MITIGATION YIELD SCALED METHANE EMISSION FROM RICE GROWN IN WATER STRESS CONDITIONS WITH BIOCHAR AND SILICATE AMENDMENTS

Climate change and water scarcity may badly affect existing rice production system in Bangladesh. With a view to sustain rice productivity and mitigate yield scaled CH4 emission in the changing climatic conditions, a pot experiment was conducted under different soil water contents, biochar and silicate amendments with inorganic fertilization (NPKS). In this regard, 12 treatments combinations of biochar, silicate and NPKS fertilizer along with continuous standing water (CSW), soil saturation water content and field capacity (100% and 50%) moisture levels were arranged into rice planted potted soils. Gas samples were collected from rice planted pots through Closed Chamber technique and analyzed by Gas Chromatograph. This study revealed that seasonal CH4 emissions were suppressed through integrated biochar and silicate amendments with NPKS fertilizer (50–75% of the recommended doze), while increased rice yield significantly at different soil water contents. Biochar and silicate amendments with NPKS fertilizer (50% of the recommended doze) increased rice grain yield by 10.9%, 18.1%, 13.0% and 14.2%, while decreased seasonal CH4 emissions by 22.8%, 20.9%, 23.3% and 24.3% at continuous standing water level (CSW) (T9), at saturated soil water content (T10), at 100% field capacity soil water content (T11) and at 50% field capacity soil water content (T12), respectively. Soil porosity, soil redox status, SOC and free iron oxide contents were improved with biochar and silicate amendments. Furthermore, rice root oxidation activity (ROA) was found more dominant in water stress condition compared to flooded and saturated soil water contents, which ultimately reduced seasonal CH4 emissions as well as yield scaled CH4 emission. Conclusively, soil amendments with biochar and silicate fertilizer may be a rational practice to reduce the demand for inorganic fertilization and mitigate CH4 emissions during rice cultivation under water stress drought conditions.

IMPROVING THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) PRECIPITATION FORECASTS AT SEASONAL SCALE OVER THE HIMALAYAN REGION USING MACHINE LEARNING

The coarse horizontal resolution global climate models (GCMs) have limitations in producing large biases over the mountainous region. Also, single model output or simple multi-model ensemble (SMME) outputs are associated with large biases. While predicting the rainfall extreme events, this study attempts to use an alternative modeling approach by using five different machine learning (ML) algorithms to improve the skill of North American Multi-Model Ensemble (NMME) GCMs during Indian summer monsoon rainfall from 1982 to 2009 by reducing the model biases. Random forest (RF), AdaBoost (Ada), gradient (Grad) boosting, bagging (Bag) and extra (Extra) trees regression models are used and the results from each models are compared against the observations. In simple MME (SMME), a wet bias of 20[Formula: see text]mm/day and an RMSE up to 15[Formula: see text]mm/day are found over the Himalayan region. However, all the ML models can bring down the mean bias up to [Formula: see text][Formula: see text]mm/day and RMSE up to 2[Formula: see text]mm/day. The interannual variability in ML outputs is closer to observation than the SMME. Also, a high correlation from 0.5 to 0.8 is found between in all ML models and then in SMME. Moreover, representation of RF and Grad is found to be best out of all five ML models that represent a high correlation over the Himalayan region. In conclusion, by taking full advantage of different models, the proposed ML-based multi-model ensemble method is shown to be accurate and effective.

ANALYSIS OF HYDROLOGICAL DROUGHT TRENDS IN AUSTRALIA WATERSHED

This paper is about the problem of drought and its future. The research methods are both theoretical and field studies. This paper presents a mathematical model for drought analysis in Australia that can predict its future trend. It analyses three meteorological indicators, including annual rainfall, increases in temperature, and water consumption volume. Surveys about the mentioned indicators are from the past to the present and now to the future intervals. This paper suggests practical solutions to change the conditions of drought-affected regions. The research method, simulated exemplary, and outcomes of this paper are applicable everywhere in the world affected by the hydro-drought crisis.

ANNUAL AND SEASONAL VARIATIONS IN TEMPERATURE EXTREMES AND RAINFALL IN BANGLADESH, 1989–2018

Based on temperature and rainfall recorded at 34 meteorological stations in Bangladesh during 1989–2018, the trends of yearly average maximum and minimum temperatures have been found to be increasing at the rates of 0.025∘C and 0.018∘C per year. Analysis of seasonal average maximum temperature showed increasing trend for all seasons except the late autumn season. The increasing trend was particularly significant for summer, rainy and autumn seasons. Seasonal average minimum temperature data also showed increasing trends for all seasons. The trend of yearly average rainfall has been found to be decreasing at a rate of 0.014[Formula: see text]mm per year in the same period; especially, for most of the meteorological stations the rainfall demonstrates an increasing trend for rainy season and a decreasing trend in the winter season. It means that in Bangladesh dry periods became drier and wet periods became wetter.

