Changes In Weather Patterns Research Articles

Community Perceptions on the Effects of Climate Change on Socio-Economic and Health Conditions of Dikgale Community, Limpopo Province South Africa

As a development issue, climate change necessitates extensive research to provide information about its effects on socioeconomic conditions and rural community health. The types of adaptation measures developed and used to adjust to its effects, also known as resilience, are a major focus among scholars in the field. There are evidence that poor and rural communities whose economies rely on favourable climatic conditions bear the harsh effects of climate change. Scientists face significant challenges in explaining climate change and its risks to rural populations. Fewer studies show that these communities are aware of the effects of changing weather patterns on their livelihoods. This study investigates the community's perspectives on the effects of climate change on socioeconomic and health situations in the Dikgale community, Limpopo province, South Africa. The findings indicate a decline in a subsistence economy, a lack of water, and exposure to dust and wind. Due to the health risks associated with climate change, adaptation initiatives in African rural communities should focus on improving people's socioeconomic conditions and long-term development projects.

Student knowledge and perceptions of climate change and environmental sustainability at the Faculty of Health Sciences, University of Cape Town, South Africa


 
 
 
 Background. Climate change and polluting healthcare systems are significant threats to public health. Education about planetary health and environmentally sustainable healthcare is needed to equip health professionals to meet these challenges.Objectives. To assess the knowledge, perceptions and understanding of climate change and environmental sustainability among undergraduate health sciences students at the Faculty of Health Sciences (FHS), University of Cape Town (UCT).
 Methods. Two student-led focus groups helped to design a cross-sectional survey of all undergraduate health sciences students. The survey findings were analysed quantitatively and thematically.Results. The 264 respondents included 211 medical and 53 health and rehabilitation students. Two-thirds of respondents (64.4%) claimed awareness but little understanding of climate change, which was understood mainly as changes in weather and climate patterns (40%) as a result of human activities (96.6%). Most (72%) were aware of the concept of environmental sustainability, but with little understanding. Students’ main sources of information about climate change were the internet (84.1%) and social media (77.3%). Two-thirds believed that climate change will highly impact their patients’ health and quality of life. Most (58.3%) thought that health professionals can help to prevent climate change by educating patients and promoting sustainable lifestyles. Nearly half (47.3%) thought it important to teach climate change and environmental sustainability in the curriculum.
 Conclusion. Climate change and environmental sustainability have been poorly incorporated into current FHS, UCT curricula. Students acknowledged that these concepts are important and should be taught. The FHS should integrate planetary health and environmental sustainability into its curricula, in line with global efforts.
 
 
 

Weather-Based Fruit Fly Population Dynamics Prediction Model for the Mid-Hills of Eastern Himalayan Region of India

Across the globe, it is widely observed that current change in climate or weather pattern has marked effect on insect population dynamics and behavior, though with varied aspects and intensity. Understanding the correlations of insect population dynamics with weather parameters is the fundamental first step in formulating an effective integrated pest management programme. In the major citrus growing mid-hill regions of Arunachal Pradesh, fruit fly, Bactrocera dorsalis, is one of the chief reasons for heavy loss leading to pre-harvest fruit drop in citrus. The current study aims to determine how changes in weather parameters influence the population dynamics of fruit flies and to develop an optimized weather-based population prediction model for fruit flies infesting citrus in the mid-hills of Arunachal Pradesh. The population was monitored through standard methyl eugenol para-pheromone traps and a model was developed using stepwise multiple regression technique. The study revealed that the population was highest during the fruit development and ripening stage and it was found that the number of flies per trap and meteorological parameters (temperature and humidity) is positively correlated with appreciable statistical significance. The optimized regression model was developed with variables temperature and humidity, and was found to be 79 percent accurate in the study site. This model will serve as an important tool in pre-estimation of the fruit fly population in the citrus growing mid-hills of Arunachal Pradesh and for formulation of effective preventive management strategies.

Weather-Based Prediction Models for the Prevalence of Dengue Vectors Aedes aegypti and Ae. albopictus.

