This thesis presents a new approach to streamflow forecasting. The approach is based on specifying the probabilities that the next flow of a stream will occur within different ranges of values. Hence, this method is different from the time series models where point estimates are given as forecasts. With this approach flood forecasting is possible by focusing on a preselected range of streamflows. A double criteria objective function is developed to assess the model performance in flood prediction. Three case studies are examined based on data from the Salt River in Phoenix, Arizona and Bird Creek near Sperry, Oklahoma. The models presented are: a first order Markov chain (FOMC), a second order Markov chain (SOMC), and a first order Markov chain with rainfall as an exogenous input (FOMCX). Three forecasts methodologies are compared among each other and against time series models. It is shown that the SOMC is better than the FOMC while the FOMCX is better than the time series models.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/278135 |
| Date | January 1992 |
| Creators | Yapo, Patrice Ogou, 1967- |
| Contributors | Sorooshian, Soroosh |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Thesis-Reproduction (electronic) |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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