Group-based estimation of missing hydrological data: II. Application to streamflows

The group approach that treats hydrological data as groups rather than as single-valued observations was proposed in a companion paper. Various models representing four techniques are briefly presented and applied to single series and bi-series cases, respectively, in this paper. The techniques represented by these models are regression, time series analysis, partitioning modelling, and artificial neural networks. The utility of the models for estimating missing streamflow data using the group approach is investigated. It turns out that the group approach is valid for estimating missing values, and possibly other applications, when data are significantly auto-correlated.