Premium
STATISTICAL IDENTIFICATION OF STORAGE MODELS WITH APPLICATION TO STOCHASTIC HYDROLOGY 1
Author(s) -
Ozaki Tohru
Publication year - 1985
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1985.tb05381.x
Subject(s) - identification (biology) , series (stratigraphy) , linearization , process (computing) , computer science , time series , system identification , algorithm , mathematics , mathematical optimization , hydrology (agriculture) , statistics , data mining , engineering , nonlinear system , geology , geotechnical engineering , paleontology , botany , physics , quantum mechanics , biology , measure (data warehouse) , operating system
This paper describes a method for the statistical identification of storage models for daily riverflow time series, together with numerical results. The first step in the identification process is to obtain a discrete time version of a storage model using a local linearization approach. It is shown that the discrete time version thus obtained may be utilized in the identification of the original storage model. A statistical method for the identification of daily rainfall time series models used in simulation is also presented. This identification procedure employs the maximum likelihood method for point process data analysis and is illustrated by means of numerical examples.