Patterns Of Climate Change Research Articles

Money Talks: A Holistic and Longitudinal View of the Budget Basket in the Face of Climate Change and Sustainable Finance Matters

This study presents a holistic and longitudinal view of the household budget basket concerning climate change and sustainable finance matters. It aims to understand their impact on the budget basket by examining the relationship between money, climate change and sustainable finance in a global economy for transition countries. Comprehensive CPI data were collected in Kosovo from 2002 to 2022, and data analysis was performed using statistical methods such as t-tests and proximity matrixes in SPSS. The results show significant differences between the average and desired values within the budget basket, indicating changes in consumer behavior, particularly in food expenditures, budget allocations, and climate change impacts. Interesting patterns emerge, such as correlations between bread, cereals, and meat, and the absence of fish in some purchases. Spending on clothing and other goods also deviates from desired values. These findings highlight the complex relationship between money, climate change, sustainable finance, and consumer spending patterns, and underscore the need to address the gap between expected and desired spending values for the global economy in transition economies. Future research should focus on analyzing household spending and its interaction with other factors to improve personal financial management and promote sustainable financial behavior in a larger number of global economies.

Surface-atmosphere energy exchanges and their effects on surface climate and atmospheric boundary layer characteristics in the forest-tundra ecotone in northwestern Canada

The circumpolar forest-tundra ecotone is experiencing rapid changes in vegetation composition and structure. Collectively, these changes modify surface-atmosphere energy exchanges and thus characteristics of the atmospheric boundary layer (ABL). Here, we characterize differences in bulk surface properties and resulting energy balance components using multi-year eddy covariance and supporting measurements made at a mineral upland tundra and a nearby subarctic woodland site between 2013 and 2022. The two sites are characteristic of the forest-tundra ecotone of northwestern Canada. A mixed-layer slab model, in combination with radiosonde observations, was used to gain more insights into differences in ABL characteristics. Compared to the tundra, the tree cover of the woodland led to an enhanced ability to transfer heat into the atmosphere, a higher resistance to evapotranspiration and a stronger coupling between surface and atmosphere. Sensible heat flux (H) was generally higher at the woodland than at the tundra. The largest difference in daily mean H was observed in late winter and spring when the albedo of the snow-covered landscape at the woodland was reduced by 45% compared to the tundra. Both sites experienced similar latent heat flux throughout the year. At the woodland, modelled afternoon air temperature in the mixed layer was up to 9 °C higher in spring and up to 3 °C higher in summer compared to the tundra. In accordance with model results, radiosonde observations indicated a deeper ABL and higher air temperature at the woodland. The presence of trees in the southern part of the forest-tundra ecotone increases air temperature throughout the year and has a drying effect in spring. Consequently, vegetation shifts in the forest-tundra ecotone can be expected to modify local surface climate change patterns, which must be considered when assessing climate change impacts in the region.

Characteristics and Comparative Study of Sodium Hydroxide Treated Banana Fiber Reinforced Composite Material

