American Journal of Climate Change

Journal Information
ISSN / EISSN : 2167-9495 / 2167-9509
Published by: Scientific Research Publishing, Inc. (10.4236)
Total articles ≅ 338
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Ladu David Morris Lemi, Michael Carnegie LaBelle
American Journal of Climate Change, Volume 11, pp 1-21;

Climate change and global warming have been identified as major threats to the development of South Sudan due to greenhouse gases (GHGs) emission responsible for the rising temperatures and erosion of existing ecosystem services that support local livelihoods. Mitigating GHGs emissions has become an urgent global policy trajectory with countries acceding to related Treaties and setting national targets. Despite having submitted its initial communications to the United Nations Framework Convention on Climate Change (UNFCCC), outlining specific sectors for GHGs reduction, the rapidly growing hotel industry has not been identified among the sectors and no GHGs emission reporting has been undertaken. Therefore, this study aimed to assess, quantify and report on the GHGs emission potential of the hotel industry in Juba-South Sudan, examine existing GHGs emission reporting mechanisms and propose a reporting framework. Using a standard quantitative methodology, the carbon footprint of twenty-seven hotels was assessed. The results showed that the hotel industry is one of the leading emitters of GHGs emission amounting to 14,624.9-ton CO2eq/year. The study also found no existing GHGs reporting systems in South Sudan and proposed a reporting framework and identified potential emissions reduction strategies for the hotel industry to deal with climate change and carbon emission issues of the hospitality industry. It recommends that further research be initiated to 1) assess the applicability of the identified strategies in the context of South Sudan and 2) to quantify GHGs emissions from cooling appliance within the hospitality industry as well as emissions from other growing sectors like the transportation.
Ibrahima Pouye, Dieudonné Pessièzoum Adjoussi, Jacques André Ndione, Amadou Sall, Kouami Dodji Adjaho, Muhammad Leroy Albert Gomez
American Journal of Climate Change, Volume 11, pp 23-36;

In the context of climate change, the study of shoreline dynamics is a critical issue concerning economic losses in coastal countries. Therefore, since it is an important parameter to study the impacts of climate change in coastal areas, scientists are more interested in littoral studies seeking deep existential knowledge. This study aims to depict separately the coastal dynamics from 1990 to 2020 in Dakar region. The difference in terms of geologic, geomorphologic and hydrodynamic conditions within the three different coasts of Dakar and the prediction until 2040 have been taken into account in comparison to the previous studies. To achieve this purpose, the Geographic Information System (GIS) approach which is among the most current methods to determine the coastline dynamics is used. Historical Landsat images from the USGS, QGIS 3.12.0, Arc GIS 10.4 and DSAS software have been used for the Landsat images pre-processing and coastline dynamic computation. After obtaining the coastline velocity rate, some predictions of future coastline position have been estimated using the formula of velocity. The results showed that the Dakar region is characterized by an average rate of retreat about −0.44 m/year on the northern coast. The western and southern coasts record respectively a rate of about 0.21 and −0.11 m/year. In 2030, the average rates of retreat of −4.4, 2.1 and −1.1 m/year were estimated respectively in the northern, western and southern coasts.
, MingChu Zhang, Robert Mark Van Veldhuizen, Charles Winsett Knight
American Journal of Climate Change, Volume 10, pp 490-511;

