Impact Of Extreme Weather Research Articles

Extreme weather impact on carbon-neutral power system operation schemes: A case study of 2060 Sichuan Province

The impact of increasingly frequent and intense climate change events on power supply and consumption has become a concern for many countries. This study evaluated the impact of extreme weather on the power system economy and security by conducting an empirical case study of the Sichuan power network to address uncertainty attributed to different extreme weather events under carbon neutrality scenarios. First, our paper proposed a CNPSSO model of four seasonal typical days by jointly considering the regional hourly disparities of load curves and renewable power output curves. Then, the model creatively introduced the effects of extreme weather on the power supply and demand curves based on various scenario analyses. The most cost-effective output strategy using various generation technologies was provided based on optimization models. Finally, Sichuan Province, a hydropower-dominated province was selected as an empirical case study. The results indicate that extreme weather significantly affects the hourly operational and dispatching schemes of Sichuan's power system. Compared to the "Normal weather scenario”, hydropower's share plummets from 83.3 % to 46.1 %, while pumped storage surges 7.1 billion kWh in summer in extremely hot and dry weather. Imported electricity rises by 24.4 %, 30.5 % and 24.8 % in dry scenario, extremely hot and dry scenario, and extremely cold scenario, respectively. Furthermore, extreme weather increases carbon emissions (mainly from thermal power and imported electricity) and costs. Specifically, when juxtaposed against normal weather, extreme drought, extreme heat drought and extremely cold lead to respective increases in carbon emissions by 32.9 %, 33.5 %, and 12.3 %. Regarding the levelized cost of electricity (LCOE), "Extremely dry scenario”, "Extremely hot and dry scenario”, and "Extremely cold scenario” drive up the LCOE by 7.2 %, 9.2 %, and 4.2 %, respectively.

Effects of climate change on firm credit risk: evidence from China

ABSTRACT Our study examines the impact of extreme weather on corporate credit risk, identifies the impact pathway, and explores the influences of different heterogeneity contexts. We propose a modified version of the climate risk measure, Modified Regional Wise Climate Risk Index for Company (MRW-CRIc), which considers not only the climate risk on financial loss but also company-specific factors. We find that climate risk significantly affects enterprise credit risk and that the impact of extreme weather varies across three industries. This research contributes to regulatory efforts by enhancing the measurement of climate risk and deepening understanding of its implications for market stability.

A Review of the Impact of Extreme Weather on Agricultural Economics

A Review of the Impact of Extreme Weather on Agricultural Economics

A cross-sectional study: the impact of extreme weather, sleep quality, and physical activity

This study aims to explore the impact of extreme weather condition on sleep quality and physical activity in Gorontalo using a cross-sectional research design. The data were collected from 220 respondents aged 18-25 using the Pittsburgh Sleep Quality Index (PSQI), International Physical Activity Questionnaire (IPAQ), and weather information from BMKG Gorontalo. The results showed that extreme weather, especially high temperatures, significantly reduced physical activity and affected sleep quality. Respondents with higher physical activity levels showed better sleep quality (lower PSQI scores). Extreme weather also reduces motivation to exercise, increasing the risk of health problems such as dehydration and heat stroke. In conclusion, public health interventions that promote physical activity and improved sleep quality during extreme weather conditions are urgently needed to reduce associated health risks. This study emphasises the importance of understanding the specific mechanisms of how extreme weather affects sleep and physical activity, and developing strategies to maintain healthy routines despite adverse weather conditions. Keywords: extreme weather, sleep quality, physical activity, public health, health interventions.

Mitigating the effects of extreme weather on crop yields: insights from farm management strategies in the Netherlands

Abstract Weather extremes can drive substantial crop losses. Farm level management strategies play a critical role in mitigating the impacts of and consequences for farmer livelihoods and food security. While the impacts of extreme weather on crop yields are well documented in recent studies, these predominantly focused on expansive geographical scales and commonly overlooked the critical role of management practices in modulating the dynamics of weather-crop sensitivities. In our study, a unique data-set is used that explores the timely relationship between extreme weather and crop yields at farm level in the Netherlands. We cover 10 types of crops and elucidate the role of soil types, irrigation and nutrient application in modulating the relationship between extreme weather and crops. We show substantial impacts from drought during the growing- and harvesting period and excessive precipitation during the planting- and growing period. Severe droughts show significant (p≤0.05) reductions in yield for all crops, and lead to yield reductions up to 24 percent relative to average yields during the growing period. Meanwhile, eight crops show significant reductions in yield due to severe water excess during the planting period, with yield reductions up to 18 percent. Soils such as sand or loess amplify the negative impact of drought on crop yield, while softening the impact of excessive precipitation. Irrigation and to a lesser extent nutrient application are shown to moderately decrease the impact of extreme weather on crop yield. Our findings contribute valuable insights to guide local adaptation priorities which are critical given the projected increase in the intensity and frequency of extreme weather under climate change.

