Impact Of Extreme Weather Events Research Articles

Effects of Extreme Weather Events on Nitrous Oxide Emissions from Rice-Wheat Rotation Croplands.

Cropland ecosystems are significant emission sources of N2O, but a limited number of studies have focused on the impact of extreme weather events on N2O fluxes from cropland. This present study integrated field observations and model simulations to explore the responses of N2O fluxes to extreme weather events in typical rice and wheat rotation croplands in the middle and lower reaches of the Yangtze River (MLRYR) in China. The findings revealed that the studied rice-wheat rotation cropland exhibited a net source of N2O over the three-year monitoring period, with annual cumulative N2O emissions ranging from 190.4 to 261.8 mg N m-2. N2O emissions during the rice and wheat growing seasons accounted for 29% and 71% of the total yearly emissions, respectively. Extreme heat events led to a 23% to 32% increase in observed N2O emissions from cropland. Observed N2O emissions from irrigated rice fields during extreme precipitation events were 45% lower than those during extreme drought events. In contrast, extreme precipitation events raised observed N2O emissions from rain-fed wheat fields by 36% compared to the multi-year average, while extreme drought events reduced N2O emissions from wheat fields by 20%. Regional simulations indicated that annual cumulative N2O emissions from croplands in the MLRYR are projected to increase from 207.8 mg N m-2 under current climate to 303.4 mg N m-2 in the future. Given the episodic nature and uncertainties associated with N2O emissions from cropland, further validation is necessary for utilizing the model to explore the effects of extreme weather events on N2O in cropland ecosystems.

Assessing the Risk of Extreme Storm Surges from Tropical Cyclones under Climate Change Using Bidirectional Attention-Based LSTM for Improved Prediction

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model’s accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA’s capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change.

Impacts of extreme weather events on mortgage risks and their evolution under climate change: A case study on Florida

We develop an additive Cox proportional hazard model with time-varying covariates, including spatio-temporal characteristics of weather events, to study the impact of weather extremes (heavy rains and tropical cyclones) on the probability of mortgage default and prepayment. We compare the survival model with a flexible logistic model and an extreme gradient boosting algorithm. We estimate the models on a portfolio of mortgages in Florida, consisting of 69,046 loans and 3,707,831 loan-month observations with localization data at the five-digit ZIP code level. We find a statistically significant and non-linear impact of tropical cyclone intensity on default as well as a significant impact of heavy rains in areas with large exposure to flood risks. These findings confirm existing results in the literature and also provide estimates of the impact of the extreme event characteristics on mortgage risk, e.g. the impact of tropical cyclones on default more than doubles in magnitude when moving from a hurricane of category two to a hurricane of category three or more. We build on the identified effect of exposure to flood risk (in interaction with heavy rainfall) on mortgage default to perform a scenario analysis of the future impacts of climate change using the First Street flood model, which provides projections of exposure to floods in 2050 under RCP 4.5. We find a systematic increase in risk under climate change that can vary based on the scenario of extreme events considered. Climate-adjusted credit risk allows risk managers to better evaluate the impact of climate-related risks on mortgage portfolios.

Analysis of Connected Vehicle Data to Quantify National Mobility Impacts of Winter Storms for Decision Makers and Media Reports

Traditional techniques of monitoring roadway mobility during winter weather have relied on embedded road sensors, roadside cameras, radio reports from public safety staff, or public incident reports. However, widely available connected vehicle (CV) data provides government agencies and media with a unique opportunity to monitor the mobility impact of inclement weather events in near real-time. This study presents such a use case that analyzed over 500 billion CV records characterizing the spatial and temporal impact of a winter storm that moved across the country from 21 to 26 December 2022. The analysis covered 97,000 directional miles of interstate roadway and processed over 503 billion CV records. At the storm’s peak on 22 December at 5:26 PM Eastern Time, nearly 4800 directional miles of interstate roadway were operating under 45 mph, a widely accepted indicator of degraded interstate conditions. The study presents a methodological approach to systematically assess the mobility impact of this winter event on interstate roadways at a national and regional level. The paper then looks at a case study on Interstate 70, a 4350 directional mile route passing through ten states. Statewide comparison showed Ohio was most impacted, with 9% of mile-hours operating below 45 mph on 23 December. High-Resolution Rapid Refresh weather data provided by the National Oceanic and Atmospheric Administration was integrated into the analysis to provide a visualization of the storm’s temporal path and severity. We believe the proposed metrics and visualizations are effective tools for communicating the severity and geographic impact of extreme weather events to broad non-technical audiences.

