Agriculture is the primary source of development for most countries in Africa, including Tanzania, where the sector contributes to about 50% of the Gross Domestic Product (GDP). The sector also employs about 80% of the labour force, contributes to about 30% of export earnings and 65% of raw materials for domestic industries. However, due to climate change the sector has performed very poorly in recent years, such that its contribution to the national economy and food security in Tanzania has not been realized. The main aim of this thesis was to evaluate the impacts of climate change on rainfed maize production in the Wami-Ruvu basin of Tanzania. Four research objectives have been analysed. The first evaluated the performance of the Coordinated Regional Downscaling Experiment Regional climate models (CORDEX RCMs) in simulating present climate condition of Tanzania, the second objective assessed the impact of climate change on rainfed maize production in the Wami-Ruvu basin of Tanzania using a process-based crop model the Decision Support System for Agro-technological Transfer (DSSAT). This model was driven by high-resolution climate change simulations for current climate condition (1971-2000) as well as future climate projections (2010-2039), (2040-2069) and (2070-2099) for two Representative Concentration Pathways (RCPs): RCP 4.5 and RCP 8.5. These data were obtained from three CORDEX RCMs driven by three different General Circulation Models (GCMs). The third objective assessed the performance of moist potential vorticity (MPV) and moist potential vorticity vector (MPVV) in describing long term annual cycles of rainfall in Tanzania, and the fourth objective evaluated the possibility of using moist potential vorticity vector (MPVV) for diagnosis of heavy rainfall events in Tanzania.
The findings of the first objective are that CORDEX RCMs capture the annual cycle of minimum temperature (TN), maximum temperature (TX) and rainfall well, however, underestimate and overestimate the amount of rainfall in March–April–May (MAM) and October–November–December (OND) seasons respectively. CORDEX RCMs reproduce interannual variations of TN, TX, and rainfall. The source of uncertainties can be analysed when the same RCM is driven by different GCMs and different RCMs are driven by same GCM simulate TN, TX, and rainfall differently. It is found that the biases and errors from the RCMs and driving GCMs contribute roughly equally. Overall, the evaluation finds reasonable (although variable) model skill in representing mean climate, interannual variability, and temperature trends, suggesting the potential use of CORDEX RCMs in simulating TN, TX and rainfall over Tanzania.
The findings of the second objective showed that due to climate change future maize yields over Wami-Ruvu basin will slightly increase relative to the baseline during current century for RCP 4.5 and RCP 8.5. Meanwhile, maize yields will decline in the mid and end centuries. The spatial distribution shows that decline in maize yields are projected over lower altitude regions due to projected increase in temperatures and decreased rainfall in those areas. The eastern part of the basin will feature more decrease in maize yields while central parts of the basin and western side of the basin will experience increased maize yields. The main reason for decreased and increased maize yield to many areas is associated with increased temperatures that reduce the length of growing season and hence cause the decrease in maize yields. However, the high altitude areas increase in temperature provides optimal temperature for maize production. It is therefore recommended that appropriate and adequate adaptation strategies need to be designed to help the communities adapt to the projected decrease in maize production particularly over lower altitude areas.
The findings of the third objective show that the patterns of MPVV compared better with rainfall than the MPV. The statistical relationship between MPVV and MPV against observed rainfall data from 22 weather stations using Pearson correlation coefficient indicated that MPVV bears strong and statistically significant correlation coefficient to rainfall than MPV suggesting its potential use as the predictor of rainfall events in Tanzania. The findings of the fourth objective are that MPVV provide accurate tracking of location received heavy rainfall, suggesting its potential use as a dynamic tracer for heavy rainfall events in Tanzania. Finally it is found that the first and second components of MPVV contribute almost equally in tracing locations received heavy rainfall events. The magnitude of MPVV provides better tracking of the locations received heavy rainfall compared to its components.