Identification of Multi-Microgrid Clusters Using Terminal Spectral Clustering Algorithm

In response to the increasing impact of extreme weather on power distribution networks (PDNs), prioritizing resilience is imperative. This study introduces an innovative k-means spectral clustering algorithm to define the boundaries of microgrids (MGs) within a multi-microgrid (MMG) system. The aim is to improve reliability by clustering PDNs into resilient MGs. The power systems are modeled with nodes representing buses, and connections are represented as edges. The analysis involves computing the adjacency matrix, degree matrix, Laplacian matrix, and applying k-means clustering to group buses based on terminal point features. Silhouette coefficients (SC) are calculated to assess the quality of the clustering. The proposed method is tested on three IEEE distribution systems: IEEE 33, 69, and 118 bus systems. Findings reveal distinct clusters within each system with SC values above 0.68, particularly emphasizing the significance of terminal points as the basis for assisting power engineers in decision-making for predetermined grid partitioning.

A study based on the impact of extreme weather on the insurance industry

A study based on the impact of extreme weather on the insurance industry

Response to Fishing Decisions and Changes in Fishing Income during Extreme Weather: A Behavioural Economy Study of Small-Scale Coastal Fisher Households

Graphical AbstractHighlight Research1. Decision response of fishermen to fish 2. Changes in fishing income 3. Extreme weather caused by climate change 4. Economic behavior of fishing households. Abstract Extreme weather, attributed to global climate change, has altered the economic behaviour of small-scale fishing households in coastal areas. This study aims to analyse the economic behaviour of fishermen by investigating their fishing decisions using a logistic regression model using the maximum likelihood estimation method and their fishing business income through a normalized profit function. The research employed a quantitative research method with a survey approach conducted in the western coastal region of Barru Regency, Indonesia. The research sample consisted of 209 fishing households. The findings reveal that fishermen fish during extreme weather conditions to meet their household's economic needs, even though their earnings are diminished. Various factors, including the power of the boat engine, fishermen's age and education level, and the potential income from fishing, influence the decision to fish during extreme weather. Additionally, engine fuel prices, education levels, and the number of dependents in the family also impact fishers' income during extreme weather. The most important factor influencing the decision to fish in extreme weather conditions is formal education. Another factor that has the greatest impact on fishermen's income, both in the fishing season and during extreme weather, is the price of engine fuel. Fishermen can adapt to events triggered by extreme weather by implementing specific strategies. For instance, diversifying income by aquaculture production can be an alternative to sea fishing. Diversifying the types of fishing gear to accommodate different weather conditions is equally crucial. Fishing periods and locations can be adjusted to align with prevailing weather conditions. The contribution and significance of this research is to provide insight and understanding of the impact of extreme weather on fishing decisions and changes in the income of capture fishermen. This condition is the economic behaviour of small-scale fishing households in coastal areas when faced with extreme weather events.

Impact of Gale Weather Events on Wind Power Generation

Abstract Due to the influence of global warming, extreme wind weather occurs frequently, especially in extreme weather such as typhoons and cold waves, problems such as wind turbine shutdown, cutting out, and sudden changes in power generation power will occur, which brings great challenges to wind power forecast and safe and stable operation of the power grid. At present, the research and analysis of strong wind weather mostly stay at the single fan level, and the causes of abnormal changes in wind power output are mostly analyzed from the physical level. However, with the large-scale access of wind power to the power grid, the impact of extreme weather on power grid operation is becoming more and more significant, and it is particularly important to study the impact of strong wind weather events on wind power cluster output. This paper summarizes various types of wind power generation weather events, then screens key meteorological factors affecting power generation under different wind weather conditions based on Gaussian search and grey correlation weighting, and finally analyzes the meteorological impact on the actual operation of wind farm stations and clusters under typhoons and cold waves respectively, aiming at the problem of huge deviations in wind power forecast caused by wind. The next research direction and suggestions are put forward.