Exploring the relationship between extreme weather events, urbanization, and food insecurity: Institutional quality perspective

This study examines the potential for institutional quality to address Africa's trilemma of food insecurity, rapid urbanization, and extreme weather events.Using a two-stage least squares regression analysis of 28 African countries between 2012Q1 and 2021Q4, our results suggest that extreme weather events have a significant negative impact on food security, particularly in urban areas.We found that institutional quality can have a significant positive impact on food security and can mitigate the negative effects of extreme weather events on food security. Our study also decomposed the food security index into its four components and found that extreme weather events negatively affect the affordability, availability, quality, and natural resource resilience of food. The findings are robust to the use of the feasible generalized least squares and panel-corrected standard errors estimation techniques.The policy implications of our study are significant, as our findings suggest that central governments can take practical steps to minimize the impact of extreme weather events on food security. Specifically, investing in disaster preparedness, improving infrastructure, and promoting agricultural development can all help improve food security. Additionally, improving institutional quality can be an effective way to mitigate the negative impact of extreme weather events on food security.

Extensive analysis of rain in australia by exploratory data analysis, feature engineering and modeling

Accurate rainfall forecasts help in planning outdoor activities, agricultural practices, and water resource management, thereby mitigating the impact of extreme weather events. This article provides an in-depth analysis of rainfall in Australia, focusing on predicting whether it will rain tomorrow using logistic regression. The research aims to develop an accurate model to help predict rainfall events for better preparedness and planning. We obtained datasets from a number of Australian weather stations. The dataset contains 142,193 daily weather observations spanning approximately ten years. The recorded information includes various details such as date, location, humidity, wind direction, clouds, temperature, etc. This shows that the model performs well in distinguishing between rainy and non-rainy days with an accuracy of about 0.875. The findings of this study have important implications for various stakeholders including meteorologists, disaster management agencies, and the public.

Seasonal differences in continental outflows and marine emissions of organic aerosols in the East China Sea: Insight from the variation of saccharides

Water-soluble organic species play an important role in cloud condensation nuclei and cloud formation in marine atmosphere. The present study investigated the influence of continental outflows and marine biogenic emission on organic aerosol in the East China Sea from the perspective of saccharides during a cold season and a warm season in 2019, respectively. Driven by the East Asian continental outflows during the cold season, levoglucosan emitted from biomass burning were the dominant species and were mostly concentrated in fine mode (<2.5 μm). Biogenic species, including primary sugars and sugar alcohols, were the dominant species in warm season, accounting for a high fraction of WSOC with 5.48 ± 3.29%, and were mostly distributed in coarse mode (2.5–10 μm). This trend was consistent with sea salt components (Na+ and Cl−) when air masses originated from oceanic areas, suggesting that marine emissions could be the major sources of saccharides during the warm season. Particularly, a dramatic increase in biogenic saccharides (glucose, sucrose, mannitol and arabitol) was observed when experienced the “Super Typhoon Lekima”. Concentrations of these species were 2 to 5 times higher than that in normal days, illustrating the significant impact of extreme weather events on the emissions of biogenic aerosols in marine area. This study improves the understanding of combined impacts of continental outflows and marine emissions on water-soluble organic species in the East China Sea, again highlighting the important contribution of marine biogenic emissions under extreme weather conditions.

Impacts of extreme weather events on microplastic distribution in coastal environments

Microplastic pollution is a pressing environmental threat to the ecosystem, which can be influenced by varying weather factors. With arising weather extremes in recent years, it is crucial to assess the weather effects on coastal microplastic pollution. In this study, we conducted a year-long baseline survey on beach sediment and surface seawater in Hong Kong, and additional surveys after rainstorm and typhoon events. Our data reveals that microplastic abundance was 5 times higher in wet season over dry season. Yet, the seasonal variation was insignificant when considering only the baseline condition, suggesting the role of extreme weather as the main driver of the seasonal variation in microplastic distribution. Typhoons and rainstorms induced 5.1 to 36.4 times and 1.9 to 11.7 times more microplastics in beach sediment, respectively, and 3.5 to 6.0 times and 2.5 to 4.3 times more microplastics in surface seawater, respectively. The larger microplastic mass in beach sediment and the increased proportion of hard fragments under extreme weather conditions indicate the larger mobility of heavier plastic debris from a wider source. We identified positive correlations between plastic levels and multiple weather factors (including rainfall, wind and tide), suggesting the potential terrestrial inputs of microplastics via surface runoff and wind transport, and the potential redistribution of microplastics from deep to surface sediment via wave agitation. We also identified a strongly positive correlation between macro- and microplastic abundance in beach sediment, suggesting the potential plastic fragmentation under strong wave abrasion, which may intensify the coastal microplastic pollution. This study sheds light on the need for reinforced security of waste management systems to avoid terrestrial plastic inputs under extreme weather forces, as well as the timing of coastal cleanup work in terms of limiting plastic fragmentation and achieving better cleanup efficiency.