DATA ANALYTICS FOR CLIMATE AND ATMOSPHERIC SCIENCE

Global climate change has already had observable effects on the environment. Glaciers have shrunk, ice on rivers and lakes is breaking up earlier, plant and animal ranges have shifted and trees are flowering sooner. Under these conditions, air pollution is likely to reach levels that create undesirable living conditions. Anthropogenic activities, such as industry, release large amounts of greenhouse gases into the atmosphere, increasing the atmospheric concentrations of these gases, thus significantly enhancing the greenhouse effect, which has the effect of increasing air heat and thus the speedup of climate change. The use of sophisticated data analysis methods to identify the causes of extreme pollutant values, the correlation of these values with the general climatic conditions and the general malfunctions that can be caused by prolonged air pollution can give a clear picture of current and future climate change. This paper presents a thorough study of preprocessing steps of data analytics and the appropriate big data architectures that are appropriate for the research study of Climate Change and Atmospheric Science.

CONTRIBUTION OF AGROFORESTS IN ADAPTATION OF CLIMATE CHANGE EFFECTS: ESTIMATING THE PHYTODIVERSITY AND C SEQUESTRATION POTENTIAL IN 8-, 16- AND 24-YEAR-OLD OF PINUS SYLVESTRIS AGROFORESTS IN NORTHERN CAMEROON (TROPICAL AFRICA)

The study aims at assessing the agrobiodiversity and carbon stocks by the pine agroforests in the Sudano-Guinean zone of Cameroon. Five [Formula: see text][Formula: see text]m sampling transects were established in each chronosequence, it was undertaken to assess the growth characteristics and biomass. Estimates of stocks of carbon in aboveground biomass, belowground biomass (BGB), total biomass (TB) and CO2 equivalent stock were incorporated in allometric equation based on nondestructive method. A total of 24 species from 23 genera and 17 families were inventoried. Annona senegalensis, Syzygium guineensis and Hymenocardia acida contributed the most to the importance value index (IVI). Density ranged between [Formula: see text]–[Formula: see text] stems/ha; basal area between [Formula: see text]–[Formula: see text][Formula: see text]m2/ha; Shannon index between [Formula: see text]–[Formula: see text] with the highest value for 8-year-old stands; Pielou’s evenness between [Formula: see text]–[Formula: see text] with the lowest value in 24-year-old stands. Aboveground biomass ranged between [Formula: see text]–[Formula: see text] Mg C/ha with the highest value in 16-year-old stands; belowground carbon from [Formula: see text] Mg C/ha to [Formula: see text] Mg C/ha and total carbon from [Formula: see text] Mg C/ha to [Formula: see text] Mg C/ha. The sequestration potential ranged from [Formula: see text] Mg CO[Formula: see text]/ha to [Formula: see text] Mg CO[Formula: see text]/ha. The sequestration rates were 84.77, 49.7 and 28.6 Mg CO[Formula: see text].ha[Formula: see text]yr[Formula: see text] in 8-, 16- and 24-year-old stands, respectively. Although our data reported that pine stands hosted a few number of species; they are true carbon sinks and useful to the REED[Formula: see text] community.

FINANCIAL INTEGRATION AND ENVIRONMENT IN AFRICA: THE ROLE OF OUTPUT GROWTH AND FOREIGN DIRECT INVESTMENT

As economies integrate financially and both investment and output increase, the environment may be affected depending on the nature of international financial resources attracted into the country. Hence, this study examines the effect of financial integration, output growth, and foreign direct investment (FDI) on the environment in selected African countries involving Nigeria, South Africa, Egypt, Algeria, and Angola between 1980 and 2017. The study uses carbon emissions and particulate emissions (PM) to proxy pollution and analyze the data through the fully modified ordinary least squares (FMOLS) technique. Empirical results show that financial integration worsens pollution in Egypt, Nigeria, Algeria, and in Africa; output growth deteriorates pollution in South Africa, Algeria, Angola, and in Africa; while FDI fuels environmental degradation in Egypt and South Africa. We recommend that African countries should strive to establish specific targets for lowering emissions even though the Kyoto Protocol did not set specific emissions reduction targets for them.