Extreme weather characteristics and influences on urban ecosystem services in Wuhan Urban Agglomeration

In recent years, there has been a pronounced increase in the frequency of extreme weather events. To comprehensively examine the impact of extreme weather on ecosystem services within the Wuhan Urban Agglomeration (WUA), this study utilized meteorological station data, the Mann-Kendall (MK) test, and the Standardized Precipitation-Evapotranspiration Index (SPEI) to quantify the variation trends in heatwaves (HW) and droughts from 1961 to 2020. Then the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model was employed to evaluate and compare the differences in water yield and climate regulation ecosystem services under various HW, droughts, and HW-drought combination scenarios. The results show that over the past 60 years, the temperature, duration, and frequency of HW have significantly increased in the WUA. Specifically, the highest HW temperature, total HW days, HW frequency, and average HW temperature showed changing trend of +0.17 ℃/decade, +1.4 day/decade, +0.19 event/decade, and +0.07 ℃/decade, respectively. The year 2000 was identified as a mutation year for HW, characterized by increased frequency and heightened severity thereafter. The SPEI value exhibited an insignificant upward trend, with 1980 marked as a mutation year, indicating a decreasing trend in drought occurrences after 1980. Heatwaves have a weakening effect on both water yield and climate regulation services, while drought significantly weakened water yield and had a relatively modest effect on climate regulation. During HW-drought composite period, the average monthly water yield showed a notable discrepancy of 60 mm compared to humid years. Besides, as heatwaves intensify, the area of low aggregation for ecosystem services expands, whereas the area of high aggregation decreases. This study provides a preliminary understanding of the impact of urban extreme weather on urban ecosystem services under changing climatic conditions.

Uncertainty graph convolution recurrent neural network for air quality forecasting

Accurate and reliable air quality forecasts are essential for informed decision-making in urban planning and environmental engineering. However, predicting air quality faces challenges due to significant fluctuations in data, including the impact of extreme weather. To improve the precision of air quality predictions, it is important to account for the long- and short-term temporal dependencies within historical data from stations, as well as the local and global spatial structures among different stations in a specific region. In this paper, we propose a novel uncertainty graph convolution recurrent neural network for air quality forecasting (AirNN). Concretely, we design an uncertainty-guided graph convolution module to enhance the robustness of air quality forecasting. It treats the parameters on each graph convolution layer as probability distributions and employs variational inference to derive optimal estimates for these distributions. In addition, to capture the global correlation of stations in the whole region, we develop a spatial information modulator. It captures the spatial correlation between long-range stations in graph data. Extensive experiments on different real-world datasets demonstrate the proposed AirNN performs favorably against state-of-the-art methods. Especially in the 24-hour prediction, AirNN reduced the root mean square error (RMSE) by 0.5 on two real datasets, indicating that our proposed AirNN exhibits good performance in long-term forecasting.

A Study on the Assessment of the Impact of Extreme Weather on the Insurance Industry and Response Strategies Based on SEIR Modeling

This paper investigates the impact of extreme weather on the insurance industry and proposes a new model for assessing the value of insurance underwriting. The study first collected data on nine indicators from 50 regions, categorized these indicators into three categories: extreme weather, insurance, and region, and then used the hierarchical analysis method (AHP), entropy weighting method (EWM), and CRITIC method to calculate the composite weights to derive the Underwriting Value Index (UVI). In addition, underwriting values for St. Louis and Kingman were evaluated using the ALARP criteria and the EIR model. In order to help communities and real estate developers to carry out rational development, the EIR model was improved to form the SEIR model, which was applied to score the development value of nine municipalities in Fujian Province, which showed that Xiamen scored the highest and was recommended to be developed and built in Xiamen. The methodological validation of this study shows that there is a significant correlation between the UVI score and the insurance coverage gap (Pearson correlation coefficient R=0.869), which indicates that the model has good rationality and practicality.

