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    A multiseries stochastic model for synthetic monthly flows
    (Taylor & Francis Ltd, 2022) HaktanIr, Tefaruk; Kara, Meltem Bircan; Acanal, Nese
    A new multiseries stochastic model is developed that takes n-year-long monthly stream flows observed at as many as 20 stations in a group and computes N-year-long synthetic monthly flows for up to 10 000 years. Initially, any series that exhibit trends with a 99% confidence level are detrended. The monthly flows of each observed series are transformed to standard-normal variates, and a variate is related to those of the other series of the same month, those of the previous month of all series, the lag-one moving-average component, and the random component. 2 M parameters of each series are iteratively computed by the least squares method, such that the variance of the random component determined using the observed variates and those computed by the model equals the variance given by the model. The model was applied to five groups in Anatolia. The relevant peculiarities of the observed and synthetic flows were in harmony.