Group-based estimation of missing hydrological data: I. Approach and general methodology

In this first paper in a set of two, the problem of estimating missing segments in streamflow records is described. The group approach, different from the traditional single-valued approach, is proposed and explained. The approach perceives the hydrological data as sequence of groups rather than single-valued observations. The techniques suggested to handle the group approach are regression, time series analysis, partitioning modelling, and artificial neural networks. Pertinent literature is reviewed and background material is used to support the group approach. Implementation and comparisons of models' performance are deferred to the second paper.