Browsing by Author "shem, Godfrey Juma"

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Logistic regression analysis of mortality among fishermen in the riperian counties of Lake Victoria, Kenya
(Science Publishing Group, 2019-01-21) Opemo, Damian Otieno; shem, Godfrey Juma
Long term assessment of aerosol radiative forcing over selected sites of East Africa
(scientific Research Publishing, 2018-03-08) Makokha, John Wanjala; Odhiambo, Jared Oloo; shem, Godfrey Juma
Atmospheric aerosols have contributed to radiative forcing through direct and indirect mechanisms. Aerosol effects are important in computing radiative forcing estimates for the past, current and future climate. In this study, a comprehensive assessment of regional aerosol radiative forcing, Optical Properties of Aerosol and Clouds (OPAC) model (wavelength range of 0.25 - 4.0 μm) over selected sites in East Africa was done. Aerosol optical properties constituted the inputs of a Radiative Transfer Model (RTM). Optical properties investigated included Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA) and Asymmetry Parameter (AP). Aerosol Radiative Forcing (ARF) during the study period at the surface (SFC), top of the atmosphere (TOA) and the atmosphere (ATM) was estimated to be –18.4 ± 1.4 W∙m−2, +1.1 ± 0.3 W∙m−2 and +19.5 ± 2.5 W∙m−2, respectively. This corresponds to an increment in net atmospheric forcing at a heating rate of about 0.55 ± 0.05 K/day (0.41 ± 0.03 to 0.78 ± 0.03 K/day) in the lower troposphere. The study points out the significant role played by atmospheric aerosols in climate modification over the area of study. It is recommended that a further assessment be done in view of uncertainties that may impact on the findings and which were not within the scope of this research.
Projected rainfall and temperature changes over Bungoma County in western kenya by the year 2050 based on PRECIS modeling system
(Ajol: Ethiopian Journal of Environmental Studies & Management, 2016-06-19) shem, Godfrey Juma; Kelonye, Festus Beru
This study investigated projected changes in rainfall and temperature over Bungoma County by the year 2050 based on the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) A1B and A2B emission Scenarios (IPCC, 2007) using the Providing Regional Climates for Impacts Studies (PRECIS); (Giorgi, 2007). The PRECIS regional Climate Model (Hadley RM3P) was configured in 0.22°×0.22° horizontal grid resolution was forced at the lateral boundaries by the UKMO-HadAM3P and UKMOHadCM3 global Models. The future projection of temperature indicates warming over Bungoma County by the year 2050 coupled with reduced precipitation. Time series analysis revealed a cyclic and seasonal trend in rainfall and temperature over the area of study. Temporal characteristics revealed a warmer and colder September-October-November (SON) season under A1B and A2B scenarios respectively. The results also revealed increasing temperatures and reducing rainfall across all seasons under both scenarios except in March-April-May (MAM) season where rainfall amounts increased and temperature reduced. A two paired t-test for the two climate variables revealed a ρ value of less than 0.05 (ρ<0.05) suggesting a statistically significant relationship between each pair of the two variables. The study recommends further evaluation of the model performance in simulating the present day climate over the area of study.
