is a eutrophic, shallow, polymictic lake that exhibits frequent mixing of the water column and resuspension of unconsolidated sediments. The objective of this study was to evaluate the temporal and spatial patterns of water and sediment characteristics and evaluate the linkage of sediment and water column total phosphorus (TP) concentrations. Spatiotemporal generalized additive models identified key periods during which both surface water total suspended solids (TSS) and TP increased, corresponding to hurricane and tropical storm activity. Our models identified regions with persistently greater water column concentrations than the lake average, indicating potential hot spots in resuspension processes and/or internal loading. Sediment bulk density (BD) and TP were inversely correlated, indicating that light, less dense sediments have greater TP concentrations, potentially contributing to the redistribution of sediments and P. An integrated evaluation of surface water and sediment characteristics using a generalized additive model revealed that sediment TP strongly influences water column TP concentrations, potentially explaining the spatial distribution of surface water TP concentrations. The area of the lake with sediment TP concentration ≤500 mg/kg is increasing while the low bulk density sediments area is decreasing, marking a potential improvement in sediment conditions. If this trend persists, it indicates that the lake is increasingly storing P in sediments that can resist entrainment, with significant implications for assessing the trajectory of lake restoration.

Climatology of Tropical Cyclone Rainfall Magnitude at Different Landfalling Stages: An Emphasis on After-Landfall Rain

Abstract Estimating the magnitude of tropical cyclone (TC) rainfall at different landfalling stages is an important aspect of the TC forecast that directly affects the level of response from emergency managers. In this study, a climatology of the TC rainfall magnitude as a function of the location of the TC centers within distance intervals from the coast and the percentage of the raining area over the land is presented on a global scale. A total of 1834 TCs in the period from 2000 until 2019 are analyzed using satellite information to characterize the precipitation magnitude, volumetric rain, rainfall area, and axial-symmetric properties within the proposed landfalling categories, with an emphasis on the postlandfall stages. We found that TCs experience rainfall maxima in regions adjacent to the coast when more than 50% of their rainfall area is over the water. TC rainfall is also analyzed over the entire TC extent and the portion over land. When the total extent is considered, rainfall intensity, volumetric rain, and rainfall area increase with wind speed intensity. However, once it is quantified over the land only, we found that rainfall intensity exhibits a nearly perfect inversely proportional relation with the increase in TC rainfall area. In addition, when a TC with life maximum intensity of a major hurricane makes landfall as a tropical depression or tropical storm, it usually produces the largest spatial extent and the highest volumetric rain. Significant Statement This study aims to describe the cycle of tropical cyclone (TC) precipitation magnitude through a new approach that defines the landfall categories as a function of the percentage of the TC precipitating area over the land and ocean, along with the location of the TC centers within distance intervals from the coast. Our central hypothesis is that TC rainfall should exhibit distinct features in the long-term satellite time series for each of the proposed stages. We particularly focused on the overland events due to their effects on human activities, finding that the TCs that at some point of their life cycle reached major hurricane strength and made landfall as a tropical storm or tropical depression produced the highest volumetric rain over the land surface. This research also presents key observational evidence of the relationship between the rain rate, raining area, and volumetric rain for landfalling TCs.

Post-disaster recovery and bargaining with patriarchy: A case study from Macapaya, Philippines

In disaster policy and implementation, there is a tendency to overestimate what women gain from participating in disaster recovery efforts and underestimate the true cost of gender impacts. This qualitative study's objective is to highlight the inadequacies of disaster recovery and reconstruction policies in the Philippines as experienced by women after Tropical Storm Sendong (international name Washi). To achieve these aims, we conducted 20 online interviews with women from the National Housing Authority-Cagayan de Oro Bayanihan Village Phase 1 or Macapaya in Cagayan de Oro City, Philippines. Using Kandiyoti's patriarchal bargaining as a framework, we argue that a more nuanced understanding of how women navigate post-disaster spaces helps deconstruct the binary framing of women as victims or agents, and understand how women cope with gender-insensitive recovery responses. It also paves the way for post-disaster policies and programs that are holistic and have greater potential to positively impact women's lives.

