Comparison of methods for filling in gaps in monthly rainfall series in arid regions

Abstract

AbstractFull and reliable rainfall data are required for hydrological modelling, integrated water resource management, and planning. However, these data suffer from record gaps and sparse rain gage distribution, which implies that the use of an imputation method is crucial. This study aims to compare the outputs from 10 imputation methods that were used to infill missing rainfall depth data (MRD) in an arid Mediterranean region. Different statistical tests were used to assess the outputs from the imputation methods. The results showed that for MRD ranges between 5 and 20% the stepwise multiple linear regression (MLRsw) method was valid and produced the best results with a root‐mean‐square error (RSME) and mean absolute error (MAE) of less than 7 and 2 mm, respectively. This was followed by the Monte Carlo Markov chain expectation–maximization‐based multiple imputation (MI‐MCMC) method, which had an RSME and MAE of 1.01 and 0.08 mm, respectively, at 20% MRD. On the other hand, the use of satellite data for imputation (LR_GPCC estimates) was appropriate for MRD ranging between 10 and 15%, while the statistical and spatial method was suitable for MRD of less than 5%.