Simulation of radiative forcing due to aerosols over some Counties in Kenya
(BEST:IJHAMS, 2015-11-05) shem, Godfrey Juma; Muthama, John Nzioka; Mutai, Bethuel K
The Coupled Ocean and Atmosphere Radiative Transfer (COART) model solved a radiative transfer equation from aerosol optical thickness data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) spanning 2000 to 2015. The temporal and spatial variation of aerosols optical depth was determined on Giovanni platform. Trajectory modelling was carried out using Hybrid Single Particle Langrangian Model (HYSPLIT). Integrated fluxes were generated from COART model. Counties investigated are Mombasa, Lamu,Nairobi, Kakamega, Bungoma, Nyeri, Meru, Machakos, Turkana, Tranzoia, Baringo, Nakuru, Narok, Kisumu, Kisii, Nyamira and Busia. Simulation of future warming over Kenya was also done using MAGGIC SCENGEN model under two scenarios. Results of the study revealed that Turkana, ASAL and Maritime Counties had the highest aerosols loading while Kisii County had the lowest aerosols loading respectively and that aerosol loading was highest during the JJA season and that Garrissa County had the highest interannual variability of aerosols. The study revealed that aerosol loading across all Kenyan counties is reducing and that long distance transport and dispersion of aerosols was facilitated by low level winds over Kenya. It was observed that Kisii County had higher radiative forcing estimate due to aerosols while counties in the ASAL, Maritime counties and Turkana County had relatively lower corresponding estimates. It was also noted that forcing due to aerosols over Kenya is reducing and lies in the range of -0.187 to -0.05 w/m2. SCENGEN Projections gave a warming of 0.17 0C, 0.45 0C, and 2.96 0C by the year
Temporal analysis of drought in Mwingi sub-county of Kitui County in Kenya using the standardized precipitation index (SPI)
(discovery journals : Climate Change, 2018-12-01) Cassim, Zuberi; shem, Godfrey Juma
This study attempts to temporally characterize drought using Standardized Precipitation Index (SPI) over Mwingi Sub-County of Kenya. Rainfall data spanning 1961-2011 over the area of study was used to determine SPI values using quantitative techniques in R programming. The SPI values were temporally characterized using series graphs and trend analysis carried out. In order to enhance understanding of vegetative characteristics over the area of study, Vegetation Cover Index data was used to generate 3 month VCI spatial characteristics. Results of this study revealed that Mwingi region has been experiencing increasing mild to moderate drought events with occasional severe cases being reported since 1961. No extreme drought event was recorded during this period. The study noted that the drought events were increasingly varying in intensity during the period of study. However, no extreme drought event was recorded during this period. The study recommends correlation analysis between the SPI values and all climate variables over the area of study.
Trend analysis of aerosol optical depth and angström exponent anomaly over East Africa
(scientific Research Publishing, 2017-10-31) Makokha, John Wanjala; Odhiambo, Jared Oloo; shem, Godfrey Juma
Trend analysis of atmospheric aerosols enhances confidence in the evaluation of both direct and indirect effects of aerosols on regional climate change. To comprehensively achieve this over East Africa, it’s important to understand aerosols temporal characteristics over well selected sites namely Nairobi (1˚S, 3 ˚E), Mbita (0˚S, 34˚E), Mau Forest (0.0˚S - 0.6˚S; 35.1˚E - 35.7˚E), Malindi (2˚S, 40˚E), Mount Kilimanjaro (3˚S, 37˚E) and Kampala (0˚N, 32.1˚E). In this context, trend analysis (annual (in Aerosol Optical Depth (AOD) at 550 nm and Angstrom Exponent Anomaly (AEA) at 470 - 660 nm) and seasonal (AOD)) from Moderate Resolution Imaging Spectroradiometer (MODIS) were performed following the weighted least squares (WLS) fitting method for the period 2000 to 2013. The MODIS AOD annual trends were ground-truthed by AErosol RObotic NETwork (AERONET) data. Tropical Rainfall Measurement Mission (TRMM) was utilized to derive rainfall rates (RR) in order to assess its influence on the observed aerosol temporal characteristics. The derived annual AOD trends utilizing MODIS and AERONET data were consistent with each other. However, monthly AOD and RR were found to be negatively correlated over Nairobi, Mbita, Mau forest complex and Malindi. There was no clear relationship between the two trends over Kampala and Mount Kilimanjaro, which may imply the role of aerosols in cloud modulation and hence RR received. Seasonality is evident between AOD and AEA annual trends as these quantities were observed to be modulated by RR. AOD was observed to decrease over East Africa except Nairobi during the study period as a result of RR during the study period. Unlike the other study sites, Nairobi shows positive trends in AOD that may be attributed to increasing populace and fossil fuel, vehicular-industrial emission and biomass and refuse burning during the study period. Negative trends over the rest of the study sites were associated to rain washout. The AOD and AEA derived annual trends were found to meet the statistical significance of 95% confidence level over each study site