Comparison of Methods for Filling Daily and Monthly Rainfall Missing Data: Statistical Models or Imputation of Satellite Retrievals?

Abstract

Accurate estimation of precipitation patterns is essential for the modeling of hydrological systems and for the planning and management of water resources. However, rainfall time series, as obtained from traditional rain gauges, are frequently corrupted by missing values that might hinder frequency analysis, hydrological and environmental modeling, and meteorological drought monitoring. In this paper, we evaluated three techniques for filling missing values at daily and monthly time scales, namely, simple linear regression, multiple linear regression, and the direct imputation of satellite retrievals from the Global Precipitation Measurement (GPM) mission, in rainfall gauging stations located in the Brazilian midwestern region. Our results indicated that, despite the relatively low predictive skills of the models at the daily scale, the satellite retrievals provided moderately more accurate estimates, with better representations of the temporal dynamics of the dry and wet states and of the largest observed rainfall events in most testing sites in comparison to the statistical models. At the monthly scale, the performance of the three methods was similar, but the regression-based models were unable to reproduce the seasonal characteristics of the precipitation records, which, at least to some extent, were circumvented by the satellite products. As such, the satellite retrievals might comprise a useful alternative for dealing with missing values in rainfall time series, especially in those regions with complex spatial precipitation patterns.