Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia

Alefu Chinasho,Bisrat Elias,Tesfaye Lemma,Bobe Bedadi,Tamado Tana,Tilahun Hordofa,Stefano Federico

doi:10.1155/2021/9657460

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. Thus, 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. The 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+). The techniques were preferred because of their computational simplicity and appreciable accuracies. Their performance was evaluated against mean absolute error (MAE), root mean square error (RMSE), skill scores (SS), and Pearson’s correlation coefficients (R). The 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. The techniques have better performance in stations located in higher altitudes. The 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.

Highlights

Rainfall is one of the key inputs in many disciplines such as climatology, meteorology, irrigation engineering, hydrology, and environmental hazard assessment
The two stations were not considered in analyses of maximum and minimum temperature. e rainfall datasets of five stations have a bimodal pattern even though the months of obtaining peak values slightly vary from station to station
It is only EQM that showed the negative of skill scores (SS −0.31). e performance levels of gap-filling techniques became poor after empirical quantile mapping plus: all the techniques overestimated the observed values (see Table 2 and Figure 3(b))

Summary

Introduction

Rainfall (precipitation) is one of the key inputs in many disciplines such as climatology (climate variability and change), meteorology (weather conditions), irrigation engineering (irrigation scheduling), hydrology (water cycle), and environmental hazard assessment (floods). The rainfall dataset of meteorological stations has gigantic missing values, mainly in developing countries [1,2,3]. Ignoring the missing values from analyses has been used as a technique to manage it [6,7,8] It leads to partial (coarse resolution) and biased results in data analyses [9,10,11]. Instead, filling the data gaps using reference datasets such as reanalysis products or estimates from the surrounding stations are better and widely used approaches [12,13,14,15]. Ample gap-filling techniques have been evaluated and suggested in the literature to Advances in Meteorology fill in the missing daily rainfall time series at different parts of the world. Ample gap-filling techniques have been evaluated and suggested in the literature to Advances in Meteorology fill in the missing daily rainfall time series at different parts of the world. e majority of gap-filling techniques are spatial interpolation methods

Methods

Results

Conclusion