The Role of Spatial Modelling in Understanding Impacts of Climate Change on Temperature Patterns in Kenya

While most developing countries contribute to less emissions globally, the effects of climate change impact them more than the developed world, causing a rise in temperature over the years. These increasing temperatures cause more damage to economies and livelihoods over years as they lead to extreme weather events, in which developing countries are highly vulnerable. The use of spatial models such as GIS and satellite models are thus essential in forecasting future climate in relation to past information if well applied. This study aimed at finding out the role of spatial modelling in understanding temperature patterns in relation to climate change in Kenya. Mann-Kendall statistical method and modelling reanalysis were used to understand the relationship between observed and modeled temperature patterns from 1980 to 2015. A direct relationship between observed and modeled data was found, indicating that observations and models can give similar output of past climatic conditions under temperature parameter. On both scenarios, the average seasonal temperatures indicated an increasing rate on DJF and MAM seasons despite them being short and long rainy seasons respectively. This indicated that more practical sessions of observations and spatial modelling relationships could be done to understand past climate, and enhance newer models for forecasting future temperature patterns in Kenya and regionally.