Dengue is an important vector-borne disease transmitted by the mosquitoes Aedes aegypti and Ae. albopictus. In the absence of an effective vaccine, vector control has become the key intervention tool in controlling the disease. Vector densities are significantly affected by the changing weather patterns of a region. The present study was conducted in three selected localities, i.e., urban Bandaranayakapura, semiurban Galgamuwa, and rural Buluwala in the Kurunegala district of Sri Lanka to assess spatial and temporal distribution of dengue vector mosquitoes and to predict vector prevalence with respect to changing weather parameters. Monthly ovitrap surveys and larval surveys were conducted from January to December 2019 and continued further in the urban area up to December 2021. Aedes aegypti was found moderately in the urban area and to a lesser extent in semiurban but not in the rural area. Aedes albopictus had the preference for rural over urban areas. Aedes aegypti preferred indoor breeding, while Ae. albopictus preferred both indoor and outdoor. For Ae. albopictus, ovitrap index (OVI), premise index (PI), container index (CI), and Breteau index (BI) correlated with both the rainfall (RF) and relative humidity (RH) of the urban site. Correlations were stronger between OVI and RH and also between BI and RF. Linear regression analysis was fitted, and a prediction model was developed using BI and RF with no lag period (R 2 (sq) = 86.3%; F = 53.12; R 2 (pred) = 63.12%; model: Log10 (BI) = 0.153 + 0.286 ∗ Log10 (RF); RMSE = 1.49). Another prediction model was developed using OVI and RH with one month lag period (R 2 (sq) = 70.21%; F = 57.23; model: OVI predicted = 15.1 + 0.528 ∗ Lag 1 month RH; RMSE = 2.01). These two models can be used to monitor the population dynamics of Ae. albopictus in urban settings to predict possible dengue outbreaks.

A Review of Modern Machine Learning Techniques in the Prediction of Remaining Useful Life of Lithium-Ion Batteries

The intense increase in air pollution caused by vehicular emissions is one of the main causes of changing weather patterns and deteriorating health conditions. Furthermore, renewable energy sources, such as solar, wind, and biofuels, suffer from weather and supply chain-related uncertainties. The electric vehicles’ powered energy, stored in a battery, offers an attractive option to overcome emissions and uncertainties to a certain extent. The development and implementation of cutting-edge electric vehicles (EVs) with long driving ranges, safety, and higher reliability have been identified as critical to decarbonizing the transportation sector. Nonetheless, capacity deteriorating with time and usage, environmental degradation factors, and end-of-life repurposing pose significant challenges to the usage of lithium-ion batteries. In this aspect, determining a battery’s remaining usable life (RUL) establishes its efficacy. It also aids in the testing and development of various EV upgrades by identifying factors that will increase and improve their efficiency. Several nonlinear and complicated parameters are involved in the process. Machine learning (ML) methodologies have proven to be a promising tool for optimizing and modeling engineering challenges in this domain (non-linearity and complexity). In contrast to the scalability and temporal limits of battery degeneration, ML techniques provide a non-invasive solution with excellent accuracy and minimal processing. Based on recent research, this study presents an objective and comprehensive evaluation of these challenges. RUL estimations are explained in detail, including examples of its approach and applicability. Furthermore, many ML techniques for RUL evaluation are thoroughly and individually studied. Finally, an application-focused overview is offered, emphasizing the advantages in terms of efficiency and accuracy.

Shoot Phenology in Bambusoideae: A Review

The study of plant phenology is important nowadays since global climate-changing phenomena are impacting the growing patterns and growing periods of plants. Bamboo is of great importance to the agriculture and forestry of temperate, subtropical to tropical regions, especially of Asia. Although some temperate genera can thrive under different climatic conditions, from the Korean Peninsula to South China, it is not known how bamboo will be affected by climate change, so the collection of data related to bamboo phenology could be of interest to research related to climate change. In this review, we describe available data on the phenology of 8 temperate genera, including 79 species, varieties and forms and 4 subtropical–tropical bamboo genera, including 19 species. Primarily, culm shoot physiology is discussed with some reference to leaf phenology data, where available, as well as their interaction. Since the data available in Western literature is often limited to the definition of season rather than exact dates and periods of given months, there is still a great need to explore more about the exact phenology of individual bamboo species to be able to determine the impact of periodic changes in weather patterns or climate change on bamboo phenology in the future.

