| dc.description.abstract | Drought in Kenya is ranked among the top and most expensive natural disaster to deal with due to its creeping phenomena. The frequency and intensity of droughts in the country have been increasing in recent years leading to great social, economic and environmental impacts. A lot of effort has been made to assess past droughts in the country with little or no information on the occurrence of future droughts. The studies carried out have only used rainfall to characterize droughts yet droughts are caused by a combination of many factors. The objective of this study was to assess the temporal and spatial characteristics of drought in the study area using a combined drought index (CDI) that incorporated three drought indices; the Precipitation Drought Index (PDI), the Humidity Drought Index (HDI) and the Temperature Drought Index (TDI). In this study, relative humidity was used instead of NDVI because NDVI is used as a proxy to monitor the condition of vegetation which is determined by the amount of soil moisture available. Information on soil moisture can better be obtained from a combination of rainfall falling in an area, temperature and relative humidity because the amount of water lost into the atmosphere through evapotranspiration depends on the amount of humidity in the atmosphere.
Data used in the study was obtained from the Kenya Meteorological Department (KMD) from 1979 to 2015 and included observed annual and dekadal rainfall, dekadal maximum temperature and dekadal relative humidity at 1200 GMT.
To achieve the objective of the study, the country was first delineated into climatologically homogenous zones using the Principal Component Analysis (PCA) after which the principal of communality was used to pick the representative station in each homogenous zone. The drought characteristics in each zone were determined using various drought categories based on CDI values. A drought forecast model was then developed using past CDI values and stochastic time series modelling (Auto Regressive Model). Nine homogenous rainfall zones with distinct rainfall characteristics were delineated by PCA. Rainfall in the zones showed high spatial and temporal variability with the highest variability being observed over the northern parts of the country, while the lowest variability was observed over the coast, western and central parts of the country. CDI is able to effectively capture drought characteristics in the study area.
The country experiences all categories of droughts (mild, moderate, severe and extreme) with the mild category being dominant in most of the zones. CDI and time series modelling can be used to develop a drought forecast model in the study area. Drought forecasts in the study area can be made with reasonable accuracy up to the ninth dekad which marks the end of a season. Since the more severe drought categories tend to be experienced during the major rainfall season of MAM, there is need for drought assessment both on the short and long term basis. Dekadal data therefore should be used in conjunction with monthly and annual data to take care of both the short and long term drought characteristics. In order to fully capture all aspects of droughts, more parameters should be incorporated into the CDI | en_US |
