Bridging data gaps: homogenisation of climate time series in the Ethiopian Abay Basin

Missing data and inhomogeneities are common challenges in climate time series. This study aimed to develop quality-controlled and homogenised rainfall and temperature data for the Kessie watershed in the Ethiopian Abay River Basin, and to assess the impact of homogenisation on the time series. The four decades (1986-2023) of observational data from fifteen meteorological stations, and the corresponding reanalysis and satellite datasets were used in this study. Four gap-filling methods—normal ratio, inverse distance weighting, modified inverse distance weighting, and correlation coefficient weighting—were compared and ranked using the combined compromise solution method. The best-performing methods were used to fill in the missing data of the observational time series, and the complete data were homogenised using the CLIMATOL R package. The reanalysis and satellite data were used as references to verify the results of the homogenised time series. The results showed that, although the normal ratio method performed comparatively better, none of the gap-filling methods consistently outperformed others across all stations and variables. CLIMATOL detected the highest (lowest) breaks in minimum temperature (rainfall), respectively. The reference data agreed better with the homogenised climate time series than the raw data. For instance, the homogenised and reference temperatures showed a consistent positive magnitude of change at all stations, reducing inconsistencies observed at some stations before homogenisation. However, changes in rainfall were insignificant because the raw data itself did not have severe inhomogeneity problems. Inhomogeneities in raw observations can lead to erroneous conclusions in climate, hydrological, and related studies, resulting in misguided management decisions and ineffective adaptation strategies. Therefore, data quality management and homogenisation should be essential preliminary research steps.