Analysis and Study of Transmission Line Icing Based on Grey Correlation Pearson Combinatorial Optimization Support Vector Machine

In recent years, ice and snow disasters have occurred frequently, and transmission line icing can pose a great threat to the operation of the power system. In order to solve this problem, the power industry should introduce an intelligent monitoring system to recognize the risk of ice-covered disasters in advance and take timely countermeasures to reduce the impact of extreme weather on the safe operation of the power system. In this paper, we first use Python to sort out and summarize multidimensional meteorological data from multiple locations in the Northeast China, and propose a method called Grey Relational Pearson Combination Analysis (GPCA). Through this method, we determine the correlation between multidimensional meteorological data and icing degree, and select the optimal combination of icing factors as the feature vector for a support vector machine. In addition, we also compare the differences in classification effects of four types of kernel functions, such as linear kernel, polynomial kernel and Gaussian kernel. Finally, considering data balance, we use Gaussian kernel to establish GPCA-SVM model and conduct experimental analysis. There was a 1.04% increase in accuracy, a 1.36% increase in recall, and 0.31% increase in F1 value when comparing gray correlation accuracy and a 21.66% increase in precision when comparing Pearson accuracy at light icing. Comparison of gray correlation accuracy at heavy icing improved by 1.04%, precision by 17.44%, and F1 value by 10.29%; comparison of Pearson accuracy improved by 0.31%, precision by 9.93%, and F1 value by 1.67%. These research results lay the foundation for the intelligent monitoring system of power grid, and provide guarantee for the operation and maintenance of power system.

Unraveling Extreme Weather Impacts on Air Transportation and Passenger Delays Using Location-Based Data

Unraveling Extreme Weather Impacts on Air Transportation and Passenger Delays Using Location-Based Data

Research on the Extreme Weather Prediction Model Based on Linear Regression and Its Application in Insurance Evaluation

This study presents a novel approach to predicting extreme weather events using a linear regression model and explores its application in the insurance evaluation process. The predictive model is developed by analyzing historical data on the frequency and intensity of extreme weather occurrences, such as floods, droughts, and hurricanes. The model employs cumulative data analysis to establish a linear relationship between the occurrence of extreme weather events and the passage of time. The results demonstrate a high correlation coefficient, indicating a reliable prediction capability. The study then extends the application of this model to the insurance sector by constructing an insurance evaluation model. This model assesses the feasibility of providing insurance services in areas prone to extreme weather by comparing insurance premiums with potential claims. The evaluation model incorporates the impact of extreme weather on three major industries: agriculture, manufacturing, and services, and assigns weights accordingly. Case studies of Japan and Florida, USA, are conducted to validate the model's effectiveness in real-world scenarios.

Impact of climate change and genetic development on Iowa corn yield

The vulnerability of corn yield to high temperature and insufficient rainfall in the US mid-west is widely acknowledged. The impact of extreme weather and genetic development on corn yield is less well known. One of the main reasons is that the multicollinearity in the variables can lead to confounding results. Here we model the impact of climate and genetic development by employing an elastic net regression model to address the multicollinearity issue. This allows us to develop a more robust multiple regression model with higher predictive accuracy. Using granular data for Iowa from 1981-2018, we find that corn yield is vulnerable to high mean summer temperatures particularly in July, a widening diurnal temperature range in June and dry summer conditions (due to extremely low rainfall) from June-August. We find that overall climate impact reduced average annual yield by 0.7%. We also find that genetic development which led to earlier planting dates, widening duration of the reproductive interval, higher growing degree day accumulation and larger net planted area had a beneficial impact on the Iowa corn yield during 1981-2018 resulting in an average annual yield improvement of 1.8% per annum. This provides a basis for optimism that these genetic developments and management practices will continue to adapt and improve in the future to counter the impact of climate change on corn yield. We have also modelled the impact of future climate change using the latest climate projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). These climate projections show that the average temperature during the growing season (MayO-October) will increase by 2.4 -2.9 o C by mid-century while the average spring temperature (March and April) will increase by a relatively slower 1.9 -2.3 o C by mid-century. Additionally, climate projections show that both temperature and rainfall will also become more extreme in the future with the changes varying from spring to summer. Our results show that, just due to climate change alone in Iowa corn yield will decline between 1.4-1.7% per annum until mid-century (or 1.2-2.1% per annum until the late twenty first century).

