Abstract
Rainfall is the most important meteorological variable that influences the economic development of Rwanda. Changes in rainfall trends and variability over recent past years have become a great concern to policymakers and scientists. This study aims at examining the spatiotemporal variability of rainfall over Rwanda and the teleconnections of rainfall with different large-scale ocean-atmospheric variables at different timescales. The study used rainfall data of Climate Hazards Category Infrared Precipitation with Stations (CHIRPS) and Climate Research Unit Time Series Version 4 (CRU) for the period 1981–2017. Several statistical methods, including standardized anomaly, Empirical Orthogonal Functions (EOF), Pearson Correlation, Mann-Kendall (MK), and Sen's gradient estimator, were used to assess the variability, trends, and teleconnections of rainfall with various driving factors. Results revealed a bimodal rainfall pattern in its annual cycle. The spatial distribution of annual and seasonal rainfall showed a southwest to northwest rainfall gradient. The MK test revealed a decreasing trend in annual rainfall in the southwest part of the country. Overall, March to May (MAM) rainy seasons showed a decreasing and September to December (SOND) rainy season an increasing trend over Rwanda. The EOF analysis revealed that the leading mode of variability for MAM rainfall parades a unimodal scheme with negative loadings that can explain 59.3% of the total rainfall variance. The dominant mode of variability of SOND rainfall revealed the same pattern but with positive loadings that can explain 58.1% of the total variance. Spatial correlation showed that the MAM (SOND) rainfall has a weak (strong) relationship with the Indian Ocean sea surface temperature (SST), which means a negative (positive) Indian Ocean Dipole can lead to anomalously wet (dry) conditions over Rwanda. A stronger influence of El-Nino Southern Oscillation (ENSO) on SOND rainfall than MAM rain was noticed. The results of this study are crucial in developing appropriate mitigation measures to curb the impacts of climate change on the agriculture and water resources of Rwanda.
•To study the spatiotemporal variability of rainfall over Rwanda and the teleconnections with ocean-atmospheric variables.•A good agreement was observed between CHIRPS and station datasets from 1981–2017.•A bimodal rainfall pattern was presented throughout the country.•The Indian Ocean has less impact on rainfall at annual and MAM, while high impact during the SOND season.•A stronger influence of ENSO on SOND rainfall than MAM rain was noticed.