Changes In Climatic Parameters Research Articles

Spatial Assessment of Temperature, Rainfall and Land Cover Change as Climate Change Monitoring Techniques in Okigwe and Its Environs

Climate change is an alarming global environmental change phenomena that is constituting a serious threat to natural, social, cultural, and human systems. Its associated risks require a wide range of policy responses and techniques at all levels from local to global. This study sought to evaluate the changes in climatic parameters over three decades as monitoring techniques in Okigwe, Imo State, Nigeria, based on evaluated land cover and temperature variation using Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 ETM+ satellite data of 1986, 2003, and 2020 respectively. Precipitation data from 1990-2020 of the study area was used to evaluate rainfall variation as climatic change parameters over three decades. Results of the geospatial analysis indicates that climate change has become a fallout from developmental activities taking place in Okigwe, which obviously is impacting on all facets of human activities thereby militating against sustainable development. Implicitly, changes in built-up areas or surfaces significantly produced a corresponding effect in escalating urban heat in the city. This study recommends the enforcement of dequate land use planning via adopting green city planning techniques in infrastructural development.

Perception of climate change from the Himalayan ‘cold desert’ Ladakh, India

Climate change perception survey is a method designed to gain insights on people’s perspectives of the changing climate and recognizing important factors, complexities, and limitations towards a climate-conscious conduct. Such surveys are particularly important in ecologically sensitive transitional climatic zones but logistically difficult terrains, where meteorological data is scarce and minor changes in climatic parameters can have unanticipated consequences for the local ecosystem. The current research is focused on one of such climate-sensitive areas, in the northwestern Himalaya. A binary question-based survey (interview) was conducted in the high-altitude, cold desert region – Ladakh, which covered all the five subdivisions of the union territory. According to the climate perception data, the majority of respondents are aware of climate change or global warming regardless of age, gender, and background. The exponential growth in tourist inflow over the last two decades (attributed to ‘war tourism’) equates with the increasing number of vehicles and is well perceived by the locals. Approximately 86 percent of respondents believe that humans have had a negative impact on the environment. They appear to be quite optimistic about curbing the effects of climate change, with approximately 91 percent willing to participate in mitigation efforts. The results of this study show an overall agreement between people’s perceptions of climate change and the scientific evidence of these changes. These findings are eventually intended to serve as an important parameter in developing adaptation and mitigation strategies in this ecologically sensitive and vulnerable region.

Assessing the impact of climate change in the wheat–maize cropping system across the Huang–Huai–Hai Plain under future climate scenarios

Abstract General circulation models suggest that changes in climatic parameters will have an effect on food production globally. Therefore, this study assessed the impact of climate change on wheat–maize rotation areas in the Huang–Huai–Hai Plain (3H Plain). The projections generated suggest an increase in precipitation during the wheat growth period (WGP) by 1.33–4.16% (2.6–8.3 mm) and 3.13–8.16% (6.2–18.0 mm) under RCP4.5 and RCP8.5, respectively, relative to the base period (1981–2016). Across the 3H Plain, the mean temperature during the WGP is projected to increase between 1.17–1.21 and 1.17–1.28 °C under RCP4.5 and RCP8.5, respectively. The projections during the maize growth period (MGP) indicate an increase in the temperature between 1.29–1.92 and 1.84–2.08 °C under RCP4.5 and RCP8.5, respectively. During the MGP, precipitation is also projected to increase by 7.41–9.73% (33.6–45.1 mm) and 6.63–14.78% (29.8–72.7 mm) under RCP4.5 and RCP8.5 scenarios, respectively. For each 1% change in climatic factors, the comprehensive effect on yield was projected to be 0.65 and 0.58% for wheat and −1.08 and −1.11% for maize, under RCP4.5 and RCP8.5, respectively, when other factors were kept constant. The change in water resources will be insignificant during the WGP and more pronounced during the MGP. The study provides an overview of changes in meteorological parameters and scientific evidence for climate change adaptation in the wheat–maize cropping system.