Chad: Human Fertility, Crop Production and Changing Weather Patterns

This paper investigates the effect of changing weather patterns on human fertility in Chad. There is literature on the effect of mortality inducing extreme weather events and fertility. However, there is less literature on the effects of less extreme changing weather patterns on fertility. We adapt Becker’s model of demand for children to a rural Chadian context and test the model with Chadian data and explore whether any effect is through biological or agricultural mechanisms. Using GIS-coded fertility and weather data, we look for correlations between the birth rate and the number of monthly high temperature days, while partitioning data by climatic zone and staple crop intensity. Running the same models with sorghum yield as the dependent variable, we find a pattern that the same planting season high temperature days that have a negative effect on the birth rate, also have a negative effect on sorghum yields, especially in the Sahel region. This accords with our model and provides tentative evidence of an agricultural mechanism by which heat affects fertility.

Debris flow occurrence under changing climate and wildfire regimes: A southern California perspective

In southern California, wildfire incidents are increasing in frequency and magnitude. There is a great deal of research predicting the impact of climate change on large scale fire regimes. These impacts, now the new normal, include increases in future fire incidents and burn severities. Yet predictive studies on how ecosystems respond to changing fire regimes are lacking. Identifying trends in post-fire erosion patterns, such as post-fire debris flows, is critical for increasing human resiliency and adaptation to changing weather patterns and eventual long-term climate change. The relative historical abundance of post-fire debris flows in southern California presents this concern: will worsening wildfire regimes result in more post-fire debris flows? Our research addresses this concern by utilizing a descriptive approach to analyze post-fire debris flows in southern California from 2001 to 2018 to elucidate possible trends. We found that 84 % of debris flows originate from watersheds with soils burnt at moderate soil burn severity. We found that debris flow occurrence is more reliant on the percent coverage of a soil burn severity classification than its areal extent. Further, larger fires also tended to produce more debris flows and most (73 %) debris flow watersheds occurred in predominantly bedrock areas. Our research suggests that the number of post-fire runoff-generated debris flows in the region will increase if fires continue to increase in size, occur on slopes of at least 20°, burn south-facing watersheds with bedrock of igneous or sedimentary origin to at least a moderate severity, and peak rainfall intensity return intervals do not change. These findings highlight the interconnectivity between natural erosive processes, wildfires, humans, and climate change.

Influence of Weather Patterns and Changes on Sickle Cell Disease Vaso-Occlusive Episodes and Acute Chest Syndrome: A Nationwide Sample Analysis

Introduction: Vaso-occlusive episodes (VOE) in sickle cell disease (SCD) contribute to significant morbidity and healthcare costs due to repeated emergency department (ED) visits and hospital stays. Recurrent VOE's also often trigger serious sequelae, such as acute chest syndrome (ACS). Known risk factors for VOE include dehydration and infection. The effect of weather on the incidence of VOE has been studied in specific geographic locations with inconsistent results. Our study aims to determine the correlation between change in weather patterns and VOE nationwide using a large dataset. Methods: After IRB approval, the Pediatric Health Information System (PHIS) database was queried for SCD patients younger than 21 years of age evaluated in the ED or admitted for VOE and ACS from 2016-2021. The PHIS is a comparative database with clinical and resource utilization data for inpatient, ambulatory surgery, ED, and observation unit encounters for more than 49 children's hospitals nationwide. Patients with type HbSS, HbSC, Hb Sβ+, and Sβ(0) SCD were included. Demographic and clinical information was collected, including age, sex, race, geographic location, and inpatient length of stay. Weather information was collected via the National Climatic Data Center database. The weather variables included average temperature (Tavg), delta in temperature (TΔ), average precipitation, and average wind speed for every city in which a pediatric hospital within the PHIS database is located. Descriptive statistics were used to describe the demographics and weather characteristics. Population-averaged GEE analysis with negative binomial distribution with log link was performed with weekly counts of VOE admissions as the outcome, weather parameters as predictors, and hospitals as clusters. The weather parameters used were the averages of the daily units by week. Results from the regression analyses were reported as incidence rate ratios with 95% confidence intervals. A two-sided p-value less than 0.05 was considered as statistically significant for the analysis. Results: Of the total 79, 872 encounters included, 86% were diagnosed with VOE and 14% with ACS. Approximately, 52% of patients presented to hospitals in the southern region (Table 1). Regions (number of participating hospitals) include midwest (MW, 8), northeast (NE, 4), south (S, 11), and west (W, 5). Information from both the PHIS and weather database were available for 11,547 weeks. The south had the highest Tavg, TΔ, and precipitation. The MW had the highest wind speed and lowest Tavg. The NE had the lowest TΔ and Tavg. The west had the lowest wind speed and precipitation. The weather variables remained non-significant for ACS admissions. Overall, Tavg, TΔ, and wind speed were significant for VOE admissions while Tavg, precipitation, and wind speed were significant for VOE ED visits. Table 2 highlights the associations between overall VOE visits, weather variables, and regions. Conclusion: Our results show average temperature and delta in temperature (TΔ) is associated with the incidence of VOE admissions and ED visits. A positive association is observed in the midwest and northeast region for Tavg and negative association in the northeast region for TΔ. Future prospective studies will need to assess the effect of weather on VOE in a larger population, so specific weather-related guidelines can be developed for SCD patients and families. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Assessment of solar load models for bifacial PV panels