Summer heatwaves, wind production and electricity demand in Southern Europe: climatic conditions and impacts

Electricity demand for cooling and heating is directly related to weather and climate, primarily through ambient temperature. In Southern Europe, the maximum electricity demand for cooling in summer can be more pronounced than in winter, especially during heat wave (HW) episodes. With the growth of renewable technologies in the energy mix, the dependency of the electricity system on the weather is becoming evident not just from the demand side, but also from the energy supply side. From the resources point of view, summer wind presents a minimum on its annual cycle, so a combination of maximum electricity demand can coincide with a minimum of wind power production. This study presents a strong multidisciplinary focus, merging climate, energy and environmental discipline, due to their relevant connections in Southern Europe where important climate change stresses are expected. The combined anomalies of electricity demand and wind production during heat wave episodes are quantified at the country level, taking into account the HW extension. The summer period (1989-2019) of ERA5 reanalysis and E-OBS-21.0e data is used for atmospheric magnitudes and the Copernicus climate change service (C3S) energy dataset for demand. In heat wave events, an increase of 3.5%–10.6% in electricity demand and a decrease up to −30.8% in wind power production is obtained, with variability depending on the country. The greater the extension of the HW, the greater the anomalies. Different weather regimes related to heatwaves also play a role on this range of values. Therefore, the impact of extreme weather events, such as heatwaves, on wind power production in conditions of high electricity demand, should be considered in the energy supply strategy and planning in order to minimize the impact of these events on an electricity system with high penetration of renewables.

Cold Season Rain Event Has Impact on Greenland's Firn Layer Comparable to Entire Summer Melt Season

AbstractRainfall high on the Greenland Ice Sheet is an emerging phenomenon with consequences for the thermal and structural makeup of the surface layer. We document changes to Greenland's firn column due to a 4‐day cold‐season warm/rain event. Heavy precipitation occurred with a sudden 30°C increase in air temperature, reaching 0°C at 2,000 m elevation. Thermistor strings within the firn layer across a 35 km transect show rapid warming of 6°–23°C reaching depths of 2–10 m. Antecedent conditions governed the magnitude, duration, and depth‐distribution of sensible and latent heat added to the firn column. Heat fluxes from the firn layer required up to 8 weeks to recover to baseline, a significant fraction of the winter period. The amount of liquid water refrozen in the firn column was ∼20%–100% of the prior summer demonstrating the impact of extreme weather events on the ice sheet's evolution and runoff characteristics.

EFFECTS OF GLOBAL CLIMATE CHANGE: ADAPTING AGRICULTURAL CROPS FOR A WARMER WORLD

With changing weather patterns, increased CO2 levels, and more frequent extreme weather events, the need to adapt crops to a warmer world is paramount. This study aims to provide a comprehensive overview of the challenges and potential solutions in adapting crops to climate change. The approach taken in this review paper involves examining the greenhouse effect, the causes of climate change, and the evidence supporting its existence. We also explore how changing weather patterns affect crop growth and the physiological effects of increased CO2 on plants. Additionally, the impact of extreme weather events on agriculture is discussed. This study reveals the importance of developing heat-resistant crop varieties and implementing climate-smart agricultural practices. By embracing genetic modifications, innovative farming practices, and technology in agriculture, we can enhance agricultural resilience and mitigate the adverse effects of climate change on crop production. Adapting crops for a warmer world is crucial for ensuring food security and sustainable agriculture. The findings of this study emphasize the need for continued research, innovation, and policy interventions to address the challenges posed by climate change. The results underscore the importance of building resilient agricultural systems and promoting sustainable practices for the well-being of present and future generations.