Climate Change Impact on Forest Cover: A Critical Review

Climate change affects forest cover through increased temperature, change in precipitation, frequent extreme events, and a shifting disturbance regime. In view of understanding the existing studies carried out on climate change impact on forest cover, we reviewed the findings of several researchers to assess the impact of changing climatic parameters on forest cover. The role of deforestation on climate change was analyzed by presenting some of the arguments made by several researchers. Further, the influence of forests in climate change was evaluated by the amount of carbon sequestrated in the forests throughout the world and the effect of climate change on carbon sequestration was also done with the help of review of the literature. Direct, Indirect and compound effects of climate change impact on forest cover were also shortlisted and discussed. Then certain coping mechanisms were also discussed by the authors for the forests whose health was highly affected due to climate change. Several research publications demonstrating the response of forest cover to climate change were critically analyzed. The review revelaed that climatic variations are very important to understand the forest cover changes of an ecosystem in which the climate prevails. It is also understood that the variability of rainfall and temperature were not the main cause for forest cover changes. Climate change impact assessment has significant implications on carbon sequestration greenhouse gas mitigation and adaptation strategies to climate change. Utilising remote sensing data for documenting and identifying forest degradation status is a viable option for planning climate change mitigation in forest regions.

Determining the Biocomfort Zones in Near Future in Antalya Province Depending on the Global Climate Change Scenarios

Climate is a factor that affects the entire life of humans such as physiological development and characteristics, housing and house structures, food and cloth selections, and distribution on land. It is projected that global climate change would cause important changes in climate parameters in near future and affect the lives of all organisms on the earth directly or indirectly. It is estimated that these changes would cause significant changes in biocomfort zones. Thus, it is important to determine the biocomfort zones depending on the climate change scenarios and to use them in urban planning studies. In this study, it was aimed to determine the change in bioclimatic comfort zones in Antalya depending on the projected climate change scenarios. Within the scope of this study, considering the SSPs 245 and SSPs 585 scenarios of Coupled Model Intercomparison Project Phase 6, that is the 6th assessment report of IPCC, it was aimed to determine the current status and possible changes in biocomfort zones in Antalya in years 2040, 2060, 2080, and 2100. The results showed that the comfort zones in Antalya will generally shift from cold to hot towards the year 2100, that this shift will be at important level, and that the highest level of increase will be seen in southern regions with high population density and intense touristic activities.

Climate Change and the Cost-Effective Governance Mode for Biodiversity Conservation

Climate change poses a key challenge for biodiversity conservation. Conservation agencies, in particular, have to decide where to carry out conservation measures in a landscape to enable species to move with climate change. Moreover, they can choose two main governance modes: (1) buy land to implement conservation measures themselves on that land, or (2) compensate landowners for voluntarily carrying out conservation measures on their land. We develop a dynamic, conceptual ecological-economic model to investigate the influence of changes in climatic parameters on the cost-effectiveness of these governance modes and specific patch selection strategies (price prioritisation, species abundance prioritisation, climate suitability prioritisation, climate change direction prioritisation). We identify five effects that explain the cost-effectiveness performance of the combinations of governance mode and patch selection strategy and find that their cost-effectiveness depends on climate parameters and is thus case-specific.

Identifying patterns in the fatty-acid composition of safflower depending on agroclimatic conditions

The object of the study reported in this paper is to establish a dependence of the fatty acid composition of the fast-growing annual plant safflower on the agroclimatic cultivating conditions. The growth rate of safflower and the characteristics of the extracted oil are highly dependent on external temperature and moisture. At low temperatures, for example, the growth of safflower is significantly inhibited. With an increase in temperature and the length of daylight, the central stem begins to branch while growing faster. Flowering is mainly affected by the length of daylight. The period from the end of flowering to maturity is typically 28‒30 days. However, the total ripening period of the crop depends on the variety, location, sowing time, and agro-climatic cultivating conditions. The need for water increases significantly during the flowering period of safflower, which ultimately affects the indicators of the fatty acid composition and yield. At the same time, safflower is sensitive to moisture in terms of disease. In case of excess water, it is subject to root rot. In addition, frequent rains and high humidity after ripening can provoke the germination of seeds on the head. Hence, it follows that in order to obtain a high yield with the specified characteristics of the fatty acid composition of safflower oil, it is necessary to take into consideration the quantitative indicators of moisture and its seasonality, as well as the temperature regime during the growing season. The study was conducted using arid or semi-arid, sharply continental Central Asia with its hot summers and cold winters as an example. The dependence of the physicochemical parameters of plant-derived oils on agroclimatic indicators has been established. The reported results and conclusions will allow farmers to predict the yield of oilseeds with specified characteristics depending on the changing climatic parameters