Environmental issues have become a worldwide crisis because of the high rate of pollution and changing climate patterns. Due to innovations and discoveries, the need for materials is increasing day by day. Here, composite materials can be substituted for any other mono-clinic materials fulfilling the requirements of those particular materials. However, the maximum composite materials are man-made or artificially fabricated fibers. These are not environmentally friendly and bio-degradable, which creates an environmental hazard. So, scientists are trying to fabricate composite materials from eco-friendly and green resources. The main advantages of natural fibers are their low cost, easy availability, and low density. Besides these acceptable specific properties, ease of separation, increasing energy recovery, carbon-oxide neutrality, and recyclable properties are increasing the popularity of natural fiber. In this study, we have used mainly two types of banana fiber for preparing the composite specimens, especially highlighting the hybrid fiber, which is made by mixing those two types of fiber: “Shobri” and “Champa” banana fibers. These names are renowned locally in Bangladesh. In this study, polyester and MEKP are used as matrix and hardener sequentially. We have accomplished a chemical treatment process. The treating process was accomplished in the alkaline medium with two different molar concentrations of NaOH. These are 1% and 5% solutions of NaOH. After the treatment neutralization process was performed with a 1 gm/L acetic acid solution. In addition, two distinct mechanical tests— the bending and tensile tests—were carried out. The results indicated that the hybrid fiber treated with 1% NaOH had the highest tensile properties; its Young’s modulus was around 625 MPa and its tensile strength was approximately 36.42 MPa. The tensile strength and Young’s modulus of Champa banana fiber treated with 1% and 5% NaOH solutions were the lowest, respectively. Approximate values are 1.82 GPa and 18.3 MPa. The flexural strength of hybrid fiber treated with 1% and 5% NaOH was the highest and lowest in that order. 69.2 MPa and 56.21 MPa, respectively, are their values. Regarding flexural modulus, Champa fiber treated with 5% and 1% NaOH exhibits the highest and lowest values, measuring roughly 4 and 1.82 GPa, respectively.

Optical flow of temperature reveals climate change patterns for agriculture and forestry

The study of climate change's impact on terrestrial ecosystems is crucial for achieving societal and economic sustainability and human well-being. Domains like agriculture and forestry respond to climate change similarly to other terrestrial natural biomes, experiencing reductions in productivity, migration, and loss of biodiversity. However, there is a research gap in understanding climate velocity for different land cover and land use types, which could reveal vulnerability patterns in agriculture and forestry. To address this gap, we present the first study of climate velocity for major land cover and land use classes with a focus on major crop growing regions and functional forest types at the continental level. To enable this analysis, we introduce a new approach for estimating climate velocity based on optical flow. This approach allows us to conduct a global study, improve ecosystem interconnection representation, and reduce the model's sensitivity to noisy data and parametrization. Our study highlights the significant vulnerability to climate change of agriculture as a land cover type with a climate velocity 15% higher than of other vegetation land cover classes. Furthermore, the analysis of climate velocity for functional tree types reveals the highest expected impact on deciduous and evergreen needleleaf trees primarily concentrated in regions with the highest climate velocity, especially in sub-polar and temperate climates. Conversely, tropical forests dominated by evergreen broadleaf tree types exhibits marked variations in climate velocity with the highest rates in South America and Africa and three times lower in Asia. Additionally, our analysis of climate velocity for agriculture shows that North American croplands experience the highest climate velocity exceeding the global average by 32%. This trend is consistent for major crop types such as soybean that have higher climate velocity than globally for 49%, rapeseed and sunflower for 39%, cotton for 35%, maize for 32%, and wheat for 30%. Notably, areas dedicated to cereals, sugar beet, and sunflowers are located in zones with climate velocities higher than the global average by 15%, 12%, and 9%, respectively. These patterns indicate potential shifts in the agriculture and forestry structures in the near future that coherent with recent climate change studies and will have significant impact on the socio-economic dimension of humanity sustainability.

Regulatory effects of non-growing season precipitation on the community structure, biomass allocation, and water-carbon utilization in a temperate desert steppe

