Extreme Weather Research Articles

Small green housing makes a big difference: the impact of natural environmental uncertainty on consumers' willingness to purchase green housing

PurposeThe residential sector is a principal contributor to global energy consumption, underscoring the critical importance of promoting green housing initiatives to mitigate energy use and environmental degradation. The prevalence of uncertainty in the natural environment, exemplified by phenomena like extreme weather events, highlights the urgent need for adaptive strategies and sustainable practices to mitigate the impact on human communities and ecosystems. Against this backdrop, this paper presents a theoretical framework examining the influence of natural environmental uncertainty on consumers' willingness to purchase green housing.Design/methodology/approachThrough three experiments, this study modeled the mechanism by which the natural environment uncertainty affects consumers' willingness to purchase green housing, and then verified the mediating effect of the threat of ontological security and the moderating effect of the degree of consumers' natural connectedness.FindingsThis paper concludes (1) natural environmental uncertainty exerts a significant positive impact on the willingness to purchase green housing, with the threat to ontological security serving as a pivotal mediating variable; (2) the degree of natural connectedness significantly moderates the effect of ontological security threats on the purchasing intent for green housing.Originality/valueThis research contributes to the marketing literature by offering a novel perspective on the impact of natural environmental uncertainty on consumer behavior, augmenting the body of knowledge concerning the determinants of green housing purchase intentions, and provides new ideas for marketers.

Analyzing dynamics of extreme weather events (EWE) in India: unfolding trends through statistical assessment of 50 years data (1970–2019)

Analyzing dynamics of extreme weather events (EWE) in India: unfolding trends through statistical assessment of 50 years data (1970–2019)

Financial Inclusion and Climate Resilience: The Role for an AI-Enhanced Digital Wallet in Caribbean SIDS

Caribbean Small Island Developing States (SIDS) are highly vulnerable to extreme weather events and climate change. Caribbean SIDS climate vulnerability is worsened by their high level of financial exclusion. Many people do not have bank accounts and access to electronic fund transfer (EFT). As such, they cannot electronically receive funds before or after a natural disaster to cope with the effects. The financial exclusion problem can be addressed through a digital wallet. A digital wallet is a financial transaction application that securely stores a user’s banking and payment information on a cloud interface and allows the user to perform a transaction while hiding their banking information from a vendor. The biggest concern of users with regards to the use of digital wallets are its convenience and security. While digital wallets offer outstanding convenience of purchasing goods and services, data privacy and fraud risks deter people from adopting mobile payment. Potential fraud risk to digital wallets can be identified with anomaly detection techniques. The research problem investigated in this study is how the implementation of an artificial intelligence (AI) enhanced digital wallet can facilitate financial inclusion in the Caribbean, particularly in the context of disaster preparedness and recovery. Since security is an important aspect of a digital wallet, a sub-objective of this study is to derive an appropriate model for anomaly detection in financial transactions in a digital wallet. This study modifies Liu et al. [1] Gated Transformer Networks (GTN) architecture to allow for univariate time series classification. The corresponding new model is referred to as the Univariate Gated Transformer Network (UGTN). The UGTN is used for anomaly detection in financial data from a digital wallet. This study also provides policy recommendations for the implementation of a digital wallet to facilitate financial inclusion and climate resilience in Caribbean SIDS.

Downy mildew of millets - An overview

Amidst global food insecurity, malnutrition, agricultural challenges, and climate change, millet farming is emerging as a viable alternativedue to its high nutritional value, resilience to extreme weather conditions, and adaptability to marginal soils. Often referred to as “super-crops”, millets gained international recognition in March 2021 during the 75th session of the United Nations General Assembly. Millets rank among the most important crops globally, following rice, wheat, maize, and sorghum. However, they are susceptible to more than fifty diseases, with the most destructive being ‘Downy Mildew’. This disease is caused by oomycete pathogens such as Sclerospora graminicola, Peronosclerospora sorghi and Sclerophthora macrospora, posing a significant threat to millet production, with yield losses ranging from 50% to 100%. This review emphasizes the importance of millet and the devastating effects of downy mildew on its yields. It explores the physiological and histological changes induced by the disease, the characterization of effector protein, and the genetic variability in millet populations. Additionally, various management techniques for combating downy mildew are examined, including chemical treatments, induced resistance, organic-based approaches, cultural practices, resistant genotypes, and advancements in nanotechnology. By compiling current knowledge on millet disease and effective management strategies, this review aims to serve as a comprehensive resource for researchers and farmers, supporting sustainable millet farming and improving global food security.

