| dc.description.abstract | Most of Eastern Africa has arid and semi-arid climate with high space-time variability in rainfall. The droughts are very common in this region, and often persist for several years, preceded or followed by extreme floods. Most of the livelihoods and socio-economic activities however remain rain-dependent leading to severe negative impacts during the periods of occurrence of climate extremes. It has been noted that one extreme event was capable of reversing national economic growth made over a period of several years. Thus no sustainable development can be attained in eastern Africa without effective mainstreaming of climate information in the development policies, plans and programmes. Many past studies in the region have focused on rainfall variability at seasonal, annual and decadal scales. Very little work has been done at intraseasonal timescale that is paramount to most agricultural applications.
This study aims at filling this research gap, by investigating the structure of rainfall season in terms of the distribution of wet and dry spells and how this distribution varies in space and time at interannual time scale over Equatorial Eastern Africa. Prediction models for use in the early warning systems aimed at climate risk reduction were finally developed. The specific objectives of the study include to; delineate and diagnose some aspects of the distribution of the wet and dry spells at interannual timescale; investigate the linkages between the aspects of the distribution of wet and dry spells identified and dominant large scale climate fields that drive the global climate; and assess the predictability of the various aspects of wet and dry spells for the improvement of the use in the early warning systems of the region.
Several datasets spanning a period of 40 years (1961 - 2000) were used.
The data included gauged daily rainfall amount for the three Eastern Africa countries namely Kenya, Uganda, and Tanzania; Hadley Centre Sea Surface Temperature (SST); re-analysis data and radiosonde observations from Nairobi (Kenya) and Bangui (Central Africa Republic) upper air stations. The indices of EI Nino-Southern Oscillation (EN SO), Indian Ocean Dipole and SST gradients which constituted the predefined predictors were also used. Missing data gaps were initially filled and the quality of rainfall data assessed. Less than seven percent of the data were estimated in all cases.
The study region was then classified into few near-homogeneous spatial and temporal rainfall regimes using empirical orthogonal function approach. Several intraseasonal statistics of the wet / dry spells were computed at both local (station) and sub-regional (near-homogeneous zone) levels to provide baseline information on the various aspects of rainfall distribution during March-May (long rains) and October-December (short rains) rainfall seasons. The interannual variation in the above intraseasonal statistics at local and sub-regional levels was also assessed for any significant trend using the non-parametric Spearman rank correlation test. The linkages between the various intraseasonal statistics of the wet / dry spells including seasonal rainfall totals and large scale climate fields were assessed using the total and partial Pearson correlation analysis. Last but not least, the stepwise regression technique was used to develop multivariate linear regression models for predicting the various intraseasonal statistics of wet / dry spells. The skill of these models was finally assessed using various statistical techniques.
The results obtained indicated that the gap-filled and quality controlled daily rainfall observations were of good quality and formed the foundation of all the analyses that were undertaken in this study. For the first time, this study delineated daily rainfall over Equatorial Eastern Africa into six near-homogeneous sub-regions for both the long and the short rainfall seasons. They are however significant spatial differences in the patterns of daily rainfall occurrences for the individual seasons which may be attributed to different climate mechanisms and systems which are in play during the specific rainfall seasons.
At interannual scale, positive (negative) relationship existed between the intraseasonal statistics of wet (dry) spells and the seasonal rainfall totals over most locations and subregions. The relationship with the intraseasonal statistics of the wet spells was mainly significant (at 95% confidence level) while those of the dry spells were generally not statistically significant. The mean frequency of dry spells of 5 days or more (the number of wet days within the season) had the least (strongest) association with the seasonal rainfall totals. The relationships were stronger during the short rainfall season compared to the long rainfall season.
For the first time, the study showed significant trends in all the intraseasonal statistics of the wet / dry spells though at few isolated locations. However, significant increasing trend in the occurrence of dry spells of 5 days or more showed organised patterns for the two seasons. Climate change is becoming a major development concern not only over the region but the world over. Further studies are therefore required to examine whether the trends observed in the daily rainfall spells in this study reflects any regional climate change signals. Results from total and partial Pearson correlation analysis identified several large scale oceanic and atmospheric signals with robust physical/dynamical linkages with the subregional intraseasonal statistics of wet / dry spells (SRISS).
The results further showed that the linkages between sub-regional intraseasonal statistics of wet spells and large scale signals were mainly from atmospheric fields of zonal and meridional components of wind and the specific humidity during the long rainfall season. For the short rainfall season, stronger linkages with oceanic variables especially SST were noted. The atmosphere has less climatic memory when compared with the oceans. Past studies have indicated stronger predictability potentials for the short rainfall season. By identifying stronger linkages between intraseasonal characteristics of wet spells for long (short) rainfall season and the atmospheric (oceanic) variables, the study has for the first time provided some insights to the prediction challenges for the specific seasons. Thus future predictability efforts for the long rainfall season should ensure the inclusion of atmospheric variables in the prediction models.
The study has produced cross-validated multivariate linear regression (MLR) models for predicting some intraseasonal characteristics of wet spells that can be used to support the current generation of models being used by the IGAD Climate Prediction and Applications Centre and National Meteorological and Hydrological Services. The results from this study have for the first time provided an in-depth knowledge on the intraseasonal modes of rainfall variability and improvement in the forecasting and early warning tools for the wet spells over the Equatorial Eastern Africa region. Better understanding and accurate prediction of rainfall totals and intraseasonal statistics of wet / dry spells is of paramount importance in the planning, development and management of all rainfall-sensitive socio-economic sectors of the economy such as agricultural and water resources; and further contribute to national efforts towards achievements of the Millennium Development Goals. | en_US |