Under the influence of global change, precipitation amounts and extreme precipitation frequency during non-growing seasons in mid-high latitude grasslands have been increasing. However, the ecological effects of non-growing season precipitation in the desert steppe have long been overlooked due to an insufficient understanding of the correlative mechanisms linking non-growing season precipitation to plant growth. Therefore, a 3-year non-growing season precipitation manipulation experiment was conducted to reveal the response of desert steppe plants to non-growing season precipitation changes. Our study indicates that, by influencing water budget and availability, non-growing season precipitation directly or indirectly impacted community structure, plant biomass allocation, and water-carbon utilization intensity. Adaptive strategies of communities and plants included: ① Dominant species enhanced their dominance in the community to adapt to non-growing season precipitation changes. ② Stipa krylovii exhibited different biomass allocation strategies in response to non-growing season precipitation variations. Plants in the precipitation shading plots tended to allocate biomass to the roots, while those in the precipitation increase plots favored aboveground development. ③ Persistent drought during the growing season intensified early insufficient development of plants in the precipitation shading plots. Upon entering the wet period, plants in the precipitation shading plots shifted into a compensatory growth mode with high water-carbon activity intensity, while those in the precipitation increase plots entered a moderate growth mode with relatively low water-carbon activity intensity. Additionally, our study found that the regulatory effects of non-growing season precipitation were more pronounced in the growing seasons with less precipitation in the early to middle stage. Moreover, increased non-growing season precipitation enhanced plant water use efficiency (WUE) and strengthened their resilience to drought conditions. Our study suggests that the ecological role of non-growing season precipitation may be further highlighted in the future climate change pattern. Given the worldwide increase in frequency of extreme precipitation events, particular vigilance should be paid to the underlying long-term adverse effects of severe droughts during the non-growing season. Our findings provide new insights and valuable experimental observational evidence for the climate change impact assessment and response in xerophytic grassland ecosystems.

Quantifying the long-term changes of terrestrial water storage and their driving factors

Global warming is expected to cause changes in terrestrial water storage (TWS) across the land surface, with widespread impacts on ecosystems and society. Although extensive research has been performed to analyze TWS changes and possible drivers during the post-2000 period, longer-term evolution of TWS and associated environmental forcings remain relatively unexplored. In this study, we evaluated the performance of the Energy Exascale Earth System model (E3SM) land model ELM version 1 (ELM v1) in simulating global TWS, and used factorial simulations of ELMv1 to quantify global TWS changes and their drivers during 1948–2012. We found that ELM’s agreed best with existing satellites and reconstruction datasets in temperate regions unaffected by irrigation. Biome- and climate zone-averaged TWS mainly increased at rates between 0 and 10 mm/year over 1948–2012, but the second half of that period saw smaller positive trends than the first half or even negative trends. Climate change explained >80 % of the TWS trends across most biomes and climate zones, followed by land use and land cover change. The physiological and phenological effects of CO2 primarily induced noticeable TWS trends in the more humid biomes and climate zones across different latitudes. In contrast, nitrogen deposition and aerosol deposition generally had smaller and negative impacts across the biomes and climate regions. Among the meteorological drivers analyzed, the long-term average imbalance between precipitation (P), evapotranspiration (E), and runoff (Q) contributed >50 % of the TWS trends in most biomes and climate zones, with nonlinearity being induced by spatially heterogenous changes in E/P and Q/P ratios. The accumulated detrended anomalies in P, E, and Q also often contributed substantially, while the trends difference between P, E, and Q contributed little. Together, these findings unveiled an intensification of the global TWS and its diverse patterns of climate change and different non-withdrawal human-induced alterations, contributing to a more comprehensive understanding and projection of the global water cycle.

Perceived Factors affecting Sugarcane Production Among Small-Scale Farmers in Kilombero District, Morogoro, Tanzania

This study investigates the nuanced perspectives of small-scale sugarcane farmers around the Kilombero Sugar Company in the Kilombero District, Morogoro, regarding the effects of weather variability, climate change, extension services, and support from sugar processing companies on sugarcane production. Cross-sectional research design was employed to gather information using a questionnaire survey from 128 farmers who were obtained through a simple random sampling technique. Descriptive statistics were used for analysis. The findings elucidate climate change and altered weather patterns are influencing agriculture, in this case, impacting the cultivation of sugarcane such that that changes in temperature and rainfall patterns have indeed resulted in unpredictable flowering and maturity cycles in sugarcane crops. Furthermore, the study highlights the significant role of extension services in enhancing agricultural practices and adequate pest and disease management practices contributes to the overall health and vigour of their sugarcane crops. It is concluded that weather variability, climate change, extension services, and support from sugar processing companies on sugarcane production are important factors influencing sugarcane production. The study emphasises the necessity of tailored education and outreach programs to bolster farmers' understanding and adoption of effective pest and disease management practices essential for sustainable sugarcane production in the Kilombero District