Biogeography of bird and mammal trophic structures

Does climate determine the trophic organization of communities around the world? A recent study showed that a limited number of community trophic structures emerge when co‐occurrence of trophic guilds among large mammals is examined globally. We ask whether the pattern is general across all terrestrial mammals (n = 5272) and birds (n = 9993). We found that the six community‐trophic structures previously identified with large mammals are largely maintained when all mammals and birds are examined, both together and separately, and that bioclimatic variables, including net primary productivity (NPP), are strongly related to variation in the geographical boundaries of community trophic structures. We argue that results are consistent with the view that trophic communities are self‐organized structures optimizing energy flows, and that climate likely acts as the main control parameter by modulating the amount of solar energy available for conversion by plants and percolated through food webs across trophic communities. Gradual changes in climate parameters would thus be expected to trigger abrupt changes in energy flows resulting from phase transitions (tipping points) between different dynamical stable states. We expect future research to examine if our results are general across organisms, ecosystems, scales and methodologies, and whether inferences rooted in complex systems theory are supported. The emergence of general patterns in the functional properties of animal communities at broad scales supports the emergence of food‐web biogeography as a sub‐discipline of biogeography focused on the analysis of the geographical distributions of trophic relationships among organisms.

Climate Change in Southeast Benin and Its Influences on the Spatio-Temporal Dynamic of Forests, Benin, West Africa

The impact of anthropogenic activities on the spatiotemporal dynamics of the forest of Dogo-Kétou has been studied in relation to climate change observed in southeastern Benin. Especially, this study has detected the changes in climate parameters in southeast Benin from 1954 to 2016 and in forestland use from 1986 to 2018. A climatic break was detected, and the annual and monthly rainfall and temperature averages, the rainfall indexes, the concentration of precipitation, the number of rainy days according to the World Meteorological Organization and the bioclimatic aridity indexes were assessed. A GIS analysis was also performed based on Landsat images from 1986, 2000 and 2018 to detect the dynamic of land use and land cover of the classified forest of Dogo-Kétou. Excel 2016, Rx64 4.1.2, Rclimdex301, Khronostat1.01 and ArcGIS were used. The rainfall series showed a break in 1969. The segment from 1954 to 1968 was a period of excess rainfall with an average of 1420.46 mm/year. The segment from 1969 to 2016 was a rainfall deficit period with a reduction of 12 to 37% of rainy days and an increase of dry days for 21.4 days/year. In this last segment, southeast Benin experienced trends of its climate towards a sub-arid and arid climate. The agglomerations and bare soil of the forest of Dogo-Kétou increased to an area almost double the size between 1986 and 2018. The tree and shrub savannas gained in space from 12.1% in 2000 to 38.9% in 2018 and the dense forest and wooded savannas regressed by 52.6% in 1986 to 36.1% in 2000 then to 4.3% of the forest area in 2018. The subequatorial climate of southeastern Benin is gradually evolving towards a dry climate. The dense forest in this area is also gradually disappearing, giving way to savannas. This transformation towards the savannas was due to anthropogenic actions whose effects were strongly accentuated by climate change in this part of Benin and West Africa.

Impact of Climate Change on Phenology of Two Heat-Resistant Wheat Varieties and Future Adaptations.