Optimal PV‐storage capacity planning for rail transit self‐consistent energy systems considering extreme weather conditions

AbstractWith the rapid development of electrified rail transportation, the traction load demand of rail transportation has increased sharply, and its operational security under extreme conditions has been highlighted. Given the above background, this paper proposes a planning method for the optimal photovoltaic (PV)‐storage capacity of rail transit self‐consistent energy systems considering the impact of extreme weather. First, the basic structure of a rail transit self‐consistent energy system is presented. Second, considering a power transmission system with line trip‐off under extreme weather conditions, a traction load reduction model is established to obtain the maximum power exchange capability between the power transmission network and rail substations. Subsequently, an optimal planning model for a hybrid energy storage system (HESS) is proposed to minimize the total HESS investment and rail transit system operation costs. Finally, the model is linearized as mixed‐integer linear programming and solved using Gurobi and the Yalmip toolbox. The simulation results verify the effectiveness of the proposed optimal PV‐storage capacity planning for rail transit self‐consistent energy systems.

Weather-related fragility modelling of critical infrastructure: a power and railway case study

Climate change has led to more frequent and severe extreme weather events, which impact critical infrastructure networks such as railway and power systems. Although infrastructure networks are interdependent, the analysis to understand the impact of weather events on infrastructure systems is usually performed in sector-specific silos. A methodology to examine how the same weather events affect different infrastructure sectors is presented, in order to understand cross-sectoral impact of extreme weather for interconnected regional infrastructure. Fragility modelling was used to examine the impact of temperature and rainfall on power and rail system failures using the West Midlands (in the UK) as a case study. The results demonstrated that the impact of temperature was broadly consistent across both infrastructure networks, showing less impact until specific upper and lower thresholds are passed; these thresholds were found to be similar for the different infrastructure networks evaluated, but railway infrastructure was found to be impacted more by lower temperatures. A growing correlation between the number of faults on power and railway systems was also found for both rainfall and temperature, indicating the value in coordinating preparation and planning efforts. For infrastructure operators and owners, regional resilience forums and other decision makers, this study provides an approach to assess the regional impact of extreme weather across multiple infrastructure sectors. The results give useful insights to inform the allocation of resources in response to extreme weather events.

Predicting the magnitude and timing of peak electricity demand: A competition case study

AbstractAs weather dependence of the electricity network grows, there is an increasing need to predict the time at which the network peak load will occur. Improving forecasts of peak hour can lead to more accurate scheduling of generation as well as the ability to use flexibility to improve system utilisation or defer capital investment. While there are extensive benchmark models for forecasting electricity demand, their efficacy at forecasting the time or shape of the peak remains to be seen. Global forecasting competitions provide a unique opportunity to compare multiple methodologies under common performance criteria and incentives. The methodology and results are detailed from the Big Data and Energy Analytics Laboratory Challenge 2022 used by the team ‘peaky‐finders’ and investigates the suitability of using hourly methods to forecast daily peak magnitude, time, and shape. The resulting approach provides a reproducible ensemble benchmark against which to evaluate more complex methods. Results indicate that simple regression techniques can perform well and outperform more complicated methods during seasons with low hourly variability, however ensemble methods show higher accuracy overall. The results also highlight the significant impact of extreme weather on forecast accuracy, demonstrating the importance of forecasting processes that are resilient to extreme weather.

Evaluating the impact of agricultural production efficiency on sustainable development goals in coffee‐producing countries in Africa

AbstractClimate change has directly impacted coffee‐producing regions in Africa. This study uses the two stage Dynamic Network Directional Distance Function (DN‐DDF) and Malmquist Productivity Index (MPI) methods to evaluate the efficiency and productivity changes of agricultural production and sustainable development in 19 African coffee‐producing countries from 2014 to 2018. According to the empirical results, Central African Republic, Ethiopia, Liberia, Uganda, and Zambia have the highest overall efficiency. This study has three main contributions: (1) using a two‐stage model to evaluate the impact of agricultural production efficiency on sustainable development efficiency, (2) using a dynamic model with forest area as a cross‐period variable to reflect the real‐world phenomenon of coffee relying on forest mixed planting, and (3) objectively evaluating the impact of extreme weather on agricultural production by using rainfall as an exogenous variable to address previous research shortcomings. The results of this study can promote the improvement of agricultural production efficiency in African coffee‐producing countries and provide important reference value for sustainable development goals.