The northern wheatear is reducing its distribution in its southernmost European range and moving to higher altitudes

Under the current pattern of climate change, mountain bird populations are generally shifting their ranges to higher elevations, tracking their climatic optima. Nevertheless, space limitations at high altitudes constrain mountain species' resilience to climatic change, making them particularly vulnerable. In extreme cases, the climatic niche of some species can move beyond mountaintops, ultimately driving such species to extinction. This study presents the case of the northern wheatear Oenanthe oenanthe in mainland Spain and compares its breeding distribution from 2003 to 2022. Spain, where the species mostly occupies mountain areas, represents its southernmost distribution limit in Europe. We built environmental favourability models using information from the two most recent Spanish bird atlases and a set of climatic, topographic, human activity and lithological variables to determine the factors affecting the occurrence of the species. The influence of climate compared to all other factors was obtained using variation partitioning analysis. The northern wheatear has suffered a strong reduction (67%) in occupied areas and also in favourability throughout mainland Spain (especially in the south) where climate change may have far‐reaching consequences, including local extinctions. Climate explained more than 90% of the variation in the model obtained for 2022. Interestingly, the occupied areas were, on average, 100 m higher in 2022 than in 2003. If the effects of climate warming persist, the northern wheatear will likely disappear in its southernmost distribution limit, being the Sierra de las Nieves National Park population the most vulnerable in mainland Spain. For this reason, it is necessary to implement monitoring programmes for northern wheatear populations, with priority for mountain systems wherein they already occupy higher elevations, as well as further studies on other mountain species.

Decadal (2010–2019) variability in the marine ecosystems of the North Atlantic

Abstract The “Symposium on Decadal Variability of the North Atlantic and its Marine Ecosystems: 2010–2019” took place in Bergen, Norway, from 20 to 22 May 2022. This event, hosted by the Institute of Marine Research, was jointly sponsored by ICES and NAFO and constitutes the fourth in a series of these symposia that started in 1991. The first symposium’s aim was to review the hydrobiological variability in the decade of the 1980s, addressing the physical environment, plankton, invertebrates, and fish. Subsequent symposia maintained the classical structure, whilst a new theme session covering social sciences was added to the programme in the 2022 edition. Studies of climate impacts on marine ecosystems and living marine resources, including trends and regime shifts, emerged from increasingly longer ecosystem time series across the North Atlantic and subarctic regions. This symposium collection presents key findings discussed during the symposium. Whilst some progress has been made in advancing our ability to understand new trends and patterns of climate change and variability across physical, plankton, and fish communities, more work is needed to integrate these physical-ecological processes and scales with social science.

Hydroclimate Changes Based on Testate Amoebae in the Greater Khingan Mountains’ Peatland (NE China) during the Last Millennium

The driving force of climate change in the monsoon margin is complex, making it a key area for regional and global climate change research. Palaeohydrological studies in the monsoon margin have increased the resolution of research in the long term, transitioning from qualitative to quantitative studies to comprehend climate change processes, patterns, and mechanisms. Testate amoebae (TA) in peat sediments are used as a proxy indicator organism for quantitative reconstruction of palaeohydrology. Thus, their community changes are directly related to precipitation, and widely used to reconstruct the patterns of summer precipitation globally. We investigated TA species and reconstructed palaeohydrological changes in the Greater Khingan Mountains’ Hongtu (HT) peatland, located in the East Asian Summer Monsoon (EASM) margin. The result showed that the most abundant TA species were Assulina muscorum (12.4 ± 5.0%) and Nebela tincta (8.9 ± 4.9%) in the HT peat core. The increase in dry indicator species (e.g., A. muscorum and Alabasta militaris) indicated a drying pattern in the HT peatland since 150 cal yr BP. Principal component analysis (PCA) explained 47.6% of the variation in the selected TA assemblages. During 400 to 250 cal yr BP, PCA axis 1 scores ranged from 0.2 to −1.3 (reflecting a drier climate), associating with the Little Ice Age. The paleohydrology of the northern part of the Greater Khingan Mountains was mainly controlled by the EASM, which was associated with changes in North Atlantic Sea surface temperature and solar radiative forcing. The apparent drying pattern may be the result of the gradual intensification of anthropogenic activities and the increase in EASM intensity.