Flood Susceptibility Mapping Using Logistic Regression Analysis In Lam Khan Chu Watershed, Chaiyaphum Province, Thailand

Due to Tropical Storm Dianmu’s influence in the Lam Khan Chu watershed (LKCW) area, central Thailand saw its worst flood in 50 years from September 23 to September 28, 2021. The flooding lasted for 1-2 months. The objective of this research is to study flood susceptibility using logistic regression analysis in LCKW area. According to the study 11 floods occurred repeatedly between 2005 and 2021, in the southern of Bamnetnarong district and continued northeast to Chaturat district and Bueng Lahan swamp. These areas are the main waterways of the LKCW area, the Lam Khan Chu stream and the Huai Khlong Phai Ngam, for which the dominant flow patterns are braided streams. The main factors influencing flooding are geology, stream frequency, topographic wetness index, drainage density, soil, stream power index, land-use, elevation, mean annual precipitation, aspect, distance to road, distance to village, and distance to stream. The results of the logistic regression analysis shed light on these factors. All such variables were demonstrated by the β value coefficient. The area’s susceptibility to flooding was projected on a map, and it was discovered to have extremely high and high levels of susceptibility, encompassing regions up to 148.308 km2 (8.566%) and 247.421 km2 (14.291%), respectively, in the vicinity of the two main river sides of the watershed. As a result of this research the flood susceptibility map will be used as a guideline for future flood planning and monitoring.

Penyediaan Rumah Panggung Aman Banjir Bagi Mama Elisabeth Seuk Bete, Warga Jemaat GMIT Ebenhaezer Motaulun Kecamatan Malaka Barat Kabupaten Malaka

The flash flood that hit Malacca district as a result of the tropical cyclone storm in several areas of NTT, had a very severe impact on one village, namely Motaulun, located in West Malacca District, Malacca Regency. Motaulun village was the first door to receive water shipments because the embankment was broken not far from the village. Flash flooding is a flood that occurs suddenly and unexpectedly due to very heavy rain in the upstream or due to a broken dam.
 Flash floods with a height of more than 1 meter of water up to the height of an adult's neck for a full day, not only washed away the houses of Motaulun villagers, but also washed away all their belongings. One of the groups affected is the GMIT Ebenhaezer Motaulun Congregation which has 125 families with 270 people.
 The only house out of 62 houses located in Lookmi A hamlet is the house belonging to Elisabeth Seuk Bete (78 years old), which was washed away by the flood and all her belongings. The provision of flood-safe stilt houses has the aim of easing the burden of victims while at the same time realizing UKAW's care and concern through community service activities.

DAMPAK SIKLON TROPIS SEROJA TERHADAP KETERSEDIAA N PAKAN, TEMPAT BERSARANG DAN PENDAPATAN PETANI LEBAH MADU HUTAN (Apis dorsata)

This study aims to determine the impact of the availability of forest honey bee feed, the impact of forest honey bee nesting sites and the impact of changes in the income of forest honey bee farmers in Uiasa Village, Semau District, Kupang Regency, East Nusa Tenggara before and after the Tropical Cyclone Storm Seroja. This research was carried out around the Pastello Amalato Protected Forest Area, Uiasa Village, Semau District, Kupang Regency, which was carried out for 1 month, from April to May 2022. This research used purposive sampling, interview and roaming methods with data analysis using descriptive qualitative and descriptive analysis. quantitative.The impact of food availability after the Seroja Tropical Cyclone Storm consists of two factors, namely reduced forage for forest honey bees and disruption of the flowering time of forage plants. The impact of forest honey bee nesting after the Seroja tropical cyclone storm was influenced by the movement of forest honey bee hives from their original place to another and the altitude where the forest honey bee nest trees were found was mostly at an altitude of 25-45 meters above sea level compared to other elevations with the number of honey bee trees is 10 trees and 7 trees at an altitude of 0-25 meters above sea level.

