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 segm ents in streamflow records is described. The group approach, different from the traditional single-valued approach, is proposed and explained. The app roach perceives the hydrological data as sequence of groups rather than sin gle-valued observations. The techniques suggested to handle the group appro ach are regression, time series analysis, partitioning modelling, and artif icial neural networks. Pertinent literature is reviewed and background mate rial is used to support the group approach. Implementation and comparisons of models' performance are deferred to the second paper.