Extreme weather events and Spatio-temporal characterization of climate change variables in Bangladesh during 1975–2019

Bangladesh is susceptible to climate change, thus a detailed study, including the analyses of trends, sub-trends, extreme events and indices was conducted to obtain a complete picture of the climate change pattern in Bangladesh utilizing daily rainfall, maximum, minimum and average temperature data of 26 stations from 1975 to 2019 using R 4.0.2 software. For the trend analysis Mann Kendal (MK), modified Mann Kendall (mMK), Innovative Trend Analysis (ITA) and Sen’s slope methods were used. Sub-trend analysis was conducted using ITA. Standard Anomaly Index (SAI) has been used to identify the frequency and severity of extreme events. ClimPACT2 software was used to check the homogeneity and calculate the extremes of temperature and rainfall data. Our analysis showed that during the last four decades, climate variables changed their patterns and trend heterogeneously over Bangladesh. Most stations showed decremental rainfall trend when central part of the country showed a substantial decrease. The northern and central parts of the country showed significant growth of trend for annual average temperature. The temperature in the monsoon season increased, whereas those in dry season decreased. The rainfall and maximum temperature were inversely related during monsoon whereas during dry season both of them decrease. The pre-monsoon and post-monsoonal rainfall also showed decreasing trends, indicating prevailing drought conditions especially in northern and central parts of the country. The SAI analysis showed alternating drought and wet years in almost all the stations. In the past 20 years, the country’s western region experienced more drought years than before whereas the coastal region experienced more wet years. The analysis of climate extreme indices suggests that, Consecutive Dry Days (CDD), Hot Days (TX90P) and Hot Nights (TN90P) show significant increasing trend throughout the country. The agricultural productivity, water resource management and food security are anticipated to benefit from this study.

Climate Change and Human Health: Impacts and Vulnerability

Background: Climate change is a global health emergency, with its impacts being profoundly felt across various regions of Pakistan. Rising temperatures, changing precipitation patterns, and increased frequency of extreme weather events pose significant threats to public health, agriculture, and water resources. Understanding these impacts is crucial for developing targeted interventions to mitigate health risks associated with climate change. Objective: This study aims to assess the effects of climate change on health outcomes in Pakistan, focusing on temperature variations, precipitation changes, agricultural impacts, vector-borne diseases, and socio-economic vulnerabilities. Methods: A cross-sectional study was conducted in four regions of Pakistan (Punjab, Sindh, KPK, and Balochistan) from January 2022 to January 2023. A sample size of 3000 participants was selected through systematic random sampling. Meteorological data was collected from local weather stations, and health data was gathered through structured surveys, interviews, and medical examinations. Descriptive and inferential statistics were used to analyze the data, employing SPSS version 25 for statistical analysis. GIS mapping visualized the spatial patterns of climate change impacts. Results: The study found significant regional variations in climate change impacts. Temperature increases were most pronounced in Sindh (1.8°C), leading to heightened risks of heat-related ailments. Precipitation changes showed decreased rainfall in Punjab, causing water scarcity and an increase in waterborne diseases. Agricultural impacts revealed a 10% crop yield reduction in Punjab, resulting in increased malnutrition rates. Vector-borne diseases saw a noticeable rise in Sindh due to habitat expansion of disease vectors. Socio-economic vulnerability analysis indicated high vulnerability in urban areas of Sindh, with limited adaptive capacities. Conclusion: Climate change significantly affects health outcomes in Pakistan, with regional disparities necessitating targeted interventions. The study underscores the importance of incorporating climate change considerations into public health planning and policy-making to enhance resilience and adaptive capacities across vulnerable regions.