Duality, Paradox and Confusion in Joseph Conrad’s “Freya of the Seven Isles”

This paper examines Conrad’s use of duality in “Freya of the Seven Isles” in relation to fundamental aspects of the text such as setting, narrative voice, characterisation and themes, and it explores the way in which the play on opposites creates a tension from the beginning between comedy and tragedy. The exotic setting in the Dutch East Indies, far from the rules of ordered society, in shallow waters between land and sea where sudden tropical storms are common, makes this account of diabolic revenge seem credible. The first-person narrator, whose ironic tone serves to free him from responsibility, as well as to distance him emotionally from the catastrophe, reassures us and alarms us in turn, revealing the contradiction between his original light-hearted view of events and his knowledge of their terrible outcome. Main characters form interchanging pairs, and all characters are paradoxical, so that they often misjudge each other and themselves. Although “Freya” is a love story, love and pride are inseparable, and pride perhaps has a greater role in the unfolding drama than love. There is also a close connection between pride and humour, with constant references to smiles and laughter, but little genuine mirth. Laughter has multiple functions, being used as a weapon or a defence mechanism reflecting ignorance, shame, fear and embarrassment. At the end nobody is laughing, except conceivably the scorned lover who precipitates the tragedy, and the reader is left ruminating causes and consequences in a tangle of possibilities.

Exploring the Real-Time WRF Forecast Skill for Four Tropical Storms, Isaias, Henri, Elsa and Irene, as They Impacted the Northeast United States

Tropical storm Isaias (2020) moved quickly northeast after its landfall in North Carolina and caused extensive damage to the east coast of the United States, with electric power distribution disruptions, infrastructure losses and significant economic and societal impacts. Improving the real-time prediction of tropical storms like Isaias can enable accurate disaster preparedness and strategy. We have explored the configuration, initialization and physics options of the Weather Research and Forecasting (WRF) model to improve the deterministic forecast for Isaias. The model performance has been evaluated based on the forecast of the storm track, intensity, wind and precipitation, with the support from in situ measurements and stage IV remote sensing products. Our results indicate that the Global Forecasting System (GFS) provides overall better initial and boundary conditions compared to the North American Model (NAM) for wind, mean sea level pressure and precipitation. The combination of tropical suite physics options and GFS initialization provided the best forecast improvement, with error reduction of 36% and an increase of the correlation by 11%. The choices for model spin-up time and forecast cycle did not affect the forecast of the storm significantly. In order to check the consistency of the result found from the investigation related to TS Isaias, Irene (2011), Henri (2021) and Elsa (2021), three other tropical storms, were also investigated. Similar to Isaias, these storms are simulated with NAM and GFS initialization and different physics options. The overall results for Henri and Elsa indicate that the models with GFS initialization and tropical suite physics reduced error by 44% and 57%, respectively, which resonates with the findings from the TS Isaias investigation. For Irene, the initialization used an older GFS version and showed increases in error, but applying the tropical physics option decreased the error by 20%. Our recommendation is to consider GFS for the initialization of the WRF model and the tropical physics suite in a future tropical storm forecast for the NE US.

Image-based machine learning for monitoring the dynamics of deltaic islands in the Atchafalaya River Delta Complex between 1991 and 2019