Econometrics of Climate Change Research

The econometrics of climate change explores the intricate interplay between economic activity and climate dynamics, offering empirical insights into the causes, impacts, and mitigation of global climate change. This research applies advanced econometric techniques to quantify the economic consequences of environmental changes and evaluate the effectiveness of climate policies. By leveraging panel data models, time-series analysis, and causal inference methods, this study investigates critical topics such as the economic costs of extreme weather events, the efficacy of carbon pricing mechanisms, and the role of renewable energy adoption in mitigating climate risks. Additionally, this research incorporates machine learning techniques to enhance predictive accuracy in modeling future climate-economic interactions. Through rigorous empirical analysis, the study provides actionable recommendations for policymakers to design sustainable strategies that balance economic growth with climate resilience. The findings contribute to a deeper understanding of the socioeconomic dimensions of climate change, emphasizing the importance of evidence-based decision-making in addressing one of humanity’s most pressing challenges.

Tribologically enhanced self‐healing hybrid laminates for wind turbine applications

AbstractWind turbines are subjected to extreme weather and load conditions; hence, high strength and impact resistance are required. Furthermore, wind turbine blades can be subjected to impact loads such as bird strikes, resulting in the formation of microcracks. Self‐healing capsules can be used to mend turbine blades for microscale damage. The incorporation of self‐healing capsules may cause a decrease in the mechanical characteristics of the composites prior to impact resistance, which can be compensated for with efficient fillers such as silicon carbide whiskers (SiCw). Thus, a novel hybrid composite structure is examined with the advantage of using a self‐healing mechanism and SiCw reinforcement. Tensile, tribological, and Charpy impact tests were performed to characterize the mechanical and tribological properties, which were supported with microscopic observations. Multiple experimental characterizations were performed to investigate the impact, and the ultimate tensile strength (UTS) and energy absorption capacity of the structure were shown to increase by 32% and 45%, respectively, with the addition of SiCw. The presence of self‐healing agents provides a 5% rise in UTS after enough time for healing following the collision. The structure's tribological performance is improved by 10% in wear resistance and 20% in friction coefficient.Highlights Hybrid laminated composite structure with silicon carbide whisker and self‐healing capsules. Tensile and Charpy impact tests conducted with microscopic observations Increased ultimate tensile strength and energy absorption capacity by 32% and 45%. Tribological improvement by 10% in wear resistance and 20% in friction coefficient.

Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks

Demand side management is a critical issue in the energy sector. Recent events such as the global energy crisis, costs, the necessity to reduce greenhouse emissions, and extreme weather conditions have increased the need for energy efficiency. Thus, accurately predicting energy consumption is one of the key steps in addressing inefficiency in energy consumption and its optimization. In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. The proposed models outperform state-of-the-art deep neural network models for predicting minute-by-minute energy consumption, with a mean square error of 0.109. The evaluated hybrid models also capture more latent trends in the data than traditional single models. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast

Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast

Future climate impacts on crop production in Bhutan

Understanding the relationship between weather extremes and crop productivity is the backbone of risk assessments on food security. The former is paramount in countries (i.e., Bhutan) where there are a limited number of impact assessments in agriculture. The analysis performed in this work highlights the trends in agricultural production under a changing climate and the attribution of yield change to a specific climatic hazard. We improve the understanding between the two (climate and yields) by applying an FAO eco-physiological model (PyAEZ) and by relying on state-of-the-art downscaled climate projections (CORDEX-CORE). We analyze climate change impacts on ten crops (maize, foxtail millet, buckwheat, wheat, rice, common beans, cabbage, white potatoes, carrots, and citrus) at national level. The main simulation findings point to a higher yield gain and loss under rainfed conditions as opposed to irrigated conditions by the end-century (2070-2099) compared to the baseline period (2010-2039), particularly under RCP 8.5 for white potatoes (+17.7%), wheat (+15.5%), cabbage (+12.8%), buckwheat (-6.6%), and citrus (-5.8%). The irrigation potential to modulate climate change impacts is reflected in a yield gain of +43.4% on average for all crops and RCP’s when compared to rainfed conditions. On average, weather extremes explain 28% and 33% of the yield variability over time, respectively under RCP’s 2.6 and 8.5. Overall, this study supports smallholder farmers, decision-makers, and project developers to implement adaptation solutions that minimize growing weather extremes such as heat-stress and dry-spells.

The Multifaceted Soils of Himachal Pradesh, India: Types and Characteristics

The soils of Himachal Pradesh face a multitude of pressing issues that threaten their health and agricultural productivity. The diverse geographical features, temperature regimes and biological zones of Himachal Pradesh have resulted into a vast range of soil types in the state. Alpine, forest, alluvial and sub-mountain soils are the four major soil categories into which the state's soils can be generally divided. Only a particular species and adapted plants can thrive in the extreme weather circumstances of alpine soils, which are located at higher elevations and possess soils having thin, stony and acidic in reaction. The mid-hill areas are dominated by forest soils which are distinguished by their high levels of organic matter and nutrients. These soils support luxuriant vegetation and make effective agricultural methods possible. Alluvial soils, which are mostly found in river valleys and are deposited by ravine systems are extremely rich and perfect in fertility and suitable for growing a range of crops including vegetables, rice and maize. Located in the foothills, sub-mountain soil exhibits a blend of traits from alluvial and forest soils, thus providing a range of uses for horticulture and agriculture. In addition to supporting a diverse array of plant and animal life, Himachal Pradesh's varied soil types are vital to the region's agricultural economy. One of the biggest problems is soil erosion, which is made worse by the steep terrain and abundant rainfall in the area. This results in the loss of topsoil and decreased fertility. Deforestation, overgrazing, and unsustainable farming methods all contribute to land degradation, which further reduces biodiversity and soil quality. Furthermore, crop development is impacted by soil acidity in high-altitude regions, which restricts nutrient availability. However, these issues need efficient conservation measures and soil management techniques. In order to maintain Himachal Pradesh's rich biodiversity and agricultural potential for future generations, sustainable agricultural development and environmental protection in this ecologically sensitive area depend on an understanding of the complex soil dynamics. Addressing these interconnected issues requires a comprehensive approach, emphasizing sustainable land management practices, community engagement, and restoration efforts to preserve the vital soil resources of this ecologically sensitive region.

A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms

The prevalence of extreme weather events gives rise to a significant degree of prediction bias in the forecasting of photovoltaic (PV) power. In order to enhance the precision of forecasting outcomes, this study examines the interrelationships between China’s 24 conventional solar terms and extreme meteorological events. Additionally, it proposes a methodology for estimating the short-term generation of PV power based on the division of solar term time series. Firstly, given that the meteorological data from the same festival is more representative of the climate state at the current prediction moment, the sample data are grouped according to the 24 festival time nodes. Secondly, a convolutional neural network–long short-term memory (CNN-LSTM) PV power prediction model based on an Attention mechanism is proposed. This model extracts temporal change information from nonlinear sample data through LSTM, and a CNN link is added at the front end of LSTM to address the issue of LSTM being unable to obtain the spatial linkage of multiple features. Additionally, an Attention mechanism is incorporated at the back end of the CNN to obtain the feature information of crucial time steps, further reducing the multi-step prediction error. Concurrently, a PV power error prediction model is constructed to rectify the outcomes of the aforementioned prediction model. The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions.