Impacts of Climate Change on Groundwater Resources in Afghanistan: A Case Study of Kandahar Province

This paper presents a case study examining the impacts of climate change on groundwater resources in the Kandahar province of Afghanistan. The study assesses the vulnerability of groundwater sources to changing climatic patterns and explores the implications for water availability, quality, and sustainability in the region. Through a combination of field surveys, hydrogeological investigations, and analysis of climatic data, the research aims to provide insights into the specific challenges faced by Kandahar province in managing its groundwater resources in the context of a changing climate. The findings of the study reveal the significant influence of climate change on groundwater availability, with altered precipitation patterns and increasing temperatures leading to shifts in recharge rates and aquifer dynamics. Moreover, the study examines the potential exacerbation of water stress, groundwater depletion, and saltwater intrusion in coastal areas due to rising sea levels and changing rainfall patterns. The insights derived from this case study contribute to a deeper understanding of the complex interactions between climate change and groundwater resources, providing valuable knowledge for policymakers, water resource managers, and stakeholders involved in sustainable water management and climate adaptation initiatives in Afghanistan.

Decoding seasonal variability of air pollutants with climate factors: A geostatistical approach using multimodal regression models for informed climate change mitigation

In response to changes in climatic patterns, a profound comprehension of air pollutants (AP) variability is vital for enhancing climate models and facilitating informed decision-making in nations susceptible to climate change. Earlier research primarily depended on limited models, potentially neglecting intricate relationships and not fully encapsulating associations. This study, in contrast, probed the spatiotemporal variability of airborne particles (CO, CH4, SO2, and NO2) under varying climatic conditions within a climate-sensitive nation, utilizing multiple regression models. Spatial and seasonal AP data were acquired via the Google Earth Engine platform, which indicated elevated AP concentrations in primarily urban areas. Remarkably, the average airborne particle levels were lower in 2020 than in 2019, though they escalated during winter. The study employed linear regression, Pearson's correlation (PC), Spearman rank correlation models, and Geographically Weighted Regression (GWR) models to probe the relationship between pollutant variability and climatic elements such as rainfall, temperature, and humidity. Across all seasons, APs showed a negative correlation with rainfall while displaying positive correlations with temperature and humidity. The GWR and PC models produced the most reliable results from all the models employed, with the GWR model superseding the rest. Moreover, heightened aerosol levels were detected within a rainfall range of 600 mm/season, a temperature range of 25–30 °C, and humidity levels of 75 %–85 %. Overall, this study emphasizes the growing levels of APs in correlation with meteorological changes. By adopting a comprehensive approach and considering multiple factors, this research provides a more sophisticated understanding of the relationship between AP variability and climatic shifts.

A Warmer and Wetter World Would Aggravate GHG Emissions Intensity in China's Cropland

AbstractMany agricultural regions in China are likely to become appreciably wetter or drier as the global climate warming increases. However, the impact of these climate change patterns on the intensity of soil greenhouse gas (GHG) emissions (GHGI, GHG emissions per unit of crop yield) has not yet been rigorously assessed. By integrating an improved agricultural ecosystem model and a meta‐analysis of multiple field studies, we found that climate change is expected to cause a 20.0% crop yield loss, while stimulating soil GHG emissions by 12.2% between 2061 and 2090 in China's agricultural regions. A wetter‐warmer (WW) climate would adversely impact crop yield on an equal basis and lead to a 1.8‐fold‐ increase in GHG emissions relative to those in a drier‐warmer (DW) climate. Without water limitation/excess, extreme heat (an increase of more than 1.5°C in average temperature) during the growing season would amplify 15.7% more yield while simultaneously elevating GHG emissions by 42.5% compared to an increase of below 1.5°C. However, when coupled with extreme drought, it would aggravate crop yield loss by 61.8% without reducing the corresponding GHG emissions. Furthermore, the emission intensity in an extreme WW climate would increase by 22.6% compared to an extreme DW climate. Under this intense WW climate, the use of nitrogen fertilizer would lead to a 37.9% increase in soil GHG emissions without necessarily gaining a corresponding yield advantage compared to a DW climate. These findings suggest that the threat of a wetter‐warmer world to efforts to reduce GHG emissions intensity may be as great as or even greater than that of a drier‐warmer world.