Deltaic islands are distinct hydro-environmental zones with global significance in food security, biodiversity conservation, and fishery industry. These islands are the fundamental building blocks of many river deltas. However, deltaic islands are facing severe challenges due to intensive anthropogenic activities, sea level rise, and climate change. In this study, dynamic changes of deltaic islands in Wax Lake Delta (WLD) and Atchafalaya Delta (AD), part of the Atchafalaya River Delta Complex (ARDC) in Louisiana, USA, were quantified based on remote sensing images from 1991 to 2019 through a machine learning method. Results indicate a significant increase in deltaic islands area for the whole ARDC at a rate of 1.29 km2/yr, with local expansion rates of 0.60 km2/yr for WLD and 0.69 km2/yr for AD. All three parts of the WLD naturally prograded seaward, with the western part (WP) and central part (CP) expanding southwestward to the sea, while the eastern part (EP) prograding southeastwards. Differently from WLD, the three parts of AD irregularly expanded seaward under the impacts of anthropogenic activities. The WP and CP of the AD expanded respectively northwestwards and southwestwards, while the EP remained stable. Different drivers dominate the growth of deltaic islands in the WLD and AD. Specifically, fluvial suspended sediment discharge and peak flow events were responsible for the shift in the spatial evolution of WLD, while dredging and sediment disposal contributed to the expansion of AD. Tropical storms with different intensity and landing locations caused short-term deltaic island erosion or expansion. Tropical storms mainly generated erosion on the deltaic islands of the WLD, while causing transient erosion or siltation on the deltaic islands of the AD. In addition, high-intensity hurricanes that made landfall east of the deltas caused more erosion in the AD. Finally, sea level rise, at the current rate of 8.17 mm/yr, will not pose a threat to the deltaic island of WLD, while the eastern part of AD may be at risk of drowning. This study recognizes the complexity of factors influencing the growth of deltaic islands, suggesting that quantitative studies on the deltaic island extent are of critical for the restoration and sustainable management of the Mississippi River Delta and other deltas around the world.

Tropical cyclones and depressions over the South Pacific Ocean since the late 19th century: Assessing synergistic relationship between the El Niño Southern Oscillation and Interdecadal Pacific Oscillation

AbstractTropical cyclones (TCs) and tropical depressions (TDs), hereafter collectively referred to as tropical storms, often exhibit large year‐to‐year variability in the South Pacific Ocean basin. Many past studies have examined this variability in relation to the El Niño Southern Oscillation (ENSO) phenomenon, particularly using observational data from the post‐satellite era (i.e., after the 1970s when TC observations became more consistent). However, less emphasis is placed on how tropical storms are modulated at interdecadal and decadal time scales such as due to Interdecadal Pacific Oscillation (IPO). This is because post‐satellite data are available for relatively short time period (i.e., post‐1970s), limiting our understanding of the IPO–TC relationship in the South Pacific. Here, using NOAA‐CIRES 20th Century Reanalysis (20CR) dataset, we reconstruct historical records (1871–2014) of TC and depression proxies for the South Pacific Ocean basin, and then utilize these reconstructed proxies to first understand the connections between TC–ENSO and TC–IPO over the 20th century, and then investigate the combined effects of ENSO–IPO effects on TCs and depressions. Results show that La Niña (El Niño) is more dominant on TC activity than El Niño (La Niña) over the western subregion 140–170° E (eastern sub‐region, 170–220° E) as expected. We also show that TC numbers are strongly modulated by the IPO phenomenon with, on average, more TCs occurring during the positive phase than during the negative phase of the IPO in both western and eastern sub‐regions. We show for the first time (using a long‐term reconstructed TC dataset) that the combined phases of El Niño and + IPO account for increased TC activity, as opposed to the combined phase of La Niña and −IPO, in the eastern sub‐region. Similarly, the combined phase of La Niña and + IPO, as opposed to the combined phase of El Niño and −IPO, account for increased TC activity in the western sub‐region. However, unlike TCs, the patterns of ENSO variability seem to be reversed for TDs. Changes in large‐scale environmental conditions, such as environmental vertical wind shear, low‐level cyclonic relative vorticity, mid‐tropospheric relative humidity and sea surface temperature are linked to the various modes of variability patterns and their synergistic relationships.

