Impact Of Climate Variability Research Articles

Assessing Commercial Sugarcane Irrigators’ Intentions to Adapt Water-Use Behaviour in Response to Climate Variability in South Africa

The scarcity of water resources in South Africa remains a considerable challenge for water users. This study evaluated the impact of climate variability on the adaptive water-use behaviour of sugarcane producers by identifying the factors influencing their adaptation decisions. A survey, the Theory of Planned Behaviour (TPB), and structural equation modelling (SEM) were used to achieve this objective. The study involved 54 sugarcane producers from the Impala Irrigation Scheme, selected through random sampling. Socio-economic profiles revealed a largely male, older demographic with varied education levels and farm characteristics. Results indicated that attitude (β = 0.349, p < 0.1) and subjective norms (β = 0.281, p < 0.05) significantly influenced farmers’ intentions to adapt, while perceived behavioural control had no significant effect (β = 0.051, p > 0.1). These findings suggest that improving farmers’ attitudes and strengthening social support systems can enhance their intentions to adopt adaptive strategies. However, the model’s explanatory power (R² = 0.276) suggests that other unexamined factors may also influence farmers’ adaptive intentions, highlighting the need for further research. Overall, our findings suggest that interventions targeting attitudes, social support, and resource access can improve adaptive behaviours.

Assessing the Impact of Climate Variability and Human Activity on Groundwater Resources in the Meskiana Plain, Northeast Algeria

Assessing the Impact of Climate Variability and Human Activity on Groundwater Resources in the Meskiana Plain, Northeast Algeria

Exploring the Impact of Climate Variables and Scenario Simulation on Ecosystem Service Value Profits and Losses in China

Ecosystem restoration can yield multiple benefits, and the quantitative accounting of ecosystem service value (ESV) profits and losses is of significant importance to the economic benefits of ecosystem restoration. This study reveals the dynamic impacts of climate change on ESVs by analyzing the effects of climate variables on ESV profits and losses across different periods and scenarios. The research findings are as follows: (1) From 1990 to 2020, and extending to simulated projections for 2030, China’s ESV exhibits a high distribution pattern in the southern regions. In 2030, under the natural development scenario (NDS), the southwestern region shows a coexistence of high and low ESVs. Under the ecological protection scenario (EPS), ESV in the southwestern region increases, whereas under the urban development scenario (UDS), ESV in the southwest decreases. (2) In both the NDS and UDS, the trends in ESV profits and losses continue from 2010 to 2020. Under the EPS, there is a significant increase in ESV in the southwestern region. The largest contributors to ESV loss are the conversion of grassland to unused land and forest to farmland. The southwestern region shows the most significant spatial differences in ESV profits and losses, with an increase in ESV profits in the northeastern region. In contrast, other regions show no significant spatial differences in ESV profits and losses. (3) From 1990 to 2000, Bio13 (the precipitation of the wettest month) and Bio12 (annual precipitation) had a significant positive impact on ESV profits and losses, indicating that increased precipitation promotes the functioning of ESVs. This study indicates that fluctuations in precipitation and temperature are significant climate factors influencing the value of ESV. Due to climate change, precipitation patterns and temperature swings are now key determinants of ESV changes. By carefully studying ESV profits and losses and their driving factors, this research can serve as the scientific basis for ecosystem restoration and management strategies.

The Impact of Climate Variability on the Livelihoods of Smallholder Farmers in an Agricultural Village in the Wider Belfast Area, Mpumalanga Province, South Africa

The purpose of this study was to investigate the impact of climate change on smallholder farmers in South Africa, particularly focusing on the relationship between agriculture and weather patterns. Understanding this connection is crucial for helping farmers adapt to changing climate conditions and improve their resilience and sustainability. This research analyses 33 years of climate data (1990–2023) from the Belfast weather station to identify long-term climate trends, seasonal shifts, and the frequency of extreme weather events. Statistical analysis, including the Mann–Kendall test, revealed significant changes in temperature, rainfall, and the intensity of extreme weather events, indicating that climate change is already affecting the region. Specifically, the research highlighted significant damage to agricultural infrastructure, such as greenhouses, due to climate-related wind events. This study emphasises the importance of using digital technologies to monitor weather patterns in real-time, aiding in decision-making, and enhancing agricultural efficiency. Additionally, it calls for further research into the social impacts of climate variability, including its effects on community cohesion, migration, and access to social services among smallholder farmers. These findings provide a foundation for developing effective interventions to support the resilience of smallholder farming communities in the face of climate change. Future studies need to consider how climate variability affects farmers’ abilities to access markets, both in terms of transport and product quality.

