A gap-filling algorithm selection strategy for GRACE and GRACE Follow-On time series based on hydrological signal characteristics of the individual river basins

Abstract Gravity recovery and climate experiment (GRACE) GRACE Follow-On (GRACE-FO) are Earth’s gravity satellite missions with hydrological monitoring applications. However, caused by measuring instrumental problems, there several temporal missing values in the dataset of two where a long gap between mission also exists. Recent studies utilized different gap-filling methodologies to fill those data gaps. In this article, we employ variety singular spectrum analysis (SSA) algorithms as well least squares-harmonic estimation (LS-HE) approach for gap-filling. These methods implemented on six basins, performance is validated artificial scenarios. Our results indicate that each basin has its special behaviour. LS-HE outperforms other half whereas half, SSA provides better performance. This highlights importance factors affecting deterministic signals stochastic characteristics climatological time series. To such series, it therefore required investigate series behaviour their time-invariant time-varying before processing