Improving the efficiency and reliability of India’s power grid through targeted EHV transmission line investments: a case of UPPCL

This study analyzes the optimization of Extra High Voltage (EHV) transmission line design parameters by Uttar Pradesh Power Corporation Limited (UPPCL) in India to improve power grid efficiency and reliability. UPPCL reduced insulation specifications for 400 kV lines from 27 to 21 disc insulators and decreased live metal clearance from 2600 to 2400 mm, resulting in 7–9% cost savings per line and reduced right-of-way requirements. While initial test lines performed satisfactorily for over 5 years, longer lines experienced operational challenges related to insulator quality, bird activity, and design margins. Tripping issues emerged during adverse weather conditions, particularly in foggy winter periods. Root cause analysis revealed the need for stringent insulator quality control, regular cleaning to mitigate bird dropping contamination, and potential corona ring design improvements. This case study demonstrates how targeted EHV transmission line optimization can yield significant cost and environmental benefits, while highlighting the importance of balancing economic gains with reliability and safety considerations. The findings provide valuable insights for future EHV transmission line designs and upgrades across India’s power grid.

Human Adaption to Climate Change: Marine Disaster Risk Reduction in the Era of Intelligence

With the intensification of global warming and sea level rise, extreme weather and climate events occur frequently, increasing the probability and destructive power of marine disasters. The purpose of this paper is to propose the specific application of artificial intelligence (AI) in marine disaster risk reduction. First, this paper uses computer vision to assess the vulnerability of the target and then uses CNN-LSTM to forecast tropical cyclones. Second, this paper proposes a social media communication mechanism based on deep learning and a psychological crisis intervention mechanism based on AIGC. In addition, the rescue response system based on an intelligent unmanned platform is also the focus of this research. Third, this paper also attempts to discuss disaster loss assessment and reconstruction based on machine learning and smart city concepts. After proposing specific application measures, this paper proposes three policy recommendations. The first one is improving legislation to break the technological trap of AI. The second one is promoting scientific and technological innovation to break through key technologies of AI. The third one is strengthening coordination and cooperation to build a disaster reduction system that integrates man and machine. The purpose of this paper is to reduce the risk of marine disasters by applying AI. Furthermore, we hope to provide scientific references for sustainability and human adaptation to climate change.

Video WeAther RecoGnition (VARG): An Intensity-Labeled Video Weather Recognition Dataset

Adverse weather (rain, snow, and fog) can negatively impact computer vision tasks by introducing noise in sensor data; therefore, it is essential to recognize weather conditions for building safe and robust autonomous systems in the agricultural and autonomous driving/drone sectors. The performance degradation in computer vision tasks due to adverse weather depends on the type of weather and the intensity, which influences the amount of noise in sensor data. However, existing weather recognition datasets often lack intensity labels, limiting their effectiveness. To address this limitation, we present VARG, a novel video-based weather recognition dataset with weather intensity labels. The dataset comprises a diverse set of short video sequences collected from various social media platforms and videos recorded by the authors, processed into usable clips, and categorized into three major weather categories, rain, fog, and snow, with three intensity classes: absent/no, moderate, and high. The dataset contains 6742 annotated clips from 1079 videos, with the training set containing 5159 clips and the test set containing 1583 clips. Two sets of annotations are provided for training, the first set to train the models as a multi-label weather intensity classifier and the second set to train the models as a multi-class classifier for three weather scenarios. This paper describes the dataset characteristics and presents an evaluation study using several deep learning-based video recognition approaches for weather intensity prediction.

