In this thesis, a stochastic model was utilized to predict the water content and sediment deposition characteristics in the Mazar Reservoir in Ecuador. The results obtained were compared with those reported in the project's feasibility study.
The methodology used in this study was based on the assumption that annual water inflow can be taken as a random variable and annual water contents in the reservoir form an independent series of first-order, homogeneous Markov Chains. The available stream flow records indicated that normal and log-normal probability distributions would adequately characterize the annual water inflows. Using Moran's storage theory, expected values of the reservoir water contents were calculated for consecutive years until the storage probability transition matrix operation yielded a stationary condition.
Annual amounts of sediment deposition in the reservoir were determined as the difference between annual sediment inflows and outflows. Two different scenarios were applied to calculate the annual sediment inflows. In the first scenario, based on the deterministic sediment rating equations, the resulting sediment rates were assigned the same probabilities as the water inflows. In the second scenario, the annual sediment rates were also considered as random variables normally distributed around the mean values.
The results obtained indicated that a stochastic model, such as the one employed in this study, can be effectively used to predict sediment deposition in a reservoir to complement the predictions obtained with deterministic methods, and can even yield a more reliable spectrum of results that can be associated with various confidence levels, particularly when the data on actual sediment measurements is scarce. / M.S.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/104532 |
| Date | January 1985 |
| Creators | Pasquel H., Renan Fernando |
| Contributors | Civil Engineering |
| Publisher | Virginia Polytechnic Institute and State University |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis, Text |
| Format | viii, 97 leaves, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | OCLC# 12965135 |
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