The climate change in Alaska has caused earlier spring snowmelt and the growing season expanded. However, the effect of climate change on crop phenological stages, heading (BBCH 55) and maturity (BBCH 85), is unknown. In this study, the trends of growing-season length (GSL), phenological stages of crops and climatic parameters, and the correlations between climatic parameters and the phenological stages were analyzed using the climate data and crop data over the period of 1978 to 2016. The longer GSL was found in Fairbanks (64.83˚N, 147.77˚W) and in Delta Junction (64.05˚N, 145.60˚W) but not in Palmer (61.60˚N, 149.11˚W). Sowing dates did not change significantly in three locations. The decreasing trends of heading and maturity of crops were observed but varied with location. Heading of barley and oat significantly advanced 3 and 3.1 d decade-1, respectively from 1989 to 2016 in Fairbanks while no change of heading was observed in Delta Junction and Palmer. Maturity of barley, oat and wheat significantly advanced 2.6, 3.8 and 3.9 d decade-1, respectively from 1978 to 2016 in Fairbanks (P -1 from 1978 to 2015, respectively in Delta Junction (P < 0.05). The increasing temperature trends and decreasing precipitation trends were found in Fairbanks and Delta Junction but varied with phenological stages of crops. Sowing was more important for heading than for maturity of crops. The effect of climate change on heading was less important than that on maturity. Earlier maturity of crops in Fairbanks may be attributed to increased temperatures, that in Delta Junction to both increased minimum temperature and decreased precipitation and that in Palmer to temperature and precipitation.
Sifat E. Rabbi, Reza E. Rabbi, Sourav Karmakar, Jürgen P. Kropp
American Journal of Climate Change, Volume 10, pp 619-638;

This study developed households’ Climate Resilient Livelihoods Index (CRLI) in Bangladesh. CRLI indicators were selected based on the Adequacy of Human livelihood conditions for Well-being and Development (AHEAD) framework and FAO resilience tools. The study was designed on cross-sectional data through a country-wide primary survey of 26,925 rural households. At first, we performed logistic regression to gauge the significance and intensity of different livelihood indicators on any specific livelihood indicator. Secondly, we scored each household with the set criteria of different livelihoods accessibility, if any households fulfill the set criteria was “scored 1” and if not “scored 0”. After scoring the households, eight different scores for each household were summed up to construct a composite score of “CRLI”. If any household scored 0 - 2 was considered as low resilient, if any household scored 3 - 5 was considered as moderate resilient and if any household scored 6 - 8 was considered as highly resilient. Additionally, we used ArcMap to visualize the percentage of households in districts with different resilience categories. Findings revealed that nationally 1.7% of households were low resilient, 60% of households were moderate resilient and only 11.48% of households were high resilient. More specifically, only 1.7% of households failed to secure any of the climate-resilient livelihood indicators, and only 0.06% of households secured all of them. Findings also revealed that food secured households had better adaptive capacity due to ensuring access to basic services, more financial capabilities, lower dependency ratio, and physical connectivity. In contrast, households with social safety net coverage had food insecurity, less financial ability, higher dependency ratio, lower education, and income sources. Among 64 counties, Cox’s Bazar, Bandarban, Chuadanga, Barguna, Bhola, Patuakhali, Narail, Kurigram, Sunamganj, Jamalpur, and Netrokona were the most vulnerable in terms of low CRLI. On the other hand, more than 25% of high resilient households were located in Dhaka, Gazipur, and Munshiganj counties. These findings would propel the government to devise appropriate steps in terms of more investment in area-specific local communities for enhancing regional resilience.
Alexander Ruzmaikin
American Journal of Climate Change, Volume 10, pp 204-236;

This brief review described spatial-time climate patterns generated by the dynamics and thermodynamics of the Earth’s climate system and methods of identifying these patterns. Specifically, it does discuss the following major climate patterns: El Ni?o-Southern Oscillation (ENSO), Cold Ocean-Warm Land (COWL) pattern, Northern and Southern Annular Patterns (NAM and SAM), Atlantic Multidecadal Oscillation (AMO) and Atlantic Meridional Overturning Circulation (AMOC), Pacific North-American Pattern (PNA) and Pacific Decadal Oscillation Pattern (PDO). In view of an extensive number of publications on some climate patterns, such as the ENSO, which discussed in many hundred of publications, this review is not intended to cover all the details of individual climate patterns but intends only to give a general overview of their structure, mechanisms of their formation and response to external forcing. It is assumed that the climate patterns can be treated as attractors of dynamical systems allowing us to extract and predict some specific features of the patterns such as the origin and evolution of the climate patterns and their role in climate change. Thus the knowledge of patterns allows the climate prediction on long time scales and understanding of how an external forcing affects the frequency of occurrence of climate patterns and their magnitude but not the spatial structure.
Yarou Halissou, Alamou Adéchina Eric, Obada Ezéchiel, Biao Ibukun Eliézer
American Journal of Climate Change, Volume 10, pp 371-385;