MODELING THE SURVIVAL OF HUMAN POPULATION IN A STRESSED ENVIRONMENT: EFFECT OF GLOBAL WARMING DUE TO TRAFFIC EMISSIONS

The survival of human population is adversely affected by the global warming due to increasing temperature of earth surface caused by emissions of various gases (such as carbon dioxide, methane, etc.) and particulate matters from vehicles in the traffic. These gases, called greenhouse gases, present in the atmosphere above a certain threshold result to elevate the average temperature of earth surface affecting the human population. To comprehend this problem, in this paper, a nonlinear mathematical model is proposed to study the detrimental effects on human population of the increased earth surface temperature caused by traffic emissions. In the model formulation, five nonlinearly interacting variables namely; the cumulative densities of human population and vehicles in traffic, cumulative concentration of greenhouse gases and particulate matters due to traffic emissions and average global warming temperature of earth surface have been used. Using stability theory, certain inferences have been drawn regarding the local and nonlinear stability behaviour of the equilibrium points. It is shown that as the cumulative concentration of greenhouse gases emitted from vehicles in the traffic reaches beyond its threshold level, the average temperature of earth surface rises above its threshold value leading to global warming with diminishing effects on the growth of human population density. The growth of human population is further declined with increase in the cumulative concentration of particulate matters emitted from vehicles in traffic. Numerical simulations have also been performed to validate the analytical findings.

THE LONG-TERM CHANGES OF EMISSION DRIVING FORCES IN EUROPEAN POWER AND HEAT SECTOR AND THEIR IMPACT ON THE DECARBONIZATION PATHWAYS IN EU ENERGY SECTOR SINCE 1990

The Public Electricity and Heat Production (PEHP) is the most emissive sector in European economy. The characteristic features of PEHP contribute to the importance of its mitigation role. In this study, the decarbonization potential of energy sector was retrospectively analyzed. Proposed algorithm contained, at the first layer, the year-by-year data analysis and calculation while at the second graphical interpretation and pattern grouping. Despite the double-layering, the developed approach included the three following stepwise stages: the CO2 emission alternatives scenarios pathways methodology, the CO2 emission driving forces modes and influence weight methodology, and the transformation pattern identifying and grouping. Based on 1990–2017 CO2 emission inventories, the investigation of long-term PEHP decarbonization in 26 European countries was performed, in year-by-year and country-by-country processing. All results and findings have become the foundation for describing achieved decarbonization goals and 27 years mitigation ways. The Energy Consumption (EC) and Emission Factor (EF) have been pointed as a key driving forces of CO2 emission and as a key development indicator of PEHP sectoral transformation structure. Based on the developed country-by-country graphical interpretation interface, the five key patterns of PEHP decarbonization processes in European countries were observed and defined. The algorithm which has been applied and tested in 26 countries for 27 years was proved to be a universal and flexible tool to determine the lack of a utilitarian path of PEHP CO2 emission mitigation in Europe.

RANKING OF CMIP5-BASED GENERAL CIRCULATION MODELS USING COMPROMISE PROGRAMMING AND TOPSIS FOR PRECIPITATION: A CASE STUDY OF UPPER GODAVARI BASIN, INDIA

This study determines the suitable general circulation models (GCMs) for the prediction of future precipitation of Upper Godavari sub-basin, India. Five performance indicators (PIs) namely correlation coefficient (CC), normalized root mean square deviation (NRMSD), absolute normalized mean biased deviation (ANMBD), skill score (SS), Nash Sutcliffe efficiency (NSE), and three different combinations (Case 1: all performance indicators, Case 2: CC, SS and ANMBD, and Case 3: CC, SS, and NRMSD) were considered to evaluate the performance of 38 GCM models for the study area. The observed precipitation data for 12 grid points covering the Upper Godavari sub-basin along with eight districts of Maharashtra were used for the selection of the suitable GCMs. The weights of the indicators were determined by the entropy method. Compromise programming (CP) and the technique for order preference to the similarity to ideal solution (TOPSIS) methods were used for ranking the GCMs. The group decision-making approach was employed to make a collective decision about the rank of 38 GCMS considering all the grid points. In view of all the three combinations of PIs, the study suggests that the effect of the performance indicator NSE on the ranking of GCM models is the most significant (weights for the grid points varying in the range 22.75%–78%) followed by ANMBD, CC, NRMSD, and SS. Including the maximum number of PIs and considering their combinations is found to be much helpful to enhance the credibility of the ranking of GCMs. From the group decision-making approach, it was observed that the ensemble of MPI-ESM-P, CNRM-CM5-2, and CNRM-CM5 is suitable for the prediction of precipitation for the study area.