The chronological account of the impact of tropical cyclones in Nicaragua between 1971 and 2020

This article provides a chronological account of the occurrence and impact of tropical cyclones in Nicaragua between 1971 and 2020. While previous research has indicated potential associations between climate change and the higher frequency of intense hurricanes, no known empirical research has focused on systematizing the chronology of the significant tropical cyclones to identify patterns of change in Nicaragua. Therefore, the principal objective of this project was to develop a general overview of the major tropical storms and hurricanes that occurred between 1971 and 2020, triggering disasters in Nicaragua. The empirical data was collected from various documents via qualitative and interpretative methodology. It included reviewing research articles, books, academic websites, databases, documents, credible reports of international organizations, and newspaper sources. This study identified that from 1971 to 2020, Nicaragua was affected by 22 tropical cyclones, which caused catastrophic damage to the territory. There are records of 17 Category 1–5 hurricanes, predominating Category 4, one with no class associated, and four tropical storms. Mitch, Felix, and Joan were the most damaging hurricanes that affected the country in the last five decades. They occurred in 1998, 2007, and 1988, respectively. Of the 22 tropical cyclones, 15 occurred during Cold ENSO, whereas only three in Warm ENSO, and four were neutral. Empirical results presented can be of value to future research on disaster risk reduction.

Probabilistic compound flood hazard analysis for coastal risk assessment: A case study in Charleston, South Carolina

Coastal communities are susceptible to flooding due to flood drivers such as high tides, surge, waves, rainfall, and river discharges. Recent hurricanes such as Harvey, Florence, and Ian brought devastating impacts from combinations of high rainfall and storm surge, highlighting the need for resilience and adaptation planning to consider compound flood events when evaluating options to reduce future flood risk. Flood risk assessments often focus on a single flood driver (e.g. storm surge) due to the complexity of accounting for compound flood drivers. However, neglecting these compound flood effects can grossly underestimate the total flood risk. A probabilistic compound flood hazard analysis considers all compound events that lead to flooding, estimates their joint probabilities, simulates the flood response, and applies a probabilistic computation technique to translate flood responses and probabilities into probabilistic flood maps (such as the 100-year flood map). Probabilistic flood maps based on compound events can be used to assess risk more accurately for current and future conditions, with and without additional adaptation measures. In this paper we present an example of a probabilistic compound flood hazard analysis for the city of Charleston, South Carolina, considering tide, surge, and rainfall, for both hurricane and non-hurricane events. Charleston is regularly confronted with compound flood events, which are expected to worsen with sea level rise and more frequent tropical storms. Starting with an initial set of over 1,000 synthetic compound events, selection techniques described in the paper led to a final set of 207 compound events. The fast compound flood model SFINCS simulated the flood response for each event and, using numerical integration, compound flood return-period maps were created for Charleston, under current and future sea level rise conditions.

The Philippine springtime (February–April) sub-seasonal rainfall extremes and extended-range forecast skill assessment using the S2S database

During the first half month of April 2022, the Philippines experienced severe disasters associated with the weak but deadly tropical storm Megi that caused 214 deaths and two sunken ships. This prompted us to investigate the extended-range prediction skill of the springtime Philippine sub-seasonal scale rainfall extremes in the subseasonal-to-seasonal (S2S) prediction database. The results suggest that the S2S models can well predict the extremity of the 2022 springtime sub-seasonal peak rainfall event (SPRE) ten days ahead. In addition to the La Niña sea surface temperature anomalies, this prolonged rainfall event, from March 26 - April 14, 2022, was associated with an anomalous cyclonic circulation straddled over the South China Sea (SCS) and the Philippine Sea and persisted for two weeks. The strong relationship between the El Niño and Southern Oscillation (ENSO) and the springtime (February–April) rainfall variability in the Philippines is clearly revealed in the analysis of 25 years of observational and hindcast data. The extremely wet SPREs tend to occur during the La Niña springs, while the extremely dry SPREs tend to occur during the El Niño springs. The Madden-Julian oscillation (MJO) and equatorial Rossby (ER) waves that are capable of modulating the sub-seasonal rainfall extremes were weak when the deadly SPRE occurred in April 2022. Thus, the extended-range forecast skill of this example can be interpreted as the baseline skill of the current S2S prediction revealed in the multi-model database. The findings suggest that the SPRE is a useful item to be included in the operational forecast as potential opportunities to harness the benefits of S2S prediction and applications.