The impact of extreme weather events on fertility preference and gender preference in Bangladesh

The link between population dynamics and climate-related severe events is complicated. Extreme weather events (EWEs), along with other factors such as socioeconomic and cultural factors, influence population dynamics, particularly changes in fertility, mortality, and migration. This study focuses solely on the fertility aspect of climate change and aims to investigate it in Bangladesh, which is extremely sensitive to climate change and EWEs such as floods, cyclones, and droughts. On a regular basis, the country is confronted with a number of EWEs. The current study examines how different types of extreme weather events affect vulnerable people’s decisions to have children or to prefer children of a certain gender. People who reside in a particular area may be more vulnerable to particular EWE types, which may result in different preferences for fertility and gender. This study employed individual-level data from three places (flood-prone, drought-prone, and cyclone-prone), each exposed to a distinct hazard, to address this issue, and collected pertinent information from 177 respondents in the susceptible areas using a survey questionnaire. The quantitative results show that the gender of the first child, the perceived risk of infant death due to EWE, the opinion on having more children to recover from the damage and losses caused by EWE, government and non-governmental organization (NGO) support during EWE, and the intended timing of child bearing (after or before EWE) are all significant factors influencing fertility preferences and gender preferences. The findings also indicate that the three regions under investigation have statistically distinct preferences for fertility and gender. There were larger differences between flood-prone areas and drought- and cyclone-prone areas. The complex issue of variations due to different EWEs requires more in-depth studies with larger samples and different methodological techniques.

Impacts of extreme weather events on enteric infectious disease outbreaks: evidence from New Zealand

Impacts of extreme weather events on enteric infectious disease outbreaks: evidence from New Zealand

China’s Inter-Provincial Energy Security Resilience Assessment over Space and Time: An Improved Gray Relational Projection Model

In recent years, with the increasing impact of extreme weather events on energy security, energy vulnerability has increased significantly, and more and more international institutions and departments have begun to incorporate resilience governance into energy security. This paper focuses on China’s inter-provincial energy security assessment. Compared with existing relevant research, the significant features of our work are (i) introducing the concept of energy resilience and presenting its evolution mechanism and evaluation criteria, (ii) developing a gray relational projection model by using the level difference maximization and optimization theory, (iii) measuring the energy resilience of 30 Chinese provinces over space and time. Our results show that the spatial–temporal patterns of energy resilience in China changed significantly from 2005 to 2018. High energy resilience moved from provinces with abundant nonrenewable energy before 2010 to provinces with high energy diversity. Energy endowment is a primary condition to ensure a region’s energy resilience. Renewable energy development, energy investment, economic development, and policy coordination play vital roles in ensuring regional energy resilience. Energy investment and economic development can effectively improve the energy resilience of resource-poor areas. This study’s results will serve as a reference for China and contribute to expanding knowledge in this field.

Age-dependent resilience assessment and quantification of distribution systems under extreme weather events

The impact of extreme weather events on power system resilience can be seen in historical electrical disruptions. Due to the increasing intensity and frequency of extreme weather events following global warming and climate change, a greater focus is required to design power systems with high resilience and low cost through assessing and quantifying power system resilience. In this way, we propose a transparent methodology and a set of metrics to quantify the resilience of distribution systems subjected to hurricanes. This methodology includes a probabilistic and detailed approach to modelling the failure probability of overhead lines and the restoration time of damaged lines. Moreover, the fragility analysis of the power system infrastructure is considered age-dependent, and the system’s resilience by considering different lifetimes is assessed. The time-dependent resilience assessment constitutes an essential component in risk-informed decision-making for resilience enhancement strategies in the future. Specifically, this paper provides two resilience curve-based metrics: vulnerability rate and restoration rate. The first metric indicates the power system’s ability to resist extreme events, and the second indicates its ability to bounce back to normal performance. Simulations are performed on the IEEE 69-bus distribution test system to validate the suggested methodologies. The results indicate the capability of the proposed methodology and metrics to precisely assess and quantify the power system resilience. A significant correlation between the age of power distribution systems and the system’s resilience can be seen. These findings can support decision-making concerning system management and plans such as expansion planning and resource allocation.

The Impact of Extreme Weather Events on the Economic Performance

With the increasing number of populations, the environment on the earth is seriously damaged, leading to a lot of weather disaster such as extreme high temperature in summer, global warming and so on. These events have a considerable number of impacts on human, especially for economy. This paper represents a comprehensive review of research dedicated to the effects of weather events on economy. During the research, a considerable number of papers about the impact on the economy were referred. Based on the statistical data and deeply analysis, it can be found that weather have a lot of negative impact on both agriculture, industry, and energy consumption. In the future, with the development of machine learning algorithms, and cloud computing, the simulation of chaotic systems such as weather and climate will be more accurate. Climate change poses great challenges in economics, and it is the largest and broadest market failure phenomenon so far. Therefore, it is necessary and urgent to study the economics of climate change.