Solar load is one of the key inputs in thermal analysis of all solar based applications using ray tracing. Commercial and academic Computational Fluid Dynamics (CFD) codes incorporate different solar load models for ray tracing, i.e., Solar Position and Intensity (SOPLOS) theoretical maximum function, American Society of Heating, Refrigeration, and Airconditioning Engineers (ASHRAE) fair weather and constant solar load models. However, solar load depends largely on weather conditions of the site whereas the solar load models in CFD software do not accommodate changing weather patterns and hence the CFD simulation results obtained are not representative of an extended period of time. This paper studies the effect of changing weather patterns on solar load assessment, using bifacial solar panels as a case study. In this study, on-site data of a humid sub-tropical region for monsoon season, mid-June to mid-August, has been used as an input for solar ray tracing due to large temperature variations and cloud cover for longer duration. Comparative study of SOPLOS and ASHRAE models with in situ model shows that they over predict front side solar load, with only 0.5% and 13% matching in situ data respectively, while both models under predict rear side solar load in the studied time period, with 2% and 24% solar load estimation agreeing with in situ data respectively.

Living with typhoons and changing weather patterns: Indigenous resilience and the adaptation pathways of smallholder farmers in Taiwan

Living with typhoons and changing weather patterns: Indigenous resilience and the adaptation pathways of smallholder farmers in Taiwan

Impact of controlled drainage on corn yield under varying precipitation patterns: A synthesis of studies across the U.S. Midwest and Southeast

Controlled drainage (CD) is a valuable management practice for reducing drainage volume and nutrient loss, but its impact on corn (Zea mays L.) production is not completely understood. The objectives of this study were to investigate the regional effect of CD on corn grain yield compared to free drainage (FD), investigate the factors influencing corn yield response to CD, provide management recommendations for optimizing corn yield under CD, and identify future research needs for corn production on poorly drained soils with subsurface drainage systems. This synthesis included data collected from 13 field sites where corn was planted under both FD and CD in six U.S. Midwestern states and North Carolina totaling 55 site-years of data from 2006 to 2017. On average, there was no statistically significant difference in corn grain yield between CD (10.62 Mg/ha) and FD (10.53 Mg ha−1). However, 42% of the dataset indicated that CD either increased or decreased corn yield by 4% or more compared to FD. Further analysis was conducted on this subset of data in order to evaluate underlying factors (i.e., weather conditions during the season, soil type, and drainage system design and management) influencing corn yield response to CD. Results of this analysis showed that CD was effective in alleviating plant stress caused by mild to moderate drought conditions and subsequently increased corn grain yield by 4–14% in 12 site-years. In contrast, CD reduced corn grain yield by 4–10% during wet growing seasons (6 site-years). Variability in growing season precipitation has been identified as a key factor influencing corn grain yield under CD, and more active management or CD system automation is recommended. General recommendations are provided for managing manually operated CD systems in the U.S. Midwest to improve growing season water management and corn yield. Additional research to develop technologically advanced water management systems for crop production on poorly drained soils is needed in order to adapt to changing weather patterns.

Climate change poses a threat to nutrition and food security in Kilifi County, Kenya.

