Extension and application of feature prediction model for synthesis of hydrologic records

Abstract

The method described in this paper for the synthesis of streamflows differs from the traditional approaches in synthetic hydrology in the sense that it utilizes the information contained in or among the groups of data in a streamflow record. The existense of such groups in geophysical records, including hydrologic records, is well emphasized by Hurst (1951). Further, in the proposed method, based on concepts of pattern recognition, neither a basic structure nor any preconceived model is imposed on the data; rather the data are allowed to speak for themselves in a most ‘democratic’ way. The preliminary details of the method were provided in an earlier paper by Panu et al. (1978). The intent of this paper is to describe a procedure whereby it is possible to specify explicitly multivariate probability distribution for the intrapattern structure and first‐order Markovian dependence for the interpattern structure in the feature prediction model (Panu et al., 1978). The various steps involved in the construction and operation of the model for streamflow synthesis are presented. The application of the model for synthesizing monthly streamflow records of three Canadian rivers exhibiting biannual cycles is explained. Statistical and hydrological tests show that these synthetic realizations possess relevant properties that are comparable with the corresponding properties contained in the historical record. This article should be read in conjunction with the previous publication by Panu et al. (1978).