Climate change (CC) is a global threat to the agricultural system. Changing climatic conditions are causing variations in temperature range, rainfall timing, humidity percentage, soil structure, and composition of gases in environment. All these factors have a great influence on the phenological events in plants’ life cycle. Alternation in phenological events, especially in crops, leads to either lower yield or crop failure. In light of respective statement, the present study is designed to evaluate the climatic impacts on two heat-resistant wheat varieties (Sialkot–2008 and Punjab–2018). During the study, impacts of CC on wheat phenology and annual yield were predicted considering six climatic factors: maximum temp, minimum temperature, precipitation, humidity, soil moisture content, and solar radiation using two quantitative approaches. First, a two-year field experimental plot was set up at five different sites of study—each plot a bisect of two sites. Phenological changes of both varieties were monitored with respect to climatic factors and changes were recorded in a scientific manner. Secondly, experimental results were compared with Global climate models (GMC) models with a baseline range of the past 40 years (1970–2010) and future fifty years (2019–2068) under Representative Concentration Pathway (RCP) 8.5 model analysis. Field experiment showed a (0.02) difference in maximum temperature, (0.04) in minimum temperature, (0.17) in humidity, and about (0.03) significant difference in soil moisture content during 2019–2021. Under these changing climatic parameters, a 0.21% difference was accounted in annual yield. Furthermore, the results were supported by GMC model analysis, which was analyzed by Decision Support System for Agrotechnology Transfer (DSSAT) model. Results depicted that non-heat-resistant wheat varieties could cause up to a 6~13% reduction in yield during future 50 years (2019–2068)) compared with the last 40 years (1970–2010). A larger decline in wheat grain number relative to grain weight is a key reducer of wheat yield, under future climate change circumstances. Using heat-tolerant wheat varieties will not only assist to overcome this plethora but also provide a potential increase of up to 7% to 10% in indigenous environment. On the other hand, it was concluded that cultivating these heat-resistant varieties that are also ripening late culminates into enhanced thermal time chucks during the grain-filling period; hence, wheat yield will increase by 8% to 12%. In changing climatic conditions and varieties, ‘Punjab–2018′ will be the better choice for peasants and farm-land owners to obtain a better yield of wheat to cope with the necessities of food on the domestic and national level.

Change in climatic elements impact on agriculture

The most essential physical factor in nature is climate. Changes in climate parameters are referred to as climate change (main parameters are temperature, rainfall, humidity and wind). It is a global and current issue that has become a well-known global calamity in recent years (IPCC, 2007). Climate change is a long-term process that has far-reaching consequences. From low to high latitude, the pace of change and the effect of that change differ. Low latitudinal places are impacted not just from an environmental standpoint, but also from a financial standpoint. Because the research location is in the tropical region of West Bengal, it is badly affected when the temperature rises over time. People's basic economy is agriculture in numerous locations, and agricultural productivity is influenced by climate factors. Agricultural productivity is influenced by climatic factors such as temperature, rainfall, and humidity. However, climate change has had an impact on lower latitudinal locations for numerous decades. Climate change is caused by the release of GHGs or Green House Gases (Gil Kim, 2009), urbanisation, and industrialisation, according to many sorts of studies. As a result, agricultural yield, area, and productivity have all dropped.

Information Extraction From the Agricultural and Weather Domains Using Deep Learning Approaches

India is an agricultural region and the economy of the country depends upon agriculture. Change in climatic parameters (like rainfall, soil, etc) directly affect the growth of crops. This parameter has an unswerving effect on the quantity of food production. Information extraction from the agricultural domain through rainfall prediction has been one of the most challenging issues around the world in recent years because of climatic changes. To evaluate the feasibility of rain by employing some data analytics and machine learning techniques are developed. This paper proposes an enhanced deep learning-based approach known as Deep Regression Network (DRN). The proposed DRN is a 6-layer deep neural network. The proposed algorithm trains and tests on the agricultural corpus, collected from Dehradun (India) region. The experimental outcomes state that the proposed DRN method attained a prediction accuracy approx 86.56%. The comparative analysis shows that the proposed method outperformed existing methods like Ensemble Neural Network, Naïve Bayes, KNN, and Weighted Self-Organizing Map.

Projection of the Near-Future PM2.5 in Northern Peninsular Southeast Asia under RCP8.5

Throughout the year, particularly during the dry season, the northern peninsula of Southeast Asia struggles with air pollution from PM2.5. In this study, we used the Nested Regional Climate and Chemistry Model (NRCM-Chem) to predict the PM2.5 concentrations over Southeast Asia’s northern peninsula during the years 2020–2029 under the Representative Concentration Pathway (RCP)8.5. In general, the model reasonably shows a good result, including temperature, precipitation, and PM2.5 concentration, compared to the observation with an Index of Agreement (IOA) in the range of 0.63 to 0.80. However, there were some underestimations for modeled precipitation and temperature and an overestimation for modeled PM2.5 concentration. As a response to changes in climatic parameters and the emission of PM2.5’s precursors, PM2.5 concentrations tend to increase across the region in the range of (+1) to (+35) µg/m3 during the dry season (November to April) and decline in the range of (−3) to (−30) µg/m3 during the wet season (May to October). The maximum increase in PM2.5 concentrations were found in March by >40 µg/m3.