Over the last decades, increased emission of greenhouse gases has led to hot weather extremes, heavy precipitation and worsening of agricultural and ecological droughts. Although Africa's contribution to climate change is minimal, the continent is especially vulnerable to its effects. This report aims to describe the effect of climate change leading to drought in Kilifi County, Kenya, and the communities' experiences of this effect on food availability. During their community rotation, residents from a university in Nairobi, Kenya, evaluated changes in weather patterns and nutrition indicators in Kilifi County and conducted focus group discussions (FGDs) with community members and health care stakeholders to explore challenges in access to adequate nutrition and possible local solutions. Kilifi County has one of the highest rates of undernutrition in Kenya, with one in five under-5 children being underweight. County data showed that rainfall in the last 4 years has become increasingly unpredictable, resulting in reduced household milk production, one of the indicators of nutrition security. Three major themes emerged from the FGDs: lack of food variety, collapse of drought mitigating projects and increasing poverty levels. Possible solutions to these problems include promoting alternatives to the current diet that are culturally sensitive and adaptable to recent climate changes, ensuring continuity of agricultural and financial support projects and improved local leadership and governance.

A spatio-temporal analysis of changing trends in rainfall patter: A case study of Kutch District

Changing trends captivate millions of analytical minds. It grabs much when the impact on human lives and properties is involved. The utmost concern is changing weather patterns, which led to a worse impact on the amount and distribution of the precipitation. Essentially, the challenge in several nations is to reach the whole population with adequate water per day. Even a country like India, which has thousands of rivers, streams, lakes, ponds, etc. unable to reach the whole population, as it depends on the uneven event, the Monsoon for its >80% annual rain during monsoon months (June-September). The Nation’s westernmost district, Kutch, which holds the title for the largest area-wise district in India, has been bearing from changing trends in rainfall for a long time. The rainfall distribution within the district is uneven and has experienced contrasting extreme events like drought and flood within consecutive years. The present study attempts to analyse the Spatio-temporal rainfall pattern for 58 years from 1961 to 2018 for the Kutch district using the IMD4 long-term daily gridded (High Spatial Resolution, 0.25° x 0.25°) rainfall dataset. In addition, the Mann-Kendall (MK) test and Sen’s slope method were performed to detect the trends and the magnitude of change over the studied period, respectively. The results indicate high rainfall variability during monsoon months in the north and northwest, while relatively moderate and low in the south and west parts of the district, respectively, and show an upward trend in monsoon rainfall at a 5% significance level in all the Taluka of the Kutch district.

Sentiment Analysis on Twitter Social Media towards Climate Change on Indonesia Using IndoBERT Model

The phenomenon of climate change is a change in temperature and weather patterns in the long term. This incident became a frightening specter for everyone because consciously or unconsciously the bad effects of climate change are already in sight. This has become an urgency for all levels of society so that this topic has become quite hot on Social Media, especially on Twitter. The topic of climate change in Indonesia on Twitter Social Media can be analyzed so that it can be seen how people's sentiments towards this phenomenon. This research utilizes the Transformer architecture, namely IndoBERT, IndoBERT itself is the development of the BERT architecture by the IndoNLU team which has 74 million words from various Bahasa Indonesia sources. Therefore, this method was chosen in the hope of helping sentiment analysis on the topic of climate change so that public sentiment can be mapped. The test results obtained an F1-Score values of 95.6% with a tuning parameter of 0.00002 learning rate and 16 of batch size. Hopefully the results of this research can be used in future research.

Climate Change and associated Glacier Retreat in Bhilangana Basin: A Case Study of Khatling Glacier, Uttarakhand from 2000 to 2018

