Browsing by Author "Kipkemoi, Isaac"

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    Hindrances to Adoption of Biomass Briquettes Technology as a Climate Change Mitigation Measure- Case Study of Maasai-Mau Forest adjacent Community, Kenya
    (International Journal of Renewable Energy Sources, 2020) Mokaya, Dennis Chweya; Koske, James Kibii; Gichuki, Cecilia; Ngare, Innocent Osoro; Kariuk, Charles Ndiritu; Kipkemoi, Isaac; Gichuki, Leah
    Climate change impacts on social, economic and political system cannot be understated. This paper outlines hindrances of briquettes uptake, anchored in socio economic factors. Briquettes are eco-friendly biomass green technology that reduces CO2 emissions a milestone towards climate change mitigation. High demand for wood products to meet the demand for energy supply in the Maasai-Mau region has seen the depletion of the forest cover thus increasing carbon dioxide emission and other greenhouse gases into the atmosphere. The main objective of this study was to assess hindrances to the adoption of briquettes technology as an alternative source of energy on mitigation measure to climate change in the Maasai-Mau region. The study employed an exploratory survey to collect information on demographics of adults in each homestead, sources of energy and economic livelihood. Data results were analyzed through Excel and Statistical Package of Social Sciences. From the results, the hypothesis was tested by Chi-square ( 2 ). The null hypothesis of a relatively low level of education hinders the adoption of biomass briquette was accepted, where ( 2=9.866, DF=6, P=0.13). From the findings, the study concluded that lack of awareness of the briquetting technology and benefits, was the primary hindrance to the adoption of biomass briquettes technology in mitigating climate change in the study area.
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    Spatio-temporal degradation detection and modeling future scenarios of embobut forest in Elgeyo Marakwet County, Kenya
    (Kenyatta University, 2018-06) Kipkemoi, Isaac
    It is evident that forests have been managed for several years in the world, but in most cases especially in the developing world, various regimes have tried to come up with an institutional framework to guide forest management with no much success due to lack of forest monitoring systems. The study entailed detecting forest degradation and modeling forest future scenarios. Analysis of Satellite imagery provided spatial temporal data with ground truthing exercise using Global positioning system was used for data validation. Population census data showing trends of population change over the study period was used to study relationship between population growth and forest trends. The study gave detailed data for the Embobut forest cover, data on trends of forest cover the relationship between forest and population change. The resultant data was used to project future forest scenarios for Embobut forest. The major forest cover types in the study area are; Cupressus lusitanica, Mixed Podocarpus latifolius, Juniperus-Nuxia-Podocarpus factus, Tree ferns Cyathea manniana and Bamboo and Acacia abyssinica and Scrabby grassland, there is also Bare land and rocky and Water Bodies. Cupressus lusitanica, Bare land and rocky and Water Bodies recorded positive changes while all other forest classes decreased in size. The study found a loss of 7,172.31 hectares or 28% forest loss over the study period study period-1986-2011. The study found that deforestation increased as population grew. Cohen’s coefficient showed that the predominant forest class in 2020 will be Tree ferns Cyathea manniana and Bamboo, the land cover that will have the highest increase will be Cupressus lusitanica with additional about 4 000 hectares. In the years 2050 and 2100 Podocarpus latifolius, Juniperus-Nuxia-Podocarpus factus, Acacia abyssica and Scrabby will reduce in size though they remain to be the highest land cover types by 65%, most of the reduction will be due to increase in Cupressus lusitanica, which will cover additional about 5 977 hectares by the year 2050 with a decline to 1 507 hectares by the year 2100. The land cover classes bare land and Rocky will also increase by 2% by the year 2100. The study therefore recommends that reforestation of the areas that were previously forested and to avoid the dominance few land cover types, efforts for reforestation to consider the use of native tree species on their respective ecosystems to retain the indigenous forest types in the study area. For further studies, the researcher recommends inclusion of socio-economical surveys which will capture the demand for forest and forestry products like fuel wood, timber, poles and animal feed.
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    Urbanization and Hydrological Dynamics: A 22-Year Assessment of Impervious Surface Changes and Runoff in an Urban Watershed
    (Frontiers in Water, 2024-12) Ongaga, Cyrus Omwoyo; Makokha, Mary; Kennedy Obiero; Kipkemoi, Isaac; Diang’a, Justus
    The frequency and intensity of flooding have been increasing in urban watersheds. Urbanization disrupts natural landscapes by replacing vegetated areas with impervious surfaces, reducing infiltration and increasing runoff. The objective of this study was to evaluate the relationship between change in impervious surface area and runoff amount of Mihang’o watershed located in the outskirts of Nairobi for the period 2000–2022. The specific objectives of this study were as follows: To determine the change in the impervious surface area of Mihang’o watershed, the trend of precipitation amount in the watershed, and the trend in runoff amount, a major source of flood water from the watershed. Supervised classification was performed on land satellite (Landsat) images to determine percentages of impervious surface cover for the study period, and linear regression analysis was used to establish the trend. Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall data were retrieved from Google Earth Engine, then processed to produce monthly and annual rainfall totals, and Mann–Kendall trend tests were used to establish the rainfall trend for the watershed. The Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model was used to simulate runoff from the watershed with the rainfall data and impervious surface area percentages as inputs; then, linear regression analysis was performed to establish the runoff trend. The impervious surface area increased by 87.03% from 2.88% (0.49 km2 ) of the total surface area of the watershed in 2000 to 22.21% (3.91 km2 ) in 2022, demonstrating an approximate increment of 3.96% (0.88 km2 ) each year. The Mann–Kendall trend test results (Sen’s slope results [β = 0.832], Kendall’s tau results [τb = 0.146], and p-value [0.625]) confirmed that there is no significant change in rainfall amounts. Runoff increased by 84.75% from 0.18 mm in 2000 to 1.18 mm in 2022; otherwise, an approximate increment of 3.85% (0.045 mm) was evident each year. Besides the impervious surface area, the HEC-HMS model factors in the length of slope, length of reach, soil type, size of subbasins, and longest flow path, thus producing accurate runoff estimations.