Metal-organic frameworks for atmospheric water extraction: Kinetic analysis and stochastic programming under climate variability

Increasing demands for sustainable and distributed freshwater sources drive the exploration of water extraction from ambient air. This study presents a comprehensive computational approach for optimizing unit harvesting cost of the adsorption-based atmospheric water extraction (AWE) systems. There are three objectives (i) assessing the impact of climate variability: utilizing k-means clustering, utilizing climate data in different regions to explore the effects of ambient conditions in dry-hot (California), humid-hot (Florida), and dry-cold (Wyoming) regions, resulting in a preference for harvesting under humid-hot conditions. (ii) performing kinetic analysis: The derived kinetic model connects climate variability to operational time and regeneration temperature, critical process design variables. (iii) assessing adsorption materials: three materials (MIL-100 (Fe), MOF-303, and ZJNU-30) were assessed revealing the impact of variations in maximum capacity and isotherm shape on performance and cost. The optimization algorithm uses a two stage stochastic programming approach to account for climate variability and enables an optimization that balances the capital and operating costs across a range of temperature and humidity conditions.

Impact of Climate Variability and Interventions on Malaria Incidence and Forecasting in Burkina Faso.

Malaria remains a climate-driven public health issue in Burkina Faso, yet the interactions between climatic factors and malaria interventions across different zones are not well understood. This study estimates time delays in the effects of climatic factors on malaria incidence, develops forecasting models, and assesses their short-term forecasting performance across three distinct climatic zones: the Sahelian zone (hot/arid), the Sudano-Sahelian zone (moderate temperatures/rainfall); and the Sudanian zone (cooler/wet). Monthly confirmed malaria cases of children under five during the period 2015-2021 were analyzed using Bayesian generalized autoregressive moving average negative binomial models. The predictors included land surface temperature (LST), rainfall, the coverage of insecticide-treated net (ITN) use, and the coverage of artemisinin-based combination therapies (ACTs). Bayesian variable selection was used to identify the time delays between climatic suitability and malaria incidence. Wavelet analysis was conducted to understand better how fluctuations in climatic factors across different time scales and climatic zones affect malaria transmission dynamics. Malaria incidence averaged 9.92 cases per 1000 persons per month from 2015 to 2021, with peak incidences in July and October in the cooler/wet zone and October in the other zones. Periodicities at 6-month and 12-month intervals were identified in malaria incidence and LST and at 12 months for rainfall from 2015 to 2021 in all climatic zones. Varying lag times in the effects of climatic factors were identified across the zones. The highest predictive power was observed at lead times of 3 months in the cooler/wet zone, followed by 2 months in the hot/arid and moderate zones. Forecasting accuracy, measured by the mean absolute percentage error (MAPE), varied across the zones: 28% in the cooler/wet zone, 53% in the moderate zone, and 45% in the hot/arid zone. ITNs were not statistically important in the hot/arid zone, while ACTs were not in the cooler/wet and moderate zones. The interaction between climatic factors and interventions varied across zones, with the best forecasting performance in the cooler/wet zone. Zone-specific intervention planning and model development adjustments are essential for more efficient early-warning systems.

The Impact of Climate Variability on Cattle Heat Stress in Vanuatu

Heat stress is a climate extreme that impacts cattle health, fertility, feed intake, production, and well-being. In Vanuatu, the beef industry is crucial to local livelihoods and the nation’s economy, thus the objective of this study was to examine the impact of heat stress on cattle health and production. This study uses the Heat Load Index (HLI) and Accumulated Heat Load (AHL) as proxies to assess the impact of heat stress on cattle in Vanuatu over a 30-year period (1994–2023), using the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) data. The analysis examines historical patterns of heat stress in cattle across Vanuatu, identifying more instances of heat stress occurring during the wet season due to characteristically elevated temperatures, humidity, and low wind speeds. Findings also suggest that El Niño events may increase the intensity and duration of heat stress events. These insights inform the development of an Early Warning System for heat stress in cattle, establishing a crucial foundation for targeted adaptation strategies aimed at enhancing the resilience and sustainability of Vanuatu’s beef industry to climate variability and change.

