Singular System Analysis with missing values: theoretical performance and application to hydrological time series
Henning Lange, Lukas Gudmundsson · EGU General Assembly Conference Abstracts · 2009
As many other analysis methods do, the classical SSA relies on gap free data. Most observational data in the environmental sciences, however, are prone to instrument failure, and do not meet this condition. Here, we investigate extensions to SSA which are designed to fill the gaps, and systematically evaluate their performance. As benchmarks we use artificial time series resembling conventional stochastic processes, and well-known measured runoff time series without missing values, such as the Danube river at Bratislava (daily values from 130 years). In particular, the stability of the estimated covariance matrix, the eigenvalue spectrum, the corresponding reconstructed components and thus the reproducibility of periodic structures either known to exist (for the artificial time series) or assumed to be present (for the measured values) are assessed.