WIT Press


Generation Of Monthly Synthetic Streamflow Series Based On The Method Of Fragments

Price

Free (open access)

Paper DOI

10.2495/WRM110201

Volume

145

Pages

11

Page Range

237 - 247

Published

2011

Size

508 kb

Author(s)

A. T. Silva & M. M. Portela

Abstract

Synthetic time series generation has long been an important tool for the planning and management of water resources systems. This technique allows for a significant reduction of the uncertainty associated with hydrological phenomena. In this article a procedure is proposed for generating synthetic series of annual and monthly flows that combines two models, a probabilistic one, applied at an annual level, and at a monthly level, a deterministic disaggregation model. The modeling of the annual flow series is based on the random sampling of the log-Pearson III law of probability. The disaggregation of annual flows into monthly flows uses the method of fragments. For the application of this method, a new procedure was developed and tested for the automatic definition of the classes of fragments, reducing the need for intervention of the modeler, resulting in a more general and robust approach. The combination of the two models was tested on a data set of 54 streamflow samples from gauging stations geographically spread over Mainland Portugal. For each gauging station, 1200 synthetic series were generated, with a length equal to that of the corresponding sample. The quality of the generated series was evaluated by their capacity to preserve the most significant statistical characteristics of the samples of annual and monthly flows, namely the mean, standard deviation, and skewness coefficient. Confidence intervals were established for this evaluation, and the results show that, generally, the statistics of the samples are contained in these intervals. Thereupon it was concluded that the developed procedure is adequate. Keywords: generation of synthetic series, disaggregation models, method of fragments, definition of classes of fragments, log-Pearson III law.

Keywords

generation of synthetic series, disaggregation models, method of fragments, definition of classes of fragments, log-Pearson III law