The Effects of Air Quality and the Impact of Climate Conditions on the First COVID-19 Wave in Wuhan and Four European Metropolitan Regions

This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, including the COVID-19 pre-lockdown, lockdown, and beyond periods, this study used a synergy of in situ and derived satellite time-series data analyses, investigating the daily average inhalable gaseous pollutants ozone (O3), nitrogen dioxide (NO2), and particulate matter in two size fractions (PM2.5 and PM10) together with the Air Quality Index (AQI), total Aerosol Optical Depth (AOD) at 550 nm, and climate variables (air temperature at 2 m height, relative humidity, wind speed, and Planetary Boundary Layer height). Applied statistical methods and cross-correlation tests involving multiple datasets of the main air pollutants (inhalable PM2.5 and PM10 and NO2), AQI, and aerosol loading AOD revealed a direct positive correlation with the spread and severity of COVID-19. Like in other cities worldwide, during the first-wave COVID-19 lockdown, due to the implemented restrictions on human-related emissions, there was a significant decrease in most air pollutant concentrations (PM2.5, PM10, and NO2), AQI, and AOD but a high increase in ground-level O3 in all selected metropolises. Also, this study found negative correlations of daily new COVID-19 cases (DNCs) with surface ozone level, air temperature at 2 m height, Planetary Boundary PBL heights, and wind speed intensity and positive correlations with relative humidity. The findings highlight the differential impacts of pandemic lockdowns on air quality in the investigated metropolises.

Holocene paleoenvironmental and paleoclimatic variability in a high mountain lake in Sierra Nevada (Spain): Insights from diatom analysis

This study examines the Holocene history of Río Seco Lake (3040 m a.s.l; Sierra Nevada, Southern Spain) by analysing diatom remains and other paleoenvironmental data. The aim is to understand the impact of long-term environmental and climatic variability on the aquatic ecosystem over the past 21,000 years. Our results suggest that shifts in diatom assemblages were mainly climate-driven in terms of temperature and water availability. The absence of diatom remains during the Late Pleistocene indicated low temperatures and prolonged lake snow cover. Five distinct periods were identified during the Holocene. The high abundance of epiphytic and bog-inhabiting taxa and tychoplanktonic Tabellaria flocculosa in the period 11,000–6700 cal yr BP were indicative of a humid climate. The onset of the tychoplanktonic Aulacoseira alpigena between 6700 and 5100 cal yr BP indicated a drop in temperature. These changes intensified during the period 5100–3300 cal yr BP, when the most significant changes in diatom assemblages took place with the dominance of A. alpigena and an abrupt increase in the abundance of the epiphytic Fragilaria radians. During the subsequent period (3300–1500 cal yr BP), the significant declines in A. alpigena and in epiphytic taxa were indicative of increased aridity and higher alkalinity values due to increased aridity and Saharan dust input during this period. The last period (1500–256 cal yr BP) was characterized by a rise in the abundance of Staurosirella pinnata, indicative of warmer temperatures and higher alkalinity values coincident with a marked increase in proxies of temperature and aridity. The increase in aridity and temperature during the last period, which has led to changes in diatom assemblages, is a matter of great concern in an ecosystem that is particularly susceptible to global warming.

Penguatan Kapasitas Adaptasi Generasi Z Pedesaan Dalam Menghadapi Variabilitas Iklim

Introduction: The climate variability that occurs in Indonesia results in various losses for society, especially in rural areas. Rural communities whose livelihoods depend on agricultural and fishing activities are very threatened by climate variability which hampers the community's economy. Objective: This service aims to analyze participation in strengthening the adaptation of rural Generation Z in facing climate variability Method: The focus of community service in this activity is the Z generation who live in rural areas. To make it easier to provide information to generation Z. A participatory approach is used in this activity. Result: There are 3 main activities in strengthening adaptation capacity for generation Z, namely: 1. Introduction stage, including a basic understanding of several forms of climate variability that occur in rural areas. 2. Advanced stage, namely providing education regarding the impact of climate variability in rural areas, especially agriculture and fisheries, 3. Adjustment stage, namely developing adaptation strategies that must be carried out when exposure to extreme climate variability occurs. Conclusion: The results of this service show an increase in generation Z's knowledge in anticipating climate variability.

