Rainfall prediction for data-scarce areas using meteorological satellites in the case of the lake Tana sub-basin, Ethiopia

Abstract

In African nations with complex topographies, alternative rainfall estimation methods such as satellites are crucial. This study aimed at predicting the spatial and temporal distribution of rainfall in the lake Tana sub-basin from 1990 to 2020. A satellite-based rainfall estimate of Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data was used with the same spanning period. The validation process employs point-to-pixel analysis, comparing CHIRPS estimates with observed data at specific gauge stations. The findings showed that CHIRPS had well estimated rainfall incidence in the highland areas and significantly overestimated it in the lowland areas. The Mann–Kendall trends for January, June, and August indicate decreasing trends, while the winter and spring seasons show notable declines. Regression analysis reveals a non-significant decrease in annual rainfall with the highest in the summer and relatively dry winters. In addition, the coefficient of variation value of 26.37% suggests a moderate level of variability around the mean annual rainfall. In conclusion, the CHIRPS satellite exhibited varied performance across the Tana sub-basin, with site-specific discrepancies and notable inaccuracies at certain stations. The study underscores the importance of considering local factors and topography in satellite-based rainfall assessments, providing valuable insights for agricultural planning in the region. HIGHLIGHTS The variability of rainfall was investigated using the standardized anomaly index, coefficient of variation, and precipitation concentration index.; The percentage of daily rainfall events with high intensity was overestimated while the number of daily rainfall events with light precipitation was underestimated by Climate Hazards Group Infrared Precipitation with Station data.;

Keywords

• chirps
• mann–kendall test
• rainfall anomaly index
• rainfall variability