Comparison of Interpolation, Statistical, and Data-Driven Methods for Imputation of Missing Values in a Distributed Soil Moisture Dataset

Missing values in in situ monitoring data is a problem often encountered in hydrologic research and applications. Values in a data set may be missing because of sensor error or failure of data recording devices. Whereas various imputation techniques have focused on hydrometeorological data, very few studies have investigated gap-filling methods for soil moisture data. This paper aims to fill that gap by investigating well-established statistical and data-driven methods for infilling missing values in a high resolution, soil moisture time series. Since 2006, the authors collected hourly soil moisture data in the Hamilton-Halton Watershed, Southern Ontario, Canada at four research sites. Each site contained nine stations with time domain reflectometry (TDR) soil sensors at six soil depths. From these distributed data sets, the authors removed values randomly (∼5%) and systematically (∼20%) from the data to evaluate the effectiveness of the monthly average replacement (MAR), soil layer relative difference (SLRD), linear and cubic interpolation, artificial neural networks (ANN), and evolutionary polynomial regression (EPR) infilling methods. When values were randomly removed, interpolation, ANN, and EPR were able to infill the missing values with similar efficiency, whereas MAR and SLRD were the least effective methods. Similarly, when large systematic gaps were present in the data, interpolation and ANN were the most effective methods of infilling, respectively. However, the effectiveness of both infilling methods is limited as serial gaps become larger than 72–100 h.