Impacts of forced and internal climate variability on changes in convective environments over the eastern United States

Hazards from convective weather pose a serious threat to the contiguous United States (CONUS) every year. Previous studies have examined how future projected changes in climate might impact the frequency and intensity of convective weather using simulations with both convection-permitting regional models and coarser-grid climate and Earth system models. We build on this existing literature by utilizing a large-ensemble of historical and future Earth system model simulations to investigate the time evolution of the forced responses in large-scale convective environments and how those responses might be modulated by the rich spectrum of internal climate variability. Specifically, daily data from an ensemble of 50 simulations with the most recent version of the Community Earth System Model was used to examine changes in the convective environment over the eastern CONUS during March-June from 1870 to 2100. Results indicate that anthropogenically forced changes include increases in convective available potential energy and atmospheric stability (convective inhibition) throughout this century, while tropospheric vertical wind shear is projected to decrease across much of the CONUS. Internal climate variability on decadal and longer time scales can either significantly enhance or suppress these forced changes. The time evolution of two-dimensional histograms of convective indices suggests that future springtime convective environments over the eastern CONUS may, on average, be supportive of relatively less frequent and shorter-lived, but deeper and more intense convection.

The Impact of Climate Variability on the Blooming of Fraxinus ornus ‘Globosa’ as a Component of Novi Sad’s (Serbia) Green Infrastructure

The Impact of Climate Variability on the Blooming of Fraxinus ornus ‘Globosa’ as a Component of Novi Sad’s (Serbia) Green Infrastructure

Investigating the impact of cross-shelf transport and local retention in the Black Sea Rim Current system on small pelagic fishes

Advection of pelagic fish larvae through ocean currents is a source of dispersal that is known to structure marine populations by directly impacting population success and persistence. To be able to understand fish population dynamics and manage existing fisheries it is therefore of importance to assess the extent of exchange between populations and determine the factors that drive it, which is still a challenge in fisheries research today. In this study, the output of a high-resolution basin-scale circulation model is used to calculate the dispersal probability from spawning areas and the remaining Black Sea for a range of commercially important fish such as anchovy, sprat, turbot, red mullet, and bluefish under different environmental conditions. The common trait of many commercially important species in the Black Sea is that they have pelagic larval stages of different time scales which are simulated by tracking virtual drifters in the simulated surface circulation of the Black Sea. Simulation results show that the dynamic current structure of the Black Sea causes low local retention of larvae. During spring and summer lower offshore transport and higher local retention is observed than in fall and winter, which is directly related to the Rim Current strength. The lowest offshore transport is observed on the northern northwestern shelf, the main spawning area for pelagic fish with still 24-32% of larvae transported offshore. Simulations show that pelagic fish species such as anchovy and sprat adopted different reproductive strategies through their species-specific traits. For the summer spawning species anchovy, timing, and location of spawning together with its short pelagic larval stage is shown to enhance retention on the northwestern shelf. Sprat with its long pelagic larval stage and winter spawning is more adapted to high offshore transport, making it unlikely that sprat is recruited to the same area as where it is spawned. This modeling framework provides a basis for investigating recruitment variability of pelagic fish species in the Black Sea specifically considering the impact of climate variability and provides a useful guide to the potential connectivity of marine populations or the spread of invasive pests in the Black Sea.