Climate Change and Trend Analysis of 24-Hourly Annual Maximum Series Using Mann-Kendall and Sen Slope Methods for Rainfall IDF Modeling

The aim of this study is the establishment of the existence of trend and variability on a typical 24-hourly sorted thirty years (1986-2015) annual maximum series (AMS) and maximum monthly series (MMS) rainfall data for Uyo metropolis in Nigeria. Data were downscaled into shorter durations of 0.25, 0.5, …,12 hours. The statistical tool applied for the study was the Mann-Kendall (MK) test and Sen Slope estimator. The results showed that there exists increasing trend for all durations analyzed with consistency in the test statistic results. The MK statistic lZl for the AMS varied between 3.1701 and 3.2827 while that of MMS was 4.756, were greater than critical Z = 1.96. Also, the computed p-value for the AMS varied between 0.0012 and 0.0015, and were lower than the significant level of alpha, = 0.05. Thus, the null hypothesis of no trend was rejected. Similarly, the Sen Slope estimator gave an average rate of change in rainfall as 2.1288 and 2.16 mm/year for AMS and MMS time series data, respectively. The result from the Sen Slope estimator indicated that the magnitude of the trend decreased as the duration of rainfall increased such that shorter duration exhibited more trend than higher duration. The results of the MK trend and Sen Slope analysis proved that both test exhibited high degree of consistency with statistically significant positive trend and variability. These results have provided further evidence of an accelerated alarming rate in climate change increasing trend in Uyo metropolis and perhaps the environs. Therefore, planning for effective and accurate rainfall prediction for annual maximum time series data with established variability in trend will require adoption of non-stationary concept to account for the influence of changing climatic parameters in intensity-duration-frequency (IDF) modeling.

Spatial and temporal variations in fractional vegetation cover and its driving factors in the Hulun Lake region

Climate change and human activities are critical to fractional vegetation cover (FVC). However, until now, their combined effects on FVC have not been well quantified. In this paper we analyze the temporal and spatial variations in the fractional vegetation cover in the Hulun Lake region from 1986 to 2017 and its response to changes in climate parameters and human activities; additionally, the impact of changes in climate parameters and human activities on FVC are discussed. The results showed that according to the Mann-Kendall (M−K) trend test analysis, 65.01% of the FVC in the Hulun Lake region decreased to varying degrees, of which 24.55% showed a significant decrease and only 8.61% increased significantly. Based on the M−K mutation test analysis of FVC and analysis of driving factors from 1986 to 2017, 1999 was the time point at which abrupt changes in FVC, annual precipitation and moisture index occurred in the Hulun Lake region, and water was an important factor affecting FVC. The regression analysis of FVC and the driving factors from 1986 to 2017 showed that the moisture index and the number of livestock could not be eliminated from the regression equation. Additionally, there was a significant correlation between the FVC and the climate livestock index (P < 0.001). Linear regression model constructed by FVC and climatic livestock index: y = 23.016ln(x)-31.454 (R2 = 0.5941), FVC increased with the increase of climatic livestock index.

Long-Term Climatic Changes in Small Ruminant Farms in Greece and Potential Associations with Animal Health

The objectives of this work were (a) to present the changes in climatic parameters from 1989 to 2019, in 444 locations throughout Greece, where small ruminant farms have been based and (b) to present associations of the changes in the climatic parameters with clinical data related to small ruminant health. Climatic variables (1989–2019) were obtained for 444 locations with small ruminant farms throughout Greece. During this period, significant increases were noted in temperature-related parameters (annually 0.05 °C for average temperature and 0.14 °C for temperature range) and precipitation (annually 0.03 mm). There were significant differences in climatic conditions between locations of farms in accord with the management system applied therein, as well as in accord with the breed of animals on the farms (e.g., higher average temperature in locations with Greek breeds, higher temperature range in locations with imported breeds). There were significant associations of temperature-related parameters with the annual frequency of cases of neonatal hypothermia seen at a veterinary teaching hospital, as well as with the average proportion of Haemonchus contortus larvae in faecal samples and the frequency of cases of H. contortus resistance reported by a veterinary parasitology laboratory.

