Missing data simulation inside flow rate time-series using multiple-point statistics

The direct sampling (DS) multiple-point statistical technique is proposed as a non-parametric missing data simulator for hydrological flow rate time-series. The algorithm makes use of the patterns contained inside a training data set to reproduce the complexity of the missing data. The proposed setup is tested in the reconstruction of a flow rate time-series while considering several missing data scenarios, as well as a comparative test against a time-series model of type ARMAX. The results show that DS generates more realistic simulations than ARMAX, better recovering the statistical content of the missing data. The predictive power of both techniques is much increased when a correlated flow rate time-series is used, but DS can also use incomplete auxiliary time-series, with a comparable prediction power. This makes the technique a handy simulation tool for practitioners dealing with incomplete data sets. © 2016 Elsevier Ltd. All rights reserved. Software availability The following information is about the software implementation of the simulation technique used in this paper: Algorithm name: Direct Sampling (Mariethoz et al. (2010)). Implementation name: DeeSse (Straubhaar (2015)). Program language: C. Developer: University of Neuchâtel, Julien Straubhaar (julien. [email protected]). Year first available: 2015. Minimal requirements: Windows/UNIX OS. Availability: free license on request for research purposes, available on purchase for commercial use e for any request please contact Philippe Renard ([email protected]). A tutorial of the application shown in the paper is available upon request. eological Survey of Denmark