Pappas, ChristoforosChristoforosPappasPapalexiou, Simon MichaelSimon MichaelPapalexiouKoutsoyiannis, DemetrisDemetrisKoutsoyiannis2025-10-092025-10-092014-08-06Journal of Geophysical Research 119 (15): 9290-9300 (2014)https://hdl.handle.net/11420/57939Data gaps are ubiquitous in hydrometeorological time series, and filling these values still remains a challenge. Since data sets without missing values may be a prerequisite in performing many analyses, a quick and efficient gap-filling methodology is required. In this study the problem of filling sporadic, single-value gaps using time-adjacent observations from the same location is investigated. The applicability of a local average (i.e., based on few neighboring in time observations) is examined and its advantages over the sample average (i.e., using the whole data set) are illustrated. The analysis reveals that a quick and very efficient (i.e., minimum mean-squared estimation error) gap filling is achieved by combining a strictly local average (i.e., using one observation before and one after the missing value) with the sample mean.en2169-929120141592909300UnionTechnology::600: TechnologyJournal Article10.1002/2014JD021633Journal Article