In the context of climate change, the study of the variability of the climatic extremes in several regions of the world is of capital importance. This study has as main objective to analyze the variability of extreme temperature events in the Beninese basin of the Niger River for the recent and the near future. To achieve this objective, seven (07) extreme temperature indices based on historical daily temperature observations (1976 to 2019) and REMO RCM simulation outputs of RCP4.5 and RCP8.5 scenarios (2021-2050) were calculated. The obtained results were represented by calculating the means for each index and analyzing the trends and their significance by the Mann-Kendall method. The results show that the indices of extreme temperature intensity (TNn, TXx, and DTR), and those related to the frequency of warm sequences (WSDI, TN90p and TX90p) have experienced a significant increase in the past. This increase will continue until 2050. In contrast, the cold sequence frequency index (CSDI) decrease over the historical period as well as over the future period. These indices show much more change with the RCP8.5 scenario than with the RCP4.5 of the REMO climate model. Only the TXx and CSDI indices show statistically significant changes at all stations.
Mary Waceke Thongoh, Henry Mikiugu Mutembei, John Mburu, Bessy Eva Kathambi
American Journal of Climate Change, Volume 10, pp 237-262;

Climate change poses great risks to poverty alleviation, food security and livelihoods sustainability in sub-Saharan Africa, declining crop yields and livestock productivity, especially in ASALs that suffer from fragile ecosystems characterized by frequent droughts and low rainfall. Climate-Smart Agriculture (CSA) objectives of improving productivity and incomes, adaptation, resilience to climate change and mitigation on GHGs emissions, are responses to these climate risks. CSA technologies, innovation and management practices (TIMPs) in general do exist, however they are concentrated in crop farming neglecting livestock production and especially in marginalized areas such as ASALs, which forms 85% of Kenyan land mass and is dominated by pastoral and nomadic livestock production. Most CSA practices are mainly at the production level and hardly extend to the entire value chain, and diffusion is slow due to several barriers. A mixed method approach was used to evaluate barriers to actors’ adoption of CSA in the pastoral Livestock red meat value chain starting from input suppliers, producers, to consumers (pasture to plate). This study used six broad perspectives to examine the barriers: 1) Knowledge and institutional; 2) Market and financial; 3) Policy and incentives; 4) Networks and engagement platforms; 5) Cultural and social; 6) Physical infrastructure barriers. These barriers can be surmounted with concerted efforts from the government, development partners, pastoral communities, value chain actors and public private partnership among others. Efforts such as modernization of the pastoral red meat value chains, integration of MSMEs into the livestock systems, access to affordable financing, availability of context based, affordable CSA TIMPs, incentives, policies and institutional support, which currently remains inadequate. Institutional barriers like lack of capacity, coupled with knowledge and behavioral barriers hinder adoption. Financial institutions and cooperative societies can be enablers, however, their reluctance to invest in the sector is a barrier too.
Yasminath Judith Follone Avaligbé, Faki Oyédékpo Chabi, Césaire Paul Gnanglè, Orou Daouda Bello, Ibouraïma Yabi, Léonard Ahoton, Aliou Saïdou
American Journal of Climate Change, Volume 10, pp 263-281;