Impact of Climatic Variability on Litchi Yields in the Tarai Region of India

Impact of Climatic Variability on Litchi Yields in the Tarai Region of India

Geostatistical evaluation of the impact of climate variability on malaria incidence In the South-West of Nigeria

Malaria remains a significant health concern in Nigeria, particularly in the South-West region. This study assesses the impact of temperature and rainfall on malaria incidence and prevalence in South-West Nigeria using remotely sensed and modelled data sourced from the Malaria Atlas Project and NASA's POWER database covering 2000 to 2020. The study adopts the Geographically Weighted Regression geostatistical model to establish the relationship between malaria and rainfall and temperature in the study area. The result shows a rising oscillating annual mean temperature trend of 0.0088oC/yr-1 from 2000 to 2020. The malaria incidence exceeds 8 million cases annually, peaking in 2020 at almost 10 million cases. The rising trend of malaria incidence highlights the inadequacy of the malaria intervention programmes to meet their goal of reducing malaria incidence by 40% by 2020. The study highlights the spatial variations, with high incidence in urban centres like Lagos and Ibadan metropolises, their satellite towns, as well as other prominent and capital towns including Oshogbo, Ilesa, Akure, Ijebu-Ode and Abeokuta. Contrary to this, the greater malaria prevalence was recorded in less densely populated areas of Oyo state, Imeko-Afon, Odeda, Yewa and Ijebu-Waterside areas in Ogun state as well as Ose and Idanre in Ondo state. The Geographically Weighted Regression equation model shows a strong positive correlation between malaria prevalence and temperature at a significance of 0.76 compared to rainfall which exhibits no association indicating the relevance of temperature as an explanatory indicator of malaria. With the continuous endemicity of malaria in the South-West, malaria management and control efforts should be focused on high-incidence areas in the South-West and Nigeria in general to fulfil the Sustainable Development Goal of Good health and well-being and the eradication of malaria by 2030.

Assessing the impact of climate variability on wheat yield in Bloemfontein wheat farms through time series analysis

Climate change, characterized by long-term shifts in global or regional weather patterns, is a consequence of natural processes and human activities. These shifts encompass al-terations in temperature, precipitation, wind patterns, and other climatic variables, all of which exert direct influence on crop growth, development, and overall agricultural productivity. Comprehending the intricate relationship between climate change and crop production is par-amount for formulating strategies to counteract adverse effects and adapt to evolving conditions. This study focuses on assessing the impact of climate variability on wheat yield in Bloemfontein wheat farms through rigorous time series analysis. The research involved the application of various time series models, including SARIMA, ARIMA, Facebook Prophet, LSTM, VAR, and Multiple Linear Regression. The investigation began with forecasting temperature patterns using SARIMA and Facebook Prophet models. SARIMA outperformed Facebook Prophet in this context, as evidenced by lower RMSE and MSE metrics. Subsequently, the study delved into predicting rainfall and precipitation, employing ARIMA and LSTM models. In this case, LSTM demonstrated superior predictive capabilities. Finally, wheat production yield was analyzed using VAR and Multiple Linear Regression, with VAR yielding more accurate predictions. The findings of this study hold profound implications for policymakers, farmers, and stakeholders deeply invested in agriculture and food security. By shedding light on the repercussions of climate change on crop production through the application of time series analysis, this project aspires to contribute to developing sustainable agricultural practices, robust farming systems and proactive policies de-signed to mitigate the adverse effects of climate change on global food production.

Climate variability impact on groundwater quality in Small Island Developing States: Mauritius Island as a case study

This study investigates the impact of climate variability on groundwater quality in Mauritius. This is achieved by analyzing the physical and chemical water quality of the five main aquifers over eleven years. Temporal variations in water quality properties were compared to the Standardised Precipitation Index (SPI), Dry Spells, the Standardised Evapotranspiration Index (SPEI), and other climate variables to gain insights into how precipitation controls groundwater quality. The results reveal that the SPI and the SPEI correlate minimally with water quality indicators. Sulphate is the only water quality indicator that showed correlations above 0.4 in aquifers 2 and 3 against a 12-month SPI. Sulphate, alongside chloride, showed what is termed ”notable correlation,” a concept defined in this paper to accommodate correlations that fall above 0.3 when assessed against global climate modes ENSO and AAO, respectively. These results signify that sulphate is the most sensitive water quality indicator to water quantity changes, notwithstanding the modesty of the correlations. Heavy storms occurring during cyclones impact groundwater quality with respect to conductivity, TDS, salinity, and nitrate, although this could not be statistically tested given the lack of water quality indicators collected on the days surrounding the storm. Therefore, the conclusion is made based on one storm event. The study revealed that individual correlations between climate indices and water quality variables are present but weak. However, the long-term trend in water quality is visible.