Emergent climate change patterns originating from deep ocean warming in climate mitigation scenarios

Emergent climate change patterns originating from deep ocean warming in climate mitigation scenarios

Changes in United States Summer Temperatures Revealed by Explainable Neural Networks

AbstractTo better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science.

Assessment of rainfall and climate change patterns via machine learning tools and impact on forecasting in the City of Kigali

Assessment of rainfall and climate change patterns via machine learning tools and impact on forecasting in the City of Kigali

Trends and impacts of temperature and fire regimes in South Africa’s coastal national parks: implications for tourism

Climate change poses significant risks on coastal national park-based tourism through its effect on accessibility, comfort levels, and spatiotemporal changes of attractions. Wildfires and extreme temperatures have become issues of concern. Due to the widespread occurrence of fires in national parks along coastal areas and the noticeable changes in temperature regimes, more needs to be done to deepen understanding of their trends and impacts and devise appropriate management regimes. This study analyses the trends and impacts of fires and temperature-related variables in six coastal South African national parks, namely Agulhas, Garden Route, West Coast, Table Mountain, Namaqua, and Addo Elephant National Park. The triangulation and convergence model of the mixed-methods research design was adopted as the inquiry strategy. Data on statistical weather variables from the South African Weather Services and the National Aeronautics and Space Administration were used to calculate temperature trends using Mann–Kendall trend tests and homogeneity analysis. Data collection included questionnaire surveys, key informant interviews, field observations, and document analysis. The study observed statistically significant (p < 0.05) changes in temperature-related variables in all coastal national parks. There is also an increase in the intensity and spatial spread of fires, resulting in increased fire damage over time. Despite the current efforts of the coastal parks to manage wildfires and the changing climatic patterns, the fire and temperature regimes are threatening the biophysical environment with resultant effects on tourism and the economic viability of the national parks. The need to reengineer the fire suppression mechanisms and temperature-resilient tourism facilities has been observed.

Assessing the Influence of Climate Change on Agricultural Yields and Crafting Farmer-Centric Adaptation Strategies: A Policy-oriented Agricultural Analysis

This journal article critically examines the influence of climate change on agricultural yields and proposes farmer-centric adaptation strategies through a policy-oriented agricultural analysis. The research employs a comprehensive approach, integrating climate science, agricultural economics, and policy studies to assess the multifaceted impacts of climate change on global agricultural productivity.&#x0D; The study analyzes empirical data on changing climate patterns, extreme weather events, and temperature variations, emphasizing their significant consequences for agricultural yields. By utilizing statistical models and econometric techniques, the research quantifies the extent of yield variations across diverse crops, providing a nuanced understanding of the challenges faced by farmers.&#x0D; In response to these challenges, the research proposes adaptation strategies centered on the needs and capacities of farmers. These strategies encompass the development and dissemination of climate-resilient crop varieties, the enhancement of irrigation infrastructure, and the implementation of precision agriculture techniques. Moreover, the study investigates the role of agricultural policies in promoting adaptive practices and explores potential policy frameworks to incentivize climate-smart agriculture.&#x0D; The findings highlight the importance of tailoring adaptation strategies to local contexts and engaging farmers in the decision-making process. The research underscores the need for policies that empower farmers, providing them with the knowledge, resources, and incentives to adapt to changing climatic conditions effectively.&#x0D; In conclusion, this article contributes to the discourse on climate change adaptation in agriculture by offering a policy-oriented perspective. It advocates for a holistic approach that integrates scientific insights, economic considerations, and farmer participation, aiming to enhance the resilience of agricultural systems in the face of climate change.