In Benin, Shea tree (Vitellaria paradoxa) is one of the agroforestry species of great socio-economic importance for local populations. Given the actual variation in the climate parameters, it is necessary to anticipate the future spatial distribution of Shea trees as an adaptation strategy and for designing relevant conservation strategies. The aim of the present research was to evaluate the influence of climate change on the distribution areas of Shea trees in Benin. Occurrence data consisting of geographic coordinates of Shea trees in Benin as well as bioclimatic variables were recorded. Furthemore, additional presence points were collected from the Global Biodiversity Information Facility database website. Current and future environmental data for the study area were obtained from the Africlim website. Bioclimatic variables (moisture and temperature), monthly maximum and minimum temperatures and annual rainfall were collected from Worldclim synoptic stations website for the period 1970-2000. The aridity index was created from the potential evapotranspiration (PET) and annual rainfall, using spatial analysis tools of ArcGIS. The impact of current and future environmental conditions on favourable Shea trees’ growing area was assessed following the maximum entropy (MaxEnt) approach under two climate scenarios (RCP 4.5 and RCP 8.5). Under the current climate conditions, 80% of Benin territory and 79% of the protected areas were highly favourable for Shea trees growing and conservation. However, all climate scenarios projected the significant decrease of 14% to 19% of the distribution of favourable for Shea tree growing area and 26% to 30% of the protected areas by 2055 in favour of non-favourable for the trees’ distribution. The protection of habitats favourable for the species development, coupled with a quick restoration of the species through the use of appropriate vegetative propagation techniques are required to sustain the species’ conservation in Benin and maintain farmers’ livelihood.
Richard Muita, Paul Kucera, Stella Aura, David Muchemi, David Gikungu, Samuel Mwangi, Martin Steinson, Paul Oloo, Nicholas Maingi, Ezekiel Muigai, et al.
American Journal of Climate Change, Volume 10, pp 300-316;

Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires a good network of manual meteorological stations and other support systems for its collection, recording, processing, archiving, communication and dissemination. In sub-Saharan Africa, such networks are limited due to low investment and capacity. To bridge this gap, the National Meteorological Services in Kenya and few others from African countries have moved to install a number of Automatic Weather Stations (AWSs) in the past decade including a few additions from private institutions and individuals. Although these AWSs have the potential to improve the existing observation network and the early warning systems in the region, the quality and capacity of the data collected from the stations are not well exploited. This is mainly due to low confidence, by data users, in electronically observed data. In this study, we set out to confirm that electronically observed data is of comparable quality to a human observer recorded data, and can thus be used to bridge data gaps at temporal and spatial scales. To assess this potential, we applied the simple Pearson correlation method and other statistical tests and approaches by conducting inter-comparison analysis of weather observations from the manual synoptic station and data from two Automatic Weather Stations (TAHMO and 3D-PAWS) co-located at KMD Headquarters to establish existing consistencies and variances in several weather parameters. Results show there is comparable consistency in most of the weather parameters between the three stations. Strong associations were noted between the TAHMO and manual station data for minimum (r = 0.65) and maximum temperatures (r = 0.86) and the maximum temperature between TAHMO and 3DPAWS (r = 0.56). Similar associations were indicated for surface pressure (r = 0.99) and RH (r > 0.6) with the weakest correlations occurring in wind direction and speed. The Shapiro test for normality assumption indicated that the distribution of several parameters compared between the 3 stations were normally distributed (p > 0.05). We conclude that these findings can be used as a basis for wider use of data sets from Automatic Weather Stations in Kenya and elsewhere. This can inform various applications in weather and climate related decisions.
Temi E. Ologunorisa, Olufemi S. Durowoju, Ademola Akinbobola
American Journal of Climate Change, Volume 10, pp 353-369;

This study examined the hydroclimatology of the Kaduna River Basin (KRB) in northern Nigeria. In achieving this, monthly data on temperature (T) and rainfall (P) were sourced from ten hydrometeorological stations across the basin from 1990 to 2018. DrinC (Drought Indices Calculator) software was deployed to calculate Potential Evapotranspiration (PET) adopting Thornthwaite approach. Water Balance (WB) model was used further to estimate other WB components i.e. soil moisture (SM), actual evapotranspiration (ETa), Water surplus (S) and Runoff (R). WB components are used to examine the temporal and spatial variability of the KRB for hydrological years (1990-2018). KRB was divided into two sub-basins (Lower and Upper KRB). The WB analyses indicated the peak of R generally occurs during the wet season (i.e. April through October) most especially at the Upper KRB. The study further reveals that the runoff efficiencies imply that i.e. November through March) across the basin while the majority of S is generated during wet season months, particularly from April through October when ~95% of S occurs on average with the peak S in August. The results of this study provide a baseline understanding of the hydroclimatology of the KRB which can be used as a starting point for further analyses, especially for water resources management.
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