Pub Date : 2022-06-22DOI: 10.1142/s2630534822800018
Andrew Breeson
{"title":"BOOK REVIEW: INTELLIGENT DECARBONISATION, OFFERS HOPE THAT ARTIFICIAL INTELLIGENCE COULD HELP END CLIMATE CHANGE","authors":"Andrew Breeson","doi":"10.1142/s2630534822800018","DOIUrl":"https://doi.org/10.1142/s2630534822800018","url":null,"abstract":"","PeriodicalId":262307,"journal":{"name":"International Journal of Big Data Mining for Global Warming","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134207188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-31DOI: 10.1142/s2630534821300013
Salah Sahabi-Abed
In this study, we assess the future changes in minimum temperature (T-min), maximum temperature (T-max), and precipitation (PRCP) for the three periods the 2020s (2011–2040), the 2050s (2041–2070), and the 2080s (2071–2100), with respect to the reference period 1981–2010 over Algeria focusing on a validation of the Statistical DownScaling Model (SDSM). In this approach, to underpin our analysis, we evaluate statistically the SDSM performance by simulating the historical temperatures and precipitation. The NCEP reanalysis data and CanESM2 predictors of three future scenarios, RCP2.6, RCP4.5, and RCP8.5 are used for model calibration and future projection, respectively. The projected climate changes resulting from the application of SDSM show a convincing consistency with those unveiled in previous studies over Algeria based on dynamical regional climate model outputs conducted in the context of Middle East-North Africa region. By the end of the century, the results exhibit strong warming for both extreme temperatures under the worst-case scenario (RCP 8.5), it is more pronounced for the T-max and over the Algerian Sahara region. Under the optimistic scenario (RCP2.6), the strength of the warming is expected to increase for both extreme temperatures. The projected changes of precipitation revealed for all scenarios several discrepancies with significant decrease over the northwest region and central Sahara, while nonsignificant change is projected for the center and eastern coastal regions. Our findings corroborate previous studies using sophisticated tools by demonstrating that Algeria’s climate is expected to warm further in the future. These primary findings could give an overview of the application of the statistical modeling approach using SDSM over a semi-arid and arid vulnerable region like Algeria and would extend our knowledge in the climate-modelling field for the North Africa zone by providing an added value to the existing GCMs and regional climate projections. In addition, reliable information regarding the magnitude of future changes at local scale may be used in impact models to assess changes of other key economic sector variables such as water resources management, energy and agriculture.
{"title":"ASSESSMENT OF FUTURE CLIMATE PROJECTIONS IN ALGERIA USING STATISTICAL DOWNSCALING MODEL","authors":"Salah Sahabi-Abed","doi":"10.1142/s2630534821300013","DOIUrl":"https://doi.org/10.1142/s2630534821300013","url":null,"abstract":"In this study, we assess the future changes in minimum temperature (T-min), maximum temperature (T-max), and precipitation (PRCP) for the three periods the 2020s (2011–2040), the 2050s (2041–2070), and the 2080s (2071–2100), with respect to the reference period 1981–2010 over Algeria focusing on a validation of the Statistical DownScaling Model (SDSM). In this approach, to underpin our analysis, we evaluate statistically the SDSM performance by simulating the historical temperatures and precipitation. The NCEP reanalysis data and CanESM2 predictors of three future scenarios, RCP2.6, RCP4.5, and RCP8.5 are used for model calibration and future projection, respectively. The projected climate changes resulting from the application of SDSM show a convincing consistency with those unveiled in previous studies over Algeria based on dynamical regional climate model outputs conducted in the context of Middle East-North Africa region. By the end of the century, the results exhibit strong warming for both extreme temperatures under the worst-case scenario (RCP 8.5), it is more pronounced for the T-max and over the Algerian Sahara region. Under the optimistic scenario (RCP2.6), the strength of the warming is expected to increase for both extreme temperatures. The projected changes of precipitation revealed for all scenarios several discrepancies with significant decrease over the northwest region and central Sahara, while nonsignificant change is projected for the center and eastern coastal regions. Our findings corroborate previous studies using sophisticated tools by demonstrating that Algeria’s climate is expected to warm further in the future. These primary findings could give an overview of the application of the statistical modeling approach using SDSM over a semi-arid and arid vulnerable region like Algeria and would extend our knowledge in the climate-modelling field for the North Africa zone by providing an added value to the existing GCMs and regional climate projections. In addition, reliable information regarding the magnitude of future changes at local scale may be used in impact models to assess changes of other key economic sector variables such as water resources management, energy and agriculture.","PeriodicalId":262307,"journal":{"name":"International Journal of Big Data Mining for Global Warming","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125646366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-26DOI: 10.1142/s2630534821500091
A. K. Misra, Maitri Verma
The expansion of the industrial sector is one of the prime contributors to the increase in the concentration of heat-trapping gases, mainly carbon dioxide ([Formula: see text]), in the atmosphere. Since the onset of the industrial revolution, industrial [Formula: see text] emissions have been contributing significantly to global warming and associated climate changes. To design strategies for the mitigation of climate changes, it is crucial to comprehend the role of industrialization in the elevation of atmospheric [Formula: see text] concentration. This paper presents a nonlinear mathematical model, comprising a set of nonlinear differential equations, to examine the impact of industrialization on the dynamics of atmospheric [Formula: see text]. Analysis of the model shows that if the industrial [Formula: see text] emission rate increases beyond a critical value, the system experiences Hopf-bifurcation about the interior equilibrium and periodic solution is generated. The direction and stability of periodic solutions arising through Hopf-bifurcation are investigated. Numerical simulation is presented to demonstrate the analytical findings.
