Impacts Of Climate Change On Spatial And Temporal Distribution Of Rangeland Vegetation In Kenya

ABSTRACT

Rangeland vegetation are important resources for livestock, wildlife and human

beings and are largely influenced by climatic conditions. The effects of climate

change are varied and among them is uncertainty in dynamics of rangelands

vegetation. The rangeland vegetation in Kenya supports the key economic activities

of livestock and tourism. This significant contribution to GDP underscores the need

to model the impacts of climate change on the rangeland vegetation for the country

to promote sustainable development. This study analysed the spatio-temporal trend

of rainfall and vegetation from 2001 to 2015 and also modelled the impacts of

climate variations on the spatial and temporal distribution of rangeland vegetation in

Kenya in the base-year and the future climatic periods of the years 2050 and 2070.

The study further conducted Key Informant interviews to analyse Kenya‟s mitigation

and adaptation measures in relation to the projected climate impacts on the

rangelands vegetation. The research was accomplished by trend analysis of Moderate

Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation

Index (NDVI) data of fifteen years (2001 to 2015), modelling the influence of

climate on the rangeland vegetation in the base-year (1960 to 2000), 2050 and 2070

climatic periods, establishing the ASALs livestock carrying capacity and a critical

analysis of Kenya‟s climate change and related policies and legislations. The spatial

data was sourced from USGS, ILRI, and Africover Project processed and analysed in

ArcGIS, DIVA-GIS, Maxent, Map Comparison Kit and Geoda softwares. The

monthly rainfall trend ranged from -15 to 20 mm while the annual trend showed a

reduction of -6 to 0 mm in the entire country which shifted spatially in some years;

the monthly MODIS NDVI trend was between -0.065 to 0.060; the dependence of

MODIS NDVI on rainfall was significant with annual coefficients of determination

of 0.541 in 2002 and 0.763 in 2006 and the fifteen years mean at 0.617. Results of

modelling show that the 2050 climate will decrease grass niche suitability by

44.99%, the unsuitable will increase by 87.01%, the grass niche suitability location

will shift by 76.7% and the category areas change by 46.4%; the 2070 climatic

period grass niche suitability will shrink by 55.21%, the unsuitable category increase

by 106.80%, the location will shift by 77.8% and the category areas will change by

66.0%; the Kenyan ASALs livestock carrying capacity patterns were the same as the

grass niche suitability; and the reviewed climate change policies and legislations

demonstrated that Kenya has responded adequately to climate change. The research

concluded that the rangeland vegetation (grass) will decline and shift location in the

both future climatic periods. The study made the following recommendations;

further research using more refined spatial datasets; inclusion of other variables in

Maxent model; modelling future seasonal grass niche suitability and livestock

carrying capacity changes; model grass niche suitability using other representative

community pathway climate data; and policy reorientation to focus on impacts in a

specific area.