The impact of climate variation on the spread of Shiga toxin-producing E. coli.

Shiga toxin-producing Escherichia coli (STEC) is influenced by seasonality, but there is limited understanding of how specific climatic variables contribute to disease spread. This information aids in understanding disease transmission dynamics and could potentially inform public health modeling. This retrospective cohort study analyzed public health data from Ontario, Canada, between 2012 and 2021, along with historical climate data from Environment Canada. We employed Seasonal Autoregressive Integrated Moving Average (S-ARIMA) models to assess how temperature and precipitation impact the incidence of STEC infections, measured per 10,000,000 population. The study included 1658 confirmed STEC cases. A significant correlation was found between STEC incidence and climatic variables. Each degree Celsius increase in maximum temperature was associated with a rise of 3 STEC cases per 10,000,000 population (Centers for Disease Control and Prevention (2024)). Additionally, each millimeter of increased precipitation correlated with an increase of 1.1 cases per 10,000,000 population. The findings demonstrate a significant impact of temperature and precipitation on STEC transmission, highlighting the importance of integrating meteorological data into public health surveillance. This integration may help inform public health responses and support healthcare systems in planning for future outbreaks. Further studies are needed to refine predictive models and develop effective early warning systems for clinical settings.

Do Climate Change Matter for Agricultural Production in an era of Globalization? Empirical Insights from Pakistan

The purpose of current research is to examine the “sunspot theory” of business cycles in the case of Pakistan in the globalization scenario. The current research uses time-series data from 1980 to 2021. To find out the impact of Climatic variation on agricultural Production, the NARDL technique is used for the estimation of results. Results confirmed that CO2 emissions and mean temperature show an asymmetric effect on Agricultural production. Variations in CO2 emissions and mean temperature pose mixed results about agricultural production both in the long-run and short-run in different globalization scenarios. Further, through discussion, it is confirmed that this decrease in agricultural production due to climatic variation has slowed down the economic activity in an economy. Based on the results, it is recommended that government must expand tree planting projects and maintain greenery at all costs. The problem is expected to worsen as the temperature rises and the population grows. Agriculture producers must be taught new and sophisticated agricultural and cultivation practices. To address the issue of water scarcity, the government of Pakistan must construct more dams and reservoirs to give farmers greater access to water. The last but not least, this research confirmed the validity of the sunspot theory in the case of Pakistan.

Spatiotemporal variability and trends of intra-seasonal rainfall and temperature in the drought-prone districts of Northwestern Ethiopia

Agriculture in Ethiopia is highly dictated by spatial patterns and temporal distributions of climate variables. The analysis of these climate variables is crucial for understanding the impacts on agricultural productivity. This study aimed to analyze spatiotemporal variability and trends of intra-seasonal rainfall and temperature using site-specific daily data from the Ethiopian Meteorology Institute (1992–2021). Standardized methods explore variability, while Mann–Kendall tests identify trends, using the Modified version for data with autocorrelation. Inverse Distance Weighted interpolation was employed for spatial analysis of rainfall, length of growing season, and temperature. The findings identified that Kiremt dominated the mono-modal rainfall pattern, contributing 72%-86% of total annual rainfall. The study found that the season typically begins early on June 13 in Adiszemen, and July 6 in Arbgebiya and ends between October 6 and October 26. The duration of the season varied across locations, averaging 95 days at Ebenat and 148 days at Adiszemen. The seasonal rainfall anomaly index shows identical patterns between ENSO episodes and seasonal rainfall. These findings inform decision-making and adaptation strategies for ENSO-driven rainfall variability. Temperatures showed predictable seasonal patterns, but have significantly increased over time, with maximum and minimum temperatures rising 0.014 °C to 0.421 °C and 0.027 °C to 0.485 °C per year respectively. This warming trend is negatively impacting water, crops, and livestock, requiring adaptation measures to build regional resilience. This study underscores the critical impact of climate variability on agriculture in the study area. The findings reveal shifts in rainfall patterns and temperature trends, providing essential insights for adapting agricultural practices.