Gap-filling of daily precipitation and streamflow time series: a method comparison at random and sequential gaps

• Published in: Hydrological sciences journal 2023-01, Vol.68 (1), p.148-160
• Main Authors: Lopes Martins, Letícia, Martins, Wander Araújo, Rodrigues, Iam Caio de Abreu, Freitas Xavier, Ana Carolina, Moraes, Jener Fernando Leite de, Blain, Gabriel Constantino
• Format: Article
• Language: English
• Subjects: climate data gaps; Daily precipitation; data filling methods; Hydrologic data; hydrological data gaps; Interpolation; Methods; missing values; Precipitation; Precipitation data; Stream discharge; Stream flow
• ISSN: 0262-6667; 2150-3435
• DOI: 10.1080/02626667.2022.2145200

This study compared the performance of seven gap-filling methods in daily streamflow and precipitation data and assessed the maximum number of gap days on which the methods perform well. Random (occurring randomly in the data series) and sequential (consecutive days with missing record) gaps were considered. Results show that the type of gaps affects the performance of the methods for gap-filling streamflow and precipitation data. Concerning random gaps, the best methods for streamflow were autoregressive integrate moving average and spline interpolation. For precipitation, the best methods were inverse distance weighting and linear regression (LR). Regarding sequential gaps, LR and multiple regression perform well in gap-filling up to 60 consecutive days in streamflow series. The other methods perform well up to 15 consecutive gap days. For precipitation series, the methods performed well up to seven consecutive missing days. For longer gaps (15, 30, 45 and 60 days), the methods performed poorly.