A probabilistic forecast for multi-year ENSO using Bayesian convolutional neural network

Abstract A robust El Niño Southern Oscillation (ENSO) prediction is essential for monitoring the global climate, regional monsoons, and weather extremes. Despite dedicated efforts spanning decades, the precise prediction of ENSO events through numerical modeling beyond a couple of seasonal lead times remains a daunting challenge. The advent of deep learning-based approaches marks a transformative era in climate and weather prediction. However, many machine learning-based studies attempting ENSO prediction are confined to singular estimates, lacking adequate quantification of uncertainty in learned parameters and overlooking the crucial need for a nuanced understanding of ENSO prediction confidence. Here, we introduce a deep learning-based Bayesian convolutional neural network model that provides robust probabilistic predictions for ENSO with a lead time of up to 9–10 months across all seasons. The Bayesian layers within the convolutional neural network maintain the capability to predict a distribution of learned parameters. Augmented with bias correction, our model reproduces the amplitude of the Niño 3.4 index with fidelity for lead up to 9–10 months. The inherent capacity for uncertainty modeling enhances the reliability of bayesian neural networks (BNNs), making them particularly valuable in operational services. This research holds substantial socio-economic implications as it enhances our forecasting capabilities and rigorously quantifies forecast uncertainties, providing valuable insights for planning and policymaking.

The effect of subsidized flood insurance on real estate markets

AbstractSubsidized insurance against extreme weather events improves affordability among households in at‐risk areas but it can weaken the risk signal via property prices. Leveraging a granular data set of all property transactions and flood events in England, we study the effects of a reinsurance scheme that lowers insurance premiums for at‐risk properties. We document that the introduction of this scheme increases prices and transaction volumes of flood‐prone properties. This fully offsets the negative direct effects of flooding on property prices, with high‐income areas and high‐value properties benefiting relatively more. Our findings speak to the debate on climate adaptation policies and their consequences for wealth distribution.

Awareness and preparedness of health systems and emergency medicine systems to the climate change challenges and threats: an international survey.

Climate change is widely recognised as a critical public health challenge. The objective of this study was to assess the awareness, preparedness and mitigation plans for climate change threats. A cross-sectional observational study targeting emergency medical societies in different countries was conducted between 15 February and 15 March 2024. The survey featured 16 closed questions on climate change awareness, preparedness and risks. Focus groups of 4-6 members were organised by country. Results were correlated to income levels, United Nations (UN) regional classification and the World Risk Index. The questions were ranked using a Likert-like scale from 0 to 9 (9 being the highest). Descriptive statistics used central tendency estimators, and inferential analysis used chi-square and Kruskal-Wallis tests, with the significance level set at P < 0.05. Forty-two focus groups responded, representing 36 countries: 21 (50%) high-income, seven (16.7%) low-income, five (11.9%) lower middle-income and nine (21.4%) upper middle-income countries, representing 31 of the 22 UN regions. According to the World Risk Index, the respondent countries belonged to the different categories as follows: very low risk, 6 (14%); low risk, 8 (19%); medium risk, 5 (12%); high risk, 8 (19%) and very high risk, 14 (34%). The estimated impact of climate change on national health systems had a mean score of 6.75 (SD = 2.16), while on Emergency Medical Systems was 6.96 (SD = 2.05). Overall, assessment and preparedness measures were reported by just 21.4 and 37.6% of respondents, respectively. Analysis by income did not show significant differences, with the exception of food supply. The main differences in the analysis by region were the risks of extreme weather events, vector-borne diseases and wildfires, whereas the World Risk Index was food and chain of supplies. Education and integration of health services were indicated by all as the main mitigation actions. Geographical position and country risk index influence risk perception among focus groups more than income economy, with vector-borne diseases, extreme weather events and food shortages being the threats with the most variability. The most important actions identified to mitigate Climate Change effects are educational and strategic plans.