{"title":"IMPACT OF INDUSTRIALIZATION ON THE DYNAMICS OF ATMOSPHERIC CARBON DIOXIDE: A MODELING STUDY","authors":"A. K. Misra, Maitri Verma","doi":"10.1142/s2630534821500091","DOIUrl":"https://doi.org/10.1142/s2630534821500091","url":null,"abstract":"The expansion of the industrial sector is one of the prime contributors to the increase in the concentration of heat-trapping gases, mainly carbon dioxide ([Formula: see text]), in the atmosphere. Since the onset of the industrial revolution, industrial [Formula: see text] emissions have been contributing significantly to global warming and associated climate changes. To design strategies for the mitigation of climate changes, it is crucial to comprehend the role of industrialization in the elevation of atmospheric [Formula: see text] concentration. This paper presents a nonlinear mathematical model, comprising a set of nonlinear differential equations, to examine the impact of industrialization on the dynamics of atmospheric [Formula: see text]. Analysis of the model shows that if the industrial [Formula: see text] emission rate increases beyond a critical value, the system experiences Hopf-bifurcation about the interior equilibrium and periodic solution is generated. The direction and stability of periodic solutions arising through Hopf-bifurcation are investigated. Numerical simulation is presented to demonstrate the analytical findings.","PeriodicalId":262307,"journal":{"name":"International Journal of Big Data Mining for Global Warming","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-30DOI: 10.1142/s2630534821500078
M. Ali, Sanjit CHANDRA BARMAN, Md. Ashraful Islam Khan, Md. Badiuzzaman Khan, Hafsa Jahan Hiya
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.
{"title":"MITIGATION YIELD SCALED METHANE EMISSION FROM RICE GROWN IN WATER STRESS CONDITIONS WITH BIOCHAR AND SILICATE AMENDMENTS","authors":"M. Ali, Sanjit CHANDRA BARMAN, Md. Ashraful Islam Khan, Md. Badiuzzaman Khan, Hafsa Jahan Hiya","doi":"10.1142/s2630534821500078","DOIUrl":"https://doi.org/10.1142/s2630534821500078","url":null,"abstract":"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.","PeriodicalId":262307,"journal":{"name":"International Journal of Big Data Mining for Global Warming","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122254205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-12DOI: 10.1142/s263053482150008x
S. Shrivastava, R. Avtar, P. K. Bal
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.
{"title":"IMPROVING THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) PRECIPITATION FORECASTS AT SEASONAL SCALE OVER THE HIMALAYAN REGION USING MACHINE LEARNING","authors":"S. Shrivastava, R. Avtar, P. K. Bal","doi":"10.1142/s263053482150008x","DOIUrl":"https://doi.org/10.1142/s263053482150008x","url":null,"abstract":"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.","PeriodicalId":262307,"journal":{"name":"International Journal of Big Data Mining for Global Warming","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117258215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-23DOI: 10.1142/S2630534821500066
A. Shahraki
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.