Climate change is the most pressing issues of 21st century which is mostly associated with anthropogenic factors caused due to rapid industaiztion There are various ways of studying climate change like changes in weather pattern ,shift in seasons ,sea level rise and Monitoring Glaciers is also one of the most efficient tool to assess climate change cause rising temperatures starts melting glacier at a faster pace .Himalayan Glaciers are more vulnerable to climate change and have recorded visible retreat in various parts . Glaciers occupy about 10% of the Earth's land surface (~ 16 million Km ) but hold roughly 77% of its fresh water; more than 96% of glacier ice lies in the Polar Regions, Antarctica and Greenland.In the Himalayas, the glaciers cover approximately 33,000 sq km area and this is one of the largest concentrations of glacier-stored water outside the Polar Regions. Post industrial revolution, with the rise in level of CO2, the natural climate of earth is susceptible to frequent unpredictable fluctuations. Since glaciers are considered as the best indicators to Climate Change, the study tries to deduce a positive relationship between the increasing temperatures and Climate change by studying Khatling Glacier, the lateral glacier situated in Tehri district(Uttarakhand) which is the source of river Bhilangana. The moraines on the side of the glaciers look like standing walls of gravel mud. In this paper Khatling Glacier is observed for a period from 2000-2017 with the help of Landsat and Sentinel Satellites . The study will help in assessing the glacial change in the region and also find out any developing Glacial Lakes (moraine dammed lakes ) in the region which will help in striking a relationship between rising temperature and climate change and also risk mitigation in case of Glacial lake outburst flood in case of dam breach in flood plains.

Pelagic seabirds reduce risk by flying into the eye of the storm

Cyclones can cause mass mortality of seabirds, sometimes wrecking thousands of individuals. The few studies to track pelagic seabirds during cyclones show they tend to circumnavigate the strongest winds. We tracked adult shearwaters in the Sea of Japan over 11 y and found that the response to cyclones varied according to the wind speed and direction. In strong winds, birds that were sandwiched between the storm and mainland Japan flew away from land and toward the eye of the storm, flying within ≤30 km of the eye and tracking it for up to 8 h. This exposed shearwaters to some of the highest wind speeds near the eye wall (≤21 m s-1) but enabled them to avoid strong onshore winds in the storm's wake. Extreme winds may therefore become a threat when an inability to compensate for drift could lead to forced landings and collisions. Birds may need to know where land is in order to avoid it. This provides additional selective pressure for a map sense and could explain why juvenile shearwaters, which lack a map sense, instead navigating using a compass heading, are susceptible to being wrecked. We suggest that the ability to respond to storms is influenced by both flight and navigational capacities. This may become increasingly pertinent due to changes in extreme weather patterns.

The role of climate change in the spread of vectors and vector-borne disease in Windsor-Essex County

West Nile virus (WNV) and Lyme disease have emerged as significant public health concerns in many parts of Canada. Vector-borne diseases (VBDs) are susceptible to climate change and changing weather patterns because mosquitoes’ and ticks’ lifecycles are significantly impacted by temperature, precipitation, and humidity. Over the last several decades, weather patterns related to climate change in Windsor-Essex County demonstrate tendencies for the further proliferation of invasive mosquito species in the area. In this study, increasing maximum January and February temperatures and number of days in May with temperatures above 30 °C demonstrated a positive impact on the number of WNV-positive pools and the annual rate of WNV. Given the results of this study, health units should consider adapting their vector-borne management strategies and risk assessment tools to include these parameters, which can help health units assess VBD risks for people during the season and develop risk communication strategies to protect public health.

Impact of planting date on CO2 fluxes, evapotranspiration and water-use efficiency in peanut using the eddy-covariance technique

The combination of increased climate variability with changing weather patterns heightens the importance of the role of planting date on water use in peanut production. Experiments addressing different peanut planting dates were conducted over three years and daytime ecosystem water-use efficiency was determined using the eddy-covaraince method. Results suggest that during the early growing stage in all three planting dates, a late planting date presented by far the highest CO2 fluxes and daytime ecosystem water-use efficiency. Of immediate significance, the latest planting date was also found to exhibit the highest yield in two of the three years in the experiment. Furthermore, an examination of the last 20 years (2001-2020) of climatological records suggests that a late planting date carries a risk of 40% chance of sub-optimal low temperatures in early fall, potentially reducing the yield in those years. In addition, the late planting date carries 15% probability of ten or more consecutive rainy days in the narrow harvest window, potentially impacting a timely harvest.

The World Starts Outside Your Comfort Zone

It's easy to feel uncomfortable as our shared world is experiencing many serious challenges. We increasingly experience loss from raging fires, extreme heat waves, floods, and changing weather patterns around the globe related to climate change. The Covid-19 pandemic has killed and disabled so many while stressing health system resources and our clinical teams’ boundaries. Another senseless war has been waged in Ukraine, with no apparent end in sight. Collectively, the current state of our world has challenged the emotional comfort of millions of people.