Genetic variation in surface temperature measured using infra-red thermography and genetic associations with production traits in grazing dairy cattle

ABSTRACT Increasing global temperatures and the incidence of extreme weather conditions will result in heat stress becoming a greater issue in production animals. Genetic selection and breeding for heat-tolerant animals have been promoted as a possible mitigation strategy in dairy cattle. The objectives of this study were to obtain in-field skin temperature measurements of the eye, muzzle and udder using infra-red thermography to examine the genetic variation in skin temperature within cows of a dairy herd and to estimate the genetic correlations between skin temperature and production traits. Thermal images and herd test records were obtained for the dairy herd at Massey University’s dairy farm 1. Estimates of (co)variances were obtained using the JWAS program with univariate and bivariate animal models. The heritability estimates for the eye, muzzle and udder temperature were low to moderate at 0.20, 0.24 and 0.39, respectively. All genetic correlations between production and temperature traits were positive except for eye temperature with milk yield and protein yield which was negative and weak. These results indicate that it may be possible to select for a greater skin temperature, however, these results need to be validated using a larger sample size.

Climate Change Impacts on Agricultural Systems Mitigation and Adaptation Strategies: A Review

Climate change poses a substantial threat to global agricultural systems, impacting crop yields, livestock productivity, and resource availability through rising temperatures, altered precipitation patterns, and increased frequency of extreme weather events. This review synthesizes current understanding of climate change impacts on agriculture and explores strategies for mitigating greenhouse gas (GHG) emissions while enhancing system resilience through adaptation. Mitigation approaches, such as conservation agriculture, agroforestry, and improved nutrient and livestock management, show significant potential to reduce emissions and increase carbon sequestration. However, economic, technological, and institutional barriers such as high implementation costs, limited access to finance, and inadequate policy support restrict their adoption. Adaptation strategies, including crop diversification, advanced water management, and climate-resilient technologies, are critical for sustaining agricultural productivity in variable climates. Synergies between mitigation and adaptation, such as integrating trees into croplands, can provide co-benefits, but trade-offs arise when resource competition occurs or land is diverted from food production to carbon sequestration. The review highlights the need for interdisciplinary research, robust data infrastructure, and tailored policies to bridge knowledge and resource gaps, particularly in vulnerable regions. Successful implementation requires coordinated efforts across stakeholders, supported by enabling policy frameworks that incentivize sustainable practices and align with global climate goals. Future research should prioritize understanding the long-term impacts of integrated strategies, enhancing monitoring systems, and fostering inclusive approaches that consider local contexts and socio-economic conditions. Addressing these challenges holistically will be crucial to building resilient agricultural systems capable of maintaining food security and supporting livelihoods in the face of accelerating climate change.

The effect of day-to-day temperature variability on agricultural productivity

Abstract With rising extreme weather events due to climate change, the impact on agricultural production has become increasingly severe. Yet, there has been a significant gap in research that assesses the influence of day-to-day temperature variability on agricultural productivity on a global scale. Our study addresses this gap by exploring the effects of day-to-day temperature variability and the change of rainfall patterns on agricultural productivity worldwide from 1961 to 2018. The results reveal that day-to-day temperature variability not only has a direct, negative impact on agricultural Total Factor Productivity (TFP), but also influences it by modulating the effects of monthly average temperatures and wet days. One unit increase of day-to-day temperature variability leads to a 2% decrease in TFP. Day-to-day temperature variability neutralizes the impact of monthly average temperature on TFP, while exacerbating the impact of wet days on TFP. Furthermore, extreme rainfall events result in a consistent negative marginal effect across all countries/seasons/rainfall intervals. This study also identifies differentiated impacts across countries with varying income levels. Low-income regions’ TFP demonstrates markedly significant sensitivities to both monthly average temperatures and daily temperature fluctuations, which means less resilient. Furthermore, the impacts of general and extreme rainfall are comparatively less pronounced in high-income countries, indicating higher resilience to climate variability in these regions and a relative vulnerability to extreme weather events in low-income regions. Our findings illuminate the intricate and multifaceted role that daily temperature variability plays in agricultural productivity, providing a theoretical basis for understanding the heterogeneous impacts of climate change on agriculture and contributing insights into the broader discourse on climate resilience and agricultural sustainability.