{"title":"ANALYSIS OF HYDROLOGICAL DROUGHT TRENDS IN AUSTRALIA WATERSHED","authors":"A. Shahraki","doi":"10.1142/S2630534821500066","DOIUrl":"https://doi.org/10.1142/S2630534821500066","url":null,"abstract":"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.","PeriodicalId":262307,"journal":{"name":"International Journal of Big Data Mining for Global Warming","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131425833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-28DOI: 10.1142/s2630534821500042
Lipon Chandra Das, Zhihua Zhang
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.
{"title":"ANNUAL AND SEASONAL VARIATIONS IN TEMPERATURE EXTREMES AND RAINFALL IN BANGLADESH, 1989–2018","authors":"Lipon Chandra Das, Zhihua Zhang","doi":"10.1142/s2630534821500042","DOIUrl":"https://doi.org/10.1142/s2630534821500042","url":null,"abstract":"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.","PeriodicalId":262307,"journal":{"name":"International Journal of Big Data Mining for Global Warming","volume":"279 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123258728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-21DOI: 10.1142/s2630534820500102
V. Noiha Noumi, P. KOUAM KAMNING, C. KAMDOUM DEMGUIA, L. Zapfack
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.
{"title":"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)","authors":"V. Noiha Noumi, P. KOUAM KAMNING, C. KAMDOUM DEMGUIA, L. Zapfack","doi":"10.1142/s2630534820500102","DOIUrl":"https://doi.org/10.1142/s2630534820500102","url":null,"abstract":"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.","PeriodicalId":262307,"journal":{"name":"International Journal of Big Data Mining for Global Warming","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114713663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-27DOI: 10.1142/s2630534820500047
F. A. Semire, Adeyanju Joshua Adekunle, Robert O. Abolade
Due to the fact that rainfall may hamper signal availability, rainfall variability and its resultant effect on environment and communication have become of global concern. In this study, we investigate the trend and variability of rainfall in southwestern Nigeria and examine its effect on radio communication. Our results reveal a steady increasing in rainfall and slightly unstable in volume variation in southwestern Nigeria. The study also reveals that the tendency of higher attenuation in years was caused by the increasing trend but showing variability with frequency. With the rising trend in view, there is therefore the likelihood that radio communication infrastructures will experience increasing outage and more signal loss in the future years. This outcome should serve as useful tools in optimizing satellite link budget and better utilization of available bandwidth.
{"title":"RAINFALL VARIABILITY AND ITS EFFECT ON COMMUNICATION LINK IN SOUTHWESTERN NIGERIA","authors":"F. A. Semire, Adeyanju Joshua Adekunle, Robert O. Abolade","doi":"10.1142/s2630534820500047","DOIUrl":"https://doi.org/10.1142/s2630534820500047","url":null,"abstract":"Due to the fact that rainfall may hamper signal availability, rainfall variability and its resultant effect on environment and communication have become of global concern. In this study, we investigate the trend and variability of rainfall in southwestern Nigeria and examine its effect on radio communication. Our results reveal a steady increasing in rainfall and slightly unstable in volume variation in southwestern Nigeria. The study also reveals that the tendency of higher attenuation in years was caused by the increasing trend but showing variability with frequency. With the rising trend in view, there is therefore the likelihood that radio communication infrastructures will experience increasing outage and more signal loss in the future years. This outcome should serve as useful tools in optimizing satellite link budget and better utilization of available bandwidth.","PeriodicalId":262307,"journal":{"name":"International Journal of Big Data Mining for Global Warming","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124174304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.1142/s2630534820500023
Danish Hassan, Muhammad Fahim Akhter, Shaheen Abbas
Sun is the main source of energy for the earth and other planets. Its activity in one or other way influences the terrestrial climate. Particularly, the solar activity manifested in the form of sunspots is found to be much more influential on the earth’s climate and on its magnetosphere. Links of the variability in terrestrial climate and sunspot cycles and associated magnetic cycles have been the concern of many recent studies. These two time series data sunspots and K-index are distributed into 22-year cycles, according to the magnetic field of the sun in which polarity reverses after 11-years. The fractal dimension of each sunspot cycle from 1 to 24 is calculated and found to be quasi-regular (persistent, [Formula: see text]). To understand the regular effects of the dynamics of sunspot cycles on the earth’s climate and magnetosphere, the sunspot cycles and K-index cycles (22 years each) from 1932 to 2014 are observed and discussed comparatively in the perspective of fractal dimension and Hurst exponent.
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