Analiza uticaja rudarskih aktivnosti na sadržaj teških metala u podzemnoj vodi i vodi za piće u zavisnosti od klimatskih parametara

The purpose of this analysis is to show whether mining activities have an impact on the grondwater and drinking water quality. Water samples were analyzed in three unfavorable climatic periods during the winter of 2018-2019. The obtained results show that the heavy metal concentrations in water samples do not have values higher than the allowable according to the current statutory regulations. Also, the heavy metal concentration did not change with changes in climate parameters. However, a higher concentration of molybdenum than statutory by the law was found in the period of snow melting. This has shown that in extremely unfavorable climatic conditions, the karst groundwater can contain certain metals that can origin either from the surrounding field or as a result of mining activities, considering that there are two open pits s in the immediate vicinity of the "Surdup" source.

The Potential of Hydro-Climate Forecast Strategies in Sustaining Agricultural Productivity amongst Rainfed Farmers in West Africa: A Review

Several studies have reported a significant yearly decrease in forest cover globally, using satellite images. This is especially true in West Africa, where rapid urbanisation acerbates the problem, and both of these changes lead to alterations in rainfall regime and other changes in climatic parameters. Several studies reveal that adaptation which reduces vulnerability to adverse climatic variation effects is the key to developing resilience against climate change. In this region, over 90% of farmers are engaged in small-scale rainfed crop cultivation and rely on their own weather perception, instincts and what they observe from the surrounding biota (flora and fauna) to forecast the weather and plan their agricultural activities. The pressing nature of the problem of climate variability in Africa had provoked a lot of research into developing and testing several adaptation strategies to control the situation. Various strategies to improve and localize global, regional and national climate services (Local Forecast, Scientific Forecast and Integrated Forecast) needed by the farmers are being developed to build resilience against climate change. This review illustrates the situation in Ghana and identifies various forecast strategies developed to mitigate the adverse effects of climate variability on agricultural productivity. These mitigation methods include the development of climate services to provide users with forecast information in order to make climate-smart decisions to minimize the risk.

Eficiência no uso de água e qualidade de cebola em cenários climáticos futuros

ABSTRACT Considering that changes in climatic parameters directly interfere in crop yields, this study aimed to evaluate the impact of changes in carbon dioxide concentrations, temperature and soil water availability on production, water-use efficiency and onion (Allium cepa L. cv. IPA 11) quality. Four levels of soil water availability (40, 60, 80 and 100 %) and two concentrations of atmospheric CO2 (770 and 390 ppm) were evaluated in three experiments with different temperature regimes (18-24-30, 22-28-34 and 26-32-38 ºC), in Phytotron growth chambers. The water-use efficiency, bulb diameter and weight, shoot dry weight, pulp frmness, soluble solids content, pH and titratable acidity were also evaluated. The increase in temperature reduces the production, and a higher soil water availability increases the production of IPA 11 onion bulbs. The temperature regime of 18-24-30 ºC and the water availability of 76.72 % provide the best water-use efficiency for the onion crop.

Fuzzy Logic based Vegetable Price prediction in IoT

Agriculture is an integral part of every economy with research in this domain mainly aimed at not only increasing food quality and production but also reducing expenditure while maximizing profit. Climate is a major factor that affects production of vegetables and other crops there by having direct impact on pricing. Among the main challenges faced by farmers is accurate prediction of vegetable prices. Farmers therefore need to acquire a better and advanced knowledgeable model to assist them in taking proper decisions when budgeting on how much to spend on planting so that it's proportional to the expected prices of the produce, in prediction of the climatic change effects on vegetable prices, a call for variables and parameters that show the response of vegetable prices, production, consumption and storage with weather conditions is necessary. The most common approach is using the statistical models that are trained using historical yield data with simplified weather measurements, for example average temperature in the growing season, humidly and rainfall. However, such approaches do not take into consideration the weather effects on entire vegetable lifecycle inclusive of the prices for yields necessitating the need for this study. This paper presents a study on Fuzzy based vegetable price prediction considering various changes in climatic parameters including humidity, rainfall and temperature. This system model was developed based on a 4- year vegetable price data and climatic data of the same period for Kisumu (Kenya). The results show that this model can indeed be used by farmers to predict prices and hence assist them to make informed vegetable farming and investment decisions.