How Climate Change Affects Young Peoples Mental Health in Southern China

We analyze the adverse impacts of extreme weather events, which frequently occur under the influence of climate change, on adolescents' mental health issues in Southern China. Further analyses on the effects of a tornado in Yancheng, Jiangsu province and posttraumatic stress disorder (PTSD) on adolescents are discussed in the paper. Potential solutions for treating mental illness include environmental improvements to upgrade psychological problems and psychological treatments to provide methods to decrease the impact of extreme weather events. The future implication and the expectation are discussed.

Assessment of the Socio-Economic Impacts of Extreme Weather Events on the Coast of Southwest Europe during the Period 2009–2020

Coastal regions in Southwest Europe have experienced major interventions and transformations of the territory with unprecedented urban development, primarily related to growing tourism activity. The coast is the place where marine and terrestrial processes converge, making it highly vulnerable to the effects of climate change. However, the lack of information on the frequency of these extreme weather events and their impacts on the coast hampers an accurate analysis of the consequences of global change. This paper provides a detailed analysis of the extreme weather events (EWE) that have affected the Atlantic and Mediterranean coasts of Southwest Europe during the period from 1 January 2009 to 28 February 2020, as well as a quantification of their impacts: fatalities, injuries and economic damage. Official sources from France, Portugal and Spain were consulted, along with technical reports, scientific articles, etc., to generate a unified database. A total of 95 significant extreme events have caused 168 fatalities, 137 injuries and almost €4000 M in direct economic losses. Cyclone Xynthia (February 2010) on the French Atlantic coast stands out, having caused 47 fatalities, 79 injuries and substantial economic losses valued at €3000 M. The study shows a slight upward trend in the number of events recorded, especially during the last three years of the analysis, as well as in human losses and damages. The results reveal a higher exposure of the Mediterranean coast of Southwest Europe when compared to the Atlantic, especially the Spanish Mediterranean coast, with 61% of the fatalities recorded there during the study period. This is primarily due to a model of exponential tourism growth on the Mediterranean coast, with an enormous urban and infrastructure development during the last decades. Traditionally, the Mediterranean coast is less prepared to reduce the effects of marine storms, extreme events that are becoming more frequent and virulent in the context of climate and global change. This work highlights the need to create a continuous monitoring system–at the European level–of the impacts of extreme weather events on the coast, where 40% of the European population is concentrated. This observatory should serve as a source of information for risk mitigation policies (predictive, preventive and corrective), as well as for emergency management during disasters.

Crop models for assessing impact and adaptation options under climate change

Increased amount of green house gases (GHG) in the atmosphere will cause climate change that will adversely impact crop production especially in the arid and semi-arid regions of the developing countries. Development and implementation of field level adaptations measures to cope up with climate change are necessary to the farmers whose livelihood depends on crop-based income. Crop simulation models that incorporate soil-crop-climate processes of plant growth and that are sensitive to climate change factors can be used to quantify impact of climate change on crop production and evaluating and prioritizing adaptation measures at farm level. This paper analyses the impacts of climate change and plausible agronomic, land and water management and genetic adaptations options for the major crops of the semi-arid tropical region with examples from selected sites in India and other developing countries. The crop models need to be linked to the improved pest, disease and weed models to analyze and predict yield losses, especially those due to climate change. The simulation models also need to incorporate the impact of extreme weather events on crop production that is projected to increase with climate change.

The impact of extreme weather events on green innovation: Which ones bring to the most harm?

This research investigates the effect of extreme weather events on green innovation by using unbalanced panel data covering 117 countries from 1971 to 2018 and documents strong evidence that extreme weather has a significantly negative impact on green innovation. We also find among the four extreme weather events such as droughts, extreme temperature, floods, and storms that floods have the most prominent adverse impact on green innovation. Furthermore, the significantly negative effect of extreme weather is manifested in countries with a high level of corruption and low level of trade openness, but not in the countries with a low level of corruption and a high level of trade openness. Lastly, we show that extreme weather events have an adverse impact on green innovation by decreasing economic growth level, hindering financial development, widening income inequality, damaging the accumulation of human capital, and increasing the political risk.