Abstract:
Meteorological stations, mainly located in developing countries, have gigantic missing values in the climate dataset (rainfall and
temperature). Ignoring the missing values from analyses has been used as a technique to manage it. However, it leads to partial and
biased results in data analyses. Instead, filling the data gaps using the reference datasets is a better and widely used approach. &us,
this study was initiated to evaluate the seven gap-filling techniques in daily rainfall datasets in five meteorological stations of Wolaita
Zone and the surroundings in South Ethiopia. &e considered gap-filling techniques in this study were simple arithmetic means
(SAM), normal ratio method (NRM), correlation coefficient weighing (CCW), inverse distance weighting (IDW), multiple linear
regression (MLR), empirical quantile mapping (EQM), and empirical quantile mapping plus (EQM+). &e techniques were preferred
because of their computational simplicity and appreciable accuracies. &eir performance was evaluated against mean absolute error
(MAE), root mean square error (RMSE), skill scores (SS), and Pearson’s correlation coefficients (R). &e results indicated that MLR
outperformed other techniques in all of the five meteorological stations. It showed the lowest RMSE and the highest SS and R in all
stations. Four techniques (SAM, NRM, CCW, and IDW) showed similar performance and were second-ranked in all of the stations
with little exceptions in time series. EQM+ improved (not substantial) the performance levels of gap-filling techniques in some
stations. In general, MLR is suggested to fill in the missing values of the daily rainfall time series. However, the second-ranked
techniques could also be used depending on the required time series (period) of each station. &e techniques have better performance
in stations located in higher altitudes. &e authors expect a substantial contribution of this paper to the achievement of sustainable
development goal thirteen (climate action